title: metaSEM: an R package for meta-analysis using structural equation modeling

text: The metaSEM package provides functions to conduct univariate, multivariate, and three-level meta-analyses using a structural equation modeling (SEM) approach via the OpenMx package in the R statistical platform. It also implements the two-stage SEM approach to conducting fixed- and random-effects meta-analytic SEM on correlation or covariance matrices. This paper briefly outlines the theories and their implementations. It provides a summary on how meta-analyses can be formulated as structural equation models. The paper closes with a conclusion on several relevant topics to this SEM-based meta-analysis. Several examples are used to illustrate the procedures in the supplementary material.


title: User-friendly 3D object manipulation gesture using kinect

text: With the rapid development and wide spread of the virtual reality technology, we can easily find VR systems in various places such as school, library and home. Translation and rotation, the most frequently used gestures for manipulating objects in the real world, need to be implemented in order to make a user feel comfortable while manipulating 3D objects in the systems. In this paper, we propose a set of user-friendly 3D object manipulation gestures and develop a recognizer using Kinect for the VR systems. The usefulness and suitableness of the proposed system is shown from the user study performed.


title: SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones

text: Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage and processing capability, it is a challenge to transfer the huge amount of crowdsourced photos. To address this problem, we propose a framework, called SmartPhoto, to quantify the quality (utility) of crowdsourced photos based on the accessible geographical and geometrical information (called metadata) including the smartphone's orientation, position and all related parameters of the built-in camera. From the metadata, we can infer where and how the photo is taken, and then only transmit the most useful photos. Three optimization problems regarding the tradeoffs between photo utility and resource constraints, namely the Max-Utility problem, the online Max-Utility problem and the Min-Selection problem, are studied. Efficient algorithms are proposed and their performance bounds are theoretically proved. We have implemented SmartPhoto in a testbed using Android based smartphones, and proposed techniques to improve the accuracy of the collected metadata by reducing sensor reading errors and solving object occlusion issues. Results based on real implementations and extensive simulations demonstrate the effectiveness of the proposed algorithms.


title: Ambiguity as a resource for design

text: Ambiguity is usually considered anathema in Human Computer Interaction. We argue, in contrast, that it is a resource for design that can be used to encourage close personal engagement with systems. We illustrate this with examples from contemporary arts and design practice, and distinguish three broad classes of ambiguity according to where uncertainty is located in the interpretative relationship linking person and artefact. Ambiguity of information finds its source in the artefact itself, ambiguity of context in the sociocultural discourses that are used to interpret it, and ambiguity of relationship in the interpretative and evaluative stance of the individual. For each of these categories, we describe tactics for emphasising ambiguity that may help designers and other practitioners understand and craft its use.


title: Robust Multi-pose Facial Expression Recognition

text: Previous research on facial expression recognition mainly focuses on near frontal face images, while in realistic interactive scenarios, the interested subjects may appear in arbitrary non-frontal poses. In this paper, we propose a framework to recognize six prototypical facial expressions, namely, anger, disgust, fear, joy, sadness and surprise, in an arbitrary head pose. We build a multi-pose training set by rendering 3D face scans from the BU-4DFE dynamic facial expression database [17] at 49 different viewpoints. We extract Local Binary Pattern (LBP) descriptors and further utilize multiple instance learning to mitigate the influence of inaccurate alignment in this challenging task. Experimental results demonstrate the power and validate the effectiveness of the proposed multi-pose facial expression recognition framework.


title: Microservices validation: Mjolnirr platform case study

text: Microservice architecture is a cloud application design pattern that implies that the application is divided into a number of small independent services, each of which is responsible for implementing of a certain feature. The need for continuous integration of developed and/or modified microservices in the existing system requires a comprehensive validation of individual microservices and their co-operation as an ensemble with other microservices. In this paper, we would provide an analysis of existing methods of cloud applications testing and identify features that are specific to the microservice architecture. Based on this analysis, we will try to propose a validation methodology of the microservice systems.


title: Robust Nonrigid Registration by Convex Optimization

text: We present an approach to nonrigid registration of 3D surfaces. We cast isometric embedding as MRF optimization and apply efficient global optimization algorithms based on linear programming relaxations. The Markov random field perspective suggests a natural connection with robust statistics and motivates robust forms of the intrinsic distortion functional. Our approach outperforms a large body of prior work by a significant margin, increasing registration precision on real data by a factor of 3.


title: Active Damping in DC/DC Power Electronic Converters: A Novel Method to Overcome the Problems of Constant Power Loads

text: Multi-converter power electronic systems exist in land, sea, air, and space vehicles. In these systems, load converters exhibit constant power load (CPL) behavior for the feeder converters and tend to destabilize the system. In this paper, the implementation of novel active-damping techniques on dc/dc converters has been shown. Moreover, the proposed active-damping method is used to overcome the negative impedance instability problem caused by the CPLs. The effectiveness of the new proposed approach has been verified by PSpice simulations and experimental results.


title: Extracting Social Structures from Conversations in Twitter: A Case Study on Health-Related Posts

text: Online Social Networks (e.g., Twitter, Facebook) have dramatically grown in usage and popularity in recent years. In addition to keeping track of friends and acquaintances, these networks provide powerful means of exchanging and sharing information on many different topics of interest (e.g., sports, religion, politics, health concerns, etc.). Moreover, the use of these networks has introduced a completely new way of collaboration among people, virtually creating spheres of friends who generally feel comfortable discussing a variety of subjects and even helping each other to grow in knowledge about certain subjects. In this work, we built and analyzed networks of social groups on Twitter related to the top leading causes of death in the United States. Due to space limitations, we present results for the state of Florida and only for the top four leading causes of death. We show that using a concept of time window in the creation of relations between users, we can reconstruct social networks for these conversations and these networks have characteristics that are similar to typical social networks. The argument is that social network information can be extracted even in cases where users are not directly talking to each other (as it is the case in most of Twitter).


title: HCI and Autonomous Vehicles: Contextual Experience Informs Design

text: The interaction between drivers and their cars will change significantly with the introduction of autonomous vehicles. The driver's role will shift towards a supervisory control of their autonomous vehicle. The eventual relief from the driving task enables a complete new area of research and practice in human-computer interaction and interaction design. In this one-day workshop, participants will explore the opportunities the design space of autonomous driving will bring to HCI researchers and designers. On the day before workshop participants are invited to visit (together with workshop organizers) Google Partnerplex and Stanford University. At Google participants will have the opportunity to explore Google's autonomous car simulator and might have the chance to experience one of the Google Cars (if available). At Stanford participants are invited to ride in a Wizard-of-Oz autonomous vehicle. Based on this first-hand experience we will discuss design approaches and prototype interaction systems during the next day's workshop. The outcome of this workshop will be a set of concepts, interaction sketches, and low-fidelity paper prototypes that address constraints and potentials of driving in an autonomous car.


title: Using BabelNet to Improve OOV Coverage in SMT

text: Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). This paper studies different strategies of using BabelNet to alleviate the negative impact brought about by OOVs. BabelNet is a multilingual encyclopedic dictionary and a semantic network, which not only includes lexicographic and encyclopedic terms, but connects concepts and named entities in a very large network of semantic relations. By taking advantage of the knowledge in BabelNet, three different methods – using direct training data, domain-adaptation techniques and the BabelNet API – are proposed in this paper to obtain translations for OOVs to improve system performance. Experimental results on English–Polish and English–Chinese language pairs show that domain adaptation can better utilize BabelNet knowledge and performs better than other methods. The results also demonstrate that BabelNet is a really useful tool for improving translation performance of SMT systems.


title: Path selection based on local terrain feature for unmanned ground vehicle in unknown rough terrain environment

text: In this paper, we propose an autonomous navigation for Unmanned Ground Vehicles (UGVs) by a path selection method based on local features of terrain in unknown outdoor rough terrain environment. The correlation between a local terrain feature obtained from a path and a value of the path obtained from the global path planning is extracted in advance. When UGV comes to a branch while it is running, the value of path is estimated using the correlation with local terrain feature. Thereby, UGV navigation is performed by path selection under the criterion of shortest time in unknown outdoor environment. We experimented on a simulator and confirmed that the proposed method can select more effective paths in comparison with a simple path selection method.


title: Behavioral Cloning from Observation

text: Humans often learn how to perform tasks via imitation: they observe others perform a task, and then very quickly infer the appropriate actions to take based on their observations. While extending this paradigm to autonomous agents is a wellstudied problem in general, there are two particular aspects that have largely been overlooked: (1) that the learning is done from observation only (i.e., without explicit action information), and (2) that the learning is typically done very quickly. In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects. First, we allow the agent to acquire experience in a self-supervised fashion. This experience is used to develop a model which is then utilized to learn a particular task by observing an expert perform that task without the knowledge of the specific actions taken. We experimentally compare BCO to imitation learning methods, including the state-of-the-art, generative adversarial imitation learning (GAIL) technique, and we show comparable task performance in several different simulation domains while exhibiting increased learning speed after expert trajectories become available.


title: Inference Over Distribution of Posterior Class Probabilities for Reliable Bayesian Classification and Object-Level Perception

text: State of the art Bayesian classification approaches typically maintain a posterior distribution over possible classes given available sensor observations (images). Yet, while these approaches fuse all classifier outputs thus far, they do not provide any indication regarding how reliable the posterior classification is, thus limiting its functionality in terms of autonomous systems and robotics. On the other hand, current deep learning based classifiers provide an uncertainty measure, thereby quantifying model uncertainty. However, they do so on a single frame basis and do not consider a sequential framework. In this letter, we develop a novel approach that infers a distribution over posterior class probabilities, while accounting for model uncertainty. This distribution enables reasoning about uncertainty in the posterior classification and, therefore, is of prime importance for robust classification, object-level perception in uncertain and ambiguous scenarios, and for safe autonomy in general. The distribution of the posterior class probability has no known analytical solution; thus, we propose to approximate this distribution via sampling. We evaluate our approach in simulation and using real images fed into a convolutional neural network classifier.


title: Protecting Your Right: Verifiable Attribute-Based Keyword Search with Fine-Grained Owner-Enforced Search Authorization in the Cloud

text: Search over encrypted data is a critically important enabling technique in cloud computing, where encryption-before-outsourcing is a fundamental solution to protecting user data privacy in the untrusted cloud server environment. Many secure search schemes have been focusing on the single-contributor scenario, where the outsourced dataset or the secure searchable index of the dataset are encrypted and managed by a single owner, typically based on symmetric cryptography. In this paper, we focus on a different yet more challenging scenario where the outsourced dataset can be contributed from multiple owners and are searchable by multiple users, i.e., multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we present the first attribute-based keyword search scheme with efficient user revocation (ABKS-UR) that enables scalable fine-grained (i.e., file-level) search authorization. Our scheme allows multiple owners to encrypt and outsource their data to the cloud server independently. Users can generate their own search capabilities without relying on an always online trusted authority. Fine-grained search authorization is also implemented by the owner-enforced access policy on the index of each file. Further, by incorporating proxy re-encryption and lazy re-encryption techniques, we are able to delegate heavy system update workload during user revocation to the resourceful semi-trusted cloud server. We formalize the security definition and prove the proposed ABKS-UR scheme selectively secure against chosen-keyword attack. To build confidence of data user in the proposed secure search system, we also design a search result verification scheme. Finally, performance evaluation shows the efficiency of our scheme.


title: Learning Structured Natural Language Representations for Semantic Parsing

text: We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general natural language representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We achieve the state of the art on SPADES and GRAPHQUESTIONS and obtain competitive results on GEOQUERY and WEBQUESTIONS. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.1


title: A four-band dual-polarized cavity-backed antenna on LTCC technology for 60GHz applications

text: In this work, we present a 4-band dual-polarized antenna designed for 8-chanel applications. Based on LTCC technology, the antenna is a patch coupled aperture with a backed cavity. Each antenna element of a designated band contains two channels through two orthogonally polarized ports. Combining four dual-polarized antenna elements under different frequency, an 8-channel antenna for 60GHz applications can be achieved. The array antenna contains 8 feeding ports, which correspond to 8 independent channels. The isolation between each port can reach 20dB in most of the frequency band.


title: Optimal Reconfiguration for Supply Restoration With Informed A$^{\ast}$  Search

text: Reconfiguration of radial distribution networks is the basis of supply restoration after faults and of load balancing and loss minimization. The ability to automatically reconfigure the network quickly and efficiently is a key feature of autonomous and self-healing networks, an important part of the future vision of smart grids. We address the reconfiguration problem for outage recovery, where the cost of the switching actions dominates the overall cost: when the network reverts to its normal configuration relatively quickly, the electricity loss and the load imbalance in a temporary suboptimal configuration are of minor importance. Finding optimal feeder configurations under most optimality criteria is a difficult optimization problem. All known complete optimal algorithms require an exponential time in the network size in the worst case, and cannot be guaranteed to scale up to arbitrarily large networks. Hence most works on reconfiguration use heuristic approaches that can deliver solutions but cannot guarantee optimality. These approaches include local search, such as tabu search, and evolutionary algorithms. We propose using optimal informed search algorithms in the A family, introduce admissible heuristics for reconfiguration, and demonstrate empirically the efficiency of our approach. Combining A with admissible cost lower bounds guarantees that reconfiguration plans are optimal in terms of switching action costs.


title: Additive Quantization for Extreme Vector Compression

text: We introduce a new compression scheme for high-dimensional vectors that approximates the vectors using sums of M codewords coming from M different codebooks. We show that the proposed scheme permits efficient distance and scalar product computations between compressed and uncompressed vectors. We further suggest vector encoding and codebook learning algorithms that can minimize the coding error within the proposed scheme. In the experiments, we demonstrate that the proposed compression can be used instead of or together with product quantization. Compared to product quantization and its optimized versions, the proposed compression approach leads to lower coding approximation errors, higher accuracy of approximate nearest neighbor search in the datasets of visual descriptors, and lower image classification error, whenever the classifiers are learned on or applied to compressed vectors.


title: Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks

text: In this paper, we propose the novel principal backpropagation networks (PBNets) to revisit the backpropagation algorithms commonly used in training two-stream networks for video action recognition. We content that existing approaches always take all the frames/snippets for the backpropagation not optimal for video recognition since the desired actions only occur in a short period within a video. To remedy these drawbacks, we design a watch-and-choose mechanism. In particular, the watching stage exploits a dense snippet-wise temporal pooling strategy to discover the global characteristic for each input video, while the choosing phase only backpropagates a small number of representative snippets that are selected with two novel strategies, i.e., Max-rule and KL-rule. We prove that with the proposed selection strategies, performing the backpropagation on the selected subset is capable of decreasing the loss of the whole snippets as well. The proposed PBNets are evaluated on two standard video action recognition benchmarks UCF101 and HMDB51, where it surpasses the state of the arts consistently, but requiring less memory and computation to achieve high performance.


title: Idea Generation Techniques among Creative Professionals

text: The creative process has been a key topic research over the past century, but it wasn't until the last decade that creativity became a hot topic of research in the HCI. It is an important commodity to businesses and individuals alike spawning numerous research studies in business, psychology and design. However, it wasn't until recently that researchers became interested in developing technologies to support creative behavior. This article outlines the role of creativity in design from the designer's perspective, provides a model for the creative process and provides a foundation and direction for future creativity support research by identifying nineteen idea generation techniques utilized by creative professionals.


title: MusicMiner : Visualizing timbre distances of music as topographical maps

text: Timbre distances and similarities are an expression of the phenomenon that some music appears similar while other songs sound very different to us. The notion of genre is often used to categorize music, but songs from a single genre do not necessarily sound similar and vice versa. Instead we aim at a visualization of timbre similarities of sound within a music collection. We analyzed and compared a large amount of different audio features and psychoacoustic variants thereof for the purpose of modelling timbre distance of sound. The sound of polyphonic music is commonly described by extracting audio features on short time windows during which the sound is assumed to be stationary. The resulting down sampled time series are aggregated to form a high level feature vector describing the music. We generated high level features by systematically applying static and temporal statistics for aggregation. Especially the temporal structure of features has previously been largely neglected. A novel supervised feature selection method is applied to the huge set of possible features. Distances between vectors of the selected features correspond to timbre differences in music. The features show few redundancies and have high potential for explaining possible clusters. They outperform seven other previously proposed feature sets on several datasets w.r.t. the separation of the known groups of timbrally different music. Clustering and visualization based on these feature vectors can discover emergent structures in collections of music. Visualization based on Emergent Self-Organizing Maps in particular enables the unsupervised discovery of timbrally consistent clusters that may or may not correspond to musical genres and artists. We demonstrate the visualizations capabilities of the U-Map and related methods based on the new audio features. An intuitive browsing of large music collections is offered based on the paradigm of topographic maps. The user can navigate the sound space and interact with the maps to play music or show the context of a song. Data Bionics Research Group, Philipps-University Marburg, 35032 Marburg, Germany


title: Rank Pooling for Action Recognition

text: We propose a function-based temporal pooling method that captures the latent structure of the video sequence data - e.g., how frame-level features evolve over time in a video. We show how the parameters of a function that has been fit to the video data can serve as a robust new video representation. As a specific example, we learn a pooling function via ranking machines. By learning to rank the frame-level features of a video in chronological order, we obtain a new representation that captures the video-wide temporal dynamics of a video, suitable for action recognition. Other than ranking functions, we explore different parametric models that could also explain the temporal changes in videos. The proposed functional pooling methods, and rank pooling in particular, is easy to interpret and implement, fast to compute and effective in recognizing a wide variety of actions. We evaluate our method on various benchmarks for generic action, fine-grained action and gesture recognition. Results show that rank pooling brings an absolute improvement of 7-10 average pooling baseline. At the same time, rank pooling is compatible with and complementary to several appearance and local motion based methods and features, such as improved trajectories and deep learning features.


title: Activity sensing in the wild: a field trial of ubifit garden

text: Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.


title: Semantic Space: an infrastructure for smart spaces

text: Semantic Space is a pervasive computing infrastructure that exploits semantic Web technologies to support explicit representation, expressive querying, and flexible reasoning of contexts in smart spaces.


title: Dyscalculia : Characteristics , Causes , and Treatments

text: Developmental Dyscalculia (DD) is a learning disorder affecting the ability to acquire school-level arithmetic skills, affecting approximately 3-6% of individuals. Progress in understanding the root causes of DD and how best to treat it have been impeded by lack of widespread research and variation in characterizations of the disorder across studies. However, recent years have witnessed significant growth in the field, and a growing body of behavioral and neuroimaging evidence now points to an underlying deficit in the representation and processing of numerical magnitude information as a potential core deficit in DD. An additional product of the recent progress in understanding DD is the resurgence of a distinction between ‘primary’ and ‘secondary’ developmental dyscalculia. The first appears related to impaired development of brain mechanisms for processing numerical magnitude information, while the latter refers to mathematical deficits stemming from external factors such as poor teaching, low socio-economic status, and behavioral attention problems or domain-general cognitive deficits. Increased awareness of this distinction going forward, in combination with longitudinal empirical research, offers great potential for deepening our understanding of the disorder and developing effective educational interventions.


title: Acquiring Hyponymy Relations from Web Documents

text: This paper describes an automatic method for acquiring hyponymy relations from HTML documents on the WWW. Hyponymy relations can play a crucial role in various natural language processing systems. Most existing acquisition methods for hyponymy relations rely on particular linguistic patterns, such as “ NP such as NP”. Our method, however, does not use such linguistic patterns, and we expect that our procedure can be applied to a wide range of expressions for which existing methods cannot be used. Our acquisition algorithm uses clues such as itemization or listing in HTML documents and statistical measures such as document frequencies and verb-noun co-occurrences.


title: Gender differences in narcissism: a meta-analytic review.

text: Despite the widely held belief that men are more narcissistic than women, there has been no systematic review to establish the magnitude, variability across measures and settings, and stability over time of this gender difference. Drawing on the biosocial approach to social role theory, a meta-analysis performed for Study 1 found that men tended to be more narcissistic than women (d = .26; k = 355 studies; N = 470,846). This gender difference remained stable in U.S. college student cohorts over time (from 1990 to 2013) and across different age groups. Study 1 also investigated gender differences in three facets of the Narcissistic Personality Inventory (NPI) to reveal that the narcissism gender difference is driven by the Exploitative/Entitlement facet (d = .29; k = 44 studies; N = 44,108) and Leadership/Authority facet (d = .20; k = 40 studies; N = 44,739); whereas the gender difference in Grandiose/Exhibitionism (d = .04; k = 39 studies; N = 42,460) was much smaller. We further investigated a less-studied form of narcissism called vulnerable narcissism-which is marked by low self-esteem, neuroticism, and introversion-to find that (in contrast to the more commonly studied form of narcissism found in the DSM and the NPI) men and women did not differ on vulnerable narcissism (d = -.04; k = 42 studies; N = 46,735). Study 2 used item response theory to rule out the possibility that measurement bias accounts for observed gender differences in the three facets of the NPI (N = 19,001). Results revealed that observed gender differences were not explained by measurement bias and thus can be interpreted as true sex differences. Discussion focuses on the implications for the biosocial construction model of gender differences, for the etiology of narcissism, for clinical applications, and for the role of narcissism in helping to explain gender differences in leadership and aggressive behavior. Readers are warned against overapplying small effect sizes to perpetuate gender stereotypes.


title: Linggle: a Web-scale Linguistic Search Engine for Words in Context

text: In this paper, we introduce a Web-scale linguistics search engine, Linggle, that retrieves lexical bundles in response to a given query. The query might contain keywords, wildcards, wild parts of speech (PoS), synonyms, and additional regular expression (RE) operators. In our approach, we incorporate inverted file indexing, PoS information from BNC, and semantic indexing based on Latent Dirichlet Allocation with Google Web 1T. The method involves parsing the query to transforming it into several keyword retrieval commands. Word chunks are retrieved with counts, further filtering the chunks with the query as a RE, and finally displaying the results according to the counts, similarities, and topics. Clusters of synonyms or conceptually related words are also provided. In addition, Linggle provides example sentences from The New York Times on demand. The current implementation of Linggle is the most functionally comprehensive, and is in principle language and dataset independent. We plan to extend Linggle to provide fast and convenient access to a wealth of linguistic information embodied in Web scale datasets including Google Web 1T and Google Books Ngram for many major languages in the world.


title: Document Language Models, Query Models, and Risk Minimization for Information Retrieval

text: We present a framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory. The framework suggests an operational retrieval model that extends recent developments in the language modeling approach to information retrieval. A language model for each document is estimated, as well as a language model for each query, and the retrieval problem is cast in terms of risk minimization. The query language model can be exploited to model user preferences, the context of a query, synonomy and word senses. While recent work has incorporated word translation models for this purpose, we introduce a new method using Markov chains defined on a set of documents to estimate the query models. The Markov chain method has connections to algorithms from link analysis and social networks. The new approach is evaluated on TREC collections and compared to the basic language modeling approach and vector space models together with query expansion using Rocchio. Significant improvements are obtained over standard query expansion methods for strong baseline TF-IDF systems, with the greatest improvements attained for short queries on Web data.


title: Top-N Recommendation with Novel Rank Approximation

text: The importance of accurate recommender systems has been widely recognized by academia and industry. However, the recommendation quality is still rather low. Recently, a linear sparse and low-rank representation of the user-item matrix has been applied to produce Top-N recommendations. This approach uses the nuclear norm as a convex relaxation for the rank function and has achieved better recommendation accuracy than the state-of-the-art methods. In the past several years, solving rank minimization problems by leveraging nonconvex relaxations has received increasing attention. Some empirical results demonstrate that it can provide a better approximation to original problems than convex relaxation. In this paper, we propose a novel rank approximation to enhance the performance of Top-N recommendation systems, where the approximation error is controllable. Experimental results on real data show that the proposed rank approximation improves the Top-N recommendation accuracy substantially.


title: Cloud RAN for Mobile Networks—A Technology Overview

text: Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can address a number of challenges the operators face while trying to support growing end-user's needs. The main idea behind C-RAN is to pool the Baseband Units (BBUs) from multiple base stations into centralized BBU Pool for statistical multiplexing gain, while shifting the burden to the high-speed wireline transmission of In-phase and Quadrature (IQ) data. C-RAN enables energy efficient network operation and possible cost savings on baseband resources. Furthermore, it improves network capacity by performing load balancing and cooperative processing of signals originating from several base stations. This paper surveys the state-of-the-art literature on C-RAN. It can serve as a starting point for anyone willing to understand C-RAN architecture and advance the research on C-RAN.


title: Therapeutic Uses of Active Videogames: A Systematic Review.

text:
BACKGROUND
Active videogames (AVGs) may be useful for promoting physical activity for therapeutic uses, including for balance, rehabilitation, and management of illness or disease. The literature from 64 peer-reviewed publications that assessed health outcomes of AVGs for therapeutic purposes was synthesized.

MATERIALS AND METHODS
PubMed, Medline, and PyschInfo were queried for original studies related to the use of AVGs to improve physical outcomes in patients who were ill or undergoing rehabilitation related to balance, burn treatment, cancer, cerebral palsy, Down's syndrome, extremity dysfunction or amputation, hospitalization, lupus, Parkinson's disease, spinal injury, or stroke. The following inclusion criteria were used: (1) human subjects; (2) English language; (3) not duplicates; (4) new empirical data; and (5) tests an AVG, including commercially available or custom-designed. Studies were included regardless of participants' age or the study design.

RESULTS AND LIMITATIONS
Overall, the vast majority of studies demonstrated promising results for improved health outcomes related to therapy, including significantly greater or comparable effects of AVG play versus usual care. However, many studies were pilot trials with small, homogeneous samples, and many studies lacked a control or comparison group. Some trials tested multiweek or multimonth interventions, although many used a single bout of gameplay, and few included follow-up assessments to test sustainability of improved health.

CONCLUSIONS AND IMPLICATIONS
AVGs were acceptable and enjoyable to the populations examined and appear as a promising tool for balance, rehabilitation, and illness management. Future research directions and implications for clinicians are discussed.


title: Logic BIST for large industrial designs: real issues and case studies

text: to Abstract This paper discusses practical issues involved in app ing logic built-in self-test (BIST) to four large industria designs. These multi-clock designs, ranging in size fr 200K to 800K gates, pose significant challenges to log BIST methodology, flow, and tools. The paper presents process of generating a BIST-compliant core along with t logic BIST controller for at-speed testing. Comparativ data on fault grades and area overhead between autom test pattern generation (ATPG) and logic BIST a reported. The experimental results demonstrate that w automation of the proposed solutions, logic BIST c achieve test quality approaching that of ATPG with min mal area overhead and few changes to the design flow.


title: Supervised and Unsupervised Intrusion Detection Based on CAN Message Frequencies for In-vehicle Network

text: Modern vehicles are equipped with Electronic Control Units (ECUs) and external communication devices. The Controller Area Network (CAN), a widely used communication protocol for ECUs, does not have a security mechanism to detect improper packets; if attackers exploit the vulnerability of an ECU and manage to inject a malicious message, they are able to control other ECUs to cause improper operation of the vehicle. With the increasing popularity of connected cars, it has become an urgent matter to protect in-vehicle networks against security threats. In this paper, we study the applicability of statistical anomaly detection methods for identifying malicious CAN messages in invehicle networks. We focus on intrusion attacks of malicious messages. Because the occurrence of an intrusion attack certainly influences the message traffic, we focus on the number of messages observed in a fixed time window to detect intrusion attacks. We formalize features to represent a message sequence that incorporates the number of messages associated with each receiver ID. We collected CAN message data from an actual vehicle and conducted a quantitative analysis of the methods and the features in practical situations. The results of our experiments demonstrated our proposed methods provide fast and accurate detection in various cases.


title: PCN: Point Completion Network

text: Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.


title: Identification of extremist videos in online video sharing sites

text: Web 2.0 has become an effective grassroots communication platform for extremists to promote their ideas, share resources, and communicate among each other. As an important component of Web 2.0, online video sharing sites such as YouTube and Google video have also been utilized by extremist groups to distribute videos. This study presented a framework for identifying extremist videos in online video sharing sites by using user-generated text content such as comments, video descriptions, and titles without downloading the videos. Text features including lexical features, syntactic features and content specific features were first extracted. Then Information Gain was used for feature selection, and Support Vector Machine was deployed for classification. The exploratory experiment showed that our proposed framework is effective for identifying online extremist videos, with the F-measure as high as 82%.


title: A Hybrid Approach for Arabic Text Summarization Using Domain Knowledge and Genetic Algorithms

text: Text summarization is the process of producing a shorter version of a specific text. Automatic summarization techniques have been applied to various domains such as medical, political, news, and legal domains proving that adapting domain-relevant features could improve the summarization performance. Despite the existence of plenty of research work in the domain-based summarization in English and other languages, there is a lack of such work in Arabic due to the shortage of existing knowledge bases. In this paper, a hybrid, single-document text summarization approach (abbreviated as (ASDKGA)) is presented. The approach incorporates domain knowledge, statistical features, and genetic algorithms to extract important points of Arabic political documents. The ASDKGA approach is tested on two corpora KALIMAT corpus and Essex Arabic Summaries Corpus (EASC). The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) framework was used to compare the automatically generated summaries by the ASDKGA approach with summaries generated by humans. Also, the approach is compared against three other Arabic text summarization approaches. The (ASDKGA) approach demonstrated promising results when summarizing Arabic political documents with average F-measure of 0.605 at the compression ratio of 40%.


title: Ring-LWE Ciphertext Compression and Error Correction: Tools for Lightweight Post-Quantum Cryptography

text:
Some lattice-based public key cryptosystems allow one to transform ciphertext from one lattice or ring representation to another efficiently and without knowledge of public and private keys. In this work we explore this lattice transformation property from cryptographic engineering viewpoint. We apply ciphertext transformation to compress Ring-LWE ciphertexts and to enable efficient decryption on an ultra-lightweight implementation targets such as Internet of Things, Smart Cards, and RFID applications. Significantly, this can be done without modifying the original encryption procedure or its security parameters. Such flexibility is unique to lattice-based cryptography and may find additional, unique real-life applications.
 Ciphertext compression can significantly increase the probability of decryption errors. We show that the frequency of such errors can be analyzed, measured and used to derive precise failure bounds for n-bit error correction. We introduce XECC, a fast multi-error correcting code that allows constant time implementation in software.
 We use these tools to construct and explore TRUNC8, a concrete Ring-LWE encryption and authentication system. We analyze its implementation, security, and performance. We show that our lattice compression technique reduces ciphertext size by more than 40% at equivalent security level, while also enabling public key cryptography on previously unreachable ultra-lightweight platforms.
 The experimental public key encryption and authentication system has been implemented on an 8-bit AVR target, where it easily outperforms elliptic curve and RSA-based proposals at similar security level. Similar results have been obtained with a Cortex M0 implementation. The new decryption code requires only a fraction of the software footprint of previous Ring-LWE implementations with the same encryption parameters, and is well suited for hardware implementation.


title: LEARNING ENGLISH IS FUN VIA KAHOOT : STUDENTS ’ ATTITUDE , MOTIVATION AND PERCEPTIONS

text: This study is aimed to investigate primary level students’ attitude and motivation and perceptions in learning English using Kahoot game. According to current development in Malaysian education field, 21st century learning style is more focused and given importance. Thus, Kahoot, game-based learning platform has been used to stimulate students’ from a rural area in southern Malaysia to learn English as their second language more effectively, actively and interestingly. The research design for this study is action research. Nine students were chosen as the target group for this study. The Kahoot game was conducted for three different topics in English after they completed the lesson each day. The data was collected using Attitude and Motivation Test Battery questionnaires consisting of 10 items, interview session with 5 semi-structured questions for three selected students and results of each Kahoot game played by the students. The data was analysed using descriptive analysis. The results of the questionnaires, interview session and results of the game were shown in figures and tables. Findings of the study show that all the nine students were able to engage actively in the game and they were able to master the target language effectively. They enjoy learning English using games and wish to have more games in future.


title: A Simplified Iron Loss Model for Laminated Magnetic Cores

text: This paper presents a new method for the prediction of iron losses in laminated magnetic cores. The method is based on the evaluation of the magnetization curves and loop shapes by which the iron losses, including hysteresis, excess, and classical eddy-current losses, are calculated from the loop area. The purpose of the method is to omit the numerical solution of the nonlinear diffusion equation (resulting from Maxwell equations) while maintaining reasonable accuracy of the iron losses. Therefore, the proposed method determines, using simple concepts, three magnetic field strength components that are responsible for generating the iron losses. The magnetic field component of the hysteresis loss is calculated by a static hysteresis model. The magnetic field component of the excess loss is calculated using the statistical loss theory. Determining the magnetic field component of the classical eddy-current loss is based upon the assumption that the flux distribution in the steel sheet is uniform, but, here, the eddy-current loss is implicitly enforced to be dependent on the magnetic characteristics of the material. The proposed method is applied to the calculation of iron losses in an electrical steel when the accuracy, stability, and generality of the method are discussed.


title: Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

text: Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e.g. hours vs. days) because they parallelize better. However, many RL problems require directed exploration because they have reward functions that are sparse or deceptive (i.e. contain local optima), and it is unknown how to encourage such exploration with ES. Here we show that algorithms that have been invented to promote directed exploration in small-scale evolved neural networks via populations of exploring agents, specifically novelty search (NS) and quality diversity (QD) algorithms, can be hybridized with ES to improve its performance on sparse or deceptive deep RL tasks, while retaining scalability. Our experiments confirm that the resultant new algorithms, NS-ES and two QD algorithms, NSR-ES and NSRA-ES, avoid local optima encountered by ES to achieve higher performance on Atari and simulated robots learning to walk around a deceptive trap. This paper thus introduces a family of fast, scalable algorithms for reinforcement learning that are capable of directed exploration. It also adds this new family of exploration algorithms to the RL toolbox and raises the interesting possibility that analogous algorithms with multiple simultaneous paths of exploration might also combine well with existing RL algorithms outside ES.


title: GlobeSen: an open interconnection framework based on named sensory date for IoT

text: How to efficiently interconnect ubiquitous wireless sensor networks (WSNs) and the Internet becomes an important challenge of Internet of Things. In this paper, we explore a route of information-centric networking (ICN) to solve this challenge and propose an open interconnection framework named GlobeSen. To overcome the problem that traditional ICN solution (such as NDN) is not suitable for resource-constrained WSNs, we present a new implementation of NDN, NDNs, for WSNs. Specifically, by extracting the spatio-temporal information and data type information of interest, we construct a globally unique name structure, and exploit the spatio-temporal relation operation as the matching method. Based on the new naming strategy and matching method, we further design packet forwarding and routing schemes. Moreover, we also develop a gateway system, NDNt, for protocol translating and an application, SenBrowser, to provide a user-friendly interface for generating interests and illustrating the returned sensory data. We implement a proof-of-concept prototype based on TelosB sensor nodes and an ARM development board, and conduct a series of experiments to evaluate the performance of GlobeSen.


title: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem

text: The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on an exact factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real-world data.


title: Simultaneous Localization and Mapping with Detection and Tracking of Moving Objects

text: Both simultaneous localization and mapping (SLAM) and detection and tracking of moving objects (DTMO) play key roles in robotics and automation. For certain constrained environments, SLAM and DTMO are becoming solved problems. But for robots working outdoors, and at high speeds, SLAM and DTMO are still incomplete. In earlier works, SLAM and DTMO are treated as two separate problems. In fact, they can be complementary to one another. In this paper, we present a new method to integrate SLAM and DTMO to solve both problems simultaneously for both indoor and outdoor applications. The results of experiments carried out with CMU Navlab8 and Navlab11 vehicles with the maximum speed of 45 mph in crowded urban and suburban areas verify the described work.


title: Credibilist occupancy grids for vehicle perception in dynamic environments

text: In urban environments, moving obstacles detection and free space determination are key issues for driving assistance systems and autonomous vehicles. When using lidar sensors scanning in front of the vehicle, uncertainty arises from ignorance and errors. Ignorance is due to the perception of new areas and errors come from imprecise pose estimation and noisy measurements. Complexity is also increased when the lidar provides multi-echo and multi-layer information. This paper presents an occupancy grid framework that has been designed to manage these different sources of uncertainty. A way to address this problem is to use grids projected onto the road surface in global and local frames. The global one generates the mapping and the local one is used to deal with moving objects. A credibilist approach is used to model the sensor information and to do a global fusion with the world-fixed map. Outdoor experimental results carried out with a precise positioning system show that such a perception strategy increases significantly the performance compared to a standard approach.


title: An evidential approach to map-building for autonomous vehicles

text: In this work, we examine the problem of constructing and maintaining a map of an autonomous vehicle’s environment for the purpose of navigation, using evidential reasoning. The inherent uncertainty in the origin of measurements of sensors demands a probabilistic approach to processing, or fusing, the new sensory information to build an accurate map. In this paper, the map is based on a two-dimensional (2-D) occupancy grid. The sensor readings are “fused” into the map using the Dempster–Shafer inference rule. This evidential approach with its multivalued hypotheses allows quantitative analysis of the quality of the data. The map building system is experimentally evaluated using sonar data from real environments.


title: Toward Abstractive Summarization Using Semantic Representations

text: We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-tograph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-totext generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on goldstandard AMR annotations and system parses show promising results. Code is available at: https://github.com/summarization


title: MANAGING HEALTH CARE SUPPLY CHAIN : TRENDS , ISSUES , AND SOLUTIONS FROM A LOGISTICS PERSPECTIVE

text: The U.S. healthcare industry is a large enterprise accounting for over 14.1% of the national economic output in 2001. It has been under pressure for cost containment and providing quality health care services to consumers. Its record of investing heavily on development of sophisticated drugs and diagnostic systems does not match that of technologies to manage its day-to-day operations. In order to achieve improved performance, healthcare supply chain must be efficient and integrated. The driver for this integration is logistics and supply chain management. This paper describes trends, issues and some solutions for logistics management for Health Care Supply Chain with concepts drawn from Industrial Engineering, and Operations Research disciplines applied to specific domains. A healthcare supply chain template utilizing Ecommerce strategy is presented. Use of simulation, optimization, and information sharing techniques are demonstrated to optimize purchasing and inventory policies. (


title: A study on peer startup process and initial offset placement in P2P live streaming systems

text: In this paper, we measure and study the peer startup process in PPLive, a popular commercial P2P streaming system, and focus on a fundamental issue in this aspect: how a peer initializes its buffer when it joins a channel, i.e., initial offset placement of peers' buffers in the startup stage. We build a general model of peer startup process in chunk-based P2P streaming systems and present an initial offset placement scheme we inferred from the measurement results, i.e., proportional placement (PP) scheme. With FP scheme, the initial buffer offset is set to the offset of the reference neighbor peer plus an advance proportional to the reference neighbor peer's offset lag or buffer width. We evaluate the performance of PP scheme and find it is stable when the placement is based on offset lag, but will be unstable when it is based on buffer width if the chunk fetching strategy and neighbor peer selection mechanism are not properly designed. We finally report our detailed measurement results of the peer startup process and initial offset placement algorithms used in PPLive. Our models and measurement results could be useful for guiding the analysis and design of buffering protocols for a real P2P live streaming system.


title: Rectal mucosal dissection commencing directly on the anorectal line versus commencing above the dentate line in laparoscopy-assisted transanal pull-through for Hirschsprung's disease: Prospective medium-term follow-up.

text:
BACKGROUND
In 2007, we began using the anorectal line (ARL) as the landmark for commencing rectal mucosal dissection (RMD) instead of the dentate line (DL) during laparoscopy-assisted transanal pull-through (L-TAPT) for Hirschsprung's disease (HD). We conducted a medium-term prospective comparison of postoperative fecal continence (POFC) between DL and ARL cases to follow our short-term study.

METHODS
POFC is assessed by scoring frequency of motions, severity of staining, severity of perianal erosions, anal shape, requirement for medications, sensation of rectal fullness, and ability to distinguish flatus from stool on a scale of 0 to 2 (maximum: 14).

RESULTS
Patient demographics were similar for ARL (2007-2014: n=33) and DL (1997-2006: n=41). There were no intraoperative complications and 2 cases of postoperative colitis in both ARL (6.1%) and DL (4.9%). Mean annual medium-term POFC scores for the 4-7 term of this study were consistently better in ARL: 9.7±1.4*, 10.1±1.6*, 10.6±1.6, and 11.3±1.4* in ARL and 8.6±1.5, 9.1±1.6, 9.8±1.9, 10.0±1.6 in DL (*: p<0.05).

CONCLUSIONS
Medium-term POFC is better when the ARL is used as the landmark for RMD during L-TAPT for HD.


title: Xpander: Towards Optimal-Performance Datacenters

text: Despite extensive efforts to meet ever-growing demands, today's datacenters often exhibit far-from-optimal performance in terms of network utilization, resiliency to failures, cost efficiency, incremental expandability, and more. Consequently, many novel architectures for high-performance datacenters have been proposed. We show that the benefits of state-of-the-art proposals are, in fact, derived from the fact that they are (implicitly) utilizing "expander graphs" (aka expanders) as their network topologies, thus unveiling a unifying theme of these proposals. We observe, however, that these proposals are not optimal with respect to performance, do not scale, or suffer from seemingly insurmountable deployment challenges. We leverage these insights to present Xpander, a novel datacenter architecture that achieves near-optimal performance and provides a tangible alternative to existing datacenter designs. Xpander's design turns ideas from the rich graph-theoretic literature on constructing optimal expanders into an operational reality. We evaluate Xpander via theoretical analyses, extensive simulations, experiments with a network emulator, and an implementation on an SDN-capable network testbed. Our results demonstrate that Xpander significantly outperforms both traditional and proposed datacenter designs. We discuss challenges to real-world deployment and explain how these can be resolved.


title: CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection

text: Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the CnnRnn Earthquake Detector (CRED), a detector based on deep neural networks. The network uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the timefrequency characteristics of the dominant phases in an earthquake signal from three component data recorded on a single station. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California and tested it with a F-score of 99.95. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. The model is applied to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 700 microearthquakes as small as -1.3 ML induced during hydraulic fracturing far away than the training region. The performance of the model is compared with STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise in lowering the detection threshold while minimizing false positive detection rates.


title: Neighborhood-Level LGBT Hate Crimes and Bullying Among Sexual Minority Youths: A Geospatial Analysis.

text: The goal of this study was to evaluate a novel measure of environmental risk factors for bullying among sexual minority youths. Data on lesbian, gay, bisexual, and transgender (LGBT) assault hate crimes were obtained from police records, geocoded, and then linked to individual-level data on bullying and sexual orientation from the 2008 Boston Youth Survey Geospatial Dataset (N = 1,292; 108 sexual minorities). Results indicated that sexual minority youths who reported relational and electronic bullying were more likely to reside in neighborhoods with higher LGBT assault hate crime rates. There was no asso- ciation between LGBT assault hate crimes and bullying among heterosexual youths, pro- viding evidence for specificity to sexual minority youth. Moreover, no relationships were observed between sexual minority bullying and neighborhood-level violent and property crimes, indicating that the results were specific to LGBT assault hate crimes.


title: Disease and pest prediction IoT system in orchard: A preliminary study

text: Each species of diseases and pests is considered to be harmful to plants and has a great adverse effect on agriculture. In addition to decreasing crop productivity and quality, pesticides or fungicides which cause environmental pollution are needed to kill diseases and pests. Therefore, the IoT system is designed to reduce the frequent use of insecticides and fungicides and to predict when the pests appear in order to lower the appearance of pests. Therefore, weather stations near the orchard have been installed and used to analyze the correlation between pests and weather data that had a large effect on pests. The prediction models have been created utilizing correlation information. In other words, we proposed a system that provides disease and pest prediction information so that farmers can quickly control them.


title: Mendeley: Recommendations for Researchers

text: For a researcher, keeping up with what is going on in their research field can be a difficult and time-consuming task. For example, a fresh PhD student may want to know what are the relevant papers matching their research interests. An assistant professor may like to be up-to-date with what their colleagues are publishing. A professor might want to be notified about funding opportunities relevant to the work done in their research group. Since the volume of published research and research activity is constantly growing, it is becoming increasingly more difficult for researchers to be able to manage and filter through the research information flow. In this challenging context, Mendeley's mission is to become the world's "research operating system". We do this not only by providing our well-know reference management system, but also by providing discovery capabilities for researchers on different kinds of entities, such as articles and profiles. In our talk, we will share Mendeley's experiences with building our article and profile recommendation systems, the challenges that we have faced and the solutions that we have put in place. We will discuss how we address different users' needs with our data and algorithm infrastructure to achieve good user experience.


title: Robust Image Registration Using Log-Polar Transform

text: This paper describes a hierarchical image registration algorithm for affine motion recovery. The algorithm estimates the affine transformation parameters necessary to register any two digital images misaligned due to rotation, scale, shear, and translation. The parameters are computed iteratively in a coarse-to-fine hierarchical framework using a variation of the Levenberg-Marquadt nonlinear least squares optimization method. This approach yields a robust solution that precisely registers images with subpixel accuracy. A log-polar registration module is introduced to accommodate arbitrary rotation angles and a wide range of scale changes. This serves to furnish a good initial estimate for the optimization-based affine registration stage. We demonstrate the hybrid algorithm on pairs of digital images subjected to large affine motion.


title: Singer Traits Identification using Deep Neural Network

text: The author investigates automatic recognition of singers’ gender and age through audio features using deep neural network (DNN). Features of each singing voice, fundamental frequency and Mel-Frequency Cepstrum Coefficients (MFCC) are extracted for neural network training. 10,000 singing voice from Smule’s Sing! Karaoke app is used for training and evaluation, and the DNN-based method achieves an average recall of 91% for gender classification and 36% for age identification.


title: Comparing Prediction Market Structures, With an Application to Market Making

text: Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Various market making algorithms have been proposed in the literature and deployed in practice, but there has been little effort to evaluate their benefits and disadvantages in a systematic manner. We introduce a novel experimental design for comparing market structures in live trading that ensures fair comparison between two different microstructures with the same trading population. Participants trade on outcomes related to a two-dimensional random walk that they observe on their computer screens. They can simultaneously trade in two markets, corresponding to the independent horizontal and vertical random walks. We use this experimental design to compare the popular inventory-based logarithmic market scoring rule (LMSR) market maker and a new information based Bayesian market maker (BMM). Our experiments reveal that BMM can offer significant benefits in terms of price stability and expected loss when controlling for liquidity; the caveat is that, unlike LMSR, BMM does not guarantee bounded loss. Our investigation also elucidates some general properties of market makers in prediction markets. In particular, there is an inherent tradeoff between adaptability to market shocks and convergence during market equilibrium.


title: Design and analysis of a hybrid energy harvester for self-powered sensor

text: This paper presents a slightly different concept of energy harvesting method as compared to the conventional energy harvesters. The new energy harvester of interest is known as Hybrid Energy Harvester (HEH). It comprises of the Piezoelectric and Magnetostrictive/Electromagnetic transducers integrated into a system. Theoretically, the hybrid energy harvester device developed is targeted to expand the vibration bandwidth as compared to the standalone energy harvesters, hence higher output power will be generated. To develop the hybrid energy harvester, mathematical models of each respective transducers are formulated, then simulations are carried out using the MATLAB/Simulink analysis tool. Next, a macro-scale prototype device is designed, fabricated and validated. From the experimental characterizations, it is unveiled that the maximum open circuit output voltage produced by the hybrid energy harvester is 6.23 V at a resonant frequency of 59.6 Hz.


title: Tracking port scanners on the IP backbone

text: Port scanning is the usual precursor to malicious attacks on today's Internet. Although many algorithms have been proposed for different aspects of the scan detection problem, their focused designed space is enterprise gateway level Intrusion Detection. Furthermore, we find few studies that track scanner behaviors over an extended period of time. Operating from a unique vantage point, the IP backbone, we put all the pieces together in designing and implementing a fast and accurate online port scan detection and tracking system. We introduce our flexible architecture, discuss trade-offs and design choices. Specifically, we go in depth to two design choices: the distinct counter data structure and the buffer size tuning. Our choice of a probabilistic counter is proved to have a low average error of 3.5%. The buffer size is derived using Lyapunov drift techniques on an equivalent queuing model. Our initial scanner tracking results show that the scanners' timing behavior follows a 90-10 curve. That is, 90% of scanners are active for a short time, with low scanning rates, while 10% are long term and fast scanners, with a few super-scanners lasting the entire duration of monitoring.


title: Evaluating enterprise resource planning ( ERP ) post-implementation problems in Egypt

text: The aim of the research is to identify and evaluate the main problems experienced in the ERP postimplementation stage of multinational, privately-owned Egyptian and governmental organizations in Egypt. Data gathering was achieved by means of a set of interviews and online questionnaire conducted to 50 companies implementing ERP in Egypt. The paper presents a descriptive analysis of the difficulties and problems encountered by organizations in Egypt following ERP implementation and how these have contributed to unsuccessful implementation overall.


title: Prediction of Indian election using sentiment analysis on Hindi Twitter

text: Sentiment analysis is considered to be a category of machine learning and natural language processing. It is used to extricate, recognize, or portray opinions from different content structures, including news, audits and articles and categorizes them as positive, neutral and negative. It is difficult to predict election results from tweets in different Indian languages. We used Twitter Archiver tool to get tweets in Hindi language. We performed data (text) mining on 42,235 tweets collected over a period of a month that referenced five national political parties in India, during the campaigning period for general state elections in 2016. We made use of both supervised and unsupervised approaches. We utilized Dictionary Based, Naive Bayes and SVM algorithm to build our classifier and classified the test data as positive, negative and neutral. We identified the sentiment of Twitter users towards each of the considered Indian political parties. The results of the analysis for Naive Bayes was the BJP (Bhartiya Janta Party), for SVM it was the BJP (Bhartiya Janta Party) and for the Dictionary Approach it was the Indian Nathional Congress. SVM predicted a 78.4% chance that the BJP would win more elections in the general election due to the positive sentiment they received in tweets. As it turned out, BJP won 60 out of 126 constituencies in the 2016 general election, far more than any other political party as the next party (the Indian National Congress) only won 26 out of 126 constituencies.


title: The double-edged sword of leader charisma: Understanding the curvilinear relationship between charismatic personality and leader effectiveness.

text: This study advanced knowledge on charisma by (a) introducing a new personality-based model to conceptualize and assess charisma and by (b) investigating curvilinear relationships between charismatic personality and leader effectiveness. Moreover, we delved deeper into this curvilinear association by (c) examining moderation by the leader's level of adjustment and by (d) testing a process model through which the effects of charismatic personality on effectiveness are explained with a consideration of specific leader behaviors. Study 1 validated HDS charisma (Hogan Development Survey) as a useful trait-based measure of charisma. In Study 2 a sample of leaders (N = 306) were assessed in the context of a 360-degree development center. In line with the too-much-of-a-good-thing effect, an inverted U-shaped relationship between charismatic personality and observer-rated leader effectiveness was found, indicating that moderate levels are better than low or high levels of charisma. Study 3 (N = 287) replicated this curvilinear relationship and further illustrated the moderating role of leader adjustment, in such a way that the inflection point after which the effects of charisma turn negative occurs at higher levels of charisma when adjustment is high. Nonlinear mediation modeling further confirmed that strategic and operational leader behaviors fully mediate the curvilinear relationship. Leaders low on charisma are less effective because they lack strategic behavior; highly charismatic leaders are less effective because they lack operational behavior. In sum, this work provides insight into the dispositional nature of charisma and uncovers the processes through which and conditions under which leader charisma translates into (in)effectiveness. (PsycINFO Database Record


title: Assessment of Factors Associated with Malnutrition among Under Five Years Age Children at Machakel Woreda, Northwest Ethiopia: A CaseControl Study

text: Introduction; malnutrition continues to be a significant public health and development concern not only in developing country but also in the world. It is a serious problem because it is causing the deaths of 3.5 million children under 5 years old peryear. Its magnitude is still highest in Ethiopia as well as in Amhara Region that remains a major public health problem. Objective: The main aim of this study is to assess associated factors of malnutrition on under five years children in Machakel Woreda. Methods: Unmatched Case control study was conducted. Cases were children of aged 6-59 months who have malnutrition (weight for height <2sd, weight for age ≤ 2Sd, height for age ≤ 2Sd, Mid-Upper Arm Circumstance (MUAC) <12 cm, if there is edema) and controls were 6-59 months of children those who have not malnutrition (weight for height ≥ 2sd, weight for age ≥ 2Sd, height for age ≥ 2sd, MUAC>12 cm, there is no edema). A consecutive sampling technique was employ to select study subjects for this study. Logistic regression was used to analyze data by using backward variable selection technique. Result: A total of 102 cases and 201 controls were included in the study with overall response rate of 94.4%. Sixty five (63.20%) of cases and 49 (24.40%) controls had fathers that cannot read and write. Thirty nine (38.23%) of cases and 44(21.89%) of controls had history of diarrheal episode. Those children whose family use drinking water from unprotected source were 3 times more likely to have malnutrition as compared to those children whose family use drinking water from protected source with [AOR=3.04, 95%CI (1.01, 9.17)]. Conclusion: The fining of this study revealed that inappropriate child carrying and feeding practice were strongly associated with under five malnutrition. Therefore, the responsible body should implement on nutritional intervention activities at all level of the community.


title: Enhancing the trust-based recommendation process with explicit distrust

text: When a Web application with a built-in recommender offers a social networking component which enables its users to form a trust network, it can generate more personalized recommendations by combining user ratings with information from the trust network. These are the so-called trust-enhanced recommendation systems. While research on the incorporation of trust for recommendations is thriving, the potential of explicitly stated distrust remains almost unexplored. In this article, we introduce a distrust-enhanced recommendation algorithm which has its roots in Golbeck's trust-based weighted mean. Through experiments on a set of reviews from Epinions.com, we show that our new algorithm outperforms its standard trust-only counterpart with respect to accuracy, thereby demonstrating the positive effect that explicit distrust can have on trust-based recommendations.


title: Trust mechanisms for cloud computing

text: Trust is a critical factor in cloud computing; in present practice it depends largely on perception of reputation, and self assessment by providers of cloud services. We begin this paper with a survey of existing mechanisms for establishing trust, and comment on their limitations. We then address those limitations by proposing more rigorous mechanisms based on evidence, attribute certification, and validation, and conclude by suggesting a framework for integrating various trust mechanisms together to reveal chains of trust in the cloud.


title: A new generation of microwave bandpass filters using multilayer ring resonators

text: A new generation of compact microwave bandpass filters (BPF) is proposed. The filters are based on multilayer ring resonators (MRR) technique which is applicable to both narrow band and ultra-wideband (UWB) bandpass filters. This class of filters has very compact circuit size and simple structure for fabrication. Multiple attenuation poles exist and can be simply placed to the left or right of the passband. The dimensions of the filters can be designed to around 2 mm × 2 mm × 1 mm. Out of the five filters presented, two of them are fabricated and measured. Good agreement between the simulation and measurements confirmed the design approach.


title: Power Amplifiers and Transmitters for RF and Microwave

text: The generation of RF/microwave power is required not only in wireless communications, but also in applications such as jamming, imaging, RF heating, and miniature dc/dc converters. Each application has its own unique requirements for frequency, bandwidth, load, power, efficiency, linearity, and cost. RF power is generated by a wide variety of techniques, implementations, and active devices. Power amplifiers are incorporated into transmitters in a similarly wide variety of architectures, including linear, Kahn, envelope tracking, outphasing, and Doherty. Linearity can be improved through techniques such as feedback, feedforward, and predistortion.


title: CMOS Outphasing Class-D Amplifier With Chireix Combiner

text: This letter presents a CMOS outphasing class-D power amplifier (PA) with a Chireix combiner. Two voltage-mode class-D amplifiers used in the outphasing system were designed and implemented with a 0.18-mum CMOS process. By applying the Chireix combiner technique, drain efficiency of the outphasing PA for CDMA signals was improved from 38.6% to 48% while output power was increased from 14.5 to 15.4 dBm with an adjacent channel power ratio of -45 dBc.


title: An extended Doherty amplifier with high efficiency over a wide power range

text: An extension of the Doherty amplifier architecture which maintains high efficiency over a wide range of output power (>6 dB) is presented. This extended Doherty amplifier is demonstrated experimentally with InGaP-GaAs HBTs at a frequency of 950 MHz. P/sub 1 dB/ is measured at 27.5 dBm with PAE of 46%. PAE of at least 39% is maintained for over an output power range of 12 dB backed-off from P/sub 1 dB/. This is an improvement over the classical Doherty amplifier, where high efficiency is typically obtained up to 5-6 dB backed-off from P/sub 1 dB/. Generalized design equations for the Doherty amplifier are derived to show a careful choice of the output matching circuit and device scaling parameters can improve efficiencies at lower output power.


title: Segmenting Salient Objects from Images and Videos

text: In this paper we introduce a new salient object segmentation method, which is based on combining a saliency measure with a conditional random field (CRF) model. The proposed saliency measure is formulated using a statistical framework and local feature contrast in illumination, color, and motion information. The resulting saliency map is then used in a CRF model to define an energy minimization based segmentation approach, which aims to recover well-defined salient objects. The method is efficiently implemented by using the integral histogram approach and graph cut solvers. Compared to previous approaches the introduced method is among the few which are applicable to both still images and videos including motion cues. The experiments show that our approach outperforms the current state-of-the-art methods in both qualitative and quantitative terms.


title: Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks

text: This paper introduces a new methodology for dynamic learning, visualization, and classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data. The method is based on an evolving spatiotemporal data machine of evolving spiking neural networks (SNNs) exemplified by the NeuCube architecture [1]. The method consists of several steps: mapping spatial coordinates of fMRI data into a 3-D SNN cube (SNNc) that represents a brain template; input data transformation into trains of spikes; deep, unsupervised learning in the 3-D SNNc of spatiotemporal patterns from data; supervised learning in an evolving SNN classifier; parameter optimization; and 3-D visualization and model interpretation. Two benchmark case study problems and data are used to illustrate the proposed methodology—fMRI data collected from subjects when reading affirmative or negative sentences and another one—on reading a sentence or seeing a picture. The learned connections in the SNNc represent dynamic spatiotemporal relationships derived from the fMRI data. They can reveal new information about the brain functions under different conditions. The proposed methodology allows for the first time to analyze dynamic functional and structural connectivity of a learned SNN model from fMRI data. This can be used for a better understanding of brain activities and also for online generation of appropriate neurofeedback to subjects for improved brain functions. For example, in this paper, tracing the 3-D SNN model connectivity enabled us for the first time to capture prominent brain functional pathways evoked in language comprehension. We found stronger spatiotemporal interaction between left dorsolateral prefrontal cortex and left temporal while reading a negated sentence. This observation is obviously distinguishable from the patterns generated by either reading affirmative sentences or seeing pictures. The proposed NeuCube-based methodology offers also a superior classification accuracy when compared with traditional AI and statistical methods. The created NeuCube-based models of fMRI data are directly and efficiently implementable on high performance and low energy consumption neuromorphic platforms for real-time applications.


title: Locked-in syndrome : a challenge for embodied cognitive science

text: Embodied approaches in cognitive science hold that the body is crucial for cognition. What this claim amounts to, however, still remains unclear. This paper contributes to its clarification by confronting three ways of understanding embodiment—the sensorimotor approach, extended cognition and enactivism—with Lockedin syndrome (LIS). LIS is a case of severe global paralysis in which patients are unable to move and yet largely remain cognitively intact. We propose that LIS poses a challenge to embodied approaches to cognition requiring them to make explicit the notion of embodiment they defend and its role for cognition. We argue that the sensorimotor and the extended functionalist approaches either fall short of accounting for cognition in LIS from an embodied perspective or do it too broadly by relegating the body only to a historical role. Enactivism conceives of the body as autonomous system and of cognition as sense-making. From this perspective embodiment is not equated with bodily movement but with forms of agency that do not disappear with body paralysis. Enactivism offers a clarifying perspective on embodiment and thus currently appears to be the framework in embodied cognition best suited to address the challenge posed by LIS.


title: Challenges of Computational Processing of Code-Switching

text: This paper addresses challenges of Natural Language Processing (NLP) on non-canonical multilingual data in which two or more languages are mixed. It refers to code-switching which has become more popular in our daily life and therefore obtains an increasing amount of attention from the research community. We report our experience that covers not only core NLP tasks such as normalisation, language identification, language modelling, part-of-speech tagging and dependency parsing but also more downstream ones such as machine translation and automatic speech recognition. We highlight and discuss the key problems for each of the tasks with supporting examples from different language pairs and relevant previous work.


title: SME e-readiness in Malaysia : Implications for Planning and Implementation

text: This study hoped to answer 2 main objectives. The first objective was to assess the level of e-readiness of SMEs in Northern Malaysia. The second objective was to investigate the factors contributing to the e-readiness of SMEs in Northern Malaysia. Questionnaires were distributed using a simple random sampling method to 300 SMEs in Penang, Kedah and Perlis. The findings of this study show that SMEs in Northern Malaysia are ready to go for e-business, e-commerce and Internet in general. The findings also showed that in general top management commitment and infrastructure and technology have significant impact on SMEs’ e-readiness. However, human capital, resistance to change, and information security do not have significant impact or contribution on e-readiness in SMEs.


title: Fame for sale: Efficient detection of fake Twitter followers

text: Fake followers are those Twitter accounts specifically created to inflate the number of followers of a target account. Fake followers are dangerous for the social platform and beyond, since they may alter concepts like popularity and influence in the Twittersphere—hence impacting on economy, politics, and society. In this paper, we contribute along different dimensions. First, we review some of the most relevant existing features and rules (proposed by Academia and Media) for anomalous Twitter accounts detection. Second, we create a baseline dataset of verified human and fake follower accounts. Such baseline dataset is publicly available to the scientific community. Then, we exploit the baseline dataset to train a set of machine-learning classifiers built over the reviewed rules and features. Our results show that most of the rules proposed by Media provide unsatisfactory performance in revealing fake followers, while features proposed in the past by Academia for spam detection provide good results. Building on the most promising features, we revise the classifiers both in terms of reduction of overfitting and cost for gathering the data needed to compute the features. The final result is a novel Class A classifier, general enough to thwart overfitting, lightweight thanks to the usage of the less costly features, and still able to correctly classify more than 95% of the accounts of the original training set. We ultimately perform an information fusion-based sensitivity analysis, to assess the global sensitivity of each of the features employed by the classifier. The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the novel issue of fake Twitter followers.


title: SVM incremental learning, adaptation and optimization

text: The objective of machine learning is to identify a model that yields good generalization performance. This involves repeatedly selecting a hypothesis class, searching the hypothesis class by minimizing a given objective function over the model’s parameter space, and evaluating the generalization performance of the resulting model. This search can be computationally intensive as training data continuously arrives, or as one needs to tune hyperparameters in the hypothesis class and the objective function. In this paper, we present a framework for exact incremental learning and adaptation of support vector machine (SVM) classifiers. The approach is general and allows one to learn and unlearn individual or multiple examples, adapt the current SVM to changes in regularization and kernel parameters, and evaluate generalization performance through exact leave-one-out error estimation. I. I NTRODUCTION SVM techniques for classification and regression provide powerful tools for learning models that generalize well even in sparse, high dimensional settings. Their success can be attributed to Vapnik’s seminal work in statistical learning theory [15] which provided key insights into the factors affecting generalization performance. SVM learning can be viewed as a practical implementation of Vapnik’s structural risk minimizationinduction principle which involves searching over hypothesis classes of varying capacity to find the model with the best generalization performance. SVM classifiers of the formf(x) = w ·Φ(x)+b are learned from the data{(xi, yi) ∈ R I m × {−1, 1} ∀ i ∈ {1, . . . , N}} by minimizing min w,b,ξ 1 2 ‖w‖ + C N ∑


title: Speaker-Independent Silent Speech Recognition From Flesh-Point Articulatory Movements Using an LSTM Neural Network

text: Silent speech recognition SSR converts nonaudio information such as articulatory movements into text. SSR has the potential to enable persons with laryngectomy to communicate through natural spoken expression. Current SSR systems have largely relied on speaker-dependent recognition models. The high degree of variability in articulatory patterns across different speakers has been a barrier for developing effective speaker-independent SSR approaches. Speaker-independent SSR approaches, however, are critical for reducing the amount of training data required from each speaker. In this paper, we investigate speaker-independent SSR from the movements of flesh points on tongue and lips with articulatory normalization methods that reduce the interspeaker variation. To minimize the across-speaker physiological differences of the articulators, we propose Procrustes matching-based articulatory normalization by removing locational, rotational, and scaling differences. To further normalize the articulatory data, we apply feature-space maximum likelihood linear regression and i-vector. In this paper, we adopt a bidirectional long short-term memory recurrent neural network BLSTM as an articulatory model to effectively model the articulatory movements with long-range articulatory history. A silent speech dataset with flesh-point articulatory movements was collected using an electromagnetic articulograph from 12 healthy and two laryngectomized English speakers. Experimental results showed the effectiveness of our speaker-independent SSR approaches on healthy as well as laryngectomy speakers. In addition, BLSTM outperformed the standard deep neural network. The best performance was obtained by the BLSTM with all the three normalization approaches combined.


title: Parallel Training for Deep Stacking Networks

text: The Deep Stacking Network (DSN) is a special type of deep architecture developed to enable and benefit from parallel learning of its model parameters on large CPU clusters. As a prospective key component of future speech recognizers, the architectural design of the DSN and its parallel training endow the DSN with scalability over a vast amount of training data. In this paper, we present our first parallel implementation of the DSN training algorithm. Particularly, we show the tradeoff between the time/memory saving via training parallelism and the associated cost arising from inter-CPU communication. Further, in phone classification experiments, we demonstrate a significantly lowered error rate using parallel full-batch training distributed over a CPU cluster, compared with sequential minibatch training implemented in a single CPU machine under otherwise identical experimental conditions and as exploited prior to the work reported in this paper.


title: Feature based Summarization of Customers' Reviews of Online Products

text: With the growing availability and popularity of opinion-rich resources such as review forums for the product sold online, choosing the right product from a large number of products have become difficult for the user. For trendy product, the number of customers’ opinions available can be in the thousands. It becomes hard for the customers to read all the reviews and if he reads only a few of those reviews, then he may get a biased view about the product. Makers of the products may also feel difficult to maintain, keep track and understand the customers’ views for the products. Several research works have been proposed in the past to address these issues, but they have certain limitations: The systems implemented are completely opaque, the reviews are not easier to perceive and are time consuming to analyze because of large irrelevant information apart from actual opinions about the features, the feature based summarization system that are implemented are more generic ones and static in nature. In this research, we proposed a dynamic system for feature based summarization of customers’ opinions for online products, which works according to the domain of the product. We are extracting online reviews for a product on periodic bases, each time after extraction, we carry out the following work: Firstly, identification of features of a product from customers' opinions is done. Next, for each feature, its corresponding opinions’ are extracted and their orientation or polarity (positive/negative) is detected. The final polarity of feature-opinions pairs is calculated. At last, feature based summarizations of the reviews are generated, by extracting the relevant excerpts with respect to each feature-opinions pair and placing it into their respective feature based cluster. These feature based excerpts can easily be digested by the user. © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of KES International.


title: Trust or consequences? Causal effects of perceived risk and subjective norms on cloud technology adoption

text: Cloud computing has become a popular alternative information curation solution for organizations. As more corporate proprietary information is stored in the Cloud, concerns about Cloud information security have also increased. This study investigates the causal effect of perceived risk and subjective norms on users’ trust intention to adopt Cloud technology. A partial least squares structural equation modeling (PLS-SEM) analysis was performed to assess latent variables and examine moderating effects to Cloud technology adoption. Our findings suggest that a user’s perceived risk and subjective norms have a significant effect on their trust intention vis-a-vis Cloud adoption, which leads to their decision on whether to adopt the Cloud technology. While a user’s attitudes do influence their intention to trust the Cloud, their attitude is not moderated by either perceived risk or subjective norms. On the other hand, a user’s perceived risk of the Cloud environment predominately moderates their knowledge and perceived behavioral control, which results in their knowledge and perceived behavioral control not having a direct effect on their intention to trust the Cloud. Moreover, a user’s


title: Managing flood disasters on the built environment in the rural communities of Zimbabwe: Lessons learnt

text: This article is about managing flood disasters affecting the built environment in the rural communities of Zimbabwe. Using Tsholotsho district in Matabeleland North province as a case study, the authors argue that flooding has adversely impacted the built environment through destroying infrastructure. The principal objectives of this study were to establish the impact of flood disasters on the built environment, to demarcate factors that perpetuate communities' vulnerabilities to flooding and to delineate challenges that negate the management of flood disasters in the built environment. This qualitative study was based on a purposive sample of 40 participants. Data were collected through semi-structured interviews and observation methods. The findings were that floods can damage human shelter, roads, bridges and dams. Locating homesteads near rivers and dams, using poor-quality construction materials, and lack of flood warning were found to perpetuate vulnerability to flooding. Poverty and costs of rebuilding infrastructure, lack of cooperation between the communities and duty-bearers, and failure to use indigenous knowledge were found to be impeding the management of flood disasters. The study concluded that flood disasters can wipe out community development gains accumulated over many years. Further, community vulnerability to flooding in the built environment is socially constructed. The study posits that addressing the root causes, reducing flood vulnerability and avoiding risk creation are viable options to development in the built environment. Lastly, reconstruction following flood disasters is arduous and gruelling, and not an easy exercise.