Query: Representation learning with complete semantic description of knowledge graphs

Relevant passages:
Modeling Relation Paths for Representation Learning of Knowledge Bases <=SEP=> Representation learning of knowledge bases aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text. The source code of this paper can be obtained from https://github.com/mrlyk423/ relation_extraction.

Transition-based Knowledge Graph Embedding with Relational Mapping Properties <=SEP=> Many knowledge repositories nowadays contain billions of triplets, i.e. (head-entity, relationship, tail-entity), as relation instances. These triplets form a directed graph with entities as nodes and relationships as edges. However, this kind of symbolic and discrete storage structure makes it difficult for us to exploit the knowledge to enhance other intelligenceacquired applications (e.g. the QuestionAnswering System), as many AI-related algorithms prefer conducting computation on continuous data. Therefore, a series of emerging approaches have been proposed to facilitate knowledge computing via encoding the knowledge graph into a low-dimensional embedding space. TransE is the latest and most promising approach among them, and can achieve a higher performance with fewer parameters by modeling the relationship as a transitional vector from the head entity to the tail entity. Unfortunately, it is not flexible enough to tackle well with the various mapping properties of triplets, even though its authors spot the harm on performance. In this paper, we thus propose a superior model called TransM to leverage the structure of the knowledge graph via pre-calculating the distinct weight for each training triplet according to its relational mapping property. In this way, the optimal function deals with each triplet depending on its own weight. We carry out extensive experiments to compare TransM with the state-of-the-art method TransE and other prior arts. The performance of each approach is evaluated within two different application scenarios on several benchmark datasets. Results show that the model we proposed significantly outperforms the former ones with lower parameter complexity as TransE.

Semantic Data Integration for Knowledge Graph Construction at Query Time <=SEP=> The evolution of the Web of documents into a Web of services and data has resulted in an increased availability of data from almost any domain. For example, general domain knowledge bases such as DBpedia or Wikidata, or domain specific Web sources like the Oxford Art archive, allow for accessing knowledge about a wide variety of entities including people, organizations, or art paintings. However, these data sources publish data in different ways, and they may be equipped with different search capabilities, e.g., SPARQL endpoints or REST services, thus requiring data integration techniques that provide a unified view of the published data. We devise a semantic data integration approach named FuhSen that exploits keyword and structured search capabilities of Web data sources and generates on-demand knowledge graphs merging data collected from available Web sources. Resulting knowledge graphs model semantics or meaning of merged data in terms of entities that satisfy keyword queries, and relationships among those entities. FuhSen relies on both RDF to semantically describe the collected entities, and on semantic similarity measures to decide on relatedness among entities that should be merged. We empirically evaluate the results of FuhSen data integration techniques on data from the DBpedia knowledge base. The experimental results suggest that FuhSen data integration techniques accurately integrate similar entities semantically into knowledge graphs.

TransG : A Generative Model for Knowledge Graph Embedding <=SEP=> Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence. This paper proposes a novel generative model (TransG) to address the issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples. The new model can discover latent semantics for a relation and leverage a mixture of relationspecific component vectors to embed a fact triple. To the best of our knowledge, this is the first generative model for knowledge graph embedding, and at the first time, the issue of multiple relation semantics is formally discussed. Extensive experiments show that the proposed model achieves substantial improvements against the state-of-the-art baselines.

Representation Learning of Knowledge Graphs with Entity Descriptions <=SEP=> Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous lowdimensional space. Most methods concentrate on learning representations with knowledge triples indicating relations between entities. In fact, in most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods. In this paper, we propose a novel RL method for knowledge graphs taking advantages of entity descriptions. More specifically, we explore two encoders, including continuous bag-of-words and deep convolutional neural models to encode semantics of entity descriptions. We further learn knowledge representations with both triples and descriptions. We evaluate our method on two tasks, including knowledge graph completion and entity classification. Experimental results on real-world datasets show that, our method outperforms other baselines on the two tasks, especially under the zero-shot setting, which indicates that our method is capable of building representations for novel entities according to their descriptions. The source code of this paper can be obtained from https://github.com/xrb92/DKRL.

Smart Societies, Infrastructure, Technologies and Applications <=SEP=> We describe a new approach to building high performance nonstop infrastructure for scalable AI and cloud computing. With AI as the technological driving force behind future smart cities and smart societies, we will need powerful new nonstop AI infrastructures at both the cloud level and at the edge.

Warm-white light-emitting diode with yellowish orange SiALON ceramic phosphor. <=SEP=> A warm-white light-emitting diode (LED) without blending of different kinds of phosphors is demonstrated. An approach that consists of a blue LED chip and a wavelength-conversion phosphor is carried out. The phosphor is a newly developed yellowish orange CaEuSiAlON ceramic phosphor with high efficiency. The CIE1931 chromaticity coordinates (x, y) are (0.458, 0.414), the color temperature is 2750 K, and the luminous efficacy of this LED is 25.9 lm/W at room temperature and with a forward-bias current of 20 mA. The chromaticity of the assembled LED is more thermally stable than that of a LED with a conventional oxide phosphor (YAG:Ce) because of the better thermal stability of the oxynitride phosphor.

A Parallel Algorithm for Enumerating All Maximal Cliques in Complex Network <=SEP=> Efficient enumeration of all maximal cliques in a given graph has many applications in graph theory, data mining and bio informatics. However, the exponentially increasing computation time of this problem confines the scale of the graph. Meanwhile, recent researches show that many networks in our world are complex networks involving massive data. To solve the maximal clique problem in the real-world scenarios, this paper presents a parallel algorithm Peamc (parallel enumeration of all maximal cliques) which exploits several new and effective techniques to enumerate all maximal cliques in a complex network. Furthermore, we provide a performance study on a true-life call graph with up to 2,423,807 vertices and 5,317,183 edges. The experimental results show that Peamc can find all the maximal cliques in a complex network with high efficiency and scalability

Evaluating mobile apps for breathing training: The effectiveness of visualization <=SEP=> Deep and slow breathing exercises can be an effective adjunct in the treatment of stress, anxiety, post-traumatic stress disorder, chronic pain and depression. Breathing techniques are traditionally learned in courses with trainers and/or with materials such as audio CDs for home practice. Recently, mobile apps have been proposed as novel breathing training tools, but to the best of our knowledge no research has focused on their evaluation so far. In this paper, we study three different designs for breathing training apps. The first employs audio instructions as in traditional training based on audio CDs, while the other two include visualizations of the breathing process, representative of those employed in current breathing training apps. We carry out a thorough analysis, focusing on users’ physiological parameters as well as subjective perception. One visualization produces better results both objectively (measured deepness of breath) and subjectively (users’ preferences and perceived effectiveness) than the more traditional audio-only design. This indicates that a visualization can contribute to the effectiveness of breathing training apps. We discuss which features could have allowed one visualization (but not the other) to obtain better results than traditional audio-only instructions.

Software Architecture Quality Measurement Stability and <=SEP=> Over the past years software architecture has become an important sub-field of software engineering. There has been substantial advancement in developing new technical approaches to start handling architectural design as an engineering discipline. Measurement is an essential part of any engineering discipline. Quantifying the quality attributes of the software architecture will reveal good insights about the architecture. It will also help architects and practioners to choose the best fit of alternative architectures that meets their needs. This work paves the way for researchers to start investigating ways to measure software architecture quality attributes. Measurement of these qualities is essential for this sub-field of software engineering. This work explores Stability and Understandability of software architecture, several metrics that affect them, and literature review of these qualities. Keywords—Software Engineering; Software Architecture; Quality Attributes; Stability; Understandability

Universal Adversarial Perturbations <=SEP=> Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such perturbations, albeit being quasi-imperceptible to the human eye. We further empirically analyze these universal perturbations and show, in particular, that they generalize very well across neural networks. The surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers. It further outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images.

Detection Based on Structural Properties <=SEP=> Phishing attacks pose a serious threat to end-users and commercial institutions alike. Majority of the present day phishing attacks employ e-mail as their primary carrier, in order to allure unsuspecting victims to visit the masqueraded website. While the recent defense mechanisms focus on detection by validating the authenticity of the website, very few approaches have been proposed which concentrate on detecting e-mail based phishing attacks based on the structural properties inherently present in the phishing e-mail. Also, phishing attacks growing in ingenuity as well as sophistication render most of existing browser based solutions weak. In this paper, we propose a novel technique to discriminate phishing e-mails from the legitimate e-mails using the distinct structural features present in them. The derived features, together with oneclass Support Vector Machine (SVM), can be used to efficiently classify phishing e-mails before it reaches the users inbox, essentially reducing the human exposure. Our prototype implementation sits between a user's mail transfer agent (MTA) and mail user agent (MUA) and processes each arriving e-mail even before it reaches the inbox. Using live email data, we demonstrate that our approach is able to detect a wide range of phishing e-mails with minimal performance overhead

Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison <=SEP=> Measures of an athlete’s heart rate variability (HRV) have shown potential to be of use in the prescription of training. However, little data exists on elite athletes who are regularly exposed to high training loads. This case study monitored daily HRV in two elite triathletes (one male: 22 year, $$ \dot{V}$$ O2max 72.5 ml kg min−1; one female: 20 year, $$ \dot{V}$$ O2max 68.2 ml kg min−1) training 23 ± 2 h per week, over a 77-day period. During this period, one athlete performed poorly in a key triathlon event, was diagnosed as non-functionally over-reached (NFOR) and subsequently reactivated the dormant virus herpes zoster (shingles). The 7-day rolling average of the log-transformed square root of the mean sum of the squared differences between R–R intervals (Ln rMSSD), declined towards the day of triathlon event (slope = −0.17 ms/week; r 2 = −0.88) in the NFOR athlete, remaining stable in the control (slope = 0.01 ms/week; r 2 = 0.12). Furthermore, in the NFOR athlete, coefficient of variation of HRV (CV of Ln rMSSD 7-day rolling average) revealed large linear reductions towards NFOR (i.e., linear regression of HRV variables versus day number towards NFOR: −0.65%/week and r 2 = −0.48), while these variables remained stable for the control athlete (slope = 0.04%/week). These data suggest that trends in both absolute HRV values and day-to-day variations may be useful measurements indicative of the progression towards mal-adaptation or non-functional over-reaching.

Evolutionary Algorithm for Extractive Text Summarization <=SEP=> Text summarization is the process of automatically creating a compressed version of a given document preserving its information content. There are two types of summarization: extractive and abstractive. Extractive summarization methods simplify the problem of summarization into the problem of selecting a representative subset of the sentences in the original documents. Abstractive summarization may compose novel sentences, unseen in the original sources. In our study we focus on sentence based extractive document summarization. The extractive summarization systems are typically based on techniques for sentence extraction and aim to cover the set of sentences that are most important for the overall understanding of a given document. In this paper, we propose unsupervised document summarization method that creates the summary by clustering and extracting sentences from the original document. For this purpose new criterion functions for sentence clustering have been proposed. Similarity measures play an increasingly important role in document clustering. Here we’ve also developed a discrete differential evolution algorithm to optimize the criterion functions. The experimental results show that our suggested approach can improve the performance compared to sate-of-the-art summarization approaches.

TEM Horn Antenna Loaded With Absorbing Material for GPR Applications <=SEP=> A novel exponentially tapered TEM horn antenna is presented. It is fed directly by a coax through a compact transition section and possesses a wide bandwidth, good radiation characteristic, and easy integration with microwave circuits. Two specially tapered metal flares have been designed to make a wideband characteristic and reduce the reflection. To overcome aperture reflections at low frequencies and unbalanced aperture field at higher frequencies, an arc surface is added at the end of the flare plates, and an absorbing material is loaded on the outer surface of the flare section. Measured results show that the presented antenna has a wide bandwidth from 0.83 to more than 12.8 GHz under a return loss <;-10 dB, small input reflection, a moderate gain from 0 dBi at 0.83 GHz to 10 dBi at 12 GHz, and a good radiation characteristics in the time domain, which can be suitable for ground penetrating radar (GPR) applications.

Eye Contact Detection via Deep Neural Networks <=SEP=> With the presence of ubiquitous devices in our daily lives, effectively capturing and managing user attention becomes a critical device requirement. While gaze-tracking is typically employed to determine the user’s focus of attention, gaze-lock detection to sense eyecontact with a device is proposed in [16]. This work proposes eye contact detection using deep neural networks, and makes the following contributions: (1) With a convolutional neural network (CNN) architecture, we achieve superior eye-contact detection performance as compared to [16] with minimal data pre-processing ; our algorithm is furthermore validated on multiple datasets, (2) Gaze-lock detection is improved by combining head pose and eye-gaze information consistent with social attention literature, and (3) We demonstrate gaze-locking on an Android mobile platform via CNN model compression.

Efficient parallel skyline processing using hyperplane projections <=SEP=> The skyline of a set of multi-dimensional points (tuples) consists of those points for which no clearly better point exists in the given set, using component-wise comparison on domains of interest. Skyline queries, i.e., queries that involve computation of a skyline, can be computationally expensive, so it is natural to consider parallelized approaches which make good use of multiple processors. We approach this problem by using hyperplane projections to obtain useful partitions of the data set for parallel processing. These partitions not only ensure small local skyline sets, but enable efficient merging of results as well. Our experiments show that our method consistently outperforms similar approaches for parallel skyline computation, regardless of data distribution, and provides insights on the impacts of different optimization strategies.

Fear of being laughed at and social anxiety : A preliminary psychometric study <=SEP=> The present study examines the relationship between questionnaire measures of social phobia and gelotophobia. A sample of 211 Colombian adults filled in Spanish versions of the Social Anxiety and Distress scale (SAD; Watson & Friend, 1969), the Fear of Negative Evaluation scale (FNE; Watson & Friend, 1969) and the GELOPH<15> (Ruch & Proyer, 2008). Results confirmed that both Social Anxiety and Distress and Fear of Negative Evaluation scale overlapped with the fear of being laughed at without being identical with it. The SAD and FNE correlated highly with the GELOPH<15> but not all high scorers in these scales expressed a fear of being laughed at. Furthermore, an item factor analysis yielded three factors that were mostly loaded by items of the respective scales. This three-factor structure was verified using confirmatory factor analysis. A measure model where one general factor of social anxiety was specified, or another one where two different factors were defined (gelotophobia vs. social anxiety assessed by SAD and FNE) showed a very poor fit to the data. It is concluded that the fear of being laughed cannot fully be accounted for by these measures of social phobia.

Caraoke: An E-Toll Transponder Network for Smart Cities <=SEP=> Electronic toll collection transponders, e.g., E-ZPass, are a widely-used wireless technology. About 70% to 89% of the cars in US have these devices, and some states plan to make them mandatory. As wireless devices however, they lack a basic function: a MAC protocol that prevents collisions. Hence, today, they can be queried only with directional antennas in isolated spots. However, if one could interact with e-toll transponders anywhere in the city despite collisions, it would enable many smart applications. For example, the city can query the transponders to estimate the vehicle flow at every intersection. It can also localize the cars using their wireless signals, and detect those that run a red-light. The same infrastructure can also deliver smart street-parking, where a user parks anywhere on the street, the city localizes his car, and automatically charges his account. This paper presents Caraoke, a networked system for delivering smart services using e-toll transponders. Our design operates with existing unmodified transponders, allowing for applications that communicate with, localize, and count transponders, despite wireless collisions. To do so, Caraoke exploits the structure of the transponders' signal and its properties in the frequency domain. We built Caraoke reader into a small PCB that harvests solar energy and can be easily deployed on street lamps. We also evaluated Caraoke on four streets on our campus and demonstrated its capabilities.

Optimal Dynamic Hotel Pricing ∗ <=SEP=> We analyze a confidential reservation database provided by a luxury hotel, ”hotel 0”, based in a major US city that enables us to observe individual reservations and cancellations at a daily frequency over a 37 month period. We show how the hotel sets prices for various classes of customers and how its prices vary over time. Hotel pricing is a challenging high-dimensional problem since hotels must not only set prices for each current date, but they must also quote prices for a range of future dates, room types and customer types. Our data reveal the full path of room rates quoted for different types of rooms and customers in advance of the arrival date. We find large within and between week variability in room prices, as well as huge seasonal variations in average daily rates and occupancy rates, not only for the hotel we study but also for its direct competitors. We formulate and estimate a structural model of optimal dynamic hotel pricing using the Method of Simulated Moments (MSM). The estimated model provides accurate predictions of the actual prices set by this firm and resulting paths of bookings and cancellations. Prices quoted for bookings generally decline as the arrival date approaches on nonbusy days, but can increase dramatically in the final days before arrival on busy days when there is a high probability of sell-out. Hotel 0’s prices co-move strongly with its competitors’ prices and we show that a simple price-following strategy where hotel 0 undercuts its competitors’ average price by a fixed percentage provides a good first approximation to its pricing behavior. However we show that simple price-following is suboptimal: when hotel 0 expects to sell out, it is optimal to depart from price-following and increase its price significantly above its competitors. On non-busy days, it is not optimal for hotel 0 to cut its prices in the final days before arrival to try to increase occupancy unless its competitors cut their prices. Though price-following has the superficial appearance of collusive behavior mediated by the use of a commercial revenue management system (RMS), our results suggest that hotel 0’s pricing is competitive and is best described as a rational best response to its beliefs about demand and the prices set by its competitors. In fact hotel 0 regularly disregards the recommended prices of its RMS, which it regards as too low compared to the prices it actually sets.

Mathematical Modeling of Hexacopter <=SEP=> The purpose of this paper is to present the basic mathematical modeling of microcopters, which could be used to develop proper methods for stabilization and trajectory control. The microcopter taken into account consists of six rotors, with three pairs of counter-rotating fixedpitch blades. The microcopter is controlled by adjusting the angular velocities of the rotors which are spun by electric motors. It is assumed as a rigid body, so the differential equations of the microcopter dynamics can be derived from both the Newton-Euler and Euler-Lagrange equations. Euler-angle parametrization of three-dimensional rotations contains singular points in the coordinate space that can cause failure of both dynamical model and control. In order to avoid singularities, the rotations of the microcopter are parametrized in terms of quaternions. This choice has been made taking into consideration the linearity of quaternion formulation, their stability and efficiency.

Sources of variance in current sense of humor inventories : How much substance , how much method variance ? <=SEP=> The present study investigates the relationship of self-report inventories of "sense of humor" and behavioral measures of humor as well as their location in the Eysenckian PEN system. 110 male and female adults in the ages from 17 to 83 years answered the following inventories: SHRQ (Martin and Lefcourt 1984), SHQZ (Ziv 1981), SHQ-3 revised (Svebak 1993), CHS (Martin and Lefcourt 1983), MSHS (Thorson and Powell 1993), HIS (Bell, McGhee, and Duffey 1986), 3 WD-K (Ruch 1983), CPPT (Köhler and Ruch 1993), TDS (Murgatroyd, Rushton, Apter, and Ray 1978), STCI-T (Ruch, Freiss, and Köhler 1993), and EPQ-R (Eysenck, Eysenck, and Barrett 1985). Reliability of the humor scales is examined and convergent and discriminant validity of homologous scales of humor appreciation and humor creation is determined. Behavioral measures and self-report instruments yield only meager correlations. While humor appreciation and humor creation form distinct traits in the behavioral measures, they can not be validly discriminated in the self-reports. Factor analysis of self-report inventories yields that the sense of humor is composed of the two orthogonal dimensions of cheerfulness and seriousness. Extraversion is predictive of cheerfulness, low seriousness, and quantity of humor production. Psychoticism is associated with low seriousness, wit and quality of humor production. Finally, emotional stability correlates with cheerfulness. All in all, the general state of the art in the assessment of the sense of humor and its components appears to be far from being satisfactory. Sources of variance 2 -

An interactive tool for manual, semi-automatic and automatic video annotation <=SEP=> The annotation of image and video data of large datasets is a fundamental task in multimedia information retrieval and computer vision applications. The aim of annotation tools is to relieve the user from the burden of the manual annotation as much as possible. To achieve this ideal goal, many different functionalities are required in order to make the annotations process as automatic as possible. Motivated by the limitations of existing tools, we have developed the iVAT: an interactive Video Annotation Tool. It supports manual, semi-automatic and automatic annotations through the interaction of the user with various detection algorithms. To the best of our knowledge, it is the first tool that integrates several computer vision algorithms working in an interactive and incremental learning framework. This makes the tool flexible and suitable to be used in different application domains. A quantitative and qualitative evaluation of the proposed tool on a challenging case study domain is presented and discussed. Results demonstrate that the use of the semi-automatic, as well as the automatic, modality drastically reduces the human effort while preserving the quality of the annotations. 2014 Elsevier Inc. All rights reserved.

Marginalized Denoising Dictionary Learning With Locality Constraint <=SEP=> Learning good representation for images is always a hot topic in machine learning and pattern recognition fields. Among the numerous algorithms, dictionary learning is a well-known strategy for effective feature extraction. Recently, more discriminative sub-dictionaries have been built by Fisher discriminative dictionary learning with specific class labels. Different types of constraints, such as sparsity, low rankness, and locality, are also exploited to make use of global and local information. On the other hand, as the basic building block of deep structure, the auto-encoder has demonstrated its promising performance in extracting new feature representation. To this end, we develop a unified feature learning framework by incorporating the marginalized denoising auto-encoder into a locality-constrained dictionary learning scheme, named marginalized denoising dictionary learning. Overall, we deploy low-rank constraint on each sub-dictionary and locality constraint instead of sparsity on coefficients, in order to learn a more concise and pure feature spaces meanwhile inheriting the discrimination from sub-dictionary learning. Finally, we evaluate our algorithm on several face and object data sets. Experimental results have demonstrated the effectiveness and efficiency of our proposed algorithm by comparing with several state-of-the-art methods.

Sentiment classification: The contribution of ensemble learning <=SEP=> Article history: Received 27 August 2012 Received in revised form 1 August 2013 Accepted 5 August 2013 Available online 15 August 2013

All your network are belong to us: a transport framework for mobile network selection <=SEP=> Mobile devices come with an assortment of networks: WiFi in two different frequency bands, each of which can run in infrastructure-mode, WiFi-Direct mode, or ad hoc mode; cellular radios, which can run in LTE/4G, 3G, or EDGE modes; and Bluetooth. But how should an app choose which network to use? There is no systematic solution to this problem today: in current practice the choice is almost always left to the user, who usually has no idea what's best. In fact, what's best for a user depends on the app's performance objectives (throughput, delay, object load time, etc.) and the user's constraints on cost and battery life. Besides, what's best for a single user or app must be balanced with what's best for the wireless network as a whole (individual optimality vs. social utility). This paper introduces Delphi, a transport-layer module to resolve these issues. Delphi has three noteworthy components: "local learning", in which a mobile device estimates or infers useful properties of different networks efficiently, "property sharing", in which mobile devices share what they learn with other nearby devices, and "selection", in which each node selects a network using what it has observed locally and/or from its neighbors.

Identifying Ghanaian pre-service teachers ’ readiness for computer use : A Technology Acceptance Model approach <=SEP=> This study extends the technology acceptance model to identify factors that influence technology acceptance among pre-service teachers in Ghana. Data from 380 usable questionnaires were tested against the research model. Utilising the extended technology acceptance model (TAM) as a research framework, the study found that: pre-service teachers’ pedagogical beliefs, perceived ease of use, perceived usefulness of computer technology and attitude towards computer use to be significant determinants of actual use of computer technology. Results obtained employing multiple stepwise regression analysis revealed that: (1) pre-service teachers’ pedagogical beliefs significantly influenced both perceived ease of use and perceived usefulness, (2) both perceived ease of use and perceived usefulness influence attitude towards computer use and attitude towards computer use significantly influences pre-service teachers’ actual use of computers. However, statistically, perceived ease of use did not significantly influence perceived usefulness. The findings contribute to the literature by validating the TAM in the Ghanaian context and provide several prominent implications for the research and practice of technology integration development.

Community Detection based on Distance Dynamics <=SEP=> In this paper, we introduce a new community detection algorithm, called Attractor, which automatically spots communities in a network by examining the changes of "distances" among nodes (i.e. distance dynamics). The fundamental idea is to envision the target network as an adaptive dynamical system, where each node interacts with its neighbors. The interaction will change the distances among nodes, while the distances will affect the interactions. Such interplay eventually leads to a steady distribution of distances, where the nodes sharing the same community move together and the nodes in different communities keep far away from each other. Building upon the distance dynamics, Attractor has several remarkable advantages: (a) It provides an intuitive way to analyze the community structure of a network, and more importantly, faithfully captures the natural communities (with high quality). (b) Attractor allows detecting communities on large-scale networks due to its low time complexity (O(|E|)). (c) Attractor is capable of discovering communities of arbitrary size, and thus small-size communities or anomalies, usually existing in real-world networks, can be well pinpointed. Extensive experiments show that our algorithm allows the effective and efficient community detection and has good performance compared to state-of-the-art algorithms.

Discovering affective regions in deep convolutional neural networks for visual sentiment prediction <=SEP=> In this paper, we address the problem of automatically recognizing emotions in still images. While most of current work focus on improving whole-image representations using CNNs, we argue that discovering affective regions and supplementing local features will boost the performance, which is inspired by the observation that both global distributions and salient objects carry massive sentiments. We propose an algorithm to discover affective regions via deep framework, in which we use an off-the-shelf tool to generate N object proposals from a query image and rank these proposals with their objectness scores. Then, each proposal's sentiment score is computed using a pre-trained and fine-tuned CNN model. We combine both scores and select top K regions from the N candidates. These K regions are regarded as the most affective ones of the input image. Finally, we extract deep features from the whole-image and the selected regions, respectively, and sentiment label is predicted. The experiments show that our method is able to detect the affective local regions and achieve state-of-the-art performances on several popular datasets.

Localization & Segmentation of Indian Car Number Plate system : A Projection based Multistage Approach <=SEP=> Enormous incorporation of information technologies into all aspects of recent life caused demand for processing vehicles as conceptual resources in information systems. This can be achieved by a human agent, or by special intelligent equipment which is be able to recognize vehicles by their number plates in a real environment and reflect it into conceptual resources. Because of this, various recognition techniques have been developed and number plate recognition systems are today used in various traffic and security applications, such as parking, access and border control, or tracking of stolen cars. This work deals with problematic from field of artificial intelligence, machine vision and neural networks in construction of an automatic car number plate Recognition (ACNPR). This techniques deals with enhanced shadow removal algorithm, along with horizontal and vertical projection method for number plate localization & segmentation..Work comparatively deals with methods achieving invariance of systems towards image illumination, translations and various light conditions during the capture.