nils-herrmann/clean_scirepeval huggingface dataset:
HYRISE - A Main Memory Hybrid Storage Engine

In this paper, we describe a main memory hybrid database system called HYRISE, which automatically partitions tables into vertical partitions of varying widths depending on how the columns of the table are accessed. For columns accessed as a part of analytical queries (e.g., via sequential scans), narrow partitions perform better, because, when scanning a single column, cache locality is improved if the values of that column are stored contiguously. In contrast, for columns accessed as a part of OLTP-style queries, wider partitions perform better, because such transactions frequently insert, delete, update, or access many of the fields of a row, and co-locating those fields leads to better cache locality. Using a highly accurate model of cache misses, HYRISE is able to predict the performance of different partitionings, and to automatically select the best partitioning using an automated database design algorithm. We show that, on a realistic workload derived from customer applications, HYRISE can achieve a 20% to 400% performance improvement over pure all-column or all-row designs, and that it is both more scalable and produces better designs than previous vertical partitioning approaches for main memory systems.
You Talking to Me? A Corpus and Algorithm for Conversation Disentanglement

When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat (IRC) dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. This is, to our knowledge, the first such corpus for internet chat. We propose a graph-theoretic model for disentanglement, using discourse-based features which have not been previously applied to this task. The model’s predicted disentanglements are highly correlated with manual annotations.
Organizational Training within Digital Transformation: The ToOW Model

Information systems and technologies (IST) are the essence of up-to date organizations, and changes in this field are occurring at an uncontrollable pace, interrupting traditional business models and forcing organisations to implement new models of business. Social media tools represent a subset of these technologies that contribute to digital transformation (DT) of organizations and which are already used within organizational training contexts. However, the adoption of social media tools, by itself, does not guarantee such a transformation; changes in the organization’s culture and behaviour are also needed. Taking advantage of the DT technology enablers and realising the need for updated approaches to address organizational workers’ training, we propose a model to guide organizational training within DT. The model, called Training of Organizational Workers (ToOW), addresses the 2nd layer of the mobile Create, Share, Document and Training (m_CSDT) framework formerly described. The advantages foreseen for the model usage are twofold. On the one hand, the model acknowledges the crucial role that an organization plays in promoting a culture of continuous learning/training of its employees; on the other hand, the model provides guidance on setting up the training strategies and activities, as well as on the monitoring of training results achieved, which are measured according to the performance metrics considered within the organizational strategy.
Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic

Marr's levels of analysis-computational, algorithmic, and implementation-have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is to push the notion of rationality, often used in defining computational-level models, deeper toward the algorithmic level. We offer a simple recipe for reverse-engineering the mind's cognitive strategies by deriving optimal algorithms for a series of increasingly more realistic abstract computational architectures, which we call "resource-rational analysis."
Cognitive radio: brain-empowered wireless communications

Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.
Anomaly Detection in Graph : Unsupervised Learning , Graph-based Features and Deep Architecture

The ability to detect anomalies in a network is an increasingly important task in many applications. In this paper a combination of graph features and unsupervised learning methods is used to tackle anomaly detection problem in a multiattributed graph. The focus is on special types of anomalies: global, neighbor-based, and community-based. We propose an algorithm for generating a graph that contains non-overlaping anomaly types. Synthetically generated anomalous graphs are analyzed with two graph-based anomaly detection methods: Direct Neighbour Outlier Detection Algorithm (DNODA); Community Neighbour Algorithm (CNA), and two unsupervised learning techniques: Isolation Forest and Deep Autoencoders.
Adaptive postfiltering for quality enhancement of coded speech

AbstrucfAn adaptive postfiltering algorithm for enhancing the perceptual quality of coded speech is presented. The posffilter consists of a long-term postfilter section in cascade with a shortterm postfilter section and includes spectral tilt compensation and automatic gain control. The long-term section emphasizes pitch harmonics and attenuates the spectral valleys between pitch harmonics. The short-term section, on the other hand, emphasizes speech formants and attenuates the spectral valleys between formants. Both filter sections have poles and zeros. Unlike earlier postfilters that often introduced a substantial amount of muffling to the output speech, our postfilter significantly reduces this effect by minimizing the spectral tilt in its frequency response. As a result, this postlilter achieves noticeable noise reduction while introducing only minimal distortion in speech. The complexity of the postfilter is quite low. Variations of this postlilter are now being used in several national and international speech coding standards. This paper presents for the first time a complete description of our original postfiltering algorithm and the underlying ideas that motivated its development.
Critical Factors Influencing E-Banking Service Adoption in Jordanian Commercial Banks : A Proposed Model

The primary purpose of this current research is to conduct an investigation into the factors affecting the acceptance of E-Banking by clients who have access to the internet and elicit the opinion of non-internet users about E-Banking. To develop a theoretical framework model for the current study, a literature review relating to the technology acceptance model (TAM) and its various adaptations is being undertaken. The E-Banking has been chosen as an innovative service, whose possible adoption by customers will be investigated. It is believed that the successful adoption of E-Banking will be cost and convenience beneficial for all stakeholders in the financial sector. The current research model purposes an extension to the technology acceptance/ adoption models that account essential factor influence E-Banking services adoption in the Jordanian commercial banks.
Empirical Evaluation of User Experience in two Adaptive Mobile Application Prototypes

Today’s applications such as ubiquitous systems are more and more aware of user’s habits and the context of use. The features of products and the context of use will affect the human’s experiences and preferences about the use of device. Thus, user experience in user-product interaction has been regarded as an important research topic in the mobile application design area. The purpose of this paper is to clarify how user experience can be evaluated in adaptive mobile applications. The user experience evaluations were performed through interviews and observation while test users were using PDA-based adaptive mobile application prototypes. As a result, this paper presents the analysis of the test methods for further user experience evaluations.  Miscellaneous; Experimentation; Human Factors
Predictors of loneliness among older women and men in Sweden: A national longitudinal study.

Longitudinal research on loneliness in old age has rarely considered loneliness separately for men and women, despite gender differences in life experiences. The objective of this study was to examine the extent to which older women and men (70+) report feelings of loneliness with a focus on: (a) changes in reported loneliness as people age, and (b) which factors predict loneliness.



Data from the 2004 and 2011 waves of SWEOLD, a longitudinal national survey, was used (n = 587). The prediction of loneliness in 2011 by variables measured in 2004 and 2004-2011 variable change scores was examined in three logistic regression models: total sample, women and men. Variables in the models included: gender, age, education, mobility problems, depression, widowhood and social contacts.



Older people moved into and out of frequent loneliness over time, although there was a general increase in loneliness with age. Loneliness at baseline, depression increment and recent widowhood were significant predictors of loneliness in all three multivariable models. Widowhood, depression, mobility problems and mobility reduction predicted loneliness uniquely in the model for women; while low level of social contacts and social contact reduction predicted loneliness uniquely in the model for men.



This study challenges the notion that feelings of loneliness in old age are stable. It also identifies important gender differences in prevalence and predictors of loneliness. Knowledge about such differences is crucial for the development of effective policy and interventions to combat loneliness in later life.
Benchmarking Android Data Leak Detection Tools

In 2017, Android hit a global mobile market share of 88% which makes it the most popular mobile platform. Application stores, such as the Google Play Store, are offering millions of mobile applications to consumers, which are installed and updated on a daily basis. However, the security of those applications is a major concern. A thorough security analysis before the publication of each application is time and resource consuming. Hence, platform providers cannot and do not manually vet every application handed in for publication. Consequently, many malicious and vulnerable applications find their way to the app stores and through there to the end users’ devices. Those applications exhibit serious security issues, such as leaking of sensitive information. During the previous years, researchers proposed a myriad of techniques and tools to detect such issues. There also exist large scale taxonomies classifying such tools into different categories. However, it is unclear how these tools perform compared to each other. Such a comparison is almost infeasible, since most tools are no longer available or cannot be set up any more. In this work, we review static analysis tools for detecting data leaks in Android applications. Out of 87 tools in the vulnerability detection domain, we are able to obtain 22 tools. We then identify 5 tools in the data leak detection domain and run them. We run them on a given data set with known data leak vulnerabilities and compare their performance. Furthermore, we run the tools on a larger set of real-world applications to assess the prevalence of data leak issues in open-source Android applications. We propose our own approach—DistillDroid—to compare security analysis tools by normalising their interfaces. This simplifies result reproduction and extension to other security vulnerability domains. In addition, the user experience and usability is highly improved.
Fuzzy Joins Using MapReduce

Fuzzy/similarity joins have been widely studied in the research community and extensively used in real-world applications. This paper proposes and evaluates several algorithms for finding all pairs of elements from an input set that meet a similarity threshold. The computation model is a single MapReduce job. Because we allow only one MapReduce round, the Reduce function must be designed so a given output pair is produced by only one task, for many algorithms, satisfying this condition is one of the biggest challenges. We break the cost of an algorithm into three components: the execution cost of the mappers, the execution cost of the reducers, and the communication cost from the mappers to reducers. The algorithms are presented first in terms of Hamming distance, but extensions to edit distance and Jaccard distance are shown as well. We find that there are many different approaches to the similarity-join problem using MapReduce, and none dominates the others when both communication and reducer costs are considered. Our cost analyses enable applications to pick the optimal algorithm based on their communication, memory, and cluster requirements.
Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting

Customer Relationship Management (CRM) enjoys increasing attention as a countermeasure to switching behaviour of customers. Because foregone profits of (partially) defected customers are significant, an increase of the retention rate can be very profitable. In this paper, we focus on the treatment of a company’s most promising customers in a non-contractual setting. We build a model in order to predict partial defection by behaviorally-loyal clients using three classification techniques: Logistic regression, ARD Neural Networks and Random Forests. Classification accuracy (PCC) and area under the receiver operating characteristic curve (AUC) are used to evaluate classifier performance. Using real-life data from an FMCG retailer we show that future partial defection can be successfully predicted. Similar to direct-marketing applications, we find that past behavioral variables, more specifically RFM variables (recency, frequency, monetary value) are the best predictors of partial customer defection.
Value Sensitive Design and Information Systems

Sensitive Design is a theoretically grounded approach to the design of technology that accounts for human values in a principled and comprehensive manner throughout the design process. It employs an integrative and iterative tripartite methodology, consisting of conceptual, empirical, and technical investigations. We explicate Value Sensitive Design by drawing on three case studies. The first study concerns information and control of web browser cookies, implicating the value of informed consent. The second study concerns using high-definition plasma displays in an office environment to provide a window to the outside world, implicating the values of physical and psychological well-being and privacy in public spaces. The third study concerns an integrated land use, transportation, and environmental simulation system to support public deliberation and debate on major land use and transportation decisions, implicating the values of fairness, accountability, and support for the democratic process, as well as a highly diverse range of values that might be held by different stakeholders, such as environmental sustainability, opportunities for business expansion, or walkable neighborhoods. We conclude with direct and practical suggestions for how to engage in Value Sensitive Design.
Semi-Supervised Learning for Relation Extraction

This paper proposes a semi-supervised learning method for relation extraction. Given a small amount of labeled data and a large amount of unlabeled data, it first bootstraps a moderate number of weighted support vectors via SVM through a co-training procedure with random feature projection and then applies a label propagation (LP) algorithm via the bootstrapped support vectors. Evaluation on the ACE RDC 2003 corpus shows that our method outperforms the normal LP algorithm via all the available labeled data without SVM bootstrapping. Moreover, our method can largely reduce the computational burden. This suggests that our proposed method can integrate the advantages of both SVM bootstrapping and label propagation.
Predicting clicks: estimating the click-through rate for new ads

Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ranking has a strong impact on the revenue the search engine receives from the ads. Further, showing the user an ad that they prefer to click on improves user satisfaction. For these reasons, it is important to be able to accurately estimate the click-through rate of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. We show that we can use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads. We also show that using our model improves the convergence and performance of an advertising system. As a result, our model increases both revenue and user satisfaction.
Channel Capacity of Electromagnetic Nanonetworks in the Terahertz Band

Nanotechnology is enabling the development of devices in a scale ranging from one to a few hundred nanometers. Coordination and information sharing among these nano-devices will lead towards the development of future nanonetworks, rising new applications of nanotechnology in the medical, environmental and military fields. Despite the major progress in nano-device design and fabrication, it is still not clear how these atomically precise machines will communicate. The latest advancements in graphene- based electronics have opened the door to electromagnetic communication among nano-devices in the terahertz band (0.1-10 THz). This frequency band can potentially provide very large bandwidths, ranging from the entire band to several gigahertz- wide windows, depending on the transmission distance and the molecular composition of the channel. In this paper, the capacity of the terahertz channel is numerically evaluated by using a new terahertz propagation model, for different channel molecular compositions, and under different power allocation schemes. A novel communication technique based on the transmission of ultra-short pulses, less than one picosecond long, is motivated and quantitatively compared to the capacity- optimal power allocation scheme. The results show that for the very short range, up to a few tens of millimeters, the transmission of short pulses offer a realistic and still efficient way to exploit the terahertz channel.
Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns

Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR). This paper comprehensively reviews the state-of-the art in AV classification methods. To improve on previous methods, a new Local Binary Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust against low contrast and low quality fundus images; and it helps the process by including additional AV texture and shape information. Experimental results compare the performance of the new method with the state-of-the art; and also methods with different feature extraction and classification schemas.
Gesture Recognition: A Survey

With increasing use of computers in our daily lives, lately there has been a rapid increase in the efforts to develop a better human computer interaction interface. The need of easy to use and advance types of human-computer interaction with natural interfaces is more than ever. In the present framework, the UI (User Interface) of a computer allows user to interact with electronic devices with graphical icons and visual indicators, which is still inconvenient and not suitable for working in virtual environments. An interface which allow user to communicate through gestures is the next step in the direction of advance human computer interface. In the present paper author explore different aspects of gesture recognition techniques.
The many faces of publish/subscribe

Well adapted to the loosely coupled nature of distributed interaction in large-scale applications, the publish/subscribe communication paradigm has recently received increasing attention. With systems based on the publish/subscribe interaction scheme, subscribers register their interest in an event, or a pattern of events, and are subsequently asynchronously notified of events generated by publishers. Many variants of the paradigm have recently been proposed, each variant being specifically adapted to some given application or network model. This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many variations on the theme of publish/subscribe are classified and synthesized. In particular, their respective benefits and shortcomings are discussed both in terms of interfaces and implementations.
Cognitive effort drives workspace configuration of human brain functional networks.

Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers (N = 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the β-band (16-32 Hz) and γ-band (32-63 Hz) networks, compared with both lower (α-band; 8-16 Hz) and higher (high γ-band; 63-125 Hz) frequency intervals. Workspace configuration of β-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting <2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.