Reviewerly/exHarmony huggingface dataset:
An exploration of user–facet interaction in collaborative-based personalized multiple facet selection

The huge amount of irrelevant and unimportant information have led to the need of using personalization in selecting the information which is relevant to searchers’ interest. Personalized faceted search has been a potential tool to support searchers to retrieve appropriate information effectively by navigating a list of selected multiple facets or categories based on the search results. To develop an effective personalized faceted search, the selection of relevant multiple facets is an important mechanism. Collaborative-based personalization was introduced for facet selection. Recently, Artificial Neural Network (ANN) has been reported that it performs better than other state-of-the-art Collaborative Filtering techniques for predicting single facet. However, analyzing the collaborative interests for multiple facets has not been studied. It is challenging if the interaction of the users on multiple facets is based on the information associated with the preferences of similar users over a group of multiple facets. This paper proposes an ANN-based facet predictive model that makes use of the collaborative-based personalization concept for multiple facet selection. The architecture of the proposed model is based on two suitable interaction schemes, the Early interaction and the Late interaction schemes. Based on experimental results, the performance was evaluated in terms of prediction accuracy and computation time. The results showed that the proposed model based on an effective interaction scheme obtained significant improvement on the prediction of personal interests on multiple facets.
Short Text Stream Clustering via Frequent Word Pairs and Reassignment of Outliers to Clusters

Short text stream clustering is an important but challenging task since massive amounts of text are generated from different social media. Given streams of texts, the proposed method clusters the streams of texts based on the frequently occurring word pairs (not necessarily consecutive) in texts. It detects outliers in the clusters and reassigns the outliers to appropriate clusters using the semantic similarity between the outliers and the clusters based on the dynamically computed similarity thresholds. Thus the proposed method efficiently deals with the concept drift problem. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art short text stream clustering algorithms by a statistically significant margin on several short text datasets.
A multi-GPU biclustering algorithm for binary datasets

Graphics Processing Units technology (GPU) and CUDA architecture are one of the most used options to adapt machine learning techniques to the huge amounts of complex data that are currently generated. Biclustering techniques are useful for discovering local patterns in datasets. Those of them that have been implemented to use GPU resources in parallel have improved their computational performance. However, this fact does not guarantee that they can successfully process large datasets. There are some important issues that must be taken into account, like the data transfers between CPU and GPU memory or the balanced distribution of workload between the GPU resources. In this paper, a GPU version of one of the fastest biclustering solutions, BiBit, is presented. This implementation, named gBiBit, has been designed to take full advantage of the computational resources offered by GPU devices. Either using a single GPU device or in its multi-GPU mode, gBiBit is able to process large binary datasets. The experimental results have shown that gBiBit improves the computational performance of BiBit, a CPU parallel version and an early GPU version, called ParBiBit and CUBiBit, respectively. gBiBit source code is available at https://github.com/aureliolfdez/gbibit.
Explainable AI and the philosophy and practice of explanation

Considerations of the nature of explanation and the law are brought together to argue that computed accounts of AI systems' outputs cannot function on their own as explanations of decisions informed by AI. The important context for this inquiry is set by Article 22(3) of GDPR. The paper looks at the question of what an explanation is from the point of view of the philosophy of science – i.e. it asks not what counts as explanatory in legal terms, or what an AI system might compute using provenance metadata, but rather what explanation as a social practice consists in, arguing that explanation is an illocutionary act, and that it should be considered as a process, not a text. It cannot therefore be computed, although computed accounts of AI systems are likely to be important inputs to the explanatory process.
Abstract Syntax Tree Based Source Code Antiplagiarism System for Large Projects Set

The paper deals with the issue of detecting plagiarism in source code, which we unfortunately encounter when teaching subjects dealing with programming and software development. Many students want to simplify the completion of the course and therefore submit modified source codes of their classmates or even those found on the Internet. Some try to modify the source code e.g. by changing the identifiers of classes, methods and variables to different ones, by changing the corresponding loops, by introducing new methods or by changing the order of methods in the source code or in other ways. We focused directly on this problem and designed our own anti-plagiarism system that we describe in this paper. The designed system consists of three parts during which the source code is processed using six designed algorithms. The basis is the processing of the source code and its transformation into an abstract syntax tree, consisting of two types of nodes, which is then vectorized using our modified DECKARD algorithm. The vectors are then clustered and stored in a database from which similar parts of the source code can be searched. The output of the system is then the final report containing a list of matches with similarities of all works that have been added to the database until then. The designed anti-plagiarism system is finally compared with the success of plagiarism detection performed by the two most used anti-plagiarism tools, namely JPlag and MOSS. It is evaluated on assignments elaborated by students from the courses dealing with object-oriented programming at our faculty.
On the Social Implications of Collective Adaptive Systems

Many Collect ive Adaptive Systems (CASs) exist in nature: think of ant colonies, where large collectives of ants operate autonomously but interact with other ants and the environment to provide resilient global behaviors that sustain their colony. Following scientific studies that were aimed at understanding and predicting the evolution of these systems, and fueled by technological advances, research has started to investigate CAS engineering: the methods, tools, and techniques for building CASs. This naturally leads to a vision where collectives of humans and computational elements, situated both in the digital and physical worlds, collaborate to give rise to "intelligent" collective behavior supporting novel kinds of applications and services. Humans can be involved in two ways: both as users and as components of the CAS, in the sense that human behaviors and limitations are often integral to the system description. This has significant social implications that need to be considered by CAS researchers: in this paper, we share a discussion that took place between some experts thinking about CAS engineering, focusing on the social implication of CASs and related open research challenges. We hope that this provides a useful context for future research projects, research grant proposals, and research directions.
Analysis of farmers’ food price volatility and Nigeria’s growth enhancement support scheme

Food prices in Nigeria have become significantly higher and more volatile since 2012. The purpose of this research was to find out what affects farmers’ participation in the growth enhancement support scheme (GESS) in the country. We determined the effect of the GESS on the ease of access to market information and agricultural inputs that influence price volatility at farm gate level. A total of 2100 rural farmers were sampled across Nigeria’s six geopolitical zones. Result from the use of recursive bivariate probit model showed that farmers depended on the GESS for the resolution of food price volatility by providing food market information and agricultural inputs that bring down the incidence and amount of anxiety-impelled price rise in Nigeria. The results advocated for the need to improve the GESS in line with the agricultural transformation agenda (ATA) by cutting down the deterrents mostly linked with the use of mobile phones, and the distance of registration and assemblage centres. In extension and contribution, the findings suggest that smallholder farmers can be part of the volatility solution when they are provided with rural roads and transportation to get their product to the market, and technology to receive and share the latest market information on prices.
Low Resource and Domain Specific English to Khasi SMT and NMT Systems

Machine translation systems for low resource languages face challenges in terms of quality and understanding. Our work focus on the translations for English to Khasi using two epitomes of translations using statistical and neural machine translation approaches. As part of this system development, we built an English-Khasi parallel dataset from existing domain specific literature. The quality of translations of statistical machine translation (SMT) and neural machine translation (NMT) systems for low resource and domain specific setting are substantially analyzed considering automatic and subjective evaluation techniques.
Semi-Supervised Face Frontalization in the Wild

Synthesizing a frontal view face from a single nonfrontal image, i.e. face frontalization, is a task of practical importance in a wide range of facial image analysis applications. However, to train the frontalization model in a supervised manner, most existing face frontalization methods rely on the availability of nonfrontal-frontal face pairs (typically from the Multi-PIE dataset) captured in a constrained environment. Such approaches, in return, limit the generalizability of their application to unconstrained scenarios. Unfortunately, although a large amount of in-the-wild face datasets are available, they cannot easily be utilized for face frontalization training since the nonfrontal and frontal facial images are not paired. To train a frontalization network which generalizes well to both constrained and unconstrained environments, we propose a semi-supervised learning framework which effectively uses both (labeled) indoor and (unlabeled) outdoor faces. Specifically, to achieve this goal, this article presents a Cycle-Consistent Face Frontalization Generative Adversarial Network (CCFF-GAN) which consists of both (1) the supervised and (2) the unsupervised components. For (1), we use the indoor paired (labeled) data to learn a roughly accurate frontalization network which may not generalize well to outdoor (in-the-wild) scenarios. For (2), to cope with the generalization issue, the unsupervised part uses the unpaired (unlabeled) images under the perceptual cycle consistency constraint in the semantic feature space to generalize the network from controlled (indoor) to uncontrolled (outdoor) environment. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art face frontalization methods, especially under the in-the-wild scenarios.
Unsupervised Text Feature Learning via Deep Variational Auto-encoder

Dimensionality reduction plays an important role in the data processing of machine learning and data mining, which makes the processing of high-dimensional data more efficient. Dimensionality reduction can extract the low-dimensional feature representation of high-dimensional data, and an effective dimensionality reduction method can not only extract most of the useful information of the original data, but also realize the function of removing useless noise. The dimensionality reduction methods can be applied to all types of data, especially image data. Although the supervised learning method has achieved good results in the application of dimensionality reduction, its performance depends on the number of labeled training samples. With the growing of information from internet, marking the data requires more resources and is more difficult. Therefore, using unsupervised learning to learn the feature of data has extremely important research value. In this paper, an unsupervised multilayered variational auto-encoder model is studied in the text data, so that the high-dimensional feature to the low-dimensional feature becomes efficient and the low-dimensional feature can retain mainly information as much as possible. Low-dimensional feature obtained by different dimensionality reduction methods are used to compare with the dimensionality reduction results of variational auto-encoder (VAE), and the method can be significantly improved over other comparison methods.
Effective approaches to combining lexical and syntactical information for code summarization

Summary Natural language summaries of source codes are important during software development and maintenance. Recently, deep learning based models have achieved good performance on the task of automatic code summarization, which encode token sequence or abstract syntax tree (AST) of code with neural networks. However, there has been little work on the efficient combination of lexical and syntactical information of code for better summarization quality. In this paper, we propose two general and effective approaches to leveraging both types of information: a convolutional neural network that aims to better extract vector representation of AST node for downstream models; and a Switch Network that learns an adaptive weight vector to combine different code representations for summary generation. We integrate these approaches into a comprehensive code summarization model, which includes a sequential encoder for token sequence of code and a tree based encoder for its AST. We evaluate our model on a large Java dataset. The experimental results show that our model outperforms several state‐of‐the‐art models on various metrics, and the proposed approaches contribute a lot to the improvements.
Algebraic dynamics of k-valued fuzzy cognitive maps and its stabilization

Fuzzy Cognitive Maps (FCMs) as knowledge-based tools are widely implemented for knowledge representation and reasoning. To reach final equilibrium states is essential for the results of causal reasoning in practical applications by using FCMs. However, the dynamics and the stabilization problem of FCMs are still not adequately studied so far. In this article, the algebraic dynamics of k-valued fuzzy cognitive maps and its stabilization is analytically and quantitatively investigated. Based on the technique of semi-tensor product (STP), k-valued FCMs are firstly converted into discrete-time linear representation. General formulas are obtained to calculate the number of fixed points and limit cycles of finite-state FCMs, and a necessary and sufficient criterion to determine attractors is proposed, by means of which an algorithm for calculating all attractors and their basins are achieved. Furthermore, the stabilization problem of k-valued FCMs is discussed. By introducing controls to certain concepts in FCMs, the system can be globally stabilized in equilibrium states. The related necessary and sufficient criterion is proposed and the corresponding method to achieve the global stabilization of FCMs under constant controls is given. Examples are shown to demonstrate the effectiveness and feasibility of the proposed scheme.
Blind 3D-Printing Watermarking Using Moment Alignment and Surface Norm Distribution

The recent development of 3D printing technology has brought concerns about its potential misuse, such as in copyright infringement and crimes. Although there have been many studies on blind 3D mesh watermarking for the copyright protection of digital objects, methods applicable to 3D printed objects are rare. In this paper, we propose a novel blind watermarking algorithm for 3D printed objects with applications for copyright protection, traitor tracing, object identification, and crime investigation. Our method allows us to embed a few bits of data into a 3D-printed object and retrieve it by 3D scanning without requiring any information about the original mesh. The payload is embedded on the object's surface by slightly modifying the distribution of surface norms, that is, the distance between the surface and the center of gravity. It is robust to resampling and can work with any 3D printer and scanner technology. In addition, our method increases the capacity and resistance by subdividing the mesh into a set of bins and spreading the data over the entire surface to negate the effect of local printing artifacts. The method's novelties include extending the vertex norm histogram to a continuous surface and the use of 3D moments to synchronize a watermark signal in a 3D-printing context. In the experiments, our method was evaluated using a public dataset against center, orientation, minimum and maximum norm misalignments; a printing simulation; and actual print/scan experiments using a standard 3D printer and scanner.
Towards a new hybrid game model: designing tangible experiences

In this paper we intend to present a theoretical discussion on the definition and specificity of hybrid games and tangible experiences, grounded in the research project “FlavourGame”. The project is developing a serious game for kids aged 10-12 years old to generate discussion and sense-making around nutrition, more specifically everyday choices of nourishment and food. We believe hybrid games are presenting themselves as a new realm of gaming, opening new possibilities, making use of the best of both worlds - physical and virtual. Therefore, we are now working in the creation of a hybrid game model (combining the digital component based on a TUI - tangible user interface with a physical board game). FlavourGame aims to become a game design model for platforms mixing real, fictional and digital worlds merged through physical interactions and rich sensory experiences, unusual in videogames. We intend to present some of the ideas we have been brainstorming and drafting, built on past experiences, in order to design a research path in the direction of the development of a hybrid game model based on embodied conversation and player participation.
Transitive Sequential Pattern Mining for Discrete Clinical Data

Electronic health records (EHRs) contain important temporal information about disease progression and patients. However, mining temporal representations from discrete EHR data (e.g., diagnosis, medication, or procedure codes) for use in standard Machine Learning is challenging. We propose a transitive Sequential Pattern Mining approach (tSPM) to address the temporal irregularities involved in recording discrete records in EHRs. We perform experiments to compare the classification performance metrics for predicting "true" diagnosis between traditional sequential pattern mining (SPM) and the proposed tSPM algorithms across multiple diseases. We demonstrate that transitive approach is superior to the traditional SPM in mining temporal representations for diagnosis prediction.
A reliable cross-site user generated content modeling method based on topic model

Nowadays, social network sites (SNSs) have been significant platforms for content sharing in our daily life. With the emergence of different kinds of social network sites and users' diverse needs for content sharing, their content sharing practices are generally taken place in multiple SNSs. To construct models that can characterize users' content sharing practices in a composite context constituted by multiple social network sites (cross-site user generated content modeling) has been an emerging research topic in web data mining and human behavior research. However, previous methods such as Dirichlet Multinomial Mixture model (DMM), Biterm Topic Model (BTM), Twitter-LDA and MultiLDA have limited representation ability or are based on unreliable assumption, which cannot characterize the user generated content (UGC) accurately from the perspective of multiple SNSs. In this paper, we first conduct an empirical study to investigate the characteristics of users' content sharing practices in cross-site context, based on which we propose a more reliable cross-site UGC model named CrossSite-LDA (C-LDA). We then evaluate the performances of the C-LDA model with four state-of-the-art models based on the two data sets sampled from Weibo–Douban and Facebook–Twitter. Results show that the C-LDA has better performances in perplexity, word coherence, topic KL divergence, UCI and UMass metrics compared with existing models, which suggests its superior accuracy on modeling users' content characteristics in cross-site context.
Requirements for computerized tools to design information security policies

Information security is a hot topic nowadays, and while top-class technology exists to safeguard information assets, organizations cannot rely on technical controls alone. Information security policy (ISP) is one of the most important formal controls when organizations work with implementing information security. However, designing ISPs is a challenging task for information security managers and to ease the burden, computerized tools have been suggested to support this design task. One important prerequisite for developing such tools is the requirements. However, existing research has, to a very limited extent, synthesized existing requirements. Against this backdrop, this study aims to elicit a set of requirements, anchored in existing ISP research, for computerized tools that support ISP design. First, we summarize existing ISP research into 14 requirement themes. Second, we suggest a set of user stories that operationalize these requirement themes from an information security manager's perspective. Third, we suggest another set of user stories that operationalize the same requirement themes from an ISP user's perspective. In total, we suggest 28 user stories that can act as a starting point for both researchers and practitioners when developing computerized tools that provide ISP design support for information security managers.
Enhanced realignment criterion vs linear entanglement witnesses

Abstract It is shown that the enhanced (nonlinear) realignment criterion is equivalent to the family of linear criteria based on correlation tensor. These criteria generalize the original (linear) realignment criterium and give rise to the family of entanglement witnesses. An appropriate limiting procedure is proposed which leads to a novel class of witnesses which are as powerful as the enhanced realignment criterion.