Reviewerly/exHarmony huggingface dataset:
Principle-to-program

Outfit recommendation automatically pairs user-specified reference clothing with the most suitable complement from online shops. Wearing aesthetically is a criterion for matching such fashion items. Fashion style tells a lot about one's personality and emerges from how people assemble clothing outfit from seemingly disjoint items into a cohesive concept. Experts share fashion tips showcasing their compositions to public where each item has both an image and textual meta-data. Also, retrieving products from online shopping catalogs in response to such real-world image query is essential for outfit recommendation. Our earlier tutorial focused on style and compatibility in fashion recommendation mostly based on metric and deep learning approaches. Herein, we cover several other aspects of fashion recommendation using visual signals (e.g., cross-scenario retrieval, attribute classification) and combine text input (e.g., interpretable embedding) as well. Each section concludes walking through programs executed on Jupyter workstation using real-world data sets.
Fast Quantum Subroutines for the Simplex Method

What would Dantzig do with a quantum computer? It is unlikely we will ever find out the answer to this question. However, we can try to understand if the simplex method can be implemented on a quantum computer, and this might have piqued Dantzig’s interest. The paper “Fast Quantum Subroutines for the Simplex Method” gives a quantum implementation of an iteration of the simplex method, in which the basis inverse is never explicitly computed: the quantum computer takes as input the current basis and certifies optimality or outputs the next basis. Because computing the basis inverse is expensive, this can lead to an asymptotically faster algorithm in terms of the problem size: in the best case, the quantum algorithm can identify pivots in essentially linear time! This, however, comes at the cost of worse dependence on some numerical parameters: all these tradeoffs are discussed in the full article.
A Survey On QoS aware Global Optimization for Dynamic Web Service Composition

To vanquish stage and language dependence, web organizations have transformed into a creating and promising development to structure and collect complex applications out of single internet programming parts. Web administration piece allows a begetter to make applications by benefiting from the administration situated engineering worldview. Administration arranged engineering (SOA) is an auxiliary model, which is unruffled basically of administrations. Its extension is to create appropriated frameworks in heterogeneous situations with area straightforwardness, accessibility, disclosure, and reusability of its segments. The quick development in the use of SOA brief the development of the quantity of administrations distributed. Web administrations offer tremendous possibilities with a huge number of basic administrations manage the cost of by a few specialist co-ops on discrete servers. Web administration organization includes the alliance of a few existing web administrations to give increasingly perplexing and amazing administration. The goal of administration piece is to reuse existing web administrations and forming them into a mind boggling process. Web administration structure method could be characterized to syntactic based and semantic based synthesis. Be that as it may, the procedure of creation is analysed as a high perplexing errand because of numerous reasons. In addition, the investigation and worldwide advancement assume a noteworthy job in a WSC.
DeepSpot

Recently opinion spam has been widespread on online review websites and has received significant research attention. Existing approaches to detecting online opinion spam can be categorized into three groups: (1) review behavior-based approaches, which use metadata associated with user review behavior and product profile, (2) language-based approaches, which focus on the characteristics of the language that the opinion spammers use, and (3) graph-based approaches, where various user-review-product networks are constructed for node connectivity and similarity analysis. Unfortunately, all the aforementioned approaches have their limitations. In this paper, we introduce a holistic system, DeepSpot, for fake review detection. DeepSpot recognizes the true and fake reviews based on both the real human-posted reviews and the synthetic machine-generated reviews leveraging sentiment classification. Specifically, DeepSpot augments the original reviews with synthetic reviews using the encoder-decoder neural networks trained by the positive and negative reviews, respectively. Extensive experiments on real-world data showed that DeepSpot outperformed the state-of-the-art approaches in terms of various effectiveness metrics for recognizing true and fake reviews.
University Affiliated Schools – the Key to a Modern Economy

The paper briefly discusses the labor market requirements and need of new school education models to face knowledge economy challenges. The survey of worldwide university integrated schools is given, which proves such institutions as an effective example for todays' education for tomorrows' jobs. The concept and experience in integration between the Technical University-Sofia and the Technological school “Electronic Systems”, integrated with the university is considered. Finally the results of close cooperation between school and university are covered.
Research on the Quality Prediction of Online Chinese Question Answering Community Answers Based on Comments

With the rapid development of online Community Question Answer (CQA), a large volume of valuable data has been accumulated in CQA sites, as well as a huge number of low-quality answers. To improve the user-friendliness of CQA sites and help users find high-quality answers quickly, in this paper, we propose a supervised learning model to evaluate the quality of answers on Chinese CQA sites. We build a quality evaluation model based on the pairwise learning-to-rank algorithm and combine a set of features to rank answers according to their quality. In the quality evaluation process, we also propose an innovative type of feature named "the sentiment polarity of comments". Experimental results on real CQA data show that our method can efficiently produce a quality-ranking list of answers. Moreover, the proposed sentiment polarity feature can improve the performance of the quality evaluation model significantly.
Stereoscopic Visual Discomfort Prediction Using Multi-scale DCT Features

Prior approaches to the problem of visual discomfort prediction (VDP) for stereo/3D images are built for the uncompressed image. This paper presents a novel VDP method based on the compressed image by using multi-scale discrete cosine transform (MsDCT). Three types of visual discomfort features, including basic disparity intensity (BDI), disparity gradient energy (DGE) and disparity texture complexity (DTC), are extracted from two-dimensional (2-D) DCT coefficients. Additionally, a multi-scale transformation approach based on the different sizes of transform units is applied to obtain the multi-scale sub-features for each of the features. Then, through experimental comparison, a random forest regressor is chosen to fuse twenty-three sub-features to get the final objective prediction value of the S3D images. Experimental results conducted on two datasets show that the proposed method improves the prediction accuracy compared to those of recent S3D visual (dis)comfort predictors.
InsideOut: Playing with Real-time Video Images of the Gastrointestinal Tract via Imaging Capsules

An imaging capsule is a pill-shaped device used to record images of the user's gastrointestinal tract. Existing research mainly focuses on the imaging capsule's functional perspective, while its experiential perspective is usually overlooked. To explore this, we introduce a playful system called InsideOut where the player wears an iPad to display the real-time images of their gastrointestinal tract captured by an imaging capsule. Players can interact with the images by performing bodily movements which are mapped to image transformations such as rotation and scaling. With this work, we aim to inspire game designers and researchers to design playful and engaging experiences around imaging capsules. Our work might further inspire future works using other bodily-integrated technologies to facilitate playful experiences.
Research on the Uniqueness and Characteristic Development of University Library Cultural Services

Characteristic development is a process of intentionally solidifying and cultivating something's uniqueness. This article analyzes the uniqueness of university library cultural services in comparison with those provided by other service institutions. Furthermore, this article reveals the differences between cultural services and other types of library services. Based on these analyses, this constructive research finds a way to achieve cultural services' characteristic development by taking service factors as the breakthrough point. The main strategies include building a professional service team, focusing on user demands, constructing characteristic resources, optimizing service spaces and facilities, and upgrading service brands; all of these strategies should be developed round the issue of culture. Finally, corresponding to the above strategies, this paper article the measures and effects of Wuhan University Library cultural services to share their experience.
Intelligent classification on images of Dongba ancient books

Dongba hieroglyphs are the only living hieroglyphs in the world today and are still used by people. The various ritual activities in Dongba culture are passed down through the Dongba hieroglyphs in the form of books. In view of the current classification and preservation of Dongba ancient books, it is a matter of labour-intensive time for translation, cataloguing, integration, and classification, so combining the methods of convolutional neural networks (CNN), an intelligent classification model is established to directly identify and classify ancient images. As the structure of the CNN has a great influence on the final recognition accuracy, a network structure that is most suitable for image recognition of Dongba ancient books is proposed. Experiments are performed on the Dongba image base established by myself and the result proves that the feasibility and effectiveness of this method.
Analysis of social network user behaviour and its influence

As a communication tool, social networks have become an important part of people’s daily lives. The proliferation of data in the big data era has spurred waves of research. Twitter is one of the most popular sites among all social network platforms. Twitter API allows researchers to easily study us er behaviour and influence. In this paper, we use data obtained through the Twitter API to study the influence maximization problem based on the relationship graph of the social network and information dissemination model. In the current independent cascade propagation model, the influence weights between nodes are based largely on the fixed-value assumption and the randomized probability of acceptance; however, this does not conform to real life. The influence weight between users is closely related to the strength of relationships, content of communication, and so on. Focusing on user relation-ships, we introduce an improved weighted cascade mode combined with a heuristic algorithm to find an approximate solution to the problem of influence maximization. Example analysis indicates that the improved weighted cascade model can obtain a more significant and influential node set compared to conventional methods.
Novel Teaching-learning-based Optimization Algorithm for Design of Digital Filters

An improved teaching-learning-based optimization algorithm named NTLBO is proposed for IIR digital design in this paper. Conventional mathematical methods have failed when reduced order adaptive models were used for the purposes of identification of problems. NTLBO utilizes a multi-learning strategy and opposition learning to overcome this disadvantage of the basic TLBO. The quasi-opposition-based learning has been applied to increase the diversity of solutions and broaden the search space to improve the global search ability. The multi-learning strategy makes local search more effective so as to speed up the convergence. To make a tradeoff between exploration and exploitation properly, the teaching factor is redesigned to increase the likelihood of solutions jumping out of local optima. Experiments are carried out on the classical examples and comparisons are made as well. The results indicate that the NTLBO algorithm achieved preferable performance in both reduced and same order models of IIR digital filters.
Dynamic Multi-Agent Systems: Conceptual Framework, Automata-Based Modelling and Verification

We study dynamic multi-agent systems (dmass). These are multi-agent systems with explicitly dynamic features, where agents can join and leave the system during the evolution. We propose a general conceptual framework for modelling such dmass and argue that it can adequately capture a variety of important and representative cases. We then present a concrete modelling framework for a large class of dmass, composed in a modular way from agents specified by means of automata-based representations. We develop generic algorithms implementing the dynamic behaviour, namely addition and removal of agents in such systems. Lastly, we state and discuss several formal verification tasks that are specific for dmass and propose general algorithmic solutions for the class of automata representable dmass.
An application of genetic algorithm to cryptanalysis of block ciphers by partitioning the key space

The genetic algorithm has been used in cryptanalysis. In this paper, we present a general attack to block ciphers by using this algorithm. This method considers the cipher as a black box and it is a known plaintext attack, whose objective is to find the key session. The crucial point of our approach is to make a partition of the key space into subsets determined by the division by a certain divisor, so that one can focus the attack in only some of these subsets. We show how this method can be successfully applied to a member of a block cipher family that contains the AES cipher.
Empirical study of abnormality in local variables and its application to fault‐prone Java method analysis

Abstract Programmers are familiar with local variables, and in many cases, they can freely define the local variables they use. Thus, the properties of these variables are widely diverse, and this may cause variations in the quality of code. Although variables are named in accordance with coding conventions, the following matters have not received much attention from an empirical viewpoint: automatically deciding whether a local variable is “abnormal” and determining the harmful effect of an abnormal variable. This study focuses on the trends in the name, type, and scope of local variables, then proposes the use of the Mahalanobis distance to evaluate their abnormality. The empirical study entailed collecting local variables from eight open‐source software projects, and the paper reports the following findings: (a) the trend in the variation of the names of variables according to their type; (b) the majority of variables have short names with narrow scopes, where a name is often a word or an abbreviation thereof; (c) methods with an abnormal variable are approximately 1.4 times more likely to be fault prone than methods that contain only normal variables; (d) the proposed abnormality metric can be useful in a random forest‐based fault‐prone method analysis model.
Medical Multimedia Systems and Applications

In recent years, we have observed a rise of interest in the multimedia community towards research topics related to health. It can be observed that this goes into two interesting directions. One is personal health with a larger focus on well-being and everyday healthy living. The other direction focuses more on multimedia challenges within the health-care systems, for example, how can multimedia content produced in hospitals be used efficiently but also on the user perspective of patients and health-care personal. Challenges and requirements in this interesting and challenging direction are similar to classic multimedia research, but with some additional pitfalls and challenges. This tutorial aims to give a general introduction to the research area; to provide an overview of specific requirements, pitfalls and challenges; to discuss existing and possible future work; and to elaborate on how machine learning approaches can help in multimedia-related challenges to improve the health-care quality for patients and support medical experts in their daily work.
PoLibSi: Path Towards Intrinsically Reconfigurable Components

One of the main research directions of polymorphic electronics is focused on various issues connected with the design of basic polymorphic components - polymorphic gates. Without a sufficient amount of polymorphic gates offering good properties, conventional electronics will be most likely the preferred way before polymorphic electronics in application scenarios targeting multifunctional behaviour or reconfiguration. The main objective of this paper is to propose a library called PoLibSi which contains eight sets of efficient bi-functional two-input polymorphic gates, whose function is selected by mutual polarity of dedicated power rails. The gate sets differ in the transistor type (conventional MOSFET, emerging double-gate ambipolar transistors), feature the gate sets were optimized to (transistor count, delay, power consumption) and input impedance constraint. The individual gates were designed by means of using an evolutionary based approach and further validated by HSPICE simulations. Each gate implementation includes a schematic, HSPICE description and simulation results. Moreover, propagation delay and power consumption is provided for all MOSFET based gates. Furthermore, each gate set is complete - it provides efficient implementation of any pair of two-input Boolean functions. Besides providing polymorphic gates with better properties to the research society, the aim of the proposed library is to improve the synthesis of polymorphic circuits in terms of the resulting size, as it is also shown in the paper. Finally, the PoLibSi library is available at: www.fit.vutbr.cz/~inevoral/polibsi.
Power Distribution Network Disconnection Fault Diagnosis Using Data Mining Method

Aiming at the problem that the 10kV distribution network branch line ungrounded disconnection fault is difficult to be detected, using the multi-source data from distribution network, the method of fault diagnosis through data mining is proposed. Firstly, based on the existing data sources, a fault diagnosis feature set with both electrical features and non- electrical features is established. Then, the Principal Component Analysis algorithm is used for feature reduction. Finally, based on the FP-Growth algorithm, the association rules are mined to determine whether the branch line ungrounded disconnection fault has happened. The case analysis using historical operation data shows that the proposed method can quickly and accurately diagnose the occurrence of the 10kV distribution network branch line ungrounded disconnection fault, and has good error tolerance.
The use of a weighted affective lexicon in a tutoring system to improve student motivation to learn

Abstract In recent years, several educational systems have integrated text‐based affective feedback. However, analyses of the educational lexicon from the affectivity perspective are scarce. The provision of affective support by these systems is significant because it improves student motivation to learn. This study analyzes the use of a weighted affective lexicon in tutoring systems to improve student motivation. First, based on the suggestions of 166 undergraduate students, this study constructed and evaluated an educational lexicon in Spanish. Additionally, after considering the assessment of another group of 185 undergraduate students, this study provides an interpretation of words/phrases on a valence and arousal scale. The authors carried out a comprehensive analysis to categorize the words/phrases. Furthermore, this study integrates an affective lexicon in a tutoring system. Finally, this paper presents a review of the impacts of this system on the motivation of its potential users. The authors believe that the results of the present study will support the development of a highly adopted Intelligent Tutoring Systems (ITSs), which will benefit prospective users.
ICA-Based EEG Feature Analysis and Classification of Learning Styles

A thorough investigation of the electroencephalograph (EEG) information may support an enriched awareness of the mechanism of understanding different learning styles patterns. Wavelet analysis is a powerful technique that uniquely permits the decomposition of complex information of trends, discontinuities, a repeated pattern. The purpose of such methods is to be able to assign simple segments at diverse locations and scales, to be remodelled afterward effectively. In this paper, we attempt to classify individual cognitive learning styles using artificial neural networks and unsupervised learning. First, we apply Independent component analysis (ICA) to extract relevant features (artefacts removal) of the EEG records. We analyse the ICA-based EEG channels data using inter-quartiles to show the degree of dispersion and skewness. Next, self-organising maps (SOM) are then created to characterise different cognitive learning styles from selected ICA-based channel data.
A Formally Verified Buddy Memory Allocation Model

Buddy memory allocation algorithms are widely adopted by various memory management systems for managing memory layouts. Rigorous mathematical proofs provide strong assurance to improve the confidence on the reliability of a memory management system. In this paper, we model and formally verify, in the interactive theorem prover Isabelle/HOL, a buddy memory allocation model, which preserves functional correctness and security properties. Firstly, we construct a specification consisting of operations to allocate and dispose memory blocks according to a buddy memory allocation algorithm. Then we verify that the specification preserves key invariants over the memory to guarantee functional correctness of the algorithm. Finally, we verify that the specification also preserves the integrity of the memory. Therefore, they do not affect other memory blocks previously allocated.
3-D Face Alignment Using A Convolutional Point-Set Representation

We present a robust method for estimating the facial pose and shape parameters with a Projected Normalized Coordinate Code (PNCC), a derived image where each pixel is annotated with its corresponding vertex position in a generic 3D model. The method relies on a Convolutional Point-set Representation (CPR), a carefully designed feature map that extracts useful information encoded in pairs or triplets of vertices of PNCC. To estimate the shape and pose parameters, different CPRs are designed to represent mutually independent pose and shape parameters. The CPRs are then fed into multiple convolutional networks (with recurrent layers) in a sequential manner based on the transformation order of weak-perspective projection model to predict the target parameters. Reprojection errors are sampled along a predicted direction and the parameters are updated based on the pattern of reprojection errors. This technique boosts the model's capability to find a local minimum under challenging scenarios. It is also demonstrated that CPRs obtained from different sources can be merged and contribute to outperforming the current state-of-the-art solutions for 3D face alignment. Experiments indicate that the proposed CPRFA (CPR-based Face Alignment) significantly improves 3D alignment accuracy. Compared to 3DDFA, the proposed method reduces the normalized mean error on AFLW2000 and AFLW database by 25.3% and 20.2% respectively.