Reviewerly/exHarmony huggingface dataset:
Task Load Estimation from Multimodal Head-Worn Sensors Using Event Sequence Features

For longitudinal behavior analysis, task type is an inevitable and important variable. In this article, we propose an event-based behavior modeling approach and employ non-invasive wearable sensing modalities (eye activity, speech and head movement) to recognize task load level under four different task load types. The novelty lies in converting physiological and behavioral signals into meaningful events and utilizing their sequence across multiple modalities to distinguish load levels and types. We evaluated this approach on head-worn sensor data from 24 participants completing four different tasks for recognizing (i) low and high load level for a given task load type, (ii) low and high load level regardless of load type, and (iii) both load level and load type. Findings show that the recognition rate is reasonable in (i), close to chance level in (ii), and well above chance level in (iii) for 8 classes using participant-dependent and -independent schemes. Further, a fusion of the proposed event-based features and conventional continuous features achieved the best or similar performance in most cases. These results suggest that task type needs to be considered when using continuous features and that the proposed event-based modeling paradigm is promising for longitudinal behavior analysis.
A Novel Architecture to verify Offline Hand-written Signatures using Convolutional Neural Network

Hand-written signatures are marked on documents to establish legally binding evidence of identity and intent. However, they are prone to forgery, and the design of an accurate feature extractor to distinguish between highly-skilled forgeries and genuine signatures is a challenging task. In this paper, we propose a Convolution Neural Network (CNN) architecture for Signature Verification (SV). The algorithm is trained using two signatures, genuine and forged. Then the SV module performs a classification task to determine if any two signatures are of the same individual or not. The simulation results show that the proposed method can achieve 27% (relatively) better results than the benchmark scheme. The paper also integrated different data augmentation techniques for the signature data, which further improved the efficiency of the proposed method by 14% (relative).
Student perceptions of virtual reality use in a speaking activity

The aim of this study was to discover what potential benefits of Virtual Reality (VR) use students perceived in comparison to smartphone use in an English-speaking activity. Two surveys were conducted over three interventions in order to ascertain these student perceptions. Ten students enrolled in an elective English class at a Japanese university engaged in an English-speaking activity centered around the VR enabled application Google Expeditions. Student perceptions showed that the immersive nature of VR prompted communication, invoked a higher sense of presence, and greater enjoyment in this speaking activity. However, a majority of students did not perceive a difference in the amount of English spoken when comparing VR and smartphone use.
SMARE: Semi-supervised Method for Activities of daily living REcognition

The significant growth of the average age of the world population requires effective solutions to meet the unsustainable number of requests for hospitalization. For this reason, the scientific community is seeking new paradigms of care that provide compensatory interventions to the first signs of warning that can be detected in the behavior of the individuals. At this aim, this work proposes a method to detect the Activities of Daily Living carried out by a subject monitored at home through unobtrusive environmental sensors. The Activity Recognition system is composed of an unsupervised segmentation layer which splits the collected sensors activation data in time periods and a semantic layer that exploits a knowledge-based approach to provide the most probable activity label for each period. The system is enriched with a further layer to adapt the base knowledge according to the information directly provided by the resident.
Frequent Item Set Mining of Large Datasets Using CUDA Computing

Karthik, Peddi Saira Banu, J.Frequent item set mining is a very popular method in data mining and is used extensively to find out the most recurring items in mainly market basket analysis. It is commonly used for association rule learning. The market basket analysis is seen to be used in many fields like Web mining and intrusion detection. There are many algorithms like Apriori algorithm, etc., to find out the frequent items in given data but these are more of a sequential approach and take huge amounts of time for large sets of data. Existing Soft computing-based approaches for solving frequent item set mining like genetic algorithm and fuzzy logic systems are proven to reduce execution time but not in a scale in which a massively parallel system does. So the main objective of this paper is to accelerate frequent item set mining process using GPU’s CUDA architecture. We have also performed a comparative study with parallel version of frequent item set mining using openMP. Our results show speedup of 2.2 for CUDA over serial implemented using genetic algorithm and 1.8 for CUDA over OpenMP.
Customer Service Automatic Answering System Based on Natural Language Processing

With the rapid development of Internet, information grows explosively, and traditional search engine have failed to meet the needs of users. This paper proposes a customer service automatic answering system with a high-quality knowledge base. First of all, based on unsupervised learning algorithm, this system extracts the question and answer pairs from documents and store them in the knowledge base. Then employing semantic analysis module and the method of Natural Language Processing (NLP), this system gains the meaning of the customers' question accurately, then retrieve the knowledge base and return a high-resolution answer to the user. Furthermore, we construct a dialog management module, which makes reasonable guesses on issues that cannot be matched, and records the dialogue history so that the question-answering system can give more intelligent responses. Finally, due to the diversity of the document structure and the complexity of Chinese natural language, this system adds an edifying function that can add, delete, and modify the question and answer pair in the knowledge. Therefore, our customer service automatic answering system can be more intelligent and efficient than the existing question and answer system.
Resource allocation in the cloud with unreliable resources

We consider a resource allocation problem in a geographically distributed cloud network, where the goal is to obtain the capacities of the servers across the network in order to minimize the overall cost. In this study, the system resources (servers) are subject to failures, due to occasional breakdowns or cyber attacks. As a result, the servers supply is of a stochastic nature. From an optimization point of view, we are facing a non-convex multi-dimensional problem. For the solution, we propose an efficient algorithm to obtain the optimal capacities of the servers in all the regions in the network, where computational experience is presented and discussed. We then numerically analyze the effect of the supply stochastic properties, namely expected volume, variability and correlation across regions, on system performance. The methodology and the results can be used to evaluate the effect of cyber attacks on resource allocation in geographically distributed systems and on the planning of these systems.
Media education and teachers’ professional training

The subject of the research is media education as a factor that has caused changes in the system of education in general and in professional training of teachers in particular. The object of the research is teachers’ professional training. The aim of the work is to consider the transformed meanings of the concept “media education” and to show the role and goals of media education for the modern education system, as well as changes in quality requirements for training teachers who work in the field of media education. It is shown how media teaching-learning model has transformed, with the example of media libraries which has been developing modern educational media space. There is an updated assessment of the quality of information that children use not only within the school information space, but also from mass media, the Internet, etc., which makes skills for analyzing media texts a crucial condition for keeping safe the national human capital and culture and educating highly marketable specialists. There is analysis of differences in understanding the term “media literacy”, which entail further misunderstand-ing of the concept “media competence”. There is a dispute on the necessity to define the media competences for mere users and for educators. The authors conclude that the results of reviewing requirements for teachers’ professional training, caused by changes in educational environment through incorporating media in teaching-learning, make it necessary to refresh the process of training, to strengthen its technological component, to intensify language and communicative aspects regardless of specialization.
An Interactive Framework for Cross-modal Attribute-based Person Retrieval

Person re-identification systems generally rely on a query person image to find additional occurrences of this person across a camera network. In many real-world situations, however, no such query image is available and witness testimony is the only clue upon which to base a search. Cross-modal re-identification based on attribute queries can help in such cases but currently yields a low matching accuracy which is often not sufficient for practical applications. In this work we propose an interactive feedback-driven framework, which successfully bridges the modality gap and achieves a significant increase in accuracy by 47% in mean average precision (mAP) compared to the fully automatic cross-modal state-of-the-art. We further propose a cluster-based feedback method as part of the framework, which outperforms naïve user feedback by more than 9% mAP. Our results set a new state-of-the-art for fully automatic and feedback-driven cross-modal attribute-based re-identification on two public datasets.
Examining Twitter as a source for address research using Colombian Spanish

The present study aims to describe pronominal address use in Colombian varieties of Spanish in a specific context of computer-mediated communication, namely Twitter. This description will serve as a discussion of Twitter as an exciting new source of linguistic data for address research. A large corpus of tweets was compiled, including memes, reported speech, and conversations. Twitter data on the geographical preference for pronoun use were in line with patterns observed using traditional data: Cali and Medellín used vos significantly more frequently than did the Colombian capital of Bogotá, which in turn used usted significantly more frequently. However, in contrast to traditional data, tú was shown to be used most frequently overall in the corpus due to the medium’s informal nature. Along with its abundance and geographical information, a strength of Twitter data is the possibility of isolating the effect of emotional and social characteristics on pronoun choice in a naturalistic setting. The expression of strong emotions significantly correlate with the use of the pronoun tú in the corpus.
Provable secure attribute-based proxy signature

Provable security theory generally adopts the method of reduction, which makes use of the unsolvable mathematical problems in number theory to reduce the scheme to be safe. The idea of proof is a method of proof by contradiction: First, it is assumed that it is not difficult to solve the scheme pre sented in this paper, then the process of proving it, and finally, it is deduced that it is not difficult to calculate a certain difficult problem, which contradicts the difficulty of the mathematical problem. Then, it means that the assumption is not valid, and the scheme is proved to be safe. In this paper, the security of the scheme proposed in our previous paper is proved in detail.the scheme is proved to be secure against existential forgery under selective attributes and adaptive chosen-message attack. Its security can be reduced to the hardness of the computational Diffie-Hellman problem.
Panel: Research Sponsors for Cybersecurity Research and the Human Factor

A panel of past or current sponsors of human-centered research in cybersecurity. Attendees from industry, government and academia alike who are interested in cybersecurity research will benefit from hearing these panelists describe their experience and their views on the state of the field, current knowledge gaps and future directions for winning proposals.
Progressive Temporal-Spatial-Semantic Analysis of Driving Anomaly Detection and Recounting

For analyzing the traffic anomaly within dashcam videos from the perspective of ego-vehicles, the agent should spatial-temporally localize the abnormal occasion and regions and give a semantically recounting of what happened. Most existing formulations concentrate on the former spatial-temporal aspect and mainly approach this goal by training normal pattern classifiers/regressors/dictionaries with large-scale availably labeled data. However, anomalies are context-related, and it is difficult to distinguish the margin of abnormal and normal clearly. This paper proposes a progressive unsupervised driving anomaly detection and recounting (D&R) framework. The highlights are three-fold: (1) We formulate driving anomaly D&R as a temporal-spatial-semantic (TSS) model, which achieves a coarse-to-fine focusing and generates convincing driving anomaly D&R. (2) This work contributes an unsupervised D&R without any training data while performing an effective performance. (3) We novelly introduce the traffic saliency, isolation forest, visual semantic causal relations of driving scene to effectively construct the TSS model. Extensive experiments on a driving anomaly dataset with 106 video clips (temporal-spatial-semantically labeled carefully by ourselves) demonstrate superior performance over existing techniques.
Kinship-based differential evolution algorithm for unconstrained numerical optimization

We propose a modification of the standard differential evolution (DE) algorithm in order to significantly make easier and more efficient standard DE implementations. Taking advantages from chaotic map approaches, recently proposed and successfully implemented for swarm intelligence-based algorithms, our DE improvement facilitates the search for the best population and then the optimal solution. More specifically, we work with a genetic memory that stores parents and grandparents of each individual (its kin) of the population generated by the DE algorithm. In this way, the new population is carried out not only on the basis of the best fitness of a certain individual, but also according to a good score of its kin. Additionally, we carried out a wide numerical campaign in order to assess the performances of our approach and validated the results with standard statistical techniques.
Prediction of hourly solar radiation using temperature and humidity for real-time building energy simulation

Abstract Solar radiation is considered one of the most substantial energy sources in our life. This paper discusses how to develop an algorithm to predict real-time hourly solar radiation based on readily available weather data such as temperature and humidity. Artificial Neural Network is one of the most effective technologies for developing algorithms to predict solar radiations. Hidden nodes, learning rates, and epochs are the main three variables. An optimization method is proposed to provide the optimum value of the variables depending on the Coefficient of Variance of the Root Mean Square Error, Normalized Mean Bias Error, and Coefficient of determination.
Finding ALL Answers to OBDA Queries Using Referring Expressions

We explore how referring expressions can be used to enhance how a conjunctive query is answered over description logic knowledge bases in a way that allows one to return not only answers explicitly named by constant symbols, but also anonymous individuals that are entailed to satisfy the query, and to provide syntactic means for referring to such individuals. In particular, for the logics $$\text {Horn-}\mathcal{ALC}$$ and $$\mathcal{EL}^{\bot }$$, we focus on reporting all entailed answers, on techniques necessary to finitely describe such sets of answers, and on extensions to more complex logics and settings.
Imputation of values above an upper detection limit in compositional data

Geochemical data frequently contain censored values. An imputation method for right-censored compositional data is proposed, based on the Tobit model, in order to get a complete and reliable data set. An algorithm is developed and implemented using regressions in an iterative scheme, where the imputed values are updated step-by-step. Optionally, classical least-squares or robust regressions can be carried out, with or without variable selection. The performance of the algorithm is evaluated using two real geochemical data sets, considering various different scenarios. Compared to commonly used substitution methods, the proposed method leads to an improved data quality. The procedure is available in the R package robCompositions.
Dynamic Management of Resources in Cloud Computing

It has already been proven that VMs are over-utilized in the initial stages and are underutilized in the later stages. Due to the random utilization of the CPU, resources are sometimes heavily loaded whereas other resources are idle. Load imbalance causes service level agreement (SLA) violations resulting in poor quality of service (QoS) aided by the imperfect management of resources. An effective load balancing mechanism helps to achieve balanced utilization, which maximizes the throughput, availability, and reliability and reduces the response and migration time. The proposed algorithm can effectively minimize the response and the migration time and maximize reliability, and throughput. This research also helps to understand the load balancing policies and analysis of other research works.
Depth-Camera Calibration Optimization Method Based on Homography Matrix

A self-calibration method of the depth-camera based on homography matrix is proposed to improve the accuracy and ease of use in calibrating the depth-camera. Firstly, the shortcomings of the existing self-calibration methods are pointed out, and the internal conversion model of the depth-camera is established. Then, the proposed self-calibration mathematical model of the depth-camera is deduced according to the imaging relationship of image pairs, and the internal and external parameters of the depth-camera are obtained by solving the essential matrix and the corresponding matrix. Finally, the distortion parameters of the depth-camera are obtained by an immune clonal selection algorithm being used for optimizing the distortion model of the depth image. Compared with Zhang's calibration method, the relative errors of the focal length and the coordinates obtained by the proposed self-calibration calibration method are smaller, in addition it has a good restoration effect for the edge distortions of the depth images.
Eliminating artefacts in polarimetric images using deep learning

Polarization measurements done using Imaging Polarimeters such as the Robotic Polarimeter are very sensitive to the presence of artefacts in images. Artefacts can range from internal reflections in a telescope to satellite trails that could contaminate an area of interest in the image. With the advent of wide-field polarimetry surveys, it is imperative to develop methods that automatically flag artefacts in images. In this paper, we implement a Convolutional Neural Network to identify the most dominant artefacts in the images. We find that our model can successfully classify sources with 98\% true positive and 97\% true negative rates. Such models, combined with transfer learning, will give us a running start in artefact elimination for near-future surveys like WALOP.
Detecting and Counting the Moving Vehicles Using Mask R-CNN

In order to make use of the existing video surveillance facilities and improve the ability of urban intelligent traffic integrated management, a novel moving vehicle detecting and tracking method based on traffic video is proposed in this paper. The Mask R-CNN algorithm based on deep learning is used to detect vehicle contours in the complex urban traffic environment, and then the improved Kalman filter is applied to track the vehicle target in the video sequence. The experimental results show that the proposed method can reach 95% average accuracy with 2.86 fps speed, and can effectively deal with different traffic and climates conditions.
A Refactoring Approach for Too Large Packages Using Community Detection and Dependency-based Impacts

Architecture smells are poor design of architectural structures which reveal negative impacts on software quality and increase risks of bugs and implementation problems in the future. Previous research defines and proposes various architecture smells and each one affects software quality in different aspects. Architecture refactoring is a process that removes an architecture smell and improves quality of system without any changes in system behavior. However, removing one of the architecture smells may cause creating of another smell in the system which directly affects software quality. This research proposes a refactoring approach for Too Large Packages smell which is one of the architecture smells by using community detection in extracting process. This process uses for extracting the package which is identified as Too Large Packages smell into small ones and avoids additional impacts from newly appeared dependency-based architecture smells by focusing on Unstable Dependency smell. The proposed approach provides feasible solution to resolve impact from Too Large Packages smell which can be evaluated by comparing number of detected architecture smells and software metrics of before and after refactoring process.
Fast Fixed-point Bicubic Interpolation Algorithm on FPGA

We propose a fast fixed-point algorithm for bicubic interpolation on FPGA. Bicubic interpolation algorithms on FPGA are mainly used in image processing systems and based on floating-point calculation. In these systems, calculations are synchronized with the frame rate and reduction of computation time is achieved designing a particular hardware architecture. Our system is intended to work with images or other similar applications like industrial control systems. The fast and energy efficient calculation is achieved using a fixed-point implementation. We obtained a maximum frequency of 27.26 MHz, a relative quantization error of 0.36% with the fractional number of bits being 7, logic utilization of 8%, and about 30% of energy saving in comparison with a C-program on the embedded HPS for the popular Matlab test function Peaks(25,25) data on SoCkit development kit (Terasic), chip: Cyclone V, 5CSXFC6D6F31C8. The experiments confirm the feasibility of the proposed method.
An Efficient Model for Sentiment Analysis of Electronic Product Reviews in Vietnamese

In the past few years, the growth of e-commerce and digital marketing in Vietnam has generated a huge volume of opinionated data. Analyzing those data would provide enterprises with insight for better business decisions. In this work, as part of the Advosights project, we study sentiment analysis of product reviews in Vietnamese. The final solution is based on Self-attention neural networks, a flexible architecture for text classification task with about $$90.16\%$$ of accuracy in 0.0124 second, a very fast inference time.