Reviewerly/exHarmony huggingface dataset:
Detection and Analysis of Periodic Actions for Context-Aware Human Centric Cyber Physical System to Enable Adaptive Occupational Therapy

In most of our daily lives, we observe repetitive (periodic) movements of our surrounding objects. In physiotherapy, for example, during the physical exercises, the analysis of the periodicity in the patient's performance would help to evaluate and monitor the his recovery process. Cyber Physical Systems for smart environments have great potential in providing context-aware, automated support in everyday life of people that need adaptive occupational therapy. We present a method and program-based algorithm for the detection and analysis of periodic activities of subjects performing physical exercises from video sequences. The algorithm detects the beginning of a series of exercises of a given type, measures the duration of each of them, detects the end of the sequence and measures the number of repetitions. Experimental results with publicly available video dataset will prove the applicability of the proposed method for detection of repetitive actions.
Sentiment Analysis for Arabic Language using Attention-Based Simple Recurrent Unit

With the growing number of people who express their opinions on the web, Sentiment Analysis have become an active research field that aims to analyze and classify the sentiment polarity of opinionated reviews. Recently, Deep Learning models have been extensively used for many Natural Language Processing tasks including Sentiment Analysis. In this paper, the authors propose a Deep Learning model for Arabic language sentence-level Sentiment Analysis. The proposed model represents an integration between an emerged variant of Recurrent Neural Networks known as Simple Recurrent Unit which is characterized by its light recurrent computations, and an attention mechanism that concentrates more on the important parts of an input text. The Simple Recurrent Unit model allows parallel recurrent calculations that lead to enhance the training process in terms of time and accuracy. Experiments were performed to evaluate the performance of the proposed model using the Large Scale Arabic Book Reviews (LABR) dataset. The proposed model obtained state of the art results compared to other Deep Learning models where it achieved 94.53% in terms of accuracy measure with faster execution time.
An Architecture for Restful Web Service Discovery Using Semantic Interfaces

In the internet environment, web services based on representational state transfer (REST) have become the de facto standard. The addition of semantics is intended to enhance the description of web services with information that enables automatic agents to understand their data. However, the existence of different languages to semantically describe services makes it difficult to discover and select the service that best meets a requirement. Furthermore, relatively few proposals have a RESTful service semantic description, making the discovery process for RESTful services more difficult. This work proposes a RESTful semantic web service discovery architecture based on semantic interfaces (SERIN). SERIN is an ontology with annotations that semantically describe RESTful web services. This architecture enables software agents to automatically discover and make service calls in order to execute a determined task.
Impression Prediction of Oral Presentation Using LSTM and Dot-Product Attention Mechanism

For automatically evaluating oral presentations, we propose an end-to-end system to predict audience's impression for speech videos. Our framework is a multimodal neural network including two Long Short-Term Memory systems and a dot-product attention mechanism to learn linguistic features and acoustic features, respectively. An attention network is also used to consider the correlation between different feature representations for feature fusion. We utilize 2,445 videos with official captions and users' ratings from TED Talks. The experimental result shows the good performance for our proposed system, which can recognize 14 types of audience impressions with an average accuracy of 85.0%. Our proposal has the advantage of making noticeable improvements to the accuracy of predicting audiences' impressions.
Quantifying the Performance Impact of SQL Antipatterns on Mobile Applications

In mobile applications, local databases have become an important component, providing mobile users with a responsive and secure service for data access and management. However, using local databases comes with a cost. Studies have shown that they are one of the most resource consuming components on mobile devices. Improper usage of the local database can even severely impact the responsiveness of an application. In this paper, we conducted a literature review and a benchmark study to investigate problematic programming practices with respect to database usage. Our results present a comprehensive overview of the current knowledge about these practices, and introduce new knowledge about the impact of these practices on the resource consumption of mobile applications.
Integrating Digital Literacy in Competency-Based Curriculum

This chapter discusses the integration of digital literacy in competency-based curriculum (CBC). In the introduction, the authors discuss the 21st century skills and their relevance to the competency-based curriculum. The discussion funnels from global, regional, and local contexts. Theoretical perspectives in ICT and the CBC are dealt with to provide a background. Multiple approaches of integrating digital literacy within the curriculum are highlighted later in the chapter. These issues are discussed in the light of the extant literature on digital literacy and the competency-based curriculum. The discussion revolves around the trends, controversies of digital literacy in the CBC with possible solutions put forth towards the end of the chapter. Finally, recommendations and future research directions are made. The chapter concludes with a summary of the major issues discussed in the chapter and recommendations for further reading.
Research on Abnormal Behavior of Electronic Invoice Based on Artificial Neural Network

The Fifth Plenary Session of the 18th CPC Central Committee proposed "implementing the "Internet +" action plan and developing the sharing economy". The Outline of the Thirteenth Five-Year Plan for National Economic and Social Development also clearly states that "electronic invoices should be promoted". Compared with traditional paper invoices, electronic invoices have the advantages of flexibility, easy storage, easy inspection, and quick reimbursement. However, there are some abnormal behaviors in the process of electronic invoice circulation, such as repeated reimbursement of electronic invoices, reimbursement of fake invoices, and frequent replacement of electronic credential status. Some abnormal behaviors can be discovered in time through simple judgments by the system. However, other abnormal behaviors need to be identified by data mining. For example, the enterprise issues a large number of invoices in a short period of time, and the same enterprise issues a large number of large-value invoices in a short period of time. Aiming at the abnormal behavior of electronic invoices in the process of circulation, this paper proposes a method for judging the abnormal behavior of electronic invoices based on multi-layer perceptron(MLP). The method uses the historical invoice data as the training data to train the model, and inputs the new data in real time. Through this method, the high efficiency of the research can be obtained, and the accuracy can reach more than 95%.
Designing Whole Journey, Multimodal Transport Provision

EU transport research is led by a vision of sustainable, accessible, inclusive, joined up transport services. The FP7 METPEX project developed a standardized tool for measuring the quality of multimodal whole journeys, to identify where public transport could be improved to attract more users, combatting the fragmentation which characterizes transport design and research. With 16 European partners in 12 countries, KPIs were developed articulating standards to ensure a satisfactory whole journey experience for different traveller groups. They framed the range of design opportunities and responsibilities, to embrace a holistic, inclusive, empathic approach. This broad case study of design integration, signals 4 design areas (infrastructure, information, system design and vehicles) involving 13 types of design specialist. The passenger is central to an integrated approach and there is a clear and timely need for a ‘Passenger Champion’ who can insure a user focus for an integrated design approach.
CLASP: a 999-style Priority Lanes Framework for 5G-era Critical Data Services

Society is increasingly reliant on digital services for its proper functioning. Yet, going into the 5G era, the prevailing paradigm for data treats all traffic as equal regardless of how critical they are to the proper functioning of society. We argue that this is a suboptimal scenario and that services such as driverless cars and road/rail traffic updates are too important for society to be treated the same way as entertainment services. Our contribution in this paper is to propose the CLASP (Critical, Localized, Authorized, Specific, Perishable) framework to guide regulators and policymakers in deciding and managing 999-style priority lanes for critical data services during atypical scenarios in the 5G era. Our evaluation shows that reserving a 100kbps `lane' for CLASP-prioritised traffic for all users does not lead to an overall statistically significant deterioration in atypical scenarios.
Search Prevention with Captcha Against Web Indexing: A Proof of Concept

A website appears in search results based on web indexing conducted by a search engine bot (e.g., a web crawler). Some webpages do not want to be found easily because they include sensitive information. There are several methods to prevent web crawlers from indexing in search engine database. However, such webpages can still be indexed by malicious web crawlers. Through this study, we explore a paradox perspective on a new use of captchas for search prevention. Captchas are used to prevent web crawlers from indexing by converting sensitive words to captchas. We have implemented the web-based captcha conversion tool based on our search prevention algorithm. We also describe our proof of concept with the web-based chat application modified to utilize our algorithm. We have conducted the experiment to evaluate our idea on Google search engine with two versions of webpages, one containing plain text and another containing sensitive words converted to captchas. The experiment results show that the sensitive words on the captcha version of the webpages are unable to be found by Google's search engine, while the plain text versions are.
Applications of blockchain in smart cities

Blockchain (BC) has become one of the most important technologies in recent years. It is a set of nodes connected to each other in a decentralized way. A smart city is a system that connects groups of devices which produce and share a vast amount of information. Because of this large amount of data that travels between devices, there must be an organizational structure to organize and manage this movement. The basic characteristics of this system are transparent, decentralized, autonomy and immutable. All of these properties can be provided by BC Technology. In this paper, we explore research that applies the BC technique in different areas of smart cities. Shedding light on ways to improve systems currently applied in those cities.
Iterative Encryption Method of Transmission Data Anti-Tampering Based on Big Data

In order to improve the security of transmission data storage in supercomputer network, a supercomputer network transmission data encryption technology based on elliptical hyperbolic iterative coding is proposed. The elliptical hyperbolic differential equation is constructed, and the big data of the supercomputer network is loaded into the elliptical hyperbolic differential equation. A set of high-order statistical characteristic solutions describing the frequency of the quantitative coding symbol are obtained. The tamper-proof iterative encryption cipher design and the public key coding configuration are carried out. The random quantitative coding fusion method is used to carry out the tampering proof iteration of the computer network transmission data. The anti-tampering encryption, secure storage and transmission of data transmission in supercomputer network are realized under Logistics chaotic mapping system. The simulation results show that the encryption technology has good tamper proof performance, strong anti-attack ability and good secure transmission performance for supercomputer network transmission data encryption.
Approximating the covariance ellipsoid

We explore ways in which the covariance ellipsoid [Formula: see text] of a centered random vector [Formula: see text] in [Formula: see text] can be approximated by a simple set. The data one is given for constructing the approximating set is [Formula: see text] that are independent and distributed as [Formula: see text]. We present a general method that can be used to construct such approximations and implement it for two types of approximating sets. We first construct a set [Formula: see text] defined by a union of intersections of slabs [Formula: see text] (and therefore [Formula: see text] is actually the output of a simple neural network). We show that under minimal assumptions on [Formula: see text] (e.g. [Formula: see text] can be heavy-tailed) it suffices that [Formula: see text] to ensure that [Formula: see text]. In some cases (e.g. if [Formula: see text] is rotation invariant and has marginals that are well behaved in some weak sense), a smaller sample size suffices: [Formula: see text]. We then show that if the slabs are replaced by well-chosen ellipsoids, the same degree of approximation is true when [Formula: see text]. The construction is based on the small-ball method.
Multi-Loss Recurrent Residual Networks for Gesture Detection and Recognition

Communication through gestures plays a relevant role in human life, in which a non-verbal language is used to propagate information among individuals. To recognize gestures, computers need to represent and interpret human appearance and motion, involving hands, arms, face, head and/or body, in a mathematical sense. Despite the high applicability in different contexts, most gesture recognition approaches in literature are not designed to deal with unsegmented videos. That is, most approaches do not temporally detect when a gesture occurs, which prevents to explore correlations between detection and recognition tasks, besides their application on real-world scenarios. In this sense, we propose the Multi-Loss Recurrent Residual Network (MLRRN), a multi-task based approach that performs both the recognition and temporal detection of gestures at once. It employs a dual loss function which takes into account the class assignment of each frame of a video to a gesture class and also determines the frame interval associated to each gesture. Our model counts with a dual input, gathering information from appearance and human pose on frames, besides bidirectional recurrent layers and residual modules. According to experiments conducted on ChaLearn Montalbano and ChaLearn ConGD datasets, our approach achieves results comparable to state-of-the-art methods considering average temporal Jaccard metric.
An assessment of tools for UML class diagram modeling

The software development process comprises a set of activities with the goal of developing high quality software. The software architecture design is a key activity of the software development processs as it allows the specification and visualization of products from different and structured point of views. One of the most used architectural views is the logical one, which represents the classes and their relationships. For this reason, the UML class diagram is one of the most used UML diagrams by software engineers [1]. Many companies use CASE (Computer-Aided Software Engineering) tools to carry out several software engineering activities, including modeling class diagrams. They are looking for independence and flexibility to adapt and integrate tools through the adoption of open-source tools, but this is not an easy task as there are a lot of CASE tools and most of them are incompatible. This problem arises when it is necessary to integrate tools to perform software engineering activity that needs to receive a UML class diagram as input. In this sense, the objective of this work is to assist software engineers in the selection of the tools by presenting results of a systematic review, whose goal was identifying existing free and open-source tools for UML class diagram modeling and analyzing their support to adaptation and integration with other tools. Twelve tools for UML class diagrams modeling were found and an analysis of their support to adaptation and integration with other tools was carried out. This analysis is useful in the choice and use of such kind of tool, either in industry or academic research.
Designing a Social Matching System to Connect Academic Researchers with Local Community Collaborators

This paper reports on efforts to design a social matching system that instigates collaborative research across multiple fields of practice, in this instance: researchers from academia and organizations in their local geographic community. A qualitative study is presented about university researchers and the design of their profile pages for the system. Findings show that university researchers prefer profile page designs that enable them to demonstrate a willingness to adapt to non-academic partners, such as by de-emphasizing esoteric markers of expertise like scholarly publications and clarifying their resources and goals. Some also wish to circumvent potential bias by omitting information about their name, physical appearance, and academic department. However, these desired omissions raise questions about how to design for sufficient distinction between profile pages and the presentation of a unique professional identity. Implications are discussed for the design of social marching systems for collaboration.
EmoteControl

Numerous computer systems have been designed for music emotion research, aiming to identify how different structural and expressive cues of a musical piece affect the emotion conveyed by the music and perceived by the listener. However, most systems are either offline systems, which work by pre-rendering different variations of the music, or real-time systems, which focus mostly on expressive cues. This paper presents a new system called EmoteControl, which allows changes to be made to both structural and expressive cues (tempo, pitch, dynamics, articulation, brightness, and mode) of a musical piece while it plays in realtime. First, a brief overview of previous computer systems will be given, followed by a detailed explanation of EmoteControl's interface design and structure. A smaller-scale version of the system will also be described, and specifications for the music inputted in the system will be outlined. Interface limitations will be noted, and user evaluation cases for both versions of the interface will be discussed. A demonstration of the interface will be presented, featuring specifically composed musical pieces.
Generic cost optimized and secured sensitive attribute storage model for template based text document on cloud

Cloud computing stands as the most powerful technology for providing and managing resources as a pay-per-usage system. Nowadays, user documents are stocked in the cloud for easy access, less maintenance cost, and better services, etc. Currently, user data stocked up in the cloud are mostly in the form of template-based unstructured text documents. These documents are stocked up in a group by various organizations. Generally, the template-based text document contains large size common information, common terms, conditions, and sensitive information. For protecting the values in the documents, present methods apply encryption algorithms. But these techniques take high encryption time and required more storage space. Applying encryption algorithms is an extremely time-consuming task since the terms and conditions and instructions are common for all documents, and they do not require any security. But the sensitive information in these documents differs as of one user to another user and as well this sensitive information requires protection. Therefore, there is a requirement for an efficient way of segregating, storing and encrypting sensitive information with minimum storage cost, and computational cost. To tackle these issues, a generic safe data storage model is proposed, which makes use of information extraction techniques of Natural Language Processing for sensitive attribute value identification, and Enhanced ECC for securing sensitive data centered on group key. When weighted against the existing entire document and partition-based encryption technique, the proposed generic secure data storage model for cloud takes lesser encryption time and storage space.
Facial Movements Recognition Using Multichannel EMG Signals

With the development of intelligent wearable technology, the need for more effective and practical human-computer interaction becomes more and more urgent. The main purpose of this paper is to recognize rich facial movements with multi-channel EMG signals and try to apply them in human-computer interaction. In offline data analysis, we tested a variety of features and classification algorithms. In the two sets of facial motion recognition, the accuracy of 11 facial motion recognition can reach 98.3% and 98.8%, respectively. In the test based on the online typing system, an average accuracy of 83.9% was also achieved.
The Linkage Between Cryptocurrencies and Macro-Financial Parameters: A Data Mining Approach

Digital currencies have increased their effectiveness in recent years and have started to see significant demand in international markets. Bitcoin stands out from the other cryptocurrencies in considering the transaction volume and the rate of return. In this study, Bitcoin is estimated by using a decision tree method which is among the data mining methodology. The variables used in the decision tree created in the estimation of Bitcoin are the S&P 500 stock index, gold prices, oil prices, Euro/Dollar exchange rate, and FED Treasury bill interest rate. When the experimental results were examined, it was observed that the decision tree C4.5 algorithm was an appropriate method with the correct classification percentage of 73% in estimating the direction of Bitcoin. Also, the results obtained from the decision tree show that Bitcoin is related to S&P 500 index among macro-financial indicators similar to the results of econometric models used in the literature.