Reviewerly/exHarmony huggingface dataset:
A declarative enhancement of JavaScript programs by leveraging the Java metadata infrastructure

Web browsers have evolved into indispensable software platforms for modern applications. Although JavaScript has become a de-facto lingua franca for developing web applications, the JavaScript software development tools and methodologies lag behind those of languages such as Java and C#. In particular, to enhance JavaScript programs with non-functional concerns (e.g., persistence, security, transactions, and logging) JavaScript developers need to modify the source code by hand. Developers then often end up having to maintain the resulting modified version separately for reasons that include poor design, the complexities of program evolution, and fundamental language limitations. The necessity to modify code by hand could be avoided if JavaScript had metadata facilities for developers to specify non-functional concerns declaratively. To address this problem, we present a novel declarative approach, Transparent Automated Enhancement for JavaScript (TAE-JS) that enhances JavaScript programs with the ability to use declarative metadata. The metadata is expressed by means of Java annotations. We have implemented our technique in an open-source plug-in for the Eclipse IDE. This plug-in allows a developer to enhance the original version of a JavaScript program by specifying concerns declaratively using Java annotations. It then analyzes an original version of the program to automatically generate enhanced program versions by adding the declared concerns. We evaluated TAE-JS with four practical examples that enhance JavaScript programs by declaratively adding concerns such as persistence, security, transactions, and logging. In a user study with ten student developers and three professional engineers at PayPal, the participants viewed favorably TAE-JS's flexible declarative enhancement capabilities, seeing them as a valuable mechanism for implementing non-functional concerns in JavaScript programs. Our evaluation results are promising in demonstrating the potential benefits of our approach to complement existing JavaScript development tools.
Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework

In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset.
Use of ICT Tools using the PBL Methodology as a Student Learning Strategy

Abstract The community of Altos de la Sabana is a population composed of families of limited economic resources and victims of the internal armed conflict in Sucre, located in low-income housing. In this urbanization is the Institution of Higher Education of the Holy Shroud. Given that at present it does not have technological and innovative tools for academic use, information provided by the rector and the academic coordinator of this institution, as well as being considered as a vulnerable community by the national government, which gives them a priority position when implementing educational and social projects; The main objective of this project is to encourage the students of the secondary school of the institution to use technological tools to solve problems in their environment. This research has a quantitative approach and the design of the research focuses on field research and with a pre-experimental method. For the execution of this project, 3 moments were concentrated; initially, an initial test was carried out to validate knowledge in technological tools and the self-directed profile, then some training was carried out using the problem-based methodology and finally a final evaluation to measure acquired competences. The results of the post-test, in contrast to the initial test, revealed that 65% of students in grade 11 of the institution demonstrate an empowerment in the use of ICT tools and concepts in the field of basic analog-digital electronics. This is how ICTs are based on the education system, as they are facilitating tools in the teaching-learning process.
50‐2: Towards Modern Burn‐in Assessment Method for HDR Displays

A novel burn‐in assessment method is strongly required for modern high dynamic range (HDR) displays. Specifically, one of the necessary requirements for the new method is to stimulate a maximum capability of HDR displays such as a wide color gamut for all colors and a wide range of luminance. In this paper, based on the requirements, a new burn‐in assessment method is proposed by employing steepest gradient weighted quantification metric. Experimental results and analysis are conducted for validating the proposed method.
Endpoint-Based Thinning with Designating Safe Skeletal Points

Thinning is an iterative object reduction: border points that satisfy some topological and geometric constraints are deleted until stability is reached. If a border point is not deleted in an iteration, conventional implementations take it into consideration again in the next step. With the help of the concepts of a 2D-simplifier point and a weak-3D-simplifier point, rechecking of some ‘survival’ points is not needed. In this work an implementation scheme is reported for sequential thinning algorithms, and it is shown that the proposed method can be twice as fast as the conventional approach in the 2D case.
AMGA: An Adaptive and Modular Genetic Algorithm for the Traveling Salesman Problem

The choice in selection, crossover and mutation operators can significantly impact the performance of a genetic algorithm (GA). It is found that the optimal combination of these operators are dependent on the problem characteristics and the size of the problem space. However, existing works disregard the above and focus only on introducing adaptiveness in one operator while having other operators static. With adaptive operator selection (AOS), this paper presents a novel framework for an adaptive and modular genetic algorithm (AMGA) to discover the optimal combination of the operators in each stage of the GA’s life in order to avoid premature convergence. In AMGA, the selection operator changes in an online manner to adapt the selective pressure, while the best performing crossover and mutation operators are inherited by the offspring of each generation. Experimental results demonstrate that our AMGA framework is able to find the optimal combinations of the GA operators for each generation for different instances of the traveling salesman problem (TSP) and outperforms the existing AOS models.
XNORNet and Minimum Barrier Detection for Efficient Face Recognition.

Deep Convolutional Neural Networks (CNNs) have been extremely effective in many areas of computer vision, particularly in the domain of facial recognition where many CNN models have delivered a near perfect performance on long-standing benchmarks. A major challenge being faced by CNN architectures today is their need for a large amount of memory and processing power to deliver their exceptional performance. This hampers their deployment on less powerful devices like smart-phones whose ubiquity is unmatched. This paper critically analyzes a CNN based network architecture wherein convolutions are approximated by XNOR operations and bit-counts which are primarily binary operations hence making the network much faster and more memory efficient than standard CNN architectures. The aim of this paper is to assess the performance of the aforementioned architecture for the task of facial recognition with Minimum Barrier Salient Object Detection (MBD) as a preprocessing step and to examine the affect self-normalizing properties induced by the Scaled Exponential Linear Unit (SELU) activation function on the convergence of the network and benchmark it against the convergence performance of the same network incorporating the widely used Rectified Linear Unit (RELU) as its activation function. Two widely known datasets, Faces95 and Faces96, pre-processed using Minimum Barrier Detection (MBD) for saliency detection were used benchmarking the performance of the proposed networks. It was observed that the network using SELU converged faster than RELU for the Faces95 dataset and both converged at similar rates for Faces96 dataset. The network with SELU gave better accuracy than RELU for both the datasets.
DCRN: Densely Connected Refinement Network for Object Detection

Abstract Object detection has made great progress in recent years, the two-stage approach achieves high accuracy and the one-stage approach achieves high efficiency. In order to inherit the advantages of both while improving detection performance, this manuscript present a useful method, named Densely Connected Refinement Network (DCRN). It adds the dense connection based on RefineDet. Compare to the RefineDet, our approach can take full advantage of the bottom feature information. DCRN is formed by three interconnected modules, the dense anchor refinement module (DARM), the dense object detection module (DODM) and the dense transfer connection block (DTCB). First module can make better use of the features from different layers to initially adjust anchors by attaching dense connection. The latter module takes the refined anchors to further improve the regression and predict multi-class label. Due to the dense connection in DCRN, the network parameters are reduced and the computing costs of this approach is also saved. Extensive experimental results on PASCAL VOC 2007 and PASCAL VOC 2012 demonstrate that DCRN achieves higher accuracy than the one-stage method and higher efficiency than the two-stage method.
A Novel Air Gesture Based Wheelchair Control and Home Automation System

With almost 1 in 10 people across the world requiring the need of assistance for mobility, the wheelchair finds itself to be the most widely used device for commuting in the world. This has led to a lot of research being carried out in the past few years to ease the way an individual can locomote with the help of the same. From eye gaze to brain waves - a lot of research has been carried out in this regard. In this paper, one such method to control the wheelchair utilizing air gestures has been described. The methodology not only paves the way for easy independent control but also proves to be highly cost effective as well. Further to this, the system is also capable of wirelessly performing various activities such as switching on lights and even opening the front door - all with the help of simple air gestures. The video processing algorithm for identifying the air gestures has been designed and developed using MATLAB 2013a and implemented on a Raspberry Pi 3 Model B. The algorithm was found to be 1.92 times faster than its predecessor algorithm.
Prioritising interventions for sustainable structural transformation in Africa: a structural equation modelling approach

Development paradigms offer competing theories on the drivers of structural transformation. Most of the analysis has so far focused on the economic factors with little emphasis placed on either the social or environmental dimensions of sustainable development. Furthermore, there has been little theoretical or empirical analysis of how the three dimensions (e.g. economic, social, and environmental) interact to impact on structural transformation. This leaves an important gap in the literature since the three sectors are interlinked, not mutually exclusive, and hence cannot be analysed in isolation from one another. Using a panel data set for a group of 29 African countries spanning the period 1995–2011, this study employs a structural equation modelling approach to estimate the direct and indirect impacts of social, economic and environmental indicators on a composite index of structural transformation. We find that strategies that prioritise investments in social inclusion programmes have the greatest catalytic effect in advancing structural transformation in Africa.
Joint Intention Detection and Semantic Slot Filling Based on BLSTM and Attention

Spoken language understanding (SLU) of the dialogue system usually involves two tasks: intent detection and semantic slot filling. The current Joint intention detection and semantic slot filling has become the mainstream method of SLU research. A Bidirectional long short-term memory (BLSTM)model based on the attention mechanism is used to jointly identify the intent and semantic slot filling of the Hohhot bus query. The experimental results show that the model achieves a good performance in the intent detection and semantic slot filling, and the result based on the character mark is better than the one based on word mark. The F1 score is better than the others based on the LSTM model.
A Formal Model for Managing Multiple Observation Data in Agriculture

Nowadays, farmers can search for treatments for their plants using search engines and applications. Most existing works are developed in the form of rule-based question answering platforms. However, an observation could be incorrectly given by the farmer. This work recommends that diseases and treatments must be considered from a set of related observations. Thus, we develop a theoretical framework for systems to manage a farmer's observation data. We investigate and formalize desirable characteristics of such systems. The observation data is attached with a geolocation in which related contextual data is found. The framework is formalized based on algebra, in which required types and functions are identified. Its key characteristics are described by: (1) the defined type called warncons for representing observation data; (2) the similarity function for warncons; and (3) the warncons composition function for composing similar warncons. Finally, we show that the framework helps observation data to become richer and improve advice-finding.
Acceleration of Feature Extraction for Real-Time Analysis of Encrypted Network Traffic

With the growing amount of encrypted network traffic, it is important to have tools for the analysis and classification of encrypted network data. Encrypted network traffic is usually analysed by statistical methods because Deep Packet Inspection or pattern matching is not applicable. However, the statistical methods are usually designed to work offline on already captured network traffic. For real-time analysis, hardware acceleration is needed to achieve wire-speed 10 Gbps throughput. Therefore, we focus on real-time monitoring of encrypted network traffic and propose a new acceleration method to extract features from encrypted network data. Approximate computing is used to speed up the computation of entropy for the input data stream and to reduce FPGA logic utilization. As can be seen in the results, the precision of classification has decreased only by 0.1 to 0.2. Moreover, proposed hardware architecture has very low FPGA logic utilization and can operate on high frequency.
Intelligent Procuratorate Depends on Big Data Investigation Technology

With the change of the times and the advent of the era of wisdom, it is urgent for procuratorial organs to apply big data investigation technology to solve difficult problems in their work. Big data can discover deep-seated rules and connections of data through valuable data processing. Big data investigation is different from the traditional reconstructing criminal process by consequence traceability. It is a process of mining effective clues on the basis of analyzing and dealing with the links between various clues. It is an effective investigation model based on a large number of data, which can make certain prediction of the direction of investigation. This is a change for ex post investigation and passive investigation in the past. It will also change the single-thread investigation mode to multi-clue cooperative investigation, which will surely lead the future development direction of investigation.
Remote corneal imaging by integrating a 3D face model and an eyeball model

In corneal imaging methods, it is essential to use a 3D eyeball model for generating an undistorted image. Thus, the relationship between the eye and eye camera is fixed by using a head-mounted device. Remote corneal imaging has several potential applications such as surveillance systems and driver monitoring. Therefore, we integrated a 3D eyeball model with a 3D face model to facilitate remote corneal imaging. We conducted evaluation experiments and confirmed the feasibility of remote corneal imaging. We showed that the center of the eyeball can be estimated based on face tracking, and thus, corneal imaging can function as continuous remote eye tracking.
Dissemination of Knowledge and Sharing Experiences in Emerging Issues through a Remote Summer School

This paper presents the analysis of innovative features of the remote mode summer school and benefits as perceived by the participants. The summer school has been identified as highly valuable for the participants to expand their knowledge on sustainable development and the economic/financial conditions on a global scale encouraging them to propose creative solutions for real problems. According to the feedback, the program features were well appreciated and inclusion of some activities is proposed to enhance the scope and further development. One of them is to include participants in different locations in the project teams. This could have been mentioned due to the fact that participants of the European countries mostly selected projects with high technological concepts and this was not case for Asian group, probably due to the difference of penetration in high technology to the society. Despite all positive feedbacks, running a summer school like this remotely does not yet deliver the expected results for a future online education. The first item is to upgrade the communication system for clear and speedy communication between different locations.
Facial Expression Recognition Based on Gabor Multi-orientation Feature Fusion

Abstract Facial expression is a key to nonverbal communication, which has been confirmed by many different research projects. A change in intensity or magnitude of even one specific facial expression can cause different interpretations. With the continuous and fast development of computer vision and pattern recognition, facial expression recognition has received significant attention recently due to the wide range of commercial and law enforcement application and the availability of feasible technology during 30 years of research. In this thesis, facial expression recognition is studied by applying several commonly used methods in the whole process. By numerical experiment, we find that our approach with Gabor based transformation for face expression feature extraction, combining the advantages of various algorithms, Gabor wavelet transform and non-negative matrix decomposition of facial expressions are used to obtain features, and CNN is used to classify and apply them to facial expression recognition.
P‐59: Robust Visual Enhancement of Moving Contents in Projected Imagery

For any projection system, one goal will surely be to maximize the quality of projected imagery at a minimized hardware cost, which is considered a challenging engineering problem. Experience in applying different image filters and enhancements to projected video suggests quite clearly that the quality of a projected enhanced video is very much a function of the content of the video itself; that is, to first order, whether the video contains content which is moving as opposed to still, since the human visual system tolerates much more blur in moving imagery. We would therefore assert that the moving and non‐moving pixels of a given video stream should be enhanced differently, using class‐dependent video enhancement filters to achieve a maximum visual quality. In this paper, we introduce such a novel motion‐dependent content enhancement scheme, based on a pixel‐wise moving / non‐moving classification, with the actual enhancement obtained via class‐dependent Wiener deconvolution filtering. Experimental results on four challenging videos show that the proposed scheme offers improved visual quality.
Using down-sampling for multiscale analysis of texture images

This work proposes the study of a simple yet efficient strategy to accomplish multiscale analysis of gray level images. The method is applied to the classification of texture/material images. The proposal employs histograms of down-sampled versions of the initial image to compose a feature vector. A statistical model based on Markov random fields is also developed and employed to explain how the information conveyed by the proposed descriptors can express degrees of homo/heterogeneity in the image. This is widely known to be a fundamental feature in texture analysis. The accuracy of our descriptors is compared to several state-of-the-art approaches and the achieved results confirm our expectation that a straightforward method can be efficiently employed even in the recognition of objects in large and complex databases.
Allowing Users to Create Similarity Relations for Their Flexible Searches over Databases

A bi-valued logic is not enough to make an intelligent search engine to give us the result for the queries like “I am looking for a cheap restaurant, Mediterranean food or similar type”. With the integration of Fuzzy Logic and Logic Programming, we were able to model and pose flexible queries over databases. Therefore, we present a framework that allows users to pose their expressive queries based on defining similar relation criteria over various modern and conventional data formats such as JSON, SQL, CSV, XLS, and XLSX. The interest is in, for example, obtaining “drama movie” when asking for “romantic movie” (only if the similarity relation between drama and romantic movie is explicitly defined in the configuration file). The uses of similarity relation between values allow us to obtain more answers apart from the identical one. The searches that use two or more criteria are much more expressive and accurate. This framework provides the facility to define, modify and remove similarity relations from a user-friendly interface (without the need to be concern about the low-level syntax of the similarity criteria).
Gamification of Eye Exercises for Evaluating Eye Fatigue

Extensive use of computer and mobile devices places large burden on our eyes. To tackle the symptoms of digital eye strain and improve eyesight, the use of eye exercises are suggested. However, few people do eye exercising as it is considered boring. In this paper we apply a serious game approach to eye exercising. We developed a PacMan style game to promote horizontal and vertical eye movements. Eye and game performance characteristics are collected during the game and used to evaluate the onset of eye fatigue during the game. The damped oscillation model is applied to observe the learning and fatigue effects. Analysis of data collected during game sessions from 14 subjects is presented.