Reviewerly/exHarmony huggingface dataset:
Lisa-life saver

Recent trends in information system shows that sharing of data among various heterogeneous system needs a proper web oriented service. Exchanging of data is mandatory in various web application systems such as Accident Detection and Ambulance Rescue System, Online transaction security system, Ticket booking system, Car detection and notification system. Passing of information is not done efficiently due to certain issues such as network failure, human errors like pronunciation, inadequate address representations, misunderstanding of the exact location. To deliver an effective web application services there exists GPS tracking facility to identify the exact location, twilio service to pass the information. API helps in fetching and accessing the data even from the operating systems. These features help any common web application to overcome the problem of connectivity and continuity. LISA is a web application system which helps in passing the accidental spot and information to nearby ambulance, police station and to the casualty peer group members. Casualty blood group donors are also spotted. To make it a successful one the above mentioned GPS tracking facility is used to overcome the common problem of manual intervention while identifying and reporting the location in emergency situations. LISA also incorporates the twilio service for faster communication. API's were adopted for better integration and data accessibility. These features helps in overcoming the problem of the rules and regulations in the hospital and FIR formalities for an accidental case by passing info to the casualty peer groups. Casualty nearby blood group donors are also identified and informed via. SMS, voice call. This helps in immediate treatment of the casualty.
Image encryption by using a specific adaptation of Lehmer’s algorithm

A congruential generator is an algorithm that yields a sequence of integers, it is defined by an arbitrary first term (seed) x0, and by a simple mathematical recursion relation which containing three parameters (a, c, m) which must satisfy the kunth criterias.The aim of this work is to propose a crypto system based on a new adaptation of the linear congruential generator in this case, it must be able to produce an indistinguishable output of a perfect randomly and, must withstand of different attacks. Also, it must be able to pass the test of the following bit, that is to say that there is no polynomial time algorithm such that knowing the first L bits of the sequence S, we can deduce the (L + 1)th bit from S with a probability greater than 0.5. There are several techniques for the construction of such a generator, we envisaged to use a linear combination of these linear congruential generators which are adapted to the cryptography.
Low-Cost Wearable Gesture Recognition System with Minimal User Calibration for ASL

In this paper, the authors design a wearable gesture recognition system that identifies the alphabetic gestures in the American Sign Language (ASL) efficiently, requiring minimal user calibration. The system uses a heterogeneous collection of sensors, viz., contact, flex and inertial, placed strategically on the hand to record movements and points of contact involved during gesture signing. In order to minimize user calibration, the authors propose a novel approach called as State-based gesture modelling, which requires only 7 gestures to be signed by a user to create a State-based model for all 26 alphabetic gestures in ASL for that user. A multi-stage gesture recognition algorithm, which uses the user-calibrated State-based model, is employed for gesture identification. The system was extensively tested for 2 trained and 1 amateur ASL signers for up to 150 times per gesture giving maximum efficiencies of 92% and 81% respectively, thus indicating the robustness of the system. The use of an elegant algorithm along with simple off-the-shelf sensors enabled the implementation of the entire system on an ATmega 328P micro-controller, resulting in a cost of 25 USD for a laboratory stage prototype.
Neural Network Based Patterns Detection in Object-Oriented Program

These days, different design methodologies are proposed for actualizing various types of answers for the product wanted for complex frameworks. Among them design patterns are as often as possible used to develop the object oriented program. The Determination of design patterns in open source program empowers the viability of conventional question situated projects. The proposed system demonstrates the assurance of software patterns in open source program. The procedure of pattern retrieval is carried out by utilizing quality estimation parameters e.g., programming measurements. The introduced strategy considers different sort of neural network models to decide the existence of patterns in open source framework. The proposed method is assessed by utilizing an open source program e.g., MapperXML.
Time-aware Session Embedding for Click-Through-Rate Prediction

TV series correlation computing is one of the most important tasks of personalized online streaming services. With the relevance of TV series and viewer feedback, we can calculate the TV series correlation table based on the viewer's implicit feedback which does not perform well for the newly added "cold start" TV series. In this paper, we aim to improve correlation computing within the cold-start phase. We propose a framework named Time-aware Session Embedding (TSE), with Item Embedding in Session and Time Decay Factor for a multimodal recommendation. We apply an lower- dimensional vector as item embedding and calculate their factor considering the time decay. The framework performed well in the Content-based Video Relevance Prediction Challenge and we get the first place in this competition.
Alignment, Scale and Skew Correction for Optical Mark Recognition Documents Based

Acquiring an OMR (Optical Mark Recognition) reading equipment can be impracticable to some companies or individuals due to its costs. Computational software solutions can be more attractive, but they require specific page format or page layout, such as specific marks to be used when recognizing any OMR document. In this paper, we propose a way to treat skew, translation, scale and alignment using a base document as reference due to the intrinsic characteristics of the problem. Key points are found by a pattern matching algorithm and used for the document image transformation. The method does not require specific layout, needing less formatting, allowing non-experts to create the form using ordinary software and scanners. Two experiments were executed: one with 40 images distorted randomly from a document clipping, and the second one with 1034 images of real student tests. Both experiments reached high overall accuracy.
Simulation of Motor Impairment with

Testing software with individuals who have differing abili­ ties such as motor impairments allows developers to create more accessible applications. Unfortunately, these individuals are not always available to assist with research or software development testing. To help address this issue, we propose a system which simulates the difficulties demonstrated by a motor-impaired user interacting with a head-controlled mouse pointer system. Through an evaluation using a Fitts' law ex­ periment, we compare the throughput rate in bits per second and percent error rate of our system between users with and without motor impairments.
Using the New VPMADD Instructions for the New Post Quantum Key Encapsulation Mechanism SIKE

This paper demonstrates the use of new processor instructions VPMADD, intended to appear in the coming generation of Intel processors (codename "Cannon Lake"), in order to accelerate the newly proposed key encapsulation mechanism (KEM) named SIKE. SIKE is one of the submissions to the NIST standardization process on post-quantum cryptography, and is based on pseudo-random walks in supersingular isogeny graphs. While very small keys are the main advantage of SIKE, its extreme computational intensiveness makes it one of the slowest KEM proposals. Performance optimizations are needed. We address here the "Level 1" parameters that target 64-bit quantum security, and deemed sufficient for the NIST standardization effort. Thus, we focus on SIKE503 that operates over Fp2 with a 503-bit prime p. These short operands pose a significant challenge on using VPMADD effectively. We demonstrate several optimization methods to accelerate Fp, Fp2, and the elliptic curve arithmetic, and predict a potential speedup by a factor of 1.72x.
MDR Model: Contributions to the Motivation and Sociocultural Integration

The aim of this article is to present the results of improvement in the motivation and sociocultural integration of ethnic minority children at risk of social exclusion (Romani children) after the implementation of the MDR model in the classroom. This model is based on the creation of digital educational resources for mobile devices adapted to the special characteristics of the sample we have worked with. They are aimed at facilitating the teaching-learning process for the overall target population. After implementing the digital educational resources provided by the MDR model, a quantitative, quasi-experimental method was used to collect data related to motivation and sociocultural integration through two questionnaires: before the implementation phase (pre-phase), and after the use of these resources (post-phase). The results show that both the motivation and the sociocultural integration of these children have increased as a consequence of the use of the MDR model in mobile devices.
Facebook as a Didactic Instrument to Improve the Teaching-Learning Process in Programming Courses

All Engineering students take a programming course as part of their professional training. The traditional didactic approach is limited and discouraging since the students are only attentive to what the teacher develops. The students do not interact with each other and less with the teacher. Using Facebook as a teaching tool for communication, participation and collaboration in the teaching-learning process breaks the traditional, passive and monolithic didactic scheme that discourages and alienates students from their classrooms and, on the contrary, generates interest and motivation. Thus, allowing participation between the teacher and students at any time without having to do it during class hours. Facebook was used as a didactic tool in the programming course at the Universidad Nacional de Ingenieria, achieving a high percentage of motivation, interest, and participation in the teaching-learning process.
Performance Comparison of Cat Swarm Optimization and Genetic Algorithm on Optimizing Functions

This study was conducted to find out the best performance resulted from Cat Swarm Optimization (CSO) and Genetic Algorithm (GA). CSO is one of the algorithms developed based on the behavior of a number of cats to solve optimization problems. It is noted that they include two problem-solving modes such as seeking and tracing. The seeking mode happens passively and cautiously. Being cautious means become aware of the prey or other cats. The latter mode, however, occurs when the cats are active in searching the prey by moving toward it. Since they spend most of the time to idle, the seeking mode is more frequently found. In order to maximize the improvement of tracing mode, a recent calculation method was added. This study implemented three experiment functions such as sphere, Rastrigin, and knapsack. Each experiment was conducted ten times to find out the number of iterations and time needed for each method. The results show that CSO is better than GA due to its performance in terms of iterations and time.
A New Tool for Linked Data Visualization and Exploration in 3D/VR Space

Linked data and knowledge graphs have become widely used in the last years. As the interest in linked data and ontologies grows, so does the interest in means of studying, presenting, developing and storing ontologies. Here, we introduce a first version of novel tool for presenting and studying ontologies and linked data which visualizes the semantic graph structure in the natural form for humans – in form of 3D objects in space. The use of 3D space provides additional means to visualize and annotate objects, and for the user to explore. The presented tool builds on an existing ontology visualization system (Ontodia), and extends it with the ability to display ontologies in 3D and virtual reality space.
Bird Watch

Birdwatching is one of the largest and most profitable outdoor wildlife activities in the world, including much of Northern America. Despite this, birdwatching enthusiasts face many difficulties in recruiting new watchers due to a multitude of limitations. The most common of these include; the unpredictability of finding live specimens in natural environments, lack of access to habitats for many individuals, and heavy reliance on teaching with illustrations and sound files which can make bird study stale or difficult for certain learners, especially members of younger generations. Our program seeks to help alleviate these issues by providing a more reliable, accessible, and entertaining means of bird education through the use of a bird identification video game. Bird Watch is a virtual reality birdwatching simulator which places participants inside a virtual forest populated by ten common North American birds. Through relaxed, repetitive action, participants improve their real world bird identification skills while hopefully gaining greater appreciation for the pastime. Which could lead to increased participation in both birdwatching and wildlife preservation.
STEM - Play, Learn and Work: STEM Education in Academic Club Based on Community and Local Products to Improve a Positive Attitude towards STEM

Abstract This paper developed a practical learning activity named “STEM - Play, Learn and Work” to increases students’ interest and engagement in the learning of STEM (Science, Technology, Engineering, and Mathematics). Because we need to show that STEM knowledge and skills can solve everyday problems and promote community and local products, one popular product in our local community is selected as a representative to be the main theme of learning. We designed the project-based learning activity combined various STEM subjects through steps of production for the representative from the beginning to the end. We used an experimental “STEM - Play, Learn and Work” to 19 students in grade ninth – twelfth in class of academic club. The learning outcomes including student performance and attitude towards STEM are discussed. The assessment results (one assessment taken before the activity, one after) show that the activity improved students’ knowledge in STEM subjects significantly. It also increased students’ positive attitude towards STEM and interest in STEM careers. Students have the opportunity to apply their meaningful STEM knowledge and skills to solve problems in everyday life through this practical activity.
Video Object Linguistic Grounding

The goal of this work is segmenting on a video sequence the objects which are mentioned in a linguistic description of the scene. We have adapted an existing deep neural network that achieves state of the art performance in semi-supervised video object segmentation, to add a linguistic branch that would generate an attention map over the video frames, making the segmentation of the objects temporally consistent along the sequence.
Causality Inspired Retrieval of Human-object Interactions from Video

Notwithstanding recent advances in machine vision, video activity recognition from multiple cameras still remains a challenging task as many real-world interactions cannot be automatically recognised for many reasons, such as partial occlusion or coverage black-spots. In this paper we propose a new technique that infers the unseen relationship between two individuals captured by different cameras and use it to retrieve relevant video clips if there is a likely interaction between the two individuals. We introduce a human object interaction (HOI) model integrating the causal relationship between the humans and the objects. For this we first extract the key frames and generate the labels or annotations using the state-of-the-art image captioning models. Next, we extract SVO (subject, verb, object) triples and encode the descriptions into a vector form for HOI inference using the Stanford CoreNLP parser. In order to calculate the HOI co-existence and the possible causality score we use transfer entropy. From our experimentation, we found that integrating casual relations into the content indexing process and using transfer entropy to calculate the causality score leads to improvement in retrieval performance.
Color Image Enhancement by Logarithmic Transformation in Fuzzy Domain

A new fuzzy logic and logarithmic transformation method for enhancing of low contrast color images is proposed. The input RGB image is converted into HSV (Hue, Saturation, Value) color space to preserve the chromatic information contained in the original image. To enhance the image, only the saturation component S and intensity value component V are transformed in fuzzy domain by using S-type membership functions with parameters calculated by solution of the maximum of fuzzy entropy optimization problem. Next, the fuzzy domain is transformed to the logarithmic one. An enhancement of local contrasts by the method of unsharp masking is fulfilled in a logarithmically transformed fuzzy domain for S and V components. After that, a number of inverse transformations is performed: from the logarithmic domain to the fuzzy one and afterwards to the spatial domain. The method of quantitative assessment of quality of color images is proposed. The examples show the effectiveness of the developed method for improving the quality of color images.
Implementing Augmented Reality as a Digital Props of Brain Anatomy using 3D Cuboid Tracker

Augmented Reality may propose several advantages over the conventional method as a teaching aid, including in learning brain anatomy in health education. To gives an alternative to the typical props, and we proposed a learning aid in the form of AR mobile application where a student can hold and observe the human brain in the form of a 3D digital props. By utilizing the 3D cuboid tracker, the proposed system offers an affordable digital prop and yet allow natural handling of the object, which gives a new learning experience. Through performance testing, we found how the device specifications' may slightly affect the proposed system performance. However, through a usability testing, the proposed system and method is preferred over other methods such as a book, information on the screen, and 2D AR object. This research shows the proposed system and method have a lot of potential in aiding much more interactive and stimulating learning activities.
Light-Weight Tracker for Sports Applications

In the paper, we describe technical details of multi-player sports tracker system. We demonstrate that proper in-depth analysis of video frames sequence may provide a lot of useful information required for sports analytics. Object detection and tracking steps are analyzed. Novel ideas for efficient filtering of false detections and irrelevant tracks are proposed. Also, we show an example of how the object tracking information used for the regions of interest location allows streaming sports events without human operators. At last, important practical implementation details, as well as initial experimental results, are discussed.
High-Dimensional Data Anomaly Detection Framework Based on Feature Extraction of Elastic Network

Although appropriate feature extraction can improve the performance of anomaly detection, it is a challenging task due to the complex interaction between features, the mixture of irrelevant features and relevant features, and the unavailability of data tags. When conventional anomaly detection methods deal with the problem of anomaly detection of high dimensional data, the performance of anomaly detection will be degraded due to the existence of irrelevant features. This paper proposed a method of feature extraction and anomaly detection for high dimensional data based on elastic network, which can filter irrelevant features and improve the accuracy and efficiency of anomaly detection. In this paper, an outlier scoring method was used to score the outliers of the original data, and then outliers and the original data were input into the elastic network for sparse regression. After feature extraction of elastic network, those irrelevant features to abnormal data are ignored, thus reducing the dimension of data. Finally, high-dimensional data are detected efficiently according to extracted features. In the experimental stage, we used the high-dimensional anomaly dataset provided by ODDS to detect the performance of the proposed method based on AUC detection accuracy, ROC curve, feature number, convergence speed and other indicators. The results show that the proposed method not only can effectively extract the features related to high-dimensional anomaly data, but also the detection accuracy of outliers has been greatly improved.
College Fees Transaction Using Hash Functions of Blockchain Model

In recent existences, the cryptocurrency has provided great opportunities and challenges for the modern financial industry. Nowadays people to pay the college fees in different ways, such as credit card, cheque, demand draft, and, direct cash. These types of transactions need an intermediate to transfer amount from the merchant to the beneficiary and the intermediate charge a transaction fee to transfer the money. In this hypothesis, we propose a decentration college fee transaction methods using blockchain technology. A blockchain is fundamentally a distributed ledger of the transcript of all transactions that has been accomplished and shared among contributing parties and it does not require any intermediate to transfer the money. Each transaction in the public ledger is confirmable by the participator in the system. Essentially, it’s a disseminated database which manages an endlessly mounting proof of blocks it grips lots of transactions. Thus the concluded blocks are added into the networks in chronological order and each block contains an index, timestamp, data, and previous hash which indicate the previous block.
Analysis and research on quality control method of global radiation observation data

Radiation observation data are important basic data for assessment and research of climate change and solar energy resources. Quality control of radiation observation data is an important guarantee for accurate and reliable observation. First, the global radiation data collection method and the quality control process are introduced, and the differences and similarities between different quality control standards are compared in detail. Then, the quality control contents of the upper and lower limits and the rate of change are analysed theoretically and verified by experiments. Theoretical analysis and experimental results showed that the lower limit of the global radiation irradiance quality control should be −20 W/m2, the upper limit should be 2221 W/m2 or based on solar position, and the wrong change rate of sampling value should be 1000 W/m2. Through the unification of the global radiation quality control standards, the difference caused by the inconsistent quality control methods can be avoided, thus improving the effectiveness of the radiation observation data and ensuring the accuracy and consistency of the radiation observation data.