Reviewerly/exHarmony huggingface dataset:
Text Sentiment Analysis Based on ResGCNN

Sentiment analysis of text is a significant task in Natural Language Processing (NLP), and Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are two commonly used deep learning models of NLP. RNN's variant Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) can solve the long-term dependence of traditional RNN. Their variant Bi-directional LSTM (BiLSTM), Bi-directional GRU (BiGRU) solve the problem that unidirectional LSTM and GRU can only access the above but not the below. However, the ability of the cyclic neural network to extract key features is not strong. CNN can extract local features of text vectors effectively, but ignores the meaning of text contexts. TextCNN can extract text location features by using convolution cores of multiple different windows. As the number of layers increases, deep learning can obtain more complex features, but it will cause the problem of gradient disappearance, and errors cannot be effectively back-propagated, and the residual networks can solve it. This paper proposes a ResGCNN network combining residual network, multilayer BiGRU, and TextCNN. The excellent classification performance is obtained on multiple English and Chinese data sets.
Automatic Recognition of Dialects of Himachal Pradesh Using MFCC &amp;GMM

In the present globalized society, people from all over the world are coming and working together. The biggest hurdle in this process is the medium of interaction, as all individuals do not speak a common language. A way to circumvent this problem is to develop a machine based system for recognition of different languages and subsequently employing automatic machine translation mechanism for communication between people from diverse language or dialect groups. A MFCC and GMM based approach for recognition of dialects of Himachal Pradesh has been presented in this paper. Around seven dialects spoken in different regions of Himachal Pradesh have been used in this study. The results obtained shows that dialects spoken in adjacent regions have significant overlap and are difficult to distinguish. Accuracy for offline and online tests was respectively around 80% and 70%.
Performance Enhancement of Solar Panel Using Dual Axis Solar Tracker

Installation of solar panel in most of solar power plant (off grid and on grid station) using fixed tilt angle. Fixed tilt angle system that implemented on solar power plant is directing solar panel to the sun in certain angle. For the extraction of maximum energy from the sun, the plane of the solar panel should always be normal to the incident radiation. Solar panel installed in a fixed tilt angle facing the sun perpendicularly only for several hours. In this experimental study, dual axis solar tracker (DAST) has been designed and implemented to facing solar panel to the sun perpendicularly. This DAST system is microcontroller-based which using arduino uno as main controller. Light dependent photo resistors (LDR) are used as the sensor of DAST. For rotating the appropriate position, servo motors are used. All experiment output parameters stored in SD card. This DAST system is designed to control the Altitude angle in the vertical plane as well as the Azimuth angle in the horizontal plane of the solar panel workspace. This experimental study shows the output of solar panel using DAST system increase significantly compared to fixed tilt angle solar panel.
A Probabilistic Approach to Quantum Inspired Algorithms

Digital computing is based on the notion of deterministic bits that are 0 or 1. At the other extreme, quantum computing is based on q-bits that are delicate, coherent superpositions of 0 and 1. In this paper, we describe an intermediate approach between these two extremes, namely Probabilistic Computing, based on the notion of probabilistic or p-bits that fluctuate between 0 and 1. Hardware p-bits can be compactly realized from emerging spintronic building blocks that are smaller, more energy efficient compared to their traditional CMOS implementations. Probabilistic circuits built out of interconnected p-bits can be useful for a host of quantum computing inspired algorithms.
Multi-robot Rounding Strategy Based on Artificial Potential Field Method in Dynamic Environment

This paper proposes an obstacle avoidance and formation rounding strategy based on the improved artificial potential field method, which can give full play to the advantages of strong real-time artificial field method, good online computing ability and fast local processing ability, so that the car formation can be based on safety. Smoothly avoid dynamic and static obstacles in the environment to complete the round-up of moving targets. The MATLAB simulation platform is used to realize the dynamic obstacle avoidance and target rounding of the car, which fully verifies the correctness and the effectiveness of the proposed strategy.
A Semiautomatic Sperm Cell Data Annotator for Convolutional Neural Network

Convolutional Neural Network (CNN) is one of deep learning methods which is very powerful for solving image processing and computer vision problems. The powerfulness of this method relies on the number and the quality of the training data. The need for numerous and valid annotated training data is crucial. Manual annotation is still the most popular option for obtaining a valid data annotation. There are some manual annotation systems available. Nevertheless, manually annotating a massive amount of data is remarkably tiresome and need plenty of time. This is the problem that we addressed. In this paper, we propose a system to annotate a CNN training data semi-automatically to accelerate the annotation process. We use an extreme case in which the number of objects to annotate inside a video frame is plentiful (334 - 472 objects). We compare the accuracy of its annotation and the speed of the annotation process to the available annotation systems. The result is that our proposed method reduces the average annotation time up to 48.7%.
Coupling-Degree-Based Heuristic Prioritized Planning Method for UAV Swarm Path Generation

This paper presents the coupling-degree-based heuristic prioritized planning method (CDH-PP) to improve the efficiency and robustness of swarm path planning. The proposed method decouples the swarm path generation problem into a series of single-UAV path planning problems. And the couplingdegree-based priority heuristic generation criterion is tailored to determine the planning order of UAV swarms. The swarm coupling degree matrix is established to determine the coupling relations among UAV swarms based on the collision-detection algorithm. Anytime repairing sparse A*algorithm (AR-SAS) is adopted to plan path for single-UAV considering obstacles and inter-UAV collisions. Simulation results show the proposed CDH-PP method can generate collision-free paths for UAV swarms. And the comparative studies with the random prioritized planning and distance-based heuristic prioritized planning method demonstrate higher computational efficiency and robustness of CDH-PP.
Adaptive Phone Orientation Method for Continuous Authentication Based on Mobile Motion Sensors

With the popularity of mobile terminals and the convenience of mobile operating, more and more private data is stored in mobile phones, which makes users pay more attention to mobile security. The data of mobile motion sensors are used to construct the user's behavioral characteristics and biometric characteristics. The principle is to capture the subtle changes in the smartphones caused by the user holding smartphones and touching screens. These changes are unique to different users. This paper studies the effects of mobile phone orientations on continuous authentication based on mobile motion sensors. Additionally, this paper constructs an adaptive phone orientation method for continuous authentication. An orientation detection model is constructed using K-means and Random Forest. The authentication model is constructed by using one-class SVM. Meanwhile, our experiments show that the data of mobile motion sensors are great difference in different phone orientations. We believe considering the situation of multi-orientations data affecting authentication accuracy. The adaptive phone orientation method can better fit for the scenarios where users use mobile devices in different phone orientations. Considering the orientation of the data will improve the robustness of the system and the accuracy of authentication.
Smart Home Monitoring and Automation Energy Efficient System Using IoT Devices

Wastage of electricity is one of the main problems which we are facing nowadays. In home and working areas, people will be engaged with too many activities. Sometimes, there might be a chance to forget to turn off various electrical appliances like lights, fans, servers, if there were nobody in the room or area/passage. This leads to huge power consumption. Also, it is very difficult to physically challenged persons and old-age people to access the appliances every time manually. To avoid all such situations, we proposed “Smart Home System.” It is the process of automating home appliances in which the various electric and electronic appliances are wired up to a central computer control system so they can be handled more efficiently. “Smart Home” helps to overcome the problems that usually people face in daily life. Behind this, it needs the involvement of multiple complex hardware components in controlling the smart home project and there is a fair amount of software architecture that is responsible for driving the hardware components. Moreover, there is a need to employ persons to monitor such simple tasks. This manual approach is time-consuming and also very costly. Thus, automation is the better alternative through which these tasks can be completed automatically without human effort and also accessed remotely from anywhere and anytime with less effort. Everyone is showing interest in making their home smarter to ease their daily activities. The proposed Smart Home system has the capability to control the home appliances based on the environment. Here, different boards like Raspberry Pi 3 and Arduino UNO are going to be deployed which can take control over the appliances and act accordingly. People can also be notified, if there are any fire/gas leakages inside the home. This security system facilitates the user to take necessary actions as soon as possible to secure their home. It also involves the deployment of different kinds of sensors which can able to detect the object motion, temperature and humidity and fire detection. This application is very simple and can be implemented in all types of homes.
Realization of a Reversible Data Hiding Framework using Histogram Bin Shifting for Color Images

Reversible data hiding (RDH) technique allows embedding of data inside an image and later can be retrieved with the original host image after extraction. In this paper, a RDH technique using histogram bin shifting (HBS) for color images has been proposed. In the proposed technique the color image is separated into three planes and each plane is divided into four blocks and a secret data is embedded into all four blocks of all the planes of the color image by using reversible histogram bin shifting. The same algorithm has been implemented on both the color image components separately (YCbCr/RGB). The proposed scheme attains 69396 and 34478 average embedding capacity for YCbCr and RGB color image components with minimum image distortion.
Colorectal Histology CSV Multi-classification Accuracy Comparison using Various Machine Learning Models

The MNIST database was derived from a larger dataset known as the NIST Special Database. In this paper, colorectal histology CSV data is multi-classified with several method: Convolutional Neural Network (CNN), K-Nearest Neighbour (KNN), Logistic Regression, and Random Forest. The aim of this paper is to compare the performance of each method and define the optimum algorithm; the best method is obtained with CNN with 82.2% accuracy.
A Framework for Automated Formative Assessment in Mathematics Courses

In general terms, the aims of formative assessment are to gather and analyze information about the progress of students, with the intention of improving instruction in real time. This can also help teachers to identify those elements that students are struggling with, and therefore to determine where adjustments must be made. There are, however, some issues that must be addressed before this approach can become more widespread. In this research, and with mathematics as our specific scope, we focus on two of these issues: we present a framework to help teachers implement assessment items that are highly dynamic and flexible, but that allows them at the same time to model common misconceptions and to provide suitable feedback. Following this, we carried out an experiment to determine (i) whether the students perceived such feedback to be useful; (ii) whether they actually used it; and ultimately (iii) whether it produced a difference in their performance. To find the answers to these questions, we used an online `pre-calculus' course with 458 students and collected data directly from those students via both a perception survey and from their interaction with the LMS. Based on these data, we performed several analyses, including process mining and statistical hypothesis tests. The results demonstrated that although many of the students had a positive opinion of the feedback received, it was not always followed. Nevertheless, when it was (or at least when students said it was), the students performed better.
How to Explain a Patch: An Empirical Study of Patch Explanations in Open Source Projects

Bugs are inevitable in software development and maintenance processes. Recently a lot of research efforts have been devoted to automatic program repair, aiming to reduce the efforts of debugging. However, since it is difficult to ensure that the generated patches meet all quality requirements such as correctness, developers still need to review the patch. In addition, current techniques produce only patches without explanation, making it difficult for the developers to understand the patch. Therefore, we believe a more desirable approach should generate not only the patch but also an explanation of the patch. To generate a patch explanation, it is important to first understand how patches were explained. In this paper, we explored how developers explain their patches by manually analyzing 300 merged bug-fixing pull requests from six projects on GitHub. Our contribution is twofold. First, we build a patch explanation model, which summarizes the elements in a patch explanation, and corresponding expressive forms. Second, we conducted a quantitative analysis to understand the distributions of elements, and the correlation between elements and their expressive forms.
Usability Analysis of TOPSIS based Mobile Recommender System of Malang Tourism

Malang City has a variety of interesting tourist objects to visit. Determining the desired place of visit requires access speed and accuracy of favorite places to visit. This can be obtained with a mobile application that utilizes the internet and GPS features of a smartphone. Therefore, the Mobile recommender system of Malang is needed, which recommends favorite place to go in Malang. The scope of this application is developed tourist destinations in Malang & Batu, beach around Malang, shopping destinations, places to buy souvenirs, traditional snacks, organic vegetables, traditional Malang cuisine, Korean cuisine, and cafe in Malang. All of these mobile recommender systems were developed under the same algorithm-based model management system by using the Technique of Order Preference for Similarity to Ideal Solution (TOPSIS). But the success rate of the TOPSIS method will not be seen without further testing (TOPSIS performance will be tested in the further research). To ensure the mobile recommender system of tourism is feasible to be used by end users in this study, it needs to test its usability with the System Usability Scale (SUS) instrument. Testing with SUS has been done in the systematic measurement using 10 statements of SUS. The test shows the score of SUS is 78.25 that means the mobile recommender system of Malang tourism is acceptable to be used by end users and classified into grade C with a good rating. Usability testing results can be improved by eliminating the TOPSIS's weight of the criteria so that it can be more user- friendly.
Research on Distributed Storage of Big Data Based on HBase Remote Sensing Image

With the rapid development of remote sensing, satellites and drone technologies, the remote sensing image datasets are exploding. The traditional storage modes can't meet the storage requirements of massive remote sensing image data. Therefore, this paper proposes a large data storage scheme for remote sensing image based on HBase. The method utilizes distributed storage and column-oriented open source database HBase as the storage model of remote sensing image big data, and uses tile pyramid technology and MapReduce parallel computing framework to construct the tile pyramid of remote sensing image. Finally, the remote sensing image data blocks are stored in the distributed database HBase. This method can effectively improve the storage problem of remote sensing image big data, and has good reliability, scalability and processing efficiency.
Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection

In this paper we present the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model applied to Unmanned Aerial Vehicles (UAV) in order to perform surveillance tasks. The use of unpredictable routes based on the chaotic solutions of a dynamic system as well as pheromone trails improves the area coverage performed by a swarm of UAVs. We propose this new application of CACOC to detect intruders entering an area under surveillance. Having identified several parameters to be optimised with the aim of increasing intruder detection rate, we address the optimisation of this model using a Cooperative Coevolutionary Genetic Algorithm (CCGA). Twelve case studies (120 scenarios in total) have been optimised by performing 30 independent runs (360 in total) of our algorithm. Finally, we tested our proposal in 100 unseen scenarios of each case study (1200 in total) to find out how robust is our proposal against unexpected intruders.
Data-driven Model-based Detection of Malicious Insiders via Physical Access Logs

The risk posed by insider threats has usually been approached by analyzing the behavior of users solely in the cyber domain. In this article, we show the viability of using physical movement logs, collected via a building access control system, together with an understanding of the layout of the building housing the system’s assets, to detect malicious insider behavior that manifests itself in the physical domain. In particular, we propose a systematic framework that uses contextual knowledge about the system and its users, learned from historical data gathered from a building access control system, to select suitable models for representing movement behavior. We suggest two different models of movement behavior in this article and evaluate their ability to represent normal user movement. We then explore the online usage of the learned models, together with knowledge about the layout of the building being monitored, to detect malicious insider behavior. Finally, we show the effectiveness of the developed framework using real-life data traces of user movement in railway transit stations.
Optimizing Breadth-First Search at Scale Using Hardware-Accelerated Space Consistency

Graph traversal is a critical building block in many algorithms. Traversing a large graph using breadth-first search, although conceptually simple, is time-consuming in distributed-memory environments due to the amount of exchanged data. In this work, we present both an efficient algorithmic approach to carry out the traversal and a low-overhead runtime that provides efficient primitives to implement the algorithm. Our algorithm relies on constructing a traversal composed of partially consistent trees until all vertices are discovered. We resolve such inconsistency through election and exchange steps. The election phase relies on communicating compressed vertices through collectives that do not stress the bisection bandwidth of the interconnect. We leverage the space consistency programming abstraction to allow an efficient overlap of computation with communication. We extend the model to leverage hardware accelerated collectives and provide primitives for one-sided broadcast and sparse reduction. We present the algorithm and runtime designs and show the results of applying our techniques on the Bluegene/Q architec-tures. We achieve 1040 GTEPS on a single rack (1K nodes), which is better than the best-known algorithms on the same architecture. We also achieve superior scalability compared with other implementations up to 32K nodes.
Consensus from Signatures of Work

Assuming the existence of a public-key infrastructure (PKI), digital signatures are a fundamental building block in the design of secure consensus protocols with optimal resilience. More recently, with the advent of blockchain protocols like Bitcoin, consensus has been considered in the "permissionless" setting where no authentication or even point-to-point communication is available. Yet, despite some positive preliminary results, all attempts to formalize a building block that is sufficient for designing consensus protocols in this setting, rely on a very strong independence assumption about adversarial accesses to the underlying computational resource. In this work, we relax this assumption by putting forth a primitive, which we call signatures of work (SoW). Distinctive features of our new notion are a lower bound on the number of steps required to produce a signature; fast verification; moderate unforgeability—producing a sequence of SoWs, for chosen messages, does not provide an advantage to an adversary in terms of running time; and honest signing time independence—most relevant in concurrent multi-party applications, as we show. Armed with SoW, we then present a new permissionless consensus protocol which is secure assuming an honest majority of computational power, thus in a sense providing a blockchain counterpart to the classical Dolev-Strong consensus protocol. The protocol is built on top of a SoW-based blockchain and standard properties of the underlying hash function, thus improving on the known provably secure consensus protocols in this setting, which rely on the strong independence property mentioned above in a fundamental way.
A Novel Recommender System for Healthy Grocery Shopping

Given here is an anonymized dataset of online grocery purchases from users; we present a recommender system framework to predict future purchases. We describe the method of constructing a utility matrix to run a collaborative filtering algorithm to pair similar and dissimilar users and ultimately provide recommendations. Given those recommendations, we further our analysis by proposing a method using natural language processing to determine the nutritional value of a food product to further improve recommendations. The results provide recommendations for the healthiest options based on historical purchase data.