Reviewerly/exHarmony huggingface dataset:
ANN Based Crime Detection and Prediction using Twitter Posts and Weather Data

Crime has grown tremendously because of a growing population. So, crime prediction is an important instrument for the safety of civilians to aid law enforcement officials. Crime prediction can be done by analyzing historical data. But, it is a challenging task due to the unavailability of data. Further, some crimes are unregistered. Currently, people are using social media like twitter to casual chats, sharing photos and ideas, and transferring information and news. Thus, Social networking sites can be used to uncover useful information. In this paper, we propose a crime prediction approach using twitter data and weather data. The proposed approach contains two modules namely crime detection and crime prediction module. The crime detection module is used to detect the crime-related twitter posts. Bidirectional Encoder Representations from Transformers (BERT) based approach is used to develop the detection module. Then, the prediction module is implemented using an Artificial Neural Network (ANN). The empirical study of our proposed approach has proved the effectiveness of the BERT and ANN-based prediction approach.
Improving Accuracy of The Sentence-Level Lexicon-Based Sentiment Analysis Using Machine Learning

Sentiment Analysis studies people's attitudes, opinions, evaluations, emotions, sentiments toward some entities such as products, topics, individuals, services, issues and classify them whether the opinion or evaluations inclines to that entities or not. It is getting more research focus in recent years due to its benefits for scientific and commercial purposes. This research aims at developing a better approach for sentiment analysis at the sentence level by using a combination of lexicon resources and a machine learning method. Moreover, as reviews data on the internet is unstructured and has much noise, this research uses different preprocessing techniques to clean the data before processing in different algorithms discussed in subsequent sections. Additionally, the lexicon building processes, how the lexicon is handled and combined with the machine learning algorithm for predicting sentiment is also discussed. In sentiment analysis, sentence's sentiment can be classified into three classes: positive sentiment, negative sentiment, or neutral. However, in this research work, we have excluded neutral sentiment for avoiding ambiguity and unnecessary complexity. The experiment results show that the proposed algorithm outperforms compared to the baseline machine learning algorithms. We have used four distinct datasets and different performance measures to check and validate the proposed method's robustness.
Revisiting Driver Anonymity in ORide

Ride Hailing Services (RHS) have become a popular means of transportation, and with its popularity comes the concerns of privacy of riders and drivers. ORide is a privacy-preserving RHS proposed at the USENIX Security Symposium 2017 and uses Somewhat Homomorphic Encryption (SHE). In their protocol, a rider and all drivers in a zone send their encrypted coordinates to the RHS Service Provider (SP) who computes the squared Euclidean distances between them and forwards them to the rider. The rider decrypts these and selects the optimal driver with least Euclidean distance. In this work, we demonstrate a location-harvesting attack where an honest-but-curious rider, making only a single ride request, can determine the exact coordinates of about half the number of responding drivers even when only the distance between the rider and drivers are given. The significance of our attack lies in inferring locations of other drivers in the zone, which are not (supposed to be) revealed to the rider as per the protocol. We validate our attack by running experiments on zones of varying sizes in arbitrarily selected big cities. Our attack is based on enumerating lattice points on a circle of sufficiently small radius and eliminating solutions based on conditions imposed by the application scenario. Finally, we propose a modification to ORide aimed at thwarting our attack and show that this modification provides sufficient driver anonymity while preserving ride matching accuracy.
Enabling scalable and fault-tolerant multi-agent systems by utilizing cloud-native computing

Abstract Multi-agent systems (MAS) represent a distributed computing paradigm well suited to tackle today’s challenges in the field of the Internet of Things (IoT). Both share many similarities such as the interconnection of distributed devices and their cooperation. The combination of MAS and IoT would allow the transfer of the experience gained in MAS research to the broader range of IoT applications. The key enabler for utilizing MAS in the IoT is the ability to build large-scale and fault-tolerant MASs since IoT concepts comprise possibly thousands or even millions of devices. However, well known multi-agent platforms (MAP), e. g., Java Agent DE-velopment Framework (JADE), are not able to deal with these challenges. To this aim, we present a cloud-native Multi-Agent Platform (cloneMAP) as a modern MAP based on cloud-computing techniques to enable scalability and fault-tolerance. A microservice architecture is used to implement it in a distributed way utilizing the open-source container orchestration system Kubernetes. Thereby, bottlenecks and single-points of failure are conceptually avoided. A comparison with JADE via relevant performance metrics indicates the massively improved scalability. Furthermore, the implementation of a large-scale use case verifies cloneMAP’s suitability for IoT applications. This leads to the conclusion that cloneMAP extends the range of possible MAS applications and enables the integration with IoT concepts.
Power-Oriented Monitoring of Clock Signals in FPGA Systems for Critical Application

This paper presents a power-oriented monitoring of clock signals that is designed to avoid synchronization failure in computer systems such as FPGAs. The proposed design reduces power consumption and increases the power-oriented checkability in FPGA systems. These advantages are due to improvements in the evaluation and measurement of corresponding energy parameters. Energy parameter orientation has proved to be a good solution for detecting a synchronization failure that blocks logic monitoring circuits. Key advantages lay in the possibility to detect a synchronization failure hidden in safety-related systems by using traditional online testing that is based on logical checkability. Two main types of power-oriented monitoring are considered: detecting a synchronization failure based on the consumption and the dissipation of power, which uses temperature and current consumption sensors, respectively. The experiments are performed on real FPGA systems with the controlled synchronization disconnection and the use of the computer-aided design (CAD) utility to estimate the decreasing values of the energy parameters. The results demonstrate the limited checkability of FPGA systems when using the thermal monitoring of clock signals and success in monitoring by the consumption current.
Compressed Coded Distributed Computing

Communication overhead is one of the major performance bottlenecks in large-scale distributed computing systems, in particular for machine learning applications. Conventionally, compression techniques are used to reduce the load of communication by combining intermediate results of the same computation task as much as possible. Recently, via the development of coded distributed computing (CDC), it has been shown that it is possible to enable coding opportunities across intermediate results of different computation tasks to further reduce the communication load. We propose a new scheme, named compressed coded distributed computing (in short, compressed CDC), which jointly exploits the above two techniques (i.e., combining the intermediate results of the same computation and coding across the intermediate results of different computations) to significantly reduce the communication load for computations with linear aggregation (reduction) of intermediate results in the final stage that are prevalent in machine learning (e.g., distributed training algorithms where partial gradients are computed distributedly and then averaged in the final stage). In particular, compressed CDC first compresses/combines several intermediate results for a single computation, and then utilizes multiple such combined packets to create a coded multicast packet that is simultaneously useful for multiple computations. We characterize the achievable communication load of compressed CDC and show that it substantially outperforms both combining methods and CDC scheme. Based on the compressed CDC technique, we then study a distributed training problem as one of its application. We characterize the communication load for this distributed training problem and show that it is asymptotically optimal.
ArrowGAN : Learning to generate videos by learning Arrow of Time

Training GANs on videos is even more sophisticated than on images because videos have a distinguished dimension: time. While recent methods designed a dedicated architecture considering time, generated videos are still far from indistinguishable from real videos. In this paper, we introduce ArrowGAN framework, where the discriminators learns to classify arrow of time as an auxiliary task and the generators tries to synthesize forward-running videos. We argue that the auxiliary task should be carefully chosen regarding the target domain. In addition, we explore categorical ArrowGAN with recent techniques in conditional image generation upon ArrowGAN framework, achieving the state-of-the-art performance on categorical video generation. Our extensive experiments validate the effectiveness of arrow of time as a self-supervisory task, and demonstrate that all our components of categorical ArrowGAN lead to the improvement regarding video inception score and Fréchet video distance on three datasets: Weizmann, UCFsports, and UCF-101.
Design and implementation of user experience model for augmented reality systems

Mobile Augmented Reality (MAR) is a system that combines digital and virtual content for users so users are not fully immersed in the virtual world but can benefit from immersive experiences while still being in the real world. However, the end-user may experience various design challenges while using such immersive technologies. To deliver positive User Experience (UX), researchers need to understand the design factors, end-user challenges, and enhance the overall immersive UX for MAR. This research follows the design science approach to present a UX model for MAR, which encapsulate components and design factors that can aid in positive UX. We developed a MAR system to verify and validate the UX model by evaluating and benchmarking it. The experimental results show excellent system novelty, good attractiveness and perspicuity compared to the benchmark. The findings suggest that the efficiency, dependability, and stimulation of MAR can be improved to enhance the UX further. This study's research knowledge can be utilized to improve the immersive design and engineering of MAR services based on understanding the practical aspirations of potential end-users.
WITHDRAWN: A new proposal for the segmentation of breast lesion in mammogram images using optimized kernel fuzzy clustering algorithm

Breast Cancer is one of the fatal diseases which is caused due to the un natural development of the tissues in the breasts which are abnormal. The level of sarcoma or the stage of the cancer is mostly relied upon the doctor’s analysis. In order to provide a technical contribution which supports the doctor to take decision, this paper is intended to develop a framework which can help in determining the stage in which the cancer is at present. The major issues in the prediction of breast cancer through mammograms are the diverged artifacts, similar breast tissues and lower contrast on the boundary between skin and air. To overcome these issues, Optimized Kernel Fuzzy Clustering Algorithm is developed (OKFCA) and to determine the cancer portions in mammogram images. The OKFCA algorithm has described to identify the segmented regions in Mammogram Image Analysis Society (MIAS) database. The proposed segmentation algorithm is carried out with pre-processed mammogram images, noise free image that was obtained by using Hybrid Denoising Filter (HDF) algorithm and the proposed OKFCA is a significant approach to find out the cancer segment of mammogram image. Data clustering facilitates to place data of similar types in one group and of dissimilar types in different group. The results from the experiments which were carried on the MIAS data confirms the efficiency of the proposed system in terms of accuracy when compared to that of the famous K-Means, OKFCA and Otsu methods.
Sentiment Analysis of Arabic Tweets Using Supervised Machine Learning

The following topics are dealt with: diseases; support vector machines; pattern classification; data mining; decision trees; microcontrollers; computerised monitoring; medical image processing; antireflection coatings; and solar cells.
Law enforcement educational challenges for mobile forensics

Training, tools, and standards are important foundations of mobile forensics. This work focuses on existing curricula and courses in the domain of mobile forensics. In order to identify courses in areas of computing where mobile forensics may be offered, this research utilises open source information gathering, in addition to questionnaire and interviews, to capture additional information and the views and experiences of educators and/or trainers. This research finds that current education and training offerings mainly include topics regarding acquisition of mobile devices and analysis of the acquired data. Current education and training do not cover the areas of a complete mobile forensic investigation, from crime scene to court. In addition, trainer opinions on skills shortages include the lack of basic knowledge, generic skills in forensics and investigation, lack of skilled practitioners, and necessary mindsets to critically think, investigate and avoid dependency on Digital Forensic software.
Better Look Twice - Improving Visual Scene Perception Using a Two-Stage Approach

Accurate visual scene perception plays an important role in fields such as medical imaging or autonomous driving. Recent advances in computer vision allow for accurate image classification, object detection and even pixel-wise semantic segmentation. Human vision has repeatedly been used as an inspiration for developing new machine vision approaches. In this work, we propose to adapt the “zoom lens model” from psychology for semantic scene segmentation. According to this model, humans first distribute their attention evenly across the entire field of view at low processing power. Then, they follow visual cues to look at a few smaller areas with increased attention. By looking twice, it is possible to refine the initial scene understanding without requiring additional input. We propose to perform semantic segmentation the same way. To obtain visual cues for deciding where to look twice, we use a failure region prediction approach based on a state-of-the-art failure prediction method. Then, the second, focused look is performed by a dedicated classifier that reclassifies the most challenging patches. Finally, pixels predicted to be errors are updated in the original semantic prediction. While focusing only on areas with the highest predicted failure probability, we achieve a classification accuracy of over 63% for the predicted failure regions. After updating the initial semantic prediction of 4000 test images from a large-scale driving data set, we reduce the absolute pixel-wise error of 232 road participants by 10% or more.
Choosing the Right Compute Resources in the Cloud: An analysis of the compute services offered by Amazon, Microsoft and Google

In this paper we present a comparison of the various compute resources offered Amazon Web Services, Microsoft Azure, and Google Cloud Platform. We further identify several platform features including geographic availability, security and compliance, operating system support, container support, and serverless computing support that are directly comparable and provide recommendations and guidance for choosing a platform. We found that overall, for compute resources, Microsoft Azure is the preferred provider for many of the features we identify. However, that differential is quite small; in reality it is difficult to find significant differences between the platforms since they are ever-changing.
Virtual Reality In Higher Education: An Experience With Medical Students

COVID-19 has changed how students access to information. In the next years, higher education should increase the number of virtual contents and Virtual Reality is a the best way to achieve it, as it offers a way to access classrooms, get interactive and immersive content and even evaluate the students. This project is born from the exploration of the educational possibilities of Virtual Reality in higher education. Therefore, the objective of this study is to validate the use of the VR platform "Medical Studium" for the teaching-learning process of medical students. For the evaluation a sample of 19 students have been selected from the Faculty of Medicine of the University of Salamanca (Spain), using both quantitative and qualitative data. Objective evidence and a questionnaire have been used as a data collection instrument. The results show that it helps them to better study some concepts that may seem difficult for students, as well as how to solve the limitation problems they have regarding the training of surgical operations. In addition, students are more motivated thanks to interaction with virtual and immersive content and they can reproduce a lesson as many times as they need. These findings can mean a change in both teaching and learning, as it is an improvement for students in their practical training.
Using Blockchain in Digitalizing Enterprise Legacy Systems: An Experience Report

Blockchain technology and distributed ledger technology (DLT) offer a secure, distributed, and tamper-proof way to store and exchange information. However, apart from standard cryptocurrency-based networks, innovations and process improvements based on the blockchain technology have mostly remained on the conceptualizing stage and have not yet reached mass adoption. There is a high demand for practical experiences from developing blockchain and DLT based systems in various domains outside FinTech. This work seeks to contribute to this gap by presenting real-world experiences from developing a proof of concept for automatizing conditional payments in social benefits and healthcare domains. We found that the key conditions for making these blockchain-based solutions viable are (1) attaining technological maturity and competences, (2) ecosystem thinking and adequate governance of these ecosystems, and finally (3) achieving legal and regulatory predictability. Furthermore, we discuss technological choice, business, and ethical considerations relevant to practitioners and research communities.
Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework

We approach the continuous-time mean-variance (MV) portfolio selection with reinforcement learning (RL). The problem is to achieve the best tradeoff between exploration and exploitation, and is formulated as an entropy-regularized, relaxed stochastic control problem. We prove that the optimal feedback policy for this problem must be Gaussian, with time-decaying variance. We then establish connections between the entropy-regularized MV and the classical MV, including the solvability equivalence and the convergence as exploration weighting parameter decays to zero. Finally, we prove a policy improvement theorem, based on which we devise an implementable RL algorithm. We find that our algorithm outperforms both an adaptive control based method and a deep neural networks based algorithm by a large margin in our simulations.
Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts

With the increase in internet access and the ease of writing comments in the Nepali language, fine-grained sentiment analysis of social media comments is becoming more and more pertinent. There are a number of benchmarked datasets for high-resource languages (English, French, and German) in specific domains like restaurants, hotels or electronic goods but not in low-resource languages like Nepali. In this paper, we present our work to create a dataset for the targeted aspect-based sentiment analysis in the social media domain, set up a dataset benchmark and evaluate using various machine learning models. The dataset comprises of code-mixed and code-switched comments extracted from Nepali YouTube videos. We present convincing baselines using a multilingual BERT model for the Aspect Term Extraction task and BiLSTM model for the Sentiment Classification Task achieving 57.978% and 81.60% F1 score respectively.
Detecting Bottleneck Use of PIE or FQ-CoDel Active Queue Management During DASH-like Content Streaming

Dynamic Adaptive Streaming over HTTP (DASH) is a widely adopted standard for delivering high Quality of Experience (QoE) for consumer video streaming applications. The progressive deployment of Active Queue Management (AQM) schemes - such as PIE and FQ-CoDel - at ISP bottlenecks or home gateways means that consumers' video streams are increasingly impacted by such AQM schemes. However, many existing approaches do not consider adjusting streaming strategies based on the bottleneck queue types. We have previously demonstrated the benefits of AQM schemes for DASH video streams, and proposed adaptive chunklets for an improved streaming performance. In this paper, we demonstrate the problems of queue-agnostic streaming and propose a queue-detection technique during DASH-like streaming. This entirely client-side and application-level technique is capable of detecting likely FIFO, PIE and FQ-CoDel AQM schemes at network bottlenecks.
Formula-E race strategy development using distributed policy gradient reinforcement learning

Energy and thermal management is a crucial element in Formula-E race strategy development. In this study, the race-level strategy development is formulated into a Markov decision process (MDP) problem featuring a hybrid-type action space. Deep Deterministic Policy Gradient (DDPG) reinforcement learning is implemented under distributed architecture Ape-X and integrated with the prioritized experience replay and reward shaping techniques to optimize a hybrid-type set of actions of both continuous and discrete components. Soft boundary violation penalties in reward shaping, significantly improves the performance of DDPG and makes it capable of generating faster race finishing solutions. The new proposed method has shown superior performance in comparison to the Monte Carlo Tree Search (MCTS) with policy gradient reinforcement learning, which solves this problem in a fully discrete action space as presented in the literature. The advantages are faster race finishing time and better handling of ambient temperature rise.