Reviewerly/exHarmony huggingface dataset:
About the Intricacy of Tasks

Without a concrete measure of the “complicatedness” of tasks that artificial agents can reliably perform, assessing progress in AI is difficult. Only by providing evidence of progress towards more complicated tasks can developers aiming for general machine intelligence (GMI) ascertain their progress towards that goal. No such measure for this exists at present. In this work we propose a new measure of the intricacy of tasks, especially designed to describe their physical composition and makeup. Our intricacy is a multi-dimensional measurement that depends purely on objective physical properties of tasks and the environment in which they are to be performed. From this task intricacy measure, a relation to the knowledge of learners can allow calculation of the difficulty of a particular task for a particular learner. The method is intended for both narrow-AI and GMI-aspiring systems. Here we discuss some of the implications of our intricacy measure and suggest ways in which it may be used in AI research and system evaluation.
Resource Optimization for Wireless Federated Learning

This chapter provides an overview of various resources such as computational, communication, and power resources, required for wireless federated learning. We perform convergence analysis of wireless federated learning. Additionally, joint resource and power allocation for wireless federated learning are proposed. Finally, we present a collaborative federated learning framework to efficiently enable the participation of communication-resource deficient devices in the federated learning process for performance improvement.
Neural network with NewSigmoid activation function

We increase the power of the Artificial Neural Networks with the help of the Activation Function (AF). The tansig and logsig are widely used AF. But there is still requires some improvement in the AF. So, in this paper, we have proposed a NewSigmoid AF in the neural network. NewSigmoid is also as powerful as tansig and logsig. In multiple cases, the NewSigmoid function gives a better or equivalent performance as compared with both these AF. Like these AF, NewSigmoid is also a smooth S-shape, bounded, continuously differentiable, and zero-centered function. Therefore the NewSigmoid is also suitable for solving non-linear problems. We have tested this AF on iris, cancer, glass, chemical, bodyfat, wine, and ovarian datasets. We use Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), and Bayesian Regularization (BR) algorithms during the optimization of the neural network. Maximum 100% accuracy in the iris dataset while using LM, and BR; 99.9% accuracy in the cancer dataset using BR; 100% accuracy in the glass dataset using BR; 100% accuracy in the chemical and bodyfat dataset using SCG, LM, and BR; 100% accuracy in the wine dataset using LM, and BR; and 99.1% accuracy in the ovarian dataset using BR has been found while working with multilayer neural networks. The NewSigmoid also achieves 100% training and validation accuracy on the mathework-cap image dataset using SCG.
Battery-Assisted Online Operation of Distributed Data Centers With Uncertain Workload and Electricity Prices

This article investigates the online operation of distributed data centers equipped with energy battery. We aim to minimize their long-term operational cost by optimally distributing workload among data centers and operating energy battery. However, future spatio-temporally variant uncertainties in both workload and electricity prices have been the main impediment for a performance-guaranteed online data center operation strategy. To address this issue, we develop a fully distributed online algorithm that decouples workload distribution and battery operation across the network and time by introducing well-designed virtual queues for workload and batteries into the framework of Lyapunov optimization. Theoretically, an analytical gap between the long-term operational cost achieved by our algorithm and the theoretical optimum is provided to corroborate the desirable operation strategy. Extensive simulations using the real-world workload and electricity price data demonstrate the cost-delay tradeoff that our algorithm strikes and validate the theoretical results that we obtained.
Comparison and Enhancement of VR Water Cycle Guide Applications

Humankind has witnessed magnificent outcomes since technology has become part of education at all levels. This paper discusses the application of a newly hyped technology in education called Virtual Reality (VR). Virtual reality is a blend of technologies to provide an interface with a virtual environment. This technology is becoming a vital part of educational applications because educators always tend to lean towards anything which can be beneficial in bringing ease in the explanation of certain topics. It will also not be wrong to say that the usage of such kind of technology is now imperative in comprehending abstract and complex concepts of the present modules of education. In this article, a discussion and comparison have been presented on existing VR-based educational applications, along with a discussion on the development of a new VR application. The purpose of this study was to amplify the structure of the virtual way of explaining concepts to the younger students. The newly developed application improves upon the existing applications and presents an enhanced learning platform. According to the survey conducted from the application users, it can be easily deduced that the developed application had a better rating in the user interface as compared to the already existing ones, and its clearly visible that the VR has made it very simple for the younger kids to view and understand the overall concept of water cycle easily.
Bridging the Language Gap: Knowledge Injected Multilingual Question Answering

Question Answering (QA) is the task of automati-cally answering questions posed by humans in natural languages. There are different settings to answer a question, such as abstractive, extractive, boolean, and multiple-choice QA. As a popular topic in natural language processing tasks, extractive question answering task (extractive QA) has gained extensive attention in the past few years. With the continuous evolvement of the world, generalized cross-lingual transfer (G-XLT), where question and answer context are in different languages, poses some unique challenges over cross-lingual transfer (XLT), where question and answer context are in the same language. With the boost of corresponding development of related benchmarks, many works have been done to improve the performance of various language QA tasks. However, only a few works are dedicated to the G-XLT task. In this work, we propose a generalized cross-lingual transfer framework to enhance the model's ability to understand different languages. Specifically, we first assemble triples from different languages to form multilingual knowledge. Since the lack of knowledge between different languages greatly limits models' reasoning ability, we further design a knowledge injection strategy via leveraging link prediction techniques to enrich the model storage of multilingual knowledge. In this way, we can profoundly exploit rich semantic knowledge. Experiment results on real-world datasets MLQA demonstrate that the proposed method can improve the performance by a large margin, outperforming the baseline method by 13.18%/12.00% F1/EM on average.
Security Analysis of AWS-based Video Surveillance Systems

In the last few years, Cloud computing technology has benefited many organizations that have embraced it as a basis for revamping the IT infrastructure. Cloud computing utilizes Internet capabilities in order to use other computing resources. Amazon Web Services (AWS) is one of the most widely used cloud providers that leverages the endless computing capabilities that the cloud technology has to offer. AWS is continuously evolving to offer a variety of services, including but not limited to, infrastructure as a service (IaaS), platform as a service (PaaS) and packaged software as a service. Among the other important services offered by AWS is Video Surveillance as a Service (VSaaS) that is a hosted cloud-based video surveillance service. Even though this technology is complex and widely used, some security experts have pointed out that some of its vulnerabilities can be exploited in launching attacks aimed at cloud technologies. In this paper, we present a holistic security analysis of cloud-based video surveillance systems by examining the vulnerabilities, threats, and attacks that these technologies are susceptible to. We illustrate our findings by implementing several of these attacks on a test bed representing an AWS-based video surveillance system. The main contributions of our paper are: (1) we provided a holistic view of the security model of cloud based video surveillance summarizing the underlying threats, vulnerabilities and mitigation techniques (2) we proposed a novel taxonomy of attacks targeting such systems (3) we implemented several related attacks targeting cloud-based video surveillance system based on an AWS test environment and provide some guidelines for attack mitigation. The outcome of the conducted experiments showed that the vulnerabilities of the Internet Protocol (IP) and other protocols granted access to unauthorized VSaaS files. We aim that our proposed work on the security of cloud-based video surveillance systems will serve as a reference for cybersecurity researchers and practitioners who aim to conduct research in this field.
Singlish to Sinhala Transliteration using Rule-based Approach

Machine translation is a difficult task in natural language processing. All text mining systems face major challenges in terms of lexicon, proper noun handling, and high-tech terminology. This paper describes a transcription technique for text mining in social media posts from English to Sinhala. This study proposes a system for Singlish to Sinhala transliteration that employs a rule-based approach with an error correction module. This method can both transliterate Sinhala words written in English characters (Singlish) and filter out original English words from the text. When compared to the Google input tool, the system has higher accuracy and a lower error rate. Thus, this study would broaden the transliteration knowledge base by introducing a novel set of transliteration rules and employing an ensemble method with a news corpus to improve transliteration accuracy.
Two-Dimensional Significant Bit Prediction of Wavelet Image Decomposition Coefficients

The paper presents the result of the development of an improved algorithm for eliminating the redundancy that present in the wavelet image decomposition coefficients. Two-dimensional prediction of significant bits of the wavelet coefficients in the horizontal and then in the vertical direction along the bit planes makes it possible to speed up the process of information transfer, and large-amplitude bursts in the most significant bits will be transmitted first. Thus, it is possible to transmit an image with ever increasing resolution or to perform a quick preview of an image at a low resolution.
StABLE: Analyzing Player Movement Similarity Using Text Mining

Digital games are increasingly delivered as services. Understanding how and why players interact with games on an ongoing basis is important for maintenance. Logs of player activity offer a potentially rich window into how and why players interact with games, but can be difficult to render into actionable insights because of their size and complexity. In particular, understanding the sequential behaviour in-game logs can be difficult. In this paper, we present the String Analysis of Behaviour Log Elements (StABLE) method, which renders location and activity data from a game log file into a sequence of symbols which can be analyzed using techniques from text mining. We show that by intelligently designing sequences of features, it is possible to cluster players into groups corresponding to experience or motivation by analyzing a dataset containing Minecraft game logs. The findings demonstrate the validity of the proposed method, and illustrate its potential utility in mining readily available data to better understand player behaviour.
A Random Online Algorithm for Reselling Reserved IaaS Instances in Amazon's Cloud Marketplace

When running applications in IaaS (Infrastructure as a Service) cloud platforms like Amazon EC2, cloud users can choose to purchase reserved instances instead of on-demand ones to save cost. However, if there are few workload arrivals after instance reservations, it will cause a waste of cost due to the idle reserved instances. At present, Amazon EC2 has launched a reserved instance market, in which cloud users can resell their idle reserved instances to avoid wastes. However, for IaaS users, it is difficult to make the optimal decisions on whether to resell idle reserved instances when they do not know the arrival situation of workloads in the future. This is because after the idle reserved instances are sold, if there arrive new workloads, additional cost for launching new reserved or on-demand instances will be incurred. In this paper, we propose a random online reselling algorithm to help cloud users make reasonable decisions on whether to sell their idle instances, while without requiring the knowledge of the future workload arrivals. We theoretically prove that our algorithm can achieve a bounded competitive ratio. Besides, we conduct extensive experiments using the real-world workload obtained from public clouds to validate our algorithm.
Sharpshooting Most Beneficial Part of AUC for Detecting Malicious Logs

Machine learning is becoming a vital component in cyber security to automatically analyze network logs and detect malware infection. Since actual network logs are imbalanced data that contain fewer malicious logs than benign logs, many studies have evaluated the classification performance of malicious log detection by using the area under the curve (AUC). However, in actual operation, a low false positive rate (FPR) is required to reduce the burden on network operators. Therefore, the area to be focused on is not the entire AUC but the partial AUC (pAUC) in a specific low FPR interval. We propose a novel supervised learning method that maximizes the pAUC, called Partial AUC Utilization for Malicious log Analysis (PUMA). Once the FPR that operators can permit is specified, PUMA optimizes the detection model to obtain the best TPR for the specified FPR interval. In addition, we describe the theoretical formulation and algorithm of PUMA to handle ties in the scores of training data that frequently occur in log analysis and often make existing pAUC maximization methods unsuitable. We also demonstrate the effectiveness of PUMA after comparing it with conventional methods with proxy logs from a real-world large enterprise network.
An Autonomous Electronic Zoological Feeder

Feeding wild animals is a significant issue at zoos, as keepers are required to come into direct contact with wildlife. When their feeding cycle is disrupted, they may become violent in their pursuit of food from zookeepers/people, leading to deadly injuries. Altering animal feeding can also affect animal behavior, causing animals to travel in greater groups to be fed by visitors, increasing the likelihood of disease transmission between animals and assaulting visitors. The paper presents an autonomous zoological feeder system that will find the shortest way feasible to feed the animal while avoiding obstructions. The road route network connecting different animal junctions/caged inside the zoo is described as a matrix with the nodes’ distances as weights. The route network has been mapped to a parameter indicating the obstruction at the identified nodes. The obtained results demonstrate the simulation of an ideal path for the feeder to follow in Proteus that is shorter in distance and free of obstructions, using supporting modules and libraries for the Arduino Nano development board and Bluetooth connectivity.
Sparse Gaussian Processes-based Black-Box Data-efficient Policy Search for Robotics

While searching for solutions for Reinforcement Learning problems Policy Search algorithms crawl through a Search Space to efficiently find a successful policy. In contrast to Deep learning methods, which rely on hundreds of trials to tune weights, such efficient methods rely on very few trials to find a solution. In this work we introduce a variant of the method Black-box Data Efficient Policy Search for Robotics, which originally relied on a Gaussian Process method to learn a dynamics model. Our algorithm is called Black-DROPS-SGP and it adopts a more efficient Gaussian Process called Sparse Gaussian Processes, and we prove the superiority of our method by performing a comparison with Deep PILCO and Black-DROPS, two highly efficient Policy Search algorithms. We use cart-pole systems with one, two and three rotating joints to assess how our method fares when dealing with different complexities, and our results show that our method can find the optimal solution with virtually the same number of trials as the current state-of-the-art, but requires much less computational time between iterations to find this solution. In fields where trials are expensive (e.g. drug discovery, robotics) a higher number of trials can lead to broken robots before new behaviors are learnt, or too many patients injected with an ineffective drug before a cure is found.
Power Marketing Risk Prevention and Control Management of Power Supply Enterprises Based on Big Data Analysis Technology

In order to improve the efficiency and accuracy of distribution network risk warning, this paper adopts the Relief algorithm. Relief algorithm is a widely used feature selection algorithm. The main point of the algorithm is to evaluate the importance of features based on their ability to distinguish short-range samples. Sex. In this experiment, the electric power big data system through GIS automatically calculates the line loss according to the topological relationship of the analysis object, and achieves the automatic statistics of line loss division, voltage division, line division, and station division, and proposes analysis and treatment methods for abnormal problems. Experimental data shows that, taking a local 4 stations as an example, the line loss operation system of the power supply system linkage of the GIS-based power big data platform enables the line loss rate to be effectively controlled. The experimental results show that, for the line loss in the sub-station area, select a certain local station area as an example, the line loss rate of the station area in 2020 compared with the same period in 2019 is reduced by 0.21, 1.77, and 4 respectively. 1.94, 1.19. Therefore, the power big data platform based on GIS is very convenient and efficient to calculate the line loss, which can save a lot of manpower and material resources, and play the greatest role for the power enterprises to specify targeted loss reduction technologies and management measures in the energy saving and consumption reduction work.
Smart Audio Glove for Deaf and Dumb Impaired

Deaf-Mute individuals all through the world using gesture-based communication to speak with others, which is feasible for the individuals who have experienced unique training. Everyday citizens face trouble in understanding the sign language. To defeat these continuous issues, this framework is created. The framework is employed to detect any gesture-based communication, and plays recorded voice. This diminishes the issues among customary individuals and enables a free flow of interactions between public citizens. This work has three modules, they are detecting, handling and voice stockpiling unit. It can be done using a flex sensor and APR9600 voice recorder. This model can be used by the people of all age groups and people can easily communicate between the normal individuals. By using this model, they can be normal as other individuals and lead the life by using their knowledge, earn and start a business too. The output from the device has high accuracy in identifying the sign languages executed, using the highly responsive flex sensors. The experimental simulations in image processing are implemented using MATLAB.
Using Bert Embedding to improve memory-based collaborative filtering recommender systems

The performance of memory-based collaborative filtering recommender systems will be severely affected when the users' item preference data is sparse. In this paper, we focus on solving this issue. Our idea is to use Bert Embedding to learn a new feature set, which is denser and more semantic, for re-representing users and items. In these new features, memory-based collaborative filtering recommender systems work more efficiently. The experiments are conducted on the Movielens 100K dataset and the Yahoo Webscope R4 dataset.
Motif-Role Extraction in Uncertain Graph Based on Efficient Ensembles

In this paper, we formulate a new problem of extracting motif-based node roles from a graph with edge uncertainty, where we first count roles for each node in each sampled graph, second calculate similarities between nodes in terms of role frequency and divide all nodes into clusters according to the similarity of roles. To achieve good accuracy of role extraction for uncertain graphs, a huge amount of graph sampling, role counting, similarity calculation, or clustering is needed. For efficiently extracting node-roles from a large-scale uncertain graph, we propose four ensemble methods and compare the similarity of results and efficiency. From the experiments using four different types of real networks with probabilities assigned to their edges, we confirmed that the graph-ensemble method is much more efficient in terms of execution time compared to other ensemble methods equipped with the state-of-the-art motif counting algorithm.
Selection of the Optimal Diagnosis Model for COVID-19 Using the SAW and PROMETHEE II Methods

The COVID-19 outbreak is one thing that is the main focus for all countries in the world, including Indonesia. The diagnosis of COVID-19 can be done in various ways, such as checking symptoms, rapid-testing, swab-testing, and checking with X-rays. The application of machine learning in diagnosing a disease is one way that can be used to maximize the diagnosis of COVID-19 disease. This is what lies behind the need for optimization in the application of machine learning. This research goal is to determine the most optimal algorithm for diagnosing COVID-19 disease with a dataset in the form of Xray photos by using a decision-making methodology. The dataset in the form of Xray images will be processed utilizing image preprocessing (resize, grayscale, augmentation) and feature extraction (GLCM) methods. The researcher uses the Backpropagation algorithm, Recurrent Neural Network, SVM Linear, SVM Non-Linear, and Naive Bayes. The decision-making methodology used is Simple Additive Weighting and PROMETHEE II. Results of research in the form of ranking the optimal algorithm model in diagnosing COVID-19 according to the two decision making methodologies used. The five algorithms will take feature extraction of data as input and output 9 assessment criteria, including Accuracy, Precision, Recall Metric, ROC Curve, F1 Score, TNR, FPR, FNR, and time. The decision-making methodology used is Simple Additive Weighting and PROMETHEE II with 9 evaluation criteria as parameters. Results of research in the form of ranking the optimal algorithm model in diagnosing COVID-19 according to the two decision making methodologies used. The conclusion is that Backpropagation was the most optimal algorithm model in diagnosing COVID-19 disease with evaluation criteria values are AUC / ROC 0.935; Accuracy 0.893; F-score 0.888; Precision 0.908; Recalls 0.869; FPR 0.084; FNR 0.131; TNR 0.916; Time 1.023.