Reviewerly/exHarmony huggingface dataset:
Solve Polyalphabetic Cipher Based on Intelligent System

Besides the development of technology, it has necessary to use sophisticated systems to maintain information confidentiality. One of the essential polyalphabetic encryption algorithms used to maintain information security is the Vigenere cipher, which has been developed over time to simulate the times' developments. Vigenere cipher's strength lies in the extended size of keyspace which can generate stream encrypted text. Hence the difficulty of analyzing cryptanalysis. This work focused on using a good and modern algorithm in cryptanalysis, which is the CLPSO algorithm. We applied this algorithm to enhance the fitness function used to attack the Vigenere cipher system with short and long keyspaces up to 26 characters. Through experiments, we noticed that the CLPSO algorithm successfully recognized key up to 11 characters based on only 80 characters of input ciphertext. It extracted 100% from the key when used to attack the Vigenere with keysize 25 based on 350 characters from the input and 100% from key length 26 based on 600 characters ciphertext using ciphertext only attack. Experiments have shown that the effectiveness of this algorithm is very promising in attacking Vigenere with long keys.
The Implementation of Reinforcement Learning Algorithm for AI Bot in Fighting Video Game

A video game is a natural and valuable medium to test AI algorithms since this virtual environment is considered safe and controllable. The finite state machine is a common technique used in developing AI in games, using a predetermined action for each situation. The problem with a predetermined action is that the scripted behaviours are predictable and easily exploit by human players. Such settings cause the game to have repetitive gameplay, leading the player to lose interest since the player knows how the AI will behave. The primary approach of this project is to use reinforcement learning to train the agent for the game. Reinforcement learning algorithms learn what to do and map the situations to maximize the cumulative reward signal from the agent environment. The agent in this project will receive raw data from the environment as input, and the agent will then act based on the environment rather than predetermined action. The results show that lowering the learning rate in deep reinforcement learning can increase the cumulative reward for the agent. This project's findings will be helpful for the game developers in developing AI for their games and benefit the game players who will interact with the AI in those games.
Salient object detection using semantic segmentation technique

Salient object detection (SOD) is the operation of detecting and segmenting a salient object in a natural scene. Several studies have examined various state-of-the-art machine learning approaches f...
Serving the Community with Trustworthy Government Information and Data: What Can We Learn from the Public Librarians?

Using the semi-structured interview method, this study identifies the roles public librarians play in assisting their communities with trustworthy government information and data, and the challenges public librarians face when providing access and mediating the use of government information and data by the community. The findings of the study provide useful insights as American libraries begin to rethink and reinvent their roles in community resilience. The paper also suggests ways library and information science education can help future public librarians prepare for their roles in mediating government information and data.
ScarceGAN

This paper introduces ScarceGAN which focuses on identification of extremely rare or scarce samples from multi-dimensional longitudinal telemetry data with small and weak label prior. We specifically address: (i) severe scarcity in positive class, stemming from both underlying organic skew in the data, as well as extremely limited labels; (ii) multi-class nature of the negative samples, with uneven density distributions and partially overlapping feature distributions; and (iii) massively unlabelled data leading to tiny and weak prior on both positive and negative classes, and possibility of unseen or unknown behavior in the unlabelled set, especially in the negative class. Although related to PU learning problems, we contend that knowledge (or lack of it) on the negative class can be leveraged to learn the compliment of it (i.e., the positive class) better in a semi-supervised manner. To this effect, ScarceGAN re-formulates semi-supervised GAN by accommodating weakly labelled multi- class negative samples and the available positive samples. It relaxes the supervised discriminator's constraint on exact differentiation be- tween negative samples by introducing a 'leeway' term for samples with noisy prior. We propose modifications to the cost objectives of discriminator, in supervised and unsupervised path as well as that of the generator. For identifying risky players in skill gaming, this formulation in whole gives us a recall of over 85% (~60% jump over vanilla semi-supervised GAN) on our scarce class with very minimal verbosity in the unknown space. Further ScarceGAN out- performs the recall benchmarks established by recent GAN based specialized models for the positive imbalanced class identification and establishes a new benchmark in identifying one of rare attack classes (0.09%) in the intrusion dataset from the KDDCUP99 challenge. We establish ScarceGAN to be one of new competitive benchmark frameworks in the rare class identification for longitudinal telemetry data.
BNN

Deep learning (DL) algorithms have played a major role in achieving state-of-the-art (SOTA) performance in various learning applications, including computer vision, natural language processing, and recommendation systems (RSs). However, these methods are based on a vast amount of data and do not perform as well when there is a limited amount of data available. Moreover, some of these applications (e.g., RSs) suffer from other issues such as data sparsity and the cold-start problem. While recent research on RSs used DL models based on side information (SI) (e.g., product reviews, film plots, etc.) to tackle these challenges, we propose boosting neural network (BNN), a new DL framework for capturing complex patterns, which requires just a limited amount of data. Unlike conventional boosting, BNN does not sum the predictions generated by its components. Instead, it uses these predictions as new SI features which enhances accuracy. Our framework can be utilized for many problems, including classification, regression, and ranking. In this paper, we demonstrate BNN's use for addressing a classification task. Comprehensive experiments conducted to illustrate BNN's effectiveness on three real-world datasets demonstrated its ability to outperform existing SOTA models for classification tasks (e.g., clickthrough rate prediction).
Interpersonal Distance in VR: Reactions of Older Adults to the Presence of a Virtual Agent

The rapid development of virtual reality technology has increased its availability and, consequently, increased the number of its possible applications. The interest in the new medium has grown due to the entertainment industry (games, VR experiences and movies). The number of freely available training and therapeutic applications is also increasing. Contrary to popular opinion, new technologies are also adopted by older adults. Creating virtual environments tailored to the needs and capabilities of older adults requires intense research on the behaviour of these participants in the most common situations, towards commonly used elements of the virtual environment, in typical sceneries. Comfortable immersion in a virtual environment is key to achieving the impression of presence. Presence is, in turn, necessary to obtain appropriate training, persuasive and therapeutic effects. A virtual agent (a humanoid representation of an algorithm or artificial intelligence) is often an element of the virtual environment interface. Maintaining an appropriate distance to the agent is, therefore, a key parameter for the creator of the VR experience. Older (65+) participants maintain greater distance towards an agent (a young white male) than younger ones (25–35). It may be caused by differences in the level of arousal, but also cultural norms. As a consequence, VR developers are advised to use algorithms that maintain the agent at the appropriate distance, depending on the user’s age.
MELex: The Construction of Malay-English Sentiment Lexicon

Currently, the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment lexicon.Thus, this issue is addressed in this paper in order to enhance the accuracy of sentiment analysis.In this study, a new lexicon for sentiment analysis is constructed.A detailed review of existing approaches has been conducted, and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated.Constructing MELex involves three activities: seed words selection, polarity assignment, and synonym expansions.Our approach differs from previous works in that MELex can analyze text for the two most widely used languages in Malaysia, Malay, and English, with the accuracy achieved, is 90%.It is evaluated based on the experimentation and case study approaches where the affordable housing projects in Malaysia are selected as case projects.This finding has given an implication on the ability of MELex to analyze public sentiments in the Malaysian context.The novel aspects of this paper are two-fold.Firstly, it introduces the new technique in assigning the polarity score, and second, it improves the performance over the classification of mixed language content.
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation

Few-shot learning is a standard practice in most deep learning based histopathology image segmentation, given the relatively low number of digitized slides that are generally available. While many models have been developed for domain specific histopathology image segmentation, cross-domain generalization remains a key challenge for properly validating models. Here, tooling and datasets to benchmark model performance across histopathological domains are lacking. To address this limitation, we introduce MetaHistoSeg – a Python framework that implements unique scenarios in both meta learning and instance based transfer learning. Designed for easy extension to customized datasets and task sampling schemes, the framework empowers researchers with the ability of rapid model design and experimentation. We also curate a histopathology meta dataset - a benchmark dataset for training and validating models on out-of-distribution performance across a range of cancer types. In experiments we showcase the usage of MetaHistoSeg with the meta dataset and find that both meta-learning and instance based transfer learning deliver comparable results on average, but in some cases tasks can greatly benefit from one over the other.
Underwater Image Enhancement Using Adaptive Algorithms

Images captured underwater suffer significantly from attenuation and degradation These characteristics entail that underwater images possess low contrast, weak natural light, and a low signal-to-noise ratio. Several image enhancement techniques have been developed to improve the visual appeal of an image, such as denoising, dehazing and contrast enhancement by histogram equalization. However, some of these methods have low adaptability and introduce discrepancies and distortions into the enhanced image. This paper proposes an adaptive dehazing method by dark channel prior (ADCP) with a combination of image enhancement methods. The proposed method can be used to improve images for real-world analysis such as feature extraction and detection in marine biology field. The proposed method shows that the enhanced images can be characterized by better exposure of the dark regions, improved global contrast, and improved image information content. The proposed method also mitigated certain limitations of the novel dehazing method.
AI Game Agents Based on Evolutionary Search and (Deep) Reinforcement Learning: A Practical Analysis with Flappy Bird

Game agents are efficiently implemented through different AI techniques, such as neural network, reinforcement learning, and evolutionary search. Although there are many works for each approach, we present a critical analysis and comparison between them, suggesting a common benchmark and parameter configurations. The evolutionary strategy implements the NeuroEvolution of Augmenting Topologies algorithm, while the reinforcement learning agent leverages Q-Learning and Proximal Policy Optimization. We formulate and empirically compare this set of solutions using the Flappy Bird game as a test scenario. We also compare different representations of state and reward functions for each method. All methods were able to generate agents that can play the game, where the NEAT algorithm had the best results, reaching the goal of never losing.
Balanced Graph Cut With Exponential Inter-Cluster Compactness

Recently, balanced graph-based clustering has been a hot issue in clustering domain, but the balanced theoretical guarantees of previous models are either qualitative or based on a probabilistic random graph, which may fail to various real data. To make up this vital flaw, this letter explores a novel balanced graph-based clustering model, named exponential-cut (Exp-Cut), via redesigning the intercluster compactness based on the exponential transformation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\exp \lbrace \mu x\rbrace$</tex-math></inline-formula> . It is worth noting that exponential transformation not only provides a bounded balanced tendency for Exp-Cut, but also helps Exp-Cut to achieve balanced results on an arbitrary graph via adjusting its curvature <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> . To solve the optimization problem involved in Exp-Cut model, an efficient heuristic solver is proposed and the computational complexity is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(n^2)$</tex-math></inline-formula> per iteration. Experimental results demonstrate that our proposals outperform competitors on all benchmarks with respect to clustering performance, balanced property, and efficiency.
Assessing the use of mobile educational tools for enhancing innovative teaching and learning in higher education institutions in Tanzania

mobile technologies have impacted people’s lives by enhancing the way activities are performed in industries in different sectors including the education sector. The use of mobile devices by teachers and students in Higher Education Institutions (HEIs) have brought up online sharing and exchange of educational materials. This mode of educational communication has influenced (or impacted) the performance of students and teachers specifically in the academic field through innovative teaching and learning. However, in Tanzania little is known on the extent of the use of mobile education tools (METs) for enhancing innovative teaching and learning. This study, therefore, applied the modified technology acceptance model (TAM) to assess the use of METs for innovative teaching and learning in Four HEIs in Tanzania. The study was mainly quantitatively combined with an initial qualitative study whereby nine (9) teachers and twenty-one (21) students were purposefully selected for semi-structured interviews. In total 600 participants from the four HEIs in Dar es Salaam participated in the study. The qualitative data were thematically analyzed and the quantitative data were analyzed using IBM Statistical Package for Social Sciences (SPSS) version 23 descriptive analysis based on means and standard deviations. Findings revealed that electronic learning (e-learning) systems are generally operative in all of the four HEIs as well as learning management systems which have some educational tools embedded for the improvement of exchange and sharing of educational materials between teachers and students. Apart from e-learning systems, the use of METs is not formal so that teachers can use their own educational tools for teaching purposes.
A text-based multi-span network for reading comprehension

Text-based reading comprehension models have great research significance and market value and are one of the main directions of natural language processing. Reading comprehension models of single-span answers have recently attracted more attention and achieved significant results. In contrast, multi-span answer models for reading comprehension have been less investigated and their performances need improvement. To address this issue, in this paper, we propose a text-based multi-span network for reading comprehension, ALBERT_SBoundary, and build a multi-span answer corpus, MultiSpan_NMU. We also conduct extensive experiments on the public multi-span corpus, MultiSpan_DROP, and our multi-span answer corpus, MultiSpan_NMU, and compare the proposed method with the state-of-the-art. The experimental results show that our proposed method achieves F1 scores of 84.10 and 92.88 on MultiSpan_DROP and MultiSpan_NMU datasets, respectively, while it also has fewer parameters and a shorter training time.
Simulation and Model Checking for Close to Realtime Overtaking Planning

Fast and reliable trajectory planning is a key requirement of autonomous vehicles. In this paper we introduce a novel technique for planning the route of an autonomous vehicle on a straight rural road using the Spin model checker. We show how we can combine Spins ability to identify paths violating temporal properties with sensor information from a 3D Unity simulation of an autonomous vehicle, to plan and perform consecutive overtaking manoeuvres on a traffic heavy road. This involves discretising the sensory information and combining multiple sequential Spin models with a Linear Time Temporal Logic specification to generate an error path. This path provides the autonomous vehicle with an action plan. The entire process takes place in close to realtime using no precomputed data and the action plan is specifically tailored for individual scenarios. Our experiments demonstrate that the simulated autonomous vehicle implementing our approach can drive on average at least 40km and overtake 214 vehicles before experiencing a collision, which is usually caused by inaccuracies in the sensory system. While the proposed system has some drawbacks, we believe that our novel approach demonstrates a potentially powerful future tool for efficient trajectory planning for autonomous vehicles.
Disturbance Evaluation in Power System Based on Machine Learning

The operation complexity of the distribution system increases as a large number of distributed generators (DG) and electric vehicles were introduced, resulting in higher demands for fast online reactive power optimization. In a power system, the characteristic selection criteria for power quality disturbance classification are not universal. The classification effect and efficiency needs to be improved, as does the generalization potential. In order to categorize the quality in the power signal disturbance, this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances. Then, a multi-label classification algorithm based on rating is proposed to understand the relationship between the labels and identify the various power quality disturbances. The two algorithms are combined to construct a multi-label classification model based on a multi-level extreme learning machine, and the optimal network structure of the multi-level extreme learning machine as well as the optimal multi-label classification threshold are developed. The proposed method can be used to classify the single and compound power quality disturbances with improved classification effect, reliability, robustness, and anti-noise performance, according to the experimental results. The hamming loss obtained by the proposed algorithm is about 0.17 whereas ML-RBF, SVM and ML-KNN schemes have 0.28, 0.23 and 0.22 respectively at a noise intensity of 20 dB. The average precision obtained by the proposed algorithm 0.85 whereas the ML-RBF, SVM and ML-KNN schemes indicates 0.7, 0.77 and 0.78 respectively.
A cutting-edge approach to generate random bit sequence with confound chaotic maps

A random number sequence is a sequence of numbers that exhibit statistical randomness. In other words, all numbers in the sequence have an equal chance and it’s hard to predict the next number in the sequence. They are most commonly used in cryptography to strengthen the security. Same key can be used by sender and receiver to generate the same set of random numbers. The pseudo-random number generation is an important task in computer science with various security features. There are 2 types of random number generators; they are True Random Number Generators (TRNG) and Pseudo Random Number Generators (PRNG). In recent years, PRNGs are implemented in hardware or software using chaotic maps. Some of the chaotic maps used in PRNG are Logistic map, Lorenz map, Henon map, Skew Tent map, Saw tooth map, etc. The Tinker Bell map is a discrete-time chaotic dynamical system with four system parameters. It is a most commonly studied examples of dynamical systems that exhibit chaotic behaviour. Chaotic maps possess several drawbacks such as the discontinuity of ranges, non-uniform distribution, periodicity in chaotic range, and small key space. In this PRNG, the numbers are generating in an unpredictable manner. To improve the uniform distribution and periodicity in random number generation, combination of Logistic and Tinker bell map is proposed. The Logistic map is a polynomial mapping. The Tinker bell map is known to have shorter periods. It is notable for having chaotic solutions for certain parameter values and initial conditions. In the proposed work, the Tinker bell map perturbs Logistic map to increase the chaotic behaviour of Logistic map. Output of the Logistic map is taken as Pseudo random bit sequence or Pseudo random number. NIST statistical test suite is used to verify the randomness of the generated random sequence.