Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning based Scheduling for Spark Jobs in Cloud Environment

Recently, big data computing paradigm has been gaining proliferation due to wide applications for processing enormous volumes of data to produce meaningful information. The big data computing frameworks perform data processing in cloud computing or physical on-premises. Cloud service providers provide flexible, affordable, and reliable resources that are easier to manage than on-premise physical data centers. So many organization are now moving their big data computing framework over to the cloud computing environment. However, due to several limitations, including the need to reduce costs for using virtual machines, optimize system performance by lowering the Average job completion time, and adhere to service level agreements for the jobs, scheduling Spark jobs efficiently in a cloud environment is a challenging problem. Numerous heuristic-based solutions are available in the literature; however, they do not work well in heterogeneous cloud environments where many constraints are present while scheduling the jobs. So, in this paper, we have optimized the use of computing resources in a cloud environment by analyzing spark job scheduling based on reinforcement learning algorithms. The case study's proposed analysis demonstrates how a reinforcement learning algorithm enables an agent to learn the inherent properties of the computing environment for job scheduling.
Implementation of Non-Linear Feedback Stream Cipher System through Hybrid block Cipher Mode to Enhance the Resistivity and Computation Speed of AES

In a modern computing world, transmission of the confidential information over public network is very challenge. Various solutions have been proposed to provide the confidentiality, authenticity against the unauthorized access the information's. One of the secure solutions considered in this work under symmetric block cipher technique is Advanced Encryption Standard (AES). To enhance the efficiency of the AES, two non-linear feedback shift operations are enabled. The first non-linearity is achieved through Cipher Feedback mode (CFB), whereas the second non-linearity is through Output Feedback mode (OFB). The non-linearity and value-based rotation in each round would help in increasing the resistivity against the attacks. Whereas the reduction of one round of AES while processing every block of data would help in reducing the overall time required to process the information's. The proposed implementation has tested to verify the possible improvement in the efficiency, the same is discussed in result and discussion.
Energy Efficient Hardware Implementation of 2-D Convolution for Convolutional Neural Network

Over the last year, Deep neural networks (DNN) have been significantly accepted for computer vision applications because of high classification accuracy and versatility. Convolutional Neural Network (CNN) is one of the most popular architectures of DNN which is widely adopted for image, speech and video recognition. Extensive computation and large memory requirement of CNN s poses the bottleneck on its application. Field Programmable Gate Arrays (FPGAs) are considered to be suitable hardware platforms for deployment of CNNs with low power requirements. This paper focus on the design and implementation of hardware accelerator to perform the convolution product (matrix-matrix multiplication. We have used two optimization techniques to achieve energy efficiency. First, dataflow of the convolution phase is rescheduled to reduce the undesired on-chip memory accesses. Further, efficiency is enhanced by reducing the internal parallelism of structure as much as possible. Our architecture is implemented on the Xilinx ZCU104 evaluation board. The implemented design attains 98.1 GOPS/Joule and 32.77 GOPS/Joule for 8-bit and 16-bit data width respectively.
DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image Superresolution

The application of single-image superresolution (SISR) in remote sensing is of great significance. Although the state-of-the-art convolution neural network (CNN)-based SISR methods have achieved excellent results, the large model and slow speed make it difficult to deploy in real remote sensing tasks. In this article, we propose a compact and efficient distance attention residual network (DARN) to achieve a better compromise between model accuracy and complexity. The distance attention residual connection block (DARCB), the core component of the DARN, uses multistage feature aggregation to learn more accurate feature representations. The main branch of the DARCB adopts a shallow residual block (SRB) to flexibly learn residual information to ensure the robustness of the model. We also propose a distance attention block (DAB) as a bridge between the main branch and the branch of the DARCB; the DAB can effectively alleviate the loss of detail features in the deep CNN extraction process. Experimental results on two remote sensing and five super-resolution benchmark datasets demonstrate that the DARN achieves a better compromise than existing methods in terms of performance and model complexity. In addition, the DARN achieves the optimal solution compared with the state-of-the-art lightweight remote sensing SISR method in terms of parameter amount, computation amount, and inference speed. Our code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/candygogogogo/DARN</uri> .
Multiple Speech Mode Transformation using Adversarial Network

The objective of Multiple Speech Mode Transformation (MSMT) is to transform speech from one form to another on the basis of their mode characteristics. In this work, we have explored three different modes of speech (conversation, extempore, and read modes) for their inter-conversion while preserving the speaker identity and the linguistic content. To accomplish this we used a variant of Star Generative Adversarial Network (StarGAN) named as StarGAN-VC. For training, our model does not require parallel occurrences of the sentences and with relatively lesser number of training example we were able to generate high quality transformed outputs. On conducting objective and subjective evaluations, it is deduced that the transformed speech mode outputs are highly comparable to the target speech mode.
OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model

In today's technological era, document images play an important and integral part in our day to day life, and specifically with the surge of Covid-19, digitally scanned documents have become key source of communication, thus avoiding any sort of infection through physical contact. Storage and transmission of scanned document images is a very memory intensive task, hence compression techniques are being used to reduce the image size before archival and transmission. To extract information or to operate on the compressed images, we have two ways of doing it. The first way is to decompress the image and operate on it and subsequently compress it again for the efficiency of storage and transmission. The other way is to use the characteristics of the underlying compression algorithm to directly process the images in their compressed form without involving decompression and re-compression. In this paper, we propose a novel idea of developing an OCR for CCITT (The International Telegraph and Telephone Consultative Committee) compressed machine printed TIFF document images directly in the compressed domain. After segmenting text regions into lines and words, HMM is applied for recognition using three coding modes of CCITT-horizontal, vertical and the pass mode. Experimental results show that OCR on pass modes give a promising results.
Comparison of facial recognition systems Fisherfaces and LBPH for access control to restricted areas

Face detection and recognition is a topic that has gained great importance in the scientific community in recent years. Its widest application focus on video surveillance, identification, and validation, among others. In order to determine which is more efficient in levels of accuracy and precision, we propose a comparison between the two most popular methods until this date for an identification system through facial recognition: Fisherface and Local Binary Patterns Histogram (LBPH).In addition, each technique was performed under different realtime capture scenarios. The result of our LBPH system obtained a better accuracy high up 90% than Fisherfaces.
Basic Research on Construction of a VR Riding Simulator for Motorcycle Safety

This paper describes construction of a riding simulator for motorcycles in which the rider can experience the riding environment using a VR instrument. The simulator reproduces the traffic situation near a campus of Kanto Gakuin University based on Google Map. Each vehicle except the rider's one autonomously drives according to signal indication in this simulator. Since the rider can control the virtual motorcycle using the handlebars, the throttle handle, the brake levers, the clutch lever, and the shift lever on the real motorcycle, he can experience virtual riding close to real riding. It is shown that the performance of rider interfaces can be evaluated safely using this riding simulator because of the implementation of the high reproducibility, intuitive and immersion feeling riding.
A Hybrid Semantic Statistical Query Expansion for Arabic Information Retrieval Systems

Query-document vocabulary mismatch, the lack of query expressiveness for user needs and the phenomenon of short queries are the main issues associated with information retrieval systems. Query Expansion (QE) is one of the well-known alternative for overcoming these problems. It mainly involves finding synonyms or related words for the query terms. There are several approaches in the query expansion field such as statistical and semantic approaches; they focus on expanding the individual query terms rather than the entire query during the expansion process. An other category of approaches deals with the whole query by using a neural approach based on Pseudo Relevance feedback (PRF) documents. In this work, we carried out an ablation study to measure the impact of the classical and semantic (word embedding, order, context) based query expansion on the retrieval performance. The experiments conducted on the Arabic EveTAR dataset reveal that our hybrid proposed approach combining classical (PRF) and transformer (AraBERT) is competitive with the state-of-the-art methods. In fact, the obtained result in terms of the Mean Average Precision (MAP) is up to 0.72.
Scatter Matrix Normalization for Unsupervised Domain Adaptation

The field of Domain Adaptation(DA) involves the usage of data from a source to train a model, and then predict the class of data samples of a different distribution. Domain Adaptation (DA) aims to leverage the available training and testing data to model a target domain classifier. Domain invariant features are extracted, and are used to minimize the distribution divergence between the source and target domains. The existing works do not consider reducing the discrepancy between the source and target covariance matrices, an important information source. No previous work has incorporated all objectives like manifold feature learning, scatter matrix normalization, discriminative information preservation, variance maximization, divergence minimization and geometric similarity preservation into a single objective function. In this work, we propose a novel domain adaptation framework for image classification that utilizes the covariance matrices of the source and target domains along with other important objectives like discrimination information preservation, divergence minimization, among others. A robust objective function that comprises of all these objectives is designed for optimal performance of the algorithm. The significance and impact of different types of normalization on the overall performance of the algorithm is also described. Experiments on benchmark domain adaptation datasets like PIE and Office-Home signify improvements over existing state of the art algorithms.
Solar Irradiance Modeling using Stochastic Methods with Probability Functions

The premise of this paper is to study the behavior of solar irradiance incident on the earth’s surface, obtaining a stochastic model using various probability distributions that can estimate and predict it; being of great importance for studies in photovoltaic systems. The probability distribution based model with parameters adjusted is obtained from the solar irradiance data using the NASA database, applying goodness-of-fit tests of the Anderson-Darling method. The models obtained are considering several probability distributions for each month and a single annual, using irradiance data for at least a 10-year period.
Sarcasm Detection in Arabic Tweets: A comparison Between deep learning and Pre trained Transformers-based Models

Sarcasm is one of the main challenges of sentiment analysis systems. This paper mainly focuses on the recognition of Arabic sarcasm on Twitter. Recognizing sarcasm in tweets is essential for understanding users' opinions on various topics and events. There are only a few attempts regarding saracsm detection in Arabic due to the challenges and complexity of the Arabic language. We propose in this paper a comparison between traditional neural network-based models and pre-trained transformers. The experimental results show that transformers are a promising approach for the task of Arabic sarcasm detection.
Complex numbers simplify solar forecasting

Accurately predicting global solar irradiance is indispensable for smart management of electric grids.
Using Functional Clustering to Diagnose Person Misfit

AbstractAbstractThe purpose of this study is to demonstrate clustering methods within a functional data analysis (FDA) framework for identifying subgroups of individuals that may be exhibiting categories of misfit. Person response functions (PRFs) estimated within a FDA framework (FDA-PRFs) provide graphical displays that can aid in the identification of persons that have responded unexpectedly to items comprising an achievement test. Typical person fit statistics are also useful for detecting unexpected response patterns, but they do not provide insight into the underlying behaviors responsible for those responses. However, different responding behaviors tend to produce FDA-PRFs of different shapes, and may provide additional information regarding the reasons for misfit. Functional clustering methods are useful for categorizing respondents into subgroups based on the shapes of their FDA-PRFs. In this study, a small simulation illustrates the potential of clustering FDA-PRFs for identifying persons displaying common types of responding behaviors. The methodology is also applied to data from a high school biology assessment and a mathematics achievement test. Clustering FDA-PRFs offers a promising methodology for operationalizing person fit evaluations in large-scale assessments, and may be a valuable step in person fit assessment when used in conjunction with traditional indices of psychometric quality.Keywords: functional data analysisfunctional clusteringperson response functionsperson fit
Charge Localization Induced by Pentagons on Ge(110)

The Ge(110) surface reconstructs into ordered and disordered phases, in which the basic unit is a five-membered ring of Ge atoms (pentagon). The variety of surface reconstructions leads to a rich electronic density of states with several surface states. Using scanning tunneling microscopy and spectroscopy, we have identified the exact origins of these surface states and linked them to either the Ge pentagons or the underlying Ge-Ge bonds. We show that even moderate fluctuations in the positions of the Ge pentagonal units induce large variations in the local density of states. The local density of states modulates in a precise manner, following the geometrical constraints on tiling Ge pentagons. These geometry-correlated electronic states offer a vast configurational landscape that could provide new opportunities in data storage and computing applications.
Reinforcement learning under temporal logic constraints as a sequence modeling problem

Reinforcement learning (RL) under temporal logic typically suffers from slow propagation for credit assignment. Inspired by recent advancements called trajectory transformer in machine learning, the reinforcement learning under Temporal Logic (TL) is modeled as a sequence modeling problem in this paper, where an agent utilizes the transformer to fit the optimal policy satisfying the Finite Linear Temporal Logic (LTLf) tasks. To combat the sparse reward issue, dense reward functions for LTLf are designed. For the sake of reducing the computational complexity, a sparse transformer with local and global attention is constructed to automatically conduct credit assignment, which removes the time-consuming value iteration process. The optimal action is found by the beam search performed in transformers. The proposed method generates a series of policies fitted by sparse transformers, which has sustainably high accuracy in fitting the demonstrations. At last, the effectiveness of the proposed method is demonstrated by simulations in Mini-Grid environments.
A computational game experience analysis via game refinement theory

From the perspective of audiences, game refinement (GR) theory is a theory and measurement that was frequently used to assess the enjoyment and complex features of games. We conjectured that when we play a strategy game, there is a physical information process in our minds which subject to Newton’s laws. It focuses on the uncertainty of game outcomes, where the game’s components were abstracted to produce the game information progression utilizing the game outcome (achievement or solved uncertainty) and game length (usually designed by total steps or moves). This quantification defines the measure of informational acceleration, in which thrills and excitement may be measured using Newton’s second law F=ma (Informational acceleration is correlated with force in mind). Changes in acceleration, on the other hand, would have an impact on game dynamics, particularly in hostile scenarios. The jerk value is used to correct the game dynamic by using a derivative of the acceleration readings. In the gaming setting, many physical factors linked to psychological feelings are explored. To illustrate the physical and psychological relationships, the game information progress is represented in our minds. By determining reasonable informational jerk values related to the game process’s success rate and informational acceleration, this development of game refinement theory provides a more player-driven experience.
The Performance Index of Convolutional Neural Network-Based Classifiers in Class Imbalance Problem

Class imbalance is a common problem in many classification domains. This paper provides an evaluation index and one algorithm for this problem based on binary classification. The Model Performance Index (MPI) is proposed for assessing classifier performance as a new evaluation metric, considering class imbalance impacts. Based on MPI, we investigate algorithms to estimate ideal classifier performance with a fair distribution (1:1), referred to as the Ideal Model Performance Algorithm. Experimentally, compared with traditional metrics, MPI is more sensitive. Specifically, it can detect all types of changes in classifier performances, while others might remain at the same levels. Moreover, for the estimation of classifier performances, the algorithm reaches small differences between predictions and the values observed. Generally, for ideal performances, it achieved error rates of 0.060% - 1.3% for rare class in four experiments, showing a practical value on estimation and representation on the classifier performances.
Privacy preserving subgraph isomorphism query for dynamic graph database

Graph is an important structure for presenting the data with internal connections. Subgraph isomorphism query is one of the most important operations in graph database. Unfortunately, the calculation of subgraph isomorphism query is very complex. The data owners choose to store the graph data in the cloud server and let the cloud server undertake the complex query calculation. In order to solve the security risks from the untrusted cloud server, several privacy preserving subgraph isomorphism query schemes have been proposed. Nonetheless, all existing schemes are designed for static graph database. Once the graph database is dynamically updated, these schemes will not work well anymore. In order deal with this problem, we propose a privacy preserving subgraph isomorphism query scheme for dynamic graph database. We use enumeration and hash mapping methods to quickly generate feature vectors for graphs. We use clustering algorithm to group feature vectors and use feature vector list to store a group of similar feature vectors, which not only reduces the size of the tree index, but also makes the index easy to be updated. In order to realize privacy protection and subgraph isomorphic query, the improved secure Euclidean distance algorithm is adopted in our scheme. We consider three types of operations: addition, deletion and modification, which are used to update the index and database synchronously. When the index needs to be updated, our scheme can quickly locate the index nodes that need to be updated and make the update operation. Security analysis and experiments show that our scheme is secure and efficient.
Texture and material classification with multi-scale ternary and septenary patterns

This paper ideates, inspired by LBP and its variants, a novel local feature extraction operator for texture classification, referred to as Multi-scale Ternary and Septenary Pattern (MTSP). MTSP is a histogram-based feature reṕresentation that is composed of two single-scale STP and SSP (single-scale ternary and septenary patterns, respectively) encoders designed according to a novel set theory based pattern encoding scheme that integrates the concepts of both LQP’s and LTP’s operators. The essence of STP and SSP is to compute several virtual pixels based on various local and global image statistics and progressively encode interactions between local and non-local pixels by examining the directional information and differential excitation according to relationships between adjacent pixels rearranged in a variety of spatial arrangements. Unlike various parametric state-of-the-art texture operators that perform thresholding based on static thresholds, MTSP incorporates dynamic thresholds estimated automatically. MTSP descriptor has good ability as faithfully as possible to capture more detailed image information via complementary texture information generated from the fusion of both STP and SSP encoders. Experimental results show that MTSP ensures reliable performance stability over ten texture datasets and against several recent representative methods. In addition, the performance of MTSP is further proved statistically via the Wilcoxon signed rank test demonstrating thus that MTSP is a good candidate for texture modeling.
Generalized trigonometry and Chebyshev functions in finite fields

Trigonometry in finite fields was introduced by de Souza et al. and further developed by Lima and Panario and others, giving functions with many properties similar to trigonometric functions over the reals. Those explorations used a degree-2 extension of a base field. While this corresponds most closely to trigonometry over the reals, in finite fields we can have extensions of other degrees. In this paper we generalize the definitions of trigonometric functions and their related Chebyshev polynomials to arbitrary degrees and explore their properties. Many familiar results carry over into the generalized setting.