Reviewerly/exHarmony huggingface dataset:
Design, Development, and Implementation of an Image Steganography Algorithm for Encrypted (Using AES) and Non-encrypted Text into an Image

Information security is a major problem today. Different approaches and methods are introduced every day for data protection [6]. One of them is steganography. The word steganography combines the Greek words “steganos” meaning “covered, concealed, or protected” and graph in meaning “writing” [6]. The purpose of steganography is to construct the stego object by placing important information invisible into the ordinary cover object (image, sound, video, text, etc.) and to transmit it to the recipient [6]. In this paper, researchers aim is to pass text data using the Image object. This experiment is done by researchers in two ways. Sender side sends encrypted and non-encrypted text data within image formats (GIF, JPEG, BMP, and GIF) with security concern. Extracted text data will be decrypted at the receiver side in case of encrypted text data, and as a part of security concern, the aim on image quality remains the same for human eyes.
Reconfigurable photonic structures for solving linear differential equations

We theoretically explore a reconfigurable, Mach—Zehnder interferometer-based photonic network capable of solving linear equations by implementing arbitrary matrix operators. The solutions of various differential equations are numerically demonstrated and discussed.
Impact of Sensor Technology Enhancing Corona Disease

An explorative field observation to determine the impact of sensor networks in different parts of the human body surrounded by body boundary areas. This study was conducted in general, overweight and thin individuals with the positions of global positioning systems and processed frequencies from sensor technology. Everyone uses mobile phones for advanced communication, but none can aware of its security systems due to cutting-edge-technology. The fieldwork details the principles, recognizing the impact of radio frequency with individual’s suffering from corona disease around the world. Quantitative and qualitative related sensor data were obtained from ISNAH experiment while secondary data were collected from diverse sources. The research highlights the impact of sensor networks with variable and processed radio frequencies among individual’s body mass indices facilitating the light and dark environment. The study represents the obese patients suffer from corona disease dies within 7 minutes in dark environment, but 12 minutes in the light condition. These results reflect the importance of human health security that the State provides. A proposed corona control model was developed on the priority of research findings. Lastly, the study recommends future research trajectories of a new collaborative alternative approach to drive the methodological agenda and to make the sound health systems on ways to further incorporate the demanding secured sensor networks for health management.
ArcGrad: Angular Gradient Margin Loss for Classification

The cosine-based softmax loss functions greatly enhance intra-class compactness and perform well on the tasks of face recognition and object classification. Outperformance, however, depends on the careful hyperparameter selection. Adaptively Scaling Cosine Logits (AdaCos) tries to propose a parameter-free version by leveraging an adaptive scaling parameter. Nevertheless, the application of AdaCos is limited in specific domains because of improper approximation.In this paper, to promote intra-class compactness and interclass separability, we propose an Angular Gradient Margin Loss (ArcGrad) that generates a gradient margin by maximizing the angular gradient. Our work suggests that the margin parameter on cosine-based methods is not necessary, and the scaling parameter is inversely proportional to the margin. Furthermore, a stable and large gradient promotes better feature representation. In experiments, we test our method, as well as other methods enhancing discriminative information, on CIFAR and 15 datasets from UCI. Experimental results show ArcGrad consistently outperforms both on large and small scale problems and has the superiority in discriminative information and time-consumption.
Enhancing EFL Students’ Cultural Awareness through Friends Sitcom

The ever-changing global practices have dramatically dissolved cultural diversity, Cultural myopia, and cultural peculiarities in an unprecedented way. These practices reveal the necessity of having a mutual understanding of cultural entities for creating and developing meanings across various cultures. Languages seem to be the vehicle that drives global communication. However, mastering linguistic skills would not be sufficient for understanding the representation of cultural-bound expressions that identify global communication. Hence, the current study attempts to investigate the cultural phenomena substantiated in a sitcom for facilitating cross-cultural communication in EFL/ESL teaching contexts. Friends, as one of the most famous American sitcoms, is used in the study as a corpus to analyze the cultural facets used by speakers in particular socio-cultural settings. The study makes use of the corpus-based method to examine how sitcoms can be utilized for developing EFL students’ cultural awareness. Throughout the study, distinct conversational patterns are meticulously analyzed through discourse perspectives to locate cultural nuances and their impact on developing the meaning of the target culture. Consequently, Sitcoms, as a corpus, have a remarkable impact on increasing EFL students’ cultural awareness of multi-cultural contexts. Building cultural-based activities around sitcoms would expose students to significant cultural aspects beyond the confinement of textbooks and course materials. Attributed to the analysis and the findings of similar studies, it could be proclaimed that sitcoms have great promises for classroom implementation as a repository of authentic resources that would be of extreme instructional value in enhancing EFL/ESL students’ communicative competence in discrete socio-cultural contexts.
SPARQLing Neo4J (Short Paper)

The growth experienced by the internet in the past few years as lead to an increased amount of available data and knowledge obtained from said data. However most of this knowledge is lost due to the lack of associated semantics making the task of interpreting data very hard to computers. To counter this, ontologies provide a extremely solid way to represent data and automatically derive knowledge from it.In this article we'll present the work being developed with the aim to store and explore ontologies in Neo4J. In order to achieve this a web frontend was developed, integrating a SPARQL to CYPHER translator to allow users to query stored ontologies using SPARQL. This translator and its code generation is the main subject of this paper.
A New Adaptation Phase for Thresholds in a CBR System Associated to a Region Growing Algorithm to Segment Tumoral Kidneys

Image segmentation is an abundant topic for computer vision and image processing. Most of the time, segmentation is not fully automated, and a user is required to guide the process in order to obtain correct results. Yet, even with programs, it is a time-consuming process. In a medical context, segmentation can provide a lot of information to surgeons, but since this task is manual, it is rarely executed because of time. Artificial Intelligence (AI) is a powerful approach to create viable solutions for automated treatments. In this paper, we reused a case-based reasoning (CBR) system previously developed to segment renal parenchyma with a region growing algorithm and we completed its adaptation phase allowing a better adjustment of parameters before segmentation. Compared to the previous system, we added an adaptation for the thresholds values in addition to the adaptation of the seeds coordinates. We compared several versions of our new adaptation in order to determine the best and we confronted it with a deep learning approach realized in similar conditions.
Relasi Makna Orang Kafir dan Muttaqȋn dalam Islam

This paper will explain the relation of eating infidels and pious people in Islam. This research is a qualitative research using Charles Sanders Pierce's semiotic approach. The data used in this paper is sourced from books, journals, and also other literature that is relevant to the theme of the writing.
Meta-Reward Model Based on Trajectory Data with k-Nearest Neighbors Method

Reward shaping is a crucial method to speed up the process of reinforcement learning (RL). However, designing reward shaping functions usually requires many expert demonstrations and much hand-engineering. Moreover, by using the potential function to shape the training rewards, an RL agent can perform Q-learning well to converge the associated Q-table faster without using the expert data, but in deep reinforcement learning (DRL), which is RL using neural networks, Q-learning is sometimes slow to learn the parameters of networks, especially in a long horizon and sparse reward environment. In this paper, we propose a reward model to shape the training rewards for DRL in real time to learn the agent's motions with a discrete action space. This model and reward shaping method use a combination of agent self-demonstrations and a potential-based reward shaping method to make the neural networks converge faster in every task and can be used in both deep Q-learning and actor-critic methods. We experimentally showed that our proposed method could speed up the DRL in the classic control problems of an agent in various environments.
Clustering Algorithms: An Application for Adsorption Kinetic Curves

Clustering algorithms have been used in different areas of knowledge with different goals such as noise detection, outliers, and descriptive tasks. The adsorption kinetics is a curve that describes the rate retention to the adsorbate on the adsorbent at time, which is represents as a two-dimensional graph. In this paper, we present a computational application to determine the experimental conditions that influence when equilibrium point is reached into adsorption kinetics curve using the K-means clustering algorithm and, Parallel Coordinates concept, in order to prove our method we used adsorption kinetic curves Q-PVA . Results obtained were compared with two designs of experiments (three-stage nested design and hierarchical design with crossed factors).
Lower Bounds for Adversarially Robust PAC Learning

In this work, we initiate a formal study of probably approximately correct (PAC) learning under evasion attacks, where the adversary's goal is to \emph{misclassify} the adversarially perturbed sample point $\widetilde{x}$, i.e., $h(\widetilde{x})\neq c(\widetilde{x})$, where $c$ is the ground truth concept and $h$ is the learned hypothesis. Previous work on PAC learning of adversarial examples have all modeled adversarial examples as corrupted inputs in which the goal of the adversary is to achieve $h(\widetilde{x}) \neq c(x)$, where $x$ is the original untampered instance. These two definitions of adversarial risk coincide for many natural distributions, such as images, but are incomparable in general. We first prove that for many theoretically natural input spaces of high dimension $n$ (e.g., isotropic Gaussian in dimension $n$ under $\ell_2$ perturbations), if the adversary is allowed to apply up to a sublinear $o(||x||)$ amount of perturbations on the test instances, PAC learning requires sample complexity that is exponential in $n$. This is in contrast with results proved using the corrupted-input framework, in which the sample complexity of robust learning is only polynomially more. We then formalize hybrid in which the evasion attack is preceded by a poisoning attack. This is perhaps reminiscent of trapdoor attacks in which a poisoning phase is involved as well, but the evasion phase here uses the error-region definition of risk that aims at misclassifying the perturbed instances. In this case, we show PAC learning is sometimes impossible all together, even when it is possible without the attack (e.g., due to the bounded VC dimension).
The Effect of Exercise Fluid Supplement on Human Metabolism after Load Exercise

Yang, Z.Y. and Xia, S.Y., 2020. The effect of exercise fluid supplement on human metabolism after load exercise. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 504–507. Coconut Creek (Florida), ISSN 0749-0208.Objective: In the process of load exercise training, with the discharge of sweat, the loss of water and electrolyte will destroy the balance of water and salt metabolism, and the athletic ability of athletes will also decline in varying degrees. Therefore, this paper studies the effect of exercise fluid supplementation on human metabolism after load exercise. The average value of the ionic concentration of athletes' sweat was used as the basis for the preparation of electrolyte sports drinks. Result: The results showed that the concentration of potassium ion in the sweat of male long-distance runners was significantly higher than that of female athletes; the concentration of potassium ion in male athletes of Sanda Team was significantly higher than that of female athletes; the test results of sodium, chlorine and potassium ion of long-distance runners were higher than that of Sanda athletes; Compared with the first week, the concentration of sodium, chloride and calcium ions in female athletes' serum increased in the fourth week, while the potassium ion concentration decreased slightly. Conclusion: After three weeks of load exercise, with the supplement of sports drinks containing appropriate electrolyte concentration, the concentration of serum chloride ion of athletes remained stable, slightly lower than that of the first week, but both were within the normal range; daily supplement of liquid supplement containing potassium ion and calcium ion had a slight decrease in serum potassium ion, and the decrease range was smaller than the former, and the calcium ion concentration was maintained compared with the first week During the period of load training, 800 ml of fluid was provided to athletes every day. After 3 weeks of rehydration, hematocrit remained stable, and there was no significant difference.
Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings

Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning, where multilingual BERT is fine-tuned on one source language and evaluated on a different target language. However, published results for mBERT zero-shot accuracy vary as much as 17 points on the MLDoc classification task across four papers. We show that the standard practice of using English dev accuracy for model selection in the zero-shot setting makes it difficult to obtain reproducible results on the MLDoc and XNLI tasks. English dev accuracy is often uncorrelated (or even anti-correlated) with target language accuracy, and zero-shot performance varies greatly at different points in the same fine-tuning run and between different fine-tuning runs. These reproducibility issues are also present for other tasks with different pre-trained embeddings (e.g., MLQA with XLM-R). We recommend providing oracle scores alongside zero-shot results: still fine-tune using English data, but choose a checkpoint with the target dev set. Reporting this upper bound makes results more consistent by avoiding arbitrarily bad checkpoints.
Efficient Dual Axis Solar Tracking System

This generation is facing problems regarding energy crisis in an enormous way. Also the level of generation of electrical energy is not reaching the demand of this energy. In order to overcome this scarcity renewable energy would be a better answer. Using solar energy which is the most dominant resources of renewable energy could be a major solution to this problem. The performance of the dual axis solar tracker using Arduino is presented in this paper. This research helps to find which one is efficient among solar tracker and static solar panel. The work involves two parts, Software and Hardware. The hardware part resembles Light dependent resistors which detects the source from sun. Servo motors are used to move the solar panel to the place where the light source is maximum indicated by LDR. Software part is carried by using C programming. The result of this tracker is compared with static solar panel and can be found that solar tracker is more efficient capturing maximum light source and also produces more power.
Optimizing Layout of Video Surveillance for Substation Monitoring

An automated video surveillance system plays a vital role in unattended substations. To minimize the camera cost and maximize the monitoring coverage of the substation, efficiently placing the camera is critical. Two optimization models are developed in this paper to satisfy two kinds of requirements of unattended substations monitoring in practice: minimizing cost with full coverage, optimizing coverage with a fixed budget. The second model is mainly proposed for the first time to get the minimum monitoring fitness function as the optimization goal. By introducing piecewise functions in the second model, it meets different monitor-ing requirements in different regions of substations and reduces the over-lapping monitoring area efficiently. The two models are tested using a genetic algorithm to get the optimal layout of the cameras. The validity and practicability of the two models of monitoring in unattended substations are verified by simulation. The results suggest that this model-based optimization approach to cameras layout can be used to improve the efficiency of substation video surveillance systems.
Deep Anomaly Detection by Residual Adaptation.

Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of differentness when given only examples of normality. In this paper we propose a novel approach to deep anomaly detection based on augmenting large pretrained networks with residual corrections that adjusts them to the task of anomaly detection. Our method gives rise to a highly parameter-efficient learning mechanism, enhances disentanglement of representations in the pretrained model, and outperforms all existing anomaly detection methods including other baselines utilizing pretrained networks. On the CIFAR-10 one-versus-rest benchmark, for example, our technique raises the state of the art from 96.1 to 99.0 mean AUC.
Diagonal Recurrent Neural Network Based Prediction Model for Sales Forecasting

This article describes the novel application of Diagonal Recurrent Neural Network (DRNN) for solving the sales forecasting problem. The proposed model is having temporal features since it consists of feedback adjustable connections that provide it with the memory feature. Gradient descent based Dynamic Back-Propagation (DBP) method is applied for generating the tuning equations. The performance evaluation of the results obtained from the proposed model is compared with that of the well-known models such as Multi-Layered Perceptron (MLP) containing single hidden layer, Radial Basis Function Network (RBFN) and Legendre-Functional Link Neural Network (LeFLNN). The simulation results obtained shows the superior performance of the DRNN model over the other considered neural network models.
Phishing URL Detection Using Machine Learning Techniques

A criminal act performed online by impersonating others to obtain confidential data like passwords, banking details, login credentials, etc., is known as phishing. Detecting such websites in real-time, is a complex and dynamic problem, which involves too many factors. This work focuses on identifying the important features that distinguish between phishing URLs and legitimate URLs. To detect significant features, statistical analysis is done on the phishing as well as legitimate datasets. Based on the statistical exploration, certain features based on the URL, HTML, JavaScript and Domain were extracted. The prominent and most relevant features to identify the phishing URLs are identified using correlation. The identified subsets of features are then used to train different machine learning based classifiers and the accuracies obtained have been compared. From the experimental analysis it is observed that the extracted features have efficiently detected phishing URLs and the Decision Tree classifier has found with highest accuracy for making the predictions.
GPGPU-based randomized visual secret sharing (GRVSS) for grayscale and colour images

Visual Secret Sharing (VSS) is a technique used for sharing secret images between users. The existing VSS schemes reconstruct the original secret image as a halftone image with only a 50% contrast. The Randomized Visual Secret Sharing (RVSS) scheme overcomes the disadvantages of existing VSS schemes. Although RVSS extracts the secret image with better contrast, it is computationally expensive. This paper proposes a General Purpose Graphics Processing Unit (GPGPU)-based Randomized Visual Secret Sharing (GRVSS) technique that leverages data parallelism in the RVSS pipeline. The performance of the GRVSS is compared with the RVSS in a generic and PARAM Shavak supercomputer architecture. The GRVSS outperforms the RVSS in both architectures.
Design of Intelligent Access Control System Based on DES Encrypted QR Code

In order to solve the problems of inconvenient carrying and management of the access card used in the existing market access control system, a set of intelligent access control system based on DES encrypted two-dimensional code is designed. The system consists of Android smart phone, embedded access controller and server. By sending and receiving QR code via smart phone, access to the door is obtained, which realizes centralized management of office buildings, companies, senior office buildings, luxury residences and other middle and high-rise places, effectively preventing unauthorized people from entering the high security area. In order to ensure information security, the two-dimensional code is encrypted by DES algorithm. This system has the characteristics of low cost, high security and flexible operation. It is still blank in the application field and has certain promotion value.
Path-Based Visual Explanation

The ability to explain the behavior of a Machine Learning (ML) model as a black box to people is becoming essential due to wide usage of ML applications in critical areas ranging from medicine to commerce. Case-Based Reasoning (CBR) received a special interest among other methods of providing explanations for model decisions due to the fact that it can easily be paired with a black box and then can propose a post-hoc explanation framework. In this paper, we propose a CBR-Based method to not only explain a model decision but also provide recommendations to the user in an easily understandable visual interface. Our evaluation of the method in a user study shows interesting results.
Security Issues in Distributed Storage Networks

Due to the constant growth of the total amount of data stored by humanity the interest in distributed storage systems significantly increases. Recently considerable effort has been made toward efficient access to stored data and repair of temporally unavailable nodes while security issues are kept outside the mainstream of research. In this paper, we present different eavesdropper models for distributed storage tasks as well as possible extensions of distributed storage frameworks to secure against them. This is ongoing research in the framework of Ph.D. study at Skolkovo Institute of Science and Technology under the supervision of Alexey Frolov and Grigory Kabatiansky started in November 2017 and tentative end date in November 2021.