Reviewerly/exHarmony huggingface dataset:
Trusted Application Using Biometrics for Android Environment

The age of depending on processing devices has passed, the current life style is more dependent on continuous connectivity to the cyber space. This requires exceeding connectivity being a need to service providers such as businesses and companies to become a need for service consumers to maintain the services. Perhaps one of the most popular tools to achieve this degree of connectivity are mobile devices, with their “anytime, anywhere” characteristics, thus providing connectivity and ensuring mobility. This characteristic comes with inherited risks from both mobility and connectivity. Therefore, efforts have been focused on providing added layers of security for mobile environment, thus causing an additional security measures that subsequently adds more complications to the environment. This research provides a secure mobile environment with a simple nonevasive biometric authentication, represented by face recognition, a backup security layer is provided, represented by a graphical password, if the user fails to unlock with the face recognition feature, the graphical password option can be used. At the same time, if any intruder tries to unlock and fails the third time, the system will capture the face and send it to a pre-defined email automatically; thus the suggested method provides an authentication mechanism that is suitable for daily use, at the same time can serve with high efficiency for users with disability or special needs. The choice of the backup authentication is also designed to take in consideration the needs of such users' categories.
Path Planning of Multiple Industrial Mobile Robots Based on Ant Colony Algorithm

At present, there are many problems in the research of multi-robot path planning, such as low efficiency of path planning algorithm, easy to fall into local optimum, and insufficient consideration of the application environment of robots. In order to improve the efficiency of multi-robot path planning algorithm, this paper generates dynamic change information according to the difference between the best path of the previous generation and the best path of the current iteration in ant colony algorithm, and improves the efficiency of the algorithm by the positive feed-forward generated by the dynamic change information, and adds mutation operation to the improved ant colony algorithm to solve the problem that the algorithm is easy to fall into local optimum. At the same time, on the premise of fully considering the working state of the robot and the urgency of the task, the rules of obstacle avoidance are optimized to improve the efficiency of obstacle avoidance of the robot and make the path planning of the multi-robot meet the requirements of the dynamic environment of the production workshop. The simulation results show the effectiveness of the algorithm.
Coverage-Based Designs Improve Sample Mining and Hyperparameter Optimization

Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning (ML), and sequential optimization has become a popular solution. Typical examples include data summarization, sample mining for predictive modeling, and hyperparameter optimization. Existing solutions attempt to adaptively trade off between global exploration and local exploitation, in which the initial exploratory sample is critical to their success. While discrepancy-based samples have become the de facto approach for exploration, results from computer graphics suggest that coverage-based designs, e.g., Poisson disk sampling, can be a superior alternative. In order to successfully adopt coverage-based sample designs to ML applications, which were originally developed for 2-D image analysis, we propose fundamental advances by constructing a parameterized family of designs with provably improved coverage characteristics and developing algorithms for effective sample synthesis. Using experiments in sample mining and hyperparameter optimization for supervised learning, we show that our approach consistently outperforms the existing exploratory sampling methods in both blind exploration and sequential search with Bayesian optimization.
Use of Fake News and Social Media by Main Stream News Channels of India

This paper discusses the use of fake news and social media by mainstream news channels of India and how they're using social media and fake news to fuel nationalism and create division between communities to avoid important issues of the country like employment, health care, education, infrastructure, crime against women and children, economy etc.. This pilot study highlights the type of topics mainstream news channels discuss on their prime-time shows and shares on social media to create division, distraction and animosity between the citizens to keep citizens away from the real issues of the country.
Tracking iron-rich rocks beneath Cenozoic tablelands: An integration of geological, airborne geophysical and remote sensing data from northern Minas Gerais State, SE Brazil

Integration of multisource data has been successfully used in geological mapping and mineral exploration projects around the world. In areas covered by extensive regolith, colluvial and/or alluvial deposits, with scarce rock outcrops, the application of airborne geophysics and remote sensing products has become a key tool to unravel the geological framework and lithological units. The integration of all those data files requires the use of geographic information systems (GIS) and spatial analysis techniques. More than 87,200 km of linear-collected airborne geophysics data have been acquired by government companies to subside mineral prospecting and geological mapping in northern Minas Gerais State, SE Brazil. Together, geomorphological, geological and geophysical data outline distinct domains on the studied region. The eastern domain shows large tablelands (plateaus) sustained by metadiamictite-quartzite-rich units of the Neoproterozoic Macaúbas Group and related glaciogenic iron deposits, like those of the Nova Aurora Formation, comprising hematite- and/or magnetite-rich metadiamictites with 10–60 wt% Fe. However, on tablelands, the known Neoproterozoic iron deposits are largely hidden beneath Cenozoic covers, although there are scattered outcrops of iron-rich rocks well-preserved from lateritization, a weathering process that can reach dozens of meters deep in the region. Applying Fuzzy logic methodology, our thematic maps provide great progress to define lithological units and structural trends, supporting new guides to track iron-rich rocks. The favorability maps allow us to suggest new targets, and a prospective model to trace locations and structural trends of iron-anomalous rock units. The regional model shows scattered favorable areas with few high-density concentrations of probable iron-rich rocks or even iron ore deposits. Magnetic data images disclose the main occurrences of iron-rich rocks, although lacking resolution for more specific studies. Although gammaspectrometry and Landsat 8 data only reflect responses from surface materials, they may reduce information ambiguity after integrated with magnetic data on areas covered by laterites richer in iron. Our final integrated models constrain the most favorable areas for the occurrence of iron-rich rocks, even in the large tableland domains.
Image DeHazing Using Deep Learning Techniques

The task of image de-hazing has been a challenge in the field of Computer Vision since its inception. The images captured during adverse weather conditions often appear to be of low quality due to the presence of various atmospheric particles, which results in the haze, fog etc. This, in turn, causes trouble in detecting objects in an image. This causes problems for many computer vision problems that rely on the visibility of these images. In this paper we are implementing a deep Generative Adversarial Network for image de-hazing. The original de-hazing approaches use per-pixel loss, which when calculating creates huge differences even when there is the only difference in a single pixel, even if these images are perceptually similar. The perceptual loss function extracts high level features of images using pre-trained models on ImageNet, which removes the problems of per-pixel loss functions.
Evaluating the Upper Bound of Energy Cost Saving by Proactive Data Center Management

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not.The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Evaluating the upper bound of energy cost saving by proactive data center management
Cyberbullying and hate speech in the debate around the ratification of the Istanbul convention in Bulgaria: a semiotic analysis of the communication dynamics

In 2018 the Bulgarian Constitutional Court declared the Istanbul Convention (IC) unconstitutional. This decision was taken after several months of fierce public debate in Bulgaria over the implications of IC adoption. The debate was overwhelmed by the populist position rejecting this convention. From this debate the word “gender,” literally transcribed in Cyrillic letters (джендър) and having no direct translation in Bulgarian, not only became a neologism with strongly offensive connotations towards LGBT community, but also a general insult. The populist position in this debate, which won the majority population support, was supported by an impressive variety of rhetorical means, most of which were pure examples of cyberbullying. Then I propose a diachronic mirror model showing alphabet evolution to reach the domesticated mind phase, as well as the reverse process just after the Internet advent, with creative undomestication of mind from a simplified online writing through emoticons and emojis, to GIFs and Internet memes. I will demonstrate that although cyber offenders act alone before their computers, their behavior is determined by crowd psychology factors (e-crowd), making them less responsible for their behavior. Some characteristics of the so-called post-truth era are seen as a consequence of this mechanism..
G&amp;#246;del semantics of fuzzy argumentation frameworks with consistency degrees

Argumentation frameworks (AF) play important roles in artificial intelligence. This paper is an exploration in establishing semantics of fuzzy AFs by fuzzy sets. There are many ways to characterize the semantics of fuzzy AFs. In this paper, our work is based on the assumption that some inconsistency of the system is permitted. Firstly, we formalize the conflict-freeness with a consistency degree x and the acceptability with a consistency degree y. Various types of extensions are then defined in a way similar to Dung's approach. The conflict-freeness and acceptability can be seen as an interpretation of the corresponding notion in Janssen's work. Formally, we add the conflict-freeness into the admissible extensions and the preferred extensions. We also introduce the complete extensions and the grounded extensions. Moreover, some basic properties are proven, such as the Fundamental Lemma, the algorithm of the grounded extension, etc. At last, it is proven to be consistent with Dung's original semantics in crisp AFs.
Automatic Annotation Service APPI: Named Entity Linking in Legal Domain

Texts referencing court decisions and statutes can be difficult to understand without context. It can be time consuming and expensive to find related statutes or to learn about context specific terminology. As a solution, we utilized a named entity linking tool for extracting information and tailored it into a service, Appi, that can automatically annotate legal documents to provide context to the readers. The service can identify and link named entities and references to legal texts to corresponding vocabularies and data sources by combining statistics- and rule-based named entity recognition with named entity linking. The results provide users with enhanced reading experience with contextual information and the possibility to access related materials, such as statutes and court decisions.
Impact of Digital Technology Training on English for Science and Technology Teachers in India

With digital literacies becoming an essential competency for teachers across the world, recent educational policies in India emphasize teachers’ ability to use digital technologies for pedagogic purposes and the use of digital technologies by teachers as one of the evaluation criteria for ranking institutions. In the absence of any properly established policy on in-service training in digital technologies for teachers, such policies cannot be expected to promote effective learning. Moreover, little effort is being made to keep track of the impact of training in digital technologies on teachers’ pedagogic practices. In light of the above background, the proposed multiple-case study aims to explore changes in the practices of three English for Science and Technology (EST) teachers in India. The main objective of the study is to compare the teachers’ technology integration practices before and after they participated in a 12-hour need-based training programme in digital technologies. Data for the study were collected through questionnaires, interviews, classroom observations, field notes and e-portfolios. Results suggest that the programme had a positive impact on their practices and there was difference in the way the three teachers selected, used and thought about digital technologies. Factors such as motivation, administrative support, students’ response to the use of technologies, and awareness about students’ language needs shaped their practices.
Aggressive Action Recognition Using 3D CNN Architectures

This paper discusses an application of the transfer learning approach concerning human aggressive actions recognition task in video content. Comparative analysis of this approach was performed using various three-dimensional convolutional network architectures (3D CNN): Convolutional 3D Neural Network (C3D), Inception 3D Neural Network (I3D), Residual 3D Neural Network (R3D) based only on RGB frames processing. These 3D CNNs have trained on a composite aggressive action video dataset, that includes benchmark datasets for aggression recognition. The neural networks were evaluated in terms of accuracy, precision, recall, f1-score and loss function values metrics. The aggressive action recognition transfer learning approach using 3D CNNs showed impressive results on the considered metrics. Moreover, learning time in context of this approach was relatively short.
On Fixpoint/Iteration/Variant Induction Principles for Proving Total Correctness of Programs with Denotational Semantics

We study partial and total correctness proof methods based on generalized fixpoint/iteration/variant induction principles applied to the denotational semantics of first-order functional and iterative programs.
Leveraging Smart Devices for Scene Text Preserved Image Stylization: A Deep Gaming Approach

With the rapid advancement of real-time rendering quality in digital art, the use of computer vision techniques has become popular in producing aesthetically pleasing stylized images of natural scenes. However, the processing time to produce a stylized image is high, especially in resource-constrained environments, such as smart devices. Most stylized imaging approaches are unable to preserve the fine details of the natural scene images, such as texts, symbols, and logos in the painted image, which leads to the loss of significant semantic information. In this article, we propose a fast image stylization framework (digital oil painting) using incremental histogramming that preserves the text content of natural scenes while efficiently painting it in resource-constrained environments. We design a stable multiplayer stochastic game with deep networks to classify regions into text or nontext using deep networks. We propose a multiscale fully convolutional character level text detector (xEASTLite) to detect the presence of a text character or part of a character with high accuracy. We have used different publicly available datasets in smart devices to illustrate the efficacy of the framework.
Wildfire forecast within the districts of Kerala using Fuzzy and ANFIS

Wildfire is an unbounded catastrophe that affects the serenity of biodiversity. Thus wildfire prediction helps to maintain the resource conservation and recovery management. This paper depicts the prediction of wildfire prevailing in 2 districts of Kerala (Idukki, Wayanad). This paper is implemented using ANFIS (Adaptive Neuro-Fuzzy-Inference System) and fuzzy clustering. Objective of this paper is to predict wildfire. Fuzzy C-means (FCM) of fuzzy clustering is used to obtain the clustered output followed by the classification using ANFIS. It enables an experimental environment on which human knowledge (rules) and learning algorithms to be combined. Based on the output value generated from ANFIS classification the objective is predicted.
Where Are You Bob? Privacy-Preserving Proximity Testing with a Napping Party

Location based services (LBS) extensively utilize proximity testing to help people discover nearby friends, devices, and services. Current practices rely on full trust to the service providers: users share their locations with the providers who perform proximity testing on behalf of the users. Unfortunately, location data has been often breached by LBS providers, raising privacy concerns over the current practices. To address these concerns previous research has suggested cryptographic protocols for privacy-preserving location proximity testing. Yet general and precise location proximity testing has been out of reach for the current research. A major roadblock has been the requirement by much of the previous work that for proximity testing between Alice and Bob both must be present online. This requirement is not problematic for one-to-one proximity testing but it does not generalize to one-to-many testing. Indeed, in settings like ridesharing, it is desirable to match against ride preferences of all users, not necessarily ones that are currently online. This paper proposes a novel privacy-preserving proximity testing protocol where, after providing some data about its location, one party can go offline (nap) during the proximity testing execution, without undermining user privacy. We thus break away from the limitation of much of the previous work where the parties must be online and interact directly to each other to retain user privacy. Our basic protocol achieves privacy against semi-honest parties and can be upgraded to full security (against malicious parties) in a straight forward way using advanced cryptographic tools. Finally, we reduce the responding client overhead from quadratic (in the proximity radius parameter) to constant, compared to the previous research. Analysis and performance experiments with an implementation confirm our findings.
Imperfect basis choice in continuous-variable quantum key distribution

In the Gaussian-modulated coherent states (GMCS) quantum key distribution (QKD), basis choice is a necessary procedure if homodyne detector is used. However, finite bandwidth of the digital-to-analog convertor (DAC) at the receiver's side may lead to the imperfection of the basis choice, namely, maybe Bob can not exactly modulate 0 or on the phase modulator to implement the homodyne detection.We investigate the GMCS QKD scheme with imperfect basis choice under realistic conditions of quantum channel and detector.We show that the imperfection causes a misestimate of the transmittance and the excess noise, the secret key rate and the security of the system are dramatically decreased consequently. To achieve a reliable quantum key distribution, a security bound to the noise induced by the imperfect basis choice is derived for reverse reconciliation and homodyne detector. Besides, we propose and demonstrate a countermeasure against the noise due to imperfect basis choice.
Color Image Encryption Depend on DNA Operation and Chaotic System

In the recent years, the interest in information security has increased due to the great development of modern technology in the world, which led to the increase of the various attacks on data and essential information. Therefore it was necessary to have encryption techniques that protect the transmitted information In this study, a new algorithm of colour image encryption based on DNA and the chaotic map is proposed. The diffusion is achieved by using the processes of shifting and permutation, and the confusion is achieved by using the processes of S-box and mathematics. This algorithm was applied to different image sizes and type (bmp, jpeg). The proposed algorithm is intended to be complex and repel attacks. The shifting process is done on colours (R, G, B) from left to right, giving new pixels different from the old ones. The hexadecimal process and box-S are applied to the blue colour to increase confusion. The process of computing of DNA takes place in two colours (red and green). Pixel locations are altered by permutation (R, G, B) become (B, R, G) this process to increase diffusion. Finally, mathematical operation take place (blue, red) decomposes pixels and increase confusion. This algorithm has a highly sensitive key and can cope with various attacks due to the use of DNA computing and chaotic system.
A Normalized Mean Algorithm for Imputation of Missing Data Values in Medical Databases

Many medical research databases commonly consist of the missing value problems, and the presence of missing data value has a negative impact on machine learning models. However, the data with missing value can decrease the classifier performance and can lead to wrong insights by introducing biases. Imputation approaches are typically employed to impute the missing data value for data analysis. In addition, imputation helps us to build an effective classification model to discover hidden patterns which can provide insightful outcomes. In this paper, the normalized mean imputation approach is designed to fill the missing data value in numerical datasets. After normalizing the data, compute the mean and cube-root-of-cubic mean. Finally, impute the missing data value from the maximum value of these two methods which are the plausible data value in a given dataset. In addition, it is observed that after imputation some of the outliers are also eliminated in a dataset in this approach. The experiments are conducted on benchmark datasets and compared with mean imputation, median imputation, and mode imputation approaches. The experimental results show that the suggested imputation technique performed superior results compared with other state-of-the-art methods.
Survey of Object Detection Algorithms and Techniques

Object detection is a field that has been in the limelight a lot in the recent years. Computer vision and image processing are involved in this computer technology and are widely used. Along the path of harnessing the power of vision, numerous algorithms have been found from simple edge detection to pixel-level object detection. In this paper, we have studied the advancements in object detection algorithms like R-CNN and the latest one being You-Only-Look-Once (YOLO). We have studied papers from 2016 to 2018 based on the types of R-CNN like Fast R-CNN, Faster R-CNN, and Mask R-CNN and various versions of YOLO. We have seen their applications in various fields, studied their efficiency, accuracy, and limitations.