Reviewerly/exHarmony huggingface dataset:
CCCiCC: A Cross-Core Cache-Independent Covert Channel on AMD Family 15h CPUs

Spectre and similar microarchitectural attacks have recently caused a major paradigm shift in hardware and software development to restrict attacker-controlled speculative execution and microarchitectural sampling. So far, research has focused on cache interaction, instruction scheduling, microarchitectural sampling and speculative side effects, whereas instruction decoding research has been notably absent. We disclose two cross-core covert channels on multiple AMD processor generations (Family 15h) spanning from Bulldozer to Excavator with partial applicability to Zen. In this work, cross-core instruction decoding and synchronization interactions are explored as a source of information leakage on these processors to yield multiple cache-independent covert channels in a non-SMT environment. In contrast to other attacks, we do not rely on memory interaction nor on speculative execution. None of the existing mitigations in the Linux kernel and processor microcode against transient execution attacks have any measurable effect on the CCCiCC covert channels. To the best of our knowledge, this is not fixable with a microcode update since any updated instruction would also become usable for signaling.
Medical image enhancement using fractional derivatives

The present paper proposes a medical image enhancement scheme using a fractional differential function that selects the order of fractional derivative dynamically corresponding to each pixel of the image. The order depends upon the type of region to which the pixel belongs. The scheme incorporates the correlation between the neighboring pixels in cementing the mask to be applied to the image. The scheme focuses on enhancing the edges and texture while keeping the smooth regions of an image intact. Experimental results are obtained over a dataset of MRI images acquired from a diagnostic center. The comparison in terms of entropy, AMBE and PSNR clearly shows that the proposed method performs better than the other method.
Pooling Modalities and Pointwise Intersection: Axiomatization and Decidability

Abstract We establish completeness and the finite model property for logics featuring the pooling modalities that were introduced in Van De Putte and Klein (Pooling modalities and pointwise intersection: semantics, expressivity, and applications). The definition of our canonical models combines standard techniques with a so-called “puzzle piece construction”, which we first illustrate informally. After that, we apply it to the weakest classical logics with pooling modalities and investigate the technique’s potential for the axiomatization of stronger logics, obtained by imposing well-known frame conditions on the models.
Learning Sparse Hypergraphs from Dyadic Relational Data

In this paper, we propose a structured hypergraph learning algorithm based on the structure of common statistical dependencies observed in network datasets. By considering the social relations regression model (SRRM) as starting point, we extend the explanatory power of the model by including third-order dependency patterns through a hypergraph which exhibit the multi-clustered behavior of the network data. To unveil the underlying structure of the data, the hypergraph learning problem is considered as a combination of a generalized factor analysis problem, regularized by a smoothness assumption over the network data feature space, and a dictionary learning problem, which can be shown to be solved efficiently. Experimental results in both synthetic and real datasets illustrate the performance of the proposed hypergraph learning method.
ANALISIS DATA DALAM RANCANGAN PENELITIAN KUALITATIF

The design of a study is a very important and major problem in a study. The skill to compose a research design is a problem that is absolutely necessary for a researcher. The skill of drafting begins with understanding the various rules in it. That is because of how important research design is to one's success in carrying out the qualitative research process. Furthermore, the research design is different from the research report, but in its application, the research report sometimes is quoted from the research design. A good qualitative research design will be able to provide an initial overview of the research process. Thus, the research design serves as a smooth implementation of research to be conducted by researchers. The several design functions of a study include the following: 1) providing directions for answering a research question; 2) determine the restrictions on activities in accordance with the focus of the research; 3) allows researchers to avoid the emergence of various problems in the middle of the research process . Keywords : data analysis, research design, qualitative research
Il corpus KIParla. Tra linguistica dei corpora e sociolinguistica dell’italiano

In this paper we introduce the main features of the KIParla corpus, a new resource for the study of spoken Italian. Among other specific features, KIParla provides access to a wide range of metadata that characterize both the participants and the settings in which the interactions take place. Furthermore, it is designed to be shared as a free resource tool through the NoSketch Engine interface and to be expanded as a monitor corpus.
Online estimation of MGGD: the Riemannian Averaged Natural Gradient method

Multivariate Generalized Gaussian Distributions (MGGD) are a rich class of multivariate distributions, which have gained importance across many engineering applications (image processing, computer vision, radar and biomedical signal processing). Unfortunately, estimating the parameters of MGGD leads to non-linear matrix equations, whose solution becomes unpractical in high-dimensional problems, or when dealing with very large datasets. To overcome this difficulty, the present paper proposes a new method for online estimation of MGGD parameters, called the Riemannian Averaged Natural Gradient (RANG) method. The RANG method is suitable for application with high-dimensional and large datasets, since it requires modest memory and computational resources. The present paper formulates this new method, and presents some computer simulations, to showcase its performance. It is seen that, while the RANG method makes less exhaustive use of available data, it still achieves identical performance, to classical maximum-likelihood estimation, for sufficiently large datasets.
Learning representations from quadrilateral based geometric features for skeleton-based action recognition using LSTM networks

With the recent developments in sensor technology and pose estimation algorithms, skeleton based action recognition has become popular. Classical machine learning methods based on hand-crafted features fail on large scale datasets due to their limited representation power. Recently, recurrent neura l networks (RNN) based methods focus on the temporal evolution of body joints and neglect the geometric relations between them. In this paper, we propose eleven quadrilaterals to capture the geometric relations among joints for action recognition. An end-to-end 3-layer Bi-LSTM network is designed as Base-Net to learn robust representations. We propose two subnets based on the Base-Net to extract discriminative spatio temporal features. Specifically, the first subnet (SQuadNet) uses four spatial features and the second one (TQuadNet) uses two temporal features. The empirical results on two benchmark datasets, NTU RGB+D and UTD MHAD, show how our method achieves state of the art performance when compared to recent methods in the literature.
Fast Scene Text Detection with RT-LoG Operator and CNN

Text detection in scene images is of particular importance for the computer-based applications. The text detection methods must be robust against variabilities and deformations of text entities. In addition, to be embedded into mobile devices, the methods have to be time efficient. In this paper, the keypoint grouping method is proposed by first applying the real-time Laplacian of Gaussian operator (RT-LoG) to detect keypoints. These keypoints will be grouped to produce the character patterns. The patterns will be filtered out by using a CNN model before aggregating into words. Performance evaluation is discussed on the ICDAR2017 RRC-MLT and the Challenge 4 of ICDAR2015 datasets. The results are given in terms of detection accuracy and time processing against different end-to-end systems in the literature. Our system performs as one of the strongest detection accuracy while supporting at approximately 15.6 frames per second to the HD resolution on a regular CPU architecture. It is one of the best candidates to guarantee the trade-off between accuracy and speed in the literature.
Deep Learning Approach to Video Frame Rate Up-Conversion Using Bilateral Motion Estimation

We propose a deep learning-based frame rate upconversion algorithm using bilateral motion estimation. We first estimate bilateral motion fields by employing a convolutional neural network. Also, we approximate intermediate bi-directional motion fields, assuming linear motions between successive frames. Finally, we develop the synthesis network to produce an intermediate frame by merging the warped frames, which are obtained using the two kinds of motion fields. Experimental results demonstrate that the proposed algorithm generates high-quality intermediate frames on challenging sequences with large motions and occlusion, and outperforms state-of-the-art conventional algorithms.
Proactive Legal Design: Embedding Values in the Design of Legal Artefacts

Legal Design is an umbrella term for merging forward-looking legal thinking with design thinking. It applies human-centered design to prevent or solve legal problems. Legal Design takes an interdisciplinary and proactive approach to law, covering not only legal information and documents, but also legal services, processes, and systems. This paper introduces Legal Design in the context of forward-looking Legal Tech, especially through the lenses of data protection, and analyses the analogies among Value-Sensitive Design, Proactive Law, and Privacy by Design.
Short Paper: Deploying PayWord on Ethereum

We revisit the 1997 PayWord credit-based micropayment scheme from Rivest and Shamir. We observe that smart contracts can be used to augment this system, apply to 'claim or refund' paradigm of cryptocurrencies to remove the counter-party risk inherent in PayWorld, and use a smart contract to 'staple' real value (in Ether) to payments in the system. Our implementation is more concise than any Ethereum payment channel we are aware of and the offline payments are very compact values (264 bits). It only uses hash functions and not digital signatures. EthWord becomes cheaper than standard Ethereum transfers when more than 16 payments between the same participants are made and appears to maintain its advantage for up to 1000+ transactions, at which point signature-based payments become cheapest. The main drawback of EthWord is the moderate gas price of using the system—despite dropping signatures, it is still priced out of the micropayments use-case. Like any payment channel, requires only two on-blockchain function calls to open and close the channel, while allowing the rest to be made off-blockchain.
STUDENT ATTITUDE TOWARD THE USE OF WHATSAPP IN EFL CLASS

This paper is grounded by the utilizing of WhatsApp as media in teaching learning process. There are a lot of education practitioners who has conducted the use of WhatsAppin classroom. Therefore, the writer interested to investigate how students attitude inutilizing WhatsApp as the media in their learning. The writer converts the questioner into descriptive research . After analyzing of the data, the researcher found that the student has a positive impact toward the use of WhatsApp in classroom. Deal to this result, the researcher suggests that the reader can utilizeWhatsApp effectively to reduce the weakness of whatApp and combine to others method or strategy to reach the goal.
QoS-Aware Path Finding and Load Balancing in Service-Composition

Service-composition is an effective way to satisfy users' complex and multi-functional requirements in the current cloud environment. Aiming at the problem that the current large-scale service-composition is inefficient and service system is instable caused by load imbalance, this paper proposes a path finding and multi-path service-composition method based on QoS. Firstly, the services with the same input and output parameters are classified into one service class. Secondly, according to the input and output parameters matching, irrelevant services are removed and effective services sets and parameter source tables are obtained. Finally, according to the user's requests and the QoS constraints, multipaths of service composition are generated, which satisfy the goal of load balancing. Simulation results show that our proposed methods not only can satisfy the user's requests, but also find more choices for the execution of service composition.
Scan.it - Text Recognition, Translation and Conversion

Electronic documents are in demand everywhere in workplace, government offices, schools, colleges. There are many texts - such as letters and documents- which are available in electronic form but need to be converted in some readable regional language. However, conventional scanners are limited by their large size and relatively cumbersome usage. In this paper we explore the need for document scanning, and how this portable application can help users overcome the language barrier among states based on regional language by converting scanned documents. Scan.it web application is a Natural Language Processing based system which is used for text recognition and translation in an intelligent way. Scan.it solves the problem of ambiguity between similar words in a text and use a more semantic approach so as to classify data according to the context it is used in. The code can be implemented using Node.js. Further Tesseract OCR gives the results of the recognized text and ImTranslator is incorporated to translate this recognized text. In contrast to other OCR techniques, Scan.it solves the problem of recognizing the Marathi language text and further also helps in maintaining the soft copy of the document.
Arabic Text-Based Video Indexing and Retrieval System Enhanced by Semantic Content and Relevance Feedback

Nowadays, the Internet remains the essential source of access to scientific and technical information. In some languages, including Arabic, the means used to search for information do not seem to perform as well as in other languages. This deficiency is probably due to the late introduction of the Internet into the Arabization of the scientific and technological world, on the one hand, and to advances in the development of Arabic-language digital processing, on the other. This article intends to identify and explain the limitations and problems of searching for video in Arabic. For this reason, we propose an Arabic video indexing and retrieval system. In the indexation phase, our method is a combination between low-level and height-level indexation. In the retrieval phase, we put the user at the center of the research process. This goal is achieved by including a relevance feedback mechanism. The proposed new system is tested and prove that is perform well in a large scale database.
Model design for grammatical error identification in software requirements specification using statistics and rule-based techniques

Abstract The statement of functional requirements statement that refers to software requirements specifications (SRS) is a reference for stakeholders in making software. The SRS document uses a different English grammar due to limited knowledge by the software development team, making it difficult for the development team to read and understand the specification documents for software requirements properly. To overcome this grammatical error, a method is needed to resolve grammatical errors. The method used to identify grammatical errors is based on a trigram language model. In the last research, trigram language models showed quite good performance in identifying grammatical errors based on probabilistic n-gram language models. To improve the performance of the n-gram language model this study also utilizes a corpus namely Penn Tree Bank Corpus. Provide recommendations, this study uses rules-based techniques. A number of rules are made to provide recommendations for inappropriate grammar. So that the software requirements specification compiler can check for grammatical errors and improve the quality of the software requirements specification document.
Multi-Branch Convolutional Descriptors for Content-based Remote Sensing Image Retrieval

Context-based remote sensing image retrieval (CBRSIR) is an important problem in computer vision with many applications such as military, agriculture, and surveillance. In this study, inspired by recent developments in person re-identification, we design and fine-tune a multi-branch deep learning architecture that combines global and local features to obtain rich and discriminative image representations. Additionally, we propose a new evaluation strategy that fully separates the test and training sets and where new unseen data is used for querying, thereby emphasizing the generalization capability of retrieval systems. Extensive evaluations show that our method significantly outperforms the existing approaches by up to 10.7% in mean precision@20 on popular CBRSIR datasets. Regarding the new evaluation strategy, our method attains excellent retrieval performance, yielding more than 95% precision@20 score on the challenging PatternNet dataset.
An Efficient Honey Bee Approach for Load Adjusting in Cloud Environment

Cloud computing is an Internet-based approach that delivers on-demand processing resources and information to the users in a shared mode. At the serving end, there is a prerequisite of proper scheduling and load adjusting to deal with the enormous measure of data. Our algorithm aims to distribute the equal load on each server in the cloud network and additionally enhances the asset usage. With the proposed approach, the honey bee inspired load adjusting (HBI-LA) method has been used for balancing the load of the virtual machine and schedule the task with respect of their priorities. Because of over-burdening of the task on a machine, there may be a chance of CPU crash. To overcome this problem, aging is applied to gradually enhance the priority of those jobs having longer waiting time as compared to the predefined time. At last, we compared our proposed work with the existing HBB-LB in terms of CPU time, execution time and waiting time. The examination of these three parameters demonstrates that the proposed algorithm requires less CPU time, less execution time and less waiting time than existing algorithm, hence it shows better performance and less energy consumption than the existing one.
Classification of Generation of Thai Facebook Users Using Deep Learning with Probability of Words

Facebook is the most popular platform in the world. Marketers would like to use Facebook user data, which comprises large amounts of information which is useful for marketing. Therefore, analyzing the generation of Facebook users for marketing research is important to successfully capture the target market. In this research, posted data of Thai Facebook users will be analyzed using the combined methods of deep learning and probability of words data. The experiment result yields an accuracy of 82.90% per user and 52.48% per status, which is better than using other models alone such as Multi-Layers Perceptron (MLP), Convolution Neural Networks (CNN), or Long Short-Term Memory (LSTM). The experiment results show that using probability of words in each generation can help to increase the efficiency of the model.
Finding all maximal perfect haplotype blocks in linear time

Recent large-scale community sequencing efforts allow at an unprecedented level of detail the identification of genomic regions that show signatures of natural selection. Traditional methods for identifying such regions from individuals' haplotype data, however, require excessive computing times and therefore are not applicable to current datasets. In 2019, Cunha et al. (Proceedings of BSB 2019) suggested the maximal perfect haplotype block as a very simple combinatorial pattern, forming the basis of a new method to perform rapid genome-wide selection scans. The algorithm they presented for identifying these blocks, however, had a worst-case running time quadratic in the genome length. It was posed as an open problem whether an optimal, linear-time algorithm exists. In this paper we give two algorithms that achieve this time bound, one conceptually very simple one using suffix trees and a second one using the positional Burrows-Wheeler Transform, that is very efficient also in practice.
Survey of Methods Applying Deep Learning to Distinguish Between Computer Generated and Natural Images

With the advent of fake news and propaganda being spread throughout the Internet using forged or computer-generated images, it is important to evolve algorithms that are able to differentiate between computer-generated images and natural ones. In this paper, we provide a high-level summary of the methods proposed recently which classify images as computer generated or natural using deep learning concepts. We spelled out the pros and cons of each method and further suggested future research paths like building a standard computer generated (CG) versus natural images (NI) dataset targeting compressed images from heterogeneous sources which ensures that the dataset models real world well, testing proposed approaches in real-life conditions and trying out training classifiers using combination of features generated from various convolutional neural networks (CNNs) as opposed to a single neural network to assess its impact on the accuracy rate for classification of compressed images.
Semantically Oriented Idioms for Sentiment Analysis. A Linguistic Resource for the Italian Language

The present paper presents a linguistic resources usable for Sentiment Analysis purposes which includes about 500 Italian Frozen Sentences formalized into electronic dictionaries and local grammars pairs. The paper will focus on the cases in which words included into sentiment lexicons, endowed with a specific Prior Polarity, take part as constituent into frozen structures.
Context-Aware Service Composition with Functionally Equivalent Services for Complex User Requests

In some situations, a complex user request cannot be satisfied by only sequential composition of services, rather by a combination of sequential and parallel composition of services. Typically user requests are represented as directed acyclic graphs (DAG). In this paper, the problem of service composition with functionally equivalent services in a context-aware setting where the user request is given as a DAG is considered. A planning based approach for service composition is suggested where a plan repair strategy adapts an existing plan to a new context by making minimalistic changes. Extensive experiments using different types of user requests have been performed. The results are quite promising and it manifest the efficacy of the proposed approach.