Reviewerly/exHarmony huggingface dataset:
Analisis Kinerja Routing Protokol AODV pada Jaringan VANET (Studi Kasus : Kecamatan Cakranegara)

Vehicular Ad-hoc Network is the concept of a subset of Mobile Ad-Hoc Networks (MANET) where vehicles act as nodes on the network. Protocol routing is very influential on the performance of the network to deal with challenges related to the rapidly changing network topology. This study analyze the routing performance of the AODV protocol on vanet networks in the district of Cakranegara by analyzing performance results using end to end delay parameters, packet delivery ratio (PDR), throughput, and routing overhead. The average Maximum value at end to end delay is 940.577 ms while the minimum value is 50,065 ms, and the average speed value decreases delay at speed 50 to 30 km / h by 78,314 ms and from 30 to 10 km / hour decreased by 102,827 ms. The maximum average value in the packet delivery ratio is 84.75% while the minimum value is 55.33%, and the average speed value decreases the ratio at the speed of 50 to 30 km / hour by 7.26% and from 30 to 10 km / h has increased by 10.39%. The average maximum value at throughput is 69,553 Kbps while the minimum value is 10.06 Kbps, and the average speed value decreases throughput at speeds of 50 to 30 km / h by 1,065 Kbps and from 30 to 10 km / h decreases amounting to 0.658 Kbps. The average Maximum value on routing overhead is 37,311 bytes while the minimum value is 7,439 bytes, and the average speed value increases at a speed of 50 to 30 km / hour by 1,321 Bytes and from 30 to 10 km / hour decreases by 5,453 Bytes.&#x0D; Key words: AODV, VANET, Routing Protocols, SUMO, NS2.
A Coin-Free Oracle-Based Augmented Black Box Framework.

After the work of Impagliazzo and Rudich (STOC, 1989), the black box framework has become one of the main research domain of cryptography. However black box techniques say nothing about non-black box techniques such as making use of zero-knowledge proofs. Brakerski et al. introduced a new black box framework named augmented black box framework, in which they gave a zero-knowledge proof oracle in addition to a base primitive oracle (TCC, 2011). They showed a construction of a non-interactive zero knowledge proof system based on a witness indistinguishable proof system oracle. They presented augmented black box construction of chosen ciphertext secure public key encryption scheme based on chosen plaintext secure public key encryption scheme and augmented black box separation between one-way function and key agreement.
"VIKTIG INFORMATION TILL SVERIGES INVÅNARE" -En kvalitativ innehållsanalys av Myndigheten för samhällsskydd och beredskaps broschyr "Om krisen eller kriget kommer"

With changes in the outside world and that we are constantly faced with potential risks, the effectiveness of risk communication has become a decisive factor. The purpose of this study is to invest ...
Compressed Video Action Recognition with Refined Motion Vector

Although CNN has reached satisfactory performance in image-related tasks, using CNN to process videos is much more challenging due to the enormous size of raw video streams. In this work, we propose to use motion vectors and residuals from modern video compression techniques to effectively learn the representation of the raw frames and greatly remove the temporal redundancy, giving a faster video processing model. Compressed Video Action Recognition(CoViAR) has explored to directly use compressed video to train the deep neural network, where the motion vectors were utilized to present temporal information. However, motion vector is designed for minimizing video size where precise motion information is not obligatory. Compared with optical flow, motion vectors contain noisy and unreliable motion information. Inspired by the mechanism of video compression codecs, we propose an approach to refine the motion vectors where unreliable movement will be removed while temporal information is largely reserved. We prove that replacing the original motion vector with refined one and using the same network as CoViAR has achieved state-of-art performance on the UCF-101 and HMDB-51 with negligible efficiency degrades comparing with original CoViAR.
Determining the Level of Readiness of Teachers to Implementation of STEM-Education in Ukraine.

Research of existing models of professional development of futureteachers of natural and mathematical disciplines and professional teacher hasshown that the creation of an educational environment for STEM-orientedlearning affects the formation and further improvement of the system of theirvalues. In this paper, the concept of STEM-education in terms of interdisciplineis considered. An attempt was also made to identify the factors thatinfluence the readiness of teachers to support STEM-education and implementit in educational institutions. This study is concerned with determining the levelof formation of teachers' readiness for the implementation of STEM-educationin Ukraine. Here we propose a methodology and a model for determining thelevel of readiness through surveys and analysis of results. On the basis of theobtained results, further prospects of the research are proposed and recommendationsfor involving young people in scientific activities, which may improvelearning of Science, Technology, Engineering, Mathematics (STEM).
Constructions of MDS Self-dual Codes from Short length

Systematic constructions of MDS self-dual codes is widely concerned. In this paper, we consider the constructions of MDS Euclidean self-dual codes from short length. Indeed, the exact constructions of MDS Euclidean self-dual codes from short length ($n=3,4,5,6$) are given. In general, we construct more new of $q$-ary MDS Euclidean self-dual codes from MDS self-dual codes of known length via generalized Reed-Solomon (GRS for short) codes and extended GRS codes.
Cloud-Based Efficient and Secure Scheme for Medical Images Storage and Sharing using ECC and SHA-3

Nowadays, healthcare institutions rely on medical imaging in the diagnosis of medical cases, where there are huge amounts of medical imaging data that are generated daily. Some of these cases are required to be shared among many doctors for teleconsultation or tele-diagnosis purposes. The reliance on the local storage systems does not guarantee the availability of sufficient storage areas. For these reasons, the cloud storage technology is introduced that provides scalable storage with online access facility. However, far about that, data storing in the cloud brings many security issues. So, in this paper, the cloud-based system is suggested for storing and sharing the medical images in secure manner. The suggested system based on number of cryptography techniques to create better security for the medical images over the transmission link and on the cloud. Elliptic Curve Cryptography (ECC), Advanced Encryption Standard (AES), and Secure Hash (SHA-3) algorithms are used here. For minimizing the computation overload on the clients' devices, the third-party auditor is used for verifying medical images integrity and authenticity before storing it in cloud. Also, a digital signature is created to ensure the data source authentication and assure the robustness of the suggested scheme against any action to disclosure or modify the data. Through security analysis, the results show that the algorithm verifies the high degree of data security. Performance analyses show that the scheme provides high storage ability with acceptable computation and storage overhead.
Students’ Perception On Remedial Teaching In Department Of English Education At Universitas Muhammadiyah Surakarta

This study discusses student perception on remedial teaching in English Education at Universitas Muhammadiyah Surakarta. The purposes of this study are to describe the students perception on remedial teaching and to find out the problems faced by the students. Type of this research is descriptive qualitative research. The subject of this research is students who have taken remedial teaching. The data of this research is interview transcipt taken from students who have taken the program. The researcher used interview to get data from the students. The results of this study are: 1) Remedial teaching was conducted in limited time, 2) the students thought that remedial teaching was expensive, 3) the students’ of remedial teaching is expected that the lecturer is able to help them in order to get the most result. The problem faced by the students’ are: 1) expensive. 2) requires lecturer not to change class hours irregularly
Discovering Emotions through the Building of a Linguistic Resource

Specific linguistic resources, syntactically annotated and distinctive for each language, related to the affective sphere are important in discovering terms or phrases associated with emotions in order to detect expressed emotions. The paper proposes the initial version of a linguistic resource for the Italian language, mapped on WordNet, where each concept, whose meaning falls into the sphere of emotions, is enriched by a category, allowing to better specify the type of emotion expressed by the term, and by a polarity value, whether the emotion is positive or negative. The resource is based on the model of emotions proposed by Robert Plutchik and has been developed, within a national project of Work-School Alternation, in collaboration with some high school students. The work has a twofold value. On one hand, the development of a linguistic resource, on the other the educational and didactic aspect of students’ involvement. Working on the analysis of literary texts with the task of elaborating and defining the emotions described, the students, assisted by their teachers and two researchers, had to face with their feelings and talk more freely about their affective states, recognizing the emotions and giving them a name.
Word recognition levels of first grade students: An application of word recognition inventory

Students are expected to recognize the vocabulary items appearing in a text in order for reading to be meaningful. The evaluation of word recognition is commonly considered in educational applications in order for reading to be meaningful for students and to identify and correct mistakes. This study aims to exhibit the effectiveness of the inventory whose stages of preparation and implementation were process-based and which was developed for word recognition on determining the first graders’ word recognition levels. For this purpose, the Type 1 development research type of design and development research model was used to develop an instrument for students’ word recognition levels and thus to demonstrate its applicability. The study group was composed of 85 first graders who had been chosen in convenience sampling method. Student Information Form, Word Recognition Inventory-1 (WRI-1) and Word Recognition Inventory-2 (WRI-2) were used as the tools of data collection. Variance analysis (ANOVA) and independent groups t-test were used in comparing the students for WRI-1 inventory which met the conditions for normal distribution whereas Kruskal Wallis and Mann Whitney U-test were used in comparing the students for WRI-2 inventory which did not meet the conditions for normal distribution. Consequently, it was found that the inventories, whose stages of preparation and implementation were process-based and which were developed to determine students’ word recognition levels, were effective in determining the primary school first graders’ word recognition levels.
Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional Systems

This paper describes a purely data-driven solution to a class of sequential decision-making problems with a large number of concurrent online decisions, with applications to computing systems and operations research. We assume that while the micro-level behaviour of the system can be broadly captured by analytical expressions or simulation, the macro-level or emergent behaviour is complicated by non-linearity, constraints, and stochasticity. If we represent the set of concurrent decisions to be computed as a vector, each element of the vector is assumed to be a continuous variable, and the number of such elements is arbitrarily large and variable from one problem instance to another. We first formulate the decision-making problem as a canonical reinforcement learning (RL) problem, which can be solved using purely data-driven techniques. We modify a standard approach known as advantage actor critic (A2C) to ensure its suitability to the problem at hand, and compare its performance to that of baseline approaches on the specific instance of a multi-product inventory management task. The key modifications include a parallelised formulation of the decision-making task, and a training procedure that explicitly recognises the quantitative relationship between different decisions. We also present experimental results probing the learned policies, and their robustness to variations in the data.
IMAGE STYLE TRANSFER MODEL WITH CONTENT PRESERVATION

The image rendered in another image style, called style transfer, is one of the most interesting applications of deep learning. Style transfer plays an increasingly important role in basic computer vision research and industrial applications. The method of iterative optimization [1] and training feedforward convolutional neural network [2-4] are used to continuously achieve fast and good effects on style transfer. However, most of these methods ignore an essential problem that the content and contour information of the original images could be lost after the transfer process. Throughout the history of style transfer, although the performance is constantly improving, pursuing higher flexibility and speed, an essential problem is ignored, that is, the main content and contour are unavoidably blurred after style transfer. It is found that in many application fields, the content/contour of the original image has great importance and thus should be kept as clear as possible after style transfer. In this paper, a sub-structure named Important Content Contour Extraction (ICCE) is proposed to generate masks and therefore preserve the clear contour and content after style transfer. The Feature Extraction Module (FEM) for extracting advanced features is added in image style transfer network which is trained by the perceptual loss function. We conducted some experiments to verify the effectiveness of the proposed method. Experiments demonstrated that the proposed framework achieves a good compromise in speed, flexibility and quality. The experiment results showed that the proposed model had good ability for style transfer.Meanwhile, it could preserve clear content and contour of the original images, which was better than [1-4].
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution

Although deep convolutional neural networks (CNNs) have obtained outstanding performance in image superresolution (SR), their computational cost increases geometrically as CNN models get deeper and wider. Meanwhile, the features of intermediate layers are treated equally across the channel, thus hindering the representational capability of CNNs. In this paper, we propose an attention-aware linear depthwise network to address the problems for single image SR, named ALDNet. Specifically, linear depthwise convolution allows CNN-based SR models to preserve useful information for reconstructing a super-resolved image while reducing computational burden. Furthermore, we design an attention-aware branch that enhances the representation ability of depthwise convolution layers by making full use of depthwise filter interdependency. Experiments on publicly available benchmark datasets show that ALDNet achieves superior performance to traditional depthwise separable convolutions in terms of quantitative measurements and visual quality.
Real-time 360 Body Scanning System for Virtual Reality Research Applications

Here, we present a low-cost solution to perform the online and realistic representation of users using an array of depth cameras.The system is composed of a cluster of 10 Microsoft Kinect 2 cameras, each one associated to a compact NUC PC to stream live depth & color images to a master PC which reconstructs live the point cloud of the scene and can in particular show the body of users standing in the capture area.A custom geometric calibration procedure allows accurate reconstruction of the different 3D data streams.Despite the inherent limitations of depth cameras, in particular sensor noise, the system provides a convincing representation of the user's body, is not limited by changes in clothing (also during immersion), can capture complex poses and even interactions between two persons or with physical objects.The advantage of using depth cameras over conventional cameras is that little processing is required for dynamic reconstruction of unknown shapes, thus allowing true interactive applications.The resulting live 3D model can be inserted in any virtual environment (e.g.Unity 3D software integration plugin), and can be subject to all usual 3D manipulation and transformations.
Fracture and Inequality of Digital Skills Among Moroccan Ph.D. Students "Hassan II University-Casablanca”, as a Case of Study

At the present time, the acquisition of digital skills or "e-skills", defined as the ability to master Information and Communication Technologies is essential for the majority of personal, educational and professional activities of Moroccan Ph.D students.How well do doctoral students master basic digital skills?Does faculty membership affect this degree of mastery?In this work, we will draw a brief portrait of the inequalities of digital skills between doctoral students from different faculties of Hassan II Casablanca University, by referring to a baseline of basic digital skills developed with the help of university professors.
The Phenomenon of Life in the Fractal Universe: The Metagalactic Hotbeds of Life Have neither Past nor Future

Background. Foundations. Twenty-four consequences" (in Russian: Мoscow
Detecting and Characterizing Lateral Phishing at Scale

We present the first large-scale characterization of lateral phishing attacks, based on a dataset of 113 million employee-sent emails from 92 enterprise organizations. In a lateral phishing attack, adversaries leverage a compromised enterprise account to send phishing emails to other users, benefitting from both the implicit trust and the information in the hijacked user's account. We develop a classifier that finds hundreds of real-world lateral phishing emails, while generating under four false positives per every one-million employee-sent emails. Drawing on the attacks we detect, as well as a corpus of user-reported incidents, we quantify the scale of lateral phishing, identify several thematic content and recipient targeting strategies that attackers follow, illuminate two types of sophisticated behaviors that attackers exhibit, and estimate the success rate of these attacks. Collectively, these results expand our mental models of the 'enterprise attacker' and shed light on the current state of enterprise phishing attacks.
ASSESSMENT OF FIXED ASSET ACQUISITION AND MANAGEMENT PRACTICE INGOVERNMENT SECTORS IN CASE OF DIRE WOREDA.

The purpose of this research was to assess the practice of fixed asset acquisition and management in government sectors in case of Dire woreda.The study employed descriptive survey design and used both qualitative and quantitative data.The study used 20 sample which drawn from 67 total population using purposive sampling method.Data were collected through questionnaire from 18 employees and heads of government sectors.Two woreda officials who were delegated to work related to property administration were interviewed.Additionally, observation was used in data gathering.The collected data were systematically arranged, organized and analyzed using SPSS to generate descriptive statistics results presented in tables, frequency and percentage then thematically interpreted.The study revealed that lack of understanding and enforcing rules, regulations and directive, established system to enhancing understanding, handling and disposing fixed asset according to rules, regulations and directive, specifying fixed asset and lack of positioning sufficient and experienced professionals.The researcher recommended enhancing understanding of heads of public sectors as well as employees by establishing systems, maintaining accountability system in fixed asset acquisition and management, fully applying of rules, regulations and directive, establishment of modern property handling method and using professionals.Copy Right, IJAR, 2019,.
A Method of Pedestrian Fine-grained Attribute Detection and Recognition

The detection and recognition of pedestrians is one of the hottest research issues. This paper presents a method that employs the fusion of convolutional neural network (CNN) models based on multi-task learning for multi-attributes, aiming to solve the problem of a low degree of accuracy in detecting and recognizing pedestrians fine-grained attributes under complex circumstances. First, the model implements double detection for data preprocessing. Specifically, this paper uses the CNN model twice to detect pedestrians under complex circumstances. In the first detection, it conducts the coarse-grained detection for the whole pedestrian; in the second detection, based on the first, it conducts fine-grained detection of and then recognizes pedestrian subcomponents. Further, to address the problem of a low rate of correct recognition for the fine-grained detection of the attributes of pedestrian subcomponents, we use the concept of the fusion of multi-task learning for multi-attributes, select the best recognized result for every attribute according to the fused results of different CNN classification models by the arrogance voting method, and then use a customized decision function to achieve a more accurate recognition of the coarse-grained attributes of pedestrians. Experimental results show that the proposed method achieved better performance than other recognition methods of pedestrians attributes.
Can Sentiment Analysis Reveal Structure in a Plotless Novel?

Modernist novels are thought to break with traditional plot structure. In this paper, we test this theory by applying Sentiment Analysis to one of the most famous modernist novels, To the Lighthouse by Virginia Woolf. We first assess Sentiment Analysis in light of the critique that it cannot adequately account for literary language: we use a unique statistical comparison to demonstrate that even simple lexical approaches to Sentiment Analysis are surprisingly effective. We then use the Syuzhet.R package to explore similarities and differences across modeling methods. This comparative approach, when paired with literary close reading, can offer interpretive clues. To our knowledge, we are the first to undertake a hybrid model that fully leverages the strengths of both computational analysis and close reading. This hybrid model raises new questions for the literary critic, such as how to interpret relative versus absolute emotional valence and how to take into account subjective identification. Our finding is that while To the Lighthouse does not replicate a plot centered around a traditional hero, it does reveal an underlying emotional structure distributed between characters - what we term a distributed heroine model. This finding is innovative in the field of modernist and narrative studies and demonstrates that a hybrid method can yield significant discoveries.