Reviewerly/exHarmony huggingface dataset:
DAMARU: A Denial-of-Service Attack on Randomized Last-Level Caches

Cross-core conflict-based timing attacks like Prime+Probe at the shared last-level cache (LLC) are practical and can cause information leakage. Cache address randomization is one of the techniques that claim to mitigate these attacks. CEASER, CEASER-S, and ScatterCache are the three recent randomized caches that use encryption engines to randomize the memory address mapping. CEASER and CEASER-S, along with encryption engines, remap the cache blocks periodically to break the static mapping of memory blocks into the LLC blocks. Encryption engine and remapping provide security to the randomized caches. However, access to encryption engines and the remapping of cache blocks are on the critical path of LLC accesses. We target encryption engine and remapping of randomized cache to mount a denial of service (DoS) attack named DAMARU. In DAMARU, the attacker frequently sends memory requests to the LLC that causes an increase in the victim's LLC access waiting time for the encryption engine. DAMARU is the first DoS attack on randomized caches where an attacker can cause a DoS even without thrashing the LLC. DAMARU provides a performance slowdown of up to 3.19X and 6X for 8-core and 16-core simulated systems, respectively. In terms of performance slowdown, the effectiveness of our DAMARU attack decreases with an increase in the number of encryption engines.
Lightnings over rose bouquets: an analysis of the topology of the Bitcoin Lightning Network

The Lightning Network is a P2P overlay that allows two Bitcoin users to create a new payment channel with a transaction, and later exchange cryptocurrency on that channel independently from the Bitcoin blockchain, improving Bitcoin’s scalability issues and making it more suitable for frequent micropayments. Moreover, the channels can also be used to route payments between two users who do not share a direct channel themselves.In this paper we study the topology of the Lightning Network in a given time period, performing a number of analysis on the channels and on the nodes composing the network. We study the churn rate, we evaluate centrality measurements and the clustering coefficient to evaluate the network connectivity, and finally we analyze the presence of a pattern that we named "bouquet". The importance of this pattern is proven by the finding that, removing specific nodes of the "bouquets" (1% of the total) causes the disconnection from the largest component of about 41% nodes.
Hidden Neuron Analysis for Detection Cataract Disease Based on Gray Level Co-occurrence Matrix and Back Propagation Neural Network

A cataract is a disease when the lens of the eye becomes cloudy. The most common cause of cataracts is the aging process. Several other conditions can cause cataracts in the lens of the eye, such as diabetes and smoking. It will be caused the decreased vision until blindness. Cataracts are the number one cause of blindness in Indonesia and the world. Blindness due to cataracts is relatively high because many sufferers do not know it. Because of that, a system for detecting cataracts is needed for taking further action quickly. The availability of medical officers and equipment is deficient in rural areas. The proposed method is expected to function as a doctor in detecting eye diseases, especially cataracts, to treat patients quickly. The combination of Gray Level Co-occurrence Matrix (GLCM) as feature extraction and Back-propagation Neural Network (BPNN) classification has been proposed. The proposed method uses four features GLCM, which are contrast, homogeneity, correlation, and energy. The angular orientation of GLCM is formed based on four angular directions, namely, 0°, 45°, 90°, and 135°, and distance between pixel uses 1, 2, 3, and 4. The highest accuracy is on 9 hidden neurons, 4 input layers, and 2 output layers with an accuracy of 0.824.
Adaptive Streaming Playback Statistics Dataset

We propose dataset capturing statistics of several large-scale real-world streaming events, delivering videos to different devices (TVs, desktops, mobiles, tablets, etc.), and over different networks (from 2.5G, 3G, and other early generation mobile networks to 5G and broadband). The data we capture include network-related statistics, playback statistics (session- and player-event-level), and characteristics of the encoded streams. Such data should enable a broad level of possible applications and uses in the research community: from analysis of the effectiveness of algorithms in streaming players to studies of QoE metrics, and end-to-end system optimizations. Examples of such possible studies based on the proposed datasets are also provided.
Online Reading Skills as an Object of Testing in International English Exams (IELTS, TOEFL, CAE)

The present article addressed the issue of university teachers' working experience in the field of class-oriented online teaching English in higher educational institutions.The central aim of this study was a detailed description of the system for assessing reading skills utilized in English proficiency tests (CAE (C1), TOEFL, IELTS), as well as a comprehensive analysis of testing results.As a part of a thorough investigation, the article detailed three major reading modes and tasks most commonly used in such tests (scan-reading, skim-reading, and in-depth reading).Besides, in order to obtain reliable conclusions about the reasonability of the test tasks, the study took into account the tests' duration, the number and types of tasks, as well as the length of reading passages proposed.The research population comprised 622 second-year students from all faculties of the National Research University "Higher School of Economics" (Nizhny Novgorod Campus, Russia) with English proficiency levels from B1 to C1+.All data required for the study were collected from the outcomes of international English exams (CAE (C1), TOEFL, IELTS), conducted in the form of testing in 2013, 2014, and 2015.The results of this examination showed that 97% of students involved successfully solved multiple choice tasks, 96% demonstrated an understanding of the dependence between headings and text fragments, and 95% succeeded in gapfilling tasks.Provided that research participants were engaged in active work on English reading comprehension improvement throughout the examination period, 95% of students have mastered their reading skills.
KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation

Self-supervised vision-and-language pretraining (VLP) aims to learn transferable multimodal representations from large-scale imagetext data and to achieve strong performances on a broad scope of vision-language tasks after finetuning.Previous mainstream VLP approaches typically adopt a two-step strategy relying on external object detectors to encode images in a multi-modal Transformer framework, which suffer from restrictive object concept space, limited image context and inefficient computation.In this paper, we propose an object-aware end-to-end VLP framework, which directly feeds image grid features from CNNs into the Transformer and learns the multi-modal representations jointly.More importantly, we propose to perform object knowledge distillation to facilitate learning cross-modal alignment at different semantic levels.To achieve that, we design two novel pretext tasks by taking object features and their semantic labels from external detectors as supervision: 1.) Object-guided masked vision modeling task focuses on enforcing object-aware representation learning in the multi-modal Transformer; 2.) Phraseregion alignment task aims to improve crossmodal alignment by utilizing the similarities between noun phrases and object labels in the linguistic space.Extensive experiments on a wide range of vision-language tasks demonstrate the efficacy of our proposed framework, and we achieve competitive or superior performances over the existing pretraining strategies.
Workload Balancing in a Multi-Cloud Environment: Challenges and Research Directions

Single cloud provider can manage their cloud on their own individually to deliver services to the users based on the need and demand of the user anywhere, anytime. Multi-cloud technology has been proposed in recent years as a revolutionary solution where a user uses more than one cloud platform and each one of them delivers a specific application or service. Thus, the total workload can be distributed across multiple clouds. The collaboration of the cloud service providers in the heterogeneous multi-cloud environment and workload balancing among multiple clouds in a multi-cloud environment is very challenging. By balancing the load among the multiple clouds, multi-cloud technology avoids overloading at any cloud, reduces waiting time, improves response time of the requests, avoids service level agreement violations, results in effective utilization of resources, and optimizes power usage at each cloud. In the first part of this book chapter, multi-cloud architecture, working functionality of multi-cloud, managing multi-cloud environment such as balanced distribution of load among multiple clouds, power management in an optimized way at each cloud, monitoring of service level agreement, and satisfying service level agreement are presented. These parameters are crucial in the operation and effectiveness in the multi-cloud environment, and the influence of these parameters on the performance of the multi-cloud is also discussed. In the second part of the book chapter, current challenges in multi-cloud like balanced distribution of workload among multiple cloud, application and service deployment in a heterogeneous multi-cloud environment, use of resources and services from different cloud service providers, security in data storage, large scale data processing, and research directions toward the multi-cloud are presented. Finally, the simulators available for multi-cloud, applicability of simulators, and best simulators to solve research challenges in multi-cloud management are elaborated. This gives an insight in choosing the best and suitable simulator for solving different research issues.
A Comparative Study of the Most Important Methods for Forecasting the ICT Systems Vulnerabilities

Nowadays, companies are facing plenty of IT secure attacks and to guarantee safe, untroubled, and continuous functioning of their business, they should detect and forecast the volume of IT security vulnerabilities and be prepared for future threats. The aim of this paper is to present a comparative study of the most important and promising methods for forecasting the ICT systems vulnerabilities.
AHNA: Adaptive representation learning for attributed heterogeneous networks

Meta-path-based random walk strategy has attracted tremendous attention in heterogeneous network representation, which can capture network semantics with heterogeneous neighborhoods of nodes. Despite the success of meta-path-based random walk strategy in plain heterogeneous networks which contain no attributes, it remains unexplored how meta-path-based random walk strategy could be utilized on attributed heterogeneous networks to simultaneously capture structural heterogeneity and attribute proximity. Moreover, the importance of node attributes and structural relations generally varies across data sets, thus requiring careful considerations when they are incorporated into representations. To tackle these problems, we propose a novel method, Attributed Heterogeneous Network embedding based on Aggregate-path (AHNA), which generates aggregate-path-based random walks on attributed heterogeneous networks and adaptively fuses topological structures and node attributes based on the learned importance. Specifically, AHNA first converts node attributes to additional links in the network to deal with the heterogeneity of structures and attributes, which is followed by an adaptive random walk strategy to strike the importance balance between node attributes and topological structures, thereby generating high-quality representations. Extensive experiments are conducted on three real-world data sets, where AHNA outperforms state-of-the-art approaches by up to 22.7%, 2.6%, and 2.3% on link prediction, community detection, and node classification, respectively. Moreover, our qualitative analysis indicates that AHNA can capture different balances of topological structures and node attributes on various data sets and thus boost the quality of node representations.
The Effects of Utilizing Telegram App to Enhance Students’ ESL Writing Skills

Mobile learning provides students to learn anywhere without the need of physical classroom. The main objective of this study is to investigate whether the utilization of Telegram app can enhance students’ ESL writing skills. The second objective is to investigate whether the utilization of Telegram app can enhance students’ perceptions towards ESL writing skills. Quasi-experimental one group pre-test post-test design was employed in this study. The students from the experimental group were given a pre-test (descriptive writing test) via Telegram app and their essays were marked by their teacher. The experiment lasted four weeks, and students were taught ESL writing skills using the Telegram app during that time. After the intervention a post-test was administered. This study used two types of instruments, pre- test, post-tests and a questionnaire. The data from the pre-test and post-test were analyzed using the paired sample t-test, and the questionnaire was analyzed using descriptive statistics. This study shows some interesting findings. Firstly, the results indicate that the utilization of Telegram app enhances students’ performance in their overall ESL writing; spelling, punctuation, organization, vocabulary, grammar, content quality and creativity. In addition, students’ perceptions towards ESL writing skills enhanced significantly. This study has effective pedagogical implication because utilization of Telegram app helps to improve students’ writing skills. Furthermore, the findings suggest that ESL teachers can use Telegram app as an alternative method in teaching writing skills.
Predicting Conversation Outcomes Using Multimodal Transformer

Analysis of communication effectiveness is an important task for understanding business outcomes. Prior research has shown that voice data can be used to predict communication effectiveness. However, to our knowledge, no existing studies have used both vocal and verbal cues to predict conversation outcomes in naturally occurring, dyadic business interactions. We use recorded audio calls collected from a partnering Fortune 500 firm that captures conversations between inside salespeople and business customers. Analysis of communication effectiveness is accomplished by transcribing these audio files and subsequently segmenting each conversation into customer and salesperson speaker turns to enable extraction of audio features and text embeddings for each speaker turn. All the speaker turns from the same conversation can be treated as a time series data, which can be modeled by temporal models, like LSTM or transformers. In this paper we propose that a multimodal transformer network (MTN) can capture the importance of different speaker turns and can be used to effectively predict the outcome of the call using both audio and text features. Results from the proposed model outperform current state-of-the-art results and reveal that text features offer superior outcome prediction compared to audio features.
Analyzing Bug Reports by Topic Mining in Software Evolution

Reporting bugs is one of the vital activities for evolving software systems. Given such reports, developers cope with unanticipated behaviors during software development, maintenance, and operations. The description of bug reports typically includes (1) what errors occurred previously and (2) how a failure can be reproduced through specific steps, test inputs, and original configurations when a failure was created. However, analyzing bug reports is a tedious and error-prone process due to overflowing, complex terminologies. For example, diverse terms are used to represent similar or divergent elucidations by surrounding contexts during software development and maintenance. To address this problem, we present an approach that applies a topic mining technique to bug reports for finding an adequate code reviewer, who can potentially cope with reported failures, by inferring some hidden topics of a textual document.
Robust Fragment-Based Framework for Cross-lingual Sentence Retrieval

Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents.The retrieved parallel sentence pairs can be used in other downstream NLP tasks such as machine translation and cross-lingual word sense disambiguation.We propose a CLSR framework called Robust Fragment-level Representation (RFR) CLSR framework to address Out-of-Domain (OOD) CLSR problems.In particular, we improve the sentence retrieval robustness by representing each sentence as a collection of fragments.In this way, we change the retrieval granularity from the sentence to the fragment level.We performed CLSR experiments based on three OOD datasets, four language pairs, and three base well-known sentence encoders: m-USE, LASER, and LaBSE.Experimental results show that RFR significantly improves the base encoders' performance for more than 85% of the cases.
Contextual Anomaly Detection Based Video Surveillance System

In this paper, a contextual anomaly event detection method is presented using a new clips boundaries detection approach and Bayesian classifier. Fall event is considered as anomaly event in our experiments and reported as case study. The anomaly score at frame levels is obtained. The proposed method involves three main phases; preprocessing for video content preparing, clips boundaries detection for anomaly behavior classification and fall event detection. The anomaly behavior - based fall event detection is classified into three main types; sudden change, gradual change and normal change within video sequence. To this end, a Bayesian classifier is trained to predict the anomaly score of video clips using similarity score prediction and acceleration raw data of sensors. We state quantitative results for clips boundaries detection, anomaly score prediction, and fall event detection rate. Further, the performance of the proposed anomaly event detection is evaluated based on results of common performance metrics (precision, sensitivity, specificity and accuracy) on public fall event datasets. The performance evaluation demonstrates a superiority of fall detection rate compared with recent researches in term of frame-level.
Homography-based model with light calibration for plenoptic cameras

Light-field and plenoptic cameras are widely available today. Compared with monocular cameras, these cameras capture not only the intensity but also the direction of the light rays. Due to this specificity, light-field cameras allow for image refocusing and depth estimation using a single image. However, most of the existing depth estimation methods using light-field cameras require a prior complex calibration phase and raw data preprocessing before the desired algorithm is applied. We propose a homography-based method with plenoptic camera parameters calibration and optimization, dedicated to our homography-based micro-images matching algorithm. The proposed method works on debayerred raw images with vignetting correction. The proposed approach directly links the disparity estimation in the 2D image plane to the depth estimation in the 3D object plane, allowing for direct extraction of the real depth without any intermediate virtual depth estimation phase. Also, calibration parameters used in the depth estimation algorithm are directly estimated, and hence no prior complex calibration is needed. Results are illustrated by performing depth estimation with a focused light-field camera over a large distance range up to 4 m.
Lecture-based, virtual reality game-based and their combination: which is better for higher education?

Purpose This paper presents an educational virtual reality (VR) game and experiments with different methods of including it into the teaching process. The purpose of this research study is to discover if immersive VR games can be used as an effective pedagogical tool if blended with traditional lectures by assisting learning gain, memory and knowledge retention while increasing edutainment value. Design/methodology/approach This research design comprises three different methods of learning: lecture-based involving lecture slides, infographics, and a video, game-based involving an immersive VR game of oil rig exploration, and the combination of lecture and game-based. Participants of each method filled up a questionnaire before and after participation to measure the learning gain, memory, and knowledge retention. Findings From the predominant findings of the study, the combined method demonstrated a significant increase in learning gain, memory, and knowledge retention and maybe a potentially suitable pedagogical tool. Research limitations/implications Limitations of the study include findings based on one VR game with a specific educational topic, additionally, it is suspected that having different participants for each of the three methods may have slightly affected the results, albeit to a limited extent. Practical implications Findings of this study will provide evidence that VR games can be used alongside traditional lectures to aid in the learning process. Educators can choose to include VR games into their curriculums to improve the educational delivery process. Originality/value This research contributes to ways of incorporating VR games into educational curriculums through findings of this study highlighting the combination of VR games with lectures.
A Unified Dialogue Management Strategy for Multi-intent Dialogue Conversations in Multiple Languages

Building Virtual Agents capable of carrying out complex queries of the user involving multiple intents of a domain is quite a challenge, because it demands that the agent manages several subtasks simultaneously. This article presents a universal Deep Reinforcement Learning framework that can synthesize dialogue managers capable of working in a task-oriented dialogue system encompassing various intents pertaining to a domain. The conversation between agent and user is broken down into hierarchies, to segregate subtasks pertinent to different intents. The concept of Hierarchical Reinforcement Learning, particularly options , is used to learn policies in different hierarchies that operates in distinct time steps to fulfill the user query successfully. The dialogue manager comprises top-level intent meta-policy to select among subtasks or options and a low-level controller policy to pick primitive actions to communicate with the user to complete the subtask provided to it by the top-level policy in varying intents of a domain. The proposed dialogue management module has been trained in a way such that it can be reused for any language for which it has been developed with little to no supervision. The developed system has been demonstrated for “Air Travel” and “Restaurant” domain in English and Hindi languages. Empirical results determine the robustness and efficacy of the learned dialogue policy as it outperforms several baselines and a state-of-the-art system.