Reviewerly/exHarmony huggingface dataset:
Designing a Decision-Support Visualization for Live Digital Forensic Investigations

Fileless Malware poses challenges for forensic analysts since the infected system often can’t be shut down for a forensic analysis. Turning off the device would destroy forensic artifacts or evidence of the fileless malware. Therefore, a technique called Live Digital Forensics is applied to perform investigations on a running system. During these investigations, domain experts need to carefully decide what tools they want to deploy for their forensic analysis. In this paper we propose a visualization designed to support forensic experts in this decision-making process. Therefore, we follow a design methodology from the visualization domain to come up with a comprehensible design. Following this methodology, we start with identifying and defining the domain problem which the visualization should help to solve. We then translate this domain problem into an abstract description of the available data and user’s tasks for the visualization. Finally, we transform these specifications into a visualization design for a Live Digital Forensics decision-support. A use case illustrates the benefits of the proposed method.
Development of Teaching Material for Short Story Writing Experience Based on 7th Grade Students of Junior High School 2 Kejuruan Muda

This study aims to develop teaching materials for writing short stories based on experience to facilitate students in writing short text. The learning approach offered by researchers is considered appropriate for writing short text material because it can help students to easily express their ideas in writing short stories by linking the material with the context of their daily lives. In addition, experience is the best teacher because from experience one can learn. Experience is a memory that is recorded and stored as a story that forms a filter of perception that ultimately guides behavior. The results of this study indicate that the effectiveness of teaching short story writing lessons based on experiences developed for 7th grade student of junior high school 2 Kejuruan Muda was stated to be more effective than student handbooks. The results of the acquisition of student learning outcomes before and after using teaching materials have increased quite significantly by 8.28. The average score of students before (pretest) using teaching materials learning experiences writing short stories based on experience 74.43 and the average value of post (testing) using teaching materials learning writing short stories based on experience 82.71.
Who is the Blockchain Employee? Exploring Skills in Demand using Observations from the Australian Labour Market and Behavioural Institutional Cryptoeconomics

LinkedIn recently predicted that blockchain skills will be the most in-demand skill in 2020, and in 2018 blockchain led the list of the fastest growing skills in demand according to Upwork.But what exactly constitutes the skill set of a blockchain employee?We use Australian labour market data to explore what skills are in demand among the blockchain workforce.We also take a deeper dive and explore what educational qualifications and experiences are required of blockchain employees, and how blockchain-related jobs perform on salary scales.We discover that alongside 'hard' software engineering skills such as programming languages or computer science, blockchain-related jobs require candidates to have 'soft' skills such as creativity, communication and leadership.To explain this, we use institutional cryptoeconomics, applied game theory and applied behavioural science to suggest that the demand for skills may be understood as a function of challenges to blockchain adoption.We suggest that for blockchain to enter a mass adoption phase, the industry will need employees with an integrated skill set of both hard software engineering skills and soft behavioural or enterprise skills.Furthermore, blockchain leaders, community leaders and end users will need to gain 'blockchain literacy' to overcome the challenge of coordinating expectations by developers and users, who will create network externalities and facilitate rapid, coordinated adoption.We contribute to the evidence-based blockchain literature by using Australian labour market data to derive insight into the challenges posed to the adoption of blockchain as (and if) it climbs out of the current 'trough of disillusionment'.
Enhancement in resource allocation system for cloud environment using modified grey wolf technique

&lt;p class="normal"&gt;Cloud computing is an advanced technology which provides services with the help of internet. These services work under the rule of pay and gain. The services consist of hardware and software used in different fields of life. Due to growth of cloud computing the number of users are increased and their demand for better services also increased with the passage of time. Cloud computing faces different issues. One of them is resource scheduling. In this paper a new technique is used for improvement of scheduling in cloud computing. The improvement took place in GWO algorithm. Two main sections of this algorithm are modified, which are local search section and fitness function value. The above proposed technique is used to improve three main parameters of scheduling that are energy consumption, throughput and average network executation time in VM for cloud computing. The techniques results are compared with ABC algorithm and GWO algorithm. The results show that proposed algorithm improves in the three main parameters of scheduling for cloud computing.&lt;/p&gt;
Junior High School Students Experiences of High Technology Based Learning in Indonesia

This study aims to understand the meaning of high technology utilization in learning for early adolescents. High-Technology Based Learning (HTBL) is for a phenomenon in every country today. However, the side effects of the use of high technology in education are troubling, especially for early adolescent junior high school students, who are still labile. This article tries to explore the meaning of HTBL in junior high school in Yogyakarta. The researchers used a phenomenological approach in collecting the data in this study in line with the idea that the people who understand the meaning of an experience are themselves, the subject of the phenomenon. Participants were chosen using purposive sampling with the following criteria: junior high school students, 11-13 years old, living in Yogyakarta and experiencing HTBL at school. The results of the research show that for junior high school students: 1) HTBL is more fun and more accessible; 2) adult guidance is necessary as a means of raising self-awareness. We suggest collaboration between parents and teachers in the implementation of all HTBL processes at school and home since they play significant roles in the use of HTBL for students. https://doi.org/10.26803/ijlter.19.5.9
Recurrent Compressed Convolutional Networks for Short Video Event Detection

Short videos are popular information carriers on the Internet, and detecting events from them can well benefit widespread applications, e.g., video browsing, management, retrieval and recommendation. Existing video analysis methods always require decoding all frames of videos in advance, which is very costly in time and computation power. These short videos are often untrimmed, noisy and even incomplete, adding much difficulty to event analysis. Unlike previous works focusing on actions, we target short video event detection and propose Recurrent Compressed Convolutional Networks (RCCN) for discovering the underlying event patterns within short videos possibly including a large proportion of non-event videos. Instead of using the whole videos, RCCN performs representation learning at much lower cost within the compressed domain where the encoded motion information reflecting the spatial relations among frames can be easily obtained to capture dynamic tendency of event videos. This alleviates the information incompleteness problem that frequently emerges in user-generated short videos. In particular, RCCN leverages convolutional networks as the backbone and the Long Short-Term Memory components to model the variable-range temporal dependency among untrimmed video frames. RCCN not only learns the common representation shared by the short videos of the same event, but also obtains the discriminative ability to detect dissimilar videos. We benchmark the model performance on a set of short videos generated from publicly available event detection database YLIMED, and compare RCCN with several baselines and state-of-the-art alternatives. Empirical studies have verified the preferable performance of RCCN.
Mapping grid based online taxi anomalous trajectory detection

This paper proposes an online taxi driving anomalous trajectory detection framework for maintaining the city public transport civilisation. The framework consists of two parts: an offline detector building and an online trajectory detection. The former employs a popular route concept to process massive trajectory data and adapts the mapping grid-based anomaly detection method by taking into account spatial and temporal characteristics of the trajectory dataset. The latter maps ongoing trajectory points and detects whether the ongoing driving route is anomalous or reliable. The proposed trajectory anomaly detection method is faster than the existing methods as it involves only simple activities of trajectory point mapping and retrieval procedure, without requiring extra distance or density calculation. In addition, the proposed method has detection accuracy comparable to that of the existing high-performance methods. The application and efficiency of the proposed method are demonstrated using extensive experiments on real datasets.
Parameter-Free Extreme Learning Machine for Imbalanced Classification

Imbalanced data distribution is a common problem in classification situations, that is the number of samples in different categories varies greatly, thus increasing the classification difficulty. Although many methods have been used for the imbalanced data classification, there are still problems with low classification accuracy in minority class and adding additional parameter settings. In order to increase minority classification accuracy in imbalanced problem, this paper proposes a parameter-free weighting learning mechanism based on extreme learning machine and sample loss values to balance the number of samples in each training step. The proposed method mainly includes two aspects: the sample weight learning process based on the sample losses; the sample selection process and weight update process according to the constraint function and iterations. Experimental results on twelve datasets from the KEEL repository show that the proposed method could achieve more balanced and accurate results than other compared methods in this work.
Lossless and Efficient Secret Image Sharing Based on Matrix Theory Modulo 256

Most of today’s secret image sharing (SIS) schemes are based on Shamir’s polynomial-based secret sharing (SS), which cannot recover pixels larger than 250. Many exiting methods of lossless recovery are not perfect, because several problems arise, such as large computational costs, pixel expansion and uneven pixel distribution of shadow image. In order to solve these problems and achieve perfect lossless recovery and efficiency, we propose a scheme based on matrix theory modulo 256, which satisfies ( k , k ) and ( k , k + 1 ) thresholds. Firstly, a sharing matrix is generated by the filter operation, which is used to encrypt the secret image into n shadow images, and then the secret image can be obtained by matrix inverse and matrix multiplication with k or more shadows in the recovery phase. Both theoretical analyses and experiments are conducted to demonstrate the effectiveness of the proposed scheme.
Complexity of planning for connected agents

We study a variant of the multi-agent path finding (MAPF) problem in which the group of agents are required to stay connected with a supervising base station throughout the execution. In addition, we consider the problem of covering an area with the same connectivity constraint. We show that both problems are PSPACE-complete on directed and undirected topological graphs while checking the existence of a bounded plan is NP-complete when the bound is given in unary (and PSPACE-hard when the encoding is in binary). Moreover, we identify a realistic class of topological graphs on which the decision problem falls in NLOGSPACE although the bounded versions remain NP-complete for unary encoding.
Barter Machine: An Autonomous, Distributed Barter Exchange on the Ethereum Blockchain

Direct bartering allows for the trading of assets with or without the use of money. In this paper, we introduce a smart contract written in the Solidity language for the Ethereum blockchain that implements a distributed and autonomous direct barter exchange operated by crowds. Since ERC20 smart contract tokens are widely used for initial coin offerings (ICOs), our implementation provides autonomous bartering services for ERC20 tokens. The non-fungible ERC721 token, as well as Ethereum Name Service (ENS) name bartering, are also supported. Because finding a feasible bartering solution for bids involving multiple tokens is NP-hard in general, our exchange only provides escrow and swapping services. It assumes feasible solutions are submitted by bartering problem solvers from the crowd who run a solver locally on their machines. Bartering problem solvers are incentivized to submit solutions to the autonomous exchange by awarding them with excess tokens that are left over after granting the bidders the tokens they requested in their bids. Our system, called BarterMachine, can perhaps be used to facilitate a global barter economy. The BarterMachine prototype is available for testing on the Ethereum Ropsten network at https://bartermachine.github.io/bartermachine/ropsten/.
Towards distributed node similarity search on graphs

Node similarity search on graphs has wide applications in recommendation, link prediction, to name just a few. However, existing studies are insufficient due to two reasons: (i) the scale of the real-world graph is growing rapidly, and (ii) vertices are always associated with complex attributes. In this paper, we propose an efficiently distributed framework to support node similarity search on massive graphs, which considers both graph structure correlation and node attribute similarity in metric spaces. The framework consists of preprocessing stage and query stage. In the preprocessing stage, a parallel KD-tree construction (KDC) algorithm is developed to form a newly defined graph so-called hybrid graph, in order to integrate node attribute similarity into the original graph. To equally divide graph vertices into subsets, KDC adopts the KD-tree partitioning after the pivot mapping. In addition, two metric pruning rules and an optimized allocation strategy are presented to reduce communication and computation costs. In the query stage, based on the formed hybrid graph, we develop similarity search methods using random walk with restart (RWR) to measure node similarity. To boost efficiency, we derive tight bounds to rapidly shrink the search region. Extensive experiments with three real massive graphs are conducted to verify the effectiveness, efficiency, and scalability of our proposed techniques.
An Architecture for Easy Onboarding and Key Life-Cycle Management in Blockchain Applications

New manufacturing paradigms require a large number of business interactions between multiple cyber-physical systems with different owners. In this context, public distributed ledgers are disruptive because they make it possible to securely and publicly record proofs of agreements between parties that do not necessarily trust each other. Many industry leaders have already achieved significant business benefits using this technology, including greater transparency, improved traceability, enhanced security, increased transaction speed and costs reduction. While the benefits of blockchain technologies for industrial applications are unquestionable, these technologies have an inherent complexity that might be overwhelming for many users. To decrease entry barriers for industry users to distributed ledger technologies, it is necessary to have an easy user onboarding process and a simple key life-cycle management. In this paper, we propose an architecture that facilitates these processes and simplifies how users utilize decentralized applications without sacrificing on the expected security. To achieve this goal, our architecture uses a middleware that allows us to decouple the digital signatures required for paying blockchain fees from the ones required for authorization. This approach has the advantage that users are not forced to create wallets, buy cryptocurrency, or protect their private keys. For these reasons, our solution is a promising way of enabling a reasonable transition to the integration of distributed ledger technologies in industrial business processes.
Collaborative boundary-aware context encoding networks for error map prediction

Accurately assessing the medical image segmentation quality of the automatically generated predictions is essential for guaranteeing the reliability of the results of computer-assisted diagnosis (CAD). Many researchers have studied segmentation quality estimation without labeled ground truths. Recently, a novel idea is proposed, which transforms segmentation quality assessment (SQA) into the pixel-wise or voxel-wise error map segmentation task. However, the simple application of vanilla segmentation structures in medical domain fails to achieve satisfactory error segmentation results. In this paper, we propose collaborative boundary-aware context encoding networks called EP-Net for error segmentation task. Specifically, we propose a collaborative feature transformation branch for better feature fusion between images and masks, and precise localization of error regions. Further, we propose a context encoding module to utilize the global predictor from the error map to enhance the feature representation and regularize the networks. Extensive experiments on IBSR V2.0 dataset, ACDC dataset and M&Ms dataset demonstrate that EP-Net achieves better error segmentation results compared with the traditional segmentation patterns. Based on error prediction results, we obtain a proxy metric of segmentation quality, which has high Pearson correlation coefficient with the real segmentation accuracy on all datasets.
Artificial Neural Network-Based 4-D Hyper-Chaotic System on Field Programmable Gate Array

In this presented study, a 4-D hyper-chaotic system newly proposed to the literature, has been implemented as Multi-Layer Feed-Forward Artificial Neural Network-based on FPGA chip with 32-bit IEEE-754-1985 floating-point number standard to be utilized in real time chaos-based applications.In the first step of the study, 4-D hyper-chaotic system has been numerically modeled on FPGA using Dormand-Prince numeric algorithm.In the second step, the data set (4X10,000) obtained from Matlab-based numeric model has been divided into two parts as training data set (4X8,000) and test data set (4X2,000) to create ANN-based 4-D hyper-chaotic system.A Multi-Layer Feed-Forward ANN structure with 4 inputs and 4 outputs has been constructed for ANN-based 4-D hyper-chaotic system.This structure has only one hidden layer and there are 8 neurons having Tangent Sigmoid activation function used as the activation function in each neuron.2.58E-07 Mean Square Error (MSE) value has been obtained from the training of ANN-based 4-D hyper-chaotic system.In the third step, after the successful training of ANN-based 4-D hyper-chaotic system, the design of ANN-based 4-D hyper-chaotic system has been carried out on FPGA by taking the bias and weight values of the ANN structure as reference.In this step, at first, Matlab-based Feed-Forward Multi-Layer 4X8X4 network structure has been coded in Very High Speed Integrated Circuit Hardware Description Language (VHDL) to be implemented on FPGA chips.Then, the bias and weight values of the ANN structure has been converted from decimal number system to floating-point number standard and these converted values have been embedded into the network structure.In the last step, the ANN-based 4-D hyper-chaotic system designed on FPGA has been synthesized and tested using Xilinx ISE Design Suite.The chip statistics have been given after the Place&Route process carried out for the Virtex XC6VHX255T-3FF1155 FPGA chip.The maximum operating frequency of ANN-based 4-D hyper-chaotic system on FPGA has been obtained as 240.861MHZ.
CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task

We present CLIReval, an easy-to-use toolkit for evaluating machine translation (MT) with the proxy task of cross-lingual information retrieval (CLIR).Contrary to what the project name might suggest, CLIReval does not actually require any annotated CLIR dataset.Instead, it automatically transforms translations and references used in MT evaluations into a synthetic CLIR dataset; it then sets up a standard search engine (Elasticsearch) and computes various information retrieval metrics (e.g., mean average precision) by treating the translations as documents to be retrieved.The idea is to gauge the quality of MT by its impact on the document translation approach to CLIR.As a case study, we run CLIReval on the "metrics shared task" of WMT2019; while this extrinsic metric is not intended to replace popular intrinsic metrics such as BLEU, results suggest CLIReval is competitive in many language pairs in terms of correlation to human judgments of quality.CLIReval is publicly available at
Distill BERT to Traditional Models in Chinese Machine Reading Comprehension (Student Abstract)

Recently, unsupervised representation learning has been extremely successful in the field of natural language processing. More and more pre-trained language models are proposed and achieved the most advanced results especially in machine reading comprehension. However, these proposed pre-trained language models are huge with hundreds of millions of parameters that have to be trained. It is quite time consuming to use them in actual industry. Thus we propose a method that employ a distillation traditional reading comprehension model to simplify the pre-trained language model so that the distillation model has faster reasoning speed and higher inference accuracy in the field of machine reading comprehension. We evaluate our proposed method on the Chinese machine reading comprehension dataset CMRC2018 and greatly improve the accuracy of the original model. To the best of our knowledge, we are the first to propose a method that employ the distillation pre-trained language model in Chinese machine reading comprehension.
Hybrid protocol for sending-or-not-sending twin-field quantum key distribution

We propose a hybrid protocol for sending-or-not-sending (SNS) twin-field quantum key distribution: replacing the signal source by heralded single-photon source (HSPS) in the original SNS protocol, while decoy sources are still unchanged. Numerical simulation shows that after adopting this HSPS, the performance in key rate and secure distance is much improved.