Reviewerly/exHarmony huggingface dataset:
EnergyTradingRank Algorithm for Truthful Auctions among EVs via Blockchain Analytics of Large Scale Transaction Graphs

In a decentralized vehicle-to-vehicle (V2V) energy trading among electric vehicles (EVs), there is a need of a highly secure and privacy protected transaction environment. With the advent of digital currencies like Bitcoin, most of the blockchain applications incorporate a similar cryptocurrency system for secure processing. In this work, we propose a permissioned blockchain based energy trading among EVs to enhance security and privacy of the EV user's information. We introduce, a new cryptocurrency "ETcoin" for energy trading among EVs. All the matched bids comprises of traded energy unit and ETcoins transferred are stored in blockchain. The transaction graph formed by energy trade can be analyzed as a large scale graph processing using Big Data analytics. Existing works have not addressed blockchain analytics for processing large transaction graphs of energy trading. All the blockchain transactions submitted in a pre-defined period are considered for analytics. We propose an EnergyTradeRank (ETR) algorithm for Blockchain analytics of the large scale transaction graphs. ETR algorithm inherently applies the weighted ranking approach, which considers both the edge as well as vertex properites of graph to calculate the ETR Score for each EV during a traded period. Transaction graph analytics on energy trading acts as a proof-of-activity and it also help in incentivizing the EVs with maximum satisfiable participation in the blockchain environment. We implemented the blockchain based proof-of-concept (POC) using IBMs hyperledger fabric and composer with minimum transaction latency and in-time modeling of each participant to the system. Transaction graph analytics is implemented via an open source Apache Spark's GraphX library for a realtime distributed dataflow processing. Transactions are modelled as edge triplets on several machines and ETR score for every vertex is computed parallelly to converge at a set tolerance value. Simulated results show that the algorithm converges faster and scales better as the number of parallel machines increases. We also analyzed that the overall incentive gain for EVs with maximum, truthful and committed participation increases and thus promotes active participation in the system.
Kinect-Based Virtual Try-on System: A Case Study

Virtual fitting rooms are a very important tool in on-line shopping, which can save time, promote products, increase sales, and reduce number of returns. The main objective of this paper is to design an interactive virtual clothes try-on system that would make the on-line shopping process much easier, and affordable at the lowest cost possible in the local country of Jordan, which we consider as a case study of this work. The novelty of our work is that we use a cost effective RGB-D sensor, simple 3D cloth textures, and alignment/fusion procedure to build robust and interactive virtual fitting room targeted for the local market. Microsoft Kinect V2 sensor what was used in implementing the proposed system. Experimentations supported by real data from the Kinect sensor demonstrate its effectiveness and robustness. A user study was conducted among 65 Jordanian university students to evaluate the effectiveness and attitude toward using the proposed system. The study results indicate the importance and usefulness of the proposed system.
Enhanced target recognition employing spatial correlation filters and affine scale invariant feature transform

A spatial domain optimal trade-off Maximum Average Correlation Height (SPOT-MACH) filter has been shown to have advantages over frequency domain implementations of the Optimal Trade-Off Maximum Average Correlation Height (OR-MACH) filter as it can be made locally adaptive to spatial variations in the input image background clutter and normalized for local intensity changes. This enables the spatial domain implementation to be resistant to illumination changes. The Affine Scale Invariant Feature Transform (ASIFT) is an extension of previous feature transform algorithms; its features are invariant to six affine parameters which are translation (2 parameters), zoom, rotation and two camera axis orientations. This results in it accurately matching increased numbers of key points which can then be used for matching between different images of the object being tested. In this paper a novel approach will be adopted for enhancing the performance of the spatial correlation filter (SPOT MACH filter) using ASIFT in a pre-processing stage enabling fully invariant object detection and recognition in images with geometric distortions. An optimization criterion is also be developed to overcome the temporal overhead of the spatial domain approach. In order to evaluate effectiveness of algorithm, experiments were conducted on two different data sets. Several test cases were created based on illumination, rotational and scale changes in the target object. The performance of correlation algorithms was also tested against composite images as references and it was found that this results in a well-trained filter with better detection ability even when the target object has gone through large rotational changes.
OntoCexp: A Proposal for Conceptual Formalization of Criminal Expressions

Internet has become the main communication instrument between criminals. Expressions used by criminals are ciphered, by replacing language terms with regionalized and mutant expressions. There is a need to reveal, understand and formalize these obscure dialects to enable the automation of searches and the analysis of intentions. OntoCexp (Ontology of Criminal Expressions) aims at providing a common and extensible model for identifying usage of crime expressions in Internet. Its foundations come from an initial terminology and a semantic analysis of written communication between criminals (from Twitter) in Brazil (Portuguese language). 17 papers on ontologies, out of 63 articles of interest, have been selected and used as input to our proposal. The initial version of OntoCexp and its core elements are presented here; the complete ontology (OWL file) is available publicly to be used. We expect it to be useful for cyber-security researchers and criminal investigators who wish to formalize knowledge on criminal communication in their systems, methods, and techniques.
VideoToVecs: a new video representation based on deep learning techniques for video classification and clustering

With the recent revolution in the field of multimedia technology, video data have become much easier and straightforward to be created, stored and transferred on a huge scale with small costs. The big amount of created data pushed the research community to delve into various study areas to aid the huge proliferation of multimedia content such as video structuring, video classification and clustering, events and objects detection, video recommendation and many other video content analysis techniques. The key success of any analysis technique relies on the audiovisual features extracted from the video. Motivated by the appearance and efficiency of deep learning techniques, we propose in this paper a new deep-learning-based features representation of videos. We depend on image-based features extracted from the sequence of frames in the video using deep learning techniques. A mapping approach named VideoToVecs is then applied to transform the extracted features into a matrix in which each row contains features of the same type. This matrix is named deep features video matrix. The efficiency of the representation is tested on 5261-video dataset for classification and clustering, and the obtained results were very promising as we will see in the paper.
Fast 2D Laser-Induced Fluorescence Spectroscopy Mapping of Rare Earth Elements in Rock Samples

Due to the rapidly increasing use of energy-efficient technologies, the need for complex materials containing rare earth elements (REEs) is steadily growing. The high demand for REEs requires the exploration of new mineral deposits of these valuable elements, as recovery by recycling is still very low. Easy-to-deploy sensor technologies featuring high sensitivity to REEs are required to overcome limitations by traditional techniques, such as X-ray fluorescence. We demonstrate the ability of laser-induced fluorescence (LIF) to detect REEs rapidly in relevant geological samples. We introduce two-dimensional LIF mapping to scan rock samples from two Namibian REE deposits and cross-validate the obtained results by employing mineral liberation analysis (MLA) and hyperspectral imaging (HSI). Technique-specific parameters, such as acquisition speed, spatial resolution, and detection limits, are discussed and compared to established analysis methods. We also focus on the attribution of REE occurrences to mineralogical features, which may be helpful for the further geological interpretation of a deposit. This study sets the basis for the development of a combined mapping sensor for HSI and 2D LIF measurements, which could be used for drill-core logging in REE exploration, as well as in recovery plants.
Framework Node2FaaS: Automatic NodeJS Application Converter for Function as a Service

Cloud computing emerged in the area of computer science as a means to achieve significant cost and time savings when starting projects. Among the various cloud models available, this work highlights Function as a Service - FaaS, and proposes the Node2FaaS framework for automatic conversion of applications written in NodeJS to work in a transparent way with the FaaS model. The experiments demonstrated significant gains of up to 170% at runtime for applications with high file I/O requirements. Applications with high CPU and RAM consumption also have benefits in adopting FaaS after conversion, but only when a threshold of competing processes is reached.
Improving Neural Network Quantization using Outlier Channel Splitting

Quantization can improve the execution latency and energy efficiency of neural networks on both commodity GPUs and specialized accelerators. The majority of existing literature focuses on training quantized DNNs, while this work examines the less-studied topic of quantizing a floating-point model without (re)training. DNN weights and activations follow a bell-shaped distribution post-training, while practical hardware uses a linear quantization grid. This leads to challenges in dealing with outliers in the distribution. Prior work has addressed this by clipping the outliers or using specialized hardware. In this work, we propose outlier channel splitting (OCS), which duplicates channels containing outliers, then halves the channel values. The network remains functionally identical, but affected outliers are moved toward the center of the distribution. OCS requires no additional training and works on commodity hardware. Experimental evaluation on ImageNet classification and language modeling shows that OCS can outperform state-of-the-art clipping techniques with only minor overhead.
Conveying Volcano Information Effectively to Stakeholders – A New Project for Promotion of Next Generation Volcano Research

A new program for the Next Generation Volcano Research and Human Resource Development started in 2016, following the government’s analysis of a volcanic disaster at Mount Ontake in 2014. One of its important purposes is the development of a technology that can provide visualized information of imminent volcanic hazards to the stakeholders. The latter include researchers in the Volcano Disaster Prevention Councils. Since the volcanic activity in Japan has been relatively less in the past few hundred years, larger eruptions are certainly expected to occur in the near future. Volcanic risk management has developed in Japan independently of university or institutional research, and by a national law, researchers are not allowed to officially forecast imminent volcanic eruptions. In the case of large eruptions never being observed, a close communication between the Japan Meteorological Agency and researchers becomes very important. Our project goal is issuing effective information on real-time observational and hazard mitigation simulation data to the stakeholders and researchers. Based on our inspection and interviews we develop information tools using which the above data are provided effectively and the dissemination and education of volcanic disasters are performed.
Gold Recovery Modeling Based on Interval Prediction for a Gold Cyanidation Leaching Plant

The production index of gold cyanidation leaching process has an important influence on the economic benefits of the plant-wide hydrometallurgical process. In the actual leaching production process, due to the fluctuations of the previous procedure, the leaching process is often affected by the uncertainty and process disturbance. And hence, the prediction accuracy of the traditional point prediction models (such as ANN: Artificial Neural Network) decreases seriously and cannot provide any quantified information about the uncertainty or process disturbance. To solve the above problem, the interval prediction technique based on Radial Basis Function (RBF) ANN is proposed and used to model a gold cyanidation leaching plant encountering uncertainty and process disturbances in this paper. The objective function trained in the interval prediction model is not based on the prediction error, but the comprehensive measure index, namely, the coverage width criteria, which consists of the prediction interval coverage probability and the prediction interval normalized averaged width. Compared to the traditional point prediction, when the process uncertainty and disturbances are present, the interval prediction model will provide more helpful process information to the operators or process designers for determining further optimization and control strategies. The simulation and practical application results show that most of the real values of gold recovery can be covered between the upper and lower bounds with a predefined probability, which indicates the effectiveness and reliability of the interval prediction model, thus laying an important foundation for plant-wide optimization and control.
Comparative Study of the Most Useful Arabic-supporting Natural Language Processing and Deep Learning Libraries

Natural Language Processing (NLP) is a key area of Artificial Intelligence (AI) that plays a critical role in many intelligent applications. To work on NLP, users can choose different Libraries, depending on their familiarity with a particular programming language. In this paper, we are focusing on Python and Java programming languages because of their libraries'richness in the Arabic Natural Language Processing (ANLP) and deep learning (DL) specifically. This paper presents a comparative study of some well-known ANLP and DL libraries considered to be the most valuable Arabic-supporting Python and Java libraries that can suitably deal with the specificities of the Arabic language. We will first focus on some libraries that are most commonly used in NLP tasks, namely NLTK, Gensim, OpenNLP, CoreNLP and GATE. Then, we will present some open-source DL libraries that are considered to be the most powerful DL libraries for ANLP, including TensorFlow, Theano, Keras and DeepLearning4j. These libraries simplify complex jobs and make data integration much easier with fewer codes and in less time.
Pique: Recommending a Personalized Sequence of Research Papers to Engage Student Curiosity

This paper describes Pique, a web-based recommendation system that applies word embedding and a sequence generator to present students with a sequence of scientific paper recommendations personalized to their background and interest. The use of natural language processing (NLP) on learning materials enables educational environments to present students with papers with content that is responsive to their knowledge history and interests. Instructors tend to focus on presentation of learning materials based on overall learning goals in a course rather than personalizing the presentation for each student. The ultimate goal of Pique is to provide learners with content that will encourage their curiosity to learn more by presenting sequences of papers with increasingly more novel content. We piloted Pique with students in a course and report on their responses to the recommended sequences. The next steps are to improve the identification of relevant keywords to represent content and the algorithm for the sequence generator.
Downgradable Identity-Based Encryption and Applications

In Identity-based cryptography, in order to generalize one receiver encryption to multi-receiver encryption, wildcards were introduced: WIBE enables wildcard in receivers’ pattern and Wicked-IBE allows one to generate a key for identities with wildcard. However, the use of wildcard makes the construction of WIBE, Wicked-IBE more complicated and significantly less efficient than the underlying IBE. The main reason is that the conventional identity’s binary alphabet is extended to a ternary alphabet $$\{0,1,*\}$$ and the wildcard $$*$$ is always treated in a convoluted way in encryption or in key generation. In this paper, we show that when dealing with multi-receiver setting, wildcard is not necessary. We introduce a new downgradable property for IBE scheme and show that any IBE with this property, called DIBE, can be efficiently transformed into WIBE or Wicked-IBE. While WIBE and Wicked-IBE have been used to construct Broadcast encryption, we go a step further by employing DIBE to construct Attribute-based Encryption of which the access policy is expressed as a boolean formula in the disjunctive normal form.
A multi-objective optimization method for service composition problem with sharing property

For service level agreement aware service composition problem, the resource sharing property is integrated into it to further decrease the cost and make more applications can be successfully deployed. Based on the characteristic of this problem, a multi-objective service sharing optimization model is built, in which the attributes of throughput, latency and cost are simultaneous considered. To make more feasible service composition candidates be included, a particle swarm optimization based multi-objective algorithm is proposed to solve it. A new particle position form is defined to reflect the sharing relationship of concrete services among different service levels composite services, and a particle deployment strategy is designed to map a particle position to a resource sharing scheme composition. The proposed algorithm is compared with some recently proposed related algorithms on different scale cases. The results show that more feasible solutions with lower cost can be found when the resource sharing property is considered.
Summarizing E-sports matches and tournaments

That video and computer games have reached the masses is a well known fact. Furthermore, game streaming and watching other people play video games is another phenomenon that has outgrown its small beginning by far, and game streams, be it live or recorded, are today viewed by millions. E-sports is the result of organized leagues and tournaments in which players can compete in controlled environments and viewers can experience the matches, discuss and criticize, just like in physical sports. However, as traditional sports, e-sports matches may be long and contain less interesting parts, introducing the challenge of producing well directed summaries and highlights. In this paper, we describe our efforts to approach the game streaming and e-sports phenomena from a multimedia research point of view. We focus on the challenge of summarizing matches from specific relevant game, Counter-Strike: Global Offensive (CS:GO). We survey related work, describe the rules and structure of the game and identify the main challenges for summarizing e-sports matches. With this contribution, we aim to foster multimedia research in the area of e-sports and game streaming.
LIDAR-based people detection and tracking for @home Competitions

People tracking is a basic capability in almost any robotic application. So it is in robotic competitions, where many robot skills rely on this ability. This problem is still challenging, particularly when are implemented using low definition sensors as Laser Imaging Detection and Ranging (LIDAR) sensors in crowded environments. This paper describes a solution based on a single LIDAR sensor that uses the gait to keep a continuous identification in time and space of the individual. The system described in this article is based on PeTra (People Tracking) package, which uses convolutional neural networks to identify legs in populated environments. Experimental validation proposes a test in an apartment replicating realistic competition arena.
Towards Zero Knowledge Learning for Cross Language API Mappings

Programmers often need to migrate programs from one language or platform to another in order to implement functionality, instead of rewriting the code from scratch. However, most techniques proposed to identify API mappings across languages and facilitate automated program translation require manually curated parallel corpora that contain already mapped API seeds or functionally-equivalent code using the APIs in two different languages so that the techniques can have an anchor to map APIs. To alleviate the need of curating parallel data and to generalize the applicability of program translation techniques, we develop a new automated approach for identifying API mappings across languages based on the idea of unsupervised domain adaption via Generative Adversarial Network (GAN) and an additional refinement procedure that can transform two vector spaces to align the API vectors in the two spaces without the need of manually provided anchors. We show that our approach can identify API mappings more accurately than Api2Api without the need of curated parallel seeds.
An IND-CCA-Secure Code-Based Encryption Scheme Using Rank Metric

The use of rank instead of Hamming metric has been proposed to address the main drawback of code-based cryptography: large key sizes. There exist several Key Encapsulation Mechanisms (KEM) and Public Key Encryption (PKE) schemes using rank metric including some submissions to the NIST call for standardization of Post-Quantum Cryptography. In this work, we present an PKE scheme based on the McEliece adaptation to rank metric proposed by Loidreau at PQC 2017. This PKE scheme based on rank metric does not use a hybrid construction KEM + symmetric encryption. Instead, we take advantage of the bigger message space obtained by the different parameters chosen in rank metric, being able to exchange multiple keys in one ciphertext. Our proposal is designed considering some specific properties of the random error generated during the encryption. We prove our proposal -secure in the QROM by using a security notion called disjoint simulatability introduced by Saito et al. in Eurocrypt 2018. Moreover, we provide security bounds by using the semi-oracles introduced by Ambainis et al.
ClinicAppChain: A Low-Cost Blockchain Hyperledger Solution for Healthcare

Unified access to anonymous records through a trustworthy system is needed in our increasingly globalised world that still suffers from profoundly disconnected health care services. Emergencies are followed by redundant, costly and slow medical examinations without a global health data sharing mechanism. We establish the foundations of a decentralised healthcare ledger where patients decide what to share, with who, and with minimal costs. We review the state-of-the-art of transparent, auditable and interactive Blockchain-based healthcare studies, developing ClinicAppChain. Our solution features authentication, confidentiality and permissioned data sharing, considering the EU data protection regulation of 2018. ClinicAppChain is a cross-platform low-cost Blockchain prototype that empowers patients, hospitals, researchers, pharmaceuticals and insurance industries without crypto-currencies involved, and with a negligible energy foot-print (7.7 watts per node).