Reviewerly/exHarmony huggingface dataset:
Empirical Analysis of Ensemble Machine Learning Techniques for Bug Triaging

Bug reports are an inescapable part of the software product framework. Nowadays, software advancements have led to the creation of beta versions of software in order to assemble the bug reports from clients. The assembled bug reports are then handled by software developers to make consequent software more reliable as well as robust. However, high recurrence of approaching bug reports forges the process of bug fixing to be a troublesome & tedious process. Bug triaging is an essential component of issue handling process and it deals with the selection of a suitable software developer for handling of reported bug such that the assigned developer is able to fix the reported issue. In the literature, different semi and fully mechanized procedures are proposed to facilitate the endeavor of developer selection in bug repositories. These techniques use historically fixed information from bug repositories to classify any new incoming bugs. In the recent years, ensemble-based classification techniques have gained popularity. These techniques use multiple classifiers for making a prediction and has proved to be outperforming classical machine learning classification. In this paper, we present an empirical study of ensemble-based techniques for classification of new incoming bug reports. We studied 5 ensemble classification techniques, namely Bagging, Boosting, Majority Voting, Average Voting, and Stacking using 25 different machine learning classifiers as base classifiers. The experimental results showed that ensemble classifiers outperform classical machine learning algorithms for selection of suitable developer for handling the bug report.
Reservoirs Learn to Learn

The common procedure in reservoir computing is to take a “found” reservoir, such as a recurrent neural network with randomly chosen synaptic weights or a complex physical device, and to adapt the weights of linear readouts from this reservoir for a particular computing task. We address the question of whether the performance of reservoir computing can be significantly enhanced if one instead optimizes some (hyper)parameters of the reservoir, not for a single task but for the range of all possible tasks in which one is potentially interested, before the weights of linear readouts are optimized for a particular computing task. After all, networks of neurons in the brain are also known to be not randomly connected. Rather, their structure and parameters emerge from complex evolutionary and developmental processes, arguably in a way that enhances the speed and accuracy of subsequent learning of any concrete task that is likely to be essential for the survival of the organism. We apply the Learning-to-Learn (L2L) paradigm to mimic this two-tier process, where a set of (hyper)parameters of the reservoir are optimized for a whole family of learning tasks. We found that this substantially enhances the performance of reservoir computing for the families of tasks that we considered. Furthermore, L2L enables a new form of reservoir learning that tends to enable even faster learning, where not even the weights of readouts need to be adjusted for learning a concrete task. We present demos and performance results of these new forms of reservoir computing for reservoirs that consist of networks of spiking neurons and are hence of particular interest from the perspective of neuroscience and implementations in spike-based neuromorphic hardware. We leave it as an open question of what performance advantage the new methods that we propose provide for other types of reservoirs.
Geometry of Pauli maps and Pauli channels

We analyze the geometrical properties of trace-preserving Pauli maps. Using the Choi-Jamio\lkowski isomorphism, we express the Hilbert-Schmidt line and volume elements in terms of the eigenvalues of the Pauli map. We analytically compute the volumes of positive, trace-preserving Pauli maps and Pauli channels. In particular, we find the relative volumes of the entanglement breaking Pauli channels, as well as the channels that can be generated by a time-local generator. Finally, we show what part of the Pauli channels are P and CP-divisible, which is related to the notion of Markovianity.
Privacy Risk Assessment for Data Subject-Aware Threat Modeling

Regulatory efforts such as the General Data Protection Regulation (GDPR) embody a notion of privacy risk that is centered around the fundamental rights of data subjects. This is, however, a fundamentally different notion of privacy risk than the one commonly used in threat modeling which is largely agnostic of involved data subjects. This mismatch hampers the applicability of privacy threat modeling approaches such as LINDDUN in a Data Protection by Design (DPbD) context. In this paper, we present a data subject-aware privacy risk assessment model in specific support of privacy threat modeling activities. This model allows the threat modeler to draw upon a more holistic understanding of privacy risk while assessing the relevance of specific privacy threats to the system under design. Additionally, we propose a number of improvements to privacy threat modeling, such as enriching Data Flow Diagram (DFD) system models with appropriate risk inputs (e.g., information on data types and involved data subjects). Incorporation of these risk inputs in DFDs, in combination with a risk estimation approach using Monte Carlo simulations, leads to a more comprehensive assessment of privacy risk. The proposed risk model has been integrated in threat modeling tool prototype and validated in the context of a realistic eHealth application.
An Application of Blockchain in Stock Market

Blockchain is one of the growing technologies used for financial management systems. Financial data must be kept secure otherwise it can create a huge loss. So, whenever security features or technologies are developed must keep financial security as a priority. Stock market management is another area of finance sector that works on two concepts, that is, minimize the risk and maximize the profit. In this chapter, the authors discuss how blockchain technology is used for stock market analysis. Mainly blockchain will help us to make optimal stock exchanges through automation and decentralization. Stock market across the globe is rapidly using blockchain technology for the market transaction. Some of the country is still preparing themselves to use the blockchain technology. This technology offers huge potential for tracing securities lending, margin financing, and surveillance of system risk.
Services Computing Systems Engineering Framework: A Proposition and Evaluation Through SOA Principles and Analysis Model

Services computing utilizes service-based technology to build IT services systems. A comprehensive understanding of service-based engineering framework is fully needed to build and to deliver the systems. This article outlines the proposition and the evaluation of services computing systems engineering framework based on service oriented architecture (SOA) principles. This framework is used as a guide to build services computing systems based on SOA. A maximize function-based analysis model is built to examine the feasibility of the framework and represent the optimization model of the processes. The findings indicate that the proposed framework can be well implemented in building the systems. The results show that the proposed framework satisfies the implementation requirements of the SOA principles. Furthermore, the analysis model demonstrates good results throughout the whole stages of the framework. This article contributes to practical knowledge in providing a systematic guide to build service-oriented systems.
R-PEKS: RBAC Enabled PEKS for Secure Access of Cloud Data

In the recent past, few works have been done by combining attribute-based access control with multi-user PEKS, i.e., public key encryption with keyword search. Such attribute enabled searchable encryption is most suitable for applications where the changing of privileges is done once in a while. However, to date, no efficient and secure scheme is available in the literature that is suitable for these applications where changing privileges are done frequently. In this paper our contributions are twofold. Firstly, we propose a new PEKS scheme for string search, which, unlike the previous constructions, is free from bi-linear mapping and is efficient by 97% compared to PEKS for string search proposed by Ray et.al in TrustCom 2017. Secondly, we introduce role based access control (RBAC) to multi-user PEKS, where an arbitrary group of users can search and access the encrypted files depending upon roles. We termed this integrated scheme as R-PEKS. The efficiency of R-PEKS over the PEKS scheme is up to 90%. We provide formal security proofs for the different components of R-PEKS and validate these schemes using a commercial dataset.
Multi-modal product title compression

Product title generation in e-commerce is a challenging task, which involves modeling multi-modal resources, i.e., textual descriptions and visual pictures, and comprising a sequence of words with proper ordering. Although myriad researches have studied this task and prompting progress has been made, there still exists a noticeable gap between generated titles and the requirements on mobile devices, especially considering the limited screen size. Towards filling this gap, we collect a large dataset from real e-commerce platforms to investigate compressing product titles for mobile devices, namely product title compression. We also propose a novel title compression model which takes the advantages of reinforcement learning and multi-modal resources. In doing so, our model is capable of retaining vital information in titles and improving the readability of generated titles. Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods by a large margin on the automatic evaluation.
A big data anonymous batch verification scheme with conditional privacy preservation for power injection over vehicular network and 5G smart grid slice

Internet of Things (IoT) is a new paradigm that has changed the way we live in our society by providing many intelligent applications such as smart healthcare, precision agriculture, smart city, smart supply chain, smart home, smart grid, and intelligent transportation. As an integration of smart grid and intelligent transportation, vehicle-to-grid (V2G) technology can make the power grid to communicate with the plugin electric vehicles (EVs), allowing the EVs to store greenpower energy from renewable energy sources and inject overplus power back to the power grid during the peak demand hours for financial gains. Nevertheless, security of transmitted information and users’ privacy preservation are very crucial to ensure fairness in the energy market and enhance users’ acceptability of this propitious technology. For instance, there is need to expeditiously authenticate massive power bids injected by EVs without exposing the owners’ private information. Therefore, a big data anonymous batch verification for injection of power over vehicular network and 5G smart grid slice based on a new ultraefficient certificateless aggregate signature (CL-AS) algorithm is proposed. The proposed scheme achieves batch-verifiable authentication of all the injected bids in an anonymous manner without compromising the privacy of participants. Moreover, we prove that the scheme is secure, assuming the Discrete Logarithm Problem (DLP) is intractable. The performance evaluation demonstrates its superiority in terms of efficiency over the related schemes.
POSDAO: Proof of Stake Decentralized Autonomous Organization

In this paper we introduce POSDAO, a Proof of Stake (POS) algorithm implemented as adecentralized autonomous organization (DAO). It is designed to provide a decentralized, fair,and energy efficient consensus for public chains. The algorithm works as a set of smartcontracts written in Solidity. POSDAO is implemented with a general purpose BFT consensusprotocol such as Authority Round (AuRa) with a proposer node and probabilistic finality, orHoney Badger BFT (HBBFT), leaderless and with instant finality. Validators are incentivized tobehave in the best interests of a network through a configurable reward structure. The algorithmprovides a Sybil control mechanism for managing a set of validators, distributing rewards, andreporting and penalizing malicious validators. The authors provide a reference POSDAOimplementation, xDai POSDAO, which uses xDai as a stable transactional coin and arepresentative ERC677 token (STAKE) as a staking token. The reference implementationfunctions on an Ethereum 1.0 sidechain and utilizes the AuRa consensus protocol. Assets arebridged between the Ethereum mainnet and the xDai POSDAO network using several instancesof the POA TokenBridge.
Symptom-Based Disease Detection System In Bengali Using Convolution Neural Network

Natural language processing (NLP) and automatic detection of the disease have become popular in the recent era. Several research work show disease detection system in several languages. We present a disease detection system from the clinical text which is in Bengali language consisting of a numerous set of diacritic character, at a sentence-level classification. The clinical dataset consisting of Bengali text which is generally user interpreted symptom for the most common disease. Also, our approach represents the NLP methodology for Bengali language processing and classification of disease using several types of neural networks with hyperparameter tuning and word vectorization. The aim of the research is the initial detection of disease from the user's voice to text data, in our case Bengali. So, a speech recognition system developed in the Bengali language is used to feed the disease detection model and finalizing the output with the model-detected disease.
A Survey on Natural Image Matting With Closed-Form Solutions

Natural image matting refers to the interactive process of estimating the foreground opacity (alpha matte) of an input image and extracting the foreground layer. Various approaches have been proposed utilizing affinity-based strategy, sampling-based strategy or hybrid of both. Affinity-based approaches and hybrid approaches generally derive a quadratic cost function concerning alpha matte smoothness as well as user input and estimate the alpha matte through solving a sparse linear system of equations. These approaches, which have closed-form solutions, are called closed-form matting approaches. This paper provides an extensive overview on the closed-form matting theory. A hierarchical matting model is presented, classifying affinity-based approaches into three categories. The basis of each model and the relationships between them are analyzed. Using manifold learning theory, we unify the closed-form matting approaches into one framework and provide explanations of: 1) how various affinity-based strategies generate the alpha matte; 2) why sampling-based strategies help improve the precision of alpha matte. Evaluation of the representative algorithms on an image matting dataset and a video matting dataset demonstrates the strength and weakness of each matting model. Not only summary of the traditional matting methods is provided but also several suggestions on future directions for matting research are proposed.
A quick convergent artificial bee colony algorithm for solving quadratic assignment problems

Quadratic assignment problem (QAP) is an NP-hard problem. Because of this computational complexity, various search processes have been developed and used in different heuristics over the years to solve the QAPs. However, in most of those heuristics, the utility of adopting the particular search processes has not been justified. Apart from this, it has been a well-established fact that the convergence rate of any algorithm reduces significantly with the increase in size and flow density of the considered problem. This paper first describes how a quick convergent artificial bee colony (qABC) algorithm is developed using various search processes, and its performance is then evaluated and compared with some of the other state-of-the-art algorithms with respect to convergence, reliability and statistical significance of the results based on the selected problems available in the QAP-library. The comparison results conclude that the qABC algorithm can converge quickly, reliably and statistically more significantly as compared to other algorithms in most of the problem instances.
Mitigating Reverse Engineering Attacks on Deep Neural Networks

With the structure of deep neural networks (DNN) being of increasing commercial value, DNN reverse engineering attacks have become a great security concern. It has been shown that the memory access pattern of a processor running DNNs can be exploited to decipher their detailed structure. In this work, we propose a defensive memory access mechanism which utilizes oblivious shuffle, address space layout randomization, and dummy memory accesses to counter such attacks. Experiments show that our defense exponentially increases the attack complexity with asymptotically lower memory access overhead compared to generic memory obfuscation techniques such as ORAM and is scalable to larger DNNs.
Quantum Aware SDN Nodes in the Madrid Quantum Network

QKD technology is mature enough to be used beyond the usual single, point to point, link by creating networks. Quantum ad hoc networks, where a separate, quantum-only network, is running in parallel to a classical network have been demonstrated in several occasions. Having to, essentially, duplicate the network to introduce quantum communications is very expensive and, beyond niche use-cases, fully integrated quantum-classical networks is what the industry demands to accept quantum technologies as a serious networking technology, ready for a broad market uptake.Recently we have reported on the successful deployment of the Madrid Quantum Network. This network is novel because it has been the first installed in production sites of a Telecommunications operator and moreover it is managed through a Software Defined Networking (SDN) structure that integrates classical and quantum channels. The network was operating for four months and we could demonstrate several technologies utilizing the integration of quantum and classical networks. Here we report on some implementation aspects and its usage in several use cases, in particular to secure the management of the SDN control plane as a critical infrastructure.
User Preferences in Intelligent Environments

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A Consumer-Oriented Cloud Forensic Process Model

In the process of cloud forensic investigation, roles and responsibilities of cloud consumers and cloud providers do not have clear delineation. Nevertheless, Consumers are responsible for collecting and analyzing data from their adopted cloud services for forensic purposes, thus, should have processes in place to identify, prioritize and collect data from cloud components that they are responsible for. Therefore, a cloud forensic process model that would guide a consumer before, during and after when an incident occurs is of vital importance. To develop such model authors have primarily defined requirements that should be met by forensic process models that aim to lead investigations in the cloud. As a result, a cloud forensic process model that takes consumer perspective into account has been proposed. Subsequently, its fulfillment of the proposed requirements is discussed.
Novel Shot Boundary Detection in News Streams Based on Fuzzy Petri Nets

With the advent of a digital era, people have encountered some difficulty in using and absorbing overwhelming information generated by technological advances in multimedia. Thus, the development of video summarization enables people to catch a general idea about videos in a short time. In this paper, we focus on the shot change, a part of the video summarization, to conduct an experimental sample on news programs. Moreover, a high-level fuzzy Petri net model is presented to describe the frame combination which indicates a shot boundary used for a video frame sequence in order to detect both cut transitions and gradual transitions. This study has used feature functions to estimate the direct shot change in consideration of video shot boundary detection which adopts the HLFPN model to find a threshold value. The experimental results manifest that the proposed system saves a lot of time and reduces the occurrence of improper shot changes caused by the motions of objects and cameras when comparing the proposed approach with other existing ones.
Virtual Machine Execution for Wearables Based on WebAssembly

Today, the programming of a complete wearable sensor system requires writing code in different programming languages for the different parts of the systems, such as the wearable sensor platform itself, the gateway, the back-end server, and the client app. In this paper, we propose to use WebAssembly, which is a simple but powerful virtual machine standard already supported by all major web browsers. We show that it is possible to implement a WebAssembly interpreter for embedded systems, such as the Texas Instruments CC2652R system-on-chip and this enables the same code to execute in all parts of the systems. In our proof-of-concept implementation, we use Bluetooth low energy, which means that smartphones can communicate with and program our device without the need for special hardware.
Design for Reliability Through Text Mining and Optimal Product Verification and Validation Planning

Failure mode and effects analysis (FMEA) has been widely used in product design process as a reliability analysis technique. Design FMEA (DFMEA), which is used in the product development phase to identify and mitigate product risk, is one of the three application scenarios of FMEA. During the DFMEA process, verification and validation (V&V) activities are proposed to mitigate the risk of the identified potential failure modes. The V&V activities can be further planned and implemented to improve the product reliability under development. However, the DFMEA report usually contains rich text descriptions of potential failure modes and causes, and it is difficult and nonintuitive to fully understand these information for design improvements. In addition, it is also very challenging to optimize the planning of V&V activities by selecting a set of V&V activities to achieve expected reliability improvement effectiveness with the minimum resource consumption requirement. To address these two challenges, this article first proposes a method of applying text mining to the DFMEA report to obtain two types of hidden reliability information, including the classifications of failure modes/causes and the correlation between keywords. Then, a mathematical model is proposed to optimize the product V&V planning by selecting an optimal set of V&V activities. The application of the above proposed methods is illustrated through the product development of a diesel engine power generation system.