Reviewerly/exHarmony huggingface dataset:
Developers' Sentiment and Issue Reopening

Since software engineering is done primarily by humans, the sentiment of the developers is crucial when trying to achieve the best possible results. This study takes a look at a specific instance of this: developer sentiment in relation to issue reopening. In general, issue reopening is something to be avoided as it indicates that something went wrong in the original issue fixing, which means extra work will have to be done to fix it entirely. In this study, we were able to apply a sentiment analysis tool to issue tracking comments and observe how the scores varied across issues with no reopenings, one reopening, and many reopenings. We found evidence that suggests that negative sentiment correlates with issue reopening, although the effect size seems to be rather small.
Design of A Double-blockchain Structured Carbon Emission Trading Scheme with Reputation

This paper proposes a double-blockchain structured trading scheme (DBR-ETS) in carbon emission permits secondary market and introduces the enterprises' reputation, which is used to urge enterprises to actively invest in carbon emission reduction, and ultimately achieve carbon emission reduction in the overall society. The application of blockchain technology guarantees the security and privacy of distributed system. The confirmation chain and financial chain in the double-blockchain structure divide the transaction into two relatively independent processes, which can not only alleviate the transaction redundancy in the system but also improve the security of the system through the mutual verification of the two chains. The reputation-based transaction fee mechanism allows enterprises to promote their reputation by increasing investment in emission reduction. In the case study, we verified the superiority of the double-blockchain structure and the effect of the transaction fee mechanism on emission reduction.
Automatic Assessment of Complex Assignments using Topic Models

Automated assessment of complex assignments is crucial for scaling up learning of complex skills such as critical thinking. To address this challenge, one previous work has applied supervised machine learning to automate the assessment by learning from examples of graded assignments by humans. However, in the previous work, only simple lexical features, such as words or n-grams, have been used. In this paper, we propose to use topics as features for this task, which are more interpretable than those simple lexical features and can also address polysemy and synonymy of lexical semantics. The topics can be learned automatically from the student assignment data by using a probabilistic topic model. We propose and study multiple approaches to construct topical features and to combine topical features with simple lexical features. We evaluate the proposed methods using clinical case assignments performed by veterinary medicine students. The experimental results show that topical features are generally very effective and can substantially improve performance when added on top of the lexical features. However, their effectiveness is highly sensitive to how the topics are constructed and a combination of topics constructed using multiple views of the text data works the best. Our results also show that combining the prediction results of using different types of topical features and of topical and lexical features is more effective than pooling all features together to form a larger feature space.
A genuine smile is indeed in the eyes – The computer aided non-invasive analysis of the exact weight distribution of human smiles across the face

Understanding the detailed differences between posed and spontaneous smiles is an important topic with a range of applications such as in human-computer interaction, automatic facial emotion analysis and in awareness systems. During the past decade or so, there have been very promising solutions for accurate automatic recognition and detailed facial emotion analysis. To this end, many methods and techniques have been proposed for distinguishing between spontaneous and posed smiles. Our aim here is to go beyond the present state of the art in this field. Hence, in this work, we are concerned with understanding the exact distribution of a smile – both spontaneous and posed – across the face. To do this, we utilise a lightweight computational framework which we have developed to analyse the dynamics of human facial expressions. We utilise this framework to undertake a detailed study of the smile expression. Based on computing the optical flow across the face – especially across key parts of the face such as the mouth, the cheeks and around the eyes – we are able to accurately map the dynamic weight distribution of the smile expression. To validate our computational model, we utilise two publicly available datasets, namely the CK + dataset in which the subjects express posed smiles and the MUG dataset in which the subjects express genuine smiles. Our results not only confirm what already exists in the literature – i.e. that the spontaneous genuine smile is truly in the eyes – but it also gives further insight into the exact distribution of the smile across the face.
Lightweight Virtualization Approaches for Software-Defined Systems and Cloud Computing: An Evaluation of Unikernels and Containers

Software defined systems use virtualization technologies to provide an abstraction of the hardware infrastructure at different layers. Ultimately, the adoption of software defined systems in all cloud infrastructure components will lead to Software Defined Cloud Computing. Nevertheless, virtualization has already been used for years and is a key element of cloud computing. Traditionally, virtual machines are deployed in cloud infrastructure and used to execute applications on common operating systems. New lightweight virtualization technologies, such as containers and unikernels, appeared later to improve resource efficiency and facilitate the decomposition of big monolithic applications into multiple, smaller services. In this work, we present and empirically evaluate four popular unikernel technologies, Docker containers and Docker LinuxKit. We deployed containers both on bare metal and on virtual machines. To fairly evaluate their performance, we created similar applications for unikernels and containers. Additionally, we deployed full-fledged database applications ported on both virtualization technologies. Although in bibliography there are a few studies which compare unikernels and containers, in our study for the first time, we provide a comprehensive performance evaluation of clean-slate and legacy unikernels, Docker containers and Docker LinuxKit.
CeMNet: Self-Supervised Learning for Accurate Continuous Ego-Motion Estimation

In this paper, we propose a self-supervised learning approach for estimating continuous ego-motion from video. Our model learns to estimate camera motion by watching RGBD or RGB video streams and determining translational and rotation velocities that correctly predict the appearance of future frames. Our approach differs from other recent work on self-supervised structure-from-motion in its use of a continuous motion formulation and representation of rigid motion fields rather than direct prediction of camera parameters. To make estimation robust in dynamic environments with multiple moving objects, we introduce a simple two-component segmentation process that isolates the rigid background environment from dynamic scene elements. We demonstrate state-of-the-art accuracy of the self-trained model on several benchmark egomotion datasets and highlight the ability of the model to provide superior rotational accuracy and handling of nonrigid scene motions.
End-to-End Chinese Dialects Identification in Short Utterances using CNN-BiGRU

The performance of dialect identificatification in short utterances is obviously degraded. To address the issue, this paper proposes an end-to-end approach to reduce frequency variations and extract the global context feature information vector using CNN-BiGRU (Convolutional Neural Networks, Bidirectional Gated Recurrent Unit), which is beneficial to enrich the feature expression of short utterances. With the method, it makes higher-order abstraction of features and increases recognition accuracy. In addition, we perform frame-level feature splicing to obtain high dimensional eigenvectors. Experiments on 10 Chinese dialects showed the proposed method achieved 9.93% relative improvement in Acc than the mainstream i-vector system.
Superresolution of MRI brain images using unbalanced 3D Dense-U-Net network

This paper proposes an unbalanced end-to-end trained 3D Dense-U-Net network for brain MRI images superresolution. We evaluated capabilites of the proposed architecture on upsampling the MRI brain scans in the factor of 2, 4 and 8 and compared the results with resampled images using lanczos, spline and bilinear interpolation achieving best results. While the network does not exceed superresolution capabilites of state-of-the-art GAN networks, it does not require large dataset, is easy to train and capable of processing 3D images in resolution suitable for medical image processing.
Toward Optimal Rate Allocation to Sampling Sets for Bandlimited Graph Signals

We study the problem of sampling signals on a graph and allocating rate to each vertex in the sampling set, in a scenario where information sampled at each of the graph nodes needs to be compressed for transmission. We formulate this problem as a constrained quadratic programming optimization and obtain analytic results stating that the reconstruction error due to quantization should be equally distributed over the nodes involved. Our solution can also be used to remove samples from an already selected sampling set with low additional complexity. We demonstrate through experiments that the optimal solution yields improved performance on various graphs.
Improving Captioning for Low-Resource Languages by Cycle Consistency

Improving the captioning performance on low-resource languages by leveraging English caption datasets has received increasing research interest in recent years. Existing works mainly fall into two categories: translation-based and alignment-based approaches. In this paper, we propose to combine the merits of both approaches in one unified architecture. Specifically, we use a pre-trained English caption model to generate high-quality English captions, and then take both the image and generated English captions to generate low-resource language captions. We improve the captioning performance by adding the cycle consistency constraint on the cycle of image regions, English words, and low-resource language words. Moreover, our architecture has a flexible design which enables it to benefit from large monolingual English caption datasets. Experimental results demonstrate that our approach outperforms the state-of-the-art methods on common evaluation metrics. The attention visualization also shows that the proposed approach really improves the fine-grained alignment between words and image regions.
BZIP: A Compact Data Memory System for UTXO-based Blockchains

Unspent Transaction Output (UTXO) set is the foundational model used in many blockchain systems to represent assets. The benefits of UTXO representation include parallel processing, privacy, etc. However, the increasing size of UTXO set is degrading the access performance and severely brings down the validation speed of blockchain further, especially in resource-constrained scenerios, such as IoT. In this paper, we present a memory-economical storage system for UTXO-based blockchain. Based on the inherent properties of UTXO set, we propose two lossless compression techniques to reduce the memory space occupied by UTXO set. Besides, the database related operations are adopted to make the proposed mechanism easily applied in current blockchain system. Taking Bitcoin as the object of study, our mechanism can deliver 2.9-4.5x memory reduction and orders of magnitude validation speed improvement in resource-constrained situations. This compact system will improve validation performance and extend applied scope of blockchains.
From Vision to Content: Construction of Domain-Specific Multi-Modal Knowledge Graph

Knowledge graphs are usually constructed to describe the various concepts that exist in real world as well as the relationships between them. There are many knowledge graphs in specific fields, but they usually pay more attention on text or structured data, ignoring the image vision information, and cannot play an adequate role in the emerging visualization applications. Aiming at this issue, we design a method that integrates image vision information and text information derived from Wikimedia Commons to construct a domain-specific multi-modal knowledge graph, taking the metallic materials domain as an example to illustrate the method. The text description of each image is regarded as its context semantic to acquire the image's context semantic labels based on the DBpedia resource. Furthermore, we adopt deep neural network model instead of simple visual descriptors to acquire the image's visual semantic labels using the concepts from WordNet. In order to fuse the visual semantic labels and context semantic labels, a path-based concept extension and fusion strategy is proposed based on the conceptual hierarchies of WordNet and DBpedia to obtain the effective extension concepts as well as the links between them, increasing the scale of the knowledge graph and enhancing the correlation between images. The experimental results show that the maximum extension level has a significant impact on the quality of the generated domain knowledge graph, and the best extension level number is respectively determined for both DBpedia and WordNet. In addition, the results of this paper are compared with IMGpedia to further show the effectiveness of the proposed method.
Providing digital identity and academic attributes through European eID infrastructures: Results achieved, limitations, and future steps

Summary Electronic identity is getting an increased importance nowadays since relentless digitalization and 24/7 connectivity continue to transform everyday life. To support the recognition of electronic identification cross‐border within the European Union, the European Commission (EC) released the eIDAS Regulation, which became effective on September 2018. To put eIDAS in practice, the EC has supported the definition of the technical specification; it provides a sample implementation; and it coordinates the eIDAS Network composed of eIDAS nodes of various countries. Each eIDAS node has a generic part required for the communication with the other nodes, and a specific part that each country has to modify independently according to its legal, operational, and technical requirements. Although this specific part is very important because it affects the node flexibility and the interaction with the other actors at the national level, it is less known in practice. We describe the adaptation of this specific part in Italy to perform authentication and provision of attributes through the STORK and STORK 2.0 infrastructures (the predecessors of eIDAS), based on our experience in the homonym projects. Significant effort is spent currently to build real services exploiting the eIDAS infrastructure, eg, the eID4U project proposes eIDAS node extension with new attributes required by some common academic services, such as student registration at a foreign university. We describe the support for these new attributes in the eIDAS nodes and the modification of the specific part of the Italian eIDAS node to allow attributes retrieval from different authorities.
Elastic Parallel Systems for High Performance Cloud Computing: State-of-the-Art and Future Directions

With on-demand access to compute resources, pay-per-use, and elasticity, the cloud evolved into an attractive execution environment for High Performance Computing (HPC). Whereas elasticity, which is often referred to as the most beneficial cloud-specific property, has been heavily used in the context of interactive (multi-tier) applications, elasticity-related research in the HPC domain is still in its infancy. Existing parallel computing theory as well as traditional metrics to analytically evaluate parallel systems do not comprehensively consider elasticity, i.e., the ability to control the number of processing units at runtime. To address these issues, we introduce a conceptual framework to understand elasticity in the context of parallel systems, define the term elastic parallel system, and discuss novel metrics for both elasticity control at runtime as well as the ex-post performance evaluation of elastic parallel systems. Based on the conceptual framework, we provide an in-depth analysis of existing research in the field to describe the state-of-the-art and compile our findings into a research agenda for future research on elastic parallel systems.
An Efficient and Robust Zero-Bit Watermarking Technique for Biometric Image Protection

Protection of data is becoming a crucial task in digital world. This paper proposes a concept for securing biometric data using zero watermarking. Biometric data like a fingerprint, iris, etc. in which unique characters reside, but it may lead to an undesired situation if an attacker tries to copy it from a distributed database. The paper introduces a zero-bit watermarking scheme in iris image. Unlike in conventional watermarking, the watermarked image gets tempered due to distortion of pixels of an image. This paper proposes a robust zero-bit watermarking scheme in which a digital pattern is generated using a unique features from the iris image without tampering the host image. This method uses SVD and DWT for performing zero-bit watermarking and making it stable against several image processing attacks. The features of iris are unique, so the developed algorithm prevents data from duplicity or piracy issues.
Traffic Risk Assessment: A Two-Stream Approach Using Dynamic-Attention

The problem being addressed in this research is performing traffic risk assessment on visual scenes captured via outward-facing dashcam videos. To perform risk assessment, a two-stream dynamic-attention recurrent convolutional neural architecture is used to provide a categorical risk level for each frame in a given input video sequence. The two-stream approach consists of a spatial stream, which analyzes individual video frames and computes high-level appearance features and a temporal stream, which analyzes optical flow between adjacent frames and computes high-level motion features. Both spatial and temporal streams are then fed into their respective recurrent neural networks (RNNs) that explicitly models the sequence of features in time. A dynamic-attention mechanism which allows the network to learn to focus on relevant objects in the visual scene is added. These objects are detected by a state-of-the-art object detector and correspond to vehicles, pedestrians, traffic signs, etc. The dynamic-attention mechanism not only improves classification performance, but also provides a method to visualize what the network "sees" when predicting a risk level. This mechanism allows the network to implicitly learn to focus on hazardous objects in the visual scene. Additionally, this research introduces an offline and online model that differ slightly in their implementations. The offline model analyzes the complete video sequence and scores a classification accuracy of 84.89%. The online model deals with an infinite stream of data and produces results in near real-time (7 frames-per-second); however, it suffers from a slight decrease in classification accuracy (79.90%).
Creation of a Modern Digital Environment for Managing the Educational Programs in University

The goal of the article is to analyze the conditions for the creation of a modern digital management environment for the main professional educational programs (MEP) in the university.
A general gamma nonlinearity compensation method for structured light measurement with off-the-shelf projector based on unique multi-step phase-shift technology

Nonlinearities in the application of fringe projection metrology make it very difficult to acquire perfect 3D data. This paper describes a six-step phase-shifting technique for a structured light measurement system with an off-the-shelf projector. First, the phase error is analysed and a gamma model is established by deriving the relative expression between the wrapped phase and input images. This is then expressed in matrix form to derive a unique solution, which is used for the gamma solver. The complex gamma calibration and projector error compensation can be removed once the gamma value of the off-the-shelf projector has been determined. The ideal model reconstruction results are obtained through simulations and experiments, and the standard deviation of the phase error is found to be only 0.0039 radians. Hence, the proposed method eliminates the nonlinear errors associated with fringe projection technology using existing projectors and improves the overall image reconstruction quality.
Biometric based User Authentication Protocol for Mobile Cloud Environment

Mobile user authentication is a challenging task in the mobile cloud computing (MCC). In 2015, Tsai and Lo's developed authentication protocol in distributed MCC. Which is vulnerable to the biometric misuse, incorrect login credentials (password and fingerprint) and attacks for service provider impersonation. It has no provision for smart-card revocation and lacks mutual authentication. To address this-mentioned issues, we propose a novel Biometric based User Authentication Protocol for MCC. The proposed protocol supports session key agreement of participants and flawless mutual authentication. Our protocol is verified using Burrows-Abadi-Needham (BAN) logic. It further withstands all known attacks and performs well with respect to computational cost.
Pareto Optimal Design Space Exploration for Accelerated CNN on FPGA

Convolutional Neural Networks (CNNs) are at the base of many applications, both in embedded and in serverclass contexts.While Graphics Processing Units (GPUs) are predominantly used for training, solutions for inference often rely on Field Programmable Gate Arrays (FPGAs) since they are more flexible and cost-efficient in many scenarios.However, existing approaches fall short to accomplish several conflicting goals, like efficiently using resources on multiple platforms while retaining deep configurability and allowing a quick Design Space Exploration (DSE) towards the best solution.This paper proposes a solution composed of highly configurable kernels designed for resources time-sharing with an analytical model of their resource/performance characteristics.Building on such models, we propose an Integer Linear Programming (ILP)-based approach to effectively identify pareto optimal kernel configurations in terms of throughput and resource consumption.We evaluate our DSE on two state-of-the-art CNNs, showing how it identifies hundreds of pareto optimal solutions in less than a minute.Guided from the DSE configurations of the AlexNet network, we quickly identified a candidate design for a Xilinx Virtex-7 XC7VX485T FPGA and achieved a peak throughput of 4.05 ms per image, while we measured a maximum estimation error of 6.69% with respect to the proposed analytical models.