Reviewerly/exHarmony huggingface dataset:
Text Classification Model Based on fastText

Most text classification models based on traditional machine learning algorithms have problems such as curse of dimensionality and poor performance. In order to solve the above problems, this paper proposes a text classification model based on fastText. Our model explores the important information contained in the text through the feature engineering, and obtains the low-dimensional, continuous and high-quality text representation through the fastText algorithm. The experiment is based on Python to classify the text dataset of “user comment data emotional polarity judgment” in Baidu Dianshi platform. In the emotional polarity judgment task, the experimental results show that the precision, recall and F values of our model are superior to the model based on traditional machine learning algorithms and have excellent classification performance.
Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval

The semantic mismatch between query and document terms—i.e., the semantic gap—is a long-standing problem in Information Retrieval (IR). Two main linguistic features related to the semantic gap that can be exploited to improve retrieval are synonymy and polysemy. Recent works integrate knowledge from curated external resources into the learning process of neural language models to reduce the effect of the semantic gap. However, these knowledge-enhanced language models have been used in IR mostly for re-ranking and not directly for document retrieval. We propose the Semantic-Aware Neural Framework for IR (SAFIR), an unsupervised knowledge-enhanced neural framework explicitly tailored for IR. SAFIR jointly learns word, concept, and document representations from scratch. The learned representations encode both polysemy and synonymy to address the semantic gap. SAFIR can be employed in any domain where external knowledge resources are available. We investigate its application in the medical domain where the semantic gap is prominent and there are many specialized and manually curated knowledge resources. The evaluation on shared test collections for medical literature retrieval shows the effectiveness of SAFIR in terms of retrieving and ranking relevant documents most affected by the semantic gap.
Self-grouping convolutional neural networks

Although group convolution operators are increasingly used in deep convolutional neural networks to improve the computational efficiency and to reduce the number of parameters, most existing methods construct their group convolution architectures by a predefined partitioning of the filters of each convolutional layer into multiple regular filter groups with an equal spatial group size and data-independence, which prevents a full exploitation of their potential. To tackle this issue, we propose a novel method of designing self-grouping convolutional neural networks, called SG-CNN, in which the filters of each convolutional layer group themselves based on the similarity of their importance vectors. Concretely, for each filter, we first evaluate the importance value of their input channels to identify the importance vectors, and then group these vectors by clustering. Using the resulting data-dependent centroids, we prune the less important connections, which implicitly minimizes the accuracy loss of the pruning, thus yielding a set of diverse group convolution filters. Subsequently, we develop two fine-tuning schemes, i.e. (1) both local and global fine-tuning and (2) global only fine-tuning, which experimentally deliver comparable results, to recover the recognition capacity of the pruned network. Comprehensive experiments carried out on the CIFAR-10/100 and ImageNet datasets demonstrate that our self-grouping convolution method adapts to various state-of-the-art CNN architectures, such as ResNet and DenseNet, and delivers superior performance in terms of compression ratio, speedup and recognition accuracy. We demonstrate the ability of SG-CNN to generalize by transfer learning, including domain adaption and object detection, showing competitive results. Our source code is available at https://github.com/QingbeiGuo/SG-CNN.git.
V-Fuzz: Vulnerability Prediction-Assisted Evolutionary Fuzzing for Binary Programs

Fuzzing is a technique of finding bugs by executing a target program recurrently with a large number of abnormal inputs. Most of the coverage-based fuzzers consider all parts of a program equally and pay too much attention to how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this article, we design and implement an evolutionary fuzzing framework called V-Fuzz, which aims to find bugs efficiently and quickly in limited time for binary programs. V-Fuzz consists of two main components: 1) a vulnerability prediction model and 2) a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of a program are more likely to be vulnerable. Then, the fuzzer leverages an evolutionary algorithm to generate inputs which are more likely to arrive at the vulnerable locations, guided by the vulnerability prediction result. The experimental results demonstrate that V-Fuzz can find bugs efficiently with the assistance of vulnerability prediction. Moreover, V-Fuzz has discovered ten common vulnerabilities and exposures (CVEs), and three of them are newly discovered.
Mining top high utility association rules using Binary Differential Evolution

In this paper, in a first-of-its-kind-study a Binary Differential Evolution (BDE) and an Adaptive Binary Differential Evolution (ABDE)-based top non-redundant high utility association rule mining (TNR-HUARM) algorithms are proposed. A real-life OnlineRetail dataset is analyzed as an application of TNR-HUARM for customer segmentation based on value, i.e. utility. Apart from this dataset, also tested the effectiveness of our proposed models on six high utility benchmark datasets. The results show that BDE based TNR-HUARM numerically outperformed ABDE based TNR-HUARM on all seven datasets. But, when a t-test is performed, it turned out that the null hypothesis is not rejected. Therefore, Differential Evolution and Adaptive Differential Evolution based rule miners are found to be statistically equal in six out of seven datasets. The proposed algorithms were compared against two state-of-the-art algorithms and mixed results were obtained.
Identification of key cyberbullies: A text mining and social network analysis approach

Cyberbullying is a major problem in society, and the damage it causes is becoming increasingly significant. Previous studies on cyberbullying focused on detecting and classifying malicious comments. However, our study focuses on a substantive alternative to block malicious comments via identifying key offenders through the application of methods of text mining and social network analysis (SNA). Thus, we propose a practical method of identifying social network users who make high rates of insulting comments and analyzing their resultant influence on the community. We select the Korean online community of Daum Agora to validate our proposed method. We collect over 650,000 posts and comments via web crawling. By applying a text mining method, we calculate the Losada ratio, a ratio of positive-to-negative comments. We then propose a cyberbullying index and calculate it based on text mining. By applying the SNA method, we analyze relationships among users so as to ascertain the influence that the core users have on the community. We validate the proposed method of identifying key cyberbullies through a real-world application and evaluations. The proposed method has implications for managing online communities and reducing cyberbullying.
Investigating Teenagers’ Ability to Detect Phishing Messages

Young people are increasingly becoming responsible for the security of their devices, yet do not appear to receive formal instruction on how to protect themselves online. In this paper, we investigate the phish detection performance of teenagers while exploring how their familiarity with a service affects their overall performance in identifying phishing messages. Our study with 83 teenagers finds that teenagers were poor at distinguishing between genuine and phishing messages in an experimental task, yet performance was not affected by the familiarity of the service. However, our participants exhibited riskier behavior when making decisions on unfamiliar messages, suggesting that this is an area for further exploration. We discuss the implications of the poor phishing performance for teenagers and explore possible avenues to improve their awareness of these attacks, e.g. through embedding training content within the school curriculum.
A clustering-based differential evolution with different crowding factors for nonlinear equations system

Solving nonlinear equations systems (NESs) is one of the most important tasks in numerical computation. It is common that most NESs contain more than one root. Generally, these roots are equally important. Therefore, locating as many of these roots as possible is extremely useful; however, it is a difficult task. Recently, the use of evolutionary algorithms (EAs) for NESs has been given more consideration. There are several issues that need to be considered when applying EAs to solve NESs: (1) How to guide the population towards multiple roots, (2) how to tackle individuals which trip into local optimum, and (3) how to minimize replacement errors. In this paper, we deal with the first issue by introducing a one-step k-means clustering method combined with niching. In this way, individuals will search for different roots in their respective directions. For the second issue, we propose two methods to form species, the goal of which is to promote individuals to get rid of local optima. Finally, different crowding factors are adopted to reduce replacement errors. By assembling these improvements, a one-step k-means clustering based differential evolution, namely KSDE, is proposed. To evaluate the effectiveness of the proposal, we use 30 NES problems from the literature as the test suite. Experimental results demonstrate KSDE has great potential to locate multiple roots in a single run. Furthermore, according to the evaluation criteria, KSDE shows better performance compared with other state-of-the-art methods.
Augmented reality smart glasses for operators in production: Survey of relevant categories for supporting operators

The aim of this paper is to give an overview of the current knowledge and future challenges of augmented reality smart glasses (ARSG) for use by industrial operators. This is accomplished through a survey of the operator perspective of ARSG for industrial application, aiming for faster implementation of ARSG for operators in manufacturing. The survey considers the categories assembly instructions, human factors, design, support, and training from the operator perspective to provide insights for efficient use of ARSG in production. The main findings include a lack of standards in the design of assembly instructions, the field of view of ARSG are limited, and the guidelines for designing instructions focus on presenting context-relevant information and limiting the disturbance of reality. Furthermore, operator task routine is becoming more difficult to achieve and testing has mainly been with non-operator testers and overly simplified tasks. Future challenges identified from the review include: longitudinal user-tests of ARSG, a deeper evaluation of how to distribute the weight of ARSG, further improvement of the sensors and visual recognition to facilitate better interaction, and task complexity is likely to increase.
Technical debt as an indicator of software security risk: a machine learning approach for software development enterprises

Vulnerability prediction facilitates the development of secure software, as it enables the identification and mitigation of security risks early enough in the software development lifecycle. Although several factors have been studied for their ability to indicate software security risk, very limited attention has been given to technical debt (TD), despite its potential relevance to software security. To this end, in the present study, we investigate the ability of common TD indicators to indicate security risks in software products, both at project-level and at class-level of granularity. Our findings suggest that TD indicators may potentially act as security indicators as well.
Conversational Bot for Newcomers Onboarding to Open Source Projects

This paper targets the problems newcomers face when onboarding to open source projects and the low retention rate of newcomers. Open source software projects are becoming increasingly more popular. Many major companies have started building open source software. Unfortunately, many newcomers only commit once to an open source project before moving on to another project. Even worse, many novices struggle with joining open source communities and end up leaving quickly, sometimes before their first successful contribution. In this paper, we propose a conversational bot that would recommend projects to newcomers and assist in the onboarding to the open source community. The bot would be able to provide helpful resources, such as Stack Overflow related content. It would also be able to recommend human mentors. We believe that this bot would improve newcomers' experience by providing support not only during their first contribution, but by acting as an agent to engage them to the project.
Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering

Two main challenges in recommender systems are modeling users with heterogeneous taste, and providing explainable recommendations. In this paper, we propose the neural Attentive Multi-Persona Collaborative Filtering (AMP-CF) model as a unified solution for both problems. AMP-CF breaks down the user to several latent ‘personas’ (profiles) that identify and discern the different tastes and inclinations of the user. Then, the revealed personas are used to generate and explain the final recommendation list for the user. AMP-CF models users as an attentive mixture of personas, enabling a dynamic user representation that changes based on the item under consideration. We demonstrate AMP-CF on five collaborative filtering datasets from the domains of movies, music, video games and social networks. As an additional contribution, we propose a novel evaluation scheme for comparing the different items in a recommendation list based on the distance from the underlying distribution of “tastes” in the user’s historical items. Experimental results show that AMP-CF is competitive with other state-of-the-art models. Finally, we provide qualitative results to showcase the ability of AMP-CF to explain its recommendations.
Energy-efficient deep learning inference on edge devices

The success of deep learning comes at the cost of very high computational complexity. Consequently, Internet of Things (IoT) edge nodes typically offload deep learning tasks to powerful cloud servers, an inherently inefficient solution. In fact, transmitting raw data to the cloud through wireless links incurs long latencies and high energy consumption. Moreover, pure cloud offloading is not scalable due to network pressure and poses security concerns related to the transmission of user data. The straightforward solution to these issues is to perform deep learning inference at the edge. However, cost and power-constrained embedded processors with limited processing and memory capabilities cannot handle complex deep learning models. Even resorting to hardware acceleration, a common approach to handle such complexity, embedded devices are still not able to directly manage models designed for cloud servers. It becomes then necessary to employ proper optimization strategies to enable deep learning processing at the edge. In this chapter, we survey the most relevant optimizations to support embedded deep learning inference. We focus in particular on optimizations that favor hardware acceleration (such as quantization and big-little architectures). We divide our analysis in two parts. First, we review classic approaches based on static (design time) optimizations. We then show how these solutions are often suboptimal, as they produce models that are either over-optimized for complex inputs (yielding accuracy losses) or under-optimized for simple inputs (losing energy saving opportunities). Finally, we review the more recent trend of dynamic (input-dependent) optimizations, which solve this problem by adapting the optimization to the processed input.
VoltJockey: A New Dynamic Voltage Scaling-Based Fault Injection Attack on Intel SGX

Intel software guard extensions (SGX) increase the security of applications by enabling them to be performed in a highly trusted space (called enclave). Most state-of-the-art attacks on SGX focus on either mining the software vulnerabilities in the enclave or speculating the secret data with side channels. In this study, we report our recent work on breaking SGX by inducing voltage-oriented hardware faults. The novelty and importance of this attack are that it is completely controlled by software and does not require any security vulnerability in the software. Our proposed attack, called VoltJockey, exploits a vulnerability in the implementation of dynamic voltage and frequency scaling (DVFS) that achieves energy saving by dynamically adjusting the processor's operating voltage and thus clock frequency. However, if the operating voltage is lower than a certain critical level, the circuit's timing constraint will fail and hardware fault would be created. We propose to deliberately trigger such voltage-oriented hardware faults by a loadable kernel module that can set the processor's voltage through Intel's undocumented model-specific register (MSR). We first utilize the module to furnish the processor with a transient low voltage with controlled timing to inject a temporal fault into the target location of the program running in the enclave. Then, we perform a differential fault attack on the outputs before and after the injection of faults. For demonstration, we successfully deploy the proposed attack to extract the key of an AES executed in the enclave and lead an SGX-protected RSA to output our specified result.
Blockchain and Machine Learning in Health Care and Management

Today we have enormous amount of data available in every sector, with the advent of technology available, it is possible to provide solutions to many problems. In this paper we are going to provide solutions to the problems related to healthcare data management using Machine Learning and Blockchain. Extracting only the relevant information from the data is possible with the use of Machine Learning. This is done using trained algorithms. Once this data is stored, the next problem is Data sharing and its reliability. This is where Blockchain comes into picture. The consensus in Blockchain technology makes sure that data is legitimate and transactions are secure. Blockchain technology can potentially change health care management for the better by placing patient at the epicentre of the healthcare system and increasing the privacy and interoperability of health data. This paper focuses primarily on solving healthcare data management problems by using Blockchain technology and including some indispensable features using Machine Learning.
Splicing Community Patterns and Smells

Software engineering projects are now more than ever a community effort. In the recent past, researchers have shown that their success may not only depend on source code quality, but also on other aspects like the balance of distance, culture, global engineering practices, and more. In such a scenario, understanding the characteristics of the community around a project and foresee possible problems may be the key to develop successful systems. In this paper, we focus on this research problem and propose an exploratory study on the relation between community patterns, i.e., recurrent mixes of organizational or social structure types, and smells, i.e., sub-optimal patterns across the organizational structure of a software development community that may be precursors of some sort of social debt. We exploit association rule mining to discover frequent relations between them. Our findings show that different organizational patterns are connected to different forms of socio-technical problems, possibly suggesting that practitioners should put in place specific preventive actions aimed at avoiding the emergence of community smells depending on the organization of the project.
GAN generation of synthetic multispectral satellite images

Generative Adversarial Networks (GAN) have been used for both image generation and image style translation. In this paper, we aim to apply GANs to multispectral satellite image. For the image generation, we take advantage of the progressive GAN training methodology, that is purposely modified to generate multi-band 16 bits satellite images that are similar to a Sentinel-2 level-1C product. The generated images that we obtained imitate closely the spectral signatures of the kind of terrain in the images, as it can be seen by comparing typical spectral view between synthetic and natural images. Furthermore, we consider the recent use of GAN architectures for transferring the style of the images and apply them to perform land-cover transfer of satellite images. Specifically, we used the unpaired style transfer method to modify images that are dominant in vegetation land cover into images that are dominated by bare land cover and vice versa. The land-cover transfer via GANs gives very promising results and the visual quality for the transferred images is also satisfactory, showing that the land-cover transfer is an easier task compared to the GAN generation from scratch. Especially, results are good when the target domain is bare land, in which the visual quality for the transferred images is also very good.
Computer aided breast cancer detection using ultrasound images

Breast cancer is the second prevalent type of cancer among women. Breast Ultrasound (BUS) imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities in the breast. To improve the diagnostic accuracy, Computer Aided Diagnosis (CAD) system is helpful for breast cancer detection and classification. Normally, a CAD system consists of four stages: pre-processing, segmentation, feature extraction, and classification. In this chapter, the pre-processing step includes speckle noise removal using Speckle Reducing Anisotropic Diffusion (SRAD). The goal of segmentation is to locate the Region of Interest (ROI) and Active contour-based segmentation is used in this work. The texture features are extracted and fed to a classifier to categorize the images as Normal, Benign and Malignant. In this work three classifiers namely K-Nearest Neighbors (KNN) algorithm, Decision tree algorithm and Random Forest classifier are used and the performance is compared based on the accuracy of classification.