Reviewerly/exHarmony huggingface dataset:
Exploring Team Competencies in Cybersecurity

With the exponential increase of cyberattacks due to the ubiquitous adoption of remote work, strong and effective cybersecurity teams are indispensable. However, in-depth knowledge of team competencies in cybersecurity literature is scarce. This article reviews and suggests areas of improvement for cybersecurity teams through the lens of the existing attitudes, behaviors, and cognitions (ABCs) framework. Suggested areas for immediate improvement within cybersecurity teams include attitudinal variables such as cohesion, mutual trust, team reward attitude, and a cognitive variable known as shared mental models. Identifying relevant ABCs associated with the team competencies necessary for cybersecurity teams to be effective will be a critical step toward creating applicable measurement and training.
Using Machine Learning Model To Predict Libyan Telecom Company Customer Satisfaction

Sentiment analysis is a field that studies the polarity of opinions from texts and determines whether they are positive, negative, or neutral. Since analyzing a large amount of data manually takes a long time, a machine learning-based system had emerged. In this study, a sentiment analysis system to determine the customer opinions of the three major Libyan telecommunication companies namely: (Libyana, Almadar Aljadid, and Libya Telecom and Technology) is proposed, where customer opinions were collected from Twitter. Several pre-processing and cleaning steps had been applied to the collected corpus to improve the performance of the models. Five machine learning models, namely: support vector machine, logistic regression, naive Bayes, K-nearest neighbor, and decision tree have been applied. An initial experiment showed that most of the models are overfitting due to class imbalance. Followed class balancing step is performed for all companies. The results showed that the support vector machine was the best in predicting the customer sentiment of the Libyana telecom company with an accuracy of 80.67%. The naive Bayes was the best on Almadar Aljadid with an accuracy of 81.19%. In Libya Telecom and Technology, the result showed that the performance of the decision tree was the best at 75%. This study showed that the sentiment of Libyan telecom companies was successfully predicted through content posted on the Twitter social media platform, which might assist those companies in improving their services.
Characterizing Python Method Evolution with PyMevol: An Essential Step Towards Enabling Reliable Software Systems

Understanding the evolution of library methods is essential for maintaining high-quality and reliable software systems as those libraries often evolve rapidly in order to meet new requirements such as adding new features, improving performance, or fixing vulnerabilities. Failing to incorporate this evolution may result in compatibility issues that may manifest themselves as runtime crashes, leading to a poor user experience. This is not uncommon for the most popular programming language, Python, for which our community has developed over 380,000 libraries. To help developers better understand their used libraries, we propose to the community a prototype tool called PyMevol to model Python libraries' APIs and their evolution. Specifically, given a Python library, PyMevol statically examines its code to extract APIs (including aliases introduced by Python's import-flow mechanism) from all its released versions to build a history-sensitive alias-aware API explorer tree, a tree structure that allows users to explore the biography of each API so as to quickly locate where and when a given API is introduced, changed, or removed. Our experimental results over five popular real-world Python libraries show that our approach is reliable in achieving its purpose (i.e., over 90 % of accuracy) and helpful in supporting further API-relevant analyses.
Twilight Rohingya: The Design and Evaluation of Different Navigation Controls in a Refugee VR Environment

Virtual reality (VR) has shown great potential in enhancing users’ empathy towards vulnerable populations. Previous work has demonstrated that navigation modes relaying different spatial information can affect a user’s presence and understanding. In this research, we designed a 360° VR video-based prototype environment to depict real-life scenarios of a refugee camp in Southern Bangladesh. Our study consists of 2 conditions: active (i.e., selecting thematic video clips freely) and passive (i.e., watching sequence-determined clips passively). Thirty-six participants evaluated the prototype’s usability and its effects on their empathy towards refugees. The results showed that active navigation resulted in higher kindness and usability scores. Finally, we provide empirical insights into navigation modes in future VR design. This research should help refugee groups receive more attention and promote recognition and empathy towards refugees and their families.
Blockchain Utility by Pioneers in Fashion and Apparel Industry

As time has progressed, it has become crucial for the society not only to progress along with time but also to stay ahead of it. The fashion industry has always incorporated innovative measures while maintaining the continuity of the business. With the advancement of science, all sectors have faced equal technological threats as much as the benefits they've enjoyed due to the same. In case of the fashion and apparel industry, issues such as counterfeiting of luxury designer outfits, increase in percentage of customers with sustainable and clean choices and the pressure to keep up with the complex supply chain management along with the increase in global market demands have risen and the industry is in need of a new strategic model that could potentially solve all the above problems at once. The technological development carried out in order to establish a transparent and decentralized platform, called the blockchain, is gathering traction for its integrity. Blockchain could be highly helpful in solving the counterfeiting of products of luxury fashion brands, helping the industry restrict the losses that it faces in billions each year owing to the traceability that the platform offers. With the growing customer percentage that prefers their clothes being ethically sourced, the blockchain could ensure the authenticity of the raw materials and every other component that goes into the making of their outfits. The global markets have given rise to complex supply chain and warehouse management systems tarnishing the face of the industry. Blockchain is a well-sophisticated platform known for its accountability. Any record created once will always be available and it can be tracked by people with appropriate access to the database. A detailed study on the intricacies of the aforementioned technological solutions by brands pioneering in the industry is carried out in this chapter.
Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs

Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and privacy-preserving alternative to optical cameras and smartphones/wearables for social distance monitoring in indoor spaces, permitting the recognition of basic shapes, without revealing the personal details of individuals. In this work, we demonstrate that an accurate detection of social distance violations can be achieved processing the raw output of a 8x8 IR array sensor with a small-sized Convolutional Neural Network (CNN). Furthermore, the CNN can be executed directly on a Microcontroller (MCU)-based sensor node.With results on a newly collected open dataset, we show that our best CNN achieves 86.3% balanced accuracy, significantly outperforming the 61% achieved by a state-of-the-art deterministic algorithm. Changing the architectural parameters of the CNN, we obtain a rich Pareto set of models, spanning 70.5-86.3% accuracy and 0.18-75k parameters. Deployed on a STM32L476RGMCU, these models have a latency of 0.73-5.33ms, with an energy consumption per inference of 9.38-68.57$\mu$J.
Metaverses and Business Transformation

Metaverses refer to virtual worlds which enable the interaction between users and simulate people, places and things of the physical world. The wider use of metaverses can disrupt how we interact and bring a plethora of implications for businesses and society. While the term metaverse received increased attention recently due to the interest of tech giants, researchers have been exploring virtual world interactions for over a decade. In light of new exciting metaverse implementations, it is necessary to consolidate information via existing research on how the use of metaverses can transform businesses. As a first step, this paper outlines the methodology to be followed by future researchers aiming to shed light on metaverse and business transformation. It proposes a framework to assist authors in organizing existing literature on metaverses and guide them in identifying future research avenues.
Decomposition of Service Level Encoding for Anomaly Detection

Application of anomaly thresholds to health provider service level data encoded in vector form can lead to unintended consequences in terms of output complexity and business appropriate interpretation. In this paper we show that specific analysis incorporating feature decomposition with the application of relative business knowledge prior to selecting anomaly thresholds is an effective strategy across multiple health provider disciplines for addressing this complexity. Cluster definitions of Modal, Specialised and Aberrant are introduced as a descriptive framework on which to interpret said feature decomposition and to aid threshold setting. This strategy furthers the work introduced in [1] as refinement to an existing anomaly detection scheme.
Reducing the Tail Latency of Microservices Applications via Optimal Configuration Tuning

The microservice architecture is an architectural style for designing applications that supports a collection of fine-grained and loosely-coupled services, called microservices, enabling independent development and deployment. An undesirable complexity that results from this style is the large state space of possibly inter-dependent configuration parameters (of the constituent microservices) which have to be tuned to improve application performance.This paper investigates optimization algorithms to address the problem of configuration tuning of microservices applications. To address the critical issue of large state space, practical dimensionality reduction strategies are developed based on available system characteristics. The evaluation of the optimization algorithms and dimensionality reduction techniques across three popular benchmarking applications highlights the importance of configuration tuning to reduce tail latency (by as much as 46%). A detailed analysis of the efficacy of different dimensionality reduction techniques in capturing the most important parameters is performed using ANOVA techniques. Results show that the right combination of optimization algorithms and dimensionality reduction can provide substantial latency improvements by identifying the right subset of parameters to tune, reducing the search space by as much as 83%.
RGA-Unet: An improved U-net segmentation model based on residual grouped convolution and convolutional block attention module for brain tumor MRI image segmentation

research-article Share on RGA-Unet: An improved U-net segmentation model based on residual grouped convolution and convolutional block attention module for brain tumor MRI image segmentation Authors: Siyi Xun School of Faculty of Applied Sciences, Macao Polytechnic University, China School of Faculty of Applied Sciences, Macao Polytechnic University, China 0000-0002-9392-5445View Profile , Yan Zhang Jinan Branch of China Telecom Co. LTD, China Jinan Branch of China Telecom Co. LTD, China 0000-0003-3139-1705View Profile , Sixu Duan School of Physics and Electronics, Shandong Normal University, China School of Physics and Electronics, Shandong Normal University, China 0000-0002-6284-7368View Profile , Huachao Chen School of Faculty of Applied Sciences, Macao Polytechnic University, China School of Faculty of Applied Sciences, Macao Polytechnic University, China 0000-0003-3603-9161View Profile , Mingwei Wang Department of Cardiovascular Medicine, Affiliated Hospital of Hangzhou Normal University, China and Clincial School of Medicine, Hangzhou Normal University, China Department of Cardiovascular Medicine, Affiliated Hospital of Hangzhou Normal University, China and Clincial School of Medicine, Hangzhou Normal University, China 0000-0001-9060-5107View Profile , Jiangang Chen Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, China and School of Communication & Eletronic Engineering, East China Normal University, China Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, China and School of Communication & Eletronic Engineering, East China Normal University, China 0000-0001-8775-0282View Profile , Tao Tan School of Faculty of Applied Sciences, Macao Polytechnic University, China School of Faculty of Applied Sciences, Macao Polytechnic University, China 0000-0002-5573-1105View Profile Authors Info & Claims CSSE '22: Proceedings of the 5th International Conference on Computer Science and Software EngineeringOctober 2022Pages 319–324https://doi.org/10.1145/3569966.3570060Published:20 December 2022Publication History 0citation34DownloadsMetricsTotal Citations0Total Downloads34Last 12 Months34Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Utilizing CNNs for Cryptanalysis of Selective Biometric Face Sample Encryption

When storing face biometric samples in accordance with ISO/IEC 19794 as JPEG2000 encoded images, it is necessary to encrypt them for the sake of users’ privacy. Literature suggests selective encryption of JPEG2000 images as fast and efficient method for encryption, the trade-off is that some information is left in plaintext. This could be used by an attacker, in case the encrypted biometric samples are leaked. In this work, we will attempt to utilize a convolutional neural network to perform cryptanalysis of the encryption scheme. That is, we want to assess if there is any information left in plaintext in the selectively encrypted face images which can be used to identify the person. The chosen approach is to train CNNs for biometric face recognition not only with plaintext face samples but additionally conduct a refinement training with partially encrypted data. If this system can successfully utilize encrypted face samples for biometric matching, we can show that the information left in encrypted biometric face samples is information actually usable for biometric recognition.The method works and we can show that a supposedly secure biometric sample still contains identifying information on average over the whole database.
Few-shot Graph Classification with Contrastive Loss and Meta-classifier

Few-shot graph-level classification based on graph neural networks is critical in many tasks including drug and material discovery. We present a novel graph contrastive relation network (GCRNet) by introducing a practical yet straightforward graph meta-baseline with contrastive loss to gain robust representation and meta-classifier to realize more suitable similarity metric, which is more adaptive for graph few-shot problems. Experimental results demonstrate that the proposed method achieves 8%-12% in 5-shot, 5%-8% in 10 shot, and 1%-5% in 20-shot improvements, respectively, compared to the existing state-of-the-art methods.
WagerWin: An Efficient Reinforcement Learning Framework for Gambling Games

Although reinforcement learning (RL) has achieved great success in diverse scenarios, complex gambling games still pose great challenges for RL. Common deep RL methods have difficulties maintaining stability and efficiency in such games. By theoretical analysis, we find that the return distribution of a gambling game is an intrinsic factor of this problem. Such return distribution of gambling games is partitioned into two parts, depending on the win/lose outcome. These two parts represent the gain and loss. They repel each other because the player keeps “raising,” i.e., making a wager. However, common deep RL methods directly approximate the expectation of the return, without considering the particularity of the distribution. This way causes a redundant loss term in the objective function and a subsequent high variance. In this work, we propose WagerWin, a new framework for gambling games. WagerWin introduces probability and value factorization to construct a more effective value function. Our framework removes the redundant loss term of the objective function in training. In addition, WagerWin supports customized policy adaptation, which can tune the pretrained policy for different inclinations. We conduct extensive experiments on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DouDizhu</i> and SmallDou, a reduced version of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DouDizhu</i> . The results demonstrate that WagerWin outperforms the original state-of-the-art RL model in both training efficiency and stability.
Efficient Two-level RNS Reverse Conversion Algorithm for Big Number of Modules

Residue Number System (RNS) has a big potential for the hardware accelerators due to its high area and performance efficiency for arithmetic computations based on multiplications and additions only. The choice of moduli set has a significant impact on the RNS efficiency. In general case, using arbitrary modulo, an increase in the number of moduli leads to the corresponding decrease of the average modulo size achieving less hardware area and power consumption, but this usually makes conversion algorithms less efficient. This article presents a two-level method for reverse conversion of a number in RNS to a Binary Number System (BNS) based on a combination of Core-function based methods and Mixed Radix Conversion (MRC) based methods for three-moduli set 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> + 1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> – 1. We demonstrated how to choose modulo properly in order to achieve up to 64% of hardware area reduction, up to 97% of power consumption reduction and become up to 53% faster than traditional CRT-based methods.
GAE-LCT: A Run-Time GA-Based Classifier Evolution Method for Hardware LCT Controlled SoC Performance-Power Optimization

Learning classifier tables (LCTs) are classifier based and lightweight hardware reinforcement learning building blocks which inherit the concepts of learning classifier systems. LCTs are used as a per-core low level controllers to learn and optimize potentially conflicting objectives e.g. achieving a performance target under a power budget. A supervisor is used at the system level which translate system and application requirements into objectives for the LCTs. The classifier population in the LCTs has to be evolved in run-time to adapt to the changes in the mode, performance targets, constraints or workload being executed. Towards this goal, we present GAE-LCT, a genetic algorithm (GA) based classifier evolution for hardware learning classifier tables. The GA uses accuracy to evolve classifiers in run-time. We introduce extensions to the LCT to enable accuracy based genetic algorithm. The GA runs as a software process on one of the cores and interacts with the hardware LCT via interrupts. We evaluate our work using DVFS on an FPGA using Leon3 cores. We demonstrate GAE-LCT’s ability to generate accurate classifiers in run-time from scratch. GAE-LCT achieves 5% lower difference to IPS reference and 51.5% lower power budget overshoot compared to Q-table while requiring 75% less memory. The hybrid GAE-LCT also requires 12 times less software overhead compared to a full software implementation.
GAN with Multivariate Disentangling for Controllable Hair Editing

Hair editing is an essential but challenging task in portrait editing considering the complex geometry and material of hair. Existing methods have achieved promising results by editing through a reference photo, user-painted mask, or guiding strokes. However, when a user provides no reference photo or hardly paints a desirable mask, these works fail to edit. Going a further step, we propose an efficiently controllable method that can provide a set of sliding bars to do continuous and fine hair editing. Meanwhile, it also naturally supports discrete editing through a reference photo and user-painted mask. Specifically, we propose a generative adversarial network with a multivariate Gaussian disentangling module. Firstly, an encoder disentangles the hair's major attributes, including color, texture, and shape, to separate latent representations. The latent representation of each attribute is modeled as a standard multivariate Gaussian distribution, to make each dimension of an attribute be changed continuously and finely. Benefiting from the Gaussian distribution, any manual editing including sliding a bar, providing a reference photo, and painting a mask can be easily made, which is flexible and friendly for users to interact with. Finally, with changed latent representations, the decoder outputs a portrait with the edited hair. Experiments show that our method can edit each attribute's dimension continuously and separately. Besides, when editing through reference images and painted masks like existing methods, our method achieves comparable results in terms of FID and visualization. Codes can be found at https://github.com/XuyangGuo/CtrlHair .
Transform Your Smartphone into a DSLR Camera: Learning the ISP in the Wild

We propose a trainable Image Signal Processing (ISP) framework that produces DSLR quality images given RAW images captured by a smartphone. To address the color misalignments between training image pairs, we employ a color-conditional ISP network and optimize a novel parametric color mapping between each input RAW and reference DSLR image. During inference, we predict the target color image by designing a color prediction network with efficient Global Context Transformer modules. The latter effectively leverage global information to learn consistent color and tone mappings. We further propose a robust masked aligned loss to identify and discard regions with inaccurate motion estimation during training. Lastly, we introduce the ISP in the Wild (ISPW) dataset, consisting of weakly paired phone RAW and DSLR sRGB images. We extensively evaluate our method, setting a new state-of-the-art on two datasets. The code is available at https://github.com/4rdhendu/TransformPhone2DSLR .