Reviewerly/exHarmony huggingface dataset:
Capture the Feature Flag

Feature flags (a.k.a feature toggles) are a mechanism to keep new features hidden behind a boolean option during development. Flags are used for many purposes, such as A/B testing and turning off a feature more easily in case of failures. While software engineering research on feature flags is burgeoning, examples of software projects using flags rarely come from outside commercial and private projects, stifling academic progress. To address this gap, in this paper we present a novel semi-automated mining software repositories approach to detect feature flags in open-source projects, based on analyzing the projects' commit messages and other project characteristics. With our approach, we search over all open-source GitHub projects, finding multiple thousand plausible and active candidate feature flagging projects. We manually validate projects and assemble a dataset of 100 confirmed feature flagging projects. To demonstrate the benefits of our detection technique, we report on an initial analysis of feature flags in the validated sample of 100 projects, investigating practices that correlate with shorter flag lifespans (typically desirable to reduce technical debt), such as using the issue tracker and having a flag owner.
Towards a Modern Ontology Development Environment

Ontologies provide engineers and developers with an unambiguous, verifiable, and expandable knowledge base related to a certain domain. Every project that requires control over consistent knowledge, which is especially relatable when using artificial intelligence with datasets increasing in size every second, would reap benefits from adding ontologies to the equation. It is a powerful asset enabling the development of a project with integrity between platforms or teams. Unfortunately, the cost of entry for a developer into the ontology engineering area is high, as it has been proven over the last decades that developing an ontology is a complex, collaborative task, which requires the support of an adequate methodology as well as software tools. The current paper’s objective is twofold. First, it provides a survey on the methodology and software tools used for the creation of the ontology, its maintenance and collaboration. The paper investigates how the tools evolved over the years and what trends have emerged. Second, as the result of the analysis conducted, we show that current solutions have deficiencies and a technological debt; therefore, we present our plan to build a modern tool that uses state-of-the-art technology.
Eye of the mind

Developers are increasingly sharing images in social coding environments alongside the growth in visual interactions within social networks. The analysis of the ratio between the textual and visual content of Mozilla's change requests and in Q/As of StackOverflow programming revealed a steady increase in sharing images over the past five years. Developers' shared images are meaningful and are providing complementary information compared to their associated text. Often, the shared images are essential in understanding the change requests, questions, or the responses submitted. Relying on these observations, we delve into the potential of automatic completion of textual software artifacts with visual content.
ZTIMM: A Zero-Trust-Based Identity Management Model for Volunteer Cloud Computing

The availability of efficient, affordable, green computing alternatives for emerging paradigms, such as edge, mobile, and volunteer cloud computing, is growing. But these models are plagued by security issues, such as in volunteer cloud computing, where trust among entities is nonexistent. This paper presents a zero-trust model that assumes no trust for any volunteer node and always verifies using a server-client model. This paper proposes an adaptive behavior evaluation model that estimates each VN’s trust score. Therefore, tasks are assigned to the most trusted VN in the system. A series of trust-aware mechanisms to handle VN’s life cycle is also presented.
Automatic profile recognition of authors on social media based on hybrid approach

In this paper, we propose a method to discover the profile of a user on social media by detecting their gender, age, and personality traits. The stated method takes advantage of NLP techniques, lexico-semantic resources and statistical measures. These techniques have allowed us to have a more general method which isn't limited to the words founded in the training corpus and preserve the principle of relativity by taking advantage of the fuzzy logic. The previously mentioned method was implemented and evaluated using the PAN 2015 corpus (Author profiling).We obtained as a result, an F-measure value which equals to 81%. The found results are indeed interesting when compared to other works.
Operation and Security Considerations of Federated Learning Platform Based on Compute First Network

To provide public federated learning services based on the Compute First Network, the Federated Learning Platform is proposed for communication service providers in this paper. Operation and security are taken into consideration to assure and secure service delivery. The generic framework of Federated Learning Platform is designed to meet the fundamental requirements. Based on the TM Forum Business Process Framework, the specific operation functionalities are introduced. Also, those security issues are addressed by adding a security domain, and a cryptography infrastructure is particularly discussed herein to make connections among the federated communication parties trusted, ensuring mutual authentication and interaction data protection.
Segmenting 2K-Videos at 36.5 FPS with 24.3 GFLOPs: Accurate and Lightweight Realtime Semantic Segmentation Network

We propose a fast and lightweight end-to-end convolutional network architecture for real-time segmentation of high resolution videos, NfS-SegNet, that can segement 2K-videos at 36.5 FPS with 24.3 GFLOPS. This speed and computation-efficiency is due to following reasons: 1) The encoder network, NfS-Net, is optimized for speed with simple building blocks without memory-heavy operations such as depthwise convolutions, and outperforms state-of-the-art lightweight CNN architectures such as SqueezeNet [2], Mo- bileNet v1 [3] & v2 [4] and ShuffleNet v1 [5] & v2 [6] on image classification with significantly higher speed. 2) The NfS- SegNet has an asymmetric architecture with deeper encoder and shallow decoder, whose design is based on our empirical finding that the decoder is the main bottleneck in computation with relatively small contribution to the final performance. 3) Our novel uncertainty-aware knowledge distillation method guides the teacher model to focus its knowledge transfer on the most difficult image regions. We validate the performance of NfS-SegNet with the CITYSCAPE [1] benchmark, on which it achieves state-of-the-art performance among lightweight segementation models in terms of both accuracy and speed.
Multi-Ontology Refined Embeddings (MORE): A hybrid multi-ontology and corpus-based semantic representation model for biomedical concepts

Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that concepts are not effectively referenced in various forms across different texts. This paper introduces Multi-Ontology Refined Embeddings (MORE), a novel hybrid framework that incorporates domain knowledge from multiple ontologies into a distributional semantic model, learned from a corpus of clinical text.We use the RadCore and MIMIC-III free-text datasets for the corpus-based component of MORE. For the ontology-based part, we use the Medical Subject Headings (MeSH) ontology and three state-of-the-art ontology-based similarity measures. In our approach, we propose a new learning objective, modified from the sigmoid cross-entropy objective function.We used two established datasets of semantic similarities among biomedical concept pairs to evaluate the quality of the generated word embeddings. On the first dataset with 29 concept pairs, with similarity scores established by physicians and medical coders, MORE's similarity scores have the highest combined correlation (0.633), which is 5.0% higher than that of the baseline model, and 12.4% higher than that of the best ontology-based similarity measure. On the second dataset with 449 concept pairs, MORE's similarity scores have a correlation of 0.481, based on the average of four medical residents' similarity ratings, and that outperforms the skip-gram model by 8.1%, and the best ontology measure by 6.9%. Furthermore, MORE outperforms three pre-trained transformer-based word embedding models (i.e., BERT, ClinicalBERT, and BioBERT) on both datasets.MORE incorporates knowledge from several biomedical ontologies into an existing corpus-based distributional semantics model, improving both the accuracy of the learned word embeddings and the extensibility of the model to a broader range of biomedical concepts. MORE allows for more accurate clustering of concepts across a wide range of applications, such as analyzing patient health records to identify subjects with similar pathologies, or integrating heterogeneous clinical data to improve interoperability between hospitals.
Application and Research of VR Virtual Technology in Film and Television Art

The development of the film is limited by the level of science and technology. Every technological revolution will bring a huge impact and change to the artistic concept of the film. Now film art is actively combined with virtual reality Virtua * Reality (VR for short) to introduce virtual reality technology into film production. A new generation of film revolution led by virtual reality is being conceived. Virtua * Reality (abbreviated as VR) is simply translated into virtual reality. It is a compound discipline that creates and integrates multi-disciplines such as graphics, psychology and electronic engineering through computer technology. Through real-time dynamic stereoscopic images and multiple information technologies to enable users to communicate with the virtual world, while providing visual and real-time perceptions such as sight, hearing, and touch, so that participants can be immersed in it. Therefore, the essence of virtual reality is to continue to approach reality and challenge the limits of simulated reality, which is the asymptote of reality. Of course, the virtual reality that needs to be applied in the movie art is not necessarily rigid, strict and true objective reality reproduction, but the fiction, imagination and stimulating non-objective "virtual reality" required by the movie itself. This article attempts to explore the concept and application of virtual reality movies from the perspective of photography composition and post-production of virtual reality movies.
Concurrent phonetic transcription, lexical stress assignment and syllabification with deep neural networks

This paper evaluates four different sequence-to-sequence deep neural network architectures aimed to jointly solve the tasks of: phonetic transcription, lexical stress assignment and syllabification. These text processing tasks are considered essential components for high quality text-to-speech or automatic speech recognition systems, with the phonetic transcription being the most frequently used in these types of applications. Although each of the tasks has been individually and extensively analyzed in the scientific literature, there are few studies which target a concurrent solution for them. In general, the lexical stress assignment and syllabification are used as augmenting input features to the phonetic transcription model and not considered as target features. The proposed network architectures include recurrent, convolution and attention neural layers and were evaluated on hand-checked English and Romanian datasets. The accuracy of the models was evaluated in terms of accuracy for the concurrent prediction of all three tasks, as well as by discarding the syllabification or lexical stress predictions. The best results were obtained with a combination of convolution and attention layers, where the accuracy of the joint prediction for the three tasks was of 58.96% for English and 86.64% for Romanian. The same model for English obtains an accuracy of 59.70% when syllables are discarded and 64% when the prediction of lexical stress is ignored. With the same best performing model for Romanian an accuracy of 88.83% without syllables and 93.84% without lexical stress is obtained.
Children's Beliefs and Understanding of Smart Objects

Children's role in the design of new technology has been widely investigated. Recently, the research focus has shifted, from the technology they help create, towards what children gain by participating in design workshops. This paper intercepts this line of research. It reports on a design workshop with 27 children, aged from 11 to 14 years old, ideating, programming and prototyping smart objects for their town park. Data were gathered in relation to children's beliefs, before and after the workshop, and in relation to their understanding of design, after the workshop. The analysis of the gathered data suggests that the workshop positively affected children's beliefs and understanding of design, giving indications for future work concerning design as means of empowerment.
A Currency Arbitrage Machine Based on the Simulated Bifurcation Algorithm for Ultrafast Detection of Optimal Opportunity

We describe a cross-currency arbitrage machine using the simulated bifurcation (SB) algorithm for finding the most profitable exchange path from among many possible paths. SB is a recently proposed quantum-inspired algorithm for solving combinatorial optimization problems. The machine is an end-to-end arbitrage system implemented on a field-programmable gate array, in which a feed handler captures market packets issued at unscheduled intervals. An SB accelerator finds the optimal arbitrage path with a success probability of 90.96% by solving an optimal path-search problem in a directed graph, then a line handler issues an order packet within 30 microseconds after receiving the last market packet.
Software development effort estimation modeling using a combination of fuzzy-neural network and differential evolution algorithm

Software cost estimation has always been a serious challenge lying ahead of software teams that should be seriously considered in the early stages of a project. Lack of sufficient information on final requirements, as well as the existence of inaccurate and vague requirements, are among the main reasons for unreliable estimations in this area. Though several effort estimation models have been proposed over the recent decade, an increase in their accuracy has always been a controversial issue, and researchers' efforts in this area are still ongoing. This study presents a new model based on a hybrid of adaptive network-based fuzzy inference system (ANFIS) and differential evolution (DE) algorithm. This model tries to obtain a more accurate estimation of software development effort that is capable of presenting a better estimate within a wide range of software projects compared to previous works. The proposed method outperformed other optimization algorithms adopted from the genetic algorithm, evolutionary algorithms, meta-heuristic algorithms, and neuro-fuzzy based optimization algorithms, and could improve the accuracy using MMRE and PRED (0.25) criteria up to 7%.
Reputation-Based Trustworthy Supply Chain Management Using Smart Contract

Supply Chain Management (SCM) has been deemed a promising method to obtain higher-level profits, while traditional SCM methods are limited by its deficiency of trust system. Blockchain is an emerging technology to achieve decentralized trusted transactions with high level automation and security, which is adoptable to address the limitation of the trust deficiency. In this paper, a blockchain-enabled reputation-based trustworthy SCM model is proposed in order to achieve trusted SCM. Besides, we propose a smart contract-based reputation rating model for achieving incentive SCM, which uses tokens to assess reputation and realize reputation reward and punishment.
The relevance of virtual reality to communication design

Many design disciplines have made use of Virtual Reality (VR) technology within the design process but is the technology appropriate for adoption into communication design? Using an interview study with professional communication design practitioners, this paper presents insights on the perceived relevance of VR technology to the profession. Findings indicate that while participants saw potential utility in VR as a tool for communication design practice, it was not perceived as immediately suitable for professional use due to various factors which are identified and discussed.
Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks

Artificial neural networks are well-known to be susceptible to catastrophic forgetting when continually learning from sequences of tasks. Various continual (or "incremental") learning approaches have been proposed to avoid catastrophic forgetting, but they are typically adversary agnostic, i.e., they do not consider the possibility of a malicious attack. In this effort, we explore the vulnerability of Elastic Weight Consolidation (EWC), a popular continual learning algorithm for avoiding catastrophic forgetting. We show that an intelligent adversary can take advantage of EWC's continual learning capabilities to cause gradual and deliberate forgetting by introducing small amounts of misinformation to the model during training. We demonstrate such an adversary's ability to assume control of the model via injection of backdoor attack samples on both permuted and split benchmark variants of the MNIST dataset. Importantly, once the model has learned the adversarial misinformation, the adversary can then control the amount of forgetting of any task. Equivalently, the malicious actor can create a "false memory" about any task by inserting carefully-designed backdoor samples to any fraction of the test instances of that task. Perhaps most damaging, we show this vulnerability to be very acute; the model memory can be easily compromised with the addition of backdoor samples into as little as 1% of the training data of even a single task.
A Novel Investigation of Attack Strategies via the Involvement of Virtual Humans: A User Study of Josh Waitzkin, a Virtual Chess Grandmaster

A growing body of evidence suggests that attack is a significant concept that has been explored by researchers from various disciplines such as marketing, psychology, and computing. Additionally, there has been substantial research undertaken on the role of attack in chess, which brought significant contributions to different fields of research. In this paper, the researcher investigates the attack concept in chess, as a strategic game by exploring virtual chess players of different strategies. In particular, the researcher explores the performance of an attacking grandmaster against three other class-A players of different chess personalities that vary in controlling the center of the chessboard. To this end, the researcher collected data from four virtual chess players: a grandmaster and three class-A players. The selected grandmaster is Josh Waitzkin who is known for his fearless attacking style and deep endgame understanding. The class-A players have different personalities: (1) a player who strongly controls the center of the board; (2) a player who ignores the center; and (3) a player who offers traps to control his opponent. The researcher measured different dependent variables including the errors of the players and the moves of the games. The findings show that class-A players of particular chess personalities perform differently. Additionally, the study reveals that there is a positive relationship between the findings and some of the existing real-life scenarios in the business domain.
HBUCNNA: Hybrid Binary-Unary Convolutional Neural Network Accelerator

Convolutional layers account for 90% of the total computational power of Convolutional Neural Networks (CNNs). Field programmable gate arrays (FPGAs) have shown great potential for accelerating inference tasks in CNNs. However, it is harder for FPGA platforms to deliver the best performance due to high computational power and high memory bandwidth requirements of today's CNNs. In this paper, we propose a reconfigurable parallel-pipelined Hybrid Binary-Unary CNN Accelerator (HBUCNNA) to implement low-cost, high-performance convolutional layers of a ResNet-18 architecture. We use the hybrid binary-unary method to implement banks of constant-coefficient multipliers, which are used to implement convolutional kernels. Moreover, we propose hybrid binary-unary batch normalization units to further improve the total hardware costs. These two units reduce {area, area×delay} costs by {50%, 30%} and {44%, 65%} on average compared to their conventional binary counterparts, respectively. The proposed accelerator stores control signals for reconfigurability instead of the numeric value of weights, which in turn reduces the memory footprint on average by 20%. Overall, the proposed HBUCNNA architecture reduces the {area, latency, power, energy, area×delay} costs on average by {25.5%, 40%, 15%, 40%, 47%} and {53%, 61%, 47%, 62%, 67%} compared to the constant-coefficient multiplier-based and variable size multiplier-based binary architectures, respectively. Moreover, the proposed accelerator improves the throughput by about 1.4 × compared to both of the mentioned architectures.
Choice of method imputation missing values for obstetrics clinical data

Clinical decision support systems use patients’ various medical records: medical history, laboratory diagnostics, doctor examination, etc. However, the data may have missing values, which may affect the diagnosis. The purpose of this article was to compare eight missing values imputation algorithms for obstetric data, which will further be used in the work of CDSS. The smallest error was shown by Iterative Imputer and fast-KNN algorithms. Preliminary transformation such as dividing data into several groups by gestational age helped to improve the results but not for all data parameters.
Designing Virtual Training Environments: Does Immersion increase Task Performance?

One of the main characteristics of virtual reality (VR) is immersion, which leads to comprehensive illusions of reality.Accordingly, VR is used in many applications like entertainment, marketing, and training.Especially in training applications, the effect of immersion on training success is still not entirely clear, since too much immersion may cause side effects such as users experiencing high mental demand whereas too little may disturb users' well-being.To further investigate the matter, we developed two virtual training environments, wherein users train a typical industrial assembly task either in low or high immersive VR.In a controlled pilot study, we additionally introduced a third condition, the control group, which justifies the necessity of the training.Immediately after the VR training session, each participant completed the corresponding real assembly task in which their performance was measured.Preliminary results from our pilot study show that participants trained in high immersive VR performed better, while negative side effects could not be detected.