Reviewerly/exHarmony huggingface dataset:
Comparative Study of Multi-hole Drill Path Optimization using Evolutionary Algorithms

Multi-hole drilling is one of the frequently used manufacturing processes in industries. Further, in mass production, multi-hole drilling path optimization plays a crucial role to remain cost-competitive. Usually, the multihole drilling problems are modeled as traveling salesman problems (TSP), and solving them is quite difficult as they fall in the category of NP-hard problems. Researchers have used various evolutionary optimization algorithms like a Genetic algorithm (GA), Particle swarm optimization (PSO), and Ant colony optimization (ACO) to optimize the multi-hole drill path sequences. Literature reveals that Simulated Annealing (SA) is one of the least used standalone algorithms in multihole drilling path optimization even though it is one of the promising algorithms in the presence of multiple local minima. This paper aims to study the performance of SA and compare it with GA, PSO, and, ACO using multi-hole drill path optimization test problems reported in the literature. The outcome reveals that the SA performs quite well.
Sentiment Analysis Model for KlikIndomaret Android App During Pandemic Using Vader and Transformers NLTK Library

COVID-19 has changed the Indonesian people’s shopping habits for consumer goods. The online retail application came as a response to social distancing and stay-at-home advice. KlikIndomaret is an online retail application that uses the omnichannel concept. As the number of downloads increased, the number of various comments and sentiments on that application also increased. In this study, the researcher did a sentiment analysis aimed to improve the quality of application experiences and retail services. The result of the analysis reflected the services given to customers thus far. The data included reviews and star ratings derived from 4,066 reviews which went under the process of data pre-processing. The methods used in this study were VADER and NLTK, improved by Transformer, without pre-training data. These methods could filter the users’ reviews with sarcasm tone. The results were sentiment labels that were appropriate based on the score comparison of positive and negative sentiments in one user’s review. This approach made the review sentiment process of thousands of data faster and more accurate.
Speech Denoising with Maximal Overlap Discrete Wavelet Transform

In this paper, the effectiveness of the maximum overlapping discrete wavelet transform (MODWT) method on denoising the speech signal is tested and examined. Ensuring the intelligibility of the speech signal in noisy environments by separating it from the noise is a widely researched topic today. On the other hand, being able to recover the original speech from the noisy signal with minimal distortion is a challenge due to the difficulties in removing the background noise. Numerous factors in environmental noise environments can interfere with the signal. In this study, the performance of some discrete wavelets transform methods is experimentally analyzed using different wavelet filters. The analysis program was carried out in the MATLAB environment. As the input noise speech signal, speech sounds containing different environmental background noises (train, car, station, plane, etc.) were analyzed. During the tests, these noisy input signals were filtered out from the speech signal by wavelet analysis. The input noisy speech signal is decomposed into wavelet coefficients with different thresholding methods. The reconstructed speech was compared by measuring the signal-to-noise ratio (SNR) values between the noisy input signal and the smoothed output signals. The scientific contributions of the study include a detailed comparative analysis of the performances of various wavelet methods against different background environmental noises.
Phishing for Legitimacy: The Use of SSL Certificates to Ensnare Internet Users

This paper examines the impact of making Hypertext Transfer Protocol Secure (HTTPS) certificates more accessible to the public. On the one hand, such an approach facilitates the process for small and large businesses to acquire certifications from Certificate Authorities (CAs), making their clients feel secure. On the other hand, such accessibility enabled many phishers to take advantage of this and pose as legitimate entities. This paper illustrates the phishers' eagerness to imitate existing websites. Furthermore, we will explore the role and responsibility of several parties, namely the Certificate Authority (CA), the browser provider, the website, and the Internet users. The paper also analyzes the results of a survey conducted to determine university students' understanding of HTTPS meaning and offers recommendations to overcome this issue.
Work-in-Progress: Victim-Aware Scheduling for Robust Operations in Safety-Critical Systems

With ever-increasing attacks against learning-enabled components (LECs) in safety-critical systems, it has become more challenging to ensure robust operations. By focusing on anterior and posterior attacks on LECs, where malicious tasks need to run before and after a victim task, respectively, to launch attacks, we study in this work the victim-aware fixed-priority scheduling in single processor systems. Specifically, by exploiting the preference-oriented fixed-priority (POFP) scheduler, we devise a Victim-Aware Priority Assignment (VAPA) scheme to assign different priorities for victim tasks that are subject to anterior and posterior attacks, respectively. VAPA aims at reducing both anterior and posterior attacking occasions in the resultant schedule and thus enhancing the robust operations of the victim tasks. Online adaptation is also considered by exploiting idle time slots to further remove such attacking occasions whenever possible. The main ideas of the victim-aware scheduling are illustrated via a concrete example and future work is discussed.
The Art Applied to Business

The article presents an overview of Russian authors’ publications, mainly from the first issues of the journal “Technical Aesthetics” of the All-Union Scientific Research Institute of Technical Aesthetics (VNIITE). The purpose of this study is to highlight the controversial issues and problems relevant at the initial stages of forming the theory of a new type of artistic and industrial activities: technical aesthetics. The study’s relevance is determined by the fact that in the theory of design, at present, the aspect of the “art — design” linking is considered systemically incomplete. While the stages of the development of Russian design in chronological and biographical perspectives are covered quite fully by K.M. Kantor, N.V. Voronov, Yu.V. Nazarov, G.V. Vershinin and a number of other authors, but the theoretical aspects require further research development. The years of the Institute’s active work (1960s—1980s) are called fruitful in conceptual, scientific and creative terms, it was then that the professional model of the service and interaction between design and the planned economy was finally formed. The article shows that, at that time, intensive discussions of the main problems of artistic design and the provisions of aesthetics were reflected in regular publications of a number of periodicals, especially the journals “Technical Aesthetics” and “Decorative Art of the USSR”; and the difficult question of the criteria for evaluating the aesthetic qualities of industrial products turned out to be paramount. The term “artistic construction” implied the solution of the problem of bringing art and science closer to industry. In this process, in the discourse of the principles of technical and aesthetic creativity, all employees of the Institute — theorists and practitioners who would become outstanding design specialists — took part. The VNIITE’s activities met the demand of the time, the task of national importance — to establish mass production of a decent aesthetic level of industrial products. The period of 1964—1969 is the first stage in the study of the Soviet design formation history and is associated with intensive studying the heritage of the 1920s, introducing new materials into scientific circulation, formulating a number of new problems, and developing criteria for an objective assessment of many phenomena. It was accompanied by a number of difficulties in the relation between the settings of artistic construction and Russian art studies. This article justifies the importance of the work of the pioneers in the field of design in connection with other fields of art, and specifically with architecture and printing.
Examining apparel design students’ self-efficacy towards using virtual reality in the design process

ABSTRACTVirtual reality (VR) is an emerging technology in garment design that has demonstrated promising student achievement results (Wyss et al., 2014 [Cotton Island: Students’ learning motivation using a virtual world. Decision Sciences Journal of Innovative Education, 12(3), 219–232]). When educators and institutions adopt new technologies, they need to consider how the unfamiliar technology will affect student’s self-efficacy levels and to design lesson plans accordingly. Therefore, the purpose of this initial study is to examine students’ self-efficacy levels toward the adoption of VR as a design sketching tool. A multiple case study design was employed in this study. Eight undergraduate Apparel Design students enrolled in an Apparel Design course constituted the sample. Data were collected using a self-efficacy scale, and open-ended questions. The data examined levels of self-efficacy in VR use as well as factors that impacted those levels. The findings failed to show differences among factors that influence students’ self-efficacy in VR usage at each self-efficacy level. Based on the findings, research propositions were confirmed or modified, or were additionally proposed.KEYWORDS: Self-efficacyvirtual realitytechnologyapparel design processteaching Disclosure statementNo potential conflict of interest was reported by the author(s).
Measuring sampling fairness and geochemical consensus for blasthole assays within grade-block mining units

In the mining industry, current grade control practices lack a standardised framework that can assess the reliability of average grade estimates computed for selective mining units (otherwise known as grade-blocks) within a mining bench. This article describes two measures that can quantify sampling fairness and geochemical consensus. Concretely, sampling fairness considers spatial factors such as sampling density and bias in the spatial distribution of blastholes whereas geochemical consensus considers the agreement between the assay samples within a grade-block. Geochemical disparity is measured using a robust distance estimator and a masking formula that takes into account the proportion of outliers and magnitude of differences observed above a threshold. The efficacy of the consensus measure is demonstrated through validation experiments. The results confirm the MCD (minimum covariance determinant) robust estimator can breakdown when the fraction of outliers exceeds (n−k−1)/(2n). For k≥2 variables and a sample size n≥10, this typically leads to an underestimation of the true extent and impact of outliers when they exceed 40%. An extension based on split-sequence analysis is proposed to overcome this limitation. The method is evaluated using production data from an open-pit iron ore deposit. An open-source implementation of the proposed algorithms is available on GitHub.
Towards better utilization of pseudo labels for weakly supervised temporal action localization

Weakly supervised temporal action localization (WS-TAL) aims to simultaneously recognize and localize action instances of interest in untrimmed videos with the use of the video-level label only. Some works have demonstrated that pseudo labels play an important role for performance improvement in WS-TAL. Since pseudo labels are inevitably inaccurate, direct adoption of noisy labels can lead to inappropriate knowledge transfer. Although some previous studies have shown the benefits of using only "reliable" pseudo labels, performance improvement is still limited. In this work, we experimentally analyze how the noise in pseudo labels affects model performance within the self-distillation framework. Motivated by the finding that incorrect pseudo labels with large confidence scores have a significant impact on performance, we propose the overconfidence suppression (OCS) strategy to mitigate the effect of the overconfident pseudo labels, and thus prevent over-fitting of the student model. In addition, a simplified contrast learning method is utilized to fine-tune the feature representation by increasing the separation of the foreground and background snippets. Equipped with the proposed methods, the benefits of pseudo labels can be better exploited and allow the model to achieve state-of-the-art performance on THUMOS'14 and ActivityNet-1.2 benchmarks.
Locating Source Code Bugs in Software Information Systems Using Information Retrieval Techniques

Bug localization is the process through which the buggy source code files are located regarding a certain bug report. Bug localization is an overwhelming and time-consuming process. Automating bug localization is the key to help developers and increase their productivities. Expanding bug reports with more semantic and increasing software understanding using information retrieval and natural language techniques will be the way to locate the buggy source code file, in which the bug report works as a query and source code as search space. This research investigates the effect of segmenting open source files into executable code and comments, as they have a conflicting nature, seeks the effect of synonyms on the accuracy of bug localization, and examines the effect of “part-of-speech” techniques on reducing the manual inspection for appropriate synonyms. This research aims to approve that such methods improve the accuracy of bug localization tasks. The used approach was evaluated on three Java open source software, namely Eclipse 3.1, AspectJ 1.0, and SWT 3.1; we implement our dedicated Java tool to adopt our methodology and conduct several experiments on each software. The experimental results reveal a considerable improvement in recall and precision levels, and the developed methods display an accuracy improvement of 4–10% compared with the state-of-the-art approaches.
Assessment of user-interaction strategies for neurosurgical data navigation and annotation in virtual reality

While virtual-reality (VR) has shown great promise in radiological tasks, effective user-interaction strategies that can improve efficiency and ergonomics are still under-explored and systematic evaluations of VR interaction techniques in the context of complex anatomical models are rare. Therefore, our study aims to identify the most effective interaction techniques for two common neurosurgical planning tasks in VR (point annotation and note-taking) from the state-of-the-arts, and propose a novel technique for efficient sub-volume selection necessary in neuroanatomical navigation. We assessed seven user-interaction methods with multiple input modalities (gaze, head motion, controller, and voice) for point placement and note-taking in the context of annotating brain aneurysms for cerebrovascular surgery. Furthermore, we proposed and evaluated a novel technique, called magnified selection diorama (Maserama) for easy navigation and selection of complex 3D anatomies in VR. Both quantitative and semi-quantitative (i.e., NASA Task Load Index) metrics were employed through user studies to reveal the performance of each interaction scheme in terms of accuracy, efficiency, and usability. Our evaluations demonstrated that controller-based interaction is preferred over eye-tracking-based methods for point placement while voice recording and virtual keyboard typing are better than freehand writing for note-taking. Furthermore, our new Maserama sub-volume selection technique was proven to be highly efficient and easy-to-use. Our study is the first to provide a systematic assessment of existing and new VR interaction schemes for neurosurgical data navigation and annotation. It offers valuable insights and tools to guide the design of future VR systems for radiological and surgical applications.
Combining hybrid DNN-HMM ASR systems with attention-based models using lattice rescoring

The traditional hybrid deep neural network (DNN)–hidden Markov model (HMM) system and attention-based encoder–decoder (AED) model are both commonly used automatic speech recognition (ASR) approaches with distinct characteristics and advantages. While hybrid systems are per-frame-based and highly modularised to leverage external phonetic and linguistic knowledge, AED models operate on a per-label basis and jointly learn the acoustic and language information using a single model in an end-to-end trainable fashion. In this paper, we propose combining these two approaches in a two-pass rescoring framework. The first-pass uses hybrid ASR systems to facilitate streaming and controllable ASR, and the second-pass re-scores the N-best hypotheses or lattices produced by the first-pass hybrid DNN-HMM system with AED models. We also propose an improved algorithm for lattice rescoring with AED models. Experiments show the combined two-pass systems achieve competitive performance without using extra speech or text data on two standard ASR tasks. For the 80-hour AMI IHM dataset, the combined system has a 13.7% word error rate (WER) on the evaluation set and is up to a 29% relative WER reduction over the individual systems. For the 300-hour Switchboard dataset, the WERs of the combined system are 5.7% and 12.1% on Switchboard and CallHome subsets of Hub5'00, and 13.2% and 7.6% on Switchboard Cellular and Fisher subsets of RT03, and are up to a 33% relative reduction in WER over the individual systems.
Optimized video compression with residual split attention and swin-block artifact contraction

Research in video compression has seen significant advancement in the last several years. However, the existing deep learning-based algorithms continue to be plagued by erroneous motion compression and ineffective motion compensation architectures, resulting in compression errors with a lower rate–distortion trade-off. To overcome these challenges, we present an end-to-end purely deep learning-based video compression method through a set of primary operations (e.g., motion estimation, motion compression, motion compensation, residual compression, and artifact contraction) differently. A deep residual attention split (DRAS) block is introduced for motion compression networks to pay more attention to certain image regions to create more effective features for the decoder while boosting the rate–distortion optimization (RDO) efficiency. A channel residual block (CRB) is proposed in motion compensation to yield a more accurate predicted frame, potentially improving the residual frame. To mitigate the compression errors, an artifact contraction module (ACM) by residual swin convolution UNet block is included in this model to improve the reconstruction quality. To improve the final frame, a buffer is added to fine-tune the previous reference frames. These modules combine with a loss function by assessing the trade-off and enhancing the decoded video quality. A comprehensive ablation study demonstrates the effectiveness of the proposed blocks and modules for video compression. Experimental results show the competitive performance of the proposed method on four benchmark datasets.
Sentiment and Storytelling: What Affect User Experience and Communication Effectiveness in Virtual Environments?

The use of virtual reality (VR) storytelling in cultural communication is increasing and has found its way into the journalism, tourism, museum, and exhibition industries. Earlier studies have examined VR storytelling to improve user experience (UX). However, there is still insufficient research on UX and communication effectiveness in an immersive virtual environment (IVE) in storytelling involving different sentiments. In this study, participants watched positive and negative news stories in three IVEs: 2D video, 360-degree video via mobile devices, and 360-degree video with a VR headset. The predictor variables of enjoyment and the impact of presence, flow, understanding, empathy, credibility, and enjoyment across the stories in IVEs were analyzed. Two models were constructed based on positive and negative stories. The findings show that predictor variables make different contributions to VR storytelling with different sentiments. The conclusions support IVE production in journalism based on sentiment to further improve UX and enhance communication effectiveness.
DeepCreativity: measuring creativity with deep learning techniques

Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores the possibility of using generative learning techniques for automatic assessment of creativity. The proposed solution does not involve human judgement, it is modular and of general applicability. We introduce a new measure, namely DeepCreativity, based on Margaret Boden’s definition of creativity as composed by value, novelty and surprise. We evaluate our methodology (and related measure) considering a case study, i.e., the generation of 19th century American poetry, showing its effectiveness and expressiveness.
Progressive unsupervised video person re-identification with accumulative motion and tracklet spatial–temporal correlation

Person re-identification (re-id) methods based on supervised learning require a large number of manually labeled samples for training, resulting in poor scalability in actual re-id applications. Existing unsupervised video person re-id methods typically focus on extracting appearance features from pedestrian videos, ignoring motion information and the fact that people usually move in groups, i.e., pedestrian spatio-temporal co-occurrence patterns. The key factor for unsupervised video person re-id is to effectively exploit both spatio-temporal clues from video sequences and cross-camera tracklet association. In this work, we propose a progressive deep learning method for unsupervised person re-id via tracklet association with spatio-temporal correlation (TASTC). Specifically, we first divide uniformly each tracklet into multiple temporally localized slices according to a time pyramid structure. Then, an initial re-id model is trained based on a two-stream convolutional architecture, and the accumulative motion context information of temporally localized slices of the tracklets per camera is learned. Finally, combining accumulative motion and tracklet spatial–temporal correlation, we associate tracklets across cameras and update the re-id model. The above steps are iterated to optimize the re-id model progressively. Experiment results demonstrate that the proposed method significantly outperforms the current state-of-the-art unsupervised video person re-identification methods on three video-based benchmark datasets, ILIDS-VID, MARS, and DukeMTMC-VideoReID.