Reviewerly/exHarmony huggingface dataset:
Literature Review: Orientation and Mobility Assistive Technology for Students with Visual Impairment

The development of increasingly sophisticated technology is accompanied by the discovery of new assistive devices that should be accessible to all people, including the blind in order to maintain or improve functions so as to improve welfare. This study uses a literature review method with descriptive analysis by analyzing 20 articles. The purpose of this research is to examine more related to assistive technology for the blind in navigating indoors and outdoors including buildings and the accessibility of public services. The literature search was carried out through an electronic search for publications from ERIC, Researchgate, Spingerlink, Sage, Science Direct, Google Scholar and IEEE Xplore. The results of the study describe the use of various assistive technologies for the visually impaired related to navigation in spaces or buildings and even the wider environment as well as public services that are commonly accessed by the public. Furthermore, it is related to the existence of a brief description of the assistive technology developed and the benefits of the results of the development for users.
Technical Debt Guild

Efficient Technical Debt Management (TDM) requires specialized guidance so that decisions taken are oriented to add value to the business. Because it is a complex problem that involves several variables, TDM requires a systemic look that considers professionals' experiences from different specialties. Guilds have been a means technology companies have united specialized professionals around a common interest, especially those using the Spotify methodology. This paper presents the experience of implementing a guild to support TDM's activities in a software development organization using the action research method. The project lasted three years, and approximately 120 developers were involved in updating about 63,300 source-code files, 2,314 test cases, 2,097 automated test scripts, and the build pipeline. The actions resulting from the TDM guild's efforts impacted the company's culture by introducing new software development practices and standards. Besides, they positively influenced the quality of the artifacts delivered by the developers. This study shows that, as the company acquires maturity in TDM, it increases the need for professionals dedicated to TDM's activities.
Efficient Private Circuits with Precomputation

At CHES 2022, Wang et al. described a new paradigm for masked implementations using private circuits, where most intermediates can be precomputed before the input shares are accessed, significantly accelerating the online execution of masked functions. However, the masking scheme they proposed mainly featured (and was designed for) the cost amortization, leaving its (limited) suitability in the above precomputation-based paradigm just as a bonus. This paper aims to provide an efficient, reliable, easy-to-use, and precomputation-compatible masking scheme. We propose a new masked multiplication over the finite field Fq suitable for the precomputation, and prove its security in the composable notion called Probing-Isolating Non-Inference (PINI). Particularly, the operations (e.g., AND and XOR) in the binary field can be achieved by assigning q = 2, allowing the bitsliced implementation that has been shown to be quite efficient for the software implementations. The new masking scheme is applied to leverage the masking of AES and SKINNY block ciphers on ARM Cortex M architecture. The performance results show that the new scheme contributes to a significant speed-up compared with the state-of-the-art implementations. For SKINNY with block size 64, the speed and RAM requirement can be significantly improved (saving around 45% cycles in the online-computation and 60% RAM space for precomputed values) from AES-128, thanks to its smaller number of AND gates. Besides the security proof by hand, we provide formal verifications for the multiplication and T-test evaluations for the masked implementations of AES and SKINNY. Because of the structure of the new masked multiplication, our formal verification can be performed for security orders up to 16.
CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking

Visual object tracking is a fundamental task in computer vision, and thrived in recent decades. With the development of snapshot hyperspectral sensors, efforts have been made to exploit tracking the object with hyperspectral (HS) videos to overcome the inherent limitation of RGB images. Existing HS tracking algorithms extract the deep features from image data separately, which break the interaction information between bands. Therefore, the discrimination ability of HS trackers is limited and the efficiency of the existing HS algorithms is low. In this paper, a novel algorithm (CBFF-Net) is proposed for HS object tracking to improve the discrimination ability and reduce the computational complexity. Specifically, the backbone and head network are implemented with modules of a transferred RGB object tracking network to carry out the HS target tracking task while maintaining the discrimination ability learned from RGB data. Moreover, a bi-directional multiple deep feature fusion (BMDFF) module is proposed to fuse the features extracted from different bands of the HS images, and a cross-band group attention (CBGA) module is introduced to learn interaction information across bands of the HS images. Experiments results indicate the superiority in performance of CBFF-Net, and it runs at 24 frames per second.
Paw Search – A Searching Approach for Unsorted Data Combining with Binary Search and Merge Sort Algorithm

Searching is one of the oldest core mechanism of nature.Nature is changing gradually along with searching approaches too.Data Mining is one of the most important industrials topic now-a-days.Under this area all social networks, governmental or non-governmental institutions and ecommerce industries produce a huge number of unsorted data and they are to utilize it.For utilizing this huge number of unsorted data there needs some specific features based unsorted data structure tools like searching algorithm.At present there are several sorted data based searching algorithms like Binary Search, Linear Search, Jump Search and Interpolation Search and so on.In this paper of Paw Search Algorithm, it is fully focused to develop a new approach of searching that can work on unsorted data merging several searching techniques and sorting techniques.This algorithm starts its operation by breaking down the given unsorted array into several blocks by making the square root of the length of the given array.Then these blocks will be searched within its specific formula till the target data is found or not, and in the inner side of each block there will be performed Merge Sort and Binary Search approach gradually.Time and Space Complexity of this Paw Search algorithm is comparatively optimal.
Analysis of Twitter Sentiment Towards Madrasahs Using Classification Methods

In today's digital era, the influence and use of the internet has become a necessity, especially in Indonesia itself, internet users in early 2021 reached 202.6 million people. The most widely used internet use by Indonesians is social media. Several incidents of sexual violence that occurred in the madrasa environment as reported in the media, the emergence of radical Islamic issues which he said were the fruit of thoughts from the madrasa environment, terrorism which was also said to come from misinterpreting knowledge from madrasahs, intolerance to different religions, changes in the character of madrasah students and so on will cause negative thoughts towards madrasah. To find out how the sentiment of social media users towards madrasahs, a study was conducted on analisis twitter sentiment towards madrasah using the classification method. The methods used are Naïve Bayes (NB), Decision Tree (DT) and K – Nearest Neighbor (K-NN). Toimprove the performance of the classification method is carried out using the Particle Swarm Optimization (PSO) selection feature. On the other hand, tools gataframework, execute Python script dan rapidminer diguna kan jug a dalam penelitian this to membantu preprocessi ng dan cleansing pa da datasethingga membantu menciptaka n corpus dan sentiment ana lysis. Acuration obtained from the Naïve Bayes algorithm accuracy: 76.86% +/- 5.24% (micro average: 76.86%), Decision Tree accuracy: 61.38% +/- 5.46% (micro average: 61.35%), K-NN accuracy: 74.70% +/- 4.83% (micro average: 74.67%), Naïve Bayes PSO accuracy: 80.80% +/- 4.86% (micro average: 80.79%, Decision Tree PSO accuracy: 65.27% +/- 5.26% (micro average: 65.28%), and K-NN PSO accuracy: 67.24% +/- 7.92% (micro average: 67.25%). The results showed that the Naïve Bayes PSO algorithm got the best and accurate results. This study succeeded in obtaining an effective and best algorithm in classifying positive comments and negative comments related to sentiment analysis towards madrasahs by classification method.
iPET: Privacy Enhancing Traffic Perturbations for Secure IoT Communications

IoT devices constantly communicate with servers over the Internet, allowing an attacker to extract sensitive information by passively monitoring the network traffic. Recent research works have shown that a network attacker with a trained machine learning (ML) model can accurately fingerprint IoT devices learned from the (encrypted) traffic flows of the devices. Such fingerprinting attacks are capable of revealing the make and model of the devices, which can further be used to extract detailed user activities. In this work, we develop and propose iPET, a novel adversarial perturbation-based traffic modification system that defends against fingerprinting attacks. iPET design employs GAN (Generative Adversarial Networks) in a tuneable way, allowing users to specify the maximum bandwidth overhead they are willing to tolerate for the defense. A fundamental idea of iPET is to deliberately introduce stochasticity between model instances. This approach limits a counter attack, as it inhibits an attacker from recreating an identical perturbation model and using it for fingerprinting. We evaluate the effectiveness of our defense against state-of-the-art fingerprinting models and with three different attacker capabilities. Our evaluations on synthetic and real-world datasets demonstrate that iPET decreases the accuracy of even the potent attackers. We also show that the traffic perturbations generated by iPET generalize well to different fingerprinting schemes that an attacker may deploy.
Sedimentologika: a community-driven diamond open access journal in sedimentology

Sedimentologika is a community-driven Diamond Open Access scientific journal for the publication of work in the broad area of sedimentology and stratigraphy. The journal aims to provide a platform to the academic community and broader society, offering and guaranteeing permanent free publication and free access to peer-reviewed scientific studies focusing on all types of sedimentary processes, deposits, and environments across all spatial and temporal scales, on Earth or any other planetary body. It will publish high-quality research that will advance the field of sedimentary science (through multidisciplinarity, new teaching practices, tools and methods, and progress in accessibility to science). Sedimentologika is part of an ongoing broader Diamond Open Access movement in geosciences aspiring freedom from the financial barriers and pressures of private publishing houses, to provide direct and equal access to science for all citizens, scientists, and institutions worldwide. The published material will include research, review, methodology and opinion articles, which will be free to share, as the authors will retain the copyright on all the submitted material. Manuscripts will be published in English. Authors can attach a second abstract in the language of their choice, further allowing local communities, students, or decisional bodies to access, at least, a summary of the latest research, thus reducing potential language barriers. Sedimentologika follows Open Science principles to promote ethical dissemination and accessibility of science and knowledge, following high equity, diversity and inclusion standards. Sedimentologika emerged as a solution for the scientific community to sidestep structural inequality of the academic publishing system that is becoming financially unsustainable to its payers (authors, funding agencies), and to commit to bibliodiversity. The objective is to ensure that scientific findings remain accessible to all in order to keep advancing research and informing society on how we understand sedimentology and stratigraphy in the world around us. Sedimentologika is driven by the academic and scientific community for the community and society, promoting self-governance and adapting to the needs expressed by the community.
Variations in timbre qualia with register and dynamics in the oboe and French horn

Many musical instruments produce a myriad of sound colors resulting from diverse playing techniques, both traditional and extended. Such techniques include parameters that are often regularly manipulated in music, such as pitch, intensity (dynamics), articulation style, and duration. Despite the likely contribution of such timbral variations to musical experience, within-instrument timbral flexibility and its semantic consequences have not been addressed empirically. Participants rated sounds produced by the oboe and the French horn on 12 combinations of register and dynamics using the 20-dimensional timbre qualia model from Reymore and Huron (2020). Data are modeled with Exploratory Factor Analysis, partial proportional odds regressions, and random forest classifiers. Although trends between ratings and register/dynamics emerged, the results illustrate the complexity of within-instrument timbral variability. Some trends were approximately linear, others demonstrated non-linear patterns, and some timbre qualia dimensions displayed interactions between register and dynamics. While certain trends were shared between the oboe and French horn, such as an increase in sparkling/brilliant ratings with register, others seem to be unique to each instrument, such as the relationship of ratings of woody to register for the oboe or of ratings of muted/veiled to dynamics for the horn. Results demonstrate that within-instrument timbral variability based on dynamic and register is apparent to listeners and that semantic interactions among parameters can be present. The methodology established in this paper can be extended to address within-instrument timbral flexibility with respect to articulation, duration, and other sources of variation for any instrument or group of instruments.
Meta-Analysis Study

This study aims to determine the effect of Augmented Reality on student learning outcomes. This study consists of two variables: Augmented Reality as the independent variable and student learning outcomes as the dependent variable using quantitative meta-analysis research. The instrument used is an article coding sheet containing the data needed for meta-analysis in the form of mean data, the number of samples, and the standard deviation of control and experimental groups from articles that have passed the selection based on inclusion and exclusion criteria obtained through Google Scholar search. The results of the meta-analysis on 13 research articles obtained a summary effect (average effect size) of 0.541 which means that augmented Reality has a significant positive effect on student learning outcomes based on the effect size category set by Cohen. The results of the publication bias test show that the conclusion of the meta-analysis is not indicated by publication bias, as evidenced by the results of the funnel plot, egger test, and fail-safe N results. This study concludes that there is an effect of Augmented Reality on student learning outcomes.
Twincode

Pair Programming (PP) is both a common practice in professional software engineering and a valuable pedagogical tool. Disciplined user-centric research on pair programming can answer important questions about how students use and benefit from PP. We have developed a platform called Twincode that includes features to support pair programming assignments, comprehensive instrumentation to enable studying how the PP partners interact, and additional tools to help in analyzing the instrumentation, in particular for utterance tagging in discourse analysis. This demo will take the audience through the entire process of conducting research on the Twincode platform: from users pair programming on the platform, to utterance tagging of user date, to data analysis in support of research questions.
An Empirical Study of Password Policy Compliance

Cybersecurity exploits that take advantage of weak passwords continue to succeed in virtually every industry. This motivates interest in empirically determining the extent to which websites that invite visitors to create new user accounts on them encourage or require users to engage in better password management practices, including strong passwords. This project examined a statistically significant sample of websites to assess how closely they voluntarily adhere to the National Institute of Standards and Technology’s authoritative guidance on password policies. Over 100 representative websites were selected from industries that consistently report the most breaches in the Verizon Data Breach Investigation Report. Their respective user account creation processes were assessed via a scorecard approach based on observations collected when following standardized experimental procedures. Scorecard data then were aggregated and analyzed for trends. The research findings highlight potential vulnerabilities that persist in online account password creation practices, leaving many websites susceptible to brute force attacks due to cyber hygiene lapses. Recommendations to help remediate compliance gaps and as paths forward to build upon this work include refining the proposed scorecard, creating and using standardized user registration and profile manager plugins, widely adopting user-friendly password management tools, and enacting tougher legal consequences for website hosts when breaches occur.
Fast inspection and accurate recognition of target objects for astronaut robots through deep learning

Target object detection based on deep learning and image processing technology is widely used in robot astronauts. However, existing detection methods have limitations in detection speed and accuracy due to the complex space environment (such as uneven illumination and particle radiation) and inadequate training samples. Inspired by the neural network structure and transfer learning, a deep learning detection method for small samples in complex environments is proposed. A depthwise separable convolution is added to a feature fusion network to reduce the number of parameters in image output feature mapping, and a linear bottleneck inverted residual structure is introduced into a backbone feature extraction network to reduce the computation and memory requirements during feature extraction. As a result, a backbone feature extraction–fusion network structure is established to solve the problem in detection speed. A squeeze-and-excitation (SE) attention module is introduced in front of the head, and an SE detector is constructed to improve the detection accuracy in a spatially complex environment by dynamically assigning image channel weights to highlight the target object features in blurred images. The learning efficiency and accuracy of the network model in the small sample case in this paper are addressed by incorporating the transfer learning idea and establishing the evaluation function of learning samples. Experimental results show that the proposed algorithm enables astronaut robots to detect object rapidly and accurately in complex environments. The average speed (frames per second) and accuracy of detection under 2200 training samples are 45.19 and 93.14%, respectively.
Directional Geospatial Interpretation of CO2 Injection at the Citronelle Field, Alabama, Using Truth Table Analysis

Aspects in Mining & Mineral Science Directional Geospatial Interpretation of CO2 Injection at the Citronelle Field, Alabama, Using Truth Table Analysis Shen-En Chen1*, Yangguang Liu2 and Peng Wang3 1University of North Carolina at Charlotte, USA 2School of Resources and Environment, Henan Polytechnic University, China 3China University of Mining Technology, China *Corresponding author: Shen-En Chen, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, North Carolina, USA Submission: June 23, 2022;Published: July 08, 2022 DOI: 10.31031/AMMS.2022.09.000715 ISSN 2578-0255Volume9 Issue3
Vector Deep Fuzzy Neural Network for Breast Cancer Classification

Breast cancer is one of the most common cancers in women worldwide and the leading cause of death in women.Medical experts use histopathological images to diagnose breast cancer, but such analysis for the effective diagnosis or detection of breast cancer is challenging.Therefore, we propose a vector deep fuzzy neural network (VDFNN) to classify breast cancer effectively and automatically from histopathological images.The VDFNN model uses four sets of vector product and pooling layers to extract features and retain important feature information.Then, a feature fusion layer uses global average pooling to reduce the dimension of the extracted feature information.Finally, a fuzzy neural network performs breast cancer classification.The VDFNN model parameters are selected using the trial-and-error method.However, we also propose the Taguchi-VDFNN (T-VDFNN), which employs the Taguchi method to determine the optimal combination of model parameters.The experimental breast cancer classification accuracy of the proposed VDFNN was 92.18%.After the application of the Taguchi method to identify the optimal parameter combination, the experimental accuracy of the proposed T-VDFNN model was 94.37%, 2.19 percentage points higher than that of the basic VDFNN model.
Digital Skills of Blind College Students in the Educational Process during a Pandemic

This article discusses the digital skills of blind students in the educational process during the pandemic. The COVID-19 pandemic forced most students to adapt to a new habit which is WFH (Working from Home). Meanwhile, blind students take longer to learn online which they barely knew about. During this pandemic, students are required to develop digital competencies even though they are studying from home. The purpose of this study is to identify and understand blind students' needs when they are studying from home during the pandemic. The research method used by the researcher in this research is descriptive qualitative, in which the data collection process is carried out online. The results of the analysis found that 7 aspects of blind students' ability in digital space were quite good. Those 7 aspects are basic knowledge of screen reader applications, basic knowledge of digital landscapes, basic knowledge of finding information, basic knowledge of tracing lecture references, basic knowledge of conversational applications and social media, basic knowledge of digital media skills, and ethics in using digital media. Although they are able in accessing or browsing in digital spaces, they are still lacking at some points regarding safety in digital spaces.