Reviewerly/exHarmony huggingface dataset:
Workplace Learning Trends

This chapter will review the concept of third-party resources for workplace learning and how organizations can use this as part of a greater learning content and knowledge strategy. This chapter will define what constitutes third-party libraries, such as LinkedIn Learning, Udemy, EmTrain, and Skillshare, and how these tools can support corporate goals. This includes looking at how third-party libraries are being used to bolster both employee retention efforts as well as general employee upskilling. Workplace learning is no longer the purview of HR or corporate leadership. The benefits and challenges of implementing such solutions are discussed. Future areas of research are also reviewed.
Using Smart Phones as Educational Technology to Promote Effective Learning

Today, smartphones have become a very popular learning tool. More specifically, educators have been using smartphones to enhance their teaching and students' learning. Using smartphones, educators can ensure flexible course delivery and provide learners access to online learning resources. In this study, the author examines how smartphones can be used as an effective learning tool for distance education. This study utilized a survey of university students in Karachi, Pakistan to find the effects of using smartphones in their learning activities and what factors inhibit their use of smartphones in their learning.
Exploring Teachers and Students Perceptions of Online Teaching in Montenegro: What Have We Learned?

The pandemic year 2020 has led to significant changes in all areas of life. Education was no exception. Due to the closure of schools and universities during the lockdown, educational systems worldwide had to be switched overnight, from face-to-face to a completely virtual education model without prior preparation. The study conducted in Montenegro, based on interviews and a survey, collected data on the basis of which it provided an insight into how the teaching staff of Montenegrin universities coped with the newly created circumstances. The aim of this primary research is to determine the challenges faced by teachers and students in the Montenegrin higher education system regarding their implementation of online teaching during the COVID-19 pandemic in 2020. It focuses on the digital competence of the teaching staff, challenges in applying digital technology, opinions regarding online assessments and students’ experiences with online learning. Despite the presented challenges all teachers experienced, the research confirmed that this was a unique experience that brought new opportunities and contributed to improving the teachers’ ability to use new technologies. It is to be expected that this unfortunate situation will trigger the development of various mechanisms for modernizing the way of providing knowledge in the future.
A Human-centered Evaluation of a Toxicity Detection API: Testing Transferability and Unpacking Latent Attributes

Perspective is a publicly available, machine learning API that can score text for toxicity. It is available for use in online platforms and communities to limit toxicity and promote civil dialogue. In this work, we adopt a human-centered approach to evaluating Perspective by investigating if human ratings of toxicity align with Perspective’s toxicity scores. We also test its transferability by making this comparison for comments from three platforms that have different commenting styles and moderation strategies: news websites, YouTube, and Twitter. Apart from toxicity, the main attribute, we collect participant ratings for three additional attributes: respectfulness, formality, and presence of stereotypes. While disrespect is part of how Perspective defines toxicity, formality and presence of stereotypes were included in the study to explore if they could be hidden/latent attributes that affect toxicity scores from Perspective. We analyzed how participant ratings for these additional attributes vary with respect to Perspective’s toxicity score for comments from each platform. We find that for high toxicity scores, Perspective strongly aligns with participant ratings of toxicity and disrespectfulness across all three platforms, providing weak evidence of its transferability. However, our evaluation also surfaced formality and presence of stereotypes as latent attributes that are unrecognized parts of Perspective’s scores. We discuss how and why this evaluation is “human-centered,” the importance of conducting such evaluations, and implications of these results for content moderation in social platforms.
A security vulnerability predictor based on source code metrics

Detecting security vulnerabilities in the source code of software systems is one of the most important challenges in the field of software security. We need an effective solution to discover and patch vulnerabilities before our valuable information is compromised. Security testing is a type of software testing that checks whether software is vulnerable to cyber attacks. This study aimed to pursue three main objectives: (1) The first goal is to identify the vulnerable functions of a C/C++ software program based on code metrics. This can reduce the cost of software security testing and also redirect the related activities to identified vulnerable functions rather than to the entire software, (2) The second goal is to identify the type of attack related to the vulnerability function, and (3) Finally, the ultimate goal is to analyze the relationship between code metrics and the vulnerabilities. This goal can help us understand which code structure is most likely to contain vulnerable code. This paper first aimed to create a comprehensive view of the source code of the target software using graph concepts. Second, a set of source code metrics and calculated by crawling on the related graph using the static analysis approach. Finally, the vulnerability prediction model presented in this paper is based on machine learning technique applied on metrics extracted from program source code. Compared to previous work, new achievements have been made in this paper. One of the most important ones is the very high accuracy detection of the proposed model in detecting the type of vulnerability. Moreover, 15 code metrics are used to predict vulnerabilities. Our analysis on feature importance indicates that what structure the software program code has, most likely, it will be vulnerable. Experimental results in 10 real projects (OpenSSL, SQLite, FreeType, LibTiff, Libxslt, Binutils, FFmpeg, ImageMagick, OpenSC, and rdesktop) indicated that the security testing predictor proposed in this paper could predict on average 89% of the really vulnerable functions of the source code and 86% of the vulnerability type of the detected functions correctly.
Why Is Online Voting Still Largely a Black Box?

Online elections and polls are increasingly gaining ground. Since the beginning of the pandemic, many associations, companies and agencies opted for online elections at some point. Yet, most of these elections use online voting systems that are a black box for voters, even though the current state of research offers cryptographic means that would allow voters to detect potential manipulations, e.g., by methods for end-to-end (E2E) verifiability. In this paper, we report on qualitative exploratory research to determine the reasons for this situation. We evaluate responses from a panel at a national conference in Germany by specialists from official agencies, industry, and academia, whom we asked why election organizers still largely opt for systems that are not verifiable and how this could be changed. We furthermore present an exploratory study in which we asked program committee members from relevant international conferences to assess the obtained panel responses on their accuracy, relevance, and completeness. Finally, we discuss possible next steps for strengthening our findings and how to implement them to see more verifiable voting systems being used in the future.
Non(c)esuch Ballot-Level Comparison Risk-Limiting Audits

Risk-limiting audits (RLAs) guarantee a high probability of correcting incorrect reported electoral outcomes before the outcomes are certified. The most efficient are ballot-level comparison audits (BLCAs), which compare the voting system’s interpretation of randomly selected individual ballot cards (cast-vote records, CVRs) from a trustworthy paper trail to a human interpretation of the same cards. BLCAs have logistical and privacy hurdles: Individual randomly selected cards must be retrieved for manual inspection; the voting system must export CVRs; and the CVRs must be linked to the corresponding physical cards, to compare the two. In practice, such links have been made by keeping cards in the order in which they are scanned or by printing serial numbers on cards as they are scanned. Both methods may compromise voter privacy. Cards selected for audit have been retrieved by manually counting into stacks or by looking for cards with particular serial numbers. The methods are time-consuming; the first is also error-prone. Connecting CVRs to cards using a unique pseudo-random number (“cryptographic nonce”) printed on each card after the voter last sees it could reduce privacy risks, but retrieving the card imprinted with a particular random number may be harder than counting into a stack or finding the card with a given serial number. And what if the system does not in fact print a unique number on each ballot or does not accurately report the numbers it printed? This paper presents a method for conducting BLCAs that maintains the risk limit even if the system does not print a genuine nonce on each ballot or misreports the identifiers it used. The method also allows untrusted technology to be used to retrieve the cards selected for audit—automation that may reduce audit workload even if cards are imprinted with serial numbers rather than putative nonces. The method limits the risk rigorously, even if the imprinting or retrieval technology misbehaves. If the imprinting and retrieval systems behave properly, this protection does not increase the number of cards the RLA has to inspect to confirm or correct the outcome.
Cybersecurity Awareness for Small and Medium-Sized Enterprises (SMEs): Availability and Scope of Free and Inexpensive Awareness Resources

Small and medium-sized enterprises (SMEs) are considered the backbone of Europe’s economy. However, SMEs are often bounded by resource constraints that also limit their cybersecurity posture. In such circumstances, SMEs could potentially benefit from the free and inexpensive cybersecurity awareness (CSA) resources produced and distributed by various public and private entities. SMEs can utilize these affordable resources to elevate the knowledge and skills of employees and transform their cybersecurity attitudes and behavior. The security-conscious employees can serve as the organization’s first line of defense against cyber-attacks and -crimes. However, prior to employing such awareness resources, it would require answering the question “how abundance and well-suited are the (affordable) awareness resources for SMEs?” To address this concern, we used an exploratory approach and examined the awareness resources from 71 sources chosen after the review of 938 potential sources. Since the primary audience of the study was European SMEs, most of the sources analyzed come from European organizations. Based on our findings, while these affordable awareness resources could benefit SMEs, they do require some adjustment to better meet the requirements and situations of SMEs. Furthermore, the awareness resources exclusively targeting SMEs and the diverse business areas SMEs serve, are insufficient. As a result, all involved entities, at the national and European levels, are encouraged to produce and distribute more localized awareness resources that are affordable and best match the demands and business areas of SMEs. Finally, the awareness resources should also include appropriate features for interested users to submit their feedback.
A Hybrid Dynamic Risk Analysis Methodology for Cyber-Physical Systems

Recent technological advances allow us to design and implement sophisticated infrastructures to assist users’ everyday life; technological paradigms such as Intelligent Transportation Systems (ITS) and Multi-modal Transport are excellent instances of those cases. Therefore, a systematic risk evaluation process in conjunction with proper threat identification are essential for environments like those mentioned above as they involve human safety. Threat modelling is the process of identifying and understanding threats while risk analysis is the process of identifying and analyzing potential risks. This research initially focuses on the most widely-used threat modelling and risk analysis approaches and reviewing their characteristics. Then, it presents a service-oriented dynamic risk analysis approach that focuses on Cyber-Physical Systems (CPS) by adopting threat modelling characteristics and by blending other methods and well-established sources to achieve automation in several stages. Finally, it provides the qualitative features of the proposed method and other related threat modelling and risk analysis approaches with a discussion regarding their similarities, differences, advantages and drawbacks.
Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition

Detection of fights is an important surveillance application in videos. Most existing methods use supervised binary action recognition. Since frame-level annotations are very hard to get for anomaly detection, weakly supervised learning using multiple instance learning is widely used. This paper explores the detection of fights in videos as one special type of anomaly detection and binary action recognition. We use the UBI-Fight and NTU-CCTV-Fight datasets for most of the study since they have frame-level annotations. We find that anomaly detection performs similarly or even better than action recognition. Furthermore, we study to use anomaly detection as a toolbox to generate training datasets for action recognition in an iterative way conditioned on the performance of the anomaly detection. Experiment results should show that we achieve state-of-the-art performance on three fight detection datasets.
Probing Contextual Diversity for Dense Out-of-Distribution Detection

Detection of out-of-distribution (OoD) samples in the context of image classification has recently become an area of interest and active study, along with the topic of uncertainty estimation, to which it is closely related. In this paper we explore the task of OoD segmentation, which has been studied less than its classification counterpart and presents additional challenges. Segmentation is a dense prediction task for which the model's outcome for each pixel depends on its surroundings. The receptive field and the reliance on context play a role for distinguishing different classes and, correspondingly, for spotting OoD entities. We introduce MOoSe, an efficient strategy to leverage the various levels of context represented within semantic segmentation models and show that even a simple aggregation of multi-scale representations has consistently positive effects on OoD detection and uncertainty estimation.
Learning to Joint Remosaic and Denoise in Quad Bayer CFA via Universal Multi-scale Channel Attention Network

The color filter array widely used in smart phones is mainly Quad Bayer and Bayer. Quad Bayer color filter array (QBC) is a filter shared by four pixels, which can improve the image quality by averaging four pixels in the 2 $$\,\times \,$$ 2 neighborhood under low light conditions. From low-resolution Bayer to full-resolution Bayer has become a very challenging research, especially in the presence of noise. Considering denoise and remosaic, we propose a general two-stage framework JRD-QBC (Joint Remosaic and Denoise in Quad Bayer CFA), including denoise and remosaic. To begin with, for the denoise phase, in order to ensure the difference of each color channel recovery, we convert the input to hollow QBC, and then enter our backbone network, including source encoder module, feature refinement module and final prediction module. After that, get a clean QBC and then use the same network structure to remosaic to generate Bayer. Extensive experiments demonstrate the proposed two-stage method has a good effect in quantitative indicators and subjective vision.
MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Report

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, Quad Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of Quad CFA to Bayer at full-resolution is introduced. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality Quad and Bayer pair. In addition, for each scene, Quad of different noise level were provided at 0 dB, 24 dB and 42 dB. All the data were captured using a Quad sensor in both outdoor and indoor conditions. The final results are evaluated using objective metrics including PSNR, SSIM [6], LPIPS [10] and KLD. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found in https://github.com/mipi-challenge/MIPI2022 .
Design and Implementation of a Collaborative Air-Ground Unmanned System Path Planning Framework

This paper proposes an air-ground collaborative unmanned system path planning framework based on a specific search and rescue environment. In recent years, UAVs have been widely used in a variety of scenarios due to their many advantages, including aiding path planning for UGVs on the ground. Most path planning assumes a digital map. However, in situations where a map is not available, especially in search and rescue operations where the operating environment is often a disaster or post-disaster area, it is very difficult for ground-based UGVs to explore the unknown environment. The use of UAVs flying in the airspace above the UGV enables a rapid search of the surrounding environment and guides the rescue operations of the UGV. Ground-based UGVs carrying rescue facilities can enable rescue operations that cannot be completed by UAVs. Therefore, we propose a collaborative air-ground cross-domain unmanned system path planning framework, with the generation of global paths based on the improved A* algorithm. Based on this, path management is used to update the waypoints. The path tracking algorithm PID and collision avoidance method VFH are used to optimise the local paths. A visual guidance method is introduced to ensure that the UAS is as close to the target as possible to complete the mission. Finally, extensive simulation experiments are conducted to verify the feasibility of the approach.
Bounded Future MS-TCN++ for Surgical Gesture Recognition

In recent times there is a growing development of video-based applications for surgical purposes. Part of these applications can work offline after the end of the procedure, other applications must react immediately. However, there are cases where the response should be done during the procedure but some delay is acceptable. In the literature, the online-offline performance gap is known. Our goal in this study was to learn the performance-delay trade-off and design an MS-TCN++-based algorithm that can utilize this trade-off. To this aim, we used our open surgery simulation data-set containing 96 videos of 24 participants that perform a suturing task on a variable tissue simulator. In this study, we used video data captured from the side view. The Networks were trained to identify the performed surgical gestures. The naive approach is to reduce the MS-TCN++ depth, as a result, the receptive field is reduced, and also the number of required future frames is also reduced. We showed that this method is sub-optimal, mainly in the small delay cases. The second method was to limit the accessible future in each temporal convolution. This way, we have flexibility in the network design and as a result, we achieve significantly better performance than in the naive approach.
Frequency Dropout: Feature-Level Regularization via Randomized Filtering

Deep convolutional neural networks have shown remarkable performance on various computer vision tasks, and yet, they are susceptible to picking up spurious correlations from the training signal. So called ‘shortcuts’ can occur during learning, for example, when there are specific frequencies present in the image data that correlate with the output predictions. Both high and low frequencies can be characteristic of the underlying noise distribution caused by the image acquisition rather than in relation to the task-relevant information about the image content. Models that learn features related to this characteristic noise will not generalize well to new data. In this work, we propose a simple yet effective training strategy, Frequency Dropout, to prevent convolutional neural networks from learning frequency-specific imaging features. We employ randomized filtering of feature maps during training which acts as a feature-level regularization. In this study, we consider common image processing filters such as Gaussian smoothing, Laplacian of Gaussian, and Gabor filtering. Our training strategy is model-agnostic and can be used for any computer vision task. We demonstrate the effectiveness of Frequency Dropout on a range of popular architectures and multiple tasks including image classification, domain adaptation, and semantic segmentation using both computer vision and medical imaging datasets. Our results suggest that the proposed approach does not only improve predictive accuracy but also improves robustness against domain shift.
An Improved Algorithm of Multi-robot Task Assignment and Path Planning

For robots to operate effectively and swiftly in complicated environments, task assignment and path planning must be reasonable. However, many of the present algorithms distribute tasks to many robots without considering the surroundings, which results in arbitrary task allocations and interferes with path planning. To address the multi-robot task allocation and path planning (MRTA-PP) issue in complicated environments, this paper proposes an enhanced algorithm of the MRTA-PP based on the integration of the improved genetic algorithm (IGA) and the improved A* algorithm (IA*). First, the multi-robot task assignment (MRTA) problem was transformed into the multiple traveling salesman problem (MTSP) in this paper. Secondly, the IA* was proposed and used to solve the distance matrix consisting of the distances between task points. Then, the IGA was proposed, and the MTSP was solved based on the distance matrix using the IGA. Finally, the overall route of each robot was planned according to the solution using the IA*. The results demonstrate the efficacy of the enhanced algorithm of the MRTA-PP based on the integration of the IGA and the IA* (IGA-IA*), as proposed in this paper, in solving the MRTA-PP issue in complex environments.