Reviewerly/exHarmony huggingface dataset:
Anti-Collusion Mechanism for Audio Signals Based on Pre-Adjustment

One of the most well-known and difficult attacks for audio transmissions that gravely infringes on copyright is collusion. The mixture attack by collaboration and other assaults, is too sophisticated for conventional defenses to handle. In this study, we propose by removing the perceived quality of a pre-adaptation process (PAP)-based strategy to collusion signals by removing the incentive for traitors to carry out covert attacks. The PAP signal is then produced by modifying the DCT coefficients in accordance with an adjustment matrix (AM) that has already been designed. The vigor of some frequency groups in the colluding signal will be lost or lowered when numerous PAP signals are averaged to produce it, which significantly lowers the colluded signal's perceptual quality. The suggested technique is resistant against hybrid as well as collusion assaults. Additionally, it is safe because the receiver won't receive access to the PAP secret keys. The suggested copyright protection technique may be further enhanced in terms of effectiveness by being integrated with other state-of-the-art watermarking algorithms. The proposed mechanism is superior, as demonstrated by theoretical analysis and experimental findings.
The Rise of Neo-Experts: Sources, Characteristics, and Implications

Experts are central to today’s knowledge economy. Yet despite their centrality, our understanding of their expertise remains surprisingly nebulous. This article offers more conceptual clarity around this question of expertise by detailing its varying sources, characteristics, and implications. The impetus for such a revisiting is the increasing lay intrusion, enabled by social media, in professional domains, and the parallel rise of what we label neo-experts. We identify four distinct kinds of actors that may claim expertise: traditional experts, lay-experts, non-experts, and neo-experts. By traditional experts, we mean people typically embodying legitimized expertise (such as lawyers). Lay experts (such as patient activists) are individuals who aspire to gain a substantive understanding of a domain and who want to convince legitimized experts to revise their views. They differ from non-experts or people who develop a limited understanding of a domain based on their unique experiences. Increasingly, however, we note the rise of neo-experts who operate in a yet to be claimed jurisdiction and self-develop a form of practice-based expertise that a following recognizes as such. By accounting for all these experts in the making of expertise, we provide a fuller account of expertise and discuss how modern technologies render it more inclusive.
Empowering the Worker in Law Enforcement: Efforts in Leadership and Change Consulting

Policing in the USA is currently at a crossroads and facing intense community and political pressure to reform. The social unrest from the murder of George Floyd and Breonna Taylor, among others, and the proposed legislative changes at the state and federal level to de-fund the police, has caused additional scrutiny for police departments. Today, police reform is a major social issue of national importance. Furthermore, intentional planned change in policing is widely viewed by scholars as difficult, if not impossible. This symposium will present on one such police department undergoing leadership and cultural changes. A newly hired Police Chief, initially acting as an internal consultant, will present the multiple change efforts undertaken at the police department. But through collaboration and empowerment, the Chief intentionally put the workers front and center during and after the leadership transition. Early results indicate that because workers felt empowered, critical changes were realized resulting in needed cultural changes which provided further momentum. After the Chief’s presentation, four panelists will analyze the success (or lack) of the leader’s change efforts through his multiple, contemporary change approaches; namely, a) appreciative inquiry; b) measurement of culture change; c) action research; and d) the leader’s use of self. Management consulting issues, organization change struggles, and social issues (at both public sector and community stakeholder levels) are at the crux of this symposium. Participation by audience will provide further discussion, integrating participants’ questions and comments.
Leveraging Artificial Intelligence and Human Intelligence for HRM in Uncertainty

The current study aims to present a new perspective on HRM functions during uncertainty. We present a novel framework of ‘AI—HI synergies’ across three critical HRM functions, viz. talent acquisition, skill development, and performance appraisal. We root our framework in dynamic capability literature. In doing so, we highlight the importance of AI and HI in building dynamic capabilities for organizations across all three stages, viz. sensing, seizing, and shifting. Our focus is to trigger fresh debates on AI’s application to HRM and the need to balance AI and HI to create synergies that can help to achieve dynamic capabilities for organizations.
Fast-talking, Loud-voiced and Silver-tongued: Patterns of Prosody and Sales Performance

Prosody or the way in which we speak carries significance. It has the power to shape perceptions of speakers such as the degree to which they are influential, charismatic or trustworthy. Much of our understanding of prosody explores the link between a speaker and his or her perceived characteristics. Using a unique dataset of 351 phone calls collected from 99 sales reps and potential customer’s phone calls, we extend our understanding of prosody by building measures of prosody for both parties and link these to sales outcomes. We suggest that base patterns of prosody, similarity in prosodic styles and mimicry of the prosodic style of the customer by the sales rep is associated with sales success. We find that the effects of prosody are more nuanced such that the customer’s prosody is more predictive of sales success. This suggests that sales successful may be better indicated by customers’ vocal prosodic features rather than by the prosodic features of sales reps
How Markets Respond to Novel Inventions; Unpacking Dissimilarity and Disruptiveness Effects

We study how the investors respond to technological novelty of patented inventions, differentiating between two dimensions of novelty: dissimilarity and disruptiveness. Building on a view of investors as market-makers when evaluating innovative firms, we show that stock returns are higher for inventions that are more dissimilar to the pre-existing patented knowledge base. We also show that this positive effect does not extend to patented inventions that are disruptive, i.e., inventions that destabilize existing technology streams and reshape networks of interlinked technologies. In fact, we demonstrate that stock markets respond negatively to disruptive inventions. Finally, we find that invention dissimilarity acts as a moderator of the latter negative effect of disruptiveness. Our contributions are developed using a patent-level analysis of 1,066,637 USPTO granted patents.
Web3 Decentralization: Blockchain Technologies’ Impact on Modern Organizational Governance

This panel symposium aims to explore the opportunities for understanding Web3 decentralization in blockchain-based organizations that arise from management theories' competing arguments. Blockchain technology, based on distributed ledger technology, is a disruptive technology that provides a decentralized database of immutable transactions maintained across peer-to-peer networks. Theoretical lenses like transaction cost economics, agency theory, mechanism design theory, institutional theory, and organization design are frequently used to examine blockchain-based organizational governance. However, these theories present competing arguments and inconsistencies regarding the effectiveness of blockchain-based organizational governance. The symposium will provide a platform for discussing the need for more theoretical clarity and for examining the impact of decentralized blockchain-based governance models on organizational processes and performance. The symposium will bring together established blockchain scholars and members of the technology and innovation management, strategic management, and organization and management theory interest groups to discuss new insights into classic managerial issues.
An Introduction to Scientific Experimentation and Benchmarking

tutorial Share on An Introduction to Scientific Experimentation and Benchmarking Authors: Anne Auger Inria and Ecole Polytechnique, IP Paris, Palaiseau, France Inria and Ecole Polytechnique, IP Paris, Palaiseau, France https://orcid.org/0009-0008-0912-2764View Profile , Nikolaus Hansen Inria and Ecole Polytechnique, IP Paris, Palaiseau, France Inria and Ecole Polytechnique, IP Paris, Palaiseau, France https://orcid.org/0000-0001-7788-4906View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 854–877https://doi.org/10.1145/3583133.3595064Published:24 July 2023Publication History 0citation19DownloadsMetricsTotal Citations0Total Downloads19Last 12 Months19Last 6 weeks14 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
How Social Versus Informational Motivations Affect the Language of User-Generated Content

This research proposes a novel conceptual distinction between two motivations of online communication – social motivation and informational motivation – and tests five linguistic features characterizing user-generated content that was posted driven predominantly by one of these two motivations. We employed an open-source text analysis tool to analyze real-world and experimentally obtained datasets and supported our theory by experimentally manipulating motivation and measuring linguistic outcomes. Theoretical and practical implications are discussed.
The Impact of the COVID-19 Pandemic on Bitcoin Prices

This study compares the impacts of the COVID-19 pandemic on Bitcoin and gold prices from January 4, 2020, to October 8, 2021. Wavelet analysis is employed since it is currently one of the most popular methods for investigating correlations. The results show that the number of confirmed cases of COVID-19 had a positive impact on Bitcoin price during the first 125 days of the pandemic, with a correlation frequency band centering around 18–68 days. In contrast, COVID-19 has a similar but much smaller positive effect on the gold price at the first 28 days of the pandemic, with a correlation frequency band centering around 8–18 days. Therefore, the positive correlation between the COVID-19 pandemic cases with Bitcoin price lasts longer than that with gold price; the same holds for the frequency span. We also find that the number of COVID-19 confirmed cases negatively affects gold prices during a relatively short period, a pattern which we do not observe for Bitcoin during the same period. Our results provide guidance for investors who might use Bitcoins to hedge their portfolio risks and policymakers who strive to evaluate the impact of the COVID-19 pandemic on financial markets.
Cryptocurrency Project Fraud and Funding Success: An Information Disclosure Quality Framework

With the popularity of blockchain technology, the potential of cryptocurrency projects to mislead or deceive investors has increased considerably. We present a study that provides a framework for detecting scam cryptocurrency projects (crypto projects for short) based on information disclosure theory, and we also illustrate how the differences in information disclosure quality will impact crypto projects’ fundraising success. Within this study, we examine whether the disclosure width and depth in whitepaper would be valuable for fraud detection and prediction of crypto projects’ fundraising success. The framework can be used to increase investors’ awareness of deception in crypto projects and help investors to discriminate between crypto projects with low vs high information disclosure quality based on crypto projects’ whitepapers. Moreover, a better understanding of the impact of information disclosure quality on deception detection can also help government agencies design more effective solutions to fight cryptocurrency fraud.
A Qualitative Study on the Performance of Online class by Music Teachers in Middle School

Objectives The purpose of this study is to analyze the music teachers’ stories about ‘how to teach regular middle school music curriculum and what to experience’ in the situation of the COVID-19 Pandemic and to suggest the implications for how to improve the online music class.&#x0D; Methods Based on the narrative inquiry method, this study focused on exploring the meaning, that is, knowledge, created by collecting stories about non-face-to-face remote classes of middle school music teachers. For this study, three middle school music teachers in Seoul, who are guiding the music curriculum of the revised 2015 curriculum in middle schools, were selected as study participants. Although it was difficult to select research participants for in-depth interviews with middle school music remote classes, the selection of research participants was centered on experienced teachers with a lot of educational experience. In-depth interviews were conducted as a qualitative research method of Hatch, and the results were analyzed.&#x0D; Results First, the music teachers said that they taught students music theory and music appreciation-oriented learning contents, one-way online classes, and reduced evaluation. Second, the stories about what music teachers experienced showed the difficulties in the urgent and rough music online class environment, using unfamiliar music platforms and making learning contents and frequently changed academic schedules.&#x0D; Conclusions Through this study, we can derive suggestions on how to improve the online music class of middle school music teachers as follows: First, music teachers should enhance technological capabilities for Digital·AI devices. Second, Music teachers should be able to produce music content for feedback and cooperative communication. Third, music teachers should develop evaluation content tools centered on each student's development process.
How Teachers’ Autonomy and Achievement Goal Orientation Affect Innovative Teaching Behavior with ICT: The Mediator Role of Teachers’ Digital Literacy

This study aims to identify the structural relationship among teachers’ autonomy, achievement goal orientation, digital literacy, innovative teaching behavior with ICT. For this purpose, this study collected data from a total of 693 elementary and secondary school teachers in Korea, and analyzed direct and indirect effects between variables by applying a structural equation model. As a result of the study, teacher's autonomy and teachers' achievement goal orientation had a statically significant effect on teachers' digital literacy, but only teacher's autonomy had a positively significant effect on innovative teaching behaviour with ICT. Teachers' digital literacy had a positively significant effect on innovative teaching behaviour with ICT, and had a mediating effect between teachers' autonomy, achievement goal orientation, and innovative teaching behaviour with ICT. Based on the research results, practical implications and future research directions related to individual and organizational factors that affect teachers' innovative teaching behavior with ICT were presented.
Implementing the FAIR Principles Through FAIR-Enabling Artifacts and Services

How does a Research Software Engineer (RSE) — often responsible for developing infrastructure to manage and share digital research objects (data, models, code, notebooks, workflows, etc.) — get from “Yes, FAIR sounds great, but how?” to “I better understand what the FAIR principles really mean and how I can put them into practice.”? I hope the diagram below can help.
SC-Chef: Turboboosting Smart Contract Concurrent Execution for High Contention Workloads via Chopping Transactions

Concurrent execution of smart contracts is a promising approach to boost their performance in current blockchain systems. However, prior work on concurrent execution only considered each smart contract transaction as a single atomic unit for the concurrent scheduling, which is not suitable for the increasingly complex smart contracts with high contention in practice. In this article, we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC-Chef</small> , a new concurrency control system to turboboost smart contract concurrent execution for high contention workloads via the transaction decomposition. The key novelty of our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC-Chef</small> is to decompose the original transaction into multiple subtransactions that can be executed independently, while still respecting the original logic dependency. We implement and evaluate our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC-Chef</small> in the private Ethereum platform. Our extensive experiments show that our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC-Chef</small> outperforms the classic optimistic concurrency control and two-phase lock by 60% and 70% on average in the case of high contention, respectively.
Using Deep Learning for Obscene Language Detection in Vietnamese Social Media

Nowadays, a vast volume of text data is generated by Vietnamese people daily on social media platforms. Besides the enormous benefits, this situation creates many challenges. One of them concerns the fact that a tremendous amount of text contains obscene language. This kind of data negatively affects readers, especially young people. Detecting this kind of text is an important problem. In this paper, we investigate this problem using Deep Learning (DL) models such as Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM), and Bidirectional Long-Short Term Memory (BiLSTM). Besides, we combine LSTM and CNN in both sequence (sequential LSTM-CNN) and parallel (parallel LSTM-CNN) forms and sequential BiLSTM-CNN to solve this task. For word embedding phrase, we use Word2vec and PhoBERT. Experiment results show that the BiLSTM model with PhoBERT gains the best results for the obscene discrimination task, with 81.4% and 81.5% for accuracy and F1-score, respectively.
An Investigation into the Cybersecurity Skills Gap in South Africa

South Africa is currently one of the largest targets for hackers and cyber terrorists in the world. With the increase in such cyber threats, there is a growing need and demand for IT professionals with the requisite cybersecurity skills and knowledge to combat and mitigate these threats. However, an ever- increasing cybersecurity skills gap has been identified both globally and in South Africa. This study investigated the cybersecurity skills demand, needs, and challenges of IT professionals in South Africa by reporting on IT professionals’ perceptions in this regard. An online questionnaire, distributed to IT professionals using various online platforms, was used to gather this information. According to the findings of the study, there are several challenges, such as lack of time, lack of funding and lack of resources, that South Africa must overcome in order to address the cybersecurity skills gap. It was also found that the perception is that higher education institutions (HEIs), organisations and the South African government are not investing enough into cybersecurity skills development. Without the right cybersecurity skills set and investment in the development of cybersecurity skills, cyber threats will continue to increase and will potentially become more harmful to South African organisations and to the South African society at large.
A reinforcement learning-based multimodal scenario hazardous behaviour recognition method

In order to solve the problems of low recognition accuracy, low accuracy of key frame extraction of dangerous behaviours, and long delay time in risk behaviour recognition methods in multimodal scenes, this paper studies a multimodal scene risk behaviour recognition method based on reinforcement learning. Firstly, static and dynamic dangerous behaviour features are extracted through long short-term memory artificial neural network. Then, the extracted dangerous behaviour key frame set is optimised by reinforcement learning. Finally, a multi-sensory risk behaviour model library is constructed to complete the multi-modal scene dangerous behaviour recognition. Experiments show that the AUC area of the proposed method is closest to 1, which proves that the recognition accuracy is high, and the accuracy of the extracted key frames of dangerous behaviours is high. When the maximum number of video frames is 500 frames, the delay time of this method is 192 ms, and the shorter the delay time; the shortest, the higher the recognition efficiency.
Facial expression recognition of high jumpers based on wearable multi-physiological parameter collection

In order to improve the recognition accuracy of athletes' facial expression, a method of high jump athletes' facial expression recognition based on wearable multi-physiological parameter collection is proposed. Firstly, through the wearable multi-physiological parameter acquisition device, the device is used to collect the high jump athletes' heart rate, skin electrical activity parameters, body temperature and other multi-physiological parameters. Secondly, the baseline drift suppression method based on wavelet transform is used to suppress the baseline drift within multi-physiological parameter signals of high jumpers. Finally, the multi-physiological parameters of high jumpers without baseline drift are inputted into the convolutional neural network model, and the model is used to output the results of high jumpers' facial expression recognition. The experimental results show that the research method can accurately recognise the facial expressions of high jumpers, with the highest recognition accuracy of 99.21%.
A Sensor System for Stair Recognition in Active Stair-Climbing Aid: Preliminary Research

Since the 2000s, the use of wheelchairs has been increasing to guarantee their right to move as the number of people with walking disabilities continues to increase. However, movement of wheelchairs on the stairs is constrained because they are structurally characterized by two large wheels and two small auxiliary wheels. Therefore, this study proposes an algorithm for estimating the alignment state of stair-climbing aid and the height of the stairs. Also, it proposes a sensor system for estimating the entry section of a flatland. As a result of the experiment, based on the proposed sensor module, the alignment state of the stair-climbing aid is confirmed, and the height of the stairs is estimated by detecting the edge of the stairs. Also, the entry position of a flatland at the end of the stairs is detected.