Reviewerly/exHarmony huggingface dataset:
A Study of Voice Recognition System Using Deep Learning Techniques

As there has been a tremendous development in “on-the-fly” computing technology, it becomes very essential to secure the system from unauthorized users and hackers. The biometrics verification system is one of the technological advancements in security measurements to restrict the unwanted users from accessing the systems. A behavioral biometric recognition and verification system performs the functions of reading unique biometric identity of an individual, such as fingerprint, iris, voice, and face, and authenticates the individual by comparing with the biometrics of the same kind in the database. As the above said behavioral biometric value can never be the same for two persons, the biometric verification and authentication system is highly accurate and reliable. The voice recognition and authentication system is one of the familiar and upcoming system that is taken for discussion in the chapter. To add more accuracy and intelligence flavor to the biometric especially voice-based verification and authentication, deep leaning is adopted as a pre-verification filter to separate out and remove bad or malicious individual from accessing the system. Deep learning puts core architectural building blocks together to build a new voice-based verification specific architecture. Deep learning allows the system to train itself again and again with test samples and makes the behavioral voice-based verification and authentication system (BVVAS) to be more secure and achieves high level of accuracy by reducing false identification. The deep learning supported BVVAS appears to be obviously fit for handling the everrising biometric identification problems ranging from mobile device to high-end security systems authentication.
Three-wavelength-based quantitative phase decoupling imaging

Measuring the refractive index (RI) and topography of samples can be realized by decoupling the obtained quantitative phase image. However, flattening the sample, changing the surrounding medium, or illuminating sample that immerse in the highly dispersive medium with different wavelength lights encounter some problems in practice. With the help of Cauchy’s dispersion formula, RI and topography can be independently calculated from a three-wavelength-based quantitative phase imaging. Neither special highly dispersive medium nor additional assistant technology is required. After characterization of the method, the capabilities for RI measurement and substance identification are demonstrated.
Recognizing human functional patterns in IADLs through hand detection and artificial intelligence

Cognitive impairment is one of the characteristics shown by patients with Alzheimer's dementia (Montenegro et al., 2020; Kelley & Petersen, 2007). This affects the executive functions of most of these patients from the early stage (Guarino et al., 2019) and consequently, have difficulty in carrying out their daily activities (Dubbelman et al., 2020). These activities require precise coordination of the person's movements and interaction with their environment, so instrumental activities of daily living (IADLs) are predictors of such impairment (Barberger-Gateau & Dartigues, 1993), as several research studies have shown(Brovelli et al., 2017). Therefore, assessing the performance of IADLs by analyzing human biomechanical markers such as hand movements is of great interest. This, together with the application of artificial intelligence techniques, in particular deep learning, will allow the development of technological tools for the creation of an automated model to support the early diagnosis of Alzheimer's disease.This work was inspired by the development of Alejandro Acosta et al, where human biomechanical markers were analyzed throughout the performance of IADL activities to recognize the human functional pattern (Acosta-Franco et al., 2021). To improve the accuracy of the baseline model, we propose a hand detection algorithm based on a probabilistic approach considering skin color as a descriptor. To test the proposed algorithm, we used the dataset of egocentric videos containing the performance of IADL activities, which is organized into four classes, based on the prehensile patterns of the hands: force and precision, and on the kinematics of the instruments: displacement and manipulation.With the improvement made to the base model we obtained an accuracy higher than 74% in recognizing movement patterns of displacement and manipulation of objects, and a good prediction of the depth of the anatomical planes.Our improved model obtained a good percentage of recognition accuracy for the instrument's kinematics, which can help in the development of technological tools for the creation of an automated model to support the diagnosis of early Alzheimer's disease.
Seeing Through Things: Exploring the Design Space of Privacy-aware Data-enabled Objects

Increasing amounts of sensor-augmented research objects have been used in design research. We call these objects Data-Enabled Objects, which can be integrated into daily activities capturing data about people’s detailed whereabouts, behaviours, and routines. These objects provide data perspectives on everyday life for contextual design research. However, data-enabled objects are still computational devices with limited privacy awareness and nuanced data sharing. To better design data-enabled objects, we explore privacy design spaces by inviting 18 teams of undergraduate design students to re-design the same type of sensor-enabled home research camera. We developed the Connected Peekaboo Toolkit (CPT) to support the design teams in designing, building, and directly deploying their prototypes in real home studies. We conducted Thematic Analysis to analyze their outcomes which led us to interpret that privacy is not just an obstacle but can be a driver by unfolding an exploration of possible design spaces for data-enabled objects.
Noise Reduction for Magnetic Resonance Imaging by Using Edge Detection and Hybrid Mean Lee Filter Techniques

In this article, spatial-domain filtering algorithms were developed to suppress additive noise in magnetic resonance (MR) imaging. It is difficult to suppress MR image noise because it corrupts almost all pixels in an image. The purpose of noise reduction is to curb the noise with high efficiency while keeping the edges and other detailed features as much as possible. The present article focused on developing quite efficient noise reduction by using an edge detection technique and hybrid mean Lee filters to suppress MR image noise quite effectively in spatial domain without yielding much distortion and blurring. The performances of the developed filter were compared with the existing filters in terms of universal quality index, method noise, and execution time. Among all existing filters, the edge detection technique and hybrid mean Lee filter was found to be best for suppressing MR image noise.
The impact of AI on designs law

This chapter seeks to challenge the assumption that just because we have become used to treating design protection as a separate field of intellectual property law in its own right, such understanding must necessarily inform how we address the impact of AI on designs. Instead, this chapter proposes that we should recognize the contradictions inherent in treating designs law as an independent area of law when in reality it is a hybrid with features of both patents and copyright, the roots of which lie in copyright but which came by accident to attract features of patent law, and address the issue of AI-generated designs accordingly. The impact of AI also presents us with an opportunity to rethink why we protect designs in this separate way (or in practice in a wide variety of separate ways) and, if we must do so, how best we should go about this.
Considering intellectual property law for embodied forms of artificial intelligence

This chapter discusses the concept of 'embodiment' in AI agents and how this concept is reflected in the debate over awarding intellectual property rights to AI-generated works. Our analysis adopts a broad conceptualization of 'embodiment' - taking in artificially intelligent agents that have a physical form but also those who do not (that is, are virtual) yet are still considered as embodied. We maintain that embodied agents (both physical and non-physical) play a unique dual role with regard to intellectual property rights (IPRs): on the one hand, they can be the subject matter of IPRs; on the other, they may be the creators or perhaps even the infringers of protected works. Following this dual role, our analysis proceeds in two parts. First, we examine how embodiment maps to discussions over AI being the subject matter of intellectual property, specifically patents and copyright. We then consider the weight embodiment carries in the narrative affording IPR protection to AI-generated works, often leading to logical fallacies of rights reserved for human authors and inventors. In doing so, the chapter aims to dissect the rather intricate matter of a traditionally human-centric intellectual property regime being influenced by AI perceived through their form. This approach appears to have influenced the wider socio-legal narrative on awarding rights to AI agents.
AI replication of musical styles points the way to an exclusive rights regime

While 'style' has been considered at the margins of copyright, it may be central to what makes a work original and protectible. At the same time, an artist's style as established over multiple works seems even more problematic for copyright's core fixation requirement. Other misappropriation regimes, such as rights of publicity or Lanham Act false affiliation/endorsement, offer limited avenues to protection of style. Yet, style standing separate from any particular work has clear aesthetic and commercial value. The success of current AI music generators using machine learning to create an algorithmic engine capable of producing musical works, performances and recordings recognizably in the style of famous composers and performers indicates that style standing alone can be quantified and fixed. This chapter thus argues that musical style should be protectable on multiple grounds, including – or if nothing else – unjust enrichment of tech companies at the expense of human artists.
Copyright law should stay true to itself in the age of artificial intelligence

Artificial intelligence has posed new challenges and exposed the inadequacies of copyright law, but a complete overhaul is not the best solution as it is not only a question of legal drafting. The well-intended proposals to substitute the sophisticated multi-layer copyright regime with a simple single test of commercial or reasonable exploitation are appealing but unlikely to solve all the problems. As shown in the EU attempt to curb hyperlinking and the decade-long copyright debates in Hong Kong, the success or otherwise of copyright reforms depends on many factors, judicial interpretation and sociopolitical considerations included. Through European and Asian examples, this chapter defends the current copyright regime and identifies the way forward from a pragmatic perspective. It advocates that, in the digital era, top-down legislative reforms can no longer encourage creativity or guarantee a hassle-free environment for authors and creators, who need to be empowered by non-statutory means.
Online shopping with artificial intelligence: what role for trade marks?

So far, the scholarly debate on how artificial intelligence (AI) influences intellectual property protection has mainly focused on its effects for patent and copyright protection. Not much attention has been devoted to the effects of the use of AI technology for trade mark law. In this chapter, we aim to explore whether trade marks still fulfil their role in a world in which consumers are assisted by AI technology when purchasing in the online marketplace. To what degree do we still need trade marks to avoid consumer confusion? Or do the other functions of trade marks justify their continuous protection? If the answer is yes, and trade marks still play a role, the concept of the average consumer in EU trade mark law may have to shift to the AI-assisted consumer. In order to do so, a better understanding of how consumers act in online purchases when confronted with different types of AI mechanisms is needed.
The elusive intellectual property protection of trained machine learning models: a European perspective

Inference models lie at the core of artificial intelligence applications. As the end-product of the machine learning phase, an inference model combines the information derived from the training data as well as the know-how required to produce a relevant solution. Once trained, it is the inference model that is deployed in the field. It can then receive new observables as input and produce relevant results as output. The value of these objects is undeniable and their protection can be of major strategic importance for their producers. However, inference models are complex hybrid entities composed of several heterogeneous subsystems: algorithmic process, data, software, processes. At the interface between computer codes, algorithms, data structures and collections of parameters, inference models cannot be cast in a single intellectual property mould and as a consequence require examination of the applicability of the various IP rights, from copyright to trade secrets, from database to patents.
Legal fictions and the corporation as an inventive artificial intelligence

This chapter's focus is an alternative form of artificial intelligence: corporations and other private organizations. Unlike their computer-bound AI cousins, corporations have already been granted the legal fiction of personhood status and many of the accompanying civil rights, including rights of property, contract, privacy and speech. This chapter walks through a historical progression of inventorship rights and corporate ownership from pre-industrial beginnings up through the elimination of statutory inventorship requirement in the Leahy–Smith America Invents Act of 2011. It concludes with recommendations on supporting the patent system while rejecting the notion that legal fictions should serve as its central tenets.
Reverse engineering (by) artificial intelligence

This chapter discusses several possible implications of artificial intelligence for trade secret law and policy, particularly with regard to reverse engineering. Reverse engineering is traditionally a legitimate way to discover a trade secret, but modern forms of artificial intelligence amount to sophisticated tools for reverse engineering and may dramatically reduce the costs of many types of reverse engineering. Moreover, some types of artificial intelligence may be especially subject to practical forms of reverse engineering by other artificially intelligent systems, even while the AI systems remain difficult for humans to understand in detail. The chapter explores these ideas; its goal is to discuss new possibilities rather than to offer specific policy prescriptions.
Ain't it just software?

Analyses of artificial intelligence are often deracinated from AI's basis in software. This chapter delves into the computer science literature to illuminate copyright treatment of software and implications for AI, copyright ownership and fair use. Building on computer science and its relationship with mathematics, engineering and experimental science, the author presents a framework for AI and copyright policy that offers an explanation for the 2021 US Supreme Court decision in Oracle v Google.
Luxury Fashion NFTs: Exploring Consumer Motivations and Behaviors

<p class="MsoNormal" style="margin: 0in; font-size: medium; font-family: Calibri, sans-serif; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span style="font-family: &quot;Times New Roman&quot;, serif;">The interest in digital technology and the rise of the metaverse has created noticeable attention to nonfungible tokens, or NFTs. One of those industries that are taking notice of NFTs is the luxury fashion industry. Given the potential growth of NFT strategies within the luxury market, a study is warranted to explore this phenomenon in depth. Therefore, the aim of the conceptual paper is to propose a study to explore NFT strategies within the luxury market among consumers and to outline their thoughts, motivations, and behaviors within this new realm of luxury consumption. The authors propose utilizing a phenomenological approach to understand the “lived experiences” of consumers during the consumption process of luxury NFTs from luxury brands. Exploring the consumer’s thoughts, motivations, and behaviors with luxury fashion NFTs can provide strategic implications for luxury fashion brands.</span>
Security and Privacy for IoT-based Smart Cities

Communities get a unique characteristic that relies on the combination of Information and Communications Technology(ICT) services and improvements to manage complex data in datacenters as well as during subway transport. The technology employed in smart cities is usually cutting-edge and unavailable elsewhere. In addition, smart cities are being used as testbeds or displays for innovative technologies whose security and privacy are unclear. As a result, critical information protected by such technologies may be subject to a range of threats. It is vital to be able to operate a smart city while retaining privacy. Industrial IoT and cyber physical systems, particularly in the smart sector, now lack proper access control and antimalware and run 24 hours a day. They're prone to distributed denial of service (DDoS) assaults, for example. Data confidentiality is critical for privacy legislation compliance in a smart setting, such as the European Union's General Data Guard Rule and individual data defense statutes in many other nations. Privacy and law compliance can be supported via cryptography, access control methods, and innovative security architectures tailored to specific settings. As a result, security and privacy are important topics for ICT audiences, with a focus on smart cities. In this chapter, various methods will be discussed, through which a concept of secured smart cities will be possible.
Network Vulnerability Analysis for Internet of Things (IoT)-based Cyber Physical Systems (CPS) Using Digital Forensics

Cyberspace has been dominated with various forms of attacks in which sniffing technique is one of the predominant approaches used in exploiting the weaknesses of cyber physical systems (CPSs). The number, variety, and advancement in technology occasioned by the Internet of Things (IoT) has made a lot of data available with significant issues for CPSs. Forensic Analysis Framework approach was implemented in the preparation, proactive, and reactive stages, and during presentation for forensic examination. The study utilized Wireshark and TCPDump to determine network attacks such as denial of service, eavesdropping, replay attack, Sybil attack, sinkhole attack, and man-in-the-middle attack. Wireshark shows that more attacks occurred on TCP as opposed to UDP; 83.65% of packets transmitted were directed to TCP port 80. TCP had a total of 1,373,445 out of 1,391,810 packets count, while UDP had a total of 18,365. This signifies that 98.68% of packets were found on the TCP. TCPDump shows that an attack occurred for 1 minute and 22 seconds, with a total of 1,373,445 packets transmitted. The study recommends other network forensic analysis tools such as Dumpcap and Ethereal for live network analysis; NetDetector for instant blockage of malicious traffic; and Fidelis XPS for recording traffic sessions for effective network post mortem analysis.
The Problem of Collecting Face Recognition Information in Colleges and Universities

As China gradually enters the “5G+artificial” intelligent information era, the vigorous development of emerging technologies such as artificial intelligence, face recognition technology has been widely used to education, security and finance and other fields, at the same time, the collection of personal face recognition information is quietly leaked, caused concern and anxiety from public. In the special group of colleges and universities, the legitimacy of collecting face recognition information is controversial. This paper centers on the different expressions of collecting face recognition information in colleges and universities.
A 75.6M Base-pairs/s FPGA Accelerator for FM-index Based Paired-end Short-Read Mapping

Next-generation sequencing (NGS) has been widely applied to genetics research and biomedical applications. It achieves a high sequencing speed by sequencing subsequences (called short-reads) in a massively parallel manner [1]. However, the succeeding data analysis for assembling short-reads still takes a couple of days on CPU and thus becomes the bottleneck. Fig. 1(a) shows the NGS data analysis workflow, consisting of short-read mapping, haplotype & variant calling, and genotype calling. Of these three steps, the execution time is dominated by short-read mapping. A CPU-FPGA heterogeneous system is presented in [2] for accelerating short-read mapping, but the performance improvement is limited. A dedicated FPGA accelerator [3] achieves a higher throughput at a cost of a larger DRAM requirement. Compared to prior arts, this work presents an FPGA accelerator that delivers a 1.7-to-18.6x higher throughput in a memory-efficient way. Paired-end short-read mapping is exploited to achieve the highest 99.3% accuracy on true human DNA.
Медийна грамотност и образование на 21. век

The article examines the concepts of information literacy and media literacy as a corrective for safe media use. Attention is drawn to the resilience of fake news and fact-checking. Emphasis is placed on the relationship between media literacy and education. The issue of the digital media skills of Bulgarian students has been examined, as well as the inclusion of topics related to the media environment in civic education classes. The research hypothesis defines the idea that media literacy as part of 21st-century education is a corrective for the development of civil society.
Cybercrime and cyber resilience

The Digital Economy has expanded exponentially in recent years. As the economy has moved online, so have the criminals. This chapter addresses the following: the development of the digital economy and cybercrime, cyber threats, cyber criminals, cyber security, artificial intelligence, cyber resilience and the relevant law, policy and cyber resilience transformation tools.
Digital Educational Tools – Potential, Application, and Perspectives

The paper examines the technologies for creating digital educational resources from the point of view of necessity, implementation, and techniques for their creation. Various digital educational tools and their capabilities according to their application are described: learning and learning management programmes, tools for creating online tests and surveys, tools for creating online quizzes and educational games, interactive worksheets, tools for video lessons, OneNote electronic notebook. A study was conducted on the opinion and attitude of teachers and students about the electronic resources used in different academic disciplines, both for teaching learning content and for control and self-control. The results of two surveys are presented – one aimed at students and the other at teachers. The opinions of 155 teachers and 180 students from four regions in Bulgaria are summarized. The two main aspects of an application – for teaching learning content and for control and self-control – have been analyzed.
Why do we silo cyber security when criminals don't?

Organisations rely on a number of suppliers to provide a wide range of security solutions and services. This creates complexity that attackers can exploit. So what can we do about this?