Reviewerly/exHarmony huggingface dataset:
Efficient Partitioning and Communication Scheme-Based Distributed Edge Computing to Accelerate Deep Neural Network

In the era of the Internet of Things (IoT), there are growing demands to deploying Deep Neural Network (DNN) on edge devices. However, edge devices typically have limited computing resources, which makes it challenging to perform the large computing workloads required for DNN inference. While several methods have been proposed to address this challenge, such as Server-Client pipeline, distributed computing, etc., the workload partitioning and transmission schemes for edge devices have not been well discussed. This paper proposes a distributed edge computing system that leverages efficient partitioning algorithms and a TCP-based communication mechanism to accelerate DNN inference. The experimental results demonstrate that the proposed system with 4 nodes reduces the computation delay by 74.39%. Furthermore, when the number of nodes increases from 2 to 4, the inference is accelerated by 1.98-3.90×, which is 12.98% better than the prior MoDNN system in [6].
Bounds for Binary Multimedia Codes with the Identifiable Parent Property

Multimedia codes with the identifiable parent property (MIPPCs) were proposed to resist collusion attacks for multimedia fingerprinting. However, the largest possible code rate of binary MIPPCs is far from being understood yet. In this paper, we aim to establish lower and upper bounds for the largest code rate of binary MIPPCs. To that end, we introduce a new concept of locally thin and fat families (LTFFs) and establish relationships between LTFFs and binary MIPPCs. Accordingly, new lower and upper bounds for binary MIPPCs and LTFFs are derived by means of the probabilistic method and combinatorial techniques. In particular, the order of magnitude for the largest rate of binary MIPPCs is determined. It is shown that the code rate of binary MIPPCs outperforms other existing binary fingerprinting codes (e.g. binary separable codes) as well.
Unsupervised Space-Time Network for Temporally-Consistent Segmentation of Multiple Motions

Motion segmentation is one of the main tasks in computer vision and is relevant for many applications. The optical flow (OF) is the input generally used to segment every frame of a video sequence into regions of coherent motion. Temporal consistency is a key feature of motion segmentation, but it is often neglected. In this paper, we propose an original unsupervised spatiotemporal framework for motion segmentation from optical flow that fully investigates the temporal dimension of the problem. More specifically, we have defined a 3D network for multiple motion segmentation that takes as input a sub-volume of successive optical flows and delivers accordingly a sub-volume of coherent segmentation maps. Our network is trained in a fully unsupervised way, and the loss function combines a flow reconstruction term involving spatio-temporal parametric motion models, and a regularization term enforcing temporal consistency on the masks. We have specified an easy temporal linkage of the predicted segments. Besides, we have proposed a flexible and efficient way of coding U-nets. We report experiments on several VOS benchmarks with convincing quantitative results, while not using appearance and not training with any ground-truth data. We also highlight through visual results the distinctive contribution of the short- and long-term temporal consistency brought by our OF segmentation method.
Constellation Mapping Design Using K-means Clustering for Analog Joint Source-Channel Coding Based on Shannon-Kotel’nikov Mappings

Analog joint source channel coding (AJSCC) schemes have gained importance, which have shown to approach the best performance theoretically obtainable when transmitted over AWGN channels. Its simplicity and zero-latency transmission make it more suitable for real-time applications than traditional digital system separation principles. However, the sources and mappings in AJSCC are inherently analog and meanwhile digital communications are standard in wire communications, so we need to digitize some aspects of mapping full-resolution constellations to finite constellations. In this work, we discuss how to digitize the Shannon-Kotel’nikov (S-K) mappings for application in real systems or for interfacing with digital systems. We propose an irregular constellation mapping method and apply it to an existing AJSCC model. The experimental results show that the performance is better than the conventional uniform quadrature amplitude modulation (QAM) constellation mapping method.
Quantum Information Theory

In the last years, a considerable amount of theoretical and experimental studies have been focussing on the impact that quantum mechanics may have on computer science, information theory and cryptography. We shall loosely refer to this vast and variegated field as quantum information [64, 90, 122, 156, 183, 266, 282, 287, 359]. In the following, we shall briefly touch upon a small fraction of its many achievements.
Development of a competency integrated cross-scientific curriculum Intelligent electronics to create innovations for industrial technicians Vocational Certificate Program (Vocational Certificate) under the Office of Vocational Education Commission in The Southern Region

The purpose of this research was to develop a competency-integrated cross-disciplinary curriculum with integrated intelligent electronics competency in order to create innovations for the field of industrial technicians, Vocational Certificate Program (Vocational Certificate) under the Office of Vocational Education Commission in the Southern Region. The research’s objectives are 1) to develop integrated competency-based teaching and learning curriculum intelligent electronics to create innovations for industrial technicians; 2) to access the quality of a competency-integrated cross-disciplinary training course outline aspect of intelligent electronics to create innovations for industrial technicians . The research instruments consisted of a training course, an experimental package, an evaluation form for content congruence with behavioral objectives, and a form to assess the suitability of the training course outline. The statistics used in the data analysis were average analysis, standard deviation, and the IOC conformity index. The research results were as follows: 1) A integrated cross-scientific instructional management model of intelligent electronic competency was consistent between content and behavioral objectives with a mean score of 0.90, and content was consistent with behavioral objectives. Moreover, the assessment’s results for the suitability of the training curriculum outline for integrated intelligent electronics competency in intelligent electronics to create innovations for industrial technicians were: the training content was high, In addition, the duration of the activities was at a high level, and the experimental design technique was at a high level.
Welcome to the glitch and make some noise: Understanding media through audio hacking

This article describes the experimental examination of various audio devices and their storage media, such as the radio, the tape recorder and the CD player, in the context of interdisciplinary university courses in arts and humanities, particularly cultural studies, with a focus on music production, sound studies and audio culture. Glitches and intrinsic noises become perceptible as aesthetic material by means of hacking and modifying audio technology failures. Following Marshall McLuhan’s concept of media, it is clear that the audio technology itself, not just the transmitted content of a piece of music or radio programme, is the message . Examples of diverse aesthetic strategies that account for various artists’ positions are presented in detail. These strategies have been the subject of a seminar that has been held several times at two different universities in Germany, addressing interdisciplinary groups of students. Unconventional, hands-on approaches to the different media devices have been evaluated positively by the participants.
Engaging the Board to Ensure Effective Cybersecurity

Abstract Grant Powell MBCS spoke to Steve Sands, BCS ISSG Chair &amp; Data Protection Officer at Synectics Solutions, and Robert Felthouse, CISO at Syntectics Solutions, about the need for greater synergy between the boardroom and security departments when it comes to dealing with cyber threats.
Vanishing Points Detection with Line Segments of Gaussian Sphere

Depth estimation using vanishing points is important in computer vision and has been widely used in various applications such as robotics and autonomous driving. A vanishing point is a point in the image where parallel lines appear to converge to a single point in 3D space. The detection of vanishing points in images plays a crucial role in estimating the depth of a scene. However, the accuracy of vanishing point detection is often affected by noisy or unconverged line segments detected by the line detectors. The problem with using line detectors is that they can produce noisy or unconverged line segments, leading to a decrease in the accuracy of vanishing point detection. Therefore, it is important to develop a method to extract accurate vanishing points from noisy line segments. This paper proposes an algorithm to detect vanishing points by projecting line segments to Gaussian sphere. The proposed method follows the steps: (1) Line segment detection by Mobile LSD [1], (2) Classifying line segments based on their angle, and (3) Projecting the classified line segments onto a Gaussian sphere and converting them into a 2D image. In the context of this transformed 2D image, it is postulated that the vanishing point corresponds to the region in which the line segments exhibit the greatest degree of overlap. In other words, the proposal does not need to compute all intersection points from line segments and recognize the vanishing points from the intersection points. The proposal can not only improves the accuracy but also reduces the time in vanishing points detection by the experiments.
Enhancing Multi-Source Open-Set Domain Adaptation through Nearest Neighbor Classification with Self-Supervised Vision Transformer

Domain adaptation mitigates the decline in performance that occurs when models are utilized in a target domain. Models designed for a limited range of categories struggle to handle real-world scenarios where unknown classes, absent from the original domain, exist. Furthermore, it is probable that multiple source domains are annotated asynchronously by distinct agencies, each with its own data distributions. The practical challenges of multi-source open-set domain adaptation (MSOSDA) have not been thoroughly investigated, despite their relevance in real-world scenarios. The main difficulty in MSOSDA lies in developing a shared discriminative feature space across all domains, while effectively separating source classes from target-specific ones. In this study, we propose a method for MSOSDA using a self-supervised vision Transformer (ViT) combined with nearest neighbor classification. Our key insight is to leverage the powerful nearest neighbor classification property of self-supervised ViT, along with supervised contrastive learning. To explicitly align the domains and accurately identify unknown classes in the target domain, we employ straightforward strategies and an adaptive data-driven threshold. Our approach has been extensively evaluated on five multi-source domain adaptation benchmarks, showcasing its effectiveness. Among these benchmarks, two are fine-grained, and it is worth noting that one of them has been introduced for the first time in this paper. Through these experiments, we provide compelling evidence of the performance and efficacy of our proposed approach.
Ukraine, Fostering Resilience, and the Importance of Preparedness

Abstract Over a year into the war in Ukraine, BCS spoke to experts from IT Ukraine Association and the UK about what we have learned about IT resilience, what Ukraine is still able to offer, and the implications for the role of IT technology in society. The webinar was chaired by Brian Runciman MBCS, BCS' Head of Content and Insight.
Can A Language Model Be Conscious?

Abstract Unseduced by ChatGPT's apparent abilities, Dr Matthew Shardlow of Manchester Metropolitan University's Department of Computing and Mathematics explores the current limits of AI.
Supporting IT Teaching in Africa

Abstract From moribund to Malawi: Martin Cooper MBCS reports on the ITSA Digital Trust, a charity which collects unwanted PCs, upcycles them and delivers them to African schools.
Case Management and Disclosure

This chapter identifies particular areas that need considering during the case management phase of the criminal investigation. Criminal investigators require a good working knowledge of the processes and procedures that come before a defendant appears at court. Matters such as knowing about threshold tests and charging policies, dealing with specific evidential points, powers to give cautions, national file standards, and last but not least the importance of giving and presenting evidence all come under the general scope of case management. Alongside court procedures, they become highly relevant and important responsibilities for a lead crime investigator. Mistakes and poor practice in this phase of the investigative process can work against any chances of mounting a successful prosecution.
Blackstone's Crime Investigators' Handbook

Now in its third edition, this book provides straightforward, practical information and advice for crime investigators. The book complements the learning outcomes from the Initial Crime Investigators Development Programme (ICIDP) Phases 2 and 3, as well as all Professionalising Investigations Programme (PIP) Level 2 investigations and provides practical operational advice for a variety of crime types that investigators and supervisors can expect to encounter. The third edition covers changes to legislative and professional practices such as the Domestic Abuse Act 2021, Achieving Best Evidence 2022, amendments to the Police and Criminal Evidence Act 1984 and Codes of Practice, the Code of Practice for Victims of Crime 2021, and the Criminal Procedure Rules and Practice Directions 2020, which consolidates the Criminal Procedure Rules 2015 and subsequent amendments. This edition also covers new and emerging crime types, such as cyber crime, honour based violence and abuse, child sexual exploitation, modern slavery, and stalking and harassment.
Crime Types and their Investigation

This chapter explores different types of crime and their investigation. It begins with volume crime, which is ‘any crime which through its sheer volume has a significant impact on the community and the ability of the local police to tackle it’. Offences include acquisitive crime such as burglary, theft and street robbery, and other ‘volume’ offences such as criminal damage and assault. Violent crime may form part of a control strategy within the Volume Crime Management Model 2009 (VCMM). Regardless of the severity of injury, it is broadly divided into three types: stranger attacks, acquaintance assaults, and domestic abuse. The chapter then looks at the Protection from Harassment Act 1997 (PHA). It also considers hate crime; rape and serious sexual offences (RASSO); child sexual exploitation (CSE); missing person inquiries; honour-based violence/abuse and forced marriage; modern slavery and exploitation; criminal use of firearms; cyber crime and cyber investigations; and terrorism and extremism.
Attending Incidents

This chapter discusses the general procedures to be followed when attending incidents, including crime scenes. The police attend volume crime scenes and minor incidents more often than major crime scenes like murder or rape. However, the principles of attending a volume crime scene are no different to those employed when attending the scene of a serious crime. The scene of an incident normally requires a police officer to make a judgement about the scale and type of response required so that the appropriate help and support are made available. The chapter notes the protocols to be followed if crimes involved deaths, fires, and railway incidents.
Markov Types and Redundancy for Markov Sources

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Shannon and Huffman FV Codes

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Incremental Learning for Simultaneous Augmentation of Feature and Class

With the emergence of new data collection ways in many dynamic environment applications, the samples are gathered gradually in the accumulated feature spaces. With the incorporation of new type features, it may result in the augmentation of class numbers. For instance, in activity recognition, using the old features during warm-up, we can separate different warm-up exercises. With the accumulation of new attributes obtained from newly added sensors, we can better separate the newly appeared formal exercises. Learning for such simultaneous augmentation of feature and class is crucial but rarely studied, particularly when the labeled samples with full observations are limited. In this paper, we tackle this problem by proposing a novel incremental learning method for Simultaneous Augmentation of Feature and Class (SAFC) in a two-stage way. To guarantee the reusability of the model trained on previous data, we add a regularizer in the current model, which can provide solid prior in training the new classifier. We also present the theoretical analyses about the generalization bound, which can validate the efficiency of model inheritance. After solving the one-shot problem, we also extend it to multi-shot. Experimental results demonstrate the effectiveness of our approaches, together with their effectiveness in activity recognition applications.
Blockchain-based knowledge graph for high-impact scientific collaboration networks

In this book chapter, we introduce a framework leveraging the intelligence of the crowd to improve the quality, credibility, inclusiveness, long-term impact and adoption of research, particularly in the academic space. This integrated platform revolves around a central knowledge graph (KG) which interacts through artificial intelligence (AI) algorithms with the community. In combination with Internet of Things (IoT) technology and blockchain, a highly productive environment including liquid governance and arbitration is created to fairly acknowledge and attractively incentivize contributions of valuable intellectual property (IP) to this knowledge base. In the proposed platform, various stakeholders customize their terms of agreement to be followed while validating the transactions on blockchain, known as smart contracts. Through the interaction of smart contracts and stakeholders, the agreement based on objective (scientific) criteria will gradually emerge from the simulated interaction and, if applicable, its experimental/empirical verification.
9 Exploration of Data Sharing and Information Exchange among Nigerian Library and Information Science Researchers

This chapter examines data sharing and reuse and associated issues. It explores how LIS researchers in Nigeria engage in data sharing practices along with the problems and challenges encountered. The chapter tracks a study which examined data sharing practices and information exchange behaviour among Nige­rian LIS researchers. Data was collected through an open-ended survey of twenty LIS researchers selected from four library schools in south-west and north-central Nigeria. The findings showed that data sharing among LIS researchers is gaining pace. Aside from conferences, workshops, seminars, and scholarly publications, LIS researchers shared data through social media and other online platforms. LIS researchers reused data for teaching and learning, replication of studies, new studies, and investigation of new problems. Challenges associated with data sharing were found to be inadequate access because of policy and guidelines failures, inad­equate skills, and fear of placing data in the public domain.
Visible Watermark Removal with Deep Learning Technology

Watermarking is a technique used to assert ownership over an image, and can be categorized into visible and invisible forms based on the detectability of the watermark. Visible watermarking is more user-friendly and intuitive than invisible methods since it allows individuals to identify image ownership with their own eyes rather than relying on machine-based watermark decoders. To enhance the visual quality of watermarked images and ensure the original images can be fully recovered after visible watermark authentication, a visible watermark removal approach using deep learning-based inpainting is proposed in this paper. Experimental results demonstrate that the watermarked images carrying the visible watermark and auxiliary information achieve peak signal-to-noise ratios (PSNRs) ranging from 41.89 dB to 43.17 dB and structural similarity indices (SSIMs) up to 0.97 to 0.98. Furthermore, our hybrid recovery operations enable the complete restoration of the original images, making them easily readable.
Initial Stages of an Investigation

This chapter details the initial stages of a criminal investigation, which begins after the discovery of the crime. The first contact could be a report to a call handling unit, a public enquiry desk, or reports to officers or other staff or agencies. Many crimes can be quickly detected through prompt and decisive resourcing and dispatch for locating and arresting offenders, obtaining witnesses, seizing CCTV footage, finding and preserving forensic evidence, etc. Initial assessments by attending officers with updates reported back to communications and control rooms should confirm the initial grading or revise the response in terms of seriousness, priority, and resources. This period is often referred to as the ‘golden hour’ (or hours). A key point for investigators, no matter what the seriousness of the crime is, is the importance of establishing exactly what has already been done, by whom, and to what level and standard.
Core Investigative Strategies

This chapter assesses the core investigative strategies, which are the methods and tactical options available and used for progressing a criminal investigation. The initial assessment of the situation should identify the primary objectives of the investigation along with deciding on the main lines of enquiry and strategies needed to achieve them. Generic considerations and aide memoires or checklists can be useful reminders to some extent, but ultimately careful consideration and decision making is required. Most of the strategies described in this chapter also feature to varying degrees in the <italic>Core Investigative Doctrine</italic> (2005). The chapter then looks at relevant time parameters; house-to-house enquiries; CCTV/Audio Visual (AV) strategy; passive data generators; digital media investigation; financial investigation; elimination enquiries; and family liaison.