Reviewerly/exHarmony huggingface dataset:
Internet of Things (IoT): Security, Cybercrimes, and Digital Forensics

Nowadays, the Internet of Things (IoT) plays a significant role in integrating various IoT-based devices over the network. These devices can be virtual or physical to facilitate various digital services to improve people's lives. To resolve security issues in the IoT systems, manufacturing companies are making significant efforts to deploy IoT-based applications over the network. Improper security measures that are implemented on IoT-based devices provide greater opportunities for attackers to indulge in cybercrimes that directly impact the real-time services of the IoT networks. The incorporation of IoT-based digital forensic frameworks can significantly resolve various issues that are being faced by cybercrimes. Cybercrimes equipped with IoT technology can penetrate the virtual space to harm people's lives; hence, IoT forensics frameworks are essential to explore and alleviate such types of attacks. Firstly, this chapter presents the layered architecture of the IoT, crucial features of the IoT, various IoT communication technologies, and issues of the IoT. Secondly, the chapter explores various IoT security threats and their solutions at different layers of IoT networks. Finally, the chapter provides an introduction to digital forensics and its process of investigation. In addition to this, various IoT forensics frameworks and their challenges are discussed. In the end, the chapter discusses the requirements for adopting real-time approaches.
Cybercrimes and Digital Forensics in Internet of Things

The Internet of Things (IoT) does not require human involvement to function. Sensors collect, communicate, analyze, and act on data as part of the IoT, providing new opportunities for technology, media, and telecommunications companies to produce value. For example, consider a garage door opener that can also deactivate the home alarm system as you enter. This is a useful function for a homeowner who needs to get into their house quickly. However, if the garage door opener is compromised, the complete alarm system may now be disarmed. TVs, home thermostats, door locks, home alarms, smart home hubs, and garage door openers, to mention a few, present a plethora of connection points for hackers to gain access to IoT ecosystems, access customer data, and even enter manufacturers' backend systems.IoT technology-based devices are becoming popular worldwide very quickly. The pervasiveness of the IoT, which is defined by its diversity, heterogeneity, and complexity, is blurring the boundaries between the physical and digital worlds. The presence of IoT technology in the business world cannot be ignored, hence the attacks and threats on IoT devices, too. Such attacks or crimes are increasing day by day. The IoT offers new ways for businesses to create value; however, the constant connectivity and data sharing also create new opportunities for information to be compromised.Cyber criminals have become a major threat to the government and business infrastructures all over the globe and are destroying these infrastructures through their criminal behaviors. Cyberattacks over IoT devices and systems are affecting the lives of users, so looking into solutions is mandatory now. Secure IoT is the need of the hour, and understanding of attacks and threats in the IoT structure should be common knowledge.Because IoT is so widely used, it makes it an ideal breeding ground for cyberattacks. The IoT infrastructure can be used as a tool to carry out a cyberattack or used as a direct target in a cyberattack. In either case, the IoT infrastructure's security is threatened. IoT forensics supports investigators in gaining intelligence from smart infrastructure in order to reconstruct historical occurrences. Due to the sophisticated IoT architecture, digital investigators face numerous obstacles when conducting IoT-related investigations using current investigation methodology, necessitating the creation of a new dedicated forensic framework.This chapter consists of the below mentioned topics and subtopics to discuss IoT technologies, IoT's main features, security challenges, proposed security solutions, and how IoT allows cybercrimes to happen. The chapter also explains digital forensics as well as IoT forensics before concluding the chapter.
GAN-Enabled Robust Backdoor Attack for UAV Recognition

Unmanned aerial vehicle (UAV) recognition is of crucial importance due to the blowout amount of UAVs and their threats on the public safety. Although many UAV recognition methods based on deep learning (DL) have been proposed by utilizing the radio frequency fingerprints and have achieved appreciable results, their vulnerability to adversarial attacks, especially backdoor attacks, has not been studied. In this pa-per, in order to reveal the serious threat for DL-based UAV recognition encountered with backdoor attacks, a novel robust generative adversarial network (GAN)-enabled backdoor attack scheme is proposed. Moreover, the proposed GAN-based trigger generator not only emerges exceptional attack effectiveness, but also performs well in terms of attack stealthiness and migration ability. Simulation results obtained with the real collected UAV recognition dataset demonstrate that our proposed scheme outperforms the benchmark BadNets backdoor attack.
SawStop Holding LLC v. Vidal <i>United States Court of Appeals of the Federal Circuit, 2022 48 F.4th 1355</i>

Biotechnology Law ReportVol. 41, No. 6 SawStop Holding LLC v. Vidal United States Court of Appeals of the Federal Circuit, 2022 48 F.4th 1355Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMANEdited for Biotechnology Law Report by CHRISTOPHER M. HOLMANChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.*The synopsis is original content and the decision itself has been condensed for ease of readability.Search for more papers by this authorPublished Online:15 Dec 2022https://doi.org/10.1089/blr.2022.29291.cipAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 41Issue 6Dec 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN.SawStop Holding LLC v. Vidal United States Court of Appeals of the Federal Circuit, 2022 48 F.4th 1355.Biotechnology Law Report.Dec 2022.311-316.http://doi.org/10.1089/blr.2022.29291.cipPublished in Volume: 41 Issue 6: December 15, 2022Online Ahead of Print:December 1, 2022PDF download
Usability Evaluation of Imikode Virtual Reality Game to Facilitate Learning of Object-Oriented Programming

Abstract Many empirical studies have shown that educational games and recent technologies impact education and increase learning effectiveness, students’ motivation and engagement. The overall aim of this study is to evaluate the usability of Imikode, a virtual reality (VR) game that was developed to introduce the concepts of object-oriented programming to novices. The improved version of the Imikode VR game consists of three features: An artificial intelligence component designed to provide real-time error feedback to users, an intelligent agent that guides and teaches users how to play the game and finally, the integration of multiple game play that gives learners more opportunities to explore the VR environment for greater immersive learning experience. This study adopted a survey approach and recruited first-year computer science students to measure learner satisfaction with educational virtual reality games and examined the correlations among the attributes of the Usefulness, Satisfaction and Ease of Use questionnaire of usage of Imikode. The results showed that the students were satisfied with Imikode and perceived the virtual reality educational game as very useful for learning object-oriented programming concepts. In addition, there was a correlation among the questionnaire variables, which means that researchers can use the instrument for future usability studies in the context. We further proffered some design recommendations for building software tools.
On resampling schemes for particle filters with weakly informative observations

We consider particle filters with weakly informative observations (or ‘potentials’) relative to the latent state dynamics. The particular focus of this work is on particle filters to approximate time-discretisations of continuous-time Feynman–Kac path integral models—a scenario that naturally arises when addressing filtering and smoothing problems in continuous time—but our findings are indicative about weakly informative settings beyond this context too. We study the performance of different resampling schemes, such as systematic resampling, SSP (Srinivasan sampling process) and stratified resampling, as the time-discretisation becomes finer and also identify their continuous-time limit, which is expressed as a suitably defined ‘infinitesimal generator.’ By contrasting these generators, we find that (certain modifications of) systematic and SSP resampling ‘dominate’ stratified and independent ‘killing’ resampling in terms of their limiting overall resampling rate. The reduced intensity of resampling manifests itself in lower variance in our numerical experiment. This efficiency result, through an ordering of the resampling rate, is new to the literature. The second major contribution of this work concerns the analysis of the limiting behaviour of the entire population of particles of the particle filter as the time discretisation becomes finer. We provide the first proof, under general conditions, that the particle approximation of the discretised continuous-time Feynman–Kac path integral models converges to a (uniformly weighted) continuous-time particle system.
Tokenised funding and initial litigation offerings: the new kids putting third-party funding on the block

This article presents a critical assessment of how blockchain technology is disrupting the monetisation of litigation claims through tokenization – and consequently further shaking up a revolutionary area in litigation financing. The significance in litigation financing is chiefly because tokenization presents securities to the general public in a far more accessible way than was previously the case – and even might enable non-accredited investors to participate in certain litigation financing investments for the first time (or with reduced barriers to entry). The assessment also considers the implications of a litigation pool that was already growing towards USD 20 billion, potentially now getting even larger. We further consider the regulatory challenges and the new moral hazards created in this fast-moving space.
Accurate diagnostic tissue segmentation and concurrent disease subtyping with small datasets

To provide a flexible, end-to-end platform for visually distinguishing diseased from undiseased tissue in a medical image, in particular pathology slides, and classifying diseased regions by subtype. Highly accurate results are obtained using small training datasets and reduced-scale source images that can be easily shared. An ensemble of lightweight convolutional neural networks (CNNs) is trained on different subsets of images derived from a relatively small number of annotated whole-slide histopathology images (WSIs). The WSIs are first reduced in scale in a manner that preserves anatomic features critical to analysis while also facilitating convenient handling and storage. The segmentation and subtyping tasks are performed sequentially on the reduced-scale images using the same basic workflow: generating and sifting tiles from the image, then classifying each tile with an ensemble of appropriately trained CNNs. For segmentation, the CNN predictions are combined using a function to favor a selected similarity metric, and a mask or map for a a candidate image is produced from tiles whose combined predictions exceed a decision boundary. For subtyping, the resulting mask is applied to the candidate image, and new tiles are derived from the unoccluded regions. These are classified by the subtyping CNNs to produce an overall subtype prediction. This approach was applied successfully to two very different datasets of large WSIs, one (PAIP2020) involving multiple subtypes of colorectal cancer and the other (CAMELYON16) single-type breast cancer metastases. Scored using standard similarity metrics, the segmentations outperformed more complex models typifying the state of the art.
An Integrating PV plant 689 kWp into Coal-Fired Power Plant (CFPP) 615 MW at Paiton: To reduce auxiliary load and coal consumption

Integrating solar energy into a coal-fired power plant is a promising way to reduce auxiliary load and numerous environmental issues related to the coal-based power generation sector. This paper will discuss the annual performance of PV plant 689 kWp at ash disposal that integrated into 615 MW coal-fired power plant PT xxx, including an estimate of total potential energy that can be generated; analysis of auxiliary load to improve performance and decreasing pollutant emissions by coal consumption reduction. The PV Plant has been operated for at least 2 years, built on an area of 5,000m2, is an On-grid PV Plant system with 11 grid Inverters and has 2,120 solar modules installed. Based on the simulation using Helioscope software, it can generate electrical energy up to 977,442 kWh per year with a performance ratio of 79.3%. The result of the PV plant utilization evaluation shows the amount of electrical energy produced is 1,096,105 kWh in 2021, above the estimation from the simulation. This result means auxiliary power load was decreasing by 0.4932%, It is also considered to be able to save the use of 263.065 kiloliters per year of diesel and also save the use of coal up to 386.634 tons or equivalent to 0.007% coal consumption per year. From an environmental point of view, it is also assessed to reduce CO2 emissions equivalent to 920.729 tons/year and the equivalent of planting 4,602 trees per year.
Direct Measurement of Higher-Order Nonlinear Polarization Squeezing

We report on nonlinear squeezing effects of polarization states of light by harnessing the intrinsic correlations from a polarization-entangled light source and click-counting measurements. Nonlinear Stokes operators are obtained from harnessing the click-counting theory in combination with angular-momentum-type algebras. To quantify quantum effects, theoretical bounds are derived for second- and higher-order moments of nonlinear Stokes operators. The experimental validation of our concept is rendered possible by developing an efficient source, using a spectrally decorrelated type-II phase-matched waveguide inside a Sagnac interferometer. Correlated click statistics and moments are directly obtained from an eight-time-bin quasi-photon-number-resolving detection system. Macroscopic Bell states that are readily available with our source show the distinct nature of nonlinear polarization squeezing in up to eighth-order correlations, matching our theoretical predictions. Furthermore, our data certify nonclassical correlations with high statistical significance, without the need to correct for experimental imperfections and limitations. Also, our nonlinear squeezing can identify nonclassicality of noisy quantum states which is undetectable with the known linear polarization-squeezing criterion.
In the Head of the Beholder: Comparing Different Proof Representations

Ontologies provide the logical underpinning for the Semantic Web, but their consequences can sometimes be surprising and must be explained to users. A promising kind of explanations are proofs generated via automated reasoning. We report about a series of studies with the purpose of exploring how to explain such formal logical proofs to humans. We compare different representations, such as tree- vs. text-based visualizations, but also vary other parameters such as length, interactivity, and the shape of formulas. We did not find evidence to support our main hypothesis that different user groups can understand different proof representations better. Nevertheless, when participants directly compared proof representations, their subjective rankings showed some tendencies such as that most people prefer short tree-shaped proofs. However, this did not impact the user’s understanding of the proofs as measured by an objective performance measure.
SiMaYang Type II Learning Model Assisted by Kahoot Application: Its Impact in Improving Student's Concept Understanding Based on Apos Theory

This research aims to determine the effect of the SiMaYang type II learning model assisted by the Kahoot application in increasing students' concept understand based on the APOS theory. This research is quasi-experimental with posttest only control group design. The sample of this research consisted of 67 students who were divided into three classes. The sampling technique used was the class random sampling technique. The research instrument was declared valid with a good difficulty level and reliable. Therefore, it could be used to collect data on students' conceptual understanding. Furthermore, the One-Way ANOVA test was used to test the hypothesis with a significant level of 5%. This research shows that the SiMaYang type II learning model assisted by the Kahoot application positively influenced students' conceptual understanding.
Research and Practice of Online and Offline Blending Teaching Mode of “Foundation of Program Design”

Aiming at the poor teaching effect of traditional teaching mode, this paper explored the online and offline blending teaching mode and practice of the course "Foundation of Programming". With "Ketangpai" as the teaching platform, teachers upload teaching resources to guide students to study independently and explore learning, strengthen communication between teachers and students in class and after class, and strengthen the process assessment. The whole teaching process data of a semester was stored in the "Ketangpai" server, with high teaching efficiency. Through the survey of students' satisfaction, we have obtained a good evaluation of the online and offline blending teaching mode of the course "Foundation of Programming". Through the analysis of the final examination of the course "Foundation of Programming" for two semesters, it was found that the students' test scores under the mode of online and offline blending teaching were better than those under the traditional teaching mode.
Identifying optimised speaker identification model using hybrid GRU-CNN feature extraction technique

Extracting vigorous and discriminative features and selecting an appropriate classifier model to identify speakers from voice clips are challenging tasks. Thus, we considered signal processing techniques and deep neural networks for feature extraction along with state-of-art machine-learning models as classifiers. Also, we introduced a hybrid gated recurrent unit (GRU) and convolutional neural network (CNN) as a novel feature extractor for optimising the subspace loss to extract the best feature vector. Additionally, space-time is contemplated as a computational parameter for finding the optimal speaker identification pipeline. Later, we have inspected the pipeline in a large-scale VoxCeleb dataset comprising 6,000 real world speakers with multiple voices achieving GRU-CNN + R-CNN for the highest accuracy and F1-score as well as GRU-CNN + CNN for maximum precision and LPC + KNN for the highest recall. Also, LPCC + R-CNN and MFCC + R-CNN are accomplished as optimal in terms of memory usage and time respectively.
Weakly-Supervised Temporal Action Localization with Multi-Head Cross-Modal Attention

Weakly-supervised temporal action localization seeks to localize temporal boundaries of actions while concurrently identifying their categories using only video-level category labels during training. Among the existing methods, the modal cooperation methods have achieved great success by providing pseudo supervision signals to RGB and Flow features. However, most of these methods ignore the cross-correlation between modal characteristics which can help them learn better features. By considering the cross-correlation, we propose a novel multi-head cross-modal attention mechanism to explicitly model the cross-correlation of modal features. The proposed method collaboratively enhances RGB and Flow features through a cross-correlation matrix. In this way, the enhanced features for each modality encode the inter-modal information, while preserving the exclusive and meaningful intra-modal characteristics. Experimental results on three recent methods demonstrate that the proposed Multi-head Cross-modal Attention (MCA) mechanism can significantly improve the performance of these methods, and even achieve state-of-the-art results on the THUMOS14 and ActivityNet1.2 datasets.
Comparing past GDPR fines to future ones under EDPB’s guidelines, and making a GDPR fine calculator

Article Comparing past GDPR fines to future ones under EDPB’s guidelines, and making a GDPR fine calculator was published on October 1, 2022 in the journal Computer Law Review International (volume 23, issue 5).
Medium term Solar Power Prediction using stacked LSTM based deep learning Technique

The grid’s safety and stability is highly influenced due to the variability in solar irradiance and power. With the decrease in fossil fuel the advancement in solar power prediction has been increasing considerably. Despite of extensive research work performed in the field of solar power there is possibility of advancement. Different parameters that affects the solar power are, solar irradiation, weather condition, cloud cover, etc. In this study solar irradiation and temperature are considered as the input for solar power prediction. A hybrid Stacked Long Short-Term Memory with dropout and optimized kernel Extreme Learning Machine is proposed for predicting solar power. The kernel parameter is optimized using the grey wolf optimization technique. Different statistical performance measures are considered for comparing the output results and it is observed that the proposed stacked LSTM based optimized kernel ELM (SLSTM-OKELM) shows better result with a MAPE value of 1.54%,1.56% and 1.34% for three different seasons as compared to other learning techniques which shows a minimum MAPE value of 4.59 %, 3.95, and 3.59% for the same seasons
Learning a Latent Space with Triplet Network for Few-Shot Image Classification

Few-shot image classification has attracted much attention due to its requirement of limited training data for target classes. Existing methods usually pretrain a network with images from the base set as feature extractor to obtain features of images from novel set. However, the pretrained feature extractor cannot extract accurate representation for categories have never seen, making images from novel set difficult to distinguish. To be specific, in the pretrained feature space, there exist a large number of overlapped areas between novel categories. To address this issue, it is crucial to acquire a space, where features from same class are gathering together and features from different classes are far away from each other. Since lots of experiments have proved that the triplet network is effective to achieve this goal, in this paper, we base our network on the Maximum a posteriori (MAP), learning a latent space with triplet network to project features from pretrained feature space into a more discriminative one. Experimental results on four few-shot benchmarks show that it significantly outperforms the baseline methods, improves around 1.09%∼13.09% than the best results in each dataset on both 1- and 5-shot tasks.