Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Traditional and Cloud-Based Disaster Recovery Methods

Regardless of size or function, every organization requires a disaster recovery plan (DRP) to ensure business continuity in case of a service interruption due to a cyber-attack or a natural calamity like a flood and fire. In addition, these plans should achieve target recovery requirements of recovery time. Earlier disaster recovery was done via storing backups at a data center. However, this required the organization to maintain every aspect of the data center. This maintenance can quickly increase the operational cost, leading to smaller organizations not implementing their disaster recovery plan. Then in the 2000s, cloud computing came along and brought an entirely new way to implement DRP. This technological leap came with its pros and cons. Traditional DR has kept the data protected for a long time, but we must evaluate the differences with the advent of novel technologies like cloud DR. This paper compares and contrasts conventional DR techniques and the newer cloud-based approach. Furthermore, we also look at current cloud-based DR solutions along with their pros and cons.
Analyzing the Error Rates of Bitcoin Clustering Heuristics

Bitcoin is a decentralized peer-to-peer cryptocurrency. Bitcoin’s strong cryptography ensures anonymity that makes it possible to profit from crimes such as ransomware attacks and money laundering. Unfortunately, developments in blockchain technology make it almost impossible to identify the owners of Bitcoin addresses. Address clustering seeks to target the pseudo-anonymity by grouping Bitcoin addresses to eventually reveal real-world identities. However, none of the heuristic-based address clustering algorithms have been successfully admitted in court proceedings because they are heuristic in nature. According to the Daubert standard, for an algorithm to be admissible, it should have a known error rate. An error rate helps determine the extent to which a court can rely on the evidence, but no address clustering algorithm is able to report an error rate. This chapter describes a simulation model for validating heuristic-based address clustering algorithms and obtaining the corresponding error rates. The evaluation results demonstrate that the model can simulate real-world transactions. Two heuristics, multi-input and one-time change, are applied. The multi-input and one-time change heuristics yield average error rates of 63.46% and 92.66%, respectively. The application of both heuristics yields the lowest average error rate of 57.47%.
Hyperbolic Embedding of Attributed and Directed Networks

Network embedding &#x2013; finding a low dimensional representation of the nodes with attributes in a hierarchical, directed network remains a challenging problem in the machine learning community. An emerging approach is to embed complex networks &#x2013; networks of real-world systems &#x2013; into hyperbolic space due to the fact that hyperbolic space can better naturally represent such a network&#x2019;s hierarchical structure. Existing hyperbolic embedding approaches, however, cannot handle the embedding of attributed directed networks to an arbitrary embedding dimension. To fill this gap, we introduce HEADNet, for Hyperbolic Embedding of Attributed Directed Networks, an algorithm based on extending previous works for embedding directed attributed networks to Gaussian distributions in hyperbolic space of arbitrary dimension. Through experimentation on a variety of both synthetic and real-world networks, we show that HEADNet can achieve competitive performance on common downstream machine learning tasks, including predicting directed links for previously unseen nodes. HEADNet provides an inductive hyperbolic embedding method for directed attributed networks, which opens the door to hyperbolic manifold learning on a wider range of real-world networks. The source code is freely available at <uri>https://github.com/DavidMcDonald1993/HEADNET</uri>.
A Joint Optimization Approach for Security and Insurance Management on the Cloud

Cloud computing is a technology that allows the access of various applications that are developed and various kinds of data that are generated from any region worldwide and any kind of device either hand held mobile device or desktop which are allowed to access the Internet. Energy efficient deployment which involves saving done in the cost of implementation, accessing the necessary resources for development of several customized applications in the industrial side. All the corporate sectors are indulged in keeping track of the security in the cloud making it even more secured for the end users. The Security-as-a-Service (SECaaS) that constitutes one of the paradigm cloud computing allows all the users who are acquiring access to the cloud to outsource the activity of applying necessary security to the information in the cloud, through the payment that is considered as a subscription fee. However, no security systems that are implemented are not so strong in real time so anyone can attack our system and no one is responsible for the loss of our data. To overcome the cost-effectiveness one must get insured their data, in case any attack occurs we can retrieve our data through the subscription which we have done by the website.
How to Sort Them? A Network for LEGO Bricks Classification

LEGO bricks are highly popular due to the ability to build almost any type of creation. This is possible thanks to availability of multiple shapes and colors of the bricks. For the smooth build process the bricks need to properly sorted and arranged. In our work we aim at creating an automated LEGO bricks sorter. With over 3700 different LEGO parts bricks classification has to be done with deep neural networks. The question arises which model of the available should we use? In this paper we try to answer this question. The paper presents a comparison of 28 models used for image classification trained to classify objects to high number of classes with potentially high level of similarity. For that purpose a dataset consisting of 447 classes was prepared. The paper presents brief description of analyzed models, the training and comparison process and discusses the results obtained. Finally the paper proposes an answer what network architecture should be used for the problem of LEGO bricks classification and other similar problems.
Optimizing the KYC Process using a Blockchain based approach

The Know Your Client (KYC) process is an essential part of the financial ecosystem. The KYC process requires banks to validate and verify primary documents. The market these days, though, is flooded with KYC utilities that facilitate this process and share these documents with multiple entities, however they provide very little value addition. Blockchain technology, with its concept of immutable timestamped ledgers and distributed systems, can effectively facilitate banks to improve their KYC methods by allowing near real-time data exchange among various entities for quicker and effective validation ensuring data integrity alongside bringing down the time and costs significantly.
A Bayesian Approach for Joint Discriminative Dictionary and Classifier Learning

Sparse representation has been widely applied to image classification, where the key issue is to extract a suitable discriminative dictionary. To this end, we propose a joint dictionary and classifier learning algorithm based on a parameterized Bayesian model. Therein, the Gaussian priors of a dictionary endow it with the capability of discrimination and representation. Moreover, we introduce a multivariate Gaussian prior for the sparse codes to achieve group sparsity, thereby substantially improving the classification performance. Furthermore, the sparse codes are estimated by a group-sparse Bayesian learning (GSBL) method, and the dictionary atoms are updated sequentially by maximizing a posterior. Moreover, to avoid manual parameter adjustment, the hyperparameters are optimized by an evidence maximization method. Accordingly, we develop a classification scheme via GSBL. Finally, extensive experiments are conducted on six benchmark datasets of face classification, object recognition, handwritten recognition, and scene categorization to substantiate the effectiveness and superiority of the proposed method.
Enhancing Sequential Recommendation via Decoupled Knowledge Graphs

Sequential recommendation can capture dynamic interest patterns of users based on user interaction sequences. Recently, there has been interest in integrating the knowledge graph (KG) into sequential recommendation. Existing works suffer from two main challenges: a) representing each entity in the KG as a single vector can confound heterogeneous information about the entity; b) triple-based facts are modeled independently, lacking the exploration of high-order connectivity between entities. To solve the above challenges, we decouple the KG into two subgraphs, namely CRoss-user Behavior-based graph and Intrinsic Attribute-based graph (Crbia), depending on the type of relation between entities. We further propose a CrbiaNet based on the two subgraphs. First, CrbiaNet obtains behavior-level and attribute-level semantic features from these two subgraphs independently by different graph neural networks, respectively. Then, CrbiaNet applies a sequential model incorporating these semantic features to capture dynamic preference of the users. Extensive experiments on three real-world datasets show that our proposed CrbiaNet outperforms previous state-of-the-art knowledge-enhanced sequential recommendation models by a large margin consistently.
A Neighborhood Framework for Resource-Lean Content Flagging

Abstract We propose a novel framework for cross- lingual content flagging with limited target- language data, which significantly outperforms prior work in terms of predictive performance. The framework is based on a nearest-neighbor architecture. It is a modern instantiation of the vanilla k-nearest neighbor model, as we use Transformer representations in all its components. Our framework can adapt to new source- language instances, without the need to be retrained from scratch. Unlike prior work on neighborhood-based approaches, we encode the neighborhood information based on query– neighbor interactions. We propose two encoding schemes and we show their effectiveness using both qualitative and quantitative analysis. Our evaluation results on eight languages from two different datasets for abusive language detection show sizable improvements of up to 9.5 F1 points absolute (for Italian) over strong baselines. On average, we achieve 3.6 absolute F1 points of improvement for the three languages in the Jigsaw Multilingual dataset and 2.14 points for the WUL dataset.
Deep Neural Networks Aiding Cryptanalysis: A Case Study of the Speck Distinguisher

At CRYPTO’19, A. Gohr proposed neural distinguishers for the lightweight block cipher Speck32/64, achieving better results than the state-of-the-art at that point. However, the motivation for using that particular architecture was not very clear; therefore, in this paper, we study the depth-10 and depth-1 neural distinguishers proposed by Gohr [7] with the aim of finding out whether smaller or better-performing distinguishers for Speck32/64 exist. We first evaluate whether we can find smaller neural networks that match the accuracy of the proposed distinguishers. We answer this question in the affirmative with the depth-1 distinguisher successfully pruned, resulting in a network that remained within one percentage point of the unpruned network’s performance. Having found a smaller network that achieves the same performance, we examine whether its performance can be improved as well. We also study whether processing the input before giving it to the pruned depth-1 network would improve its performance. To this end, convolutional autoencoders were found that managed to reconstruct the ciphertext pairs successfully, and their trained encoders were used as a preprocessor before training the pruned depth-1 network. We found that, even though the autoencoders achieved a nearly perfect reconstruction, the pruned network did not have the necessary complexity anymore to extract useful information from the preprocessed input, motivating us to look at the feature importance to get more insights. To achieve this, we used LIME, with results showing that a stronger explainer is needed to assess it correctly.
Self-Supervised Moving Vehicle Detection From Audio-Visual Cues

Robust detection of moving vehicles is a critical task for any autonomously operating outdoor robot or self-driving vehicle. Most modern approaches for solving this task rely on training image-based detectors using large-scale vehicle detection datasets such as nuScenes or the Waymo Open Dataset. Providing manual annotations is an expensive and laborious exercise that does not scale well in practice. To tackle this problem, we propose a self-supervised approach that leverages audio-visual cues to detect moving vehicles in videos. Our approach employs contrastive learning for localizing vehicles in images from corresponding pairs of images and recorded audio. In extensive experiments carried out with a real-world dataset, we demonstrate that our approach provides accurate detections of moving vehicles and does not require manual annotations. We furthermore show that our model can be used as a teacher to supervise an audio-only detection model. This student model is invariant to illumination changes and thus effectively bridges the domain gap inherent to models leveraging exclusively vision as the predominant modality.
Semi-Supervised Leukocyte Segmentation Based on Adversarial Learning With Reconstruction Enhancement

The number, relative ratio, and appearance of peripheral blood leukocytes can assist doctors to diagnose diseases such as lymphoma and leukemia. Therefore, segmentation of peripheral blood leukocytes in blood smear images plays a crucial role in the diagnosis of certain diseases. We propose a semi-supervised leukocyte segmentation method under the framework of adversarial learning, in which a discriminator is trained to differentiate the segmentation maps coming either from the ground truth or from the segmentation network. Specifically, we first construct a lightweight leukocyte segmentation network by modifying DeepLabV3+ with simplified MobileNetV2. In addition, a reconstruction decoder is developed as a constraint on feature extraction, to recover the original leukocyte image from its segmentation result. Then, we design the discriminator in a fully convolutional manner, which as well enables semi-supervised learning by inferring reliable supervisory information for unlabeled images. Quantitative and qualitative experimental results on four image datasets demonstrate the superiority of the proposed method over several state-of-the-art methods, achieving the highest average F1 score of 0.9717 and mIoU of 0.9493 with only 20% labeled training samples.
Virtual Usability Testing (VIRTUSE) - Development of a Methodical Approach for Usability Testing in VR

In several domains of product design - like medical device design - risk related use scenarios have to be analyzed in an early stage of design process. Virtual reviews with users make it possible to get early insights in use problems. Also, situations that are difficult to imitate in reality can be modeled and simulated in virtual reality without risking the health of user. Therefore, virtual usability tests are a promising approach which allow testing different scenarios under controlled conditions. We chose a sample scenario from medical device design and compare and evaluate different technical systems which can be used for virtual usability tests. Aim is to derive guidance for virtual usability tests including systems that can be used for specific conditions and the qualitative and quantitative data, which can be collected with these systems. A formative test is performed to evaluate and compare different systems. Also, a summative test is performed to evaluate the selected systems. Results show that virtual usability tests make it possible to test scenarios with users in an early stage and thus to encounter possible interaction problems. But there are also many additional and new things to consider compared to normal usability tests, such as checking motion sickness, maintaining presence and the extensive operation of the technology. Finally, a proposed method for virtual usability testing is described which also comprises our equipment recommendations for virtual usability tests.
Novel Dynamic Segmentation for Human-Posture Learning System Using Hidden Logistic Regression

In this letter, we propose a novel automatic-segmentation technique for a dynamic human-posture learning system using skeletal-graph time-series. The focused problem is very challenging as no fixed segment-size is appropriate for capturing precise human postures. Our proposed novel dynamic-segmentation scheme will first estimate the number of segments and then the optimal segmentation can be determined using hidden logistic regression subject to the estimated number of segments. Experimental results from the realworld Kinect data are compared with the well-known dynamic-time-warping (DTW) segmentation method. Based on our experiments, our proposed new scheme greatly outperforms the DTW method in terms of miss-detection probability and miss-alignment percentage.
Location Based Services

Location-based services (LBS)location-based service (LBS) have been a research field since the early 2000s. With the recent rapid advances in mobile computing technologies and the ubiquity of mobile devices, LBS are entering into many aspects of our daily life. This chapter provides an up-to-date overview of the field of LBS. Particularly, it introduces the essential components within the LBS ecosystem, i.e., mobile devices/users, positioning, communication networks, service and content provider; typical application domains of LBS; and key research areas, i.e., positioning, context modeling, and information communication and adaptation, as well as recent research trends and open research challenges currently presented in the domain of LBS.
Extracting Preference Rules Using &lt;i&gt;Kansei&lt;/i&gt; Retrieval Agents with Fuzzy Inference

This study examines a Kansei retrieval agent (KaRA) model based on fuzzy reasoning in terms of optimizing fuzzy rules. The KaRA model learns the user’s preferences based on the user’s sensory evaluation, and retrieves what the user wants from a large amount of data. The KaRA model based on fuzzy reasoning has information on membership functions and fuzzy rules. Previous studies have demonstrated the effectiveness of the KaRA model in terms of learning the user’s evaluation criteria by optimizing the membership function of fuzzy reasoning using numerical simulation. However, the fuzzy rules of the model have not been optimized. By optimizing fuzzy rules, the KaRA model can acquire the user’s sensibility evaluation information in linguistic expressions (fuzzy rules). Therefore, we confirmed the effectiveness of fuzzy rule optimization in the KaRA model. We conducted numerical simulations using pseudo-users and experiments with real users. Consequently, we examined the effectiveness of fuzzy rule optimization in the KaRA model.
Forensic Intelligence and Traceology in Digitalised Environments: The Detection and Analysis of Crime Patterns to Inform Practice

Abstract Different data processing methods can support the detection and analysis of various forms of crime patterns. The authors document the influence and role of forensic science and how this has been transformed by digitalization. Forensic intelligence is key and they illustrate their argument of its potential by discussing two new forensic intelligence systems and their underlying digital infrastructure, one facilitating the forensic comparison of fraudulent ID documents (ProFID) and the other the monitoring of online frauds (PICSEL).
Performance Measures of Blockchain-Based Mobile Wallet Using Queueing Model

Smart and cashless payments through mobile wallets are the recent implementation of digital payment alternatives in developing countries to achieve competitive advantage. Blockchain is an egressing digitally managed technology (BT) which allows ubiquitous monetary transactions with the untrusted entities. In blockchain, it is impossible to change records without notifying all participants and validating signatures. Hence, it overcomes the security challenges that exist in the mobile wallet. BT can revolutionize a change in paradigm by which the transactions will become more efficient by reduced number of intermediaries and faster payment procedures among the parties. In this paper, we develop a queueing model which evaluates in detail the characteristics of a mobile wallet system and measures the various performances of the blockchain-based mobile wallet system.
Association Rules Extraction From the Coronavirus Disease 2019

This research was aimed to extract association rules on the morbidity and mortality of corona virus disease 2019 (COVID-19). The dataset has four attributes that determine morbidity and mortality; including Confirmed Cases, New Cases, Deaths, and New Deaths. The dataset was obtained as of 2nd April, 2020 from the WHO website and converted to transaction format. The Apriori algorithm was then deployed to extract association rules on these attributes. Six rules were extracted: Rule 1. {Deaths, NewDeaths}=&gt;{NewCases}, Rule 2. {ConfCases, NewDeaths}=&gt;{NewCases}, Rule 3. {ConfCases, Deaths}=&gt;{NewCases}, Rule 4. {Deaths, NewCases}=&gt;{NewDeaths}, Rule 5. {ConfCases, Deaths}=&gt;{NewDeaths}, Rule 6. {ConfCases, NewCases}=&gt;{NewDeaths}, with confidence 0.96, 0.96, 0.86, 0.66, 0.59, 0.51 respectively. These rules provide useful information that is vital on how to curtail further spread and deaths from the virus, both in areas where the pandemic is already ravaging and in areas yet to experience the outbreak.
A Methodology Review on Multi-view Pedestrian Detection

Although surprisingly good progress has been made in monocular pedestrian detectionPedestrian detection with emerging deep learning techniques, the existing algorithms still suffer from heavy occlusion between people. To cope with this problem, multiple cameras are often used to provide complimentary information. This chapter presents a comprehensive review on the algorithms for multi-view pedestrian detectionPedestrian detection. The existing methods are sorted into low-level fusion, intermediate-level fusion, high-level fusion and deep-learning based fusion, in terms of the degree of information fusion. The challenges in developing deep multi-view algorithms are also described in this chapter.