Reviewerly/exHarmony huggingface dataset:
Automatic Multi-Class Collective Motion Recognition Using a Decision Forest Extracted from Neural Networks

This paper presents an approach to machine recognition of multiple classes of collective motion behaviours. Previous work has demonstrated that it is possible to distinguish structured collective motion from random, unstructured motion. However, it has proved difficult to use such techniques for automatically recognising specific collective motion variants such as moving in a line versus moving in a group. To enable a knowledge base to recognise multiple classes of collective motion, this paper proposes a decision forest approach. The proposed approach extracts machine-understandable knowledge from a neural network trained to automatically recognise collective motions. The main advantage of this approach is that besides being automatic, it is fast, accurate and easy to use. We show that our deep neural network achieves 90.30% accuracy for multi-class labelling of collective motion behaviours, which is more accurate than shallow neural networks for this problem. Furthermore, a knowledge base extracted using the decision forest on the deep neural network can recognise the class of random behaviour and the eight classes of collective motion behaviours with 88.81% accuracy in just 0.03 seconds, which is only 1.49% less accurate than the original deep neural network, but over 100 times faster.
The Question Concerning Technology in Northeast India

In Northeast India, an extremely rich ecological, cultural and historical setting, the challenge of the technological evolution of education makes it crucial to revisit the historical question concerning technology, as it was posed by Heidegger in his eponymous 1977 essay, then revisited by Chinese philosopher Yuk Hui in 2017, who coined the term “cosmotechnics” as a result. This paper discusses the project Library in the Forest, developed by research and design studio Forest Cybernetics, whose goal is to re-imagine how technology might serve, and not undermine, education, nature, and the environment. Against universalizing notions of technology, it develops a cosmotechnical position—at once technological and ethical. The evolving curriculum brings together design practice, scientific investigation, coding, and philosophy, with local/ indigenous knowledge systems, to re-situate technology in a specific set of histories of place, settlement, and change.
Automata with Timers

In this work, we study properties of deterministic finite-state automata with timers, a subclass of timed automata proposed by Vaandrager et al. as a candidate for an efficiently learnable timed model. We first study the complexity of the configuration reachability problem for such automata and establish that it is $$\textsf{PSPACE}$$ -complete. Then, as simultaneous timeouts (we call these, races) can occur in timed runs of such automata, we study the problem of determining whether it is possible to modify the delays between the actions in a run, in a way to avoid such races. The absence of races is important for modelling purposes and to streamline learning of automata with timers. We provide an effective characterization of when an automaton is race-avoiding and establish that the related decision problem is in $$\textsf{3EXP}$$ and $$\textsf{PSPACE}$$ -hard.
Fast Parameter Estimation in TV-Based Image Restoration

The regularization parameter plays the role of balancing the fidelity term and the regularization term. The adaptive selection of this parameter is essential for the automated implementation of image restoration. In this chapter, the adaptive estimation of regularization parameter in the objective function of image restoration is studied, which balances the prior regularization term and the fidelity term of observation data. A fast algorithm is proposed to estimate the regularization parameter and restore the image at the same time. The adaptive estimation of regularization parameter is an important basis for the automatic image restoration. Experimental results show that, compared with some famous algorithms, the proposed algorithm has simpler structure, more accurate parameter estimation, and faster convergence speed.
FHC-DQP: Federated Hierarchical Clustering for Distributed QoS Prediction

With the overwhelming explosion of Web services, how to effectively predict unknown QoS has become a key issue of differentiating large-scale similar or functionally equivalent Web services. However, current state-of-the-art QoS prediction approaches based on deep learning still suffer from two deficiencies. First, they mainly focus on predicting vacant QoS in a centralized manner and scarcely take into account distributed QoS prediction, which makes difficult to protect the privacy information of users invoking Web services. Second, they have ignored the hierarchical collaborative relationship to better extract latent features of users and services, reducing the accuracy of QoS prediction. To address these two issues, we propose a novel framework called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ederated</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ierarchical</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lustering for</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">istributed</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oS</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rediction</i> (FHC-DQP). It collaboratively performs distributed federated training on independent users’ QoS invocations, and then the extracted federated users’ private features are fed to clustering algorithm for partitioning them into a set of clusters. By iteratively federated hierarchical clustering, users are fine-grained partitioned together and those users within the same cluster have stronger collaborative relevance for more effectively learning the latent features of users and services leading to the performance improvement of distributed QoS prediction, where contextual-aware deep neural network is designed for personalized QoS prediction. Extensive experiments are conducted based on a public real-world benchmarking dataset called WS-DREAM with almost 2,000,000 user-service historical QoS invocations. Compared with both centralized and federated competing baselines, the results demonstrate FHC-DQP receives superior performance for distributed QoS prediction, when it provides privacy-preserving of users’ QoS invocations.
Exploring the Mutual Influence Between Self-Supervised Single-Frame and Multi-Frame Depth Estimation

Although both self-supervised single-frame and multi-frame depth estimation methods only require unlabeled monocular videos for training, the information they leverage varies because single-frame methods mainly rely on appearancebased features while multi-frame methods focus on geometric cues.Considering the complementary information of singleframe and multi-frame methods, some works attempt to leverage single-frame depth to improve multi-frame depth.However, these methods can neither exploit the difference between single-frame depth and multi-frame depth to improve multi-frame depth nor leverage multi-frame depth to optimize single-frame depth models.To fully utilize the mutual influence between single-frame and multi-frame methods, we propose a novel self-supervised training framework.Specifically, we first introduce a pixel-wise adaptive depth sampling module guided by single-frame depth to train the multi-frame model.Then, we leverage the minimum reprojection based distillation loss to transfer the knowledge from the multi-frame depth network to the single-frame network to improve single-frame depth.Finally, we regard the improved single-frame depth as a prior to further boost the performance of multi-frame depth estimation.Experimental results on the KITTI and Cityscapes datasets show that our method outperforms existing approaches in the self-supervised monocular setting.
Cloud Computing Data Breaches in News Media: Disclosure of Personal and Sensitive Data

Cloud computing has changed how businesses adopt information and communication technology (ICT) services that can be provisioned dynamically, providing more capacity and capability as required without the huge upfront capital expenditure. As consumers continue to use more and more online self-service portals, they increasingly leave digital footprints and personally identifiable information (PII) behind. Hackers, cloud configuration vulnerabilities, insider attacks, and accidental information security leaks are commonplace today, affecting tens of millions, if not hundreds of millions of end-users. This article recounts the story of three of the most significant data breaches internationally, Sony PSN (2011), eBay (2014) and Yahoo! (2014), through the lens of news media at the height of the data breaches. The article captures a variety of cloud computing stakeholder perspectives, identifying key socio-technical considerations that need to be addressed over the longer term for the protection of the end-user, and the continuous improvement of cloud services.
The Prevalence and Mechanisms of cyber fraud activities among Ghanaian Youths; Routine Activity, Space Transition, and Rational Choice from Differential Association Perspective

This study examines the impact of four components of a refined routine activity theory model, namely the offenders’ motivations, suitable targets, capable guardians, and online routines, on the desistance desires of cyberfraudsters. A largescale self-report survey of 515 cyberfraudsters in Ghana is utilized. The results suggest that traditional criminological theories are still applicable for analyzing cybercrime, especially cyber fraud, even though it is a more sophisticated form of traditional crime with criminals employing various tactics and security experts putting various preventive measures in place. Due to a variety of reasons, cybercriminals continue to target unprotected internet users. The study concludes that depending just on police patrol is insufficient for stopping cyber fraud attempts and that investing in secure systems and solutions is essential for protecting netizens’ assets and data from cyber dangers.
Neuro-Fuzzy Logic Application in Speech Recognition

Speech recognition is the ability of a system to recognise words and phrases in speech and convert them to readable or text format. In general, speech recognition will be accomplished by activities such as call routing, speech-to-text processing, voice dialling, voice audibility, and language modelling. Although neural networks are good classifiers, their effectiveness is based on the calibre and quantity of training data they are given. The use of fuzzy approaches enhances performance when training data is lacking or not entirely representative of the possible range of values. In other words, adding fuzzy approaches enables the classification of erroneous data. This paper presents a neural network that handles fuzzy numbers as the neuro-fuzzy system. This characteristic gives the system the ability to classify erroneous input in the proper way. The performance of the neuro-fuzzy system for speaker-independent voice recognition is significantly better than a regular neural network, according to experimental findings. Due to variances in voice frequency and pronunciation, speaker-independent speech recognition is a particularly challenging categorisation challenge in this study.
Causal Reversibility for Timed Process Calculi with Lazy/Eager Durationless Actions and Time Additivity

A reversible computing system features backward computations along which the effects of forward ones are undone when needed. This is accomplished by reverting executed actions from the last one. Since the last performed action may not be uniquely identifiable in a concurrent system, causal reversibility is considered: an executed action can be undone provided that all of its consequences have been undone already. We investigate causal reversibility in a timed setting by defining a reversible calculus in the style of Phillips and Ulidowski in which action execution is separated from time passing, actions can be lazy or eager, and time is described via numeric delays subject to time additivity. We show that the calculus meets causal reversibility through an adaptation of the technique of Lanese, Phillips, and Ulidowski that ensures a proper treatment of action laziness/eagerness as well as time-additive delays.
Unsupervised vehicle extraction of bounding boxes in UAV images

Various studies have been conducted to detect objects in urban areas by applying machine learning algorithms to UAV high-resolution images. However, most vehicle detection studies have limitations in that vehicle detection is performed as a bounding box instead of instance segmentation. Since instance segmentation requires labor-intensive labeling work of each object to train individual objects, research on how to perform unsupervised automatic instance segmentation is needed. Therefore, this study proposed unsupervised SVM classification of the vehicle bounding boxes in UAV images for instance segmentation. As a result of the extraction, it was confirmed that the vehicle could be detected with an accuracy of 89%. It was also confirmed that the vehicle could be detected even if the spectral characteristics within the vehicle were significantly different.
Pandai Smart Highway

It is crucial to identify the flow of traffic based on unexpected factors such as obstacles on the road and accidents. The proposed road traffic monitoring “Pandai Smart Highway” is based on the traffic camera placed above the digital signboard, it allows for capturing the obstruction on the road, density, and speed of the vehicles. Emergency services on highways are not well developed by solely relying on the siren only works around a limited radius. Smart highway systems play a crucial role in road traffic management, and planning, and reduce frequent traffic jams, speed violations, and road accidents. Our solution is based on sensors to detect traffic patterns, emergency vehicles, and obstructions. At the same time, digital signage and LED bars are being implemented to notify drivers regarding the road situation. A prototype is used to assess the viability of the model. The results of the investigations demonstrate good efficiency in vehicle detection and accurate messages provided to the drivers.
Grammar-Based Question Classification Using Ensemble Learning Algorithms

Question Classification is one of the important applications of information retrieval, as it plays a crucial role in improving the performance of question-answering systems. Differentiating between factoid and non-factoid questions is a particularly difficult task. Different methods have been suggested to improve the identification and classification of factoid questions. Most of these methods rely on semantic features and bag-of-words. This research paper explores the utilization of a Grammar-based framework for Questions Categorization and Classification (GQCC) to identify question types. This framework incorporates features such as grammatical features, domain-specific features, and patterns. These features leverage the question structure. By employing Ensemble Learning models, experimental findings demonstrate that the integration of question grammatical features with Ensemble Learning models contributes to achieving a good level of accuracy.
Question Answering Versus Named Entity Recognition for Extracting Unknown Datasets

Dataset mention extraction is a difficult problem due to the unstructured nature of text, the sparsity of dataset mentions, and the various ways the same dataset can be mentioned. Extracting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unknown</i> dataset mentions which are not part of the training data of the model is even harder.We address this challenge in two ways. First, we consider a two-step approach where a binary classifier filters out positive contexts, i.e., detects sentences with a dataset mention. We consider multiple transformer-based models and strong baselines for this task. Subsequently, the dataset is extracted from the positive context. Second, we consider a one-step approach and directly aim to detect and extract a possible dataset mention. For the extraction of datasets, we consider transformer models in named entity recognition (NER) mode. We contrast NER with the transformers’ capabilities for question answering (QA). We use the Coleridge Initiative "Show US the Data" dataset consisting of 14.3 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> scientific papers with about 35 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> mentions of datasets. We found that using transformers in QA mode is a better choice than NER for extracting unknown datasets. The rationale is that detecting new datasets is an out-of-vocabulary task, i.e., the dataset name has not been seen once during training. Comparing the two-step versus the one-step approach, we found contrasting strengths. A two-step dataset extraction using an MLP for filtering and RoBERTa in QA mode extracts more dataset mentions than a one-step system, but at the cost of a lower F1-score of 62.7%. A one-step extraction with DeBERTa in QA achieves the highest F1-score of 92.88% at the cost of missing dataset mentions. We recommend the one-step approach for the case when accuracy is more important, and the two-step approach when there is a postprocessing mechanism for the extracted dataset mentions, e.g., a manual check. The source code is available at https://github.com/yousef-younes/dataset_mention_extraction.
Self-Supervised Cluster-Contrast Distillation Hashing Network for Cross-Modal Retrieval

Unsupervised cross-modal hashing aims to train models in an unsupervised environment so that they can achieve efficient and fast retrieval between multi-modal data. The key to the cross-modal hashing model is feature extraction. However, the quality of the features largely depends on the semantic similarity between the multi-modal data, and the existing methods do not effectively utilize the semantic information between the data. In this paper, we attempt to explore the semantic information inherent within the data using contrastive learning. Specifically, we propose a cluster-level contrastive learning method (SCCDH) for unsupervised cross-modal hashing. The method utilizes the clustering results to guide feature learning in an appropriately designed contrast framework. In SCCDH, feature-level and hash cluster-level contrastive learning are used to help the model learn discriminative features among multimodal data. In addition, we propose a distillation filtering method to filter out a large amount of noise in the data. Extensive experiments were conducted on the MIRFLICKR-25K, NUS-WIDE, and MS-COCO datasets, and the results demonstrate that the proposed method outperformed several state-of-the-art methods.
Soft Web Intelligence with the J-CO Framework

In the last two decades a plethora of approaches have been proposed to perform Web Intelligence to discover useful knowledge over the World-Wide Web. However, variety and vastness of the Web are still making this task a hard challenge. Nonetheless, the Web is evolving. An example is the advent of the JSON format as the practical standard for exchanging data over the Internet. In our previous work, we proposed the concept of Soft Web Intelligence: it is a modern interpretation of Web Intelligence based on the current technological panorama, in which JSON data sets can be gathered and stored within JSON document stores and processed by means of Soft Computing so as to Soft Querying them. Soft Web Intelligence is enabled by the J-CO Framework, a software tool that is natively able to manage, soft-query and transform collections of JSON documents, located either in NoSQL repositories or over the Internet. The paper illustrates our vision by presenting a plausible case study based on a weekly-updated data set that reports COVID-19 cases in European Countries.
Deep-Learning Based Nonprofiling Side-Channel Attack on Mask Leakage-Free Environments Using Broadcast Operation

With the recent development of artificial intelligence (AI), efforts to apply related technologies to various fields are rapidly increasing. In the field of cryptanalysis, research utilizing deep learning is continuously being published in order to keep up with this trend. Side-channel analysis is a a type of cryptanalysis that uses physical information and can be classified into profiling and nonprofiling analyses. Nonprofiling attacks using deep learning take advantage of the fact that training is performed relatively well when the right key is compared to the wrong key. Masking countermeasures are applied to design a secure cipher against side-channel analysis. The traditional second-order attack for analyzing masked ciphers is used by preprocessing the side channel information to remove the mask value. However, deep learning has the advantage of being able to omit this process. Related works proposed so far attempted to analyze the masked cipher, but focused only on 1-byte analysis using the masking information itself. In reality, grasping the time-points, in which only the masking information is revealed, is difficult and far from the secret key analysis area. In this study, we attempt to analyze the case of combining masked 2-byte information, not only using the masking information. We also propose a neural network design scheme to perform more effective attacks. The proposed method highlights the relative difference between the right and wrong keys. Previous research on analysis evaluation criteria has been lacking. Therefore, we propose herein new evaluation metrics that can be easily used and demonstrate their validity using simulation and actually collected data. As a result of the experiment, the proposed methods based on the loss metric improved by approximately 228.59% in the simulation dataset and 739.46% in the real dataset compared to the binary labeling. And it reduced the minimum number of analytical traces by approximately 78.95% and 72.5%, respectively.