Reviewerly/exHarmony huggingface dataset:
Supporting crime script analyses of scams with natural language processing

Abstract In recent years, internet connectivity and the ubiquitous use of digital devices have afforded a landscape of expanding opportunity for the proliferation of scams involving attempts to deceive individuals into giving away money or personal information. The impacts of these schemes on victims have shown to encompass social, psychological, emotional and economic harms. Consequently, there is a strong rationale to enhance our understanding of scams in order to devise ways in which they can be disrupted. One way to do so is through crime scripting, an analytical approach which seeks to characterise processes underpinning crime events. In this paper, we explore how Natural Language Processing (NLP) methods might be applied to support crime script analyses, in particular to extract insights into crime event sequences from large quantities of unstructured textual data in a scalable and efficient manner. To illustrate this, we apply NLP methods to a public dataset of victims’ stories of scams perpetrated in Singapore. We first explore approaches to automatically isolate scams with similar modus operandi using two distinct similarity measures. Subsequently, we use Term Frequency-Inverse Document Frequency (TF-IDF) to extract key terms in scam stories, which are then used to identify a temporal ordering of actions in ways that seek to characterise how a particular scam operates. Finally, by means of a case study, we demonstrate how the proposed methods are capable of leveraging the collective wisdom of multiple similar reports to identify a consensus in terms of likely crime event sequences, illustrating how NLP may in the future enable crime preventers to better harness unstructured free text data to better understand crime problems.
Energy-Constrained Deep Neural Network Compression for Depth Estimation

Many applications, such as autonomous driving, robotics, etc., require accurately estimating depth in real time. Currently, deep learning is the most popular approach to stereo depth estimation. Some of these models have to operate in highly energy-constrained environments, while they are usually computationally intensive, containing massive parameter sets ranging from thousands to millions. This makes them hard to perform on low-power devices with limited storage in practice. To overcome this shortcoming, we model the training process of a deep neural network (DNN) for depth estimation under a given energy constraint as a constrained optimization problem and solve it through a proposed projected adaptive cubic quasi-Newton method (termed ProjACQN). Moreover, the trained model is also deployed on a GPU and an embedded device to evaluate its performance. Experiments show that the stage four results of ProjACQN on the KITTI-2012 and KITTI-2015 datasets under a 70% energy budget achieve (1) 0.13% and 0.61%, respectively, lower three-pixel error than the state-of-the-art ProjAdam when put on a single RTX 3090Ti; (2) 4.82% and 7.58%, respectively, lower three-pixel error than the pruning method Lottery-Ticket; (3) 5.80% and 0.12%, respectively, lower three-pixel error than ProjAdam on the embedded device Nvidia Jetson AGX Xavier. These results show that our method can reduce the energy consumption of depth estimation DNNs while maintaining their accuracy.
BadDGA: Backdoor Attack on LSTM-Based Domain Generation Algorithm Detector

Due to the outstanding performance of deep neural networks (DNNs), many researchers have begun to transfer deep learning techniques to their fields. To detect algorithmically generated domains (AGDs) generated by domain generation algorithm (DGA) in botnets, a long short-term memory (LSTM)-based DGA detector has achieved excellent performance. However, the previous DNNs have found various inherent vulnerabilities, so cyberattackers can use these drawbacks to deceive DNNs, misleading DNNs into making wrong decisions. Backdoor attack as one of the popular attack strategies strike against DNNs has attracted widespread attention in recent years. In this paper, to cheat the LSTM-based DGA detector, we propose BadDGA, a backdoor attack against the LSTM-based DGA detector. Specifically, we offer four backdoor attack trigger construction methods: TLD-triggers, Ngram-triggers, Word-triggers, and IDN-triggers. Finally, we evaluate BadDGA on ten popular DGA datasets. The experimental results show that under the premise of 1‰ poisoning rate, our proposed backdoor attack can achieve a 100% attack success rate to verify the effectiveness of our method. Meanwhile, the model’s utility on clean data is influenced slightly.
Document Image Restore via SPADE-Based Super-Resolution Network

With the development of deep learning technology, various structures and research methods for the super-resolution restoration of natural images and document images have been introduced. In particular, a number of recent studies have been conducted and developed in image restoration using generative adversarial networks. Super-resolution restoration is an ill-posed problem because of some complex restraints, such as many high-resolution images being restored for the same low-resolution image, as well as difficulty in restoring noises such as edges, light smudging, and blurring. In this study, we applied super-resolution restoration to text images using the spatially adaptive denormalization (SPADE) structure, different from previous methods. This paper used SPADE for document image restoration to solve previous problems such as edge unclearness, hardness to catch features of texts, and the image color transition. As a result of this study, it can be confirmed that the edge of the character and the ambiguous stroke are restored more clearly when contrasting with the other previously suggested methods. Additionally, the proposed method’s PSNR and SSIM scores are 8% and 15% higher compared to the previous methods.
Computational Pathology for Breast Cancer and Gynecologic Cancer

Advances in computation pathology have continued at an impressive pace in recent years [...].
Image Inpainting Anti-Forensics Network via Attention-Guided Hierarchical Reconstruction

Privacy security and property rights protection have gradually attracted the attention of people. Users not only hope that the images edited by themselves will not be forensically investigated, but also hope that the images they share will not be tampered with. Aiming at the problem that inpainted images can be located by forensics, this paper proposes a general anti-forensics framework for image inpainting with copyright protection. Specifically, we employ a hierarchical attention model to symmetrically reconstruct the inpainting results based on existing deep inpainting methods. The hierarchical attention model consists of a structural attention stream and a texture attention stream in parallel, which can fuse hierarchical features to generate high-quality reconstruction results. In addition, the user’s identity information can be symmetrically embedded and extracted to protect copyright. The experimental results not only had high-quality structural texture information, but also had homologous features with the original region, which could mislead the detection of forensics analysis. At the same time, the protection of users’ privacy and property rights is also achieved.
Construction of recipe knowledge graph based on user knowledge demands

Building a recipe knowledge graph from the perspective of user knowledge demands can provide users with accurate and efficient retrieval results and recipes. Relatively a few ontology studies have been conducted on Chinese recipes. In addition, some existing recipe ontology models are relatively rough, with few dimensions, and others have too many dimensions, that is, they lack versatility and cannot achieve efficient recipe recommendation and query functions. Therefore, this article proposes a general recipe ontology based on user knowledge demands analysed from multi-source recipes. Then, this article selects recipes for common edible flowers with health benefits, an ontology model and constructs a knowledge graph of flower recipes via knowledge extraction, knowledge fusion and other techniques. Finally, the application of constructed flower recipe knowledge graph is discussed, and the results indicated that it can effectively feedback the query results and satisfy the knowledge demands of users.
A Robust Adversarial Example Attack Based on Video Augmentation

Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems. Video adversarial attacks add subtle noise to the original example, resulting in a false classification result. Thorough studies on how to generate video adversarial examples are essential to prevent potential attacks. Despite much research on this, existing research works on the robustness of video adversarial examples are still limited. To generate highly robust video adversarial examples, we propose a video-augmentation-based adversarial attack (v3a), focusing on the video transformations to reinforce the attack. Further, we investigate different transformations as parts of the loss function to make the video adversarial examples more robust. The experiment results show that our proposed method outperforms other adversarial attacks in terms of robustness. We hope that our study encourages a deeper understanding of adversarial robustness in video classification systems with video augmentation.
SelfCCL: Curriculum Contrastive Learning by Transferring Self-Taught Knowledge for Fine-Tuning BERT

BERT, the most popular deep learning language model, has yielded breakthrough results in various NLP tasks. However, the semantic representation space learned by BERT has the property of anisotropy. Therefore, BERT needs to be fine-tuned for certain downstream tasks such as Semantic Textual Similarity (STS). To overcome this problem and improve the sentence representation space, some contrastive learning methods have been proposed for fine-tuning BERT. However, existing contrastive learning models do not consider the importance of input triplets in terms of easy and hard negatives during training. In this paper, we propose the SelfCCL: Curriculum Contrastive Learning model by Transferring Self-taught Knowledge for Fine-Tuning BERT, which mimics the two ways that humans learn about the world around them, namely contrastive learning and curriculum learning. The former learns by contrasting similar and dissimilar samples. The latter is inspired by the way humans learn from the simplest concepts to the most complex concepts. Our model also performs this training by transferring self-taught knowledge. That is, the model figures out which triplets are easy or difficult based on previously learned knowledge, and then learns based on those triplets in the order of curriculum using a contrastive objective. We apply our proposed model to the BERT and Sentence BERT(SBERT) frameworks. The evaluation results of SelfCCL on the standard STS and SentEval transfer learning tasks show that using curriculum learning together with contrastive learning increases average performance to some extent.
Online Hand Gesture Detection and Recognition for UAV Motion Planning

Recent advances in hand gesture recognition have produced more natural and intuitive methods of controlling unmanned aerial vehicles (UAVs). However, in unknown and cluttered environments, UAV motion planning requires the assistance of hand gesture interaction in complex flight tasks, which remains a significant challenge. In this paper, a novel framework based on hand gesture interaction is proposed, to support efficient and robust UAV flight. A cascading structure, which includes Gaussian Native Bayes (GNB) and Random Forest (RF), was designed, to classify hand gestures based on the Six Degrees of Freedom (6DoF) inertial measurement units (IMUs) of the data glove. The hand gestures were mapped onto UAV’s flight commands, which corresponded to the direction of the UAV flight.The experimental results, which tested the 10 evaluated hand gestures, revealed the high accuracy of online hand gesture recognition under asynchronous detection (92%), and relatively low latency for interaction (average recognition time of 7.5 ms; average total time of 3 s).The average time of the UAV’s complex flight task was about 8 s shorter than that of the synchronous hand gesture detection and recognition. The proposed framework was validated as efficient and robust, with extensive benchmark comparisons in various complex real-world environments.
Gaussian Markov Random Fields and totally positive matrices

The present paper focuses on the study of the conditions under which the covariance matrix of a multivariate Gaussian distribution is totally positive, paying particular attention to multivariate Gaussian distributions that are Gaussian Markov Random Fields. More specifically, it is proven that, if the graph over which the Gaussian Markov Random Field is defined consists of path graphs and the covariances between adjacent variables on the graph are non-negative, then there always exists a reordering of the variables that renders the resulting covariance matrix totally positive. Moreover, this reordering is identified and some cases for which the conditions for the covariance matrix of a multivariate Gaussian distribution to be totally positive are necessary and sufficient are provided.
Machine Learning for Cyber-Physical Systems

Machine Learning plays a crucial role for many innovations for Cyber-Physical Systems such as production systems. On the one hand, this is due to the availability of more and more data in ever better quality. On the other hand, the demands on the systems are also increasing: Production systems have to support more and more product variants, saving resources is increasingly in focus and international competition is forcing companies to innovate faster. Machine Learning leverages data to solve these issues. The goal is to have self-learning systems which improve over time. There are various algorithms and methods for this, for which an overview is given here. Furthermore, this article discusses special requirements of Cyber-Physical Systems for Machine Learning processes.
Symbolic Artificial Intelligence Methods for Prescriptive Analytics

Prescriptive analytics in supply chain management and manufacturing addresses the question of “what” should happen “when”, where good recommendations require the solving of decision and optimization problems in all stages of the product life cycle at all decision levels. Artificial intelligence (AI) provides general methods and tools for the automated solving of such problems. We start our contribution with a discussion of the relation between AI and analytics techniques. As many decision and optimization problems are computationally complex, we present the challenges and approaches for solving such hard problems by AI methods and tools. As a running example for the introduction of general problem-solving frameworks, we employ production planning and scheduling. First, we present the fundamental modeling and problem-solving concepts of constraint programming (CP), which has a long and successful history in solving practical planning and scheduling tasks. Second, we describe highly expressive methods for problem representation and solving based on answer set programming (ASP), which is a variant of logic programming. Finally, as the application of exact algorithms can be prohibitive for very large problem instances, we discuss some methods from the area of local search aiming at near-optimal solutions. Besides the introduction of basic principles, we point out available tools and practical showcases.
Runtime Monitoring for Systems of System

Software-intensive systems in general and Cyber-Physical Systems (CPS) in particular have drawn considerable attention from both industry and academia in recent years, with companies increasingly adopting Cyber-Physical Production Systems (CPPS). Regardless of the domain in which these systems are deployed, what they have in common is a shift from traditional software engineering principles towards a development process where software, hardware, and human actors controlling these systems are deeply interwoven and dependent upon each other. To ensure safe operation, it is crucial that an SoS complies with its requirements. Engineers and maintenance personnel must monitor if and how the SoS meets its requirements at runtime, e.g., to correctly verify timing behavior, or measure performance and resource consumption. In this chapter, we provide a brief introduction to SoS and CPPS, and investigate runtime monitoring from two different angles. First, we discuss requirements and challenges from the machine vendor's perspective. Second, we focus on the customer itself, i.e., typically a shop floor owner who has to combine a multitude of different components, both machinery and software systems for her production system. Finally, we discuss potential applications and benefits of runtime monitoring and provide an outlook presenting current research lines in SoS monitoring.
Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training

Smart grid-training systems enable trainers to achieve the high safety standards required for power operation. Effective methods for the rational segmentation of continuous fine actions can improve smart grid-training systems, which is of great significance to sustainable power-grid operation and the personal safety of operators. In this paper, a joint algorithm of a spatio-temporal convolutional neural network and multidimensional cloud model (STCNN-MCM) is proposed to complete the segmentation of fine actions during power operation. Firstly, the spatio-temporal convolutional neural network (STCNN) is used to extract action features from the multi-sensor dataset of hand actions during power operation and to predict the next moment’s action to form a multi-outcome dataset; then, a multidimensional cloud model (MCM) is designed based on the motion features of the real power operation; finally, the corresponding probabilities are obtained from the distribution of the predicted data in the cloud model through the multi-outcome dataset for action-rsegmentation point determination. The results show that STCNN-MCM can choose the segmentation points of fine actions in power operation in a relatively efficient way, improve the accuracy of action division, and can be used to improve smart grid-training systems for the segmentation of continuous fine actions in power operation.
Super‐resolution using deep residual network with spectral normalization

In this letter, the authors present a single-image super-resolution method based on introducing a novel spectral normalization to the convolution of a deep residual network. Moreover, the authors construct a new residual block (RB) and assemble it in a cascade form. The new RB was restructured by the spectrally normalized convolution layers and activated function. In addition, the RB allows the spectral normalization introduced on the trained network to update the additional weights. Furthermore, it minimizes pixel loss, thereby helping to obtain enhanced reconstruction results, limiting the number of parameters, and facilitating a low computational cost. The experimental results demonstrate that the proposed model shows the superior performance over that of state-of-the-art methods in terms of visual quality metrics such as UQI and PIQE.
On the maximum empty hyper-rectangle problem

Given a rectangle [Formula: see text] containing points, we consider the problem of detecting the largest rectangle that is totally contained in [Formula: see text] and does not include any of the points. In other words, we want to find the biggest hole in the dataset that can contain the biggest possible rectangle. A new algorithm for dealing with this problem is, therefore, suggested. Existing algorithms are exact but cannot deal efficiently with problems in high dimensions and large instances. In fact, only computing maximum empty rectangles in a set of points in [Formula: see text] has been well addressed. In high dimensions, the problem is shown to be NP-complete. Our suggested approach is evolutionary in nature and is an innovative implementation of the genetic algorithm. The approach solves a more general form of the stated problem in that it ignores the axis-parallel condition imposed on the hyper-rectangles to be found. This paper includes computational results and their discussions.
Generalized Zero-Shot Activity Recognition with Embedding-Based Method

Sensor-based human activity recognition aims to recognize the activities performed by people with the sensor readings. Most of existing works in this area rely on supervised classification algorithms, and can only recognize activities covered by the training data. Whereas, in many practical applications, while performing activity recognition, not only the activities covered by the training data, but also some previously unseen activities need to be recognized. In this paper, we study the problem of generalized zero-shot activity recognition. In this problem, the activities that need to be recognized contain both the activities covered by the training data and the previously unseen activities. We firstly give a formulation of this problem, and then propose an embedding-based method to address it. In this method, an embedding-compatibility model is learned. When performing activity recognition, the learned model and the calibrated stacking mechanism are employed. Extensive experiments on publicly available datasets demonstrate the effectiveness of our method.
Contributions of Social Networks to Employee Socialization and Performance in Cameroonian SMEs during the Covid-19 Pandemic

The rapid evolution of the internet has given birth to social networks, which are a dedicated website or other application that enables users to communicate by posting information, comments, messages, images, etc.Generally, the utilization of social networks tends towards entertainment; however, they are also used for professional purposes.In the context of the Covid-19 pandemic, this study aims to respond to the following question: What factors favor the adoption of social networks as tools to maintain operating activities in Cameroonian SMEs, and what is the impact of this utilization during the Covid-19 pandemic?At the centre of this study is a research model design based on four conceptual elements: TAM, the theory of socialization, the theory of connectivism, and the theory of performance.Data collected from 271 employees in Cameroon constitutes the dataset to test the model using the PLS-SEM approach.The results prove that TAM and the theory of connectivism are relevant to identifying the factors driving employees' behavioural decisions to use social networks during the Covid-19 pandemic.Furthermore, the study confirms the adequacy of both theories (performance and socialization) as conceptual frameworks by which to explain the impacts of the utilization of social networks.