Reviewerly/exHarmony huggingface dataset:
Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity

Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking), harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Then, we show that our proposed active learning solution is capable of matching the performance of a fully supervised baseline by using just 5% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts.
Scalable Multi-view Subspace Clustering with Unified Anchors

Multi-view subspace clustering has received widespread attention to effectively fuse multi-view information among multimedia applications. Considering that most existing approaches' cubic time complexity makes it challenging to apply to realistic large-scale scenarios, some researchers have addressed this challenge by sampling anchor points to capture distributions in different views. However, the separation of the heuristic sampling and clustering process leads to weak discriminate anchor points. Moreover, the complementary multi-view information has not been well utilized since the graphs are constructed independently by the anchors from the corresponding views. To address these issues, we propose a Scalable Multi-view Subspace Clustering with Unified Anchors (SMVSC). To be specific, we combine anchor learning and graph construction into a unified optimization framework. Therefore, the learned anchors can represent the actual latent data distribution more accurately, leading to a more discriminative clustering structure. Most importantly, the linear time complexity of our proposed algorithm allows the multi-view subspace clustering approach to be applied to large-scale data. Then, we design a four-step alternative optimization algorithm with proven convergence. Compared with state-of-the-art multi-view subspace clustering methods and large-scale oriented methods, the experimental results on several datasets demonstrate that our SMVSC method achieves comparable or better clustering performance much more efficiently. The code of SMVSC is available at https://github.com/Jeaninezpp/SMVSC.
Medical Image Encryption Scheme Using Multiple Chaotic Maps

Telemedicine and various tele-medicinal applications are revolutionizing a variety of healthcare departments through its innovative means of remote diagnosing and faster first aid administration. Digital images play an important role in these applications for ensuring better and faster health carfigure. These digital images, which usually contain confidential and diagnostic information about the patients, are usually transmitted through public networks among hospitals, doctors and patients. Consequently, there is a need to secure them while being stored and in transit to guarantee the privacy of the patient. However, unique properties of digital image data, such as high redundancy and correlation between the pixels of the image and their large size, make the conventional cryptographical algorithms insufficient for ensuring proper security while encrypting them. As the conventional algorithms seize to be a reliable solution, the need to develop improved and dedicated image encryption algorithms also arises. Chaotic systems are systems that appear random and unpredictable from the outside but are governed by deterministic equations or rules on the inside. Due to these properties, along with ergodicity and its heightened sensitivity to the initial conditions, chaotic systems are devised to be one of the best candidates for securing the storage and transmission of digital images. However, the security of a chaotic image encryption system depends upon the chaotic behavior demonstrated by the chaotic map applied in the image encryption system. Because of this, different attacks can be used to break a chaotic encryption system if the scheme is not well-structured. This paper introduces a chaotic image encryption scheme that incorporates two chaotic maps, namely, Arnold's Cat Map and 2D Logistic-Sine-Coupling Map(2D-LSCM), for increased randomness and security of the encrypted image. We also analyze the performance and security of our scheme and compare it with other prominent chaotic image encryption schemes.
An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO's issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.
Multi-Object Tracking in Satellite Videos With Graph-Based Multitask Modeling

Recently, satellite video has become an emerging means of earth observation, providing the possibility of tracking moving objects. However, the existing multi-object trackers are commonly designed for natural scenes without considering the characteristics of remotely sensed data. In addition, most trackers are composed of two independent stages of detection and reidentification (ReID), which means that they cannot be mutually promoted. To this end, we propose an end-to-end online framework, which is called TGraM, for multi-object tracking in satellite videos. It models multi-object tracking as a graph information reasoning procedure from the multitask learning perspective. Specifically, a graph-based spatiotemporal reasoning module is presented to mine the potential high-order correlations between video frames. Furthermore, considering the inconsistency of optimization objectives between detection and ReID, a multitask gradient adversarial learning strategy is designed to regularize each task-specific network. In addition, aiming at the data scarcity in this field, a large-scale and high-resolution Jilin-1 satellite video dataset for multi-object tracking (AIR-MOT) is built for the experiments. Compared with state-of-the-art multi-object trackers, TGraM achieves efficient collaborative learning between detection and ReID, improving the tracking accuracy by 1.2 multiple object tracking accuracy. The code and dataset will be available online ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HeQibin/TGraM</uri> ).
BlockMedCare: A healthcare system based on IoT, Blockchain and IPFS for data management security

Nowadays, healthcare is growing rapidly due to the large development of new technologies such as IoT and wearable devices. These devices are widely used to ensure remote patient monitoring. The current implementation is based on a client/server architecture. This raises several challenges regarding security and privacy that make healthcare systems more susceptible to several attacks. Therefore, health data are subject to strict regulatory and security requirements. To overcome these challenges and comply with security regulations, the adoption of a distributed architecture is a necessity. Due to its distributed nature and its security promises, Blockchain has a large interest as a sophisticated technology to solve the security challenges in IoT-based systems. Motivated by these factors, this work proposes BlockMedCare, a secure healthcare system that integrates IoT with Blockchain. The system is designed to support remote patient monitoring, especially when it comes to chronic diseases that require regular monitoring. We took into consideration three main parameters: security, scalability, and processing time. The security is ensured by using the re-encryption proxy combined with Blockchain to store hash data. Smart contracts are used for access control. To ensure Blockchain scalability, an off-chain database based on IPFS is used to store data. To speed up the data storage process, we use an Ethereum Blockchain-based proof of authority. As a use case, we applied the system to diabetes management and showed the execution results based on the system interfaces. The experimental system has demonstrated a good improvement of healthcare systems in terms of security face to the existing methods.
Cyber risk and cybersecurity: a systematic review of data availability

Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.The online version contains supplementary material available at 10.1057/s41288-022-00266-6.
Wearable Sensor-Based Human Activity Recognition with Transformer Model

Computing devices that can recognize various human activities or movements can be used to assist people in healthcare, sports, or human-robot interaction. Readily available data for this purpose can be obtained from the accelerometer and the gyroscope built into everyday smartphones. Effective classification of real-time activity data is, therefore, actively pursued using various machine learning methods. In this study, the transformer model, a deep learning neural network model developed primarily for the natural language processing and vision tasks, was adapted for a time-series analysis of motion signals. The self-attention mechanism inherent in the transformer, which expresses individual dependencies between signal values within a time series, can match the performance of state-of-the-art convolutional neural networks with long short-term memory. The performance of the proposed adapted transformer method was tested on the largest available public dataset of smartphone motion sensor data covering a wide range of activities, and obtained an average identification accuracy of 99.2% as compared with 89.67% achieved on the same data by a conventional machine learning method. The results suggest the expected future relevance of the transformer model for human activity recognition.
Blockchain in logistics and production from Blockchain 1.0 to Blockchain 5.0: An intra-inter-organizational framework

Blockchain technology is a ground breaking disruptive technology in production and logistics systems. However, how it affects logistics and production over the past years as well as what should be examined next remain unclear. This paper fills this gap by first discussing the evolution of blockchain (from Blockchain 1.0 to Blockchain 5.0). Then, we examine the related production and logistics management literature and highlight the current states of knowledge in the field. We review in more details the analytical and empirical studies and uncover the research methods, topics, insights and implications. After that, with references to both the examined literature and observed industrial practices, we identify various important research gaps one by one. We build the intra-inter-organizational (IIO) operational framework to highlight the importance of both internal (i.e., intra-organizational level) and inter-organizational considerations in order to adopt blockchain successfully in production and logistics systems. Critical factors for implementing blockchain under the IIO framework are uncovered. Finally, based on the IIO framework, a future research agenda, which relates to the different stages of blockchain development as well as different topical areas, is established.
Large pre-trained language models contain human-like biases of what is right and wrong to do

Artificial writing is permeating our lives due to recent advances in large-scale, transformer-based language models (LMs) such as BERT, GPT-2 and GPT-3. Using them as pre-trained models and fine-tuning them for specific tasks, researchers have extended the state of the art for many natural language processing tasks and shown that they capture not only linguistic knowledge but also retain general knowledge implicitly present in the data. Unfortunately, LMs trained on unfiltered text corpora suffer from degenerated and biased behaviour. While this is well established, we show here that recent LMs also contain human-like biases of what is right and wrong to do, reflecting existing ethical and moral norms of society. We show that these norms can be captured geometrically by a ‘moral direction’ which can be computed, for example, by a PCA, in the embedding space. The computed ‘moral direction’ can rate the normativity (or non-normativity) of arbitrary phrases without explicitly training the LM for this task, reflecting social norms well. We demonstrate that computing the ’moral direction’ can provide a path for attenuating or even preventing toxic degeneration in LMs, showcasing this capability on the RealToxicityPrompts testbed. Large language models identify patterns in the relations between words and capture their relations in an embedding space. Schramowski and colleagues show that a direction in this space can be identified that separates ‘right’ and ‘wrong’ actions as judged by human survey participants.
A Fully Automated Multimodal MRI-Based Multi-Task Learning for Glioma Segmentation and IDH Genotyping

The accurate prediction of isocitrate dehydrogenase (IDH) mutation and glioma segmentation are important tasks for computer-aided diagnosis using preoperative multimodal magnetic resonance imaging (MRI). The two tasks are ongoing challenges due to the significant inter-tumor and intra-tumor heterogeneity. The existing methods to address them are mostly based on single-task approaches without considering the correlation between the two tasks. In addition, the acquisition of IDH genetic labels is expensive and costly, resulting in a limited number of IDH mutation data for modeling. To comprehensively address these problems, we propose a fully automated multimodal MRI-based multi-task learning framework for simultaneous glioma segmentation and IDH genotyping. Specifically, the task correlation and heterogeneity are tackled with a hybrid CNN-Transformer encoder that consists of a convolutional neural network and a transformer to extract the shared spatial and global information learned from a decoder for glioma segmentation and a multi-scale classifier for IDH genotyping. Then, a multi-task learning loss is designed to balance the two tasks by combining the segmentation and classification loss functions with uncertain weights. Finally, an uncertainty-aware pseudo-label selection is proposed to generate IDH pseudo-labels from larger unlabeled data for improving the accuracy of IDH genotyping by using semi-supervised learning. We evaluate our method on a multi-institutional public dataset. Experimental results show that our proposed multi-task network achieves promising performance and outperforms the single-task learning counterparts and other existing state-of-the-art methods. With the introduction of unlabeled data, the semi-supervised multi-task learning framework further improves the performance of glioma segmentation and IDH genotyping. The source codes of our framework are publicly available at https://github.com/miacsu/MTTU-Net.git.
Novel Meta-Heuristic Algorithm for Feature Selection, Unconstrained Functions and Engineering Problems

This paper proposes a Sine Cosine hybrid optimization algorithm with Modified Whale Optimization Algorithm (SCMWOA). The goal is to leverage the strengths of WOA and SCA to solve problems with continuous and binary decision variables. The SCMWOA algorithm is first tested on nineteen datasets from the UCI Machine Learning Repository with different numbers of attributes, instances, and classes for feature selection. It is then employed to solve several benchmark functions and classical engineering case studies. The SCMWOA algorithm is applied for solving constrained optimization problems. The two tested examples are the welded beam design and the tension/compression spring design. The results emphasize that the SCMWOA algorithm outperforms several comparative optimization algorithms and provides better accuracy compared to other algorithms. The statistical analysis tests, including one-way analysis of variance (ANOVA) and Wilcoxon’s rank-sum, confirm that the SCMWOA algorithm performs better.
Service robots: Applying social exchange theory to better understand human–robot interactions

Despite service robots' importance in the tourism industry, few studies have investigated human–robot interaction from a reciprocal exchange perspective. This paper applies social exchange theory to the context of human–robot interaction along with relational and psychological states based on two empirical studies with two service robots (i.e., Pepper and Connie) in hotel settings. Findings reveal significant positive effects of perceived intelligence, perceived social presence, and perceived social interactivity on trust, leading to usage intentions. Similarly, perceived social presence and perceived social interactivity are positively related to rapport, driving usage intentions. Perceived intelligence and perceived social interactivity negatively lead to uniqueness neglect to contribute to usage intentions. We additionally identify mediating roles of rapport, trust, and uniqueness neglect in the relationships between human–robot attributes and usage intentions. Our results deliver key insight into how human–robot interaction attributes influence users’ relational and psychological states and subsequent usage intentions.
Recommending the long tail items through personalized diversification

Recommender systems which focus only on the improvement of recommendations’ accuracy are named “accuracy-centric”. These systems encounter some problems the major of which is their failure in recommending long tail items. Long tail items are the ones rated by a few users, thus, their rare participation in recommendations. To overcome this problem, it is necessary to provide recommendations by considering other aspects in addition to accuracy. One of these aspects is diversity in recommendations. As to different users who may prefer different levels of diversity in recommendations, here diversification of recommendations in a personalized manner is suggested in order to increase the participation of long tail items. The recommendation list is optimized based on three objectives of increasing the accuracy, personalizing the diversity, and reducing the popularity of the recommended items to meet the purpose. The defined multi-objective optimization problem is solved through the archived multi-objective simulated annealing algorithm. The evaluation of this proposed method on the Netflix datasets reveals that this method overcomes the long tail recommendation problem and diversifies the recommendations according to user needs while maintaining an acceptable level of accuracy.
Privacy Preserving Collaborative Computing: Heterogeneous Privacy Guarantee and Efficient Incentive Mechanism

Collaborative computing uses multiple data servers to jointly complete data analysis, e.g., statistical analysis and inference. One major obstruction for it lies in privacy concern, which is directly associated with nodes' participation and the fidelity of received data. Existing privacy-preserving paradigms for cloud computing and distributed data aggregation only provide nodes with homogeneous privacy protection without consideration of nodes' diverse trust degrees to different data servers. We propose a two-phase framework that computes the average value while preserving heterogeneous privacy for nodes' private data. The new challenge is that in the premise of meeting privacy requirements, we should guarantee the proposed framework has the same computation accuracy with existing privacy-aware solutions. In this paper, nodes obtain heterogeneous privacy protection in the face of different data servers through one-shot noise perturbation. Based on the definition of KL privacy, we derive the analytical expressions of the privacy preserving degrees (PPDs) and quantify the relation between different PPDs. Then, we obtain the closed-form expression of computation accuracy. Furthermore, an efficient incentive mechanism is proposed to achieve optimized computation accuracy when data servers have fixed budgets. Finally, extensive simulations are conducted to verify the obtained theoretical results.
Distributed Gradient Descent with Coded Partial Gradient Computations

Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity. Second, they ignore computations carried out by straggling servers; and they are typically designed to recover the full gradient, and thus, cannot provide a balance between the accuracy of the gradient and per-iteration completion time. Here we introduce a hybrid approach, called coded partial gradient computation (CPGC), that benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and decoding complexity.