Reviewerly/exHarmony huggingface dataset:
Extended High-Utility Pattern Mining: An Answer Set Programming-Based Framework and Applications

Abstract Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High-Utility Pattern Mining; in particular, we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach.
Korn—Software Verification with Horn Clauses (Competition Contribution)

Abstract Korn is a software verifier that infers correctness certificates and violation witnesses sutomatically using state-of-the-art Horn-clause solvers, such as Z3 and Eldarica. The solvers are used in a portfolio together with cheap random sampling where the latter can be very effective at finding counterexamples. Korn perfomend best in the sub-category of SV-COMP 2023.
Qiskit Experiments: A Python package to characterize and calibrate quantum computers

Qiskit Experiments is a Python package for designing and running quantum computing experiments with a focus on calibration and characterization of quantum devices.It consists of a general purpose experiments framework that can be used by researchers to rapidly implement new experiments and a library of common experiments for calibration, characterization, and verification of quantum devices.
SecureMed: A Blockchain-Based Privacy-Preserving Framework for Internet of Medical Things

The Internet of Medical Things (IoMT) connects a huge amount of smart sensors with the Internet for healthcare service provisioning. IoMT’s privacy-preserving becomes a challenge considering the life-saving data collected and transferred through IoMT. Traditional privacy protection techniques use centralized management strategies, which lead to a single point of failure, lack of trust, state modification, information disclosure, and identity theft. Edge computing enables local computation of IoMT data, which reduces traffic to the cloud and also helps in accomplishing latency-sensitive healthcare applications and services. This paper proposes a novel framework (i.e., SecureMed) that uses blockchain-based distributed authentication implemented at the edge cloudlets to enforce privacy protection. In SecureMed, IoMT devices interact with edge cloudlets using smart contracts. It uses trusted edge nodes to implement an authentication algorithm that uses public/private key matching to authenticate IoMT. Experimental evaluation performed using the Pythereum blockchain shows that SecureMed outperforms the traditional blockchain scheme based on latency, bandwidth consumption, deployment time, scalability, and accuracy. Therefore, it can be used to protect the edge-enabled IoMT from privacy attacks and to ensure end-to-end healthcare service provisioning.
3D convolutional neural Network-based 3D mineral prospectivity modeling for targeting concealed mineralization within Chating area, middle-lower Yangtze River metallogenic Belt, China

The Chating area is situated within the Middle-Lower Yangtze River Metallogenic Belt, China. Several concealed skarn and porphyry-type deposits have been discovered in this area, indicating high potential for hosting hydrothermal deposits. However, due to the complex geological structure, exploration risks significantly increase with increasing depth. To overcome this challenge, three-dimensional mineral prospectivity modeling (3DMPM) has begun to be widely applied for mapping the prospectivity of deep-seated and concealed mineralization. However, most previous studies on 3DMPM were based on shallow supervised machine learning models and dimensionality-reduced 3D predictive maps. Although these models have shown good results, they may lose spatial correlation within the 3D predictive maps and fail to explore nonlinear correlations between the 3D predictive maps and mineralization. Meanwhile, 3D geological models are the most important basis of the 3DMPM, however, in the past, few studies have incorporated the optimization of the 3D geological models into the process of 3DMPM. Therefore, this paper initially builds and optimizes 3D geological models through implicit 3D geological modeling and "total litho-inversion" approach. Subsequently, the 3D predictive maps are generated by employing various 3D methods, which are further integrated using a 3D convolutional neural network (3D CNN) model to identify highly prospective areas for mineralization. The results show that the highly prospective areas identified by the 3DMPM include not only the training data but also other mineral deposits that have previously been discovered within the study area. In addition, compared with the Logistic Regression model (LR), Support Vector Machines (SVM), and Radom Forest (RF), the 3D CNN performs better prediction capabilities due to its enhanced ability to capture the correlations between 3D predictive maps and multiple types of mineral deposits. It suggests that the 3DMPM based on the 3D CNN model has commendable predictive capabilities in identifying prospective mineralization areas, and some new highly prospective areas can be considered as priority areas for future exploration of concealed mineralization within the Chating Area.
A Fuzzy Model for Reasoning and Predicting Student’s Academic Performance

Evaluating students’ academic performance is crucial for assessing the quality of education and educational strategies. However, it can be challenging to predict and evaluate academic performance under uncertain and imprecise conditions. To address this issue, many research works have employed fuzzy concepts to analyze, predict, and make decisions about students’ academic performance. This paper investigates the use of fuzzy concepts in research related to evaluating, analyzing, predicting, or making decisions about student academic performance. The paper proposes a fuzzy model, called FPM (Fuzzy Propositional Model), for reasoning and predicting students’ academic performance. FPM aims to address the limitations of previous studies by incorporating propositional logic with fuzzy sets concept, which allows for the representation of uncertainty and imprecision in the data. FPM integrates and transforms if-then rules into weighted fuzzy production rules to predict and evaluate academic performance. This paper tests and evaluates the FPM in two scenarios. In the first scenario, the model predicts and examines the impact of absenteeism on academic performance where there is no clear relation between the two parts of the dataset. In the second scenario, the model predicts the final exam results using the lab exam results, where the data are more related. The FPM provides good results in both scenarios, demonstrating its effectiveness in predicting and evaluating students’ academic performance. A comparison study of the FPM’s results with a linear regression model and previous work showed that the FPM performs better in predicting academic performance and provides more insights into the underlying factors affecting it. Therefore, the FPM could be useful in educational institutions to predict and evaluate students’ academic performance, identify underlying factors affecting it, and improve educational strategies.
On the Philosophy of Unsupervised Learning

Abstract Unsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date. This stands in stark contrast to supervised and reinforcement learning algorithms, which have been widely studied and critically evaluated, often with an emphasis on ethical concerns. In this article, I analyze three canonical unsupervised learning problems: clustering, abstraction, and generative modeling. I argue that these methods raise unique epistemological and ontological questions, providing data-driven tools for discovering natural kinds and distinguishing essence from contingency. This analysis goes some way toward filling the lacuna in contemporary philosophical discourse on unsupervised learning, as well as bringing conceptual unity to a heterogeneous field more often described by what it is not (i.e., supervised or reinforcement learning) than by what it is . I submit that unsupervised learning is not just a legitimate subject of philosophical inquiry but perhaps the most fundamental branch of all AI. However, an uncritical overreliance on unsupervised methods poses major epistemic and ethical risks. I conclude by advocating for a pragmatic, error-statistical approach that embraces the opportunities and mitigates the challenges posed by this powerful class of algorithms.
Histopathological bladder cancer gene mutation prediction with hierarchical deep multiple-instance learning

Gene mutation detection is usually carried out by molecular biological methods, which is expensive and has a long-time cycle. In contrast, pathological images are ubiquitous. If clinically significant gene mutations can be predicted only through pathological images, it will greatly promote the widespread use of gene mutation detection in clinical practice. However, current gene mutation prediction methods based on pathological images are ineffective because of the inability to identify mutated regions in gigapixel Whole Slide Image (WSI). To address this challenge, hereby we propose a carefully designed framework for WSI-based gene mutation prediction, which consists of three parts. (i) The first part of cancerous area segmentation, based on supervised learning, quickly filters out a large number of non-mutated regions; (ii) the second part of cancerous patch clustering, based on the representations derived from contrastive learning, ensures the comprehensiveness of patch selection; and (iii) the third part of mutation classification, based on the proposed hierarchical deep multi-instance learning method (HDMIL), ensures that sufficient patches are considered and inaccurate selections are ignored. In addition, benefiting from a two-stage attention mechanism in HDMIL, the patches that are highly correlated with gene mutations can be identified. This interpretability can help a pathologist to analyze the correlation between gene mutation and histopathological morphology. Experimental results demonstrate that the proposed gene mutation prediction framework significantly outperforms the state-of-the-art methods. In the TCGA bladder cancer dataset, five clinically relevant gene mutations are well predicted.
Efficient skeleton-based action recognition via multi-stream depthwise separable convolutional neural network

Skeleton-based human action recognition has attracted considerable attention and achieved great success in several engineering fields, which is also one of the most active research topic in computer vision community. However, the existing methods may suffer from a large model size and slow execution speed. In addition, some useful information embedded in skeleton sequence, such as the motion information with different scales, had not been fully utilized, such that the performance of recognition is compromised or decreased. To this end, we propose an efficient skeleton-based action recognition approach based on multi-stream neural networks, namely Lightweight Double-feature Triple-scale motion Network (LDT-NET). Precisely, by endowing a lightweight network structure, i.e., multi-stream Depthwise Separable Convolutional Neural Network, LDT-NET is capable of being employed efficiently on a single CPU/GPU. The experimental results on several famous datasets tested by almost all studies in this field, such as SHREC (i.e., hand actions) and JHMDB (i.e., body actions), the proposed LDT-NET achieves promising performance against the state-of-the-art methods. More promisingly, compared to the latest representative work, such as DD-NET, LDT-NET achieves a speedup both in training and inferring by 25%, with only about 20% of its model parameter. In a nutshell, our method not only achieves higher recognition rate but can be used in practical application comparing to the state-of-the-art methods.
Virtual Reality for Addressing Depression and Anxiety: A Bibliometric Analysis

Virtual reality is an emerging field in mental health and has gained widespread acceptance due to its potential to treat various disorders, such as anxiety and depression. This paper presents a bibliometric analysis of virtual reality (VR) use in addressing depression and anxiety from 1995 to 2022. The study analysed 1872 documents using the Scopus database, identifying the field's most relevant journals and authors. The results indicate that using VR for addressing anxiety and depression is a multidisciplinary field with a wide variety of research topics, leading to significant collaborative research in this area. The Annual Review of Cybertherapy and Telemedicine was identified as the most relevant journal, while Behavior Research and Therapy was found to be the most cited journal. The analysis of keywords suggests that there is more research on using VR for anxiety and related disorders than for depression. Riva G. was identified as the top author in producing research outputs on VR-AD, and the University of Washington emerged as the leading institution in scientific outputs on VR-AD. Thematic and intellectual analyses helped identify the main themes within the research domain, providing valuable insight into the current and future directions of the field.
Reversible image-hiding algorithm based on singular value sampling and compressive sensing

A reversible image-hiding algorithm based on a novel chaotic system is proposed using compressive sensing (CS) and singular value sampling (SVS) techniques. In the first stage, a novel mathematical model is constructed to extract the plain messages from the secret plain image, and the public-key Rivest–Shamir–Adleman (RSA) algorithm is adopted to encrypt these messages, obtaining the corresponding cipher messages. Then, another mathematical model of key transformation is constructed to transform above messages into the initial keys, which is used to produce a random key stream. In the second stage, the secret plain image is scrambled by a pre-encryption operation, and the corresponding singular values are obtained by singular value decomposition (SVD). Then, these values are partitioned, and zero blocks are identified and removed. Thereafter, the singular values of the blocks with non-zero elements are sampled by CS, with filled by zero elements again. In the third stage, high energy coefficients are removed and replaced by zero elements to obtain new sampling values. Then, the carrier image is processed by discrete wavelet transform (DWT). Next, new sampling values are embedded into the wavelet coefficients, and the inverse DWT is performed. Thus, a new carrier image containing the secrets is obtained. The advantages are: (1) A new chaotic system ImpTDCS is proposed to have a better behavior. (2) A novel model EMM is built to extract plain messages from secret image. (3) Multi-images can be embedded, which can hide more secret information each time. (4) SVD is operated followed by SVS on non-zero blocks, reducing transmission bandwidth. (5) High energy coefficients of SVS are removed before embedding operation, guaranteeing effectively the visual quality of carrier image containing secrets.
BFG: privacy protection framework for internet of medical things based on blockchain and federated learning

The deep integration of Internet of Medical Things (IoMT) and Artificial intelligence makes the further development of intelligent medical services possible, but privacy leakage and data security problems hinder its wide application. Although the combination of IoMT and federated learning (FL) can achieve no direct access to the original data of participants, FL still can't resist inference attacks against model parameters and the single point of failure of the central server. In addition, malicious clients can disguise as benign participants to launch poisoning attacks, which seriously compromises the accuracy of the global model. In this paper, we design a new privacy protection framework (BFG) for decentralized FL using blockchain, differential privacy and Generative Adversarial Network. The framework can effectively avoid a single point of failure and resist inference attacks. In particular, it can limit the success rate of poisoning attacks to less than 26%. Moreover, the framework alleviates the storage pressure of the blockchain, achieves a balance between privacy budget and global model accuracy, and can effectively resist the negative impact of node withdrawal. Simulation experiments on image datasets show that the BFG framework has a better combined performance in terms of accuracy, robustness and privacy preservation.
Event-based incremental recommendation via factors mixed Hawkes process

Incremental recommendation systems have garnered significant research interest since they ideally adapt to users' ongoing events (such as clicking, browsing, and reviewing) and recommend items without retaining the model. Many methods have tracked the event generation sequences for incremental recommendation. However, most existing models treat the event as a static snapshot or a black box, ignoring the underlying factors that may trigger the event generation. Underlying such inner factors can help RS reasonably and foreseeingly evaluate the potential items for the user next time. Along this vein, we propose the Factors Mixed Hawkes Process (FMHP) for event-based incremental recommendations. First, we extend each event to a notion of factor-driven event sequence. Next, we consider three factors that may influence the occurrence of an event: intrinsic intensity, external intensity, and historical intensity. An intrinsic intensity function, multi-type temporal attention, and a hybrid time decay function are incorporated in FMHP to evaluate the intrinsic, external, and historical intensity, respectively. In addition, an incremental updating strategy is implemented in FMHP, continuously updating event intensity as new events occur. We conduct extensive experiments on four public datasets (e.g., Amazon Beauty, LastFM, Movielens, and Amazon Book). Compared with state-of-the-art incremental recommendation methods, our proposed FMHP model achieves superior performance, with up to 9.77%, 9.35%, 9.32%, 10.10% w.r.t. HR, 8.86%, 10.26%, 9.81%, 9.38% w.r.t. NDCG, and 9.64%, 9.32%, 8.97%,9.85% w.r.t. Recall in Beauty, LastFM, MovieLens, and Book, respectively. Besides, the case study shows that the three factors in our proposed FMHP method play a vital role in triggering event generation.
Denoising and Prompt-Tuning for Multi-Behavior Recommendation

In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e.g., click, add-to-cart, and purchase). Traditional collaborative filtering techniques typically assume that users only have a single type of behavior with items, making it insufficient to utilize complex collaborative signals to learn informative representations and infer actual user preferences. Consequently, some pioneer studies explore modeling multi-behavior heterogeneity to learn better representations and boost the performance of recommendations for a target behavior. However, a large number of auxiliary behaviors (i.e., click and add-to-cart) could introduce irrelevant information to recommenders, which could mislead the target behavior (i.e., purchase) recommendation, rendering two critical challenges: (i) denoising auxiliary behaviors and (ii) bridging the semantic gap between auxiliary and target behaviors. Motivated by the above observation, we propose a novel framework–Denoising and Prompt-Tuning (DPT) with a three-stage learning paradigm to solve the aforementioned challenges. In particular, DPT is equipped with a pattern-enhanced graph encoder in the first stage to learn complex patterns as prior knowledge in a data-driven manner to guide learning informative representation and pinpointing reliable noise for subsequent stages. Accordingly, we adopt different lightweight tuning approaches with effectiveness and efficiency in the following stages to further attenuate the influence of noise and alleviate the semantic gap among multi-typed behaviors. Extensive experiments on two real-world datasets demonstrate the superiority of DPT over a wide range of state-of-the-art methods. The implementation code is available online at https://github.com/zc-97/DPT.
On How Zero-Knowledge Proof Blockchain Mixers Improve, and Worsen User Privacy

Zero-knowledge proof (ZKP) mixers are one of the most widely-used blockchain privacy solutions, operating on top of smart contract-enabled blockchains. We find that ZKP mixers are tightly intertwined with the growing number of Decentralized Finance (DeFi) attacks and Blockchain Extractable Value (BEV) extractions. Through coin flow tracing, we discover that 205 blockchain attackers and 2, 595 BEV extractors leverage mixers as their source of funds, while depositing a total attack revenue of 412.87M USD. Moreover, the US OFAC sanctions against the largest ZKP mixer, Tornado.Cash, have reduced the mixer’s daily deposits by more than .
Behavioral drivers of blockchain assimilation in supply chains – A social network theory perspective

This study investigates decision drivers for assimilating blockchain technology in supply chains. The benefits of blockchain accrue when there is collaborative acceptance of supply chain members. We ground our hypotheses in tenets of social network theory to propose that supply chain learning, supply chain collaboration, and network prominence will influence blockchain assimilation by organizations. Based on a cross-sectional group of blockchain users (150 supply chain professionals familiar with blockchain), the study is the first to develop an understanding of behavioral drivers of blockchain assimilation at the organizational level and complements earlier studies focused on the intention to adopt the technology and identification of blockchain benefits. In addition to contributing to a nascent body of literature on the blockchain, we provide psychometrically validated scales for blockchain assimilation and network prominence constructs. The findings have implications for blockchain adoption in supply chain literature.