Reviewerly/exHarmony huggingface dataset:
Performance optimization of criminal network hidden link prediction model with deep reinforcement learning

The scale of criminal networks (e.g. drug syndicates and terrorist networks) extends globally and poses national security threat to many nations as they also tend to be technologically advance (e.g. Dark Web and Silk Road cryptocurrency). Therefore, it is critical for law enforcement agencies to be equipped with the latest tools in criminal network analysis (CNA) to obtain key hidden links (relationships) within criminal networks to preempt and disrupt criminal network structures and activities. Current hidden or missing link predictive models that are based on Social Network Analysis models rely on ML techniques to improve the performance of the models in terms of predictive accuracy and computing power. Given the improvement in the recent performance of Deep Reinforcement Learning (DRL) techniques which could train ML models through self-generated dataset, DRL can be usefully applied to domains with relatively smaller dataset such as criminal networks. The objective of this study is to assess the comparative performance of a CNA hidden link prediction model developed using DRL techniques against classical ML models such as gradient boosting machine (GBM), random forest (RF) and support vector machine (SVM). The experiment results exhibit an improvement in the performance of the DRL model of about 7.4% over the next best performing classical RF model trained within 1500 iterations. The performance of these link prediction models can be scaled up with the parallel processing capabilities of graphical processing units (GPUs), to significantly improve the speed of training the model and the prediction of hidden links.
Improving Cross-lingual Entity Alignment via Optimal Transport

Cross-lingual entity alignment identifies entity pairs that share the same meanings but locate in different language knowledge graphs (KGs). The study in this paper is to address two limitations that widely exist in current solutions: 1) the alignment loss functions defined at the entity level serve well the purpose of aligning labeled entities but fail to match the whole picture of labeled and unlabeled entities in different KGs; 2) the translation from one domain to the other has been considered (e.g., X to Y by M1 or Y to X by M2). However, the important duality of alignment between different KGs (X to Y by M1 and Y to X by M2) is ignored. We propose a novel entity alignment framework (OTEA), which dually optimizes the entity-level loss and group-level loss via optimal transport theory. We also impose a regularizer on the dual translation matrices to mitigate the effect of noise during transformation. Extensive experimental results show that our model consistently outperforms the state-of-the-arts with significant improvements on alignment accuracy.
Image Denoising via Sequential Ensemble Learning

Image denoising is about removing measurement noise from input image for better signal-to-noise ratio. In recent years, there has been great progress on the development of data-driven approaches for image denoising, which introduce various techniques and paradigms from machine learning in the design of image denoisers. This paper aims at investigating the application of ensemble learning in image denoising, which combines a set of simple base denoisers to form a more effective image denoiser. Based on different types of image priors, two types of base denoisers in the form of transform-shrinkage are proposed for constructing the ensemble. Then, with an effective re-sampling scheme, several ensemble-learning-based image denoisers are constructed using different sequential combinations of multiple proposed base denoisers. The experiments showed that sequential ensemble learning can effectively boost the performance of image denoising.
The Use of UTAUT and Post Acceptance Models to Investigate the Attitude towards a Telepresence Robot in an Educational Setting

(1) Background: in the last decade, various investigations into the field of robotics have created several opportunities for further innovation to be possible in student education. However, despite scientific evidence, there is still strong scepticism surrounding the use of robots in some social fields, such as personal care and education. (2) Methods: in this research, we present a new tool named the HANCON model, which was developed merging and extending the constructs of two solid and proven models—the Unified Theory of Acceptance and Use of Technology (UTAUT) model used to examine the factors that may influence the decision to use a telepresence robot as an instrument in educational practice, and the Post Acceptance Model used to evaluate acceptability after the actual use of a telepresence robot. The new tool is implemented and used to study the acceptance of a double telepresence robot by 112 pre-service teachers in an educational setting. (3) Results: the analysis of the experimental results predicts and demonstrate a positive attitude towards the use of telepresence robot in a school setting and confirm the applicability of the model in an educational context. (4) Conclusions: the constructs of the HANCON model could predict and explain the acceptance of social telepresence robots in social contexts.
A New Method for Automatic Determining of the DBSCAN Parameters

Abstract Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the eps radius and the MinPts density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its k -th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.
Exploring interpretations of blockchain's value in healthcare: a multi-stakeholder approach

Purpose Drawing broadly from the technology frame (Davidson, 2002) and organizing vision perspectives (Swanson and Ramiller, 1997) which consider the business value of information technology as resulting from actors' efforts to make sense of new technology, the study applies Ojala's (2016) business model framework to examine how different sets of actors understand the value of blockchain within the healthcare sector. Design/methodology/approach To include the perspective of different sets of actors, the research combines a systematic literature review to capture academic research, semi-structured interviews with blockchain experts, with an analysis of blockchain healthcare vendors. Findings The study finds a high degree of congruence between the perspective of different actors, with key sources of blockchain value concentrated around value proposition, particularly enhancing privacy and security; value capture, specifically cost savings, and value network, mostly enhancing data accessibility and reducing intermediation. Value delivery is the least emphasized value creation mechanism and concerns primarily improvements in supply chain transparency. Minor variations between actors' interpretations of value exist, mostly around the contribution of blockchain to support the value proposition and include the provision of social value, the creation of trust, supporting automation and improving employment. Originality/value Recognizing that the value of new technology is as much the result of actors' interpretations, as the objective outcome of its deployment, this study takes a multi-stakeholder perspective to examine blockchain's business value and highlights new aspects of value associated with blockchain deployments. The findings include a value outcome framework that allows systematic comparisons between blockchain implementations across contexts.
A Data Augmentation-Based Defense Method Against Adversarial Attacks in Neural Networks

Deep Neural Networks (DNNs) in Computer Vision (CV) are well-known to be vulnerable to Adversarial Examples (AEs), namely imperceptible perturbations added maliciously to cause wrong classification results. Such variability has been a potential risk for systems in real-life equipped DNNs as core components. Numerous efforts have been put into research on how to protect DNN models from being tackled by AEs. However, no previous work can efficiently reduce the effects caused by novel adversarial attacks and be compatible with real-life constraints at the same time. In this paper, we focus on developing a lightweight defense method that can efficiently invalidate full whitebox adversarial attacks with the compatibility of real-life constraints. From basic affine transformations, we integrate three transformations with randomized coefficients that fine-tuned respecting the amount of change to the defended sample. Comparing to 4 state-of-art defense methods published in top-tier AI conferences in the past two years, our method demonstrates outstanding robustness and efficiency. It is worth highlighting that, our model can withstand advanced adaptive attack, namely BPDA with 50 rounds, and still helps the target model maintain an accuracy around 80%, meanwhile constraining the attack success rate to almost zero.
Heart Failure Diagnosis, Readmission, and Mortality Prediction Using Machine Learning and Artificial Intelligence Models

Abstract Purpose of Review One in five people will develop heart failure (HF), and 50% of HF patients die in 5 years. The HF diagnosis, readmission, and mortality prediction are essential to develop personalized prevention and treatment plans. This review summarizes recent findings and approaches of machine learning models for HF diagnostic and outcome prediction using electronic health record (EHR) data. Recent Findings A set of machine learning models have been developed for HF diagnostic and outcome prediction using diverse variables derived from EHR data, including demographic, medical note, laboratory, and image data, and achieved expert-comparable prediction results. Summary Machine learning models can facilitate the identification of HF patients, as well as accurate patient-specific assessment of their risk for readmission and mortality. Additionally, novel machine learning techniques for integration of diverse data and improvement of model predictive accuracy in imbalanced data sets are critical for further development of these promising modeling methodologies.
Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls

Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We analyzed the acceleration signals recorded by an inertial sensor on the lower back during 143 real-world falls (the most extensive collection to date) from the FARSEEING repository. Such data were obtained from continuous real-world monitoring of subjects with a moderate-to-high risk of falling. We designed and tested fall detection algorithms using features inspired by a multiphase fall model and a machine learning approach. The obtained results suggest that algorithms can learn effectively from features extracted from a multiphase fall model, consistently overperforming more conventional features. The most promising method (support vector machines and features from the multiphase fall model) obtained a sensitivity higher than 80%, a false alarm rate per hour of 0.56, and an F-measure of 64.6%. The reported results and methodologies represent an advancement of knowledge on real-world fall detection and suggest useful metrics for characterizing fall detection systems for real-world use.
INSPIRED: Toward Sociable Recommendation Dialog Systems

In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner.However, this is a challenge in developing a sociable recommendation dialog system, due to the lack of dialog dataset annotated with such sociable strategies.Therefore, we present INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations.To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs.Our analysis shows that sociable recommendation strategies, such as sharing personal opinions or communicating with encouragement, more frequently lead to successful recommendations.Based on our dataset, we train end-to-end recommendation dialog systems with and without our strategy labels.In both automatic and human evaluation, our model with strategy incorporation outperforms the baseline model.This work is a first step for building sociable recommendation dialog systems with a basis of social science theories 1 .
Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting

Solar irradiance forecasting is an inevitable and most significant process in grid-connected photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid operation efficiently, with irradiance forecasting for various timescales, such as an hour ahead, a day ahead, and a week ahead, strategies are developed and analysed in this article. However, the single time scale model can perform better for that specific time scale but cannot be employed for other time scale forecasting. Moreover, the data consideration for single time scale forecasting is limited. In this work, a multi-time scale model for solar irradiance forecasting is proposed based on the multi-task learning algorithm. An effective resource sharing scheme between each task is presented. The proposed multi-task learning algorithm is implemented with a long short-term memory (LSTM) neural network model and the performance is investigated for various time scale forecasting. The hyperparameter estimation of the proposed LSTM model is made by a hybrid chicken swarm optimizer based on combining the best features of both the chicken swarm optimization algorithm (CSO) and grey wolf optimization (GWO) algorithm. The proposed model is validated, comparing existing methodologies for single timescale forecasting, and the proposed strategy demonstrated highly consistent performance for all time scale forecasting with improved metric results.
A Full Featured Configurable Accelerator for Object Detection With YOLO

Object detection and classification is an essential task of computer vision. A very efficient algorithm for detection and classification is YOLO (You Look Only Once). We consider hardware architectures to run YOLO in real-time on embedded platforms. Designing a new dedicated accelerator for each new version of YOLO is not feasible given the fast delivery of new versions. This work's primary goal is to design a configurable and scalable core for creating specific object detection and classification systems based on YOLO, targeting embedded platforms. The core accelerates the execution of all the algorithm steps, including pre-processing, model inference and post-processing. It considers a fixed-point format, linearised activation functions, batch-normalisation, folding, and a hardware structure that exploits most of the available parallelism in CNN processing. The proposed core is configured for real-time execution of YOLOv3-Tiny and YOLOv4-Tiny, integrated into a RISC-V-based system-on-chip architecture and prototyped in an UltraScale XCKU040 FPGA (Field Programmable Gate Array). The solution achieves a performance of 32 and 31 frames per second for YOLOv3-Tiny and YOLOv4-Tiny, respectively, with a 16-bit fixed-point format. Compared to previous proposals, it improves the frame rate at a higher performance efficiency. The performance, area efficiency and configurability of the proposed core enable the fast development of real-time YOLO-based object detectors on embedded systems.
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design

The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-the-art object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented using a lightweight model but sacrificing accuracy. In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. To improve computational efficiency on mobile devices, a GPU-CPU collaborative scheme is adopted along with advanced compiler-assisted optimizations. Experimental results indicate that our pruning scheme achieves 14x compression rate of YOLOv4 with 49.0 mAP. Under our YOLObile framework, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme, the inference speed is increased to 19.1 FPS, and outperforms the original YOLOv4 by 5x speedup. Source code is at: https://github.com/nightsnack/YOLObile.
Multi-objective optimization for rebalancing virtual machine placement

Load balancer, as a key component in cloud computing, seeks to improve the performance of a distributed system by allocating workload amongst a set of cooperating hosts. A good balancing strategy would make the distributed system efficient and enhance user satisfaction. However, the balance of Host Machines (HMs) in a real cloud environment often breaks due to frequently occurred addition and removal of Virtual Machines (VMs). Therefore, it is essential to schedule the VMs to be reBalanced (VMrB). In this paper, we first summarize and analyze the existing studies on load rebalancing. We then propose a novel solution to the VMrB problem, namely a Pareto-based Multi-Objective VM reBalance solution (MOVMrB), which aims to simultaneously minimize the disequilibrium of both inter-HM and intra-HM loads. It is one of the first solutions that leverages the inter-HM and intra-HM loads and applies a multiple objective optimization strategy to overcome the virtual machine rebalance problem. In our work, we keep migration cost in mind and propose a hybrid VM live migration algorithm that significantly reduces the I/O complexity of VMrB processing. The proposed rebalancing solution is evaluated based on two synthetic datasets and two real-world datasets under a CloudSim framework. Our experimental results show that MOVMrB outperforms other existing multi-objective solutions and also demonstrate its extensibility to support complex scenarios in cloud computing.
Stronger Leakage-Resilient and Non-Malleable Secret Sharing Schemes for General Access Structures

In this work we present a collection of compilers that take secret sharing schemes for an arbitrary access structure as input and produce either leakage-resilient or non-malleable secret sharing schemes for the same access structure. A leakage-resilient secret sharing scheme hides the secret from an adversary, who has access to an unqualified set of shares, even if the adversary additionally obtains some size-bounded leakage from all other secret shares. A non-malleable secret sharing scheme guarantees that a secret that is reconstructed from a set of tampered shares is either equal to the original secret or completely unrelated. To the best of our knowledge we present the first generic compiler for leakage-resilient secret sharing for general access structures. In the case of non-malleable secret sharing, we strengthen previous definitions, provide separations between them, and construct a non-malleable secret sharing scheme for general access structures that fulfills the strongest definition with respect to independent share tampering functions. More precisely, our scheme is secure against concurrent tampering: The adversary is allowed to (non-adaptively) tamper the shares multiple times, and in each tampering attempt can freely choose the qualified set of shares to be used by the reconstruction algorithm to reconstruct the tampered secret. This is a strong analogue of the multiple-tampering setting for split-state non-malleable codes and extractors. We show how to use leakage-resilient and non-malleable secret sharing schemes to construct leakage-resilient and non-malleable threshold signatures. Classical threshold signatures allow to distribute the secret key of a signature scheme among a set of parties, such that certain qualified subsets can sign messages. We construct threshold signature schemes that remain secure even if an adversary leaks from or tampers with all secret shares.
An Efficient ABE Scheme With Verifiable Outsourced Encryption and Decryption

Attribute-based encryption (ABE) is a promising cryptographic tool for data owner (DO) to realize fine-grained date sharing in the cloud computing. In the encryption of most existing ABE schemes, a substantial number of modular exponentiations are often required; the computational cost of it is growing linearly with the complexity of the access policy. Besides, in the most existing ABE with outsourced decryption, the computation cost of generating transformation key is growing linearly with the number of attributes associated with user private key; these computations are prohibitively high for mobile device users, which becomes a bottleneck limiting its application. To address the above issues, we propose a secure outsourcing algorithm for modular exponentiation in one single untrusted server model and a new method to generate the transformation key. Based on these techniques and Brent Waters's ciphertext-policy ABE scheme, we propose an ABE scheme with verifiable outsourced both encryption and decryption, which can securely outsource encryption and decryption to untrusted encryption service provider (ESP) and decryption service provider (DSP), respectively, leaving only a constant number of simple operations for the DO and eligible users to perform locally. In addition, both DO and the eligible users can check the correctness of results returned from the ESP and the DSP with a probability, respectively. Finally, we provide the experimental evaluation and security analysis of our scheme, which indicates that our construction is suitable for the mobile environment.
In a Silent Way

Collaboration is built on trust, and establishing trust with a creative Artificial Intelligence is difficult when the decision process or internal state driving its behaviour isn't exposed. When human musicians improvise together, a number of extra-musical cues are used to augment musical communication and expose mental or emotional states which affect musical decisions and the effectiveness of the collaboration. We developed a collaborative improvising AI drummer that communicates its confidence through an emoticon-based visualisation. The AI was trained on musical performance data, as well as real-time skin conductance, of musicians improvising with professional drummers, exposing both musical and extra-musical cues to inform its generative process. Uni- and bi-directional extra-musical communication with real and false values were tested by experienced improvising musicians. Each condition was evaluated using the FSS-2 questionnaire, as a proxy for musical engagement. The results show a positive correlation between extra-musical communication of machine internal state and human musical engagement.