Reviewerly/exHarmony huggingface dataset:
Visual Data Mining: A Comparative Analysis of Selected Datasets

This paper presents data preprocessing operations and visualisation techniques, carried out on the following datasets: Teaching Assistant Evaluation dataset, Statlog (Australian Credit Approval) dataset, Letter Recognition, Connectionist Bench (Sonar, Mines vs. Rocks) dataset, and Poker Hand dataset. These datasets are from the University of California Irvine (UCI) Machine Learning Repository. Further, appropriate visualisation techniques are applied to the five selected datasets depending on the properties that are supported by the visualisation techniques used. In the end, this paper offers a template for researchers, data scientists, and other data users, in selecting the right preprocessing operations and appropriate visualisation techniques when using these datasets.
LraSched: Admitting More Long-Running Applications via Auto-Estimating Container Size and Affinity

Abstract Many long-running applications (LRAs) are increasingly using containerization in shared production clusters. To achieve high resource efficiency and LRA performance, one of the key decisions made by existing cluster schedulers is the placement of LRA containers within a cluster. However, they fail to account for estimating the size and affinity of LRA containers before executing placement. We present LraSched, a cluster scheduler that places LRA containers onto machines based on their sizes and affinities while providing consistently high performance. LraSched introduces an automated method that leverages historical data and collects new information to estimate container size and affinity for an LRA. Specifically, it uses an online machine learning method to map a new incoming LRA to previous workloads from which we can transfer experience and recommends the amount of resources (size) and the degree of collocation (affinity) for the containers of the new incoming LRA. By means of recommendations, LraSched adapts the heuristic for vector bin packing to LRA scheduling and places LRA containers in a manner that both maximizes the number of LRAs deployed and minimizes the resource fragmentation, but without affecting LRA performance. Testbed and simulation experiments show that LraSched can improve the resource utilization by up to 6.2% while meeting performance constraints for LRAs.
Camera calibration using projection properties of conics of equal eccentricity

Conics typically include ellipses, hyperbolas, and parabolas with equal eccentricities that intersect infinite line at absolute points. According to the imaging characteristics of conics under a pinhole camera, a dual image of the absolute points can be obtained. By analysing the representative coefficient matrix of the dual image, the algebraic relationship between the image of the centre of the conic and its projection matrix can be obtained; thus, the vanishing line can be determined by the pole-polar relationship of the dual image of the absolute points. A group of orthogonal vanishing points can be obtained to determine the camera intrinsic parameters by two conics with aligned principal axes. The feasibility and robustness of the algorithm are verified by simulation data and real experiments.
Rate-Distortion Optimized Hierarchical Deep Feature Compression

Deep learning has proved to be a vital tool for many computer vision applications. In a distributed environment, an efficient way to analyze the sensed visual information is to extract features for deep learning and then transmit the features to powerful processing hubs for further analysis. This demands for efficient compression of the feature maps before transmission. This paper proposes a hierarchical framework for the compression of deep features from the key frames of a video. First, a feature selection strategy is introduced to reduce the redundancy among different features. The selected features are then quantized, repacked as videos, and further compressed using a standardized video encoder. Furthermore, a rate-distortion optimization module is integrated in the proposed framework to enhance the quality of deep-learning-based computer vision tasks under rate constraints.
Digital image watermarking in sparse domain

A watermarking method based on a robust sparse domain is proposed in this paper, which integrates the secret information into the significant sparse elements of the original image. Our algorithm protects the original data by a two-way security process to embed confidential information. First of all, converting the watermark logo into a discrete transform coefficient (DCT) is the protection process. Then, using the dictionary learning method, the transformed coefficient is embedded in the selected effective sparse coefficient in the original image. The embedded logo is extracted from the selected effective sparse coefficient using the sparse orthogonal matching tracking algorithm (OMP) domain. Then, the discrete inverse transformation is performed. To check the algorithm's efficiency, numerous specific attacks are checked. The experimental results show that the algorithm can recover the embedded watermark with precision without losing any information.
Thermal Quantum Correlations in Two Gravitational Cat States

We consider the effect of a thermal bath on quantum correlations induced by the gravitational interaction in the weak field limit between two massive cat states, called gravitational cat (gravcat) states. The main goal of this paper is to provide a good understanding of the effects of temperature and several parameters in the entanglement (measured by the concurrence) and quantum coherence (measured by the $l_1$-norm that is defined from the minimal distance between the quantum state and the set of incoherent states) which are derived from the thermal quantum density operator. Our results show that the thermal concurrence and $l_1$-norm can be significantly optimized by increasing the masses or decreasing the distance between them. We investigate and discuss the behavior of these quantities under temperature variations in different regimes, including some that are expected to be experimentally feasible in the future. In particular, we observe that thermal fluctuations raise non-entangled quantum correlations when entanglement suddenly drops.
A Brief Review of Performance and Interpretability in Fuzzy Inference Systems

Business analytics allows understand the current state of an organization and identify the needs of the company. Currently, there are commercial and scientific tools, which focus on trying to solve some of the stages of a business analytics system based on machine learning that helps in decision making through the discovery of knowledge. Network-based learning methods solve problems that were challenging for many years but the results they produce are not interpretable. On the other hand, methods based on fuzzy logic allow the discovery of predicates and their evaluation of unseen data, which is called inference. Due to their construction based on predicates, fuzzy logic facilitates the presentation of results in an interpretable way, but with less precision than network-based methods. A general line open to research is to integrate learning methods that compensate for accuracy and interpretation. This work presents a scoping review on compensatory fuzzy logic, Archimedean compensatory fuzzy logic as well as works based on inference, accuracy, and interpretability. Also, it presents a list of lines open to research on these topics.
Internet of things-based energy-efficient optimized heuristic framework to monitor sportsperson’s health

Recently, wearable technologies have gained attention in diverse applications of the medical platform to guarantee the health and safety of the sportsperson with the assistance of the Internet of things (IoT) device. The IoT device's topology varies due to the shift in users' orientation and accessibility, making it impossible to assign resources, and routing strategies have been considered the prominent factor in the current medical research. Further, for sportspersons with sudden cardiac arrests, hospital survival rates are low in which wearable IoT devices play a significant role.In this paper, the energy efficient optimized heuristic framework (EEOHF) has been proposed and implemented on a wearable device of the sportsperson's health monitoring system.The monitoring system has been designed with cloud assistance to locate the nearest health centers during an emergency. The wearable sensor technologies have been used with an optimized energy-efficient algorithm that helps athletes monitor their health during physical workouts. The monitoring system has fitness tracking devices, in which health information is gathered, and workout logs are tracked using EEOHF. The proposed method is applied to evaluate and track the sportsperson's fitness based on case study analysis.The simulation results have been analyzed, and the proposed EEOHF achieves a high accuracy ratio of 97.8%, a performance ratio of 95.3%, and less energy consumption of 9.4%, delay of 13.1%, and an average runtime of 98.2% when compared to other existing methods.
With whom should I work? Ratings consideration for partner selection in a P2P supply chain network

Partner selection is a challenge central to dynamic peer-to-peer (P2P) supply chain networks with decentralized architecture. In this study, we examine how ratings information can be used to select the right trading partners in a P2P network. Using data collected from a bitcoin platform, we first show that the behavior patterns of fraudsters differ from those of other users. We analyze the topology of a P2P network and demonstrate that it is a small-world network and that various internal and external factors can affect the ratings. We find that it is also temporal and that effective rating indices for partner selection are also time-varying. We design a system to simulate the partner selection process and access its impacts on the effectiveness of the ratings information under different information revelation policies—all past information, three-month information, and one-month information—along with different levels of tolerance for deception. Our results provide practical insights into reputation system design in a P2P platform and have implications for partner selection in a P2P supply chain network.
ASTM: An Attention based Spatiotemporal Model for Video Prediction Using 3D Convolutional Neural Networks

Video prediction has always been a challenging task in video representation learning due to the diversity of spatial-temporal evolution in videos. In this paper, we propose an Attention based SpatioTemporal Model for Video Prediction based on 3D Convolutional Neural Networks and Long Short-Term Memory (LSTM), which is named ASTM. In our method, we leverage both multi-term and short-term inter-frame dependencies in temporal domain to capture reliable motion information for videos. In particular, we design an Efficient Inter-Frame Attention Gate (EIFAG) to efficiently aggregate the multi-term inter-frame dependencies and integrate 3D convolutional operations into the proposed model to further improve the local perception to videos by capturing more accurate short-term temporal dependency. In addition, we make use of the multilayer Spatiotemporal LSTM (PredRNN) structure to preserve more spatial appearance details for videos. To evaluate the adaptability of our model on more complex real scenes, we collect a multi-level spatiotemporal (MLST) dataset. Experimental results show that the proposed model can achieve state-of-the-art performance on both widely used datasets and the proposed MLST dataset.
Demystifying the Challenges of Formally Specifying API Properties for Runtime Verification

Runtime Verification (RV) is a technique to monitor formally-specified properties of the software during its execution. RV has shown to be very effective for bug finding. Unfortunately, RV typically relies on formal specification languages and learning those languages be costly for developers. This paper reports on a study to assess the challenges to specify API properties for the purpose of RV. To that end, we wrote SIESTA, a minimalist specification language, extending Java with two features (the ability to catch calls to specified methods and the ability to access the event history of a given object), and asked inexperienced developers (students) to write specifications in that language for certain parts of the Java API. Among our findings, we observed that 40% of the specifications written by the students matched the ground truth perfectly. The main messages of this work are that 1) it is feasible to use a simple imperative language for specifying properties without significant loss of generality; and that 2) developers are capable of writing specifications in the (programming) language they feel comfortable.
Privacy preserving model-based authentication and data security in cloud computing

Purpose Cloud computing plays a significant role in the initialization of secure communication between users. The advanced technology directs to offer several services, such as platform, resources, and accessing the network. Furthermore, cloud computing is a broader technology of communication convergence. In cloud computing architecture, data security and authentication are the main significant concerns. Design/methodology/approach The purpose of this study is to design and develop authentication and data security model in cloud computing. This method includes six various units, such as cloud server, data owner, cloud user, inspection authority, attribute authority, and central certified authority. The developed privacy preservation method includes several stages, namely setup phase, key generation phase, authentication phase and data sharing phase. Initially, the setup phase is performed through the owner, where the input is security attributes, whereas the system master key and the public parameter are produced in the key generation stage. After that, the authentication process is performed to identify the security controls of the information system. Finally, the data is decrypted in the data sharing phase for sharing data and for achieving data privacy for confidential data. Additionally, dynamic splicing is utilized, and the security functions, such as hashing, Elliptic Curve Cryptography (ECC), Data Encryption Standard-3 (3DES), interpolation, polynomial kernel, and XOR are employed for providing security to sensitive data. Findings The effectiveness of the developed privacy preservation method is estimated based on other approaches and displayed efficient outcomes with better privacy factor and detection rate of 0.83 and 0.65, and time is highly reduced by 2815ms using the Cleveland dataset. Originality/value This paper presents the privacy preservation technique for initiating authenticated encrypted access in clouds, which is designed for mutual authentication of requester and data owner in the system.
Explainable Activity Recognition over Interpretable Models

The majority of the approaches to sensor-based activity recognition are based on supervised machine learning. While these methods reach high recognition rates, a major challenge is to understand the rationale behind the predictions of the classifier. Indeed, those predictions may have a relevant impact on the follow-up actions taken in a smart living environment. We propose a novel approach for eXplainable Activity Recognition (XAR) based on interpretable machine learning models. We generate explanations by combining the feature values with the feature importance obtained from the underlying trained classifier. A quantitative evaluation on a real dataset of ADLs shows that our method is effective in providing explanations consistent with common knowledge. By comparing two popular ML models, our results also show that one versus one classifiers can provide better explanations in our framework.
Guided Intra-Patch Smoothing Graph Filtering for Single-Image Denoising

Graph filtering is an important part of graph signal processing and a useful tool for image denoising.Existing graph filtering methods, such as adaptive weighted graph filtering (AWGF), focus on coefficient shrinkage strategies in a graph-frequency domain.However, they seldom consider the image attributes in their graph-filtering procedure.Consequently, the denoising performance of graph filtering is barely comparable with that of other state-of-the-art denoising methods.To fully exploit the image attributes, we propose a guided intra-patch smoothing AWGF (AWGF-GPS) method for single-image denoising.Unlike AWGF, which employs graph topology on patches, AWGF-GPS learns the topology of superpixels by introducing the pixel smoothing attribute of a patch.This operation forces the restored pixels to smoothly evolve in local areas, where both intra-and inter-patch relationships of the image are utilized during patch restoration.Meanwhile, a guided-patch regularizer is incorporated into AWGF-GPS.The guided patch is obtained in advance using a maximum-a-posteriori probability estimator.Because the guided patch is considered as a sketch of a denoised patch, AWGF-GPS can effectively supervise patch restoration during graph filtering to increase the reliability of the denoised patch.Experiments demonstrate that the AWGF-GPS method suitably rebuilds denoising images.It outperforms most state-of-the-art single-image denoising methods and is competitive with certain deep-learning methods.In particular, it has the advantage of managing images with significant noise.
Keynote: Explainable AI in Pervasive Healthcare: Open Challenges and Research Directions

In our ageing society, there is increasing need for innovative tools to early detect fragility and non communicable diseases of the elderly population. To this aim, in the last few years, several research efforts have been made to exploit sensorized smart-homes and artificial intelligence (AI) methods to detect health issues of the elderly. However, most of these AI systems act as black-boxes, leading to a low level of trust by the clinicians and by the final users. Moreover, they provide limited support to clinicians in making a diagnosis, since they do not provide any explanation of the reason why a given prediction was computed. This talk addresses this challenging problem and our research efforts on this topic. The talk will present a novel AI system to detect cognitive decline symptoms in smart homes, which is supported by explainable AI capabilities. The system relies on clinical indicators of abnormal behaviors, spatial disorientation, and wandering. An AI-fueled dashboard allows clinicians to inspect anomalies together with the explanations of predictions. The system was experimented with a large set of real-world subjects, including people with MCI and people with dementia.
Enhancement the ChaCha20 Encryption Algorithm Based on Chaotic Maps

As a result of the increasing demand for lightweight applications, the researchers set out to implement appropriate security mechanisms, one of which is lightweight cryptographic hash functions. Light hash functions are a common topic in cryptography, and its strength depends primarily on cryptographic analysis of hash functions. Therefore, it always requires an improvement in the designs of light hash functions in addition to paying attention to their performance in terms of implementing software and hardware. In this paper, we propose a lightweight hash functions scheme that consolidates the method of the ChaCha20 data encryption with chaotic maps to keys generation. It has made the existing ChaCha20 encryption method stronger when chaos-based random number generation method has been utilized. Based on these random numbers, the ChaCha20 would generate keys with high randomness and perform cryptographic operations. The analysis results show that improving the ChaCha20 algorithm by chaotic maps generated keys with high randomness and lightweight hash functions efficiency and thus increased security.
Logical Inference in Predicate Calculus with the Definition of Previous Statements

At the present stage of computer engineering and information technology development, one of the most actual problems is the creation of high-performance artificial intelligence systems. Along with the successful use of neural networks and various machine learning algorithms, the theory and methods of logical inference play an important role. These mechanisms are indispensable in the design of such knowledge processing systems as expert systems, decision support systems, enterprise management systems, software verification systems, medical and technical diagnostics systems, battle control systems, etc. One of the promising applications of inference is logical prediction. The authors propose an original method for the inference of conclusions in predicate calculus with the definition of previous statements. This approach allows us to reduce the problem of forecasting the situation development, including the transition of the situation to a desired phase, to the task of deductive inference. The method is based on an iterative repetition of the partial and complete "disjunct division" procedure. The article gives a formal and meaningful definition of the problem of the logical inference of conclusions in predicate calculus with the definition of previous statements. Features of all method's stages implementation are illustrated by examples. The main advantage of the proposed method is the expansion of the intellectual system functionality and the possibility of "disjunct division" operations parallel execution in the inference procedure.
Perceptual similarity measurement based on generative adversarial neural networks in graphics design

Measuring the similarity between images is of paramount importance in computer vision. However, the commonly used pixelwise similarity metrics do not match well with perceptual similarity. The purpose of this paper is to propose a visual similarity measurement method, which can be effectively used for plagiarism detection in graphic design. Plagiarism detection of designs refers to the identification and determination of major similarities. It is difficult to carry out the similarity learning process in traditional deep neural network due to the insufficient of training samples. To overcome this problem, a novel scheme is proposed for measuring perceptual similarity of graphics by using a constraint Generative Adversarial Network (GAN) model. The generator of GAN is used to create similar graphics following the common plagiarism features of logo design. Unlike the traditional discriminator which judges the authenticity of the generated image and the original image, the modified discriminator is used to calculate the perceptual similarity of the graphics pair. In graphics design, plagiarism mainly focuses on the changes of shape, color and style, which has certain cognitive subjectivity. Therefore, design experts were invited to participate in a group of cognitive analysis experiments. A perceptual constraint model is established to limit the generation of plagiarized graphics according to “design and visual rationality”. Promising results demonstrate that the proposed method can be used for plagiarism detection of logo design. Given its effectiveness and conceptual simplicity, I hope it can serve as a baseline and contribute to the future research on plagiarism detection of artworks.