Reviewerly/exHarmony huggingface dataset:
High-dimensional multi-objective optimization strategy based on directional search in decision space and sports training data simulation

In many cases, some programs often have some milestone activities that decision makers need to pay attention to. In this case, managers must efficiently describe these different decision-making spaces and use this as a basis to optimize possible solutions. Choosing a suitable decision-making space has always been a major factor in decision-making management, and it is also one of the key factors that managers must determine when making plans. Multi-objective optimization is to optimize multiple objectives at the same time. Multi-objective optimization algorithms have been widely used in many technology industries. This article researched from two parts: model display and optimization algorithm. The first is to deepen the basic characteristics of project planning and decision-making when there are more decision-making spaces. Based on the existing decision-making network planning methods, combined with the situation analysis method, a decision-making network planning model that can show more decision-making space is established. Secondly, based on the actual improvement requirements, the multi-objective optimization method in the multi-decision network design model and the simulation model of activity exercise data are established. The purpose is to create a relatively complete project planning decision model and optimization algorithm that can select multiple decision spaces at the same time through the above experimental research.
RanBox: anomaly detection in the copula space

A bstract The unsupervised search for overdense regions in high-dimensional feature spaces, where locally high population densities may be associated with anomalous contaminations to an otherwise more uniform population, is of relevance to applications ranging from fundamental research to industrial use cases. Motivated by the specific needs of searches for new phenomena in particle collisions, we propose a novel approach that targets signals of interest populating compact regions of the feature space. The method consists in a systematic scan of subspaces of a standardized copula of the feature space, where the minimum p -value of a hypothesis test of local uniformity is sought by greedy descent. We characterize the performance of the proposed algorithm and show its effectiveness in several experimental situations.
Comparison of Poisson process and machine learning algorithms approach for credit card fraud detection

This article describes the financial fraud detection in imbalanced data. We compare various approaches for credit card fraud detection problem. On the one hand, we use homogeneous and heterogeneous Poisson process to determine the probability of predicting fraud with the various intensity parametric functions. On the other hand, we solve classification problem using machine learning algorithms and different family of ensemble methods like boostings. The results of both methods are compared. The “false positive” problem is also discussed in the article.
Illuminating and bridging the vortex between tacit and explicit knowledge: Counterbalancing information asymmetry in high-value low-frequency transactions

Asymmetric information could harm a stakeholder in a transaction when they are not aware of the disparity of the information among the various stakeholders. Understanding the relationship and interaction among stakeholders is imperative for making informed decisions. It becomes even more critical in the context of high-value low-frequency transaction (HVLFT). This paper delves into the different forms of knowledge (data, information, knowledge, wisdom, understanding, experience and learning) that are essential to support HVLFT. If a form of knowledge can be categorised as neither tacit nor explicit, it belongs to the proposed knowledge dimension X. This paper further explores the transformation of different dimensions of knowledge at the disruptive vortex between tacit, explicit, and X. The interactions between these three dimensions of knowledge are validated through a study of knowledge asymmetry among HVLFT stakeholders in the real estate industry of NZ. We conclude with the proposal of a blockchain-based architecture to counterbalance information asymmetry caused by X.
Application of chaotic Fish School Search optimization algorithm with exponential step decay in neural network loss function optimization

The Fish School Search (FSS) algorithm is a heuristic technique for finding globally optimal solutions. This algorithm is characterized by its simplicity in implementation, and high performance. Since the first mention of FSS, this effective optimization algorithm has been of a great interest among researches and practitioners around the globe. Modifications of FSS exist, applied to solve practical problems, including image reconstruction in electrical impedance tomography, finding optimal solutions in assembly line balancing problems, neural network structure optimization. In this paper, we consider a modification of the FSS algorithm, which uses chaos theory to generate uniformly distributed pseudorandom numbers, and incorporates exponential step decay. The described modified optimization algorithm is known as ETFSS, and is characterized by faster convergence speed and better performance. In order to further investigate the performance of the novel optimization algorithm, we apply ETFSS to neural network loss function optimization. In addition, we compare the described approach with other machine learning techniques, such as the support vector machine (SVM) algorithm, k-nearest neighbors (KNN) algorithm and back propagation-based neural network, trained using the adaptive moment estimation (Adam) optimizer. We visualize classification results using T-distributed stochastic neighbor embedding (TSNE) method, and uniform manifold approximation and projection (UMAP) method, in order to provide more details considering classification performance and dataset shape. The obtained results confirm, that ETFSS can produce slightly more accurate classifications when compared to backpropagation.
Determination of the optimum tilt angle for photovoltaic modules in Senegal

This paper deals with finding the optimum tilt angle of solar panels for solar energy applications.The objective is to maximize the output electrical energy of the photovoltaic (PV) modules.A mathematical model was used to determine the optimum tilt angle of solar collectors in Senegal on a daily and monthly basis, as well as for a specific period.Then, the configuration of the optimum tilt angle was analyzed by studying four specific cases in different typical climatic zones in Senegal.On a horizontal plane, the sun spends more time in the south than in the north.This means that in Senegal, the optimum tilt angle is often set equal to latitude.On the other hand, the optimum latitude angle is only for annual optimization.This study shows that the optimum tilt angle equal to latitude does not produce maximum output.For monthly optimum tilt angle, only the month of November gives tilt optimum angles equal to latitude.It is preferable to change the tilt angle of solar conversion systems monthly instead of fixing them, to gain more energy.
Joint Adaptive Dual Graph and Feature Selection for Domain Adaptation

Domain adaptation aims to exploit domain-invariant features by aligning the cross-domain distributions in the manifold subspace for applying the classifier trained on the source domain to the target domain. However, two limitations may still deteriorate their performances: (1) the influences of noisy or irrelevant features in the original feature space are ignored, which may unexpectedly hurt the classification of target samples; (2) the graph constructed directly in the original data space cannot accurately capture the inherent local manifold structures of high-dimensional data due to the curse of dimensionality, which may seriously mislead the transferable features learning. In this paper, we propose a novel approach to address these problems, referred to as joint Adaptive Dual Graph and Feature Selection for domain adaptation (ADGFS). Specifically, feature selection can characterize the relative importance of different features through a scaling factor, which enables ADGFS to not only reduce the impacts of noisy or irrelevant features on knowledge transfer but also learn informative domain-invariant features. Meanwhile, ADGFS adaptively optimizes the dual graph by learning the similarity matrices of both instance-level and feature-level graphs in the projected low-dimensional manifold subspace rather than the original high-dimensional space, such that the intrinsic local manifold structures of data can be captured precisely. Moreover, ADGFS simultaneously aligns the marginal and conditional probability distributions in the nonnegative matrix factorization framework to narrow the distribution discrepancies between the two different domains, which can adequately transfer knowledge from the source domain to the target domain. Comprehensive experiments on four benchmark datasets can demonstrate that the effectiveness of the proposed approach in cross-domain image classification.
DLIO: A Data-Centric Benchmark for Scientific Deep Learning Applications

Deep learning has been shown as a successful method for various tasks, and its popularity results in numerous open-source deep learning software tools. Deep learning has been applied to a broad spectrum of scientific domains such as cosmology, particle physics, computer vision, fusion, and astrophysics. Scientists have performed a great deal of work to optimize the computational performance of deep learning frameworks. However, the same cannot be said for I/O performance. As deep learning algorithms rely on big-data volume and variety to effectively train neural networks accurately, I/O is a significant bottleneck on large-scale distributed deep learning training. This study aims to provide a detailed investigation of the I/O behavior of various scientific deep learning workloads running on the Theta supercomputer at Argonne Leadership Computing Facility. In this paper, we present DLIO, a novel representative benchmark suite built based on the I/O profiling of the selected workloads. DLIO can be utilized to accurately emulate the I/O behavior of modern scientific deep learning applications. Using DLIO, application developers and system software solution architects can identify potential I/O bottlenecks in their applications and guide optimizations to boost the I/O performance leading to lower training times by up to 6.7x.
An Efficient Approach for Interpretation of Indian Sign Language using Machine Learning

Non-verbal communication involves the usage of Sign Language. The sign language is used by people with hearing / speech disabilities to express their thoughts and feelings. But normally, people find it difficult to understand the hand gestures of the specially challenged people as they do not know the meaning of the sign language gestures. Usually, a translator is needed when a speech / hearing impaired person wants to communicate with an ordinary person and vice versa. In order to enable the specially challenged people to effectively communicate with the people around them, a system that translates the Indian Sign Language (ISL) hand gestures of numbers (1-9), English alphabets (A-Z) and a few English words to understandable text and vice versa has been proposed in this paper. This is done using image processing techniques and Machine Learning algorithms. Different neural network classifiers are developed, tested and validated for their performance in gesture recognition and the most efficient classifier is identified.
Investigations on the Implementation of Blockchain Technology in Supplychain Network

The attacks against supply chain has increased recently in an alarming levels and the implementation of blockchain to secure the IoT data of the supply chain has received positive results as it provides transparency, traceability and involvement of all the agents. In this paper, a comprehensive exploratory reports of several blockchain based supply chain implementations has been given. The challenges and benefits of implementing blockchain in supply chain, various methods and techniques, different fields of supply chain especially focusing on Pharmaceutical supply chain and the working of blockchain has all been discussed here thoroughly. It has been concluded that the application of blockchain can enhance the security of supply chain significantly by providing transparency, authenticity and confidentiality.
Analysing Public Sentiments Regarding COVID-19 Vaccine on Twitter

In March 2020, Coronavirus disease was officially announced as a pandemic all over the world by the World Health Organization (WHO). Since then, the whole pharmaceutical world is in a state of war with COVID-19 and has a responsibility to provide its vaccine for the entire world as soon as possible. People have come forward and spoken about their emotions on both the mainstream and the social media. This study aims to analyze COVID-19 Vaccine related tweets and generate a report on that analysis. The motivation behind this study is the overspread of the virus and the increasing number of cases daily. The method used for topic modeling is a latest statistical model in Natural Language Processing (NLP), and it belongs to the machine learning toolbox and artificial intelligence toolbox, Latent Dirichlet Allocation (LDA) to determine popular themes, and for sentiment analysis, VADER and TextBlob are used on the dataset of 980,557 tweets which were scraped from Twitter based on a particular set of 14 keywords namely, "Covaxin," "BNT162b2", "SputnikV," and "mRNA-1273" from November 1 to December 16 in 2020.
Learning and Executing Re-Usable Behaviour Trees From Natural Language Instruction

Domestic and service robots have the potential to transform industries such as health care and small-scale manufacturing, as well as the homes in which we live. However, due to the overwhelming variety of tasks these robots will be expected to complete, providing generic out-of-the-box solutions that meet the needs of every possible user is clearly intractable. To address this problem, robots must therefore not only be capable of learning how to complete novel tasks at run-time, but the solutions to these tasks must also be informed by the needs of the user. In this letter we demonstrate how behaviour trees, a well established control architecture in the fields of gaming and robotics, can be used in conjunction with natural language instruction to provide a robust and modular control architecture for instructing autonomous agents to learn and perform novel complex tasks. We also show how behaviour trees generated using our approach can be generalised to novel scenarios, and can be re-used in future learning episodes to create increasingly complex behaviours. We validate this work against an existing corpus of natural language instructions, demonstrate the application of our approach on both a simulated robot solving a toy problem, as well as two distinct real-world robot platforms which, respectively, complete a block sorting scenario, and a patrol scenario.
The impact of the Blockchain technology on the global Sukuk industry: smart contracts and asset tokenisation

This paper aims to investigate the Sukuk industry and Distributed Ledger Technologies (DLTs) applications exploring their fundamentals and criticisms to shed light on potential opportunities both for Sukuk issuances and for the securitisation industry. DLTs are characterised by intense competition and potential future disruption in the banking and finance industry for both conventional and IF arena and Sukuk industry representing a leading sector. Using a case-study analysis on some pioneers’ Blockchain-based Smart Sukuk and tokenisation projects, we try to identify the potential streamlining of the inquiry and issuance process. This paper contributes to the literature on Sukuk and the use of DLTs matching the need demand and supply-side, highlighting new opportunities for the Islamic Finance (IF) standardisation, considering the different interpretation of Shariah between countries making up a barrier to its growth. Therefore, the ShariahTech industry's outgrowth might contribute to developing new instruments as ancillary or alternative to conventional bonds providing new policy implications for regulatory authorities and stimulating the global market competition.
Technique to Adjust Adaptive Digital Filter Coefficients in Residue Number System Based Filters

The paper discusses adaptive filtering using Least Mean Square (LMS) and Recursive Least Square (RLS) algorithms. An algorithm for adjusting the coefficients of an adaptive digital filter in the Residue Number System and a procedure of developed algorithm applying depending on filter length and signal length are proposed. Mathematical modeling of the considered algorithms is performed. Examples are presented to demonstrate how the proposed technique can help the designer in the adjustment of the filter coefficients without the need for extensive trial-and-error procedures. The analysis of the denoising quality and computational complexity is made. Synthetic and real data (earthquake recording) were used while testing. The proposed algorithm surpasses the existing ones like LMS and RLS, and their modifications in a number of parameters: adaptation (denoising) quality, ease of implementation, execution time. The main difference between the developed algorithm is the sequential adaptation of each coefficient with zero error. In the known algorithms, the entire vector of coefficients is iteratively adapted, with some specified accuracy. The iterations (steps) number is determined by the input signal length for all algorithms.
Boosting the Performance of Reference-Frame- Independent Measurement-Device-Independent Quantum Key Distribution

Among all present quantum key distribution protocols, the reference-frame-independent measurement-device-independent quantum key distribution (RFI-MDI-QKD) is standing out due to its high security and practicability, since it can not only remove all detector-side-channel attacks, but also relax the requirement of reference-frame calibrations. However, till today, the performance degradation of RFI-MDI-QKD caused by finite-size effects is one of the biggest impediment to its practical implementations. Here, we propose an enhanced RFI-MDI-QKD protocol which is highly resistant to the finite-size effect and immensely boosts both the key rate and transmission distance. As a result, it can even surpass the most advanced MDI-QKD protocol under larger rotation angles of reference frames. Furthermore, with the state-of-the-art RFI-MDI-QKD setup, we successfully carry out a proof-of-principle experimental demonstration at a record distance of 300km. The experiments under non-ideal condition verify the advantage and robustness of our present work.
Context-Aware Local Information Privacy

In this paper, we study Local Information Privacy (LIP). As a context-aware privacy notion, LIP relaxes the de facto standard privacy notion of local differential privacy (LDP) by incorporating prior knowledge and therefore achieving better utility. We study the relationships between LIP and some of the representative privacy notions including LDP, mutual information and maximal leakage. We show that LIP provides strong instance-wise privacy protection compared to other context-aware privacy notions. Moreover, we present some useful properties of LIP, including post-processing, linkage, composability, transferability and robustness to imperfect prior knowledge. Then we study a general utility-privacy tradeoff framework, under which we derive LIP based privacy-preserving mechanisms for both discrete and continuous-valued data. Three types of perturbation mechanisms are studied in this paper: 1) randomized response (RR), 2) random sampling (RS) and 3) additive noise (AN) (e.g., Gaussian mechanism). Our privacy mechanisms incorporate the prior knowledge into the perturbation parameters so as to enhance utility. Finally, we present a comprehensive set of experiments on real datasets to illustrate the advantage of context-awareness and compare the utility-privacy tradeoffs provided by different mechanisms.
Global–local image enhancement with contrast improvement based on weighted least squares

Contrast improvement as well as detail preservation is important for human visualization in image enhancement. Traditional histogram equalization methods are easy to produce over-enhancement results, resulting in the loss of fine details. In order to deal with this problem, this paper proposes an image enhancement scheme (called as global–local image enhancement, GLIE) based on weighted least squares (WLS) for contrast enhancement and structural preservation, which includes the following key parts: First, we employ the WLS method to separate a low-quality image into a base image and a detail image. Then, the base image is enhanced by adaptive gamma correction based on cumulative histogram. Next, the enhanced base image jointing with the original image is used to obtain an enhancement coefficient for correcting details. Finally, the high-quality image is generated by adding the enhanced base and detail images. Experimental results in both qualitation and quantitation verify that the proposed GLIE method is effective, and even is better than the state-of-the-arts.
“Emoji, I can feel your pain” – Neural responses to facial and emoji expressions of pain

The facial expression of pain is a universal social language that transcends cultures and societies. Gaining increasing popularity, emoji is now widely used to express emotional states during digital communication. Yet, it remains unclear whether emoji expression of pain can be processed as effectively as facial expression. This event-related brain potential (ERP) study investigates whether perception of pain expressions in emoji shares similarity with faces. Participants judged pain or neutral expressions in emoji and faces in a classic empathy paradigm. A dynamic convergent-divergent-convergent process was found during the observation of pain expressions. While the recognition for pain is better for faces than for emoji (both in accuracy and reaction time), we found similar P2 amplitudes during the early stage of processing and similar LPP amplitudes during later stage. Interestingly, the neural response diverges during P3 and N2 between faces and emoji: P3 respond more strongly to pain expression (relative to neutral) in faces, whereas N2 respond more strongly to pain expression (relative to neutral) in emoji. Our findings point to the commonality of perceiving pain in faces and in emoji, as well as the distinct salience of detecting pain in faces.
A Method for Protecting Private Data in IPFS

IPFS is a distributed file system. Each node in the system works in a collaborative manner to implement decentralized file access. Anyone can share files through the system. However, there are some data privacy issues of the system during the sharing of file data. Therefore, we need to add the data access mechanism to alleviate data privacy issues in IPFS. In response to this problem, we propose a privacy protection method based on blockchain and improved IPFS. The method utilizes the blockchain to store file information and user permissions, and the improved IPFS can control file data sharing according to the user's permission. At the same time, we have extended the user group and file directory management functions based on smart contracts. Through this method, the user can centrally manage the shared users in the system and efficiently organize the file directory while realizing the protection of the private files.