Reviewerly/exHarmony huggingface dataset:
Adaptive Fuzzy Approach for Image Contrast Enhancement

The contrast of an image plays a crucial role in distinguishing the object from the other objects and the background, especially for analyzing and diagnosing essential information. Contrast enhancement improves the quality of the image for any subjective evaluation. This paper presents novel fuzzy- based techniques for contrast enhancement. In the developed method, absolute luminance difference (ALD) have utilized to decide the threshold for contrast enhancement. We have used Gaussian fuzzy membership function to find the appropriate weight corresponding to the pixels. The mean of the Gaussian membership function is determined using the means of k-middle, and the variance of the MF in a window is evaluated by taking the average deviation from the mean. The proposed approach is validated on the standard datasets and compared with various state-of-the-art methods.
Detection of Architectural Distortion using Deep Convolutional Neural Network

Breast cancer is one of the threatening diseases among women throughout the world. The early detection is the only way to cure from cancer. Architectural distortion (AD) is one of the earliest symptoms of breast cancer which is mostly malignant in nature. Computer-aided detection (CAD) and particularly deep learning (DL) gives prominent solution for the detection and diagnosis of breast cancer. This paper presents a deep convolutional neural network (DCNN) architecture designed for the automatic detection of AD in digital mammography images. The proposed deep learning based model consists of series combination of down sampler and ResNet blocks. Due to stacking of these blocks, these layers learn more complex features which help to improved in sensitivity and performance of the model. A total of 150 mammograms are considered for experimentation purpose from publicly available dataset namely, DDSM. Hence the best result obtained in the proposed approach with Leave-One-Out cross validation technique, in terms of true positive rate 86% at 0.42 false positives per image (FPs/I).
Multi-lingual Spam SMS detection using a hybrid deep learning technique

Nowadays, the incremental usage of mobile phones has made spam SMS messages a big concern. Sending malicious links through spam messages harms our mobile devices physically, and the attacker might have a chance to steal sensitive information from our devices. Various state-of-the-art research works have been proposed for SMS spam detection using feature-based, machine, and deep learning techniques. These approaches have specific limitations, such as extracting and selecting signifi-cant and quality features for efficient classification. Very few deep learning techniques are only used for classifying spam detection. Moreover, the benchmark spam datasets written in English are mostly used for evaluation. Very few papers have detected spam messages for other languages. Hence, this paper creates a multilingual SMS spam dataset and proposes a hybrid deep learning technique that combines the Convolutional Neural Network and Long Short-Term Memory (LSTM) model to classify the message dataset. The performance of this proposed hybrid model has been compared with the baseline deep learning models using accuracy, precision, recall, and F1-score metrics.
Analysis of User-Support Tickets in the Lifetime of the Blue Waters System

We present an analysis of the collection of user support tickets created during nearly nine years of operation of the Blue Waters supercomputer. The analysis is based on information obtained from the Jira ticketing system and its corresponding queues. The paper contains a set of statistics showing, in quantitative form, the distribution of tickets across system areas. It also shows the computed metrics related to management of tickets by our staff. Additionally, we present an analysis, based on Machine Learning and Sentiment Analysis techniques, conducted over the text entered in tickets, targeting detecting trends on users' views and perspectives about the Blue Waters system. This kind of study, which is uncommon in the literature, could provide guidance for operators of future large systems about the expected volume of user support demanded by each system area, and about how to allocate support staff such that users receive the best possible assistance.
Goal-oriented requirement language model analysis using analytic hierarchy process

We present the application of multi-objective optimisation analytic methodologies to goal models in this research, with the intention of providing various benefits beyond the initial modelling act. Optimisation analysis can be used by modellers to evaluate goal satisfaction, evaluate high-level design alternatives, aid analysts in deciding on high-level requirements and system design, verify the sanity of a model, and improve communication and learning. Goal model analysis may be done in a variety of methods, depending on the nature of the model and the study’s goal. In our work, we use the Goal-Oriented Requirement Language (GRL), which is part of the User Requirements Notation (URN), a new International Telecommunication Union (ITU) recommendation that offers the first standard goal-oriented language. Existing optimisation methods are geared towards maximising objective functions. On the other hand, real-world problems necessitate simultaneous optimisation of both maximising and minimising objective functions. This work explores a GRL model analysis that may accommodate the conflicting goals of various inter-dependent actors in a goal model using the Analytic Hierarchy Process (AHP). By evaluating the qualitative or quantitative satisfaction levels of the actors and intentional elements (e.g., objectives and tasks) that make up the model, we construct a multi-objective optimisation method for analysis using the GRL model. The proposed hybrid technique evaluates the contribution of alternatives to the accomplishment of top softgoals. It is then integrated with the top softgoals’ normalised relative priority values. The integration result may be utilised to assess multiple alternatives based on the requirements problem. Although the URN standard does not mandate a specific propagation algorithm, it does outline certain criteria for developing evaluation mechanisms. Case studies were used to assess the viability of the suggested approach in a simulated environment using JAVA Eclipse and IBM Cplex. The findings revealed that the proposed method can be used to analyse goals in goal models with opposing multi-objective functions.
A visual analytics approach for national intangible cultural heritage in Heilongjiang province

The work of traditional intangible cultural heritage analyzes the intangible cultural works in restricted scales way. So, it is difficult to multi-scale understand the closely related to intangible cultural heritage. In this paper, visual analytics approaches are proposed for the preserve and protection those historic cultural heritages, which include multi-dimensional visualization, temporal visualization, and geospatial visualization. We take national intangible cultural heritage in Heilongjiang province as an example. Through visual analysis of intangible cultural heritage in Heilongjiang province, experts can form multi-dimensional understanding the culture of Heilongjiang province, including the categories, numbers and distributions. Furthermore, those methods we proposed can effective help experts make an in-depth analysis of those intangible cultural heritage, and provide them a new insight in a comparative way.
Construction of quasi-cyclic self-dual codes over finite fields

Our goal of this paper is to find a construction of all ℓ-quasi-cyclic self-dual codes over a finite field Fq of length mℓ for every positive even integer ℓ. In this paper, we study the case where xm−1 has an arbitrary number of irreducible factors in Fq[x]; in the previous studies, only some special cases where xm−1 has exactly two or three irreducible factors in Fq[x], were studied. Firstly, the binary code case is completed: for any even positive integer ℓ, every binary ℓ-quasi-cyclic self-dual code can be obtained by our construction. Secondly, we work on the q-ary code cases for an odd prime power q. We find an explicit method for construction of all ℓ-quasi-cyclic self-dual codes over Fq of length mℓ for any even positive integer ℓ, where we require that q≡1(mod4) if the index ℓ≥6. By implementation of our method, we obtain a new optimal binary self-dual code [172,86,24], which is also a quasi-cyclic code of index 4.
Is Bitcoin ESG-Compliant? A Sober Look

Much of the media focus surrounding Bitcoin has been on the “E” (environmental) element of the ESG investing approach. Given the amount of electricity consumed by Bitcoin mining, and the resulting large carbon emissions, Bitcoin has faced substantial criticism of its overly negative environmental impact. This one-sided discussion, however, ignores the “S” (social) and “G” (governance) elements almost entirely. To remedy that, this article first estimates Bitcoin’s current mining-related energy consumption and resulting related carbon emissions in order to gauge its impact on “E” more fully. Building on the status quo, we use Monte Carlo simulations, vector autoregression (VAR) models, and vector error correction (VEC) models to forecast the realistic environmental impact of Bitcoin mining over the next ten years. We also explore Bitcoin’s positive impact on the “S” (user satisfaction, data protection and privacy, human rights, and criminal activity), and “G” (accounting integrity and transparency, compensation, and principles of good governance) components.
Towards a New Model for the Production of Civil Status Records Using Blockchain

The work presented in this article focuses on document forgery.Document fraud is a generic term used to designate a fraud, a falsification of a document.The said falsification can be observed in several sectors of everyday life.For a better illustration, let's consider a system for producing civil status forms.The birth certificate is generally the basis of any individual's life, and if this document is falsified, it can have repercussions at several levels, particularly in the academic field, where false diplomas, false national identity cards, false passports, false marriage certificates, etc. are obtained.It is therefore demonstrated that a secure production unit for civil status forms can be beneficial to both developed and developing countries.In addition, smart contract is proposed using a private Hyperledger fabric blockchain for validation of civil status documents, and a non-state public key management infrastructure is also proposed to authenticate the nodes that will validate the transactions.This improved blockchain solves the problem of falsification of civil status documents, as false nodes can contribute to the validation of a transaction.The contribution of such a hybrid system is of paramount importance for the identity of civil status documents on the one hand and for the identity of documents in general, and the blockchain to guarantee the distributed, unforgeable, non-repudiation, transparency and disintermediation character.
Water outlet temperature prediction method of nuclear power plant based on echo state network with variable memory length

As a new type of energy which is developing vigorously in China, nuclear energy has been widely concerned in all aspects. The circulating water system in the nuclear power plant takes water from seawater, cools the steam engine through the condenser, and then carries waste heat from the outlet to the sea. If the temperature of the outlet is too high, it will not only cause the temperature rise near the water surface of the atmosphere and the ground layer near the shore, but also affect the ecological environment inside the ocean. In this paper, a model based on the echo state network with variable memory length (VML-ESN) is proposed to predict outlet temperature of the nuclear power plant. It can get memory according to the different input autocorrelation characteristic length to adjust the status update equation. The simulation results show that compared with ESN, Leaky-ESN, and Twi-ESN, the proposed model has better prediction performance, with a MAPE of 3.42%. In addition, when the reservoir size is 40, the error of VML-ESN is smaller than that of other models.
Multiple independent losses scheduling: A simple training method for deep neural networks

In recent years, various loss functions have been proposed to boost the performance of deep neural networks. Every loss function has its own specific theoretical motivation, and can easily learn its preference features of training data compared with other loss functions. Thus, combining multiple loss functions to capture more data features becomes an attractive idea for model performance improvement. In this paper, instead of using a single loss function or a linear weighted sum of multiple loss functions, we present the method named Multiple Independent Losses Scheduling (MILS), which allows multiple loss functions to independently participate in the training process according to their performance. Specifically, for all candidate loss functions, one loss function will be predefined as the primary loss function before training, and the other loss functions will play auxiliary roles for possible contributions to improve the model performance. In order to avoid auxiliary loss functions bringing a negative effect on the model performance in the training process, we developed a simple but effective performance-based scheduling algorithm to prevent auxiliary loss functions from dragging down the model performance. Extensive experiments using various deep architectures on various recognition benchmarks demonstrate our scheme is simple, robust, lightweight, and effective for typical classification tasks.
Promotion of Public Health or Digital Dictatorship? The Use of Digital Technologies for Crisis Management during COVID-19 and their Impact on Civil and Political Rights

This chapter will focus on the governance challenges arising from the question on how to manage the COVID-19 crisis with a specific focus on the use of digital technologies. Many governments have used digital technologies in response to the COVID-19 pandemic. This chapter will ask what potential impacts this has on civil and political rights and liberties such as the right to privacy, freedom of association and peaceful assembly, and freedom of movement, which are, among other things, also key to entrepreneurial activities. Civil and political rights are a backbone of human rights and humanistic values, including human dignity and personal freedom. Without civil and political rights, it is difficult to imagine a free exchange of opinions, arguments based on science and rational thought, and a free and public debate. Free deliberation in society and politics is at the base of rational reasoning. In order to shape a truly human society, we need to respect the human rights and dignity of all people, which includes absolute respect for their personal privacy, freedom and autonomy. This discussion is timely and relevant noting that a number of states have used digital technology in their emergency response to the COVID-19 pandemic, giving rise to concerns that these technologies could curtail personal freedoms and rights. Examples of the types of digital technology employed include the use of existing digital surveillance technology being employed to track the locations of individuals via their smartphones without consent, and also the rapid development and implementation of specific smartphone applications designed to be downloaded by individuals, allowing state authorities to track their location and cross-reference or contact-trace individuals.
Preference-aware Bayesian Personalized Ranking for Point-of-interest recommendation

Point-of-interest (POI) recommendation has become one of the research highlights in the field of recommender systems due to the prosperity of location-based social networks in recent years. Various techniques have been proposed to improve the performance of the personalized recommendation service. Embedding-based methods have shown promising effect and attracted great attention for their flexibility and efficiency. Bayesian Personalized Ranking (BPR), as a famous optimization algorithm, has been widely used to learning the parameters of Embedding-based models in the recommendation scenario. However, existing Bayesian Personalized Ranking and its follow-up methods ignore the unique user preference when constructing the positive and negative samples, leading a suboptimal performance. To overcome this limitation, we propose a novel method named preference-aware Bayesian Personalized Ranking (PABPR) according to empirical analyses on real-world datasets. The empirical analyses show that a user tend to visit a POI with categories which have been visited before. Thus, the key idea of PABPR is to introduce such user behaviors into the sample constructing process. PABPR is a general method which could be used for training various Embedding methods. Extensive experiments show that PABPR can lead a superior model performance compare to BPR and its variant methods.
An adaptive active learning algorithm with informativeness and representativeness

Active learning focuses on selecting a small subset of the most valuable instances for labeling to learn a highly accurate model. Considering informativeness and representativeness of unlabeled instances is significant for a query, some works have been done about combing informativeness and representativeness criteria. However, most of them are generally in a fixed manner to balance these criteria, and difficult to find suitable sampling strategies and weights of informativeness and representativeness for various datasets. In this paper, an adaptive active learning method ALIR is proposed to address these limitations. Firstly, an adaptive active learning framework is represented, in which the weight of informativeness and representativeness criteria can be dynamically updated by the feedback of previous learning processes. Secondly, by formulating the active learning as a Markov decision process, ALIR can adaptively select the suitable sampling strategies according to the reward of the learning process. Finally, extensive experimental results over several benchmark datasets and two real classification datasets demonstrate that ALIR outperforms several state-of-the-art methods. Different from traditional active learning algorithms, ALIR can adaptively select sampling strategies and adjust the weights simultaneously, which helps it more feasible in the application.
State polynomials: positivity, optimization and nonlinear Bell inequalities

This paper introduces state polynomials, i.e., polynomials in noncommuting variables and formal states of their products. A state analog of Artin’s solution to Hilbert’s 17th problem is proved showing that state polynomials, positive over all matrices and matricial states, are sums of squares with denominators. Somewhat surprisingly, it is also established that a Krivine–Stengle Positivstellensatz fails to hold in the state polynomial setting. Further, archimedean Positivstellensätze in the spirit of Putinar and Helton–McCullough are presented leading to a hierarchy of semidefinite relaxations converging monotonically to the optimum of a state polynomial subject to state constraints. This hierarchy can be seen as a state analog of the Lasserre hierarchy for optimization of polynomials, and the Navascués–Pironio–Acín scheme for optimization of noncommutative polynomials. The motivation behind this theory arises from the study of correlations in quantum networks. Determining the maximal quantum violation of a polynomial Bell inequality for an arbitrary network is reformulated as a state polynomial optimization problem. Several examples of quadratic Bell inequalities in the bipartite and the bilocal tripartite scenario are analyzed. To reduce the size of the constructed SDPs, sparsity, sign symmetry and conditional expectation of the observables’ group structure are exploited. To obtain the above-mentioned results, techniques from noncommutative algebra, real algebraic geometry, operator theory, and convex optimization are employed.
Parallel Acquisition of Uncorrelated Sequences does Not Provide Firm Evidence for a Modular Sequence-Learning System

Dual-systems theories of sequence learning assume that sequence learning may proceed within a unidimensional learning system that is immune to cross-dimensional interference because information is processed and represented in dimension-specific, encapsulated modules. Important evidence for such modularity comes from studies investigating the absence or presence of interference between multiple uncorrelated sequences (e.g., a sequence of color stimuli and a sequence of motor keypresses). Here we question the premise that the parallel acquisition of uncorrelated sequences provides convincing evidence for a modularized learning system. In contrast, we demonstrate that parallel acquisition of multiple uncorrelated sequences is well predicted by a computational model that assumes a single learning system with joint representations of stimulus and response features.
An efficient multi-metric learning method by partitioning the metric space

Metric learning has attracted significant attention due to its high effectiveness and efficiency for pattern recognition task. Traditional supervised metric learning algorithms attempt to seek a global distance metric with labeled samples. When data are represented with multimodal and only limited supervision information is available, these approaches are insufficient to obtain satisfactory results. In this paper, we develop a robust semi-supervised multi-metric learning method (RSMM) to improve classification performance. The proposed RSMM learns multiple local metrics and a background metric instead of a single global metric. Specifically, we divide the metric space into influential regions and background region, and then regulate the effectiveness of each local metric to be within the related regions. Simultaneously, a geometrically interpretable, symmetric distance is defined with local metrics and background metric. Based on the resultant learning bounds, we obtain the regularization term to improve the classifier's generalization ability. Moreover, the manifold regularization term is introduced to preserve the supervision information as well as geometry structure. The substantial unlabeled samples may cause potential threats and large uncertainties, so the logarithmic loss function is utilized to enhance the robustness. An efficient gradient descent algorithm is exploited to solve the non-convex challenging problem. To further understand the proposed algorithm, we theoretically derive its robustness and generalization error bounds. Finally, numerical experiments on UCI datasets and image datasets demonstrate the feasibility and validity of the RSMM.