Reviewerly/exHarmony huggingface dataset:
Enhanced Competitive Swarm Optimizer for Multi-task Optimization

In the real world, many problems possess the high degree of similarity. Inspired by multifactorial inheritance model, evolutionary multi-task optimization paradigm is proposed to simultaneously solve multiple optimization problems. However, experimental results have revealed that the performance of multifactorial evolutionary algorithm deteriorates with negative knowledge transfer between uncorrelated tasks. To alleviate this issue, we proposed an enhanced competitive swarm optimizer to explore the generality of the multitasking paradigm. A new velocity update mechanism for losers is proposed to improve the search ability. Further, a mating approach is proposed to transfer implicit knowledge among tasks for generating offspring. Experimental and statistical analyses are performed on CEC2017 evolutionary multitask optimization competition. Results show that the proposed algorithm is competitive in comparison with other state-of-the-art multifactorial optimization algorithms.
Blockchain and corporate fraud

Purpose The purpose of this paper is to develop conceptual designs for blockchain implementations aimed at reducing corporate fraud. The proposed framework consists of different levels of implementation with specific examples for each level. Design/methodology/approach The paper uses a multi-level framework to highlight the properties of blockchain technology as suitable for reducing corporate fraud. The five levels of technological complexity designed for this research include information storage, information flow, information processing, information enhancement and information and financial integration. Specific cases of corporate fraud are discussed to complement the proposed methodology. Findings The potential ability to limit fraud and increase transparency could greatly improve faith in financial reporting. These benefits accrue to all capital market participants. The blockchain infrastructure can significantly improve the existing monitoring system and provide value added in detecting, deterring, and documenting possible fraud. Originality/value The paper contributes to the growing field on corporate fraud and blockchain technology. The paper is novel in the implementation of the nascent blockchain methods to detect and deter fraud at the organizational level. The proposed five conceptual levels provide practical use.
A Survey of Intelligent Agro-climate Decision Support Tool for Small-Scale Farmers: An Integration of Indigenous Knowledge, Mobile Phone Technology and Smart Sensors

Food security in Africa and the rest of the globe has come under tremendous threat, meaning that agriculture which is the driving force behind many economies is under threat. Seventy per cent of the food produced in sub-Saharan Africa is produced in the rainfed small-holder agriculture, which in turn is the most devastated by any disasters experienced in the agricultural sector. Although the indigenous knowledge system has been used by many small-scale farmers as the basis for their day-to-day agricultural decision support, climate change and global warming have rendered this knowledge unreliable. Documented limitations on the isolated use of indigenous knowledge and modern scientific systems are the basis of this study. We investigate the effectiveness of the integration of indigenous knowledge interpreted through fuzzy inference systems, mobile phone and smart sensor technology with intelligence, on farmers decision support systems.
Predicting Adverse Events and their Precursors in Aviation Using Multi-Class Multiple-Instance Learning

In recent years, there has been a rapid growth in the application of machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen prior to the adverse event itself. Therefore, precursor mining provides many benefits including understanding the reasons behind a safety incident and the ability to identify signatures, which can be tracked throughout a flight to alert the operators of the potential for an adverse event in the future. This work proposes using the multiple-instance learning (MIL) framework, a weakly supervised learning task, combined with carefully designed binary classifiers leveraging a Multi-Head Convolutional Neural Networks-Recurrent Neural Networks (MHCNN-RNN) architecture. The classifiers are then combined to perform a multi-class task, which enables the prediction of different adverse events for any given flight and the identification of their precursors with minimum post-processing. Results obtained show that the MHCNN-RNN is able to accurately forecast high speed and high path angle events during the approach, and that it is also capable of determining the aircraft's parameters that are correlated to these events. The identified parameters can be considered precursors to the events and may be studied/tracked further to prevent these events in the future.
Locality Bounds for Nonredundant Binary-Integer Representations

Genetic and Evolutionary Algorithms (GEAs) rely on operators such as mutation and recombination to introduce variation to the genotypes. Because of their crucial role and effect on GEA performance, several studies have attempted to model and quantify the variation induced by different operators on various genotypic representations and GEAs. One metric of particular interest is the locality of genetic operators and representations, or how sensitive the phenotype is to small changes in genotype. Consequently, there is a considerable body of empirical work on the effects that different representations have on locality, with an emphasis on several popular representations, such as Gray encoding, and popular variation operators, such as single-bit mutation and single-point crossover.Here, we compute and prove tight upper and lower bounds on locality. We first precisely define our locality metrics for the single-point mutation and generic crossover operators by reformulating Rothlauf's seminal definitions specific to the binary-to-integer domain of representations. We then prove lower and upper bounds for single-point mutation locality by reducing the problem to mappings on hypercubes, and present constructive algorithms to generate representations of both optimal and pessimal locality. We also compute asymptotic bounds for generalized locality under any crossover operator.Our primary result is that the single-point locality of standard binary encoding is provably as good as Binary-Reflected Gray encoding, while other Gray encodings, which we construct, have worse locality. Another important result is that the generalized locality of any nonredundant binary-to-integer representation quickly converges to the same value, meaning that this metric cannot discriminate among representations and may therefore lose its usefulness for binary-integer representations.
Multi-Thread Approach to Object Detection Using YOLOv3

Recently, In the field of the object detection process, Convolutional Neural Network (CNN) methods have been applied as more efficient solutions. In the CNN methods, You Look Only Once, version 3 (YOLOv3) more powerful than classic CNN methods such as Fast-RCNN and Faster-RCNN in terms of accuracy and speed. Even though YOLOv3 can obtain more accuracy and speed compared to other CNN approaches, it needs to be used in a system with a powerful single Graphics Processing Unit (GPU). The YOLOv3 still requires the heavy computational cost to maintain good detection performance; it brings high computation overhead. It is needed to design a more stable algorithm to increase frames per second (fps) in the object detection process. The main contribution is proposing a Multi-thread approach that uses YOLOv3 to perform realtime object detection in the large scale of video streams. In this paper, we design a Multi-thread approach that uses YOLOv3 to perform real-time object detection with decreasing detection time per frame. In the proposed multi-thread approach, we aim to decrease the idle times between CPU and GPU. The proposed approach is evaluated using a public dataset and the result shows improvements in performance compared to the YOLOv3. The multi-thread model is evaluated on two different servers with two GPU cards and the results yield improvement by an average 26% in fps. Contribution-The main contribution is a Multi-thread approach that uses YOLOv3 to perform real-time object detection in the large scale of video streams.
Distributed MLPerf ResNet50 Training on Intel Xeon Architectures with TensorFlow

MLPerf benchmarks, which measure training and inference performance of ML hardware and software, have published three sets of ML training results so far. In all sets of results, ResNet50v1.5 was used as a standard benchmark to showcase the latest developments on image recognition tasks. The latest MLPerf training round (v0.7) featured Intel’s submission with TensorFlow. In this paper, we describe the recent optimization work that enabled this submission. In particular, we enabled BFloat16 data type in ResNet50v1.5 model as well as in Intel-optimized TensorFlow to exploit full potential of 3rd generation Intel Xeon scalable processors that have built-in BFloat16 support. We also describe the performance optimizations as well as the state-of-the-art accuracy/convergence results of ResNet50v1.5 model, achieved with large-scale distributed training (with upto 256 MPI workers) with Horovod. These results lay great foundation to support future MLPerf training submissions with large scale Intel Xeon clusters.
A Graph-Based Method for Health Care Joint Fraud Detection

The development of health care insurance has brought many benefits to the Chinese people. However, the frequent occurrence of health care fraud has caused great damage to health insurance funds. Common fraud is mainly discovered by analyzing a patient's medication abnormalities and his/her abnormal costs. However, this individual fraud analysis method is not applicable to joint fraud. The fraud group needs to be explored through the potential relationships among patients. Nevertheless, due to the limited amount of available medical data, we cannot directly obtain the relationships among patients. Thus, this paper proposes a health care fraud detection method based on graph analysis to identify the joint fraud group. This method uses the similarity of patients' medical behaviors to mine the latent relationships between patients. Afterwards, it uses the community detection algorithm to find suspicious groups in the weighted patient network. Eventually, the clustering coefficient strategy is used in the community to find highly suspicious people. Experiments on a real medical dataset show that the proposed method can effectively identify fraud groups and achieves better performance than that of the existing fraud detection method.
University education and its pandemic response COVID-19

The COVID-19 pandemic was probably the fastest and most intensive intervention in university teaching on a global scale in history.It paved the way for online education.At the same time, however, it also raised the question of what we can learn better through e-learning than in classical face-to-face teaching and where, on the contrary, we need personal contact.The review study analyzes 16 papers indexed in the SCOPUS database, which provide insight into the reactions of universities to the new social situation.Univerzitní vzdělávání a jeho reakce na pandemii COVID-19
Abandoned Object Detection Method Using Convolutional Neural Network

Automatic surveillance is an effort to detect anomalies that occur in the surrounding environment such as stations, offices and other public spaces. One of the anomalies that occurs is neglected objects. Abandoned objects will become annoying or dangerous if left unattended. Abandoned object detection process begins by detecting a stationary object using Gaussian mixture models, then abandoned recognize objects using convolutional neural network. The recognition of stage objects is very helpful in determining the bounding box of objects that are left behind, thereby reducing the bias arising from shadows or lighting changes. The resulting effective method to detect abandoned objects and recognize it.
Management of Risks for Complex Computer Network

The paper is concerned with a general model of complex computer network, within which a risk manager exercises efficient management of risks of a complex system through distribution of available resource among its elements (units of computer networks). A tasks of risks management are considered in a context of uncertainty and mutual influence of system elements on each other and methods for solving them are proposed.
A modified cognitive complexity metric to improve the readability of object-oriented software

Complexity of software can be identified as a term which expresses the difficulty level of reading, understanding, maintaining and modifying the software. This helps to the quality improvement of the software and maintenance process of the software through a long time period without any obstacle. Therefore, software complexity metrics have been introduced to calculate the complexity of a software using numerical values. While there are number of metrics which calculate the complexity of object-oriented programs, they only consider one or two object-oriented concepts. As a result of that, there is no single metric which has the capability of measuring the complexity of a program based on multiple object-oriented concepts. This research aims to build a new metric to evaluate the complexity of an object-oriented program in order to improve the readability. The new metric has been built based on the influence of previous object-oriented metrics and some disregarded factors in calculating the complexity. In order to evaluate the new metric, Weyuker's properties and Briand's properties are used. The new metric acquires most of the object-oriented concepts in calculating the complexity and helps to improve the readability of the software as well. In fact, it makes it easy to handle the maintainability, reusability, portability and reliability of the software, when readability is high. This will result in increasing the overall software quality.
Discovering Higher-Order Interactions Through Neural Information Decomposition

If regularity in data takes the form of higher-order functions among groups of variables, models which are biased towards lower-order functions may easily mistake the data for noise. To distinguish whether this is the case, one must be able to quantify the contribution of different orders of dependence to the total information. Recent work in information theory attempts to do this through measures of multivariate mutual information (MMI) and information decomposition (ID). Despite substantial theoretical progress, practical issues related to tractability and learnability of higher-order functions are still largely unaddressed. In this work, we introduce a new approach to information decomposition—termed Neural Information Decomposition (NID)—which is both theoretically grounded, and can be efficiently estimated in practice using neural networks. We show on synthetic data that NID can learn to distinguish higher-order functions from noise, while many unsupervised probability models cannot. Additionally, we demonstrate the usefulness of this framework as a tool for exploring biological and artificial neural networks.
Background Subtraction Algorithm Based on Combination of Grabcut and Improved ViBe

Background subtraction algorithm is essential for video processing. such as target tracing, gesture recognition and gait recognition. ViBe has been widely used because of easy implementation and high efficiency. However, the algorithm would produce a ghost imaging when the speed of the moving target changes. On the other hand, ViBe is challenged to adapt to the change of environment and by misjudge the shadow as the foreground target. Moreover, it is also inability to handle well the interference caused by camera jitter. Aiming to the deficiencies of ViBe, we propose a new algorithm Gc_IViBe, which takes advantages from both Grabcut and Improved ViBe (IViBe). Based on the ability of IViBe to eliminate ghost imaging, the proposed algorithm utilizes mask in HSV space to remove background shadows. A further improvement of the algorithm in handling cavity problem and camera jitter is achieved by combinating IViBe and Grabcut. The experimental results show that Gc_IViBe performs better than ViBe in Pixel-level measure Precision, Structural measures S-measure and E-measure. This paper also discusses the evaluation methods. The evaluation results of Precision and S-measure in some cases are apparently different from the truth. while E-measure performs relatively better consistent, which capable to accurately evaluate the problems raised in this article.
Multi-Target Stance Detection with Multi-Task Learning

Stance detection plays an important role in the field of online public opinion analysis. The majority of methods of stance detection are for a single target at a once. However, in some situations, multi-target to be analyzed are interrelated, such as several different candidates in an election and different brands of the same product. Detecting the stance of a single target in isolation may lose some information. Some studies have shown that using multi-task learning mechanism for multi-target performs better results than only using a single task. Considering the effectiveness of sentiment features for stance detection in existing researches, we propose a multi-task learning model with sentiment features and at the same time use background lexicon to guide the attention mechanism. We reduce the work of manual labeling and use automatic methods to obtain background knowledge and sentiment information. We use BiLSTM to extract the semantic features and use Multi-Kernel Convolution to get the local features. We use the widely accepted evaluation method and our experiments achieve state-of-the-art results on a benchmark dataset.
Phobia Therapy Using Virtual Reality

In today's world, many people suffer from various phobias and for these phobias, psychiatrists and therapists recommend Cognitive Behavioural Therapy (CBT). The goal of our project is to develop a virtual environment which can be used in the field of Phobia treatment as a base exposure therapy session. Implementation of this virtual environment not only cuts down cost expenses of conducting actual physical sessions, but also helps the patient to experience a glimpse of the physical exposure, helping the patient to become more comfortable and overcome their fear of particular phobia. The implemented application is installed on an android device. This will guide the patient by exposing to different virtual environment. An X-Box controller allows the patient to have full control of his/her moments and exposure.
Experimental demonstration of the violation of the temporal Peres-Mermin inequality using contextual temporal correlations and noninvasive measurements

We present a generalized quantum scattering circuit which can be used to perform non-invasive quantum measurements, and implement it on NMR qubits. Such a measurement is a key requirement for testing temporal non-contextual inequalities. We use this circuit to experimentally demonstrate the violation of the Peres-Mermin inequality (which is the temporal analog of a Klyachko-Can- Binicioglu-Shumovsky (KCBS) inequality), on a three-qubit NMR quantum information processor. Further, we experimentally demonstrate the violation of a transformed Bell-type inequality (the spatial equivalent of the temporal KCBS inequality) and show that its Tsirelson bound is the same as that for the temporal KCBS inequality. In the temporal KCBS scenario, the contextual bound is strictly lower than the quantum temporal and nonlocal bounds.
DeepHealth: Deep Representation Learning with Autoencoders for Healthcare Prediction

General practitioners/doctors usually make disease diagnoses based on some predefined rules, which may lead to omitting some important hidden factors. Deep neural networks are well known for their ability to extract features from large amounts of labeled data, which can be used to learn and discover knowledge from electronic medical records (EMRs). Existing methods have mostly focused on treating deep networks as black-box feature extractors based on the information of labels. However, it is labor-intensive and expensive to manually generate ground-truth labels for EMRs. In this paper, we propose DeepHealth, a general semi-supervised learning framework for automatic diagnoses and feature interpretation based on autoencoders. Specifically, our end-to-end DeepHealth framework is constructed of two parts: a supervised classifier network for extracting high-level representation for classification; an unsupervised autoencoder network to make sure the features extracted from the input are meaningful and can be further used for reconstruction. Compared with existing methods, the experimental results show that our method, on both numerical data and medical images, can achieve the state-of-the-art results for EMRs classification. Hence, DeepHealth is a general flexible framework for high-precision diagnoses in healthcare, it can utilize both labeled and unlabeled data to address the problem of insufficient labels in EMRs, meanwhile, it can also reconstruct medical images to help interpret the features learned by a deep neural model.
Dissimilarity Based Regularized Deep Learning Model for Information Charts

The charts are very much convenient way to represent the complex data into simple pictorial based representation. Every chart type has variations in its characteristics, structure, and appearances making every type and subtype of chart different from each other on its physical outlook. Classification of such similar outlook charts still remains an untouched area. This paper presents a model that computes chart dissimilarity index, which is amalgamated with regularization on input layers of the learning model. Thus, all structural variations of charts are integrated into the model which produces 96.66% accuracy rate outperforming existing state-of-the-art models. We proposed a novel approach to learn structure invariant dissimilar features using regularized learning techniques, by incorporating the dissimilarity index with the learning model to ease in learning dissimilar and hidden features of chart images.
ML Training on a Tiny Microcontroller for a Self-adaptive Neural Network-Based DC Motor Speed Controller

Neural-Network (NN)-based controllers have the potential to achieve better control performance than classical PID controllers. Yet NN deployment on tiny microcontrollers, which are used in DC motor control due to strict cost requirements, is challenging as NNs are computationally intensive and memory demanding. We propose a lightweight direct inverse NN-based control approach for controlling the angular speed of a permanent magnet DC motor, which runs on a tiny Arm Cortex-M0 microcontroller with only 4 kB of RAM. Moreover, the NN-based controller can self-adapt to the DC motor characteristics without the need of any external machine learning frameworks such as TensorFlow. For this, we are not deploying a pre-trained network for inference but implement a fully automated training process on the microcontroller, which also includes the dataset collection. The result is a self-adaptive control algorithm that is able to drive the motor at the desired speed after it learned the motor characteristics in an initial training phase. Furthermore, the approach is extended such that it enables the controller to constantly self-adapt to later changes in the motor characteristics caused by heating or wear-out while it is operating in standard control mode.