Reviewerly/exHarmony huggingface dataset:
Using Artificial Neural Network Condensation to Facilitate Adaptation of Machine Learning in Medical Settings by Reducing Computational Burden: Model Design and Evaluation Study

Machine learning applications in the health care domain can have a great impact on people's lives. At the same time, medical data is usually big, requiring a significant number of computational resources. Although this might not be a problem for the wide adoption of machine learning tools in high-income countries, the availability of computational resources can be limited in low-income countries and on mobile devices. This can limit many people from benefiting from the advancement in machine learning applications in the field of health care.In this study, we explore three methods to increase the computational efficiency and reduce model sizes of either recurrent neural networks (RNNs) or feedforward deep neural networks (DNNs) without compromising their accuracy.We used inpatient mortality prediction as our case analysis upon review of an intensive care unit dataset. We reduced the size of RNN and DNN by applying pruning of "unused" neurons. Additionally, we modified the RNN structure by adding a hidden layer to the RNN cell but reducing the total number of recurrent layers to accomplish a reduction of the total parameters used in the network. Finally, we implemented quantization on DNN by forcing the weights to be 8 bits instead of 32 bits.We found that all methods increased implementation efficiency, including training speed, memory size, and inference speed, without reducing the accuracy of mortality prediction.Our findings suggest that neural network condensation allows for the implementation of sophisticated neural network algorithms on devices with lower computational resources.
The Choreography of the Creative Process: Drawing in Time and Space

Abstract This article provides a new perspective on the contribution of computers to visual art, exploring how the visual product’s aesthetic qualities and the making process itself can render a hybrid artistic outcome. A medium that unifies the physical product with the spirit of the making process has innovative potential for computational artistic practice. The article demonstrates various techniques to visualize artists’ motor performance in activities such as drawing and carving. The authors digitally track artists’ movements and computer graphic tools to expose expressive performance, highlight working styles and bring their strokes’ hidden paths to the artwork’s forefront. The authors discuss contextual application of this form of visualization to new domains of visual art.
Multi-scale Adaptive Residual Network Using Total Variation for Real Image Super-Resolution

Single image super-resolution (SISR) has developed fast for recent years. Most of the SISR models are trained and evaluated with simulated data where low-resolution (LR) images are generated from high-resolution (HR) images using pre-defined degradation. In contrast, real-world image super-resolution (RealSR) is more challenging since the process of obtaining LR images is formulated by complex degradation. To solve this problem, we propose the multi-scale adaptive real image super-resolution (MARS). Our model extracts complex features in the image and uses them for upscaling adaptively. Experimental results show that the proposed method can improve the quality of the super-resolved images in RealSR.
Blockchains from Non-idealized Hash Functions

The formalization of concrete, non-idealized hash function properties sufficient to prove the security of Bitcoin and related protocols has been elusive, as all previous security analyses of blockchain protocols have been performed in the random oracle model. In this paper we identify three such properties, and then construct a blockchain protocol whose security can be reduced to them in the standard model assuming a common reference string (CRS). The three properties are: collision resistance, computational randomness extraction and iterated hardness. While the first two properties have been extensively studied, iterated hardness has been empirically stress-tested since the rise of Bitcoin; in fact, as we demonstrate in this paper, any attack against it (assuming the other two properties hold) results in an attack against Bitcoin. In addition, iterated hardness puts forth a new class of search problems which we term iterated search problems (ISP). ISPs enable the concise and modular specification of blockchain protocols, and may be of independent interest.
WITHDRAWN: Revocable storage identity based encryption for protected shared data in cloud computing

Cloud computing is the internet based modern computing paradigm it provides resources as services dynamically pay and use basis. It has experienced tremendous growth in the last decade due to its cost effective high quality computing and improved performance. It is becoming a necessity as a larger number of devices are connected and sharing of information has becoming ubiquitous. It accommodates data sharing in a more pliable way. Most of the users are averse to outsource their sensitive data to the cloud computing platform because of the security concerns. Consequently it is imperative to employ some kind of encryption methodology and access control on shared data. Similarly, there is a need for a contrivance to manage permissions and revoked users. This paper proposes an approach coined as Revocable Storage Identity Based Encryption, it handles the backward and forward secrecy of the encrypted text with expired user revocation and encryption text update functionalities. Additionally, Revocable Storage IBE is evaluated against earlier IBE methods and illustrated the leverages in terms of performance and adequacy.
Formation of personal qualities by means of acmeological approach in the context of professional becoming

The article deals with the problem of personal skills formation in the context of modern system of higher education. It is proved that acmeological approach provides the graduate’s ability to adapt to practical activity successfully in contradictory and constantly changing conditions. The growing interest in this scientific problem is due to the fact that professional training at the university today is actively involved in the global processes of education modernization for the purposes of sustainable development.
Spherical Bounding Classifier using CGP Generated Transforms

A Cartesian Genetic Programming (CGP) approach was applied to generate data projection equations (transforms) to project original N-dimensional features space into new 3-dimensional coordinate space followed by a spherical bounding for classification. The solution finding mechanism differs slightly from conventional CGP based machine learning in four ways. Firstly, inputs were weighted to introduce more flexibility for solution finding. Secondly, a dual mutation sequence was introduced to encourage faster convergence. Thirdly, chromosome vector included a 3-dimensional coordinate point and spherical bounding mechanism for classification. Fourthly, the probability of selecting either of the input types (feature input, node sequence, or constant) are made approximately equal to ensure that the input types have equal probability to be in the active equation nodes. The best classification rate on test datasets achieved using 10 fold cross validation was 98.57% (Wisconsin Breast Cancer dataset), 87.78% (Heart disease dataset) and 80.5% (PIMA Indian diabetes dataset).
Multi‐swarm competitive swarm optimizer for large‐scale optimization by entropy‐assisted diversity measurement and management

Abstract As a crucial factor, population diversity greatly affects performances of swarm intelligence algorithms. Especially, for large‐scale optimization problems (LSOPs), the searching space is huge and the number of local optima dramatically increases. Hence to well address LSOPs, a healthy population diversity is helpful to prevent a swarm from premature convergence. However, this is a big challenge to balance exploration and exploitation for swarm intelligence algorithms. To handle with this issue, in this paper, we design a novel algorithm structure for swarm update. In the proposed algorithm, a swarm is divided into several groups and conduct competition in each group where the loser will learn from the winner and meanwhile the winner does nothing in the corresponding iteration. For diversity measurement, we abandon the distance‐based measurement, but employ a frequency‐based measurement, namely entropy indicator, so that the diversity maintenance can be conducted with a different measurement of convergence situation. In this way, the diversity maintenance and convergence can be conducted simultaneously and independently. The benchmarks on the suite of LSOPs are employed to validate the performance of a proposed algorithm. By comparing several state‐of‐the‐art competitor algorithms, the results demonstrate that the proposed algorithm is effective and competitive in dealing with LSOPs.
Relaxed Low Tensor Train Rank Representation with Structural Smoothness for Hyperspectral Image Super-resolution

We propose a super-resolution method for hyperspectral image (HSI) that utilizes relaxed low tensor train (TT) rank representation with structural smoothness in this paper. Nonlocal similarity is exploited by grouping the similar HSI cubes. The 4D tensor formed by similar cubes is highly low-rank. The good balanced matricisation scheme of TT and rational shrinkage strategy of log-sum norm motivated us to design the relaxed low TT rank regularization in the model. It can learn the spatial and spectral correlations hidden in these 4-D tensors. The structural smoothness is captured by the three-dimensional total variation (3DTV) regularization in the model. We solve our model via ADMM. Compared with existing state-of-art super-resolution approaches, quantitative and qualitative reconstruct results on typical HSI data indicate that our method is effective.
A Novel Local Selective Ensemble-based AdaBoost Method for Fault Detection of Industrial Process

For the sake of guaranteeing the security of complex industrial system, it is important to accurately and efficiently detect the faults. AdaBoost algorithm is an effective fault detection method. It can generate a large number of weak classifiers in iterations and combine many of these weak classifiers into the strong classifier to solve the classification problem for fault detection. For the traditional AdaBoost, several of these poor weak classifiers are often ignored and not fully used. However, the weak classifiers with poor performance may store the significant information and pay more attention to the difficult samples. To solve these problems, we propose a local selective ensemble-based AdaBoost (AdaBoost-LSE) in this article. Firstly, error feedback ELM (EFELM) is introduced to establish the basic weak classifier. Through the iteration of AdaBoost, these weak classifiers based on EFELM are generated. Secondly, these weak classifiers are divided into good weak classifiers and bad weak classifiers based on the classification accuracy. The poor weak classifiers with good performance are selected by calculating the classification accuracy for the targeted samples. Thirdly, the strong classifier of AdaBoost-LSE is constructed by integrating the original good weak classifiers and some of these poor weak classifiers with good performance. To verify the efficiency of AdaBoost-LSE, the Tennessee Eastman (TE) simulation process is used. The experimental results reveal that the proposed AdaBoost-LSE can greatly improve the accuracy of fault detection.
An Effective Drone Surveillance System Using Thermal Imaging

Surveillance is the process of continuous observation of circumstances, an area, or person. This generally occurs in a military scenario where surveillance of war areas and adversary territory are crucial for the nation's security. Human surveillance is accomplished by conveying workforce in close sensitive areas to continually screen for changes. In this paper, we propose to design a drone that is used to detect people by using a smart thermal imaging surveillance system with the help of neural networks. The thermal imaging dual-spectrum camera placed on the drone is capable of working under cover of darkness and in conditions where there is condensation from fog and rain with no external illumination. The drone is capable of differentiating native military men from intruders by implementing RFID technologies. The combination of the drone and long-range surveillance capability along with secured data communication makes the system ideal for addressing security-related challenges in critical areas on guarded premises.
Deep Learning Based Human Locomotion Recognition in Video Sequences

The paper proposes a classification method of human locomotion types from video sequences based on machine learning methods. The purpose is to discriminate between three locomotion types: walking, jogging and running, using only postural features. It is obvious that the result of the classification may be wrong for some frames due to the similarity of some transition postures. For this reason, a temporal filtering procedure is applied for smoothing the results and makes the final decision for the entire video sequence. For some video sequences, the obtained result is different from the a priori known movement type. However, a careful analysis shows that the dynamics of the movement is closer to that determined by classification than the known one. Therefore, the procedure can be applied to correct the movement in sports activities or in medical recovery.
Moment Dynamics and Observer Design for a Class of Quasilinear Quantum Stochastic Systems

This paper is concerned with a class of open quantum systems whose dynamic variables have an algebraic structure, similar to that of the Pauli matrices pertaining to finite-level systems. The system interacts with external bosonic fields, and its Hamiltonian and coupling operators depend linearly on the system variables. This results in a Hudson--Parthasarathy quantum stochastic differential equation (QSDE) whose drift and dispersion terms are affine and linear functions of the system variables. The quasilinearity of the QSDE leads to tractable dynamics of mean values and higher-order multipoint moments of the system variables driven by vacuum input fields. This allows for the closed-form computation of the quasi-characteristic function of the invariant quantum state of the system and infinite-horizon asymptotic growth rates for a class of cost functionals. The tractability of the moment dynamics is also used for mean square optimal Luenberger observer design in a measurement-based filtering problem for a quasilinear quantum plant, which leads to a Kalman-like quantum filter.
An Improved Flower Pollination Algorithm for Global Numerical Optimization

A new bionic algorithm named as Flower Pollination algorithm (FPA) was proposed by Yang. FPA has some shortcomings, such as premature convergence, low precision, et al. So an improved flower pollination algorithm (IFPA) is proposed in this paper. IFPA combines three aspects: global pollination with quantum search mechanics, local pollination with DE/rand/1 mutation, and switch on dimensions. The experimental results show that IFPA can speed up convergence and improve accuracy.
Connecting the Dots Between Human Factors and Software Engineering

Software engineering (SE) is a human-intensive activity where human factors play a fundamental role. As such, SE is an inherently sociotechnical endeavor on which different social and technical aspects are involved. In fact, it is recognized that successful SE not only depends on technical or process issues, but also it is influenced by human factors. They have been proved to have impact on software process and their study is a growing research field. The summary presented in this chapter highlights the results obtained in a five-year effort research aiming at understanding the role that human factors play in SE. As a result, a holistic view of human factors on software process is given.
Motion Path Planning of Two Robot Arms in a Common Workspace

Avoiding collision between two robot arms in a common workspace is non-trivial, since each arm acts as a dynamic obstacle for the other one. In this context, Motion Path Planning (MPP) is the process of finding an optimal and collision-free track that a robot/robot arm can follow to get to the target position starting from any point in its workspace. We propose a reinforcement learning approach to MPP for two manipulators, the first one of which tries to avoid collision with the second one. Initially, the first manipulator has no knowledge about the environment, but it successfully learns optimal collision-free paths through a Team Q-learning algorithm. We present experiments using two different methods for state discretization, namely General State (GS) Discretization and Tile Coding (TC) Discretization, as well as two different Q-learning methods, namely single-agent (SA) and multi-agent (MA) approaches.
The Performance Evaluating of Kotlin and Java Implementations

In this article the performance analysis of two modern programming languages Java and Kotlin is carried out. The main methods of comparing programming languages in the modern world are considered. Each performance test is described in detail. The results of the research are presented, analyzed and evaluated. The results of this work can help determine the development language for a future project, depending on its specifics.
IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification

Medical data classification (MDC) refers to the application of classification methods on medical datasets. This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis. To gain experts’ trust, the prediction and the reasoning behind it are equally important. Accordingly, we confine our research to learn rule-based models because they are transparent and comprehensible. One approach to MDC involves the use of metaheuristic (MH) algorithms. Here we report on the development and testing of a novel MH algorithm: IWD-Miner. This algorithm can be viewed as a fusion of Intelligent Water Drops (IWDs) and AntMiner+. It was subjected to a four-stage sensitivity analysis to optimize its performance. For this purpose, 21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine. Interestingly, there were only limited differences in performance between IWD-Miner variants which is suggestive of its robustness. Finally, using the same 21 datasets, we compared the performance of the optimized IWD-Miner against two extant algorithms, AntMiner+ and J48. The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner, as confirmed by the Wilcoxon nonparametric statistical test. Results suggest that IWD-Miner is more efficient than AntMiner+ as measured by the average number of fitness evaluations to a solution (1,386,621.30 <i>vs</i>. 2,827,283.88 fitness evaluations, respectively). J48 exhibited higher accuracy on average than IWD-Miner (79.58 <i>vs</i>. 73.65, respectively) but produced larger models (32.82 leaves <i>vs</i>. 8.38 terms, respectively).
Forecasting of power production of a parabolic trough plant using EO and NWP data

TSK Flagsol Engineering GmbH through a cooperation with the DLR (the German Aerospace Center) and sponsored by the European Space Agency (ESA) has developed a weather and energy production forecasting system called CSP-FoSyS for the operation of CSP power plants. CSP-FoSyS is both a meteorological and an electrical and thermal power forecast system based on satellite based nowcasting, numerical weather prediction model and cloud cameras based forecasts. The system integrates these several forecasting methods through a concept called “the merger”, providing forecasts for the use in concentrated solar power (CSP) plants.
Sub-word Based End-to-End Speech Recognition for an Under-Resourced Language: Amharic

In this work, we focused on end-to-end speech recognition for less-resourced language, Amharic. The result can be integrated with other tasks such as spoken content retrieval. We explored three models, which consist of Convolutional Neural Networks, Recurrent Neural Networks, and Connectionist Temporal Classification, towards end-to-end speech recognition on less-resourced language. Further, we studied the possibility of having an end-to-end system with 1-best output keeping the network parameters and computational resource minimal. The paper gives attention to finding a more suitable sub-lexical unit for the Amharic end-to-end speech recognition system which can be used as an audio indexing unit. We present the first result comparing grapheme, phoneme, and syllable-based end-to-end speech recognition systems for our target language. The models are evaluated on approximately 52 hours of Amharic speech corpus containing read-speech, audiobooks, and multi-genre radio programs. On the test set, we report a character error rate (CER) of 19.21% and a syllable error rate (SER) of 39.98% for a syllable-based end-to-end model without lexicons and language model integrated.
Residual Waste Quality Detection Method Based on Gaussian-YOLOv3

In the process of garbage collection, the water flow in residual waste directly affects the process of collecting residual waste. Therefore, detecting the water flow in residual waste at the garbage transfer station is of great guiding significance for garbage disposal. In this paper, the Gaussian-YOLOv3 algorithm with high accuracy and real-time performance is used to identify and detect the water flow during the dumping process of residual waste, and determine the quality of the classification of residual waste according to the recognition situation. The experimental results show that the residual waste quality detection method based on the Gaussian-YOLOv3 algorithm can accurately identify the amount of the water flow during the dumping of the residual waste. At the same time, the back annotation and retraining method significantly reduces the model's impact on similar residual waste in complex environments. The false recognition rate satisfies the actual needs of residual waste water flow identification and improves the efficiency of residual waste classification quality determination.
Rebuilding Food Supply Chain with Introducing Decentralized Credit Mechanism

Although the trust mechanism for agricultural product transactions has systems and laws to endorse the contract, this kind of "credit relationship" and security mechanism lack sufficient trust. Currently, the design of the RFID traceability system is used to solve the problem of data reading, but situation such as data can be tampered with and problem of data security exist. Blockchain is a distributed, shared and encrypted database. Through distributed accounting, a decentralized credit system is established to enhance data security, which is a new mode to save time and costs. In this paper, On the basis of systematically sorting out the focal points and problems of food supply chain system, reanalyzes the framework of traditional food supply chain systems. Moreover, after analyzing the characteristics the blockchain technology, a decentralized credit mechanism of food supply chain is proposed. Furthermore, the traceability of agricultural products based on the blockchain can realize fast and secure authentication permissions and achieve data security in which data cannot be tampered with or forged from anti-counterfeiting, and information privacy is protected.