Reviewerly/exHarmony huggingface dataset:
Optimal foraging algorithm with direction prediction

Optimal foraging algorithm (OFA) was presented as a stochastic search algorithm to solve global optimization problems in 2017. As an emerging algorithm, there are many excellent potentials to be explored. To enhance the performance of OFA, a novel optimal foraging algorithm with direction prediction is presented in this paper, named OFA/DP. During the iterations, the population information can be fully utilized by OFA/DP. Once a new optimal solution is found, the evolutionary direction prediction strategy is applied to generate more potential candidates. In addition, considering the situation that the population does not evolve for a long time, which means that the algorithm has achieved the global optima or trapped into local optima. In this case, the Gaussian oscillation strategy is adopted to attempt to find a better solution, and escaping from local optima. To validate the efficiency of the proposed algorithm, the numerical experiments on 20 benchmark functions, 30 CEC test functions, 6 large scale functions, 28 CEC2017 constrained problems, 3 engineering problems, 6 unconstrained multi-objective functions and 10 constrained multi-objective functions are executed. The simulation results and the statistical test demonstrate that OFA/DP has a superior performance in most of functions with faster convergence speed.
Thinking of College Students' innovation and Entrepreneurship Education under the background of big data

In order to solve the problem of insufficient effectiveness of traditional college students' innovation and entrepreneurship education, this paper proposes a new method of College Students' innovation and Entrepreneurship Education under the background of big data. In order to achieve more effective innovation and entrepreneurship education of College Students under the background of big data, K-means clustering method is needed to deeply mine the innovation and entrepreneurship data of college students. Mining more accurate entrepreneurial data of college students, based on the above mining entrepreneurial data of college students as the basis of index construction, the maximum eigenvalue of College Students' entrepreneurial risk evaluation index is calculated, and the consistency test of evaluation index is completed according to the calculation results. According to the test results, calculate the correlation degree of College Students' entrepreneurial risk evaluation index, screen out the evaluation index of College Students' entrepreneurial risk, and construct the evaluation function of College Students' entrepreneurial risk according to the evaluation index. The experimental results show that, compared with the traditional methods, the proposed method can achieve higher accuracy of data mining for college students' entrepreneurship, and the risk evaluation results are more accurate.
Importing Custom DNN Models on FPGAs

With the recent advances and the proliferation of low-power and resource constraint embedded hardware being deployed in the field, the edge computing paradigm has become more prominent. Nowadays, it is possible to apply Machine Learning (ML) algorithms and Deep learning (DL) architectures, like Deep Neural Networks (DNNs), by exploiting these edge devices. There is also a plethora of software frameworks to transform the typically complex computations and accommodate the implementation process and specifically for those who are targeted to field-programmable gate arrays (FPGAs), quite a few challenges need to be addressed. Therefore, there is an active research development towards providing efficient techniques for applying DNNs models to these devices. In this paper, we propose a methodology which combines two research tools for the generation of a register-transfer Level (RTL) design using a hardware description language (HDL). We evaluate different implementations experimenting with a set of DNN models in different FPGA boards.
Parasitic Egg Detection and Classification in Low-Cost Microscopic Images Using Transfer Learning

Abstract Intestinal parasitic infection leads to several morbidities in humans worldwide, especially in tropical countries. The traditional diagnosis usually relies on manual analysis from microscopic images which is prone to human error due to morphological similarity of different parasitic eggs and abundance of impurities in a sample. Many studies have developed automatic systems for parasite egg detection to reduce human workload. However, they work with high-quality microscopes, which unfortunately remain unaffordable in some rural areas. Our work thus exploits a benefit of a low-cost USB microscope. This instrument however provides poor quality images due to the limitation of magnification (10 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> ), causing difficulty in parasite detection and species classification. In this paper, we propose a CNN-based technique using transfer learning strategy to enhance the efficiency of automatic parasite classification in poor-quality microscopic images. The patch-based technique with a sliding window is employed to search for the location of the eggs. Two networks, AlexNet and ResNet50, are examined with a trade-off between architecture size and classification performance. The results show that our proposed framework outperforms the state-of-the-art object recognition methods. Our system combined with the final decision from an expert may improve the real faecal examination with low-cost microscopes.
Optional monitoring for long-lived transactions

Runtime monitoring comes at a runtime cost. Overheads induced by monitoring and verification code may be necessary, and yet prohibitive in certain circumstances. When verification is local to a single unit of execution in a system, one can choose whether or not to monitor based on the risk of that individual unit. In this paper, we propose a monitoring and verification approach for a class of long-lived transaction-based systems whose execution can be partitioned into separate subtraces, one for each such transaction, and which are independent of each other from a correctness perspective. We focus on the use of this approach for the monitoring of smart contracts on distributed ledger technologies to show how we can reduce overheads in this manner.
An English Handwriting Quality Evaluation Algorithm Based on Machine Learning

English handwriting assessment is an important part of basic English teaching. Started in 2001 in the United States, the International English Calligraphy Contest is a worldwide non-profit event sponsored by the International English Calligraphy Contest Association. Due to the large number of participants, the evaluation of English calligraphy is labor-intensive, so we would like to have English calligraphy evaluation algorithms that can evaluate English calligraphy and thus reduce the burden of judges to read the scripts. In the context of the epidemic, students need to complete the competition at home and upload photos using their cell phones. Therefore, in this paper, we propose an algorithm to automatically evaluate the quality of English handwriting, hoping to achieve the function of correcting the test papers uploaded by cell phones and giving scoring results. In this paper, we use traditional image processing to calibrate the photos of the test papers taken by cell phones and combine traditional feature extraction methods with convolutional neural networks to jointly extract the features of English handwriting. Our algorithm not only enables us to achieve 99% accuracy in our experiments, but also improves the interpretability of the English handwriting evaluation algorithm to some extent.
ArgVote: Which Party Argues Like Me? Exploring an Argument-Based Voting Advice Application

Brenneis, MarkusMauve, MartinA lof of people use voting advice applications (VAAs) as a decision-making tool to assist them in deciding which political party to vote for in an election. We think that arguments for/against political positions also play an important role in this decision process, but they are not considered in classical VAAs. Therefore, we introduce a new kind of VAA, ArgVote, which considers opinions on arguments when calculating voter–party similarity. We present the results of an empirical study comprising two groups who used ArgVote with and without arguments. Our results indicate that arguments improve the understanding of political issues and different opinions, and that people enjoy the interaction with arguments. On the other hand, the matching algorithm which considers arguments was not better, and user interface improvements are needed. The user profiles we collected are provided to assist further research.
Research on Optimization of Data Balancing Partition Algorithm Based on Spark Platform

Aiming at the data skew problem in the Spark system caused by the unbalanced distribution of the input data and the default partition algorithm, this paper proposes an optimized partition method to solve the data skew problem. Firstly, the parallel cluster sampling algorithm is used to sample the intermediate data processed by each Map task to predict the data distribution. Then, the frequency of each Key is obtained according to the sampling prediction, and the weight is assigned to each Key. Finally, combining the greedy algorithm to divide the intermediate data to make the amount of data in each partition more balanced. Compared with the Hash and Range partitioning methods of the Spark platform and the SCID algorithm proposed by predecessors, experiments show this method effectively reduces the load deviation and reduces the task execution time.
Coarse-to-Fine Spatial-Temporal Relationship Inference for Temporal Sentence Grounding

Temporal sentence grounding aims to ground a query sentence into a specific segment of the video. Previous methods follow the common equally-spaced frame selection mechanism for appearance and motion modeling, which fails to consider redundant and distracting visual information. There is also no guarantee that all meaningful frames can be obtained. Moreover, this task needs to detect the location clues precisely from both spatial and temporal dimensions, but the relationship between spatial-temporal semantic information and query sentence is still unexplored in existing methods. Inspired by human thinking patterns, we propose a Coarse-to-Fine Spatial-Temporal Relationship Inference (CFSTRI) network to progressively localize fine-grained activity segments. Firstly, we present a coarse-grained crucial frame selection module, where the query-guided local difference context modeling from adjacent frames helps discriminate all the coarse boundary locations relevant to the sentence semantics, and the soft assignment vector of locally aggregated descriptors are employed to enhance the representation of selected frames. Then, we develop a fine-grained spatial-temporal relationship matching module to refine the coarse boundaries, which disentangles the spatial and temporal semantic information from query sentence to guide the excavation of visual grounding clues of corresponding dimensions. Furthermore, we devise a gated graph convolution network to incorporate the spatial-temporal semantic information by leveraging a gate operation to highlight frames referred to by the query sentence from spatial and temporal dimensions, and propagate fused information on the graph. Extensive experiments on two benchmark datasets demonstrate that our CFSTRI significantly outperforms most state-of-the-art methods.
Execution of Smart Contacts Concurrently Using Fine-Grain Locking and Asynchronous Functions

Smart contracts are widely popularized to transfer an asset securely from one device to another. Smart contracts contain the blockchain’s business logic that revolutionized the concept of self-enforcing contracts without the need for a middle man. Many use cases thus are being generated day by day. Smart contracts allow us to build decentralized applications through blockchains. The transactions in the smart contract are interrelated; they define the overall flow of the application. To maintain integrity between the transactions, the execution of the smart contracts is sequential and has no conflicts. These contracts’ execution is slow and time-consuming. Thus, we cannot leverage the current systems’ complete resource utilization. To overcome this, we propose a safer multi-threading model using fine-grain locking and thread executor service mechanism to concurrently execute the smart contract which also provides integrity among the transactions. Our results prove the faster and safer execution of smart contracts.
A Non-immersive Virtual Reality Application for Children with Autism Spectrum Disorder

Children with autism spectrum disorder (ASD) are progressively acquainted with digital technologies in their training and diagnosis. Due to their growing accessibility and practicality, the potential should not be overlooked. Virtual Reality (VR) has become a noticeable tool to help children with ASD in their social training due to its competitive potential that provides extensive interaction, economical cost, safe environment and an enjoyable experience. The implementation of VR is often associated with heavy usage of head-mounted display (HMD) where through it, the immersive experience can be achieved. However this may not be applicable to children with ASD. Side effects such as motion sickness and claustrophobia may affect their training and performance. Therefore, this paper aims to present the development of non-immersive VR application with a serious game concept with inclusion of analytics, where progresses can be calculated, compared and observed. Upon post evaluation by experts, this game is improved and holds a larger potential to achieve its objectives.
Determining a consistent experimental setup for benchmarking and optimizing databases

The evaluation of the performance of an IT system is a fundamental operation in its benchmarking and optimization. However, despite the general consensus on the importance of this task, little guidance is usually provided to practitioners who need to benchmark their IT system. In particular, many works in the area of database optimization do not provide an adequate amount of information on the setup used in their experiments and analyses. In this work we report an experimental procedure that, through a sequence of experiments, analyzes the impact of various choices in the design of a database benchmark, leading to the individuation of an experimental setup that balances the consistency of the results with the time needed to obtain them. We show that the minimal experimental setup we obtain is representative also of heavier scenarios, which make it possible for the results of optimization tasks to scale.
An Alternative Practice of Tropical Convolution to Traditional Convolutional Neural Networks

Convolutional neural networks (CNNs) have been used in many machine learning fields. In practical applications, the computational cost of convolutional neural networks is often high with the deepening of the network and the growth of data volume, mostly due to a large amount of multiplication operations of floating-point numbers in convolution operations. To reduce the amount of multiplications, we propose a new type of CNNs called Tropical Convolutional Neural Networks (TCNNs) which are built on tropical convolutions in which the multiplications and additions in conventional convolutional layers are replaced by additions and min/max operations respectively. In addition, since tropical convolution operators are essentially nonlinear operators, we expect TCNNs to have higher nonlinear fitting ability than conventional CNNs. In the experiments, we test and analyze several different architectures of TCNNs for image classification tasks in comparison with similar-sized conventional CNNs. The results show that TCNN can achieve higher expressive power than ordinary convolutional layers on the MNIST and CIFAR10 image data set. In different noise environments, there are wins and losses in the robustness of TCNN and ordinary CNNs.
Analysis of Models for Selection of Investment Strategies

The article discusses mathematical models of investment strategies for cybersecurity systems. The purpose of the work is to analyze and compare existing models in the field of information technology and cybersecurity in order to increase the efficiency and optimize the procedures for evaluating projects and supporting decision-making related to investment. Data analysis is often used to improve efficiency and streamline project evaluation procedures and support decision-making related to investing. The optimal value of resources depends not only on the vulnerability of the system, but also on the cost of the information to be protected. However, studies are very often purely economic and do not take into account trends regarding the introduction of information technology in the control and decision-making procedures for investment projects, this fact determines the need to develop new models for decision support systems. This can be achieved by applying mathematical models of game theory, which are used to make decisions in the design of information protection systems.
Monitoring and control of temperature, humidity using machine learning

In this paper, In Recent Year Machine learning plays important role in analyzing the various time of weather condition all around the world especially Indian Subcontinent. Data is available in the government website to analyze the data for few long years. This should be matched with UCI technique for the machine learning and obtain the condition of different level of data repository. Temperature and humidity level condition of different parameters were analyzed and taken as example of weather condition to monitor the weather condition. We need to design a model to fit the different condition and need to extrapolating the needs of information, and optimize the technique using some algorithm and variation should be targeted and value should be analyzed.
Building an Open Source Big Data Platform Based on Milis Linux

With technology, all internet connected devices have become data producers and that lead to exponential growth in data. Data and processing are now increasingly taking place on Cloud while the cost of cloud services are decreasing and the capacity is increasing. This is because the high availability of the servers, their ease of creation, configuration and management. Open source Apache Hadoop and its add-on components are frequently used in cloud systems for the handling Big Data. The installation of the Hadoop infrastructure and its compatibility with other components is only possible through a proper deployment process. The package manager of the operating system in which it is hosted, ensures that the compatible versions of the Big Data frameworks and applications are installed and updated together. At present, global companies that used to produce open source integrated solutions are also purchased, privatized and hence now closed sourced. However, the need of a Linux compatible big data platform based on open source Hadoop framework is still demanded. Therefore, in this paper, the components, the methodology and a case study based on novel big data platform that is high-performing, light-weight, scalable based on Milis Linux is presented.
Near Optimal Solving of the (N$$^2$$–1)-puzzle Using Heuristics Based on Artificial Neural Networks

We address the design of heuristics for near-optimal solving of the (N2–1)-puzzle using the A* search algorithm in this paper. The A* search algorithm explores configurations of the puzzle in the order determined by a heuristic that tries to estimate the minimum number of moves needed to reach the goal from the given configuration. To guarantee finding an optimal solution, the A* algorithm requires heuristics that estimate the number of moves from below. Common heuristics for the (N2–1)-puzzle often underestimate the true number of moves greatly in order to meet the admissibility requirement. The worse the estimation is the more configurations the search algorithm needs to explore. We therefore relax from the admissibility requirement and design a novel heuristic that tries estimating the minimum number of moves remaining as precisely as possible while overestimation of the true distance is permitted. Our heuristic called ANN-distance is based on a deep artificial neural network (ANN). We experimentally show that with a well trained ANN-distance heuristic, whose inputs are just the positions of the tiles, we are able to achieve better accuracy of estimation than with conventional heuristics such as those derived from the Manhattan distance or pattern database heuristics. Though we cannot guarantee admissibility of ANN-distance due to possible overestimation of the true number of moves, an experimental evaluation on random 15-puzzles shows that in most cases the ANN-distance calculates the true minimum distance from the goal or an estimation that is very close to the true distance. Consequently, A* search with the ANN-distance heuristic usually finds an optimal solution or a solution that is very close to the optimum. Moreover, the underlying neural network in ANN-distance consumes much less memory than a comparable pattern database. We also show that a deep artificial neural network can be more powerful than a shallow artificial neural network, and also trained our heuristic to prefer underestimating the optimal solution cost, which pushed the solutions towards better optimality.
Literature Review of People Counting

People counting systems can provide statistical trend information used for human behavior analysis. Intuitively people counting system works based on sequence detection, tracking, and counting. However, with active research activities in Artificial Intelligence in Computer Vision, there are many alternatives available for implementing a people counting system. A systematic literature review is used to obtain the latest research information that accurately following the requirements of the system being designed as well as the gap areas that arise in implementation. The gap areas found are in the design of an implementation that suits your needs and increases the accuracy of the counters who enter or leave the frame. Several strategies can be done to achieve this goal by conducting head detection and training with a pretrained model in the object detection and tracking framework. This strategy is expected to increase accuracy through stabilizing object detection and tracking.