Reviewerly/exHarmony huggingface dataset:
Modelling of the Impact of Socio-Demographic Indicators on the Gross National Income by CORAL GMDH

The inductive approach algorithm CORAL GMDH was applied in order to solve the problem of recovering of gaps in data, modelling, and forecasting of impact of socio-demographic indicators on the gross national income in Ukraine. The advantage of this algorithm is selection of arguments in the model, which are often included in the best models for different options for dividing the training sample of data, ie they have the highest rating. Based on the obtained models, recommendations for the impact on the level of human capital development in the country have been developed.
On Time-sensitive Control Dependencies

We present efficient algorithms for time-sensitive control dependencies (CDs). If statement y is time-sensitively control dependent on statement x , then x decides not only whether y is executed but also how many timesteps after x . If y is not standard control dependent on x , but time-sensitively control dependent, then y will always be executed after x , but the execution time between x and y varies. This allows us to discover, e.g., timing leaks in security-critical software. We systematically develop properties and algorithms for time-sensitive CDs, as well as for nontermination-sensitive CDs. These work not only for standard control flow graphs (CFGs) but also for CFGs lacking a unique exit node (e.g., reactive systems). We show that Cytron’s efficient algorithm for dominance frontiers [ 10 ] can be generalized to allow efficient computation not just of classical CDs but also of time-sensitive and nontermination-sensitive CDs. We then use time-sensitive CDs and time-sensitive slicing to discover cache timing leaks in an AES implementation. Performance measurements demonstrate scalability of the approach.
Towards Trustworthy Outsourced Deep Neural Networks

The rising complexity of deep neural networks has raised rigorous demands for computational hardware and deployment expertise. As an alternative, outsourcing a pre-trained model to a third party server has been increasingly prevalent. However, it creates opportunities for attackers to interfere with the prediction outcomes of the deep neural network. In this paper, we focus on integrity verification of the prediction results from outsourced deep neural models and make a thread of contributions. We propose a new attack based on steganography that enables the server to generate wrong prediction results in a command-and-control fashion. Following that, we design a homomorphic encryption-based authentication scheme to detect wrong predictions made by any attack. Our extensive experiments on benchmark datasets demonstrate the invisibility of the attack and the effectiveness of our authentication approach.
Learning Cooperative Intrinsic Motivation in Multi-Agent Reinforcement Learning

The cooperative behavior is important skill in many real-world applications. Recently, many works have used the multi-agent platform to solve the real-world applications. However, it is difficult to learn the cooperative behaviors with equal rewards that the environment provides without considering the contributions. In this paper, we propose a method for learning cooperative behaviors in the centralized multi-agent environment. Firstly, we implement a reward model to predict the average rewards of all agents. And then, we use the reward model for calculating the contributions. The proposed method allows the model to distinguish which agent behaves better for team success. In order to evaluate the performance of the proposed method, we compute the average team rewards on the multiagent battle environment. Experimental results show that the proposed method has better performance than the baseline using the cooperative behaviors.
A Two-step Model for Multi-object Tracking

Multi-object tracking is widely used in video analysis. However, due to the limitation of detector performance, many multi-object tracking models have the problem of detecting two objects into one object in some occlusion scenes. In this paper, we propose a two-step model for handling this problem. In the first step model, the non-occlusion targets are detected and embeddings are extracted, while the occlusion areas are identified. The second step model processes the occlusion areas to obtain occlusion targets' accurate positions and embeddings. Finally, we integrate and optimize the output results of the two steps models. Experiments show that the number of false positives and missed positives in our model's object detection is significantly reduced. The multi-object tracking performance (MOTA metric) is improved by nearly 3% compared with other models.
Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP

Mining association rules is a process which consists in extracting knowledge from datasets. This is a widely used technique to analyze customer purchasing patterns, and its process is segmented in two main phases: mining frequent sets and formulating association rules. Several approaches were developed for the first phase of the mining process whose main objective was to reduce execution time. However, as all available datasets are very large (Big Data), there is a limitation regarding its application in these new sets due to excessive memory usage. We propose the Apriori-Roaring-Parallel which explores parallelism in shared memory and demands less memory usage during the mining process. In order to achieve this memory usage reduction, the Apriori-Roaring-Parallel method employs compressed bitmap structures to represent the datasets. The results obtained show that the Apriori-Roaring-Parallel method uses memory efficiently when compared to other methods.
Memory Mountain Experiment Analysis

In this paper, we made a reflection based on the development and analysis of future CPUs. We measured the performance of two machines based on memory mountain experiments. TXT files, 2D images and 3D images were output and analyzed to consider the effect of Stride as well as Size changes on CPU read throughput rate. Finally, we conclude that the future development of CPUs is based on the development of caches and optimization of temporal and spatial localization, as well as the creation of multi-core and multi-threaded CPUs.
Chinese Word Segmentation for Sub-character Representation

Nowadays, bidirectional long short-term memory neural network(Bi-LSTM) becomes the main structure for Chinese word segmentation tasks, which can obtain text information with time series. As a sequence model, the training speed of Bi-STM is very slow, while dilated convolution neural networks(DCNN) have a natural advantage in it which is designed to obtain information with a long length. In this paper, the sub-character information is concatenated with the ordinary features to enrich the input. Multiple contrast experiments are designed to verify the effect of applying DCNN and adding Conditional Random Fields (CRF). Experiments on the four datasets in SIGHAN2005 show that DCNN structure can improve the word segmentation effect in terms of F1 value and efficiency. The main advantage of the DCNN is that the speed is greatly faster than Bi-LSTM.
The hazard level set

The hazard rate function plays a fundamental role in survival analysis. Its statistical inference methods have been systemically and exclusively studied. When does the hazard rate reach a particular warning level? This is a basic question of interest to the investigator but largely left to be explored in practice. We define a level set of hazard rate to address this issue and propose a kernel smoothing estimator for such a level set. In terms of the Hausdorff distance, we establish the consistency, convergence rate and asymptotic distribution of the level set estimator. The validity of the proposed confidence set, based on the bootstrap method, for the level set of hazard rate function is theoretically justified. We conduct comprehensive simulation studies to assess the finite-sample performance of the proposed method, which is further illustrated with a breast cancer study.
Organization of Information Search in the Global Network Based on the Information-graph Data Model

in recent decades, a new scientific direction related to optimal storage and retrieval of information, called the theory of information retrieval, has been actively developing. Specific examples of methods for storing and presenting data and information search algorithms corresponding to these methods arose in it: lexicographic, treelike, relational, etc. Search problems for them have specific types and modifications: for example, the problem of finding identical objects, the problem of proximity, including search and etc. They play the role of model problems for the selected methods of storing information and have been studied over the years. Each time, to solve them, special research funds were involved, which were of a limited nature. Further, the organization of information retrieval using an innovative information-graph data model is proposed.
Machine Learning Approach for the Game Mechanics Problem in Computer Games

The purpose of this work is to develop algorithms and game mechanics for controlling the car driving along a track using a genetic algorithm for training a neural network with the ability to save and load the obtained weights during training. A genetic algorithm and a neural network were developed using the C ++ programming language without extraneous libraries. To find effective approaches and parameters of the genetic algorithm are experimentally tested and statistically analyzed some various of fitness function, selection methods and population sizes. Due to the results of experiments the final training is done. Moreover, comparison with the result of real player is made.
Study of power flow in electricity system using extreme learning machine

Abstract Load Flow or Power Flow Analysis in the power system in used to determine the power system parameters such as voltage, current, active power, and reactive power contained in the power grid. The method that has long been used in the calculation of load flow or power flow is the Newton-Raphson iteration method. As for its development, to complete the power flow study, it is carried out by implementing the Artificial Intelligence method, one of which is the Extreme Learning Machine method. This method is used in the simulation of the simple 39 Bus system calculation from IEEE. In this Extreme Learning Machine, the testing analysis is carried out with 2 inputs, 1 hidden layer, 5 neurons, and 2 outputs and the number of datasets is 39 to produce MAE and MAPE respectively 2.02 and 0.76% and with a very fast processing time of 0.010s
A Random Walks Image Segmentation Method Combined with KNN Affine Class

In the image binary segmentation technology, accurately solving the boundary between the foreground object and the background is a key issue, and the typical unsupervised interactive binary segmentation algorithms include GrabCut, random walk, k-means clustering, etc. Among them, the traditional random walk algorithm has the advantages of strong interpretability, fast speed, and high efficiency. The segmentation effect of the hard boundary is integrated into the user's experience knowledge and subjective requirements, and the outline of the target object can be extracted accurately and meticulously. The segmentation effect of images such as soft borders such as hair and complex background textures is not ideal. Therefore, this paper proposes a random walk segmentation algorithm combined with the KNN affine class. The algorithm is based on the random walk method, but the neighborhood search space for unknown pixels is larger than the traditional random walk algorithm, and the weight is calculated. At the time, samples with similar color characteristics to the unknown pixels are selected, and the foreground probability of the unknown pixels is obtained by solving a sparse linear system, to realize the binary segmentation of the image. Experiments show that the combination of random walk algorithm and KNN algorithm improves the accuracy of foreground probability value and enhances the edge fineness of image segmentation compared with traditional random walk algorithm and KNN algorithm.
A Suggested Integrated Approach for the Evaluation of Remote Intranet Usability

As the societal impacts of the Covid-19 progress, so do the workplace and business challenges the pandemic has brought on. It is now crucial to conduct user testing to measure and optimize the usability of corporate Intranets under pandemic conditions. This paper suggests and validates an integrated approach for Intranet remote usability evaluation validated by user testing in a work from home context during a pandemic. The main contribution of the current research is the discovery of some users preferences specific to work from home workers during a pandemic not previously addressed in the literature on intranets.
Application of the Digital Footprint in Teaching English to Engineering Students

Using the empirical method of investigation the authors developed a professionally oriented course of English based on the didactic potential of digital technologies. The approach includes the stages of teaching the language in integration with various forms of interaction between subjects of the educational process through persistent chat-based collaboration platform and online services. The authors determined the structural and informative characteristics of the educational footprint as a way to organise individual educational paths in the developed course. Practical use of the research results contributes to the improvement of teaching English to students of technical specialities.
A High-Performance Mel-scale Frequency Cepstral Coefficients Digital Circuit Used on Keyword-Spotting Chip

This paper introduces a high-performance mel-scale frequency cepstral coefficients (MFCC) digital circuit which can be used on keyword-spotting (KWS) chip cooperating with DS-CNN. The parameters of the MFCC are chosen specially to reduce the consumption of hardware resources. For the same reason, some of the factors of MFCC are carefully neglected. To reduce the power consumption, pipelines are used in the architecture of MFCC circuit as well as in the fast Fourier transform (FFT) module. In order to keep the balance of performance and consumption of hardware resources, the format (the word length and the fraction length) of fixed-point variable is chosen though three steps. The performance of MFCC circuit is evaluated on software by using the extracted features to train a DS-CNN, obtaining accuracy of 95% finally.
Accelerate CNN Models via Filter Pruning and Sparse Tensor Core

Convolutional neural network (CNN) is a state-of-the-art technique in machine learning and has achieved high accuracy in many computer vision tasks. However, the number of the parameters of the models is fast increasing for accuracy improvement; therefore, it requires more computation time and memory space for training and inference. Thus, compressing the model size and improving the inference speed has become an important issue. This paper focuses on filter pruning and NVIDIA sparse tensor core. Filter pruning is one of the model compression methods which uses a method that evaluates the importance of filters in the CNN model and removes the less important ones. NVIDIA sparse tensor core is the hardware support provided by NVIDIA Ampere GPU architecture. The sparse tensor core can speed up the matrix multiplication if the matrix has a structure that manifests as a 2:4 pattern. In this paper, we proposed a hybrid pruning metric to prune the CNN model. The hybrid pruning combines filter pruning and 2:4 pruning. We apply filter pruning to remove the redundant filters in convolutional layers to make the model smaller. Next, we use 2:4 pruning to prune the model according to a 2:4 pattern to utilize the sparse tensor core hardware for speedup. In this hybrid pruning situation, we have also proposed a hybrid ranking metric to decide the filter's importance during filter pruning. In hybrid ranking metric, we will preserve the filters that are important for both of the pruning steps. By considering both metrics, we can achieve higher accuracy than traditional filter prunings. We test our hybrid pruning algorithm on MNIST, SVHN, CIFAR-10 datasets using AlexNet. From our experiments, we concluded that our hybrid ranking method achieves better accuracy than the classic L1-norm metric and the output L1-norm metric. When we prune away 40 percent of filters in the model, our method has 2.8 %, 2.9 %, 2.7% higher accuracy than the classic L1-norm metric and the output L1-norm metric on these three datasets. Next, we evaluate the inference speed. We compare the hybrid pruning model with the models that result from either filter pruning or 2:4 pruning. We find that a hybrid pruning model can be 1.3x faster than the filter pruning model with similar accuracy.
Predictive data analysis subsystem for patients with subjective and subtle cognitive impairment

The progress of cognitive impairments at the initial stage is poorly researched and is difficult to be diagnosed. This paper proposes an integrated approach based on the initial data analysis and the subsystem construction to predict the progression of further complications in patients with cognitive impairment symptoms. The combined use of statistical analysis and machine learning methods made it possible to achieve 78% accuracy in diagnosing subtle cognitive impairments in a sample of 526 patients. Visualization methods helped to improve the created model’s interpretability.
The Italian Academic Research System and Its Evaluation: A Conceptual Framework Inception

In this paper, we introduce the main topics and the initial settings of an Italian PRIN project aimed at investigating how the systematic adoption of systems for the evaluation of research in the Italian academic context may influence research outcomes. We motivate the need to adopt and adapt a conceptual framework, which may identify, define and describe the relevant entities involved in the evaluation process, their measurable properties and relations. We then present the first draft of an ontology derived from an existing ontology about the academic world, namely the VIVO ontology, and the criteria for its design. We report the steps taken to modify the received ontology in order to fit it to our purposes, with an interdisciplinary contribution to the selection and adaptation of entities. Novel considerations about the use of formal conceptual systems and the contribution of our work to the socio-technical view are finally drawn, and some further directions of the project are proposed.
Positional Mask Attention for Video Sequence Modeling

The attention mechanism has been widely developed in different domains. Some recent studies apply position embedding to encode relative positions in the attention mechanism for learning better representations in both natural language processing and computer vision tasks. However, this position embedding method is limited to the “fixed input size” problem and requires large additional memory to store the position embedding parameters. In this paper, we present the positional mask attention, which is a new approach to incorporate position information into the attention mechanism. Specifically, a positional distance mask is proposed to encode the relative positions as a type of prior knowledge, which is different from the existing position embedding methods. To verify the generality and effectiveness of the proposed method, we evaluate our positional mask attention on two general video understanding tasks, i.e., video object detection and video instance segmentation. Experimental results demonstrate that our method can achieve significant improvement by aggregating the position information.
Super Mario A-Star Agent Revisited

Since the first Mario AI Competition in 2009 where Mario AI Framework made a debut, there is a continuous interest to use the game and the framework for research in AI. Decent amount of these papers is using the A-Star algorithm along with the game forward model and some results are directly depending on the quality of the best known A-Star-based agent by Robin Baumgarten. In this paper, we are revisiting and improving the implementation of Mario AI Framework’s forward model by two orders of magnitude and also presenting a new A-Star-based agent that outperforms the current state-of-the-art agent by a large margin.
A case study comparing static analysis tools for evaluating SwiftUI projects

Abstract Declarative programming languages such as SwiftUI have gained increasing relevance for user interface implementation in mobile applications. A tool for evaluating and improving the quality of such projects is static analysis (SA). This study compares the usefulness of two of the most popular SA tools (SonarQube and Codacy) for evaluating real-world SwiftUI projects. Moreover, it recommends setup and adjustments to promote SA tools for SwiftUI projects that can be extended to other languages.
A Fractal Geometry Based Method for Dataset Augmentation by Automatically Generation of Multi-View Images

Large-scale image datasets are constructed with a significant amount of time and human effort, however, an image may have biases in the camera positions that render the target object. In this study, we propose a framework that automatically conducts 3D models, multi-view images, and even category definition. We automatically generate 3D models based on fractal geometry, which is the regularity behind natural phenomena and render images from multiple viewpoints. By following those generation processes, we can automatically construct a generally large-scale image dataset that takes into account the viewpoint position. The experimental results show that the classification accuracy is improved over the conventional baseline in the context of pre-training for image recognition tasks. We show that our proposed method provides an effective method for automatically constructing pre-training datasets for image recognition tasks.
QUDOS

Distributed machine learning algorithms that employ Deep Neural Networks (DNNs) are widely used in Industry 4.0 applications, such as smart manufacturing. The layers of a DNN can be mapped onto different nodes located in the cloud, edge and shop floor for preserving privacy. The quality of the data that is fed into and processed through the DNN is of utmost importance for critical tasks, such as inspection and quality control. Distributed Data Validation Networks (DDVNs) are used to validate the quality of the data. However, they are prone to single points of failure when an attack occurs. This paper proposes QUDOS, an approach that enhances the security of a distributed DNN that is supported by DDVNs using quorums. The proposed approach allows individual nodes that are corrupted due to an attack to be detected or excluded when the DNN produces an output. Metrics such as corruption factor and success probability of an attack are considered for evaluating the security aspects of DNNs. A simulation study demonstrates that if the number of corrupted nodes is less than a given threshold for decision-making in a quorum, the QUDOS approach always prevents attacks. Furthermore, the study shows that increasing the size of the quorum has a better impact on security than increasing the number of layers. One merit of QUDOS is that it enhances the security of DNNs without requiring any modifications to the algorithm and can therefore be applied to other classes of problems.