Reviewerly/exHarmony huggingface dataset:
Recognition of Tennis Shots Using Convolutional Neural Networks Based on Three-Dimensional Data

The article explores the possibilities of using convolutional neural networks to recognize the type of tennis shots. The study compares two architectures of such networks: Inception-v3 and MobileNet. Two datasets consisting of images of tennis players making an impact are analyzed in order to identify the type and phase of impact. The images in each of the collections were assigned to the respective five following classes: backhand preparation phase, backhand shot, forehand preparation phase, forehand shot and non-shot. In each of them there is a tennis player silhouette and a racket skeleton consisting of markers. Images of tennis players are generated from motion recordings made using motion capture technology. The networks were trained with different values of the learning rate. In addition, the network training time and match results for the best-trained models are presented. In view of the parameters considered, the MobileNet network has proved to be a better model.
Analysis of Global Spatial Statistics Features in Existing Contrast Image Quality Assessment Algorithm

Most of existing image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for CDI, e.g. Contrast distortion may be caused by poor lighting condition and poor-quality image acquisition device. No Reference-Image Quality Assessment (NR-IQA) for Contrast-Distorted Images (NR-IQA-CDI) is one of these few IQAs. The five features used in NR-IQA-CDI are the global spatial statistics of an image including the mean, standard deviation, entropy, kurtosis and skewness. Unfortunately, the performance of NR-IQA-CDI are not encouraging in two of the three test image databases, TID2013 and CSIQ, where the Pearson Linear Correlation Coefficients are only around 0.57 and 0.76, respectively. Therefore, this paper presents the reason which led to poor results in existing NR-IQA-CDI. This paper also can address the problem of existing NR-IQA-CDI which the weakness of the global features in assessing images with uneven contrast.
Modelling Negotiations of Construction Subcontract based on a Game Theory – Results of an Experiment

Abstract The process of negotiation can be analyzed considering some scientific issues (e.g. psychological, sociological). When a construction subcontract is negotiated, as it is business activity, the rational choices should be considered more than other factors influencing the decisions of the negotiating parties. A game theory supports rational decision making based on calculations, expected values and strategic choices. An experiment was carried with the participation of the students of Civil Engineering Faculty. Five different games were prepared. The students started playing with the “nature”. Then the probabilities of the “nature” strategies were introduced. Finally, they played three one-on-one sets of repetitive games. The results prove that knowing the needs of the opposite player (knowing his/her possible strategies) leads to a win-win result where the score of both players is maximized. The experiment and its result are analyzed in the paper. The multiple negotiations, and then multiple cooperation of a general contractor with the same subcontractor on construction projects is an often case. These parties discuss the rules of their cooperation formalized in subcontracts several times. The typical image of a general contractor’s need is a low price given by a subcontractor. In fact, it the is only one of the needs. Moreover, it is not a rare case where flexibility of engagement on a building site or a high number of equipment units or favorable terms of payment are desired by a general contractor more than the low price given by a subcontractor. A subcontractor’s standing can be different too. They can search for profitable cooperation or they have to provide work for some brigades not to have them unengaged. They can afford long term payment or they have to be paid in advance. The experiment proves that disclosure of the needs (strategies) by negotiating parties leads to achievement of higher gain for both of them (when the game is repetitive). The paper sets the basis for modelling negotiations of a construction subcontract based on a game theory.
A Novel Color Image Encryption Scheme based on Hybrid Chaotic Maps

A new adaptive color image encryption algorithm based on hybrid chaotic map is proposed. The algorithm is divided into three steps: key sequence generation, position scrambling and RGB component cross-diffusion. In the key sequence generation stage, improved one-dimensional chaotic map can be used to generate a key stream; in the location scrambling stage, the adaptive strategy is used to select the key stream, so that the generated scrambling sequence is related to the image data content; in the third stage, the RGB components are cross-diffused by adaptively selecting the encryption method to modify the pixel gray value to destroy the strong correlation between adjacent pixels. The use of adaptive strategies in the scrambling and diffusion correlates the key stream with the image data, making the encryption algorithm robust against known/selected plaintext attacks. Histogram analysis, correlation analysis between adjacent pixels, information entropy analysis and differential attack analysis show that the algorithm is safe and effective.
An intelligent wearable to aid speech impaired people by detection of specific hand gestures using flex sensors

This paper presents a special method of communication for the speech impaired people and other patients using their hand gestures. A microcontroller based system has been developed for measuring fingers flexion to identify specific hand gestures using flex sensors. The system is made wearable and has been deployed on a glove. To measure the fingers flexion for a specific hand gesture five flex sensors are tailored for each finger on the glove. These flex sensors produce signals corresponding to different hand gestures and finger movements. Flex sensors simply change their resistance values according to the degree of bend angle made. They convert the change in bend to electrical resistance-the more the bend, the more is the resistance value. The system has been built around a widely used eight bit microcontroller ATMega328P that has a built-in 10-bit multiplexed channel Analog-to-Digital Converter for the signal conditioning purpose. The important issues while designing a glove is the calibration, the finger size, height and thickness varies from patient to patient and these inaccurate measurements of parameter leads to overlap of the sign.
Visual Attention Guided Video Object Segmentation

Recently, video object segmentation (VOS) is a new challenging research direction from DAVIS competition. Carrying on with these researches, we propose a visual attention guided framework in video object segmentation, which includes four main components: segmentation network, visual encoder, spatial encoder and guide. The segmentation network predicts the object mask in the current video frame, and the visual guide force segmentation network to focus on the annotated object by visual information from visual encoder, and the spatial guide provide spatial location by spatial encoder from previous frame. Visual attention mechanism plays an important role in the model on capturing annotated object without online fine-tuning as previous models. This approach has an advantage over previous methods on accuracy and efficiency, especially avoid the online fine-tuning in those one-shot learning approaches.
Teaching Concurrency with the Disappearing Formal Method

The Gries-Owicki non-interference condition is fundamental to concurrent programming, but difficult to explain as it relies on proof outlines rather than only pre- and postconditions. This paper reports on teaching a practical course on concurrent programming using hierarchical state diagrams to visualize concurrent programs and argue for their correctness, including non-interference.
Attention Guided Relation Network for Few-Shot Image Classification

Few-shot Learning is an object categorization problem where the classifier attempts to distinguish new classes with very few labeled examples. There has been significant progress in this field, which includes complex network architectures. Most of the works done in this field were focused on small datasets and longer training. In this paper, the experimentation was done with limited episodic training architecture, which consists of Relation Network as classification network, ResNet Embedding as embedding module, and Self Attention as attention mechanism. The experimentation and comparison with the state-of-the-art models show that attention with metric-based meta-learning generalizes quicker in short training and yields good results. The architecture was tested on the complex dataset miniImageNet. The accuracy was found to be 62.9%, which is close to the state-of-the-art architecture described on metric based meta-learning.
3D Reconstruction of Archaeological Pottery from Its Point Cloud

This paper presents a 3D reconstruction process for an archaeological pottery. Because of the shape of the pottery and its fracture, applying existing methods of registration did not yield the desired result. Thus, to improve the registration output and accuracy, a custom acquisition setup was used to acquire the point cloud data after it was calibrated. The setup was also cost effective, a consideration that is useful for archaeologists. Furthermore, the acquired point clouds were pre-processed to remove noise and outliers. Key-points were extracted from the point clouds using Principal Component Analysis (PCA) and descriptors were computed adapting the ColorSHOT descriptor. With correspondences between the key-points of point clouds established, a pairwise alignment of the point clouds was done. Finally, the global registration was done on all the point clouds, with ICP used to refine the alignment for all the point clouds. The peculiarity of the approach of this paper is in adapting and adjusting parameters as required due to the peculiar nature of the data acquired. This improved the robustness of this approach, evident in the final registration output.
Robust Hazy QR Code Recognition based on Dehazing and Improved Adaptive Thresholding Method

Quick Response (QR) code has been extensively used in our daily life. But in some complex environments such as hazy conditions, QR code recognition is difficult. In this paper, a robust QR code recognition algorithm in complex hazy environments is proposed. First, contrast-limited adaptive histogram equalization is implemented to enhance original pictures. Then Gated Context Aggregation Network is modified to obtain dehazed QR code images. After that, an adaptive thresholding method is used to obtain binary images. Finally, binary QR code is decoded. For training and testing our algorithms, we collect a set of benchmark of hazy QR code images including hazardous chemical name, Chemical Abstracts Service number, shape and properties such as flammable, explosive, corrosive and toxic. Ablation comparison and experimental results on our own database demonstrate our proposed algorithm achieves superior performance on hazy QR code recognition tasks.
The Landscape of PPM Informatics and the Future of Medicine

This chapter summarizes and synthesizes the key areas of today’s precision medicine landscape as described by the book’s contributing authors. We also attempt to project into the future by developing a view of likely developments in the short and longer term horizons. The chapter is divided in four parts: part 1 draws heavily on the contributions in the present volume to summarize the current state of PPM and the present role of informatics; part 2 identifies areas of scientific and implementation maturity; part 3 identifies areas of growth opportunity; part 4 is more speculative and seeks to highlight a series of possibilities and developments that may affect the trajectory of PPM as a discipline, and that are worth considering and exploring. We generally find that the broad implementation of scientifically mature PPM formats like molecular profiling, clinical risk modeling, and pharmacogenomics (PGx) are especially appealing with respect to their readiness for immediate clinical impact. PPM areas such as genomic trials, disease subtyping, tumor sequencing, and genomic EHRs on the other hand, will require substantial research efforts in addition to translation to care. Realizing the full potential of PPM requires therefore, on one hand, overcoming a variety of barriers to implementation (notably the health economics of PPM, knowledge barriers and ethical, legal and social issues (ELSI), and on the other hand developing substantial research programs in the PPM areas that are not yet fully clinically operational. There are many identifiable areas of fruitful informatics research and deployment that promise to be instrumental to PPM and the future of healthcare.
Using transitivity information for morphological and syntactic disambiguation of pronouns in Ukrainian

The paper presents a short introduction to several electronic resources for Ukrainian language, namely, two treebanks: the Gold standard (ab.130 thousand tokens), manually annotated in the Universal Dependencies flavour (https://universaldependencies.org/), which comprises the training data for a machine-trained syntactic parser, and a big (near 3 billion tokens), automatically annotated General Treebank (also known as Zvidusil), as well as a valency dictionary, developed by the Institute for Ukrainian, NGO (Kyiv) in 2015-2019 (https://mova.institute/).We also describe an experimental usage of the valency dictionary information to boost the performance of the syntactic parser.As a proof of concept, we discuss the case of syntactic and morphological ambiguity of frequently used Ukrainian pronouns його, її, їх 'his, her, their' and ways of improving the syntactic parser's performance using the supervised machine learning techniques with a theoretical linguistic support.Apart from the multiple morphological ambiguity (24+ possible tags for each of these forms), one of the challenges connected with the presented linguistic phenomenon, is that its correct disambiguation involves anaphora resolution and semantic roles identification.On the one hand, this makes the disambiguation process much more complicated, given the followed annotation design, on the other hand, by resolving a seemingly low-level (morphological) problem we gain a bonus in the form of significant textual analysis hints which can be later used in various NLP applications for Ukrainian.The present article is a practical follow-up of its more theoretical predecessor (Kotsyba, Moskalevskyi 2018 [11]), where the linguistic underpinnings of the syntactic and morphological interpretation of the pronouns його, її, їх in comparison with other Slavic languages are presented in greater detail.
Floating Data Window Movement Influence to Genetic Programming Algorithm Efficiency

Presented paper deals with problem of large data series modeling by genetic programming algorithm. The need of repeated evaluation constraints size of training data set in standard Genetic Programming Algorithms (GPAs) because it causes unacceptable number of fitness function evaluations. Thus, the paper discusses possibility of floating data window use and brings results of tests on large training data vector containing 1 million rows. Used floating window is small and for each cycle of GPA it changes its position. This movement allows to incorporate information contained in large number of samples without the need to evaluate all data points contained in training data in each GPA cycle. Behaviors of this evaluation concept are demonstrated on symbolic regression of Lorenz attractor system equations from precomputed training data set calculated from original difference equations. As expected, presented results points that the algorithm is more efficient than evaluating of whole data set in each cycle of GPA.
Target Mask Reduction in Pulse-Doppler Radar Using Wavelet Domain Spatial Filtering

The shift-invariant properties of the redundant discrete wavelet transform (RDWT) produce spatial correlation between adjacent scales for significant features of a signal. In previous work, edges were detected in medical imaging based directly on the locations of high correlation. The technique presented in this work follows the same principle of spatial correlation between adjacent scales to identify interference in target detection of range-Doppler map applications. A filtering technique is presented to determine the location of interfering targets in constant false alarm rate (CFAR) detection and remove them as a means to reduce mutual target masking. In this paper, the filtering technique is applied to radar detection of 2-D range-Doppler maps produced by LFM pulse-Doppler radar. A CFAR detection algorithm using this filtering technique is presented and the performance is compared with other robust CFAR detectors. It is shown that the proposed CFAR detector utilizing the interference filtering technique performs better in suppressing target masking when compared to the other CFAR detectors.
On Construction and Evaluation of Analogical Arguments for Persuasive Reasoning

Analogical reasoning is a complex process based on a comparison between two pairs of concepts or states of affairs (aka. the source and the target) for characterizing certain features from one to another. Arguments which employ this process to support their claims are called analogical arguments. Our goals are to study the structure and the computation for their defeasibility in light of the argumentation theory. Our proposed assumption-based argumentation with predicate similarity ABA(p) framework can be seen as an extension of assumption-based argumentation framework (ABA), in which not only assumptions can be used but also similarity of predicates is used to support a claim. ABA (p) labels each argument tree with an analogical degree and different ways to aggregate numerical values are studied toward gullible/skeptical characteristics in agent reasoning. The acceptability of analogical arguments is evaluated w.r.t. the semantics of abstract argumentation. Finally, we demonstrate that ABA (p) captures the argumentation scheme for argument from analogy and provides an explanation when it is used for persuasion.
Design of Fuzzy Logic-Based PID Controller for Heat Exchanger Used in Chemical Industry

In industries, the use of heat exchanger is badly necessary to transfer heat through fluid. For such purpose, control of temperature has a great role, so that PID controller is used vastly. This paper basically focuses upon the feed-forward–feedback control strategy involved along with the PID controller. This control strategy cancels the effect of the measured disturbances and load changes in the system. The use of fuzzy controller tuning of PID controller makes the system intelligent and robust. The simulation results endorse the effectiveness of fuzzy controller-based tuning along with the feed-forward–feedback control strategy.
Rapid filament prototyping of a bionic hand

Abstract No matter how we call it: additive manufacturing, fast manufacturing, fast prototyping, fabrication in layers or DMF (desktop manufacturing) manufacturing, 3D printing is rapidly developing due to the diversity of applicability areas. In this paper, we would like to introduce the way of realizing a bionic hand that imitates human hand movements with the help of the 3D printer. The actual function that can perform is the movement of the finger segments for closing and opening the fist. The imprint was made using the Creality 3D Direct Store 3D Ender 3 3D Printer Original. As components, we have the handprint, the print of servomotor holder, the Arduino uno board, the breadboard, 5 actuators, male jumpers and fishing thread for ligaments. For programming, the integrated development IDE environment based on the Processing project has been used, which includes support for the C and C ++ programming languages.
Efficient Large-Scale Multi-graph Similarity Search Using MapReduce

A multi-graph is a set consisting of multiple graphs. Multi-graph similarity search aims to find the multi-graphs similar to the query multi-graphs from the multi-graph datasets. It plays important role in a wide range of application fields, such as finding similar drugs, searching similar molecule groups and so on. However, existing algorithms of multi-graph similarity search are memory-based algorithms, which are not suitable for the large amount of multi-graph scenarios. In this paper, we propose a parallel algorithm based on the MapReduce programming model to solve the problem of the large-scale multi-graph similarity search. Our proposed algorithm consists of two MapReduce jobs, one for indexing and the other for filtering and validation. Specially, we adapt the localization strategy to further improve the performance of our algorithm, which not only reduces the communication cost, but also mitigates the load imbalance. Extensive experimental results show that our algorithm is effective and efficient.
Multi-granularity Convolutional Neural Network with Feature Fusion and Refinement for User Profiling

User profiling is an important research topic in social media analysis, which has great value in research and industries. Existing research on user profiling has mostly focused on manually handcrafted features for user attribute prediction. However, the research has partly overlooked the social relation of users. To address the problem, we propose a multi-granularity convolutional neural network model with feature fusion and refinement. Our model leverages the convolution mechanism to automatically extract user latent semantic features with respect to their attributes from social texts. We also combine different machine learning methods using the stacking mechanism for feature refinement. The proposed model can capture the social relation of users by combining semantic context and social network information, and improve the performance of attribute classification. We evaluate our model based on the dataset from SMP CUP 2016 competition. The experimental results demonstrate that the proposed model is effective in automatic user attribute classification with a particular focus on fine-grained user information.
Perceptual Comparison of Procedural and Data-Driven Eye Motion Jitter

Research has shown that keyframed eye motions are perceived as more realistic when some noise is added to eyeball motions and to pupil size changes. We investigate whether this noise, in contrast to being motion captured, can be synthesized with standard techniques, e.g., procedural or data-driven approaches. In a two-alternative forced choice task, we compare eye animations created with four different techniques: motion captured, procedural, data-driven, and keyframed (lacking noise). Our perceptual experiment uses three character models with different levels of realism and two motions. Our results suggest that procedural and data-driven noise can be used to create animations at similar perceived naturalness to our motion captured approach. Participants’ eye movements when viewing the animations show that animations without jitter yielded fewer fixations, suggesting ease of dismissal as unnatural.
A Novel Adaptive Tuning Mechanism for Kafka-Based Ordering Service

Thousands of resumes are sent to companies every year and it takes abundant time to authenticate the resumes. Blockchain (Hyperledger fabric) with its consensus algorithm, kafka-based ordering service, is a new solution to this issue, but it can not adapt to the dynamic workloads in real-time. This paper investigates an adaptive tuning mechanism based on feedback control theory to adjust the parameters connected to its consensus algorithm. In order to evaluate its efficiency, experiments have been done to compare the performance with the original kafka-based ordering service.