Reviewerly/exHarmony huggingface dataset:
Analysis of Integration Methods of Simulation, Evolutionary, and Expert Modeling to Optimize of Resource Conversion Processes

An approach of the resource conversion processes as a model for formalization of the processes occurring in organizational and technical systems is considered. For analysis and optimization of such processes, simulation is widely used. The disadvantage of using computer modeling is the need to formulate such an experiment plan with the model that would contain an optimal control impact. This is impossible without using of additional heuristic or experiments planning methods. Analysis of hybrid heuristic methods for optimizing of the resource conversion processes is presented. These methods integrate simulation modeling and artificial intelligence methods, in particular expert systems, multi-agent and evolutionary modeling. Integration of simulation and heuristic methods for solving the problem considered is confirmed. Application of genetic algorithm for optimizing the controlled parameters of the simulation model or expert system is considered. A hybrid method of using the expert system for solving the problem of optimizing the operation of genetic algorithm is considered. Application of evolutionary modeling for centralized and decentralized coordination of agent behavior in multiagent systems is also considered. A hybrid heuristic method for solving the problem of forming various types of the agent's interactions in the multi-agent systems depending on the relationships of agents to common resources has been analyzed. Choice of the hybrid method for integrating multi-agent simulation and genetic algorithm for solving the problem of optimizing resource conversion processes is substantiated. This method is implemented in the multi-agent genetic optimization information technology in the BPsim. MAS dynamic situations modeling system and in the metallurgical enterprise information automated system.
Smart Wearable Speaking Aid for Aphonic Personnel

Gesturing is an instinctive form of non-verbal or non-vocal communication in which perceptible bodily actions are used to communicate definite messages in conjunction with or without speech. But, aphonic personnel rely solely on this way of communication. This inhibits them from interacting or communicating freely with other people. This paper deals with a Smart Wearable Speaking aid which translates Indian Sign Language into text and voice in real time based on SVM (Support Vector Machine) Model using Raspberry Pi, Flex Sensors, and Accelerometer. Additionally, LCD is used to display the text corresponding to gestures and equivalent output in the form of voice signal is given to the Bluetooth Speaker. The prototype was designed to identify 5 gestures. During the testing of this device, it was observed that the accuracy of the prototype is 90%.
Image Stitching Algorithm Based on Area Blocking and Optimal Seam

Aiming at the problem of high time complexity and poor real-time performance of image matching algorithm due to the calculation of invalid image areas and it is easy to fuse ghosts with classical fusion algorithms. An image stitching algorithm based on area blocking and optimum seam is proposed.Firstly, the overlapping areas between the matched image and the matched image are extracted quickly by using the normalized cross correlation coefficient of image energy.Then the SURF algorithm is used to extract image features and feature matching in overlapping regions.Finally, the optimal seam algorithm is used to fuse the two images to eliminate ghosts.The experimental results show that the algorithm reduces a large number of useless computations of image matching algorithm, improves the speed of image matching, and improves the quality of image mosaic. Computing time is 3.25 times faster than traditional SIFT algorithm and 1.76 times faster than traditional SURF algorithm.The proposed algorithm can realize seamless mosaic of deformed and scaled images accurately and quickly.
RSM: An Explainable Predictive Sales Route Selector

We present Route Selection Model (RSM), an online data-driven sales route selector to help firms decide on how to respond to new business opportunities. The system addresses sales route selection (also known as sales channel selection) to determine if the opportunity can be handled by business partners, within the firm using sales agents on the field, or aim to close remotely using digital sellers. Given a new opportunity, RSM recommends an optimal sales route with the highest win probability predicted by machine learning models and provides explanation by meaningful clauses. Compared to the traditional manual passing approach based on business rules, RSM makes faster and more objective recommendations. Our pilot evaluation study shows our recommendations are not only accurate but also interpretable, which is crucial in business decision making. The main features of RSM are: (1) automatically merges multiple sales databases and produce timely recommendations, (2) allows users to navigate through the opportunity information and evidence which supports the recommendation. In this paper we describe the methodology and demonstrate the main functions of RSM.
Definitional Question Answering Using Text Triplets

Definitional question answering deals with answering questions of the type "Who is X" and "What is X." The techniques used in the literature extract long sentences that may not only give irrelevant facts, but also pose difficulty in evaluating the performance of the system. In this paper, we propose a technique that uses text triplets. We further choose relevant triplets based on a manually built list of terms that are found in definitions in general. The selected triplets give simple, short, and precise definitions of the target. We also show that evaluation becomes easy.
Evaluating a Side-Channel Resistance against Order 4 Rational Points in Arduino Cryptography Library

Curve25519 has been used in various security protocols for its efficiency and rapidity. However, Curve25519 is known to have rational points whose orders are 2, 4, and 8. In this research, the authors focus on rational points of order 4 and demonstrate a side-channel attack (SCA) against a scalar multiplication algorithm for Curve25519. It purposes to compare two scalar multiplication algorithms in terms of the difficulty of estimating a secret key via the SCA, where the algorithms are differentiated whether it uses a conditional swap function or not. Furthermore, we examine to attack an open-source library for Arduino UNO which includes the scalar multiplication algorithm using a conditional swap function in practice. Since this library takes a countermeasure against the SCA during key exchange, we can use this secure library as a protocol tool. However, there is a threat when we implement a scalar multiplication by using some functions in this library without considering the SCA.
Geochemical features of soils in an undulating glaciolacustrine sandy plain in the middle taiga subzone of Karelia

The geochemically conjugate series of soils (Albic Podzol – Albic Podzol Gleyc – Hystosol) formed on an undulating glaciolacustrine plain in the middle taiga of Karelia was investigated. Surveys for redox conditions showed them to vary from oxidizing in automorphic soils to reducing in soils occupying accumulation-favoring locations. The geochemical coefficients descriptive of the features of accumulation and directions of migration in the studied soils were calculated and analyzed. The distribution of silicic acid and a majority of sesquioxides inside the profile of the studied soils is typical of podzols. The podzolic horizons of the soils occupying interstitial positions have a faster outmigration of elements than in automorphic soils, while their Al-Fe-humic horizons accumulate aluminum, iron, titanium and phosphorus. All the soils are deficient in a majority of microelements as compared to their background levels; very low concentrations were determined for nickel, cobalt and manganese. The content of copper and sometimes zinc is at the background level. The distribution of the studied elements across the soil profile follows the accumulation-eluviation-illuviation pattern, but the scope of variation in the migration of elements varies among topographic positions. In well-drained locations microelements are quite monotonously distributed through the lower part of the soil profile, while soils in transitional landscapes have a higher differentiation of microelements. The differentiation of the catena through lateral migration is of the transient eluviation type, i.e. soils in lower-lying positions are poorer in the studied elements than soils in automorphic positions. This pattern is due to the natural characteristics of the area: low surface slope, homogeneous sandy parent material, low humus content in soil.
On the Prerequisite of Coprimes in Double Hashing

Hashing is a widely used technique for performing dictionary operations. Even though it is selected from the problem of collision, alternative hashing technique, open addressing has been developed. One such open addressing scheme, double hashing uses two hash functions to perform the search. The paper discusses the implication of choosing the hash function and table size, m, to be relatively prime and why it is necessary for searching the hash table in its entirety.
CloudTraceViz: A Visualization Tool for Tracing Dynamic Usage of Cloud Computing Resources

This paper introduces CloudTraceViz, a visual analytic tool for analyzing the characteristics of modern cloud data centers. The goals of this tool are: 1) to fulfill a set of visual tasks on cloud computing retrieved from in-depth interviews with domain experts, 2) to visualize and monitor large real-world data in terms of both the number of profiles and number of time steps, and 3) to aid system administrator to trace and understand the causal relationship of multivariate data. To reach these goals, our system composes several interconnected visual components. The customized heatmap is used to capture the pattern of a single machine as well as a group of machines, the progressive rendering of parallel coordinate allows users to see the dynamic behavior of running task/job over time, the scatterplot matrices are used in conjunction with the parallel graph for anomaly extraction. The results on the Alibaba Cloud Trace dataset show that the visualization tools offer great support for users to have a high-level overview of a large dataset as well as understand the causal relations within multivariate data.
Teachers’ Personal Homepage Construction of Medical University Based on the Website Group

The teachers in medical colleges lack the knowledge of dynamic webpage programming, so they cannot design complicated personal homepage by themselves, due to the heavy teaching tasks, even public course teachers majoring in computer science, they also cannot spend more energy designing personal homepage. Therefore, there is great practical significance to establishing a good operation interface personal homepage system which can customize the page content independently for medical college teachers. In this paper, we proposed how to establish a simple operation and rich content customization homepage system based on the website group platform for the professional teachers. The studies show that with this system, all professional teachers can quickly establish a personalized personal homepage according to their own needs, the basic information, teaching achievements, scientific research achievements and other important information could be shown in the personal homepage, for society and students, they could know the teachers and the level of college better. For teachers, with the rapid development of information technology and increasing demand of construction of personal homepage, it will play more important role to construct teachers’ personal homepage.
De-Anonymization of the Author of the Source Code Using Machine Learning Algorithms

The authors of the article consider the relevant problem of determining the author of the anonymous source code of program. The importance of these solutions for the field of computer forensics is due to the rapid increase in the number of cybercrimes and necessity to disclose authors of various viruses and cyberattacks. In addition, methods of determining the author of the source code can be used as a test for plagiarism of student programming works, which makes the study valuable for educational organizations. Finally, such decisions may be useful in resolving intellectual property and copyright disputes in a commercial environment. The most popular among researchers approaches to the identification the author of the source code for the last 5 years are described in the article. The authors propose approaches based as on classical machine learning algorithms as on modern architectures of deep neural networks for this purpose. Experiments were conducted out on the corpus includes the most popular and rapidly developing programming languages (just above 200 thousand samples). The effectiveness of these methods in comparison with their analogues is confirmed by the accuracy of more than 95% and independence from language in which the author's programs were written.
Effective Backbone Network for 3D Object Detection in Point Cloud

Abstract Three-dimensional (3D) object detection is composed of object classification and object localization, and has been used in many applications, such autonomous driving and mobile robot. However, the accuracy of classification and localization is greatly affected by the depth of the network. Shallow networks tend to cause poor classification, but as the depth of the network increases, the network degradation will become more obvious, which is not conducive to the training of network. To solve this problem, a novel Backbone Network is proposed in this paper for 3D object detection in point cloud, which consists of multiple residual modules. Experimental results on the KITTI 3D object detection benchmarks show that Backbone Network proposed can effectively improve the accuracy of 3D object detection.
Design of UAV Bird-Driving Linkage System

Based on the analysis of the bird situation in airport and the means of bird repelling, a kind of UAV bird repelling linkage system is proposed. The system is mainly composed of UAV, ground bird drive equipment and monitoring center. Unmanned aerial vehicle first finds the bird situation through visual capture and voice recognition, and then reports the bird situation to the monitoring center through the Internet of Things. After the analysis of the monitoring center, the ground bird-repelling equipment is controlled to select sound, laser, gas gun and other means to cooperate with UAV to complete the bird-repelling task.
They Know What You Will Do Next Click

This chapter focuses on predicting web user behaviors. When web users enter a website, every move they make on that website is stored as web log files. Unlike the focus group or questionnaire, the log files reflect real user behavior. It can easily be said that having actual user behavior is a gold value for the organizations. In this chapter, the ways of extracting user patterns (user behavior) from the log files are sought. In this context, the web usage mining process is explained. Some web usage mining techniques are mentioned.
Optimising Analytical Software Quality Assurance

While optimising quality assurance has been an important research area for many years, we still see interesting new ideas in this area such as incorporating psychological factors, detecting pseudo-tested code and detecting code with low fault risk.
Data-driven Optimization of Row-Column Transforms for Block-Based Hybrid Video Compression

In state-of-the-art video compression residual coding is done by transforming the prediction error signals into a less correlated representation and performing the quantization and entropy coding in the transform domain. For complexity reasons usually separable transforms are used. A more flexible transform structure is given by row-column transforms, which apply a separate transform to each row and each column of a signal block. This paper describes a method for training such structured transforms by maximizing the data likelihood under a parameterized probabilistic model with a compelled structure. An explicit model is derived for the case of row-column transforms and its efficiency is demonstrated in the application of video compression. It is shown that trained row-column transforms achieve almost the same coding gain as unconstrained KLTs when applied as secondary transforms, while the encoder and decoder runtime are the same as in the separable transform case.
Gesture Classification with Low-Cost Capacitive Sensor Array for Upper Extremity Rehabilitation

Machine Learning and artificial intelligence play major roles in understanding human activity through various classification and regression tasks. However, for many lowresource devices, high computation cost resulting from the construction of AI models may limit their applications. To that end, this work explores gesture recognition through a low-cost capacitive sensor matrix overlayed on a rehabilitation activity table. For gesture recognition, a convolutional long short-term memory (C-LSTM) neural network structure is applied and hyper-parameters are varied to determine what resources are necessary to perform classification tasks. The 8 X 8 mutual capacitive sensor array (CSA) is constructed with low-cost copper adhesive. The designed capacitive sensors capture hand motions performed by patients during rehabilitative exercise. The motions cause changes in the electric field that is quantified through sampling the changing capacitance between the copper tape electrodes. An MSP430 MCU computes the capacitance-todigital conversion at a 50 Hz sampling rate. To identify low computation cost models for the C-LSTM neural network, we evaluate different numbers of capacitor sensors, kernels, convolutional layers, and hidden nodes. Six subjects performed 1200 gestures, and the accuracy metrics are calculated using fivefold cross-validation.
Self-position estimation of Mini 4WD AI using digital camera images

Mini 4WD machine has to run in high speed but remains quite stable while a racing game. In order to achieve the previous, Mini 4WD should adjust its speed depending on its running course such as a straight course, a curve and a jump ramp. In this paper, we propose a self-position estimation method for our Mini 4WD AI utilizing digital camera images.
The Use of Context-Free Probabilistic Grammar to Anonymise Statistical Data

In the following article, a proprietary method of anonymisation of identifiable statistical data using context-free probabilistic grammar is proposed. The advantage of this method is that it is simple and thanks to this, the identifier is easy to retrieve after masking the identifiable data, e.g. when it is necessary to modify or update the micro-data. This can be done using public-key cryptography, i.e. encrypting some probabilistic context-free grammar with this method. In the case of public statistics, there is often a need to use an anonymised source value, for example when economic operators’ reports are verified by statistical officers. With appropriate information generated by context-free grammar, the verifier can easily identify an economic operator or a natural person. The idea of the anonymising algorithm used in the proposed method is presented by means of an example. According to the authors, the combination of the proposed method with asymmetric encryption of the definition of context-free grammar using public key infrastructure, makes it probable that its resistance to attacks will be quite high. This is because statistical methods that are used in the analysis of natural languages are not susceptible to attacks.
Body Part Labelling with Minkowski Networks

Labelling body parts in depth images is useful for a wide variety of tasks. Many approaches use skeleton-based labelling, which is not robust when there is a partial view of the figure. In this work we show that Minkowski networks, which have recently been developed for 3D point cloud labelling of scenes, can be used to label point clouds with body part categories, achieving 85.6% accuracy with a full view of the figure, and 82.1% with partial views. These results are limited by a small sample size of our training data, but there is evidence that some of these `misclassifications' may be correcting mistakes in the reference labelling. Overall, we demonstrate that Minkowski networks are effective for body part labelling in point clouds, and are robust to occlusion.
A Scrolling Approach for Gaze-Based Interaction

Gaze-based interaction approaches like the Multi Modal Interaction Concept for Efficient Input (M²ice) minimize the need for using pointing devices like a computer mouse in human computer interaction. While most core functionalities of pointing devices can be addressed in a more or less straightfor-ward manner, secondary functions like scrolling remain to be a challenge. This demo paper presents a gaze-based extension for the M²ice interaction concept that is realized by dividing the screen into distinct areas and combining scrolling areas with a reading area in the middle. The approach is compared to other gaze-based solutions.
Формулы речевого этикета в русском языке

In this work we will take into consideration some aspects of the part of vocabulary that students usualy memorize as a series of fixed sentences. The etiquette formulas are means of expression determined by cultural traditions and interpersonal relationships. A conscious approach to the learning of these units would help to avoid mistakes and make the correct choice between the synonimic formulas featured by different register, social context and pragmatic intentions. By placing the formulas, even non multiword ones, in the field of the phraseology, it is necessary to investigate their structure, degree of semantic transparency, literal meaning and non-conventional use.
A Case Study on Qualitative Efficiency of National R&amp;D Projects on Technical Performances : Focused on Livestock Quarantine

Korea’s R&D investment has significantly increased in recent years and the quantitative outputs such as number of papers and patents have also increased with the investment. However, the quality of R&D outputs has not been fully addressed. In particular, quality of technical performance, such as the quality of patents, has attracted little attention. In this paper, a Data Envelopment Analysis (DEA) method was used to construct models for efficiency analysis of R&D investment, focused on quality of technical performance. Indices were proposed to analyze the quantitative and qualitative efficiencies of R&D investment. In order to effectively analyze R&D efficiencies, the measurement units of the input and output variables were standardized. Based on cases of livestock quarantine R&D projects of Korea, the quantitative and qualitative efficiencies of national R&D projects were analyzed and factors that would influence R&D efficiencies were identified. This paper suggests that both quantitative and qualitative efficiencies should be considered when measuring R&D efficiency. Also, it is recommended to carefully consider the characteristics of R&D projects during project selection stage.