Reviewerly/exHarmony huggingface dataset:
An Empirical Study of Object Detectors and Its Verification on the Embedded Object Detection Model Competition

As one of the most active research fields in computer vision, the performance of object detection has been boosted by recent advancement of deep learning. However, performance improvements are often accompany with more resource consumption, which in turn restricts the application. There has been a rising interest in building object detectors that run well on embedded systems, i.e., with a better tradeoff among accuracy, speed, resource consumed, etc. To this end, we analyze popular object detectors, with an emphasis on taking typical evaluation metrics from the above aspects to assess both the selection of backbone network and the effects of input size. With these empirical studies, we propose to integrate MobileNet series into SSD to generate efficient yet accurate models and discuss their training tricks. The models are verified on the embedded object detection model competition held in conjunction with ICME 2020, where we gain the best accuracy on scooter detection.
Experience in Designing and Developing the Educational Game BlockSolver

This article examines the gamification learning method and demonstrates the examples of applications that use it. The article shows an attempt to design and develop a modern 3D graphics game application based on Unity3D platform to use it for teaching of LIFO and FIFO data manipulation methods. The developed prototype of the BlockSolver game includes a game module, a level builder, and a navigation module. The game module is responsible for the player’s interaction with the virtual world. The level builder allows developing new interaction schemes. The navigation module provides a graphical interface which the user uses to move between the game module levels, level builder, and other additional information presented in BlockSolver. This paper describes the main stages of development and the technologies used. The hardware and technical requirements necessary for the application to work are presented. Development scenarios and prospectives of a prototype are described.
Determining Algorithmic Performance with Uncertainty Analysis

The uncertainty analysis of controller function in embedded autonomous driving software is a strong nonlinear problem, and for the real-time performance determination to be approximated by adaptive structure based on reference measurements. The performance is the metric of the uncertainty. In this work the fuzzy FxLMS algorithm is used for the performance determination, which is a fuzzy-filtered (Fx) version of the LMS (Least Mean Square) algorithm. Mathematically we investigate the effect of uncertain model on the performance of a regularized l2 model predictive controller with input and input-rate constraints in the autonomous driving software platform of the Óbuda University. The fuzzy FxLMS model is used for prediction of the controller outputs. In contrast to state space parameterizations, the fuzzy FxLMS model is in a form that can easily be applied in robust predictive control for determining possible reliability of autonomous driving software based on spectral theory.
Mixture modelling of categorical sequences with secondary components

In this paper, the forward selected first‐order Markov mixture (FSFOMM) is proposed for modelling heterogeneous categorical sequences with secondary components capable of detecting outlying sequences within each cluster. Such sequences are assumed to have different transition probabilities in certain states. The model provides an attractive and flexible tool for diagnostics of unusual behaviours and parsimonious modelling of transition probabilities. The algorithm is tested on simulated as well as real‐life datasets with promising results.
Secure Data Travelling User using Hybrid Cryptosystem with User Privacy Protection

Nowadays traveling is the activity that everyone likes the most, but sometimes there is one traveling member who is lost and confused looking for the location of the other members. When traveling, they must bring a smartphone because of its small size and easy to carry anywhere. For this reason, an Android-based smartphone application that is able to send GPS data to all travelling members is proposed. In order to secure data transmission, cryptography and group signature to ensure that only traveling members could find out the location are applied. We use hybrid cryptography, which is a combination of symmetric cryptography using AES and asymmetric cryptography using IB-mRSA. We also add group signature as verification that members are in the same traveling group. The test result showed that the proposed method is safer than the comparison method because the symmetric key is encrypted before the key is distributed, so the attacker can not know the key. The total processing time needed to send data until member get data is 2.01 s.
A Comparison of Particle Swarm optimization and Global African Buffalo Optimization

Abstract The performance of Particle Swarm Optimization (PSO) brings attention to the field of algorithms when deals with different optimization problems. Due to her simple implementation, small consumption, and very effective in finding a solution in many problems, (PSO) becomes well known to the field of algorithms. In addition, the late proposed algorithms mostly are compared to the well-known algorithm such as PSO. Thus, the Global African Buffalo Optimization (GABO) was proposed lately and yet not been compared to the old well-known algorithms in terms of accuracy and time consumption. However, in this paper, a comparison between Particle Swarm Optimization (PSO) and Global African Buffalo Optimization (GABO) algorithms was performed. Five different nonlinear equations with their upper and lower boundaries values were selected as the test optimization functions problem in addition to PSO was applied to real case study. The experimental results illustrated the differences in the performances of both algorithms toward the optimum solution. At the end of the experiments, the PSO algorithm quickly convergence towards the optimum solution using a few particles and iterations rather than GABO. However, the experimental result showed that PSO achieved good results in all the test cases within a short time. In many cases, PSO and GABO are promising optimization methods.
Adaptive Stabilization of Certain Dominant Poles of Control System with Interval Parametric Uncertainty for Providing Constant Indices of Control Quality

The paper is dedicated to a problem of manipulating objects and systems with interval parametric uncertainty. Rapid and wide-range variations of system parameters may lead to its instability. To solve this problem, a method of synthesizing a controller for the systems with interval parameters, which provides constant values for the root indices of control quality, is proposed. The method is based on a combination of the adaptive and robust approach to controller synthesis and a principle of pole dominance; it allows placing any amount of dominant poles in any configuration in certain points of a complex plane. The example of the proposed method application is also provided. According to the results of the analysis of the control quality, conducted for the system synthesized, the method proposed is effective.
A verifiable ranked ciphertext retrieval scheme based on bilinear mapping

Summary It is prevalent nowadays for data owners to outsource their data to the cloud. Most of the traditional searchable schemes are proposed under the honest and curious model, lacking verification of integrity and correctness of the retrieval results. Because the cloud server is not completely trustworthy and there may be improper execution of the users' retrieval requests, it is necessary to verify the retrieval results returned by the cloud server. In order to address these issues, we propose a verifiable ranked ciphertext retrieval scheme (VRCRS) based on bilinear mapping. For the purpose of ranking the keywords and improving the security of ciphertext retrieval scheme, we utilize the lucene search engine toolkit to score keywords, and improve the traditional inverted index structure for building a secure inverted index structure. By adding dummy words into the dictionary of keywords, the proposed scheme can resist the keywords statistics attack of malicious server. To ensure the correctness and integrity of returned ciphertext, we apply bilinear mapping to generate validation tags for keywords. The theoretical analysis and experimental results show that the proposed scheme is secure and efficient. VRCRS can actually identify the illegal behavior of cloud server such as tampering and forgery.
Teaching Word Order Variation with a Constraint-Based View on Grammar

One prominent linguistic feature of German is that it exhibits rather flexible word order in the so-called middle field. And while much theoretical research has been carried out on this phenomenon, not much of the insights gained there has been carried over to teaching German in schools (in fact, in many school text books, word order is depicted as rather rigid). Using optimality theory as an example, this article sketches how modern linguistic theories can be used in the classroom to teach aspects of the Grammar of German and to raise the language awareness of the students. For this, we lay out the basics of optimality theory (OT) and how it applies to word order variation, before we sketch different ways how the basic of OT can be taught to younger and older students and why this would be a perfect fit for the officially supported curricula of the German school system.
Cython for Speeding-up Genetic Algorithm

This paper proposes a library for implementing the genetic algorithm using Python mainly in NumPy and speeding-up its execution using Cython. The preliminary Python implementation is inspected for possible optimizations. The 4 main changes include statically defining data types for the NumPy arrays, specifying the data type of the array elements in addition to the number of dimensions, using indexing for looping through the arrays, and finally disabling some unnecessary features in Cython. Using Cython, the NumPy array processing is 1250 times faster than CPython. The Cythonized version of the genetic algorithm is 18 times faster than the Python version.
Graph Learning with Loss-Guided Training

Classically, ML models trained with stochastic gradient descent (SGD) are designed to minimize the average loss per example and use a distribution of training examples that remains static in the course of training. Research in recent years demonstrated, empirically and theoretically, that significant acceleration is possible by methods that dynamically adjust the training distribution in the course of training so that training is more focused on examples with higher loss. We explore loss-guided training in a new domain of node embedding methods pioneered by DeepWalk. These methods work with implicit and large set of positive training examples that are generated using random walks on the input graph and therefore are not amenable for typical example selection methods. We propose computationally efficient methods that allow for loss-guided training in this framework. Our empirical evaluation on a rich collection of datasets shows significant acceleration over the baseline static methods, both in terms of total training performed and overall computation.
A method to reduce the number of sizes in apparel industry

Abstract The aim of this research is to define a methodology for reducing the number of garments sizes after building a sizing system for a given population. Thus, we introduce the process of development of sizing system in accordance with the literature. Despite the importance of developing appropriate sizing systems in the textile industry, there has been very little research focused on the task of reducing the huge number of sizes generated. The intention to keep the number of garment sizes to a minimum is to reduce confusion among customers, in addition to the logistics costs associated with maintaining a large number of sizes. Therefore, we propose a method to reduce the number of sizes by removing the sizes that cover fewer people. The challenge is to find the most convenient order of removal, how to maintain the linearity of the sizing system as well as to accommodate a larger number of people in the population.
Risk Factors Selection for Predicting Thalassemia Patients using Linear Discriminant Analysis

Thalassemia is an inherited blood disorder caused by abnormal production of hemoglobin. In order to establish an efficient thalassemia prognosis process, the practitioners suggest using complete blood count (CBC) report. Based on CBC report, practitioners often use professional experience and domain knowledge to discover the cause and relevant risk factors. Thus, to the best of our knowledge, this research is the first that uses machine learning techniques to accurately classify and predict thalassemia patients using the parameters of CBC report. WBC, RBC, HB, HCT, Platelets and an additional parameter Ferritin (Iron) are the selected parameters for the experimentations. The experimental analysis of the results show that RBC, HB, and Ferritin (Iron) plays a vital role in the establishment of an efficient thalassemia prognosis process.
The theft criminality forecasting in the surabaya district police region using poisson GARMA model and negative binomial GARMA model

Abstract Theft is a crime that often occurs in the community. According to the Police, theft consists of ordinary theft, petty theft, weight theft, and theft of violence. According to BPS, the highest number of incidents of theft occurred during 2018, one of which was in the Surabaya City Police Region. Based on these facts forecasting is done to provide material for consideration of the number of incidents of theft in the future. Forecasting generally uses the ARMA model. The ARMA model is less suitable for use in count data. The ARMA model was developed into the GARMA model if the data is assumed to be a Poisson distribution called the Poisson GARMA model. If the data is assumed to be a Negative Binomial distribution is called the Negative Binomial GARMA model. The estimation of the Poisson GARMA model and the Binomial Negative GARMA model uses MLE and is optimized using IRLS. This model is applied in cases of theft crimes in the Surabaya City Police Region and compares the accuracy of forecasting between the Poisson GARMA model and the Negative Binomial GARMA model. Based on the analysis carried out, the Negative Binomial GARMA (1.0) model is better for Central Surabaya, East Surabaya, and South Surabaya than Poisson GARMA (1.0) model. The Negative Binomial GARMA (0.2) model is better for the West Surabaya region rather than the Poisson GARMA (0.2) model. The best model in the Surabaya City Police Region area is the Binomial Negative GARMA (0.2) model in the West Surabaya region. The selection of the best model uses the lowest value of AIC.
Design of RTD-PID optimized neural networks controller for non-holonomic wheeled mobile robot

In this paper, a new Real Time Dynamic Proportional Integrated Derivative approach "RTDPID" controlled by neural networks for non-holonomic wheeled mobile robot control is presented.The structure of the PID and the parameters that intervene in the control process are real time adjusted depending on the trajectory type and obstacles detected.The new proposed method is compared with the classical PID controller and has proved high efficiency and precision in real time control, especially when evaluated in a hostile environment, which leads to an autonomous decision making to cross over an obstacle or avoid it and stop the engine to protect it.The control process takes into account disturbances appearing during the mobile robot assignments evaluated in a hostile environment, reacts quickly, and adapts to changing environment conditions.In fact, both robot and the control process, faced with obstacles such as holes or stones, are autonomous when it comes to decision making.In case the obstacles are insurmountable, it is necessary to take a decision and stop the engine to avoid damaging it.In addition, an implementation of a kinematic and a dynamic model based on Lagrangien mechanics, are presented regarding non-holonomic and rolling without sliding constraints.This implementation has been developed and stimulated in real time on Matlab/Simulink environments.Incidentally, simulation results are given based on the effect of load torque applications on the mobile robot behavior.These simulation results are satisfactory and have demonstrated the effectiveness, robustness and high stability of the proposed technique.
High Dimensional Massive Data Processing Based on Locality-Sensitive Hashing and Double-Layer Skiplist

When processing the high-dimension and massive data collected in the production process of ultra-precision machining tool, there is the issue of low efficiency in query operation of multi-key query and time range. For solving this issue, this paper propose a high-dimensional and massive data storage query method based on locality-sensitive hashing algorithm and double-layer skiplist index structure to improve the efficiency of multi-key query and time range query. In this method, by using the locality-sensitive hashing algorithm, the machine state parameters are stored in multiple similar data nodes, and the double-layer skiplist index structure is established in each data node. This method reduces the search range of multi-key query operations at the cost of a certain probability error, thereby indirectly improving query efficiency. Experimental results show that the method is effective.
Adenoma recurrence after piecemeal endoscopic mucosal resection: methodological issues on interobserver agreement and prediction

"Adenoma recurrence after piecemeal endoscopic mucosal resection: methodological issues on interobserver agreement and prediction." Scandinavian Journal of Gastroenterology, 55(7), p. 869
Training Specialists for Developing Digital Power Equipment

The report considers the training of specialists for the development of modern energy equipment: the requirements to the competence of such specialists within the realities of the fourth technological revolution, methodological approaches to practice-oriented training and the application of modern software products. The features of the fourth technological order ('Industry 4.0'), requirements to equipment developers and the structure of the competence model of such specialists are under review. The report also reviews the experience of the National Research University "MPEI" in organization of master's final qualification works within the framework of participation in real projects on energy equipment manufacturing.
Vehicle’s Density Prediction Based on History Data of e-Toll in PT Jasamarga Pandaan Tol Using Hidden Markov Model

The Gempol-Pandaan toll is a strategic center for regional development. In 2017, the number of vehicles passing through this toll has increased. This condition could impact on traffic jam and road damage because of passes by many cars, especially trucks with loads that exceed their capacity. Therefore, it is necessary to do a vehicle's density prediction not only from the Gempol toll gate but also from Kejapanan, Bangil and Rembang toll gates that will exit through Pandaan toll gate. In this research, vehicle's density is viewed from the probability and number of each vehicles category I, II, III, IV, V. The origin gate and vehicle category can not be observed directly, but the car must pass the toll gate to do tapping the e-toll card, so the method used in this research is the Hidden Markov Model (HMM). Two estimate methods for HMM parameters are employed in this research, the Expectation-Maximization (EM) algorithm and Bayesian approach. The result shows that HMM parameters estimation using Bayesian is better than the EM algorithm. The Bayesian estimated parameter values are closer to the input parameters so that the model is more representative to explain the vehicle's density prediction.
Digilantism, discrimination, and punitive attitudes: A digital vigilantism model

In this article, I propose and apply a digital vigilantism model to a specific incident that occurred in Mexico, where the death of two innocent people was filmed through Facebook Live. Using a mixed methods approach and content analysis, I analyzed digilante Facebook posts ( N = 942) coding gender, digital vigilantism categories, discriminatory comments, and punitive attitudes aimed at the perpetrators and the inciter of the lynching. The categories include investigating, blaming, or rebuking, while the discriminatory comments include classism, racism, homophobia, and body-shaming. I coded the punitive attitudes distinguishing four categories: non-physical punishment (calling for God’s wrath and the guilty conscience of the targets), legal sanction, death, and other punishment. The findings reveal the key role gender played in digilantism: females tend to conduct more investigations and low level attacks (blaming) than males, but males tend to perpetrate more harsh attacks (rebuking) than females. The most popular punitive attitude is calling for the death of targets, revealing tensions between legal sanctions and digilantes’ desired punishment. This study suggests the presence of different expressions of discrimination and reasons to engage in digilantism, encompassing both legal and illegal behavior deployed in a mainstream social media platform such as Facebook.
Secured Data Transmission through Dual Domain Reversible Data Hiding and Encryption in Images

Communication of images has become very easy and frequent due to popularity of internet. But this fast moving world is prone to various threats from unauthorized users. To protect images and maintain privacy of data many data hiding and encryption techniques are developed. In this paper, a novel reversible 2-stage data hiding algorithm with chaotic based encryption using DNA encoding is proposed. Firstly, Integer Wavelet Transform (IWT) is applied on cover image to convert it from spatial to transform domain. Then, 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> level of data hiding is carried out using threshold based histogram shifting algorithm. Resultant image is interpolated to double of its size and second level of data hiding is done using adaptive prediction error expansion. This stego image is scrambled using chaotic logistic map, result is then DNA encoded using complementary rules. The encoded image is then XOR with a key image generated using hash function and chaotic map. Finally, encrypted image is obtained by DNA decoding the image. Simulation results obtained show the algorithm gives good embedding capacity; also the encrypted images are validated against key sensitivity, statistical and differential attacks.
A Framework for the Evaluation of NoSQL Databases for Big Data Use Cases

This article provides a comprehensive use case-based comparison framework for the selection of the most suitable database for specific requirements and application domains. The concept of a NoSQL Enterprise Readiness Index which is a comparable numeric measurement for the fitness of a NoSQL database in enterprise use cases is proposed. It is calculated by the fulfillment of a comprehensive set of weighted criteria distilled from comprehensive research. The goal of the developed NoSQL Enterprise Readiness Index (NERI) is to provide a numeric index value for the comparison and benchmarking of NoSQL databases in enterprise use cases. The calculation of NERI is based on 43 criteria in which the single database product is evaluated. To be reproducible with more products and to guarantee a consistent evaluation among the products, a fulfillment matrix is used. The fulfillment matrix describes for each of the 43 criteria the four different levels of fulfillment in a mostly qualitative way and therefore guides the evaluator in choosing the right point value.