Reviewerly/exHarmony huggingface dataset:
Intelligence, Creativity, Psychological and Personal Qualities of Employees As Factors of Real Achievements

In the work, we analyzed the relationship of indicators of psychometric intelligence and psychometric creativity with intellectual, creative and social achievements. On the other hand, we tried to identify the role of different intellectual abilities and different forms of creativity, taking into account the different measures of their conjugation with real achievements, and also to analyze the role of those personal properties that, related to the peculiarities of organizing different forms of individual mental (conceptual, metacognitive, intentional) experience, act as a condition for real creative achievements of employees. The main approaches to the study of intelligence and creativity in connection with various aspects of individual productivity were examined, the results of studies of the ratio of intellectual and creative abilities were analyzed, and ideas about the role of personal properties as a factor in the employee's real achievements were analyzed.
Formalization and taxonomy of compute-aggregate problems for cloud computing applications

Efficient representation of data aggregations is a fundamental problem in modern big data applications, where network topologies and deployed routing and transport mechanisms play a fundamental role in optimizing desired objectives such as cost, latency, and others. In traditional networking, applications use TCP and UDP transports as a primary interface for implemented applications that hide the underlying network topology from end systems. On the flip side, to exploit network infrastructure in a better way, applications restore characteristics of the underlying network. In this work, we demonstrate that both specified extreme cases can be inefficient to optimize given objectives. We study the design principles of routing and transport infrastructure and identify extra information that can be used to improve implementations of compute-aggregate tasks. We build a taxonomy of compute-aggregate services unifying aggregation design principles, propose algorithms for each class, analyze them theoretically, and support our results with an extensive experimental study.
Discrete Fourier Transforms: DtFT and DFT

In this chapter we investigate a big family of Fourier transforms: continuous Fourier transform (CFT), Fourier series (FS), discrete-time Fourier transform (DtFT), and discrete Fourier transform (DFT). The DFT, in its fast form (FFT), is probably one of the most frequently used orthogonal transforms in the world apart from the discrete cosine transform (DCT). We learn that DtFT originates from continuous Fourier transform, while DFT from Fourier series. We become familiar with transform features and understand their strong and weak points. We learn the fundamentals of frequency analysis of discrete-time signals by DtFT and DFT. We see the significance of special signal shaping functions called windows in spectral analysis of signals.
Small Target Segmentation Method in Complex Background Based on Attention Mechanism

Aiming at the problem that the accuracy of small target segmentation is not high in complex scenes, this paper proposes a target segmentation method based on attention mechanism. The convolutional neural network extract the feature maps based on VGG16, and extract multi directional feature as the horizontal and vertical feature maps in low layer networks, which can effectively fuse and enrich the context information. Moreover, combined with the attention mechanism, the relationship between the channels of feature maps is learned adaptively. The useful information is emphasized, and the redundant information is suppressed. The discrimination ability of feature maps is improved. The enhanced feature maps are then used for segmentation. The experimental results show that, compared with similar algorithms, the proposed algorithm has better segmentation effect for most small targets and significantly improves the segmentation accuracy.
Point of Interest and Stop Type Prediction for Paratransit Transportation Data

Paratransit vehicles often do not have an uncertain stop point Sometimes it causes a traffic jam, especially in Southeast Asia. In Indonesia, Angkot is a well-known paratransit vehicle. Angkot often causes congestion because it can stop anywhere and anytime. It is quite difficult to predict when and where the Angkot stops. This paper proposes a machine learning model design that can predict the types of Angkot stops, as well as its Point of Interest. This paper using Angkot trips history data on Bandung City in 2018. Data processing is carried out in several stages, consisting of manual labeling with google maps and google road maps as a reference, grouping data, increasing the correlation matrix to the output data, normalizing GPS data, finally entering the data into the Multi-Layer Perceptron (MLP) model for training. Several experiments were tested, such as conducting training on the Artificial Neural Network model without normalizing the data beforehand or without entering the clustering results into the training data as input. A model that has been trained can labeling raw data that has the same route as trained data. The model also shows us 95% accuracy with the GPS data that has been normalized and clustered first.
The Future Prospects of Adversarial Nets

Machine learning has obtained remarkable achievement in longstanding tasks in various domains of artificial intelligence. However, machine learning certainly has some security threats, such as adversarial examples that hamper the machine learning models from correctly classifying the data. The adversarial examples are minor perturbations in the actual inputs to detract the model from its original task. Adversarial Attacks and their defenses are found in parallel when it comes to the literature of machine learning adversaries. In this paper, we have tried to inspect the adversarial attack types and their defenses by comprehensively classifying different techniques.
Remote sensing image registration based on dual-channel neural network and robust point set registration algorithm

Remote sensing image registration technology has important applications in military and civilian fields such as ground target recognition, urban development evaluation, and geographic change evaluation. In this paper, a remote sensing image registration method based on dual-channel convolutional neural network (DCCNN) is proposed. Firstly, the dual-channel neural network model with improved dense structure is used to extract the features of the input image pair and generate the corresponding feature points. Then the affine transformation coefficient is obtained by feature matching using the robust point set registration algorithm (TPS-RPM) based on thin-plate spline. Finally, the image to be registered can be transformed according to the coefficient to achieve the purpose of registration.The experimental results show that the registration accuracy of this method is higher than that of the comparison method.
Genetic Algorithms and Neural Networks by Data Mining

We propose, in our study, an approach of hybridization (Genetic Algorithms/Neural Networks) in order to apply it to solve our problems, namely “optimization of the budget estimates of the Aval activity of Sonatrach”. Indeed, within the downstream activity, the budget estimates of the expenditure are done every 5 years. Those must approach reality as much as possible in theory; therefore we took as bases training the table which contains the real expenditure (entries) 3 years previous. While studying the genetic algorithms and the networks of neurons separately, the idea came to us to use the genetic algorithms for the adjustment of the synaptic weights at the time of the phase of training of our network of neurons, and to use the capacity of prediction of the network of neurons to predict our budgetary expenditure. One starts from a population of more than 100 individuals (chromosomes), each one of them represents a configuration of weight, for the network of neurons, drawn by chance between [-1,1]. For each individual, one will calculate the average error between the calculated exits and the exits wished for all the examples of our base of training. The function fitness is anything else only this average error. One selects then the individuals ready to reproduce who are those which have the weakest error. One uses the operators of crossing and change to generate a new generation. This process will be repeated to L `obtaining of an individual whose error will be minimal. The configuration of weight represented by this individual will be to use for our prediction.
Information Security as Digital Economy Critical Success Factor

In an increasingly complex and competitive world, information is a valuable asset and a difference maker. It contributes to better government through supporting efficient business, assisting decision-making, mitigating risks, and adding economic value. This case study reviews Portuguese military information security requirements and its potential application on business company's crucial information protection. It's a military security policy, procedures, and measures approach to commercial environment. It's defined a security checklist to be applied by companies which want to achieve success. The explosive growth of information and communication technologies and their global dissemination and penetration have been a special impact on commercial activities, making them an attractive target to competitors and other agents. Cybersecurity is an organization's top priority. It's necessary to build an increasingly effective security policy in order to protect critical information. Keeping safe business competitive information advantages will be the key to success.
Lecturer Achievement Index Measurement Using Fuzzy Inference System at STIKOM PGRI Banyuwangi

As an effort to measure lecturer performance, STIKOM PGRI Banyuwangi implements the measurement of the Lecturer Achievement Index (LAI). The size is conducted by collecting student ratings of the lecturer's performance using a Likert scale on nine performance indicators. However, this method only produces an average value from the indicator scores. This method has not been able to describe the quality of a lecturer's performance. Therefore, another mechanism is needed so that the lecturer's performance quality can be known. One method that can be used is the Fuzzy Inference System (FIS). The technique is used to map the lecturer's performance average scores given by students into three levels of performance quality, i.e., Good, adequate, and PO, or based on predefined membership functions. These fuzzy values then evaluated using predefined rules to determine the lecturer's performance quality. The LAI score is then obtained from the defuzzification stage of these values. By using this method, the LAI score obtained is different from the old mechanism. It can enhance the score based on performance quality. The better the quality, the better the score that the lecturer gets, and vice versa.
A Novel Color Image Encryption Mechanism Based on Visual Cryptography, SHA-512 and One-time Password

With the rapid development of information sciences and communication networks, the security of digital images has been concerned seriously. However, many of the current image encryption mechanisms trend to adopt complex algorithms and functions, resulting in high calculation power consumption, which is not suitable for mobile devices. Aiming to this issue, a color image encryption mechanism using visual cryptography, SHA-512 and one-time password is proposed. Based on visual cryptography, the encryption mechanism works through split the plain image into several shares and encrypted for transmission. For image decryption, only XOR operations are required and the computation complexity is greatly reduced. Besides, the SHA-512 and one-time password are utilized to protect the security of cipher keys and reduce the possibility of correct cipher images be intercepted. The experimental results demonstrate that the proposed mechanism has high security with low computational time consumption.
A Domain Adaptation Method for Object Detection in UAV Based on Semi-Supervised Learning

In recent years, the objects detection algorithm of deep learning neural network has been widely used in scene monitoring fields such as intelligent transportation and ground observation. But scene changes always happen caused by the seasons and the ground feature cover change, which makes the training sample (source domain) different from the application scene (target domain), and causes the original effective model to generate a lot of false alarms. This paper proposes a domain adaptation algorithm for Unmanned Aerial Vehicle (UAV) objects detection based on semi-supervised learning. Firstly applying the source domain model on target domain to generate pseudo-labels for semi-supervised learning, during fine-tuning the model with Pseudo-Label, the scale-aware loss is introduced to suppress false alarms in abnormal scale, which realize the domain adaptability of network model migration from source domain to target domain. Experiments on our dataset for monitoring ground objects show that: the proposed method avoids the catastrophic forgetting caused by the direct domain adaptation method without denoising, and maintains the target detection rate of the network model in different scenarios.
The Future of Audit: Literature Review of Possibilities of Automation and Blockchain Technology

The aim of this paper is to analyze the possibilities of automation and Blockchain technology in audit. Blockchain has potential to develop new audit techniques and change the view on audit procedures. Otherwise, this new technology brings new risks that has to be solved with new audit procedures. The possibilities are presented on audit of revenues. One of the benefits of Blockchain and Smart contracts can be seen in automation of tests of controls. The greatest risk that evolves with Blockchain is to ensure the integrity of Blockchain.
FYLO Gallery: Handicraft business Branding application (a case study of Indonesia Handicraft Branding portal)

Based on research, Indonesia is one of country that have various types of handicrafts that are famous in the world. Most of the small economic society in Indonesia is depend on handicraft. This study is aiming provide to a solution with FYLO, an application that give a platform for handcrafters to promotes their handmade products. The study consists of business plan, analysis process and system design. In the business plan, the writers analyse the business environment and collect the requirement with reference and literature review. In the analysis and design process, the system is created by using the Satzinger day 1 to day 6 models which is reflected by several steps such as Planning the Overall Project, Discovering and Understanding Details, Analysing The details, and Designing Various Components. This analysis and design will create UML diagrams, database design, and user interface design as an output.
An Unsupervised Feature Selection Method for Data-Driven Anomaly Detection Systems

Feature selection has been widely used as a pre-processing step that helps to optimise the performance of data-driven intrusion/anomaly detection systems in achieving their tasks. For example, when grouping the data into normal and outlier groups, the existence of redundant and non-representative features would reduce the accuracy of classifying the data points and would also increase the processing time. Therefore, feature selection is applied as a pre-processing step for anomaly detection systems in order to optimize their classification accuracy and running time. Most of the existing feature selection methods have limitations when dealing with high-dimensional data, as they search different subsets of features to find accurate representations of all features. Obviously, searching for different combinations of features is computationally very expensive, which makes existing work not efficient for high-dimensional data. The work carried out here, which relates to the design of a similaritybased unsupervised feature selection method for an efficient and accurate anomaly detection (UFSAD), tackles mainly the selection of reduced set of representative features from high-dimensional data without the data class labels. The selected features should improve the accuracy and performance of anomaly detection systems due to the elimination of redundant and non-representative features. The proposed UFSAD method extends the k-mean clustering algorithm to partition the features into k clusters based on a similarity measure (e.g. PCC - Pearson Correlation Coefficient, LSRE - Least Square Regression Error or MICI - Maximal Information Compression Index) in order to accurately partition the features. Then the proposed centroid-based feature selection method is used, where the feature with the closest similarity to its cluster centroid is selected as the representative feature while others are discarded. Extensive experimental work has shown that UFSAD can generate a reduced representative and non-redundant feature set that achieves good classification accuracy in comparison with well-known unsupervised features selection methods.
Using Sub-Word Units for Low-Resource Language Keyword Searching

The traditional pipelined keyword search system suffered from the out-of-vocabulary (OOV) keywords for low-resource languages. To investigate the performance of different sub-word units on OOV recovery, we make some experiments with BABEL Pashto dataset. The experiment results indicate the phoneme search method combined with grapheme-to-phoneme (G2P) converter gets the best actual term-weighted value (ATWV) score of all the sub-word units. In addition, to solve the high false alarm rate of the sub-word method, we employ the system combination method and get the comparable results to the proxy word method in Kaldi toolkit.
Large Vocabulary Continuous Speech Recognition with Deep Recurrent Network

Speech recognition mainly refers to making the machine understand what people say, that is, in various environments, it can accurately recognize the speech content. According to the voice information, the machine can execute the intention of human. In this paper, the feature extraction algorithm of speech data set is designed. The voice data set used is thchs30, which contains 13388 voice files. The fbank feature of speech is input into the recurrent neural network for training. And, the training method is end-to-end, and the decoding result is the corresponding syllable in the dictionary. Among them, the initial and final of syllable is used as the voice label for training, and the accuracy is about 70%. After changing the mapping relationship between speech sequence and Pinyin label, about 1209 Pinyin are sorted out, and the speech features with Pinyin labels are trained. The accuracy is about 80%.
HDR Image Generator Focused on Saturated Region Restoration with Contextual Loss

In this paper, we propose a novel method for generating a tone mapped high dynamic range (HDR) image from a single low dynamic range (LDR) image. At this time, in order to concentrate on restoring the saturated region of 0 or 255 in the image, in the proposed method, the ratio of L1 loss and GAN loss is set differently according to the ratio of the saturated region. In addition, contextual loss is applied to solve the blur and stain artifacts when L1 loss and GAN loss are applied. In the performance evaluation, the peak signal-to-noise ratio and structural similarity of the proposed method are 29.09 and 0.869, respectively, which outperform the benchmark method.
DO WE NEED PRIVATE UNIVERSITIES?

The theses of the report are devoted to the problem of functioning of private universities in society, deter-mining their place in the educational process. A parallel is drawn with the work of private educational institutions in the United States, which have a glorious history of development dating back to the XVII century. The author of the theses relies on the analysis of special literature on this issue, formulates the success factors of American universities and the «failure factors» of Yakut private universities, and also refers to a large-scale sociological study of graduates of the Yakut Institute of Economics conducted under her leadership (Yakutsk, 469 respondents, 2016, 2017, 2018). The Author of the theses of the report con-cludes that in Russia the history of private universities began in the 90s of the twentieth century, it is quite dramatic. Branches of the capital's private universities operating in the province are at the greatest disad-vantage today. At the same time, sociologists should work on mistakes, study in detail the pros and cons of non-state universities and suggest ways to correct the situation.
Research on Traffic Classification of Auto Machine-learning Base on Meta-QNN

Abstract The traditional traffic classification task relies on feature engineering by experts with specialized knowledge. With the advent of representation-learn-based traffic classification techniques, machines learned to extract features from traffic data and classify them. In this paper, we research traffic classification technology based on representational learning and import auto-machine learning technology to solve the problems of network architecture design and parameter tuning. We also design a befitting reward function for the network architecture model. The experimental results on the USTC-TF2016 dataset and USTC-TF2016-PLUS shows that the network architecture generated by auto-machine learning technology has better training performance and classification accuracy than a traditional neural network.
Improved antlion Optimizer algorithm base on Dynamic random hill-climbing mechanism

In this paper, a dynamic random hill-climbing mechanism is proposed to solve the problems that the ant lion optimization algorithm is easy to fall into the local optimal. The algorithm adjusts the position of ant lion by hill-climbing mechanism to enhance the ability to jump out of local optimal. In addition, the global search ability of the algorithm is improved by dynamically hill-climbing mechanism by balancing exploration and development ability. The performance of the proposed algorithm is evaluated on 7 benchmark function and compared with original ALO and an improved ant lion optimization algorithm. The experimental results prove that the proposed algorithm can effectively improve the performance of the algorithm.
Receiver Distortion Nornalization Method for Specific Emitter Identification

Specific emitter identification (SEI) is an identification technique that identify the transmitting device with the hardware imperfection. However, the identification features are influenced by the receiver distortions and can be different when the receivers are changed. Previous SEI studies rarely concern the method to normalize the distortions among different receivers. To solve this problem, a SEI receiver normalization method is proposed. In the proposed method, a frequency-domain adaptive equalization system is contained to normalize the distortions in receivers. According to the experiment, this method can effectively eliminate the SEI feature difference caused by receivers. Then the robust of the proposed method is discussed.