Reviewerly/exHarmony huggingface dataset:
Metric-Guided Multi-task Learning

Multi-task learning (MTL) aims to solve multiple related learning tasks simultaneously so that the useful information in one specific task can be utilized by other tasks in order to improve the learning performance of all tasks. Many representative MTL methods have been proposed to characterize the relationship between different learning tasks. However, the existing methods have not explicitly quantified the distance or similarity of different tasks, which is actually of great importance in modeling the task relation for MTL. In this paper, we propose a novel method called Metric-guided MTL (M\(^2\)TL), which explicitly measures the task distance using a metric learning strategy. Specifically, we measure the distance between different tasks using their projection parameters and learn a distance metric accordingly, so that the similar tasks are close to each other while the uncorrelated tasks are faraway from each other, in terms of the learned distance metric. With a metric-guided regularizer incorporated in the proposed objective function, we open a new way to explore the related information among tasks. The proposed method can be efficiently solved via an alternative method. Experiments on both synthetic and real-world benchmark datasets demonstrate the superiority of the proposed method over existing MTL methods in terms of prediction accuracy.
Deep Person Identification Using Spatiotemporal Facial Motion Amplification

We explore the capabilities of a new biometric trait, which is based on information extracted through facial motion amplification. Unlike traditional facial biometric traits, the new biometric does not require the visibility of facial features, such as the eyes or nose, that are critical in common facial biometric algorithms. In this paper we propose the formation of a spatiotemporal facial blood flow map, constructed using small motion amplification. Experiments show that the proposed approach provides significant discriminatory capacity over different training and testing days and can be potentially used in situations where traditional facial biometrics may not be applicable.
참여자 지향적 대학 창업교육 프로그램 개발을 위한 실증적 연구

With the decline in the college student population and the increase in the youth unemployment rate, the university began to be interested in starting...
Generating Synthesized CT from Cone-Beam Computed Tomography (CBCT) Using Artifact Disentanglement Network for Image-Guided Radiotherapy (IGRT)

Recently, image generation, including generating synthesized CT (sCT) from CBCT, has become a research hotspot in medical image analysis. Many sCT generation architectures learn the mapping from CBCT images to corresponding CT images based on an unpaired image-to-image translation method, named cycle-consistent generation adversarial network (CycleGAN), but the anatomy accuracy of the sCT images generated by them needs to be improved. To address this problem, we propose a novel CBCT-based sCT generation architecture, which generates sCT images with CBCT anatomy and CT image quality and is trained with unpaired data. The sCT images generated by the proposed architecture have impressively high anatomy accuracy. Experiments show that the sCT images generated by the proposed method are more visually and quantitatively similar to real CT images than some recently-developed CycleGAN-based sCT generation architectures with the mean peak-signal-to-noise ratio (PSNR) improved from 26.15 dB to 29.66 dB.
Lightweight Neural Networks From PCA &amp; LDA Based Distilled Dense Neural Networks

This paper presents two methods for building lightweight neural networks with similar accuracy than heavyweight ones with the advantage to be less greedy in memory and computing resources. So it can be implemented in edge and IoT devices. The presented distillation methods are respectively based on Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The two methods basically rely on the successive dimension reduction of a given dense neural network (teacher) hidden features, and the learning of a smaller neural network (student) which solves the initial learning problem along with a mapping problem to the reduced successive features spaces. The presented methods are compared to baselines -learning the student networks from scratch-, and we show that the additional mapping problem significantly improves the performance (accuracy, memory and computing resources) of the student networks.
Personal Component as a Key Element of the Structure of Professional Competence of Modern Teachers

The present study considers the place of personal component in the complex structure of professional competence of a teacher, as well as its influence on the process of education and children upbringing. On the one hand, it is connected with the essence of the profession itself, and on the other hand, with the mandatory requirement for the presence of specific qualities inherent in teacher’s personality. Other-wise, the teaching process may become ineffective. The main aim of the author is to draw attention to the considered aspect of professional competence. This approach is reflected in the new documents in the field of education and in particular, in preschool education. Based on the analysis of various aspects of the content of the concept, the study presents a model of psychological competence, which com-plements the relationship of personal, theoretical and practical components. There is also defined the structure of competence regarding a preschool teacher who is engaged in the first stage of educa-tion process. The author focuses, first of all, on the practical aspect, tools and technologies of an edu-cational and nurturing process as a priority in the teacher’s work. The novelty of the study is deter-mined by the practice-oriented approach to solving the problem of providing a teacher with necessary tools with regard to personal self-realization in the modern educational system in accordance with the empirically emerging needs of modern society. Thus, practical implementation, technology and tools (diagnostics, forecasting and analysis) are the novelty factor of the present research. The publica-tion is intended for specialists in the field of peda-gogy and psychology, preschool workers, under-graduate and postgraduate students.
Operational Visual Control of the Presence of Students in Training Sessions

The problem of automating the students’ attendance in the classroom is solved by using computer vision. A convolutional neural network is used to recognize a person’s face. The recognition process is implemented in real time. Localization of faces in frames from a video camera is performed by the Viola-Jones method. The convolutional neural network of the VGGFace model forms the features of a person’s face. Identification of the person occurs by the facial features similarity. The software is implemented by using the Keras and OpenCV libraries. The control system performs the following functions: captures the faces of students on a video camera when entering the classroom, compares faces with a database of students, notes the presence at the lesson (or being late) in case of successful identification, saves the data in attendance register. For the convenience of video monitoring, the color of the student’s line changes depending on his condition: not present, present, late, absent. The system provides for manual editing of the electronic register and the choice of the subject name. The student’s photo can be uploaded into the database from a file or directly from the camera.
Video Dehazing Hardware Accelerator Design based on Dark Channel Prior with Sky Preservation

With the increasingly serious haze disaster, the development of dehazing technology is highly concerned. But the current software dehazing algorithm suffers from too high computational complexity. We selects the algorithm based on dark channel prior with sky preservation [1] as the base software algorithm, and a hardware accelerator is designed to simplify the data transmission process and optimize the data processing. The whole operation simulation of the accelerator is realized in NC-Verilog hardware design environment. The accelerator can achieve 75fps@640x480 video dehazing under 0.18μm CMOS technology. It can be applied to driving assistance and surveillance system.
k-Nearest Neighbor based Clustering with Shape Alternation Adaptivity

Existing clustering algorithms aim at identifying clusters from a single dataset. However, many applications generate a series of datasets. For example, scientists need to repeat an experiment many times to ensure reproducibility; sensors collect information day after day. In such scenarios, we need to identify clusters separately from a large number of datasets, which can contain an unknown number of clusters with various densities and shapes.Density-based clustering algorithms are commonly used in identifying arbitrary shaped clusters when the cluster number is unknown. Most density-based clustering algorithms are "DBSCAN-alike", where clusters are formed by connecting consecutive high dense regions. Therefore, points are grouped as one cluster as long as they are densely connected. When the distribution shape of points is changed across different datasets, parameter tuning on each dataset is necessary to obtain proper results, which is time-consuming.In this work, we developed a new kNN density-based clustering algorithm, which does not adopt the DBSCAN paradigm. Instead, we identify clusters by maximizing the intra-cluster similarities, which are estimated using: 1) the probability that two points belong to the same cluster; 2) the probability that a point is a cluster center. The kNN concept and minimum spanning tree are used to compute both probabilities. Our approach is capable of extracting clusters in arbitrary shapes using the single parameter k, and can handle a series of datasets with less parameter tuning effort. Experiments on both synthetic and real-world datasets show that our approach outperforms other recent kNN clustering algorithms.
Gesture-Based Intelligent User Interface for Control of an Assistive Mobile Information Robot

This article presents a gesture-based user interface for a robotic shopping trolley. The trolley is designed as a mobile robotic platform helping customers in shops and supermarkets. Among the main functions are: navigating through the store, providing information on availability and location, and transporting the items bought. One of important features of the developed interface is the gestural modality, or, more precisely, Russian sign language elements recognition system. The notion of the interface design, as well as interaction strategy, are presented in flowcharts, it was made an attempt to demonstrate the gestural modality as a natural part of an assistive information robot. Besides, a short overview of mobile robots is given in the paper, and CNN-based technique of gesture recognition is provided. The Russian sign language recognition option is of high importance due to a relatively large number of native speakers (signers).
Numerical Performance Comparison of Distributed Photovoltaic Power Station (DPV) Forecasting Model Based on Two Neural Network Approaches

The power output of distributed photovoltaic power station (DPV) depends on many factors including solar radiation, temperature, wind speed and so on. Because the power output is greatly affected by environmental conditions, it has the characteristics of fluctuation, intermittence, and instability, which brings much challenge for power forecasting [1-2]. In this paper, after studying and analyzing the existing neural network approaches, two photovoltaic power station output prediction methods based on Convolutional Neural Network(CNN) and Long-Short Term Memory(LSTM) are established along with verifying the effectiveness of the algorithm by case studies via the evaluation index.
A Watermarking Method for Selective Image Authentication Tollerant to JPEG Compression, Brightness and Contrast Adjustments

One of the most promising application of the digital watermarking is the selective image authentication (SIA) systems. In order to implement such a system one requires an embedding algorithm with an appropriate capacity. In addition, an embedding method is to be robust for the class of non-malicious manipulations which the SIA system is designed for. We propose the new method which has a significant embedding capacity while still being tolerant to JPEG compression, brightness and contrast adjustments. This was possible due to the extension of the well-known discrete wavelet transform embedding technique. We propose two-step embedding scheme and the use of image histogram equalisation and recovering operations. The experiment results show acceptable tolerance to JPEG compression, brightness and contrast adjustments with good visual quality in terms of PSNR just after embedding.
Dealing with Data Streams: Complex Event Processing vs. Data Stream Mining

Recently, data generation rates are getting higher than ever before. Plenty of different sources like smartphones, social networking services and the Internet of Things (IoT) are continuously producing massive amounts of data. Due to limited resources, it is no longer feasible to persistently store all that data which leads to massive data streams. In order to meet the requirements of modern businesses, techniques have been developed to deal with these massive data streams. These include complex event processing (CEP) and data stream mining, which are covered in this article. Along with the development of these techniques, many terms and semantic overloads have occurred, making it difficult to clearly distinguish techniques for processing massive data streams. In this article, CEP and data stream mining are distinguished and compared to clarify terms and semantic overloads.
The Impact of Object Immutability on the Java Class Size

According to the subjective opinions of many industry experts, object immutability is a virtue in object-oriented programming, since it leads to side-effect-free design, cleaner code, better concurrency, and many other factors. However, it has never been empirically demonstrated exactly how immutability affects quality metrics of object-oriented programs. In the following research, we analyzed 97508 classes from 240 public Java repositories to find out how immutability affects the size of the code.
Self-Adaptive Hybrid Extreme Learning Machine for Heterogeneous Neural Networks

This paper presents a hybrid algorithm for the creation of heterogeneous single layer neural networks (SLNNs). The proposed self-adaptive heterogeneous hybrid extreme learning machine (SA-He-HyELM) trains a series of SLNNs with different neuron types in the hidden layer utilizing the extreme learning machine (ELM) algorithm. These networks are evolved into heterogeneous networks (networks having different combinations of hidden neurons) with the help of a modified genetic algorithm (GA). The algorithm is able to handle two architecturally different neuron types: traditional low order (linear) units and higher order units with different transfer functions. The GA is fully self-adaptive and uses one novel hybrid crossover operator along with a self-adaptive mutation operator in order to retain ELM's simplicity and minimize the number of parameters need tuning. The experimental part of the current paper involves testing SA-He-HyELM with traditional ELM and other three ELM-based methods. The experimental part utilized a series of regression and classification experiments on relatively large datasets. In all cases the proposed method managed to get lower MSE or higher classification accuracy when compared to the aforementioned methods.
Folding-BSD Algorithm for Binary Sequence Decomposition

The IoT revolution leads to a range of critical services which rely on IoT devices. Nevertheless, they often lack proper security, becoming the gateway to attack the whole system. IoT security protocols often rely on stream ciphers, where PRNGs are an essential part of them. In this article, a family of ciphers with strong characteristics that make them difficult to be analyzed by standard methods is described. In addition, we will discuss an innovative technique of sequence decomposition and present a novel algorithm to evaluate the strength of binary sequences, key part of the IoT security stack.
Relations of Logical Consequence in Program-oriented Logics of Quasiary Predicates

We study new classes of program-oriented logical formalisms-pure first-order logics of quasiary predicates with extended renominations. Logics with predicates of weak equality and strong equality are considered; special attention is paid to logics with composition of predicate complement. For the introduced logics, composition algebras and languages are described and various logical consequence relations are specified including relations with undefinedness conditions. Relationships between different logical consequence relations are defined and conditions for elimination of undefinedness for the logical consequence relations of types T and F are determined.
Joint-Distribution And Gain Rate Based Saliency Model For Circular Tank Detection In Remote Sensing Images

Oil tank detection plays an important role in object detection for remote sensing images. While the existence of the complex cases affects the detection accuracy, this paper proposes a joint-distribution and gain rate based saliency analysis model for circular tank detection. First, the joint-feature residual is utilized to extract common parts among the selected feature maps for intensity analysis. Besides, the local gradient specificity and local flatness descriptor are introduced to assess the texture characters. Second, the introduced feature vector is used for the clustering of the input series, and the joint-distribution is utilized to label a coarse binary mask. Third, the coarse labeled mask is used to assist to calculate the gain rate for different elements of the feature vector and generate the corresponding weights to get the saliency map. Finally, the desired targets are extracted according to the local salient parts in the saliency map. Experiments are conducted on two aspects including the qualitative evaluation and the quantitative evaluation. The assessment of pixel level and geometrical segmentation shows the superiority of the proposed method compared with the listed competing algorithms.
A Lightweight Neural-Net with Assistive Mobile Robot for Human Fall Detection System

Falls are a major health issue, particularly among the elderly. Increasing fall events require high service quality and dedicated medical treatment which is an economic burden. In the lack of appropriate care and support, serious injuries caused by fall will cost lives. Therefore, tracking systems with fall detection capabilities are required. Static-view sensors with machine learning techniques for human fall detection have been widely studied and achieved significant results. However, these systems unable to monitor a person if he or she is out of viewing angle which greatly impedes its performance. Mobile robots are an alternative for keeping the person in sight. However, existing mobile robots are unable to operate for a long time due to battery issues and movement constraints in complex environments. In this paper, we proposed a lightweight deep learning vision-based model for human fall detection with an assistive robot to provide assistance when a fall happens. The proposed detection system requires less computational power which can be implemented in a low-cost 2D camera and GPU board for real-time monitoring. The assistive robot equipped with various sensors that can perform SLAM, obstacle avoidance and navigation autonomously. Our proposed system integrates these two sub-systems to compensate for the weakness of each other to constitute a system that robust, adaptable, and high performance. The proposed method has been validated through a series of experiments.
Towards More Efficient And Effective Inference: The Joint Decision Of Multi-Participants

Existing approaches to improve the performances of convolutional neural networks by optimizing the local architectures or deepening the networks tend to increase the size of models significantly. In order to deploy and apply the neural networks to edge devices which are in great demand, reducing the scale of networks is quite crucial. However, It is easy to degrade the performance of image processing by compressing the networks. In this paper, we propose a method which is suitable for edge devices while improving the efficiency and effectiveness of inference. The joint decision of multiparticipants, mainly contain multi-layers and multi-networks, can achieve higher classification accuracy (0.26% on CFAR-10 and 4.49% on CFAR-100 at most) with similar total number of parameters for classical convolutional neural networks.
Analytical Design of the DIS Architecture: The Hybrid Model

In the last few decades, and due to emergence of internet appliance, there is a strategical increase in the usage of data which had a high impact on the storage and mining technologies. It is also observed that the scientific/research field produces the zig-zag structure of data, viz., structured, semi-structured, and unstructured data. Comparably, processing of such data is relatively increased due to rugged requirements. There are sustainable technologies to address the challenges and to expedite scalable services via effective physical infrastructure (in terms of mining), smart networking solutions, and useful software approaches. Indeed, the cloud computing aims at data-intensive computing, by facilitating scalable processing of huge data. But still, the problem remains unaddressed with reference to huge data, and conversely, the data is growing exponentially faster. At this juncture, the recommendable algorithm is a well-known model, that is, MapReduce, to compress the huge and voluminous data. Conceptualization of any problem with the current model is less fault-tolerant and reliable, which may be surmounted by Hadoop architecture. On the contrary case, Hadoop is fault-tolerant and has the high throughput which is recommendable for applications having huge volume of data sets, thus file system requiring the streaming access. The paper examines and unravels what efficient architectural/design changes are necessary to bring the benefits of the Everest model, HBase algorithm, and the existing MR algorithms.
Project Team Recommendation Model Based on Profiles Complementarity

An influencing factor in integration and development of a project team is personality trait. Considering the specificities of project teams, which usually requires multidisciplinary knowledge, there was a need to develop a team recommendation system model that considers, in addition to technical characteristics (training, skills, competences, experiences), personality traits of its participants. Some researches have applied personality traits in systems that recommend people, however, the works in this line, make the recommendation based on the principles of similarity of profiles. Thus, the recommendation model proposed in this paper is based on the principle of profile complementarity. The profile complementarity model aims to achieve the best possible personality combination so that one member’s strengths complement the other’s weaknesses. From the proposed model, the prototype of a recommendation system was developed