Reviewerly/exHarmony huggingface dataset:
Solving computer vision tasks with diffractive neural networks

Modern computer vision tasks are achieved by first capturing and storing large-scale images and then performing the processing electronically, the paradigm of which has the fundamentally limited speed and power efficiency with the continuous increase of the data throughput and computational complexity. We propose to build the all-optical artificial intelligent for light-speed computing, which performs advanced computer vision tasks during the imaging so that the detector can directly measure the computed results. The proposed method uses light diffraction property to build the optical neural network, where the neuron function is achieved by tuning the optical diffraction with a nonlinear threshold. Since every target scene has different frequency components, the proposed diffractive neural network is trained to perform various filtering on different frequency components and achieves different transform functions for the target scenes. We demonstrate the proposed approach can be used for high-speed detecting and segmenting visual saliency objects of the microscopic samples and macroscopic scenes as well as performing the task of object classification. The low power consumption, light-speed processing, and high-throughput capability of the proposed approach can serve as significant support for high-performance computing and will find applications in self-driving automobile, video monitoring, and intelligent microscopy, etc.
Evaluation of automatic algorithm for solving differential equations of plane problems based on BP neural network algorithm

Plane problem is a typical combinatorial optimization problem. Aiming at the difference method of plane problem, BP neural network is proposed, the algorithm of solving difference equation is established, and the corresponding program is compiled. By calculating the calculation example, the continu ity condition under the condition of modulus abruption is further discussed. The correctness and practicability of the difference equation algorithm are verified. A dynamic model of the parallel difference equation is constructed according to the characteristics of the parallel structure of BP neural network. The study shows that the two groups of differential equations are used to identify and verify the model, and the energy function that satisfies both the linear embedding condition and the correct wiring is given. Furthermore, BP neural network is used to realize the search and routing of the maximum plane subgraph of the planable line and the non-planar plan. The results show that the verification used is effective. Difference equation calculations have the ability to help BP networks get rid of local minima and get better results.
Single Underwater Image Enhancement Based On UCIQE

Abstract In order to deal with the problems of low contrast and color distortion of underwater images, a single underwater image enhancement method based on UCIQE is designed in this paper. It estimates background light intensity by a quad-tree search algorithm and divides the image into many blocks based on the assumption that the transmittance of local blocks is equal. The objective function of each block is composed of UCIQE and the information loss function, and the transmittance is obtained at maximum value of the objective function. The global optimization of the overall transmittance image is carried out by using the guided filter. Experimental results demonstrate that this method can effectively solve the problem of color deviation and blur of underwater degraded images.
Training of future teachers of mathematics and informatics to teaching schoolchildren to the bases of cybersecurity

The active development of the cybersecurity industry has led not only to the need to develop specialized software, improve the skills of employees of large companies, but also to the formation of future teachers’ skills and abilities of competent behavior in cyberspace. The analysis of the normative documentation indicates that the problem of training future teachers of mathematics and computer science for teaching schoolchildren the basics of cybersecurity comes to the fore. The article is devoted to the description of a possible way to solve the identified problems. The article presents four levels of formation of knowledge, skills of students of pedagogical areas of training in the field of cybersecurity, means and methods of their achievement, structural components of cybersecurity and the disciplines of the curriculum (bachelor’s degree), allowing to master the content of these components, describes the special features of lecture and practical lessons. The authors of the article believe that the proposed approach allows us to implement the idea of lifelong cybersecurity education according to the scheme: schoolchildren — undergraduate students — graduate students — acting teachers.
Deep Spectral Super-Resolution with Noisy Input

Learning based methods, e.g., sparse coding or deep convolutional neural networks (DCNNs) have underpinned much of recent progress in increasing the spectral resolution of an RGB image for hyperspectral image (HSI) super-resolution. However, these methods suffer severe performance loss, when the test RGB image distributed differently from the training set, e.g., being corrupted with random noise. To mitigate this problem, we propose an unsupervised deep spectral super-resolution method, which employs a DCNN to generate the latent HSI from an input RGB and encourages it to fit the input RGB image through down-sampling in spectral domain as well as a sparse gradient prior in spatial domain. Due to the powerful capacity of DCNN in capturing the low-level image statistics, the proposed method is able to automatically accommodate the noise corruption in the input RGB image. Experimental results shows the superior performance of the proposed method.
Computer crimes in Serbia

riminal Legislation of the Republic of Serbia, which started applying as of 1 January 2006, provides for criminal responsibility and punishment for several criminal offences against the safety of computer data.Those are computer criminal offences, which a perpetrator (who obviously has unique, special knowledge of information technology, computing -IT sector) commits by the abuse of computers, computer systems or network, thereby causing material or non-material damage to other natural or legal persons, as well as the whole social community.The basis of those incriminations is the European standards established under the Budapest Convention on Cybercrime and Additional Protocol to this Convention, as well as many other European documents.The paper analyses the basic characteristics of computer criminal offences in Serbia and the degree of their compatibility with the European standards.
Compensating for Object Variability in DNN–HMM Object-Centered Human Activity Recognition

This paper describes a deep neural network - hidden Markov model (DNN-HMM) human activity recognition system based on instrumented objects and studies compensation strategies to deal with object variability. The sensors, comprising an accelerometer, gyroscope, magnetometer and force-sensitive resistors (FSRs), are packaged in a coaster attached to the base of an object, here a mug. Results are presented for recognition of actions involved in manipulating a mug. Evaluations are performed using over 24 hours of data recordings containing sequences of actions, labelled without time-stamp information. We demonstrate the importance of data alignments. While the DNN-HMM system achieved error rate below 0.1% for matched train-test conditions, this increased up to 26.5% for highly mismatched conditions. The error rate averaged over all conditions was 1.4% when using multi-condition training and decreased to 0.8% by employing feature augmentation. The use of FSR feature compensation, specific to weight variability, resulted in 0.24% error rate.
State Estimation for Sound Environment System with Nonlinear Observation Characteristics by Introducing Wide-Sense Particle Filter

In this study, a modified particle filter considering non-Gaussian properties of noises is proposed in a form applicable to real situation in sound environment system where the observation data are contaminated by the external noise (i.e., background noise) of arbitrary probability distribution and measured in decibel scale.More specifically, a nonlinear observation model in decibel scale with a quantized level is first paid considered by introducing the additive property of energy variables (i.e., sound intensity) in sound environment system.Next, a wide-sense particle filter of an expansion expression type is derived in a form suitable for the nonlinear observation characteristics and the signal processing considering higher-order correlation information between the specific signal and observation.Furthermore, the effectiveness of the proposed theory is confirmed by applying it to the observed data measured in real sound environment.
Small Traffic Sign Detection Through Selective Feature Fusion Based Faster R-CNN With Arc-Softmax Loss

Traffic signs are basic and important elements in maps. They are related to traffic regulations, profoundly affecting/managing the travel mode of human beings and efficiency of vehicle running. Traffic sign mining technology is applied in many research fields such as traditional map update, high-precision map establishment and automatic driving. Image based traffic sign identification technology has the advantages of low cost and high efficiency over manual processing mode, and traffic sign detection has thus become a significant task with the pacing advancement of autonomous driving. However, many common object detection methods cannot be directly applied to this task, as the size of traffic signs are very small yet they vary considerably. Due to such characteristics, features of traffic signs are difficult to capture, and are harder to discriminate between classes. To address this problem, we proposed a selective feature fusion based Faster R-CNN with Arc-Softmax loss, which optimizes the detection performance from the two following ways: network structure and loss function. We discover that each Faster R-CNN layer is only capable of detecting targets within a certain size range. By carefully selecting and combining different layers' feature maps, we can extract features that effectively represent traffic signs of various sizes. Then, Arc-Softmax loss penalizes the angular distances between the feature vectors of different signs, and their corresponding weight vectors of the last fully connected layers, thereby encouraging intra-class compactness and inter-class separability between learned features. Extensive analysis and experiments on the challenging Tsinghua-Tencent 100K benchmark demonstrate the superiority and implementation simplicity of our proposed method. Code will be made publicly available.
Using depth map algorithms to improve the quality of object identification on digital stereo images

Abstract For the first time experimentally investigated the use of a vision system using the technology of depth maps, infrared depth sensor, laser rangefinder, lidar to determine the relief and geometric dimensions of the object on digital stereo images, which allows continuous monitoring of the state of any process. Methods of digital image processing are presented. The main advantages of such systems are considered.
Teachers’ perspective on the application of information and communication technology (ICT) in mathematics learning

Abstract This research aims to comprehend how the impact of ICT in mathematics learning, particularly in middle school, and to study teachers’ perspective toward implementation of ICT application in mathematics learning based on the gender of teaching teacher. The research method isdescriptive, giving questionnaires and interviewing 56 mathematics’ teacher in West Java, Indonesia. The results showed in applying ICT in mathematics learning, categories show many times 45%, ever applied 54%, and never applied 1 %.
RecKGC: Integrating Recommendation with Knowledge Graph Completion

Both recommender systems and knowledge graphs can provide overall and detailed views on datasets, and each of them has been a hot research domain by itself. However, recommending items with a pre-constructed knowledge graph or without one often limits the recommendation performance. Similarly, constructing and completing a knowledge graph without a target is insufficient for applications, such as recommendation. In this paper, we address the problems of recommendation together with knowledge graph completion by a novel model named RecKGC that generates a completed knowledge graph and recommends items for users simultaneously. Comprehensive representations of users, items and interactions/relations are learned in each respective domain, such as our attentive embeddings that integrate tuples in a knowledge graph for recommendation and our high-level interaction representations of entities and relations for knowledge graph completion. We join the tasks of recommendation and knowledge graph completion by sharing the comprehensive representations. As a result, the performance of recommendation and knowledge graph completion are mutually enhanced, which means that the recommendation is getting more effective while the knowledge graph is getting more informative. Experiments validate the effectiveness of the proposed model on both tasks.
Active Learning using Uncertainty Sampling and Query-by-Committee for Software Defect Prediction

In the process of software defect prediction dataset construction, there are problems such as high labeling costs.Active learning can reduce labeling costs when using uncertainty sampling.Samples with the most uncertainty will be labeled, but samples with the highest certainty will always be discarded.According to cognitive theory, easy samples can promote the performance of the model.Therefore, a hybrid active learning query strategy is proposed.For the sample with lowest information entropy, query-by-committee will analyze it again using vote entropy.Empirical studies show that the proposed HIVE approach outperforms several state-of-the-art active learning approaches.
Moment subset sums over finite fields

The k-subset sum problem over finite fields is a classical NP-complete problem. Motivated by coding theory applications, a more complex problem is the higher m-th moment k-subset sum problem over finite fields. We show that there is a deterministic polynomial time algorithm for the m-th moment k-subset sum problem over finite fields for each fixed m when the evaluation set is the image set of a monomial or Dickson polynomial of any degree n. In the classical case m=1, this recovers previous results of Nguyen-Wang (the case m=1, p>2) [24] and the results of Choe-Choe (the case m=1, p=2) [3].
Attention-guided GANs for human pose transfer

This paper presents a novel generative adversarial network for the task of human pose transfer, which aims at transferring the pose of a given person to a target pose. In order to deal with pixel-to-pixel misalignment due to the pose differences, we introduce an attention mechanism and propose Pose-Guided Attention Blocks. With these blocks, the generator can learn how to transfer the details from the conditional image to the target image based on the target pose. Our network can make the target pose truly guide the transfer of features. The effectiveness of the proposed network is validated on DeepFasion and Market-1501 datasets. Compared with state-of-the-art methods, our generated images are more realistic with better facial details.
NCR-KG: news community recommendation with knowledge graph

With the pervasiveness of smart equipment and social networking, it has become an urgent task to recommend personalized news to users. For news recommendation, researchers have proposed to finish the news recommendation task based on collaborative filtering algorithms, and also proposed to employ additional information solving high data sparsity problem. A typical method is to use the structure of knowledge graph to represent heterogeneous knowledge. But in existing methods, it is difficult to define the relationship among entities, and can only use the static knowledge without utilization and consideration of the evolution of knowledge graph. To solve these problems, in this paper, we first propose a novel algorithm to efficiently extract the most relevant tags in the news to represent the news. Then we construct the structure of the knowledge graph to represent the personal information and historical click records of a user. Those extracted knowledge is used to build a user profile and further to form a community of users, which can also alleviate the cold start problem. We conducted experiments on a real-world news website, including the labels extraction. We report the experimental results of metrics evaluation and manual evaluation, and the results demonstrate the effectiveness and efficiency of the proposed framework.
Perazuhan: A Mobile Application for Solid Waste Micro-Management Framework

Improper solid waste management poses a major concern not only in the Philippines but also on a global scale. Metro Manila has more serious concerns in waste management than in other urban areas in the Philippines affecting nearly twelve (12) million of its people. In response to the efforts initiated by the Philippine government to help reduce solid waste problems, the Local Government Units (LGUs) have recognized the role of junk shops in promoting proper waste management. The community, on the other hand, is encouraged to collect recyclable materials to be sold to junkshop owners and scrap buyers. This collect-sell method to the community has been referred to as “may pera sa basura” or there is money in waste to promote proper waste management. This study promotes proper solid waste management in the barangay level by presenting a mobile application framework dubbed as Perazuhan, a contraction between Pera (Money) and Basura (Trash or waste) which signifies that you can get money out of the selling solid wastes. This technologically driven approach provides a manageable and accessible selling of recyclable materials like bottles, newspapers, papers, and plastics to junk shops. To regulate the collection and selling of recyclable materials, the barangay officials will serve as an intermediary between the junk shops and community to develop full inclusive waste management.
Building an Knowledge Base of a Company Based on the Analysis of Employee’s Behavior

The approach is proposed for building the enterprise knowledge base utilizing technologies of cyber-physical systems operating in virtual space. Integration of the enterprise information system (IS) with the knowledge base is considered as the cyber-physical system; employees act as physical objects. The knowledge base model has been developed, whose articles are matched with the objects and business processes of the main information system, based on a three-level semantic network. The algorithm for determining priority materials for writing into the knowledge base driven by the analysis of the user actions track was built. The user actions track is formed according to statistical data obtained from the event logs of the knowledge base and the IS using the sequential analysis to identify patterns of typical operations. The formal model of the interaction process of employees with the knowledge base was built, taking into account the activities of its formation and use. Simulation of the interaction process with the knowledge base was carried out, the results of which confirmed the effectiveness of the proposed approach. With an increase in the number of users, the cost of seeding the knowledge base, taking into account the relevance of its materials, pays its way due to reducing the employees idle time. The proposed models and algorithms contribute to reducing the cost of developing the enterprise knowledge base and increasing its utilization efficiency.
A Comparative Study of Two Different Spam Detection Methods

With the development of the Internet, the problem of spam has become more and more prominent. Attackers can spread viruses through spam or place malicious advertisements, which have seriously interfered with people's life and internet security. Therefore, it is of great significance to study efficient spam detection methods. Currently using machine learning methods for spam detection has become a mainstream direction. In this paper, the machine learning method of Bayesian linear regression and decision forest regression are used to conduct experiments on a data set from UCI Machine Learning Repository. We use the trained models to predict whether a mail is spam or not, and find better prediction scheme by comparing quantitative results. The experimental results show that the method of decision forest regression can get better performance and is suitable for numerical prediction.
Which Country Epitomizes the World? A Study from the Perspective of Demographic Composition

Demographic indicators are an essential element in considering various problems in the social economy, such as predicting economic fluctuations and establishing policies. Although literature extensively discusses the growth of the world population or issues pertaining to its aging, it has given little to no attention to population structures and transition patterns while considering compositional data problems. This study considers the characteristics of compositional data to examine the transition of the world population structure. The Aitchison distance examines the similarity of the world population structure from 1990 to 2080 and that of countries and regions in 2015, and creates maps to illustrate the results. Accordingly, the results identified the following countries and regions as epitomes of the world’s population structure through different periods: India, Northern Africa and South Africa, in the 1990s, South America in 2015 to 2030, Oceania and Northern America in 2040, Uruguay and Puerto Rico in 2050 to 2060, and Italy and Japan in the distant future.
Expressive cardinality restrictions on concepts in a description logic with expressive number restrictions

In two previous publications we have, on the one hand, extended the description logic (DL) ALCQ by more expressive number restrictions using numerical and set constraints expressed in the quantifier-free fragment of Boolean Algebra with Presburger Arithmetic (QFBAPA). The resulting DL was called ALCSCC . On the other hand, we have extended the terminological formalism of the well-known description logic ALC from concept inclusions (CIs) to more general cardinality constraints expressed in QFBAPA, which we called extended cardinality constraints. Here, we combine the two extensions, i.e., we consider extended cardinality constraints on ALCSCC concepts. We show that this does not increase the complexity of reasoning, which is NExpTime-complete both for extended cardinality constraints in the DL ALC and in its extension ALCSCC . The same is true for a restricted version of such cardinality constraints, where the complexity of reasoning decreases to ExpTime, not just for ALC , but also for ALCSCC.
Perceptual visual quality assessment using deeply-learned gaze shifting kernel

Image quality assessment (IQA) is a useful technique in computer vision and machine intelligence. It is widely applied in image retrieval, image clustering and image recognition. IQA algorithms generally rely on human visual system (HVS), which can reflect how human perceive salient regions in the image. In this paper, we leverage both low-level features and high-level semantic features to select salient regions, which will be concatenated to form GSPs by the designed saliency-constraint algorithm to mimic human visual system. We design an enhanced IQA index based on the GSPs to calculate the simialrity between reference image and test image to achieve image quality assessment. Experiments demonstrate that our IQA method can achieve satisfactory performance.