Reviewerly/exHarmony huggingface dataset:
Solving Well-posed Shape from Shading Problem Using Implicit Neural Representations

We propose a method for solving well-posed shape from shading problem by using implicit neural representations. We build an image irradiance equation and solve the equation by a sinusoidal representation network called SIREN, which is proposed by Sitzmann et al. in 2020. Object surface is expressed by Oren-Nayar model and a perspective projection model with light source located at the optical center is considered. Based on the above models, image irradiance equation is constructed, which is a partial differential equation (PDE). We introduce a neural network SIREN to solve this PDE, where implicit neural representations use the sine as a periodic activation function. Experiments are performed on three synthetic images and two real images. Results demonstrate that our proposed method performs with much higher accuracy.
Plausible Heterogeneous Graph $k$-Anonymization for Social Networks

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods, aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector representations. Graph embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data continuously. Publishing social network data not only bring benefit for public health, disaster response, commercial promotion, and many other applications, but also give birth to threats that jeopardize each individual's privacy and security. Unfortunately, most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social networks. To be specific, attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network embedding. In this paper, we propose a novel link-privacy preserved graph embedding framework using adversarial learning, which can reduce adversary's prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph embedding. Extensive experiments are conducted to evaluate the proposed framework using ground truth social network datasets.
Progressive Standard Operating Procedures for Darkweb Forensics Investigation

With the advent of information and communication technology, the digital space is becoming a playing ground for criminal activities. Criminals typically prefer darkness or a hidden place to perform their illegal activities in a real-world while sometimes covering their face to avoid being exposed and getting caught. The same applies in a digital world where criminals prefer features which provide anonymity or hidden features to perform illegal activities. It is from this spirit the Darkweb is attracting all kinds of criminal activities conducted over the Internet such as selling drugs, illegal weapons, child pornography, assassination for hire, hackers for hire, and selling of malicious exploits, to mention a few. Although the anonymity offered by Darkweb can be exploited as a tool to arrest criminals involved in cybercrime, an in-depth research is needed to advance criminal investigation on Darkweb. Analysis of illegal activities conducted in Darkweb is in its infancy and faces several challenges like lack of standard operating procedures. This study proposes progressive standard operating procedures (SOPs) for Darkweb forensics investigation. We provide the four stages of SOP for Darkweb investigation. The proposed SOP consists of the following stages; identification and profiling, discovery, acquisition and preservation, and the last stage is analysis and reporting. In each stage, we consider the objectives, tools and expected results of that particular stage. Careful consideration of this SOP revealed promising results in the Darkweb investigation.
Research on user granularity-level personalized social text generation technology

Abstract With the introduction of large-scale pre-trained language models, breakthroughs have been made in text generation technology research. On this basis, in order to assist users to complete personalized creation, this paper proposes a user-level fine-grained control generation model. First, we design the Encoder-Decoder framework based on the GPT2 structure, and model and encode the user’s static personalized information on the Encoder side. Then add a bidirectional independent attention module to receive the personalized feature vector on the Decoder side. The attention module in the original GPT2 structure captures the dynamic personalized features in the user text, namely writing style, expression way, etc. Next, the scores of each attention module are weighted and fused to participate in the subsequent decoding to automatically generate social texts constrained by the user’s personalized feature attributes. However, the semantic sparsity of the user’s basic information will cause occasional conflicts between the generated text and some personalized features. Therefore, we use the Alignment module to perform the secondary enhancement and generation of consistent understanding between the output data of the Decoder and the user’s personalized features, and finally realize the personalized social text generation. Experiments show that compared with the GPT2 baseline model, the fluency of the model is improved by 0.3%-0.6%, and on the basis of no loss of language fluency, the social text generated by the model can have significant user personalization characteristics, among which personalization and consistency the two evaluation indicators of sexuality both increased significantly by 8.4% and 9%.
Digital Forensics Lab Design: A framework

Internet connectivity and digital technologies have experienced exponential growth in the past few years. This explosion has spurred a significant increase in crime while also creating a new definition of cybercriminals. Digital Forensics plays an important role in crime reconstruction and thus the need for skilled forensics experts has multiplied. As a result, digital forensics education and training has also experienced radical growth. Teaching digital forensics has always been a challenge as the creation of suitable hands-on digital forensics labs has always been the core of these training programs. There are several challenges faced by both the educators and the students when it comes to the creation and implementation of digital forensics labs. This paper aims to address some of these issues by providing a framework that can be used by educators to establish educational hands-on labs for digital forensics. Firstly, we identify all the challenges faced by digital examiners, educators, and training professionals to deliver high-quality forensic labs. Secondly, we identify specific common technical pitfalls that professionals run into when designing digital forensics labs such as the creation of large image files. We thus, offer tips and tricks to make the process of creating digital forensic labs easier. Finally, we also provide a data set of small-sized image files that can be used by educators for the creation of a digital forensic lab infrastructure.
An intelligent monitoring system based on computer vision

Abstract Aiming at the customs supervision area of the comprehensive bonded area, this paper proposes an intelligent monitoring system based on computer vision through the research of computer vision, photoelectric sensors, digital image processing, data transmission network, automatic control and intelligent video analysis. We have realized the comprehensive supervision of people, vehicles, and things in the park, uploading all collected information to the centralized storage server, realizing uninterrupted data tracking, analysis, alarming, centralized management, unified scheduling, and command of front-end equipment. We have comprehensively improved the level of supervision of the comprehensive bonded area, provided the most intuitive information for emergency response, and performed the best response to emergencies according to the emergency plan to build a more intelligent comprehensive bonded area.
A New Highly Secure Optical Image Security Technique Using Gyrator Transform for Image Security-Related Applications

New methods and apparatuses for information security have evolved as a result of the rapid expansion of optical information processing. Security is one of the major issues in digital image transmission because it can deliver very secret information to any corresponding agency such as the military, biomedical, and security agencies. Previously, various techniques are proposed to perform optical image encryption techniques using different transformation and pixel-level techniques. Each work has its advantages and disadvantages in terms of computational complexity, security level, flexibility, quality, and so on. To overcome the security issues present in the previous works, a novel optical image encryption standard is proposed in this paper. This work uses information hiding followed by image encryption using Gyrator Transform (GT) using mean gradient key-based block swapping techniques. The main advantage of this work is that the key generation is dynamic and it depends upon the pixel intensity of 8 × 8 blocks. Secret information hiding is performed in the Discrete Cosine Transform (DCT) domain to protect the data against noise attacks. To analyze the performance, various evaluation metrics are used to measure the quality of the decrypted image under various distortions such as cropping and rotation. The robustness of information hiding is analyzed using a noise attack on the received image. This work achieved 45.6 dB of Peak Signal-to-Noise Ratio (PSNR) and 0.965 of Structural Similarity Index (SSIM), which is the best when compared to the conventional image encryption standards.
Automated Single-Label Patent Classification using Ensemble Classifiers

Many thousands of patent applications arrive at patent offices around the world every day. One important task when a patent application is submitted is to assign one or more classification codes from the complex and hierarchical patent classification schemes that will enable routing of the patent application to a patent examiner who is knowledgeable about the specific technical field. This task is typically undertaken by patent professionals, however due to the large number of applications and the potential complexity of an invention, they are usually overwhelmed. Therefore, there is a need for this code assignment manual task to be supported or even fully automated by classification systems that will classify patent applications, hopefully with an accuracy close to patent professionals. Like in many other text analysis problems, in the last years, this intellectually demanding task has been studied using word embeddings and deep learning techniques. In this paper these research efforts are shortly reviewed and re-produced with similar deep learning techniques using different feature representations on automatic patent classification in the level of sub-classes. On top of that, an innovative method of ensemble classifiers trained with different parts of the patent document is proposed. To the best of our knowledge, this is the first time that an ensemble method was proposed for the patent classification problem. Our first results are quite promising showing that an ensemble architecture of classifiers significantly outperforms current state-of-the-art techniques using the same classifiers as standalone solutions.
Hardware-aware Workload Distribution for AI-based Online Handwriting Recognition in a Sensor Pen

Time series-based applications such as recognition of handwriting benefit from using Deep Neural Networks (DNNs) in terms of accuracy and efficiency. Due to strict power and memory limitations of embedded platforms in the Internet-of-Things (IoT), the inference of such DNNs is usually performed on more powerful and less constrained devices. However, inference on mobile devices such as smartphones or tablets leads to high system requirements. In this paper, we present our approach for distributing computational workload between sensor pen and a mobile device for handwriting recognition. This not only results in lower system requirements for mobile devices, but also opens up the possibility of storing compressed sensor data on the pen when remote devices are not available or connected. Therefore, we first quantize weights of the DNN layers to significantly reduce the memory footprint and optimize a Connectionist Temporal Classification (CTC) algorithm to minimize the link bandwidth. The latter allows transmitting only a small matrix from the pen to the mobile device, on which the decoding is performed. To further improve the accuracy of handwriting recognition, we add a Language Model (LM) to the decoding algorithm executed on the mobile device. As a result, we are able to reduce the memory footprint of the DNN weights by almost a quarter to 736.64 KB. Additionally, applying CTC-based beam search and an LM, we can reduce the link bandwidth by almost 80%. Based on the RISC-V Instruction Set Architecture (ISA), we finally define memory and performance requirements to enable in-pen DNN inference.
Adaptive few-shot learning with a fair priori distribution

Few-shot learning is a special challenge in pattern recognition, which identifies unseen categories given only limited samples. In the past few years, various methods have been proposed to solve few-shot problems. Based on the predecessors, we discuss the prior influence of the training set bring into the model, the relationship between data quality and feature performance, as well as the method and reason to expand negative samples. We consequently improve the models and learning strategies in these aspects, then we obtain a novel framework named AFSL: Adaptive Few-Shot Learning, which can eliminate the impact of data sets on the model, and also performs well on several public datasets. Specifically, we achieved a percentage increase of verification accuracy of 7.8% and 1.5% for 1-shot and 5-shots tasks comparing to prototype network on mini-Imagenet dataset, 9.8% and 2.4% on tiered-Imagenet, and 28.4% and 14.0% on the CUB dataset.
A Seamless Integration of Fault-Tolerant and Real-Time Capabilities for Robot Operating System (ROS)

Robot Operating System (ROS) is an open-source framework for the development of modular robotics applications. It provides a component-based middleware and interface abstraction to standardize the integration of distributed applications using the DDS standard. However, ROS in its current version is not suitable for safety and real-time systems. It does not support QoS policies for dynamic reconfiguration and fault recovery. This work starts by identifying the relevant QoS policies in the DDS standard related to fault-tolerant to integrate them in ROS-2. Second, these QoS policies are completed by the specification of timing constraints, and then an approach is proposed for seamless integration of fault tolerance and real-time capabilities in ROS-2. A typical case study of a real-time geo-localization application has been developed to evaluate the proposed approach for autonomous vehicle.
Development of logical thinking of high school students through a problem-based approach to teaching mathematics

Abstract This article considers the search for new methodological approaches to teaching high school students mathematics that will promote the development of their logical thinking. Based on the analysis conducted by the authors of the study identified an urgent need to develop the logical thinking of students in mathematics lessons through various methods of solving problems. The authors of the article set out to investigate the factors that will ensure the implementation of a problem-based approach to teaching mathematics. These factors include the choice of a certain type of mathematical problem; selection of a method for solving mathematical problems; compiling a system of problems of one type that require the selection of methods for solving them. The authors reveal the introduction of the problem approach as a method of developing students’ logical thinking in the article on the example of solving irrational equations with parameters. The authors of the study explain the value of problems with parameters by the fact that the process of solving them contributes to the formation of students’ research skills. In the study, the authors focused on the analytical method of solving problems with parameters. Structural analysis of problems allowed the authors to formulate the following didactic requirements for the system of mathematical problems such as compliance with the problems of the course content and the principle of gradual increase in complexity; the rational relationship between the logical and heuristic components of educational activities; the presence of ideological and technical complexity, etc. To confirm the effectiveness of the introduction of the problem-based approach to the educational process, as a method of developing students’ logical thinking, an experiment was conducted. The main objectives of the experiment were to identify the level of logical thinking of students; development and implementation in the educational process of a system of problems with parameters in the study of the topic “Irrational equations”, which contribute to the development of logical thinking of students; analysis of the results of the experiment. The obtained results and their statistical processing confirmed significant differences in the levels of development of logical thinking in the control and experimental groups, respectively. This proved the effectiveness of a problem-based approach to teaching mathematics and the development of logical thinking of students.
Pairnorm based Graphical Convolution Network for zero-shot multi-label classification

Zero-shot learning transfers the knowledge from the seen labels available during training to the unseen labels. In this paper, we propose a Pairnorm based Graphical Convolution Network for zero-shot multi-label classification (ML-ZSLPGCN). The proposed approach uses the label features obtained from the images during training and semantic embedding for the unseen labels. The ML-ZSLPGCN first creates the features corresponding to the images, and the label aware module creates the feature vector of the labels corresponding to the seen labels using the attention region embedding. A graphical convolution network takes the feature vector of seen labels during training and semantic word embedding for the unseen labels as input and learns the classifier. The proposed approach uses a pairnorm-based normalization scheme to tackle the over smoothing problem in the graphical convolution network. The experimental results on the NUSWIDE and MS-COCO datasets show that the proposed approach provides significant performance in terms of precision, recall, and F1 score in comparison to state-of-the-art approaches.
International Experience of Digital Technologies Applying in Secondary Education Organizations Activities

The paper presents materials on the analysis of international experience in the use of digital technologies in the activities of educational institutions in foreign countries with high quality education: analysis of international experience in the use of digital technologies in the management of educational institutions of general education in Australia, Singapore, Sweden; analysis of the main features of the use of digital technologies in the activities of educational institutions of general education in France; comparative analysis of the problems of digital transformation of secondary education in Russia and abroad (the experience of the USA and Western Europe); analysis of the experience of using virtual, augmented, mixed, augmented reality technologies in the activities of educational organizations of foreign countries with high quality education; comparative analysis of international experience in the implementation of organizational and methodological conditions to ensure information security of the personality of subjects of the educational process in the activities of educational institutions in foreign countries with high quality education; analysis of international experience in the implementation of health-preserving approaches to organizing the learning process using digital technologies in educational institutions of foreign countries with high quality education.
Reliable Service-Oriented Architecture for Nasa's Mars Exploration Rover Mission

The Collaborative Information Portal (CIP) for NASA's Mars Exploration Rover (MER) mission was created by NASA's Ames Research Center and Jet Propulsion Laboratory (JPL). Both MER and CIP have outperformed their forecasts. CIPs are carried out by mission managers, engineers, scientists, and mission researchers at JPL mission control, as well as by scientists in their laboratories, residences, and offices. Users are linked to the internet in a secure manner. The CIP displayed the current time in different time zones of the planet Mars and Earth, as well as personnel and event schedules using Martian time scales, because the expedition took place at Mars' time. In this paper, we present the MER and a CIP mission summary. We show how the CIP was put to work to meet some of the mission's requirements. We discuss the criteria for choosing its architecture, web services (current time ratio between planet Earth and the planet Mars, conversion of Martian (Sol) and Earth Day, and direct connection to planet Mars) and how developers made the software so reliable.
A Deep Neural Networks model for Restaurant Recommendation systems in Thailand

In the age of flooded information, Recommender Systems play a crucial role as long as consumers consume more content and submit more data. Many businesses have implemented Recommender Systems to assist users find items based on their previous interactions. Deep neural networks have demonstrated promising results in a variety of disciplines, including recommendation systems in the past few years. However, such studies ignore auxiliary information input. In this work, we purpose a deep recommendation system with neural networks which consists of deep collaborative filtering to learn user and item interaction latent factor and enrich the performance with textual information by using multi-layer perceptrons and combining these two models under our framework, called DNNRecs. Apart from our model framework, we also contribute a feature engineering method to create new features from review text by using technique tf-idf. Extensive experiments on one real-life dataset in Thailand demonstrate the effectiveness of the proposed model.