Reviewerly/exHarmony huggingface dataset:
5 Extraction of Common Feature of Dysgraphia Patients by Handwriting Analysis Using Variational Autoencoder

Nowadays, handwritten document analysis using intelligent computing technology is a demanding research area, considering its usefulness in identifying a person and human characteristics, particularly that of persons having typical disabilities such as dyslexia, dysgraphia, and Parkinson’s disease. Analysis of handwriting, falling under the broad purview of graphology, helps us understand the writer’s psychology, emotional outlays, and noticeable disorders as well. Since there prevails a broad spectrum of cursive nature and high inconsistency of handwriting styles, the techniques for modern handwriting analysis need to be more robust and sensitive to different patterns compared to the traditional graphological techniques. Herein lies the necessity of computing technology, which should intelligently analyze handwritten texts to find out the similarity of finer aspects of handwritings of children or adult with some kind of learning/writing disability. Deep learning technology is chosen as the technical tool to identify and classify common features of handwriting of children with developmental dysgraphia. Variational autoencoder, a deep unsupervised learning technique, is presently used for this purpose. This chapter reports successful extraction and interpretation of significant number of distinguishable handwriting characteristics that are clinically proved to be symptoms of dysgraphia.
Application of Backpropagation Algorithm for Handwriting Recognition

Abstract Handwritten letter recognition is one form of pattern recognition. The introduction of letters looks simple to humans, but it becomes a tough task for computer programs to complete. In recognizing someone’s handwriting, a computer program trained first. This study discusses how a computer recognizes a digital image pattern in the form of handwritten letters using the backpropagation algorithm. The backpropagation algorithm used in making this system is backpropagation with momentum. The network architecture used consists of an input screen with 64 neurons, a hidden screen consisting of 46 neurons, and an output screen with five neurons. Before the recognition process, input image with image format (jpg) processed first, which includes scaling, grayscale, and binarization. The trial results show that the system can recognize handwriting with an accuracy of up to 65%.
Engineering Impacts of Anonymous Author Code Review: A Field Experiment

Code review is a powerful technique to ensure high quality software and spread knowledge of best coding practices between engineers. Unfortunately, code reviewers may have biases about authors of the code they are reviewing, which can lead to inequitable experiences and outcomes. In principle, anonymous author code review can reduce the impact of such biases by withholding an author's identity from a reviewer. In this paper, to understand the engineering effects of using author anonymous code review in a practical setting, we applied the technique to 5217 code reviews performed by 300 software engineers at Google. Our results suggest that during anonymous author code review, reviewers can frequently guess authors’ identities; that focus is reduced on reviewer-author power dynamics; and that the practice poses a barrier to offline, high-bandwidth conversations. Based on our findings, we recommend that those who choose to implement anonymous author code review should reveal the time zone of the author by default, have a break-the-glass option for revealing author identity, and reveal author identity directly after the review.
Densely Connected Convolutional Network for Audio Spoofing Detection

Anti-spoofing has attracted increasing attention since the inauguration of the ASVspoof Challenges, due to the fact that automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. The latest ASVspoof 2019 Challenge was dedicated to addressing attacks in three major classes: speech synthesis, voice conversion, and replay audio. In this paper, we propose a novel method that includes feature extraction, a densely connected convolutional network, and fusion strategies to answer the ASVspoof 2019 Challenge and to defend against spoofing attacks. Features are extracted using different algorithms and then fed separately into variants of our model, which differ only in terms of the kernel size of the global average pooling layer. A dense connectivity pattern with better parameter efficiency is introduced to the proposed network to strengthen the propagation of the audio features. The experimental results show that the proposed method improves the tandem decision cost function and equal error rate scores by 75% and 78%, respectively, in the logical access challenge. In the physical access challenge, the proposed method improves the t-DCF and EER scores by 73% and 72%, respectively, compared with state-of-the -art methods.
Position Paper: Low-Cost Solutions for Home-Based Healthcare

This paper describes approaches to the use of wearables and related monitoring and diagnostic tools to glean and present data to patients and their caregivers in the form of Personal Health Dashboard. Raw data from devices such as fitness watches, blood glucose and blood pressure monitors can be analyzed by machine learning algorithms ensembles over the cloud or at the edge, and the resulting patterns can be presented to allow for actionable responses. The advent of low-cost computational, sensing, monitoring and diagnostic tools allow for cost-effective solutions that can have wide adaptability. The paper draws from prior and ongoing research conducted by the team to present these positions in the advancement of healthcare.
Mitigating Row-hammering by Adapting the Probability of Additional Row Refresh

Continuous scaling-down of the DRAM manufacturing process technology has achieved a dense chip capacity with a low cost-per-bit. On the other hand, it has introduced a new reliability problem called row-hammering, in which, in case that a certain row is activated too frequently, one or more bits in the adjacent rows are unintentionally corrupted. It is crucial to address row-hammering errors because they not only can be exploited by a malicious attack for modern computing systems but also may occur in general applications stored in a highly scaled-down DRAM. Even if several methods have been proposed to resolve row-hammering, existing solutions have limited capability to prevent row-hammering from occurring. Hence, a more robust solution for row-hammering is necessary. In this paper, we propose a novel row-hammering mitigation mechanism, called Adaptive-probabilistic Additional Row Refresh (AARR). The main observation exploited by the proposed method is that each memory access does not have an equal degree of threat to cause row-hammering: accessing a row that has been frequently activated is much vulnerable to row-hammering rather than a barely activated row. In AARR, a small table and a few logic blocks are added to keep track of the threat level that causes row-hammering. Then, one of the adjacent rows of an accessed row is refreshed with an adaptive probability that corresponds to the threat level of that memory access. Our evaluation results show that the proposed method renders the most reliable protection against row-hammering with the lowest overhead on performance and energy compared to two well-known existing solutions.
Hiding Short Message Text In the Uzbek Language

Steganography is a method that is based on hiding or embedding additional information in digital objects while causing some distortion of these objects. In this case, an object or container can be used image, audio, video, network packets, etc. Recently, there have been a lot of publications in the field of hiding information in a text container. To embed a secret, steganographic methods rely on redundant information about the covering medium used or properties that the human perception system cannot distinguish. Since text documents are widely used in organizations, using a text document as a storage medium may be the preferred choice in such an environment, while having little excess information. Therefore, the choice of using a text document as a storage medium is the most difficult, since it contains less redundant information. In this article, we present a simple and new approach to steganography based on the Uzbek language. We analyze the characteristics of the Uzbek language and offer some effective methods for converting messages to bits. We find that a single Uzbek text message can be converted to multiple secret bits. The results show that the proposed method is very important for a successful steganographic technique. Besides, we find that embedded bits can be correctly and easily extracted by human observers without using a special decoder. Thus, the proposed scheme is practical and effective for hiding messages and is a new approach for the Uzbek language.
Partial Label Learning via Subspace Representation and Global Disambiguation

Partial Label Learning (PLL) learns from the training data where each example is associated with a set of candidate labels, among which only one is valid. Most existing methods deal with such problem by disambiguating the candidate labels first and then inducing the predictive model from the disambiguated data. However, these methods only focus on disambiguation for each single candidate label set, while the global label context tends to be ignored. Meanwhile, these methods induce the model by directly utilizing the original feature information, which may lead the model overfitting due to high-dimensional redundant feature. To tackle the above issues, we propose a novel feature \({\varvec{S}}ubspac{\varvec{E}}\ {\varvec{R}}{epresentation}\) and label \({\varvec{G}}{lobal\ Disambiguat}{\varvec{IO}}{n}\) (SERGIO) PLL approach, which improves the generalization ability of learning system from the perspective of both feature space and label space. Specifically, we project the original high-dimensional feature space into a low-dimensional subspace, where the projection matrix is regularized with an orthogonality constraint to make the subspace more compact. Meanwhile, we introduce a label confidence matrix and constrain it with \(\mathcal {\mathbf {\ell _{1}}}\)-norm regularization, where such constraint can be well in accordance with the nature of PLL problem and explore more global partial label correlations. Extensive experiments on various data sets demonstrate that our proposed method achieves competitive performance against state-of-the-art approaches.
Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference

Utilization of latent space to capture a lower-dimensional representation of a complex dynamics model is explored in this work. The targeted application is of a robotic manipulator executing a complex environment interaction task, in particular, cutting a wooden object. We train two flavours of Variational Autoencoders-standard and Vector-Quantised-to learn the latent space which is then used to infer certain properties of the cutting operation, such as whether the robot is cutting or not, as well as, material and geometry of the object being cut. The two VAE models are evaluated with reconstruction, prediction and a combined reconstruction/prediction decoders. The results demonstrate the expressiveness of the latent space for robotic interaction inference and the competitive prediction performance against recurrent neural networks.
A Nonpenalty Neurodynamic Model for Complex-Variable Optimization

In this paper, a complex-variable neural network model is obtained for solving complex-variable optimization problems described by differential inclusion. Based on the nonpenalty idea, the constructed algorithm does not need to design penalty parameters, that is, it is easier to be designed in practical applications. And some theorems for the convergence of the proposed model are given under suitable conditions. Finally, two numerical examples are shown to illustrate the correctness and effectiveness of the proposed optimization model.
Publicly auditable conditional blind signatures

This work formalizes Publicly Auditable Conditional Blind Signatures (PACBS), a new cryptographic primitive that allows the verifiable issuance of blind signatures, the validity of which is contingent upon a predicate and decided by a designated verifier. In particular, when a user requests the signing of a message, blinded to protect her privacy, the signer embeds data in the signature that makes it valid if and only if a condition holds. A verifier, identified by a private key, can check the signature and learn the value of the predicate. Auditability mechanisms in the form of non-interactive zero-knowledge proofs are provided, so that a cheating signer cannot issue arbitrary signatures and a cheating verifier cannot ignore the embedded condition. The security properties of this new primitive are defined using cryptographic games. A proof-of-concept construction, based on the Okamoto–Schnorr blind signatures infused with a plaintext equivalence test is presented and its security is analyzed.
A reliability index for fuzzy expert group criterion

Abstract In the paper, a fuzzy expert group criterion is presented in the form of a linguistic variable with the properties of completeness and orthogonality. The expert group criterion is created based on individual expert criteria of evaluation a quantitative or qualitative characteristic. The construction is carried out on the basis of the minimum loss of information contained in the individual criteria. For neighboring terms (concepts) of the criterion, analogues of probabilities of errors of the first and the second kinds are determined, as well as the reliability index, taking into account the zones of uncertainty of their membership functions. The reliability index of fuzzy expert group criterion is determined based on the reliability indexes of all the neighboring terms.
KNOBIE: A Design Intervention for Supporting Chefs’ Sustainable Recipe Planning Practices

Reducing the environmental impact of current food production and consumption practices is a significant challenge for a more sustainable future. Even though previous HCI studies illustrated that design interventions could support more sustainable food practices in domestic contexts, little attention has been given to hospitality contexts (e.g., restaurants). Addressing this gap, we first investigated food preparation practices in restaurants (i.e., how recipes, menus and meals are prepared) through interviews with 10 chefs and instructors of culinary arts. Then, we designed KNOBIE, a design intervention aimed at supporting chefs’ sustainable food preparation practices through better recipe and menu planning. In this paper, we present the results of these interviews, KNOBIE as a concept to support sustainable recipe planning and how interviews guided its design.
Text Block Segmentation in Comic Speech Bubbles

Comics and manga text recognition are attracting an increasing research and industrial interest. Also, the state of the art text detection and OCR performances is starting to be mature enough to provide automatic text recognition for a variety of comics and manga writing styles. However, comics text layout sometimes prevents usual text line detection to be applied successfully, even within speech bubbles. In this paper, we propose a domain specific text block detection method able to detect single and multiple text block regions inside speech bubbles, in order to enhance OCR transcription and further post-processing. This approach presents very satisfactory results on all tested bubble styles from Latin and non-Latin scripts.
Hybrid-S2S: Video Object Segmentation with Recurrent Networks and Correspondence Matching

One-shot Video Object Segmentation~(VOS) is the task of pixel-wise tracking an object of interest within a video sequence, where the segmentation mask of the first frame is given at inference time. In recent years, Recurrent Neural Networks~(RNNs) have been widely used for VOS tasks, but they often suffer from limitations such as drift and error propagation. In this work, we study an RNN-based architecture and address some of these issues by proposing a hybrid sequence-to-sequence architecture named HS2S, utilizing a dual mask propagation strategy that allows incorporating the information obtained from correspondence matching. Our experiments show that augmenting the RNN with correspondence matching is a highly effective solution to reduce the drift problem. The additional information helps the model to predict more accurate masks and makes it robust against error propagation. We evaluate our HS2S model on the DAVIS2017 dataset as well as Youtube-VOS. On the latter, we achieve an improvement of 11.2pp in the overall segmentation accuracy over RNN-based state-of-the-art methods in VOS. We analyze our model's behavior in challenging cases such as occlusion and long sequences and show that our hybrid architecture significantly enhances the segmentation quality in these difficult scenarios.
Smart Elevator Cotrol System Based on Human Hand Gesture Recognition

The rapid development of computer vision technology makes human-computer interaction possible, which has a wide range of application prospects. In this paper, we propose a gesture recognition system that can be applied to the operation of smart elevators. It can recognize different gestures of people without touching the buttons and reach the designated floor. The training data set used to train the hand gesture recognition consists of pictures and real-time frames taken by the camera. We utilize gesture segmentation, gesture tracking and other methods to preprocess the image. Then we use CNN to train the preprocessed pictures. At last, We design the user interface for computer and human interaction. The experiment shows 98.1% accuracy of static images.
Approaches for Representing Software as Graphs for Machine Learning Applications

Machine learning (ML) is making its way into the source code analysis. Most of the time, this happens with the help of Natural Language Processing (NLP) techniques. However, NLP techniques often represent their input as a sequence of tokens. This assumption is reasonable when processing text because the words related to the same object usually follow each other. However, in source code, this assumption can be inadequate simply because of the source code execution nature. Graphs can be much more adequate for representing source code. They can capture the dependency structure of a program. Due to the recent advances in the area of machine learning on graphs, researchers started to explore the graph-based representation of software in the scope of machine learning applications. There is no single way to represent a program in the form of a graph. For this reason, researchers explored different alternatives, such as function call graphs (FCG), data flow graphs (DFG), control flow graphs (CFG), or their mixtures. In this survey, we overview approaches for representing software as graphs and how these representations help to solve machine learning tasks.
Analysis of Changing Trends in Textual Data Representation

The most efficient way to convey human knowledge is through natural language text. In order to manage the exponential growth of digital data, it is high time to build a robust text information system. The system performs various techniques such as classification, clustering, summarization, etc. to organize the huge unstructured text data into well-defined forms. Text representation is the fundamental task in text mining and text retrieval. Just like a backbone gives structure to human body, text representation lays the foundation step for various other downstream tasks of Natural Language Processing (NLP), text mining and text retrieval. This paper concentrates on the analysis of various text representation models and also discusses major changing trends in textual data representation. This paper could be beneficial to those who wish to study and work on text data in NLP domain. The challenges and drawbacks of the existing text representation models are also discussed in this paper.
Research on CPU Instruction Security

Based on the research of abnormal instruction, Meltdown, Spectre, undocumented instruction and so on in the processor architecture, this paper considers that instruction security is one of the core issues of processor security. Firstly, this paper analyzes the instruction execution standard, optimization technology and the relationship between instruction and microinstruction, and then summarizes four existing security problems including instruction violation description, utilization of optimization technology, hijacking updating mechanism, undocumented instruction. Then, from the mitigation measures and hardware protection technologies, the security measures for the instruction problems are explained. Finally, it concludes that the research on undocumented instruction, semantic ambiguity of documented instruction and reverse analysis of CPU internal mechanism are the development trends of future security research.
ONLINE EDUCATION - PROBLEMS AND SOLUTIONS

The online platforms have been in a continuous development since the beginning of their appearance, being classified according to the field of activity. This article discusses some features of the online educational platforms, that serve the distance learning adopted during this pandemic period by the majority of nations on the planet, to continue the educational progress, at all levels of learning. The article is intended exclusively for the platforms used at the pre-university level of education and illustrates theoretical notions about them, represented by typologies, characteristics, and the features that platforms have been applying. The online platforms, as any other software device, possess advantages and also limitations of the available functions, and this article illustrates their approach in the context of the formative type of assessments, as well as of the summative ones. The effects of using these platforms are highlighted by using the questionnaire survey. This has the final aim to identify the perceptions of the respondents on the use of the online educational platforms for conveying the school information and also to identify the psycho-social aspects that this approach entails. At the end of the article the results of students' attendance at the online courses are presented, results that were collected based on the monitoring study conducted on a sample of students.
BD-D1Sec: Protocol of security authentication for BeiDou D1 civil navigation message based on certificateless signature

The BeiDou navigation satellite system (BDS) lacks a security authentication mechanism for BeiDou civil navigation message (BD-CNAV), which faces the threat of information spoofing attacks that may cause errors in positioning and timing information. This paper focuses on the security authentication of BeiDou D1 civil navigation message (BD-D1-CNAV) in BD-CNAV, trying to design a security authentication protocol from the perspective of information authentication. Hence, a protocol of security authentication for BD-D1-CNAV based on certificateless signature is proposed, called as BD-D1Sec protocol. For the purpose of anti-spoofing attacks on BD-D1-CNAV, BD-D1Sec protocol inserts a certificateless signature into the transmission process of BD-D1-CNAV to guarantee the integrity and authenticity of BD-D1-CNAV information without requiring certificate management, which will not reduce the availability of BD-D1-CNAV information. The simulation results show that BD-D1Sec protocol has good authentication and timeliness, a strong ability to resist spoofing attack against BD-D1-CNAV, and has the advantages of low computational complexity and communication cost.