Reviewerly/exHarmony huggingface dataset:
Handwritten Chess Scoresheet Recognition Using a Convolutional BiLSTM Network

Chess players while playing in Over-the-Board (OTB) events use scoresheets to record their moves by hand, and later the event organizers digitize these sheets for an official record. This paper presents a framework for decoding these handwritten scoresheets automatically using a convolutional BiLSTM neural network designed and trained specifically to handle chess moves. Our proposed network is pretrained with the IAM handwriting dataset [] and later fine-tuned with our own Handwritten Chess Scoresheet (HCS) dataset [, ]. We also developed two basic post-processing schemes to improve accuracy by cross-checking moves between the two scoresheets collected from the players with white and black pieces. The autonomous post-processing involves no human input and achieves a Move Recognition Accuracy (MRA) of 90.1% on our test set. A second semi-autonomous algorithm involves requesting user input on certain unsettling cases. On our testing, this approach requests user input on 7% of the cases and increases MRA to 97.2%. Along with this recognition framework, we are also releasing the HCS dataset which contains scoresheets collected from actual chess events, digitized using standard cellphone cameras and tagged with associated ground truths. This is the first reported work for handwritten chess move recognition and we believe this has the potential to revolutionize the scoresheet digitization process for the thousands of chess events that happen each day.
Designing a Cybersecurity Curriculum Library: Best Practices from Digital Library Research

The global cybersecurity crisis has forced academic institutions to expand cybersecurity education, and accessible, quality cybersecurity curriculum is needed. The CLARK cybersecurity curriculum resource library is a large-scale Design Science Research project intended to address critical demands in cybersecurity. This paper focuses on the need for a living digital library, surveys current cybersecurity repositories, and discusses factors that determine the success of a digital library. We also discuss CLARK, a multifaceted solution that includes various lenses for building and sustaining a living library of cybersecurity curriculum.
Understanding and Improving User Engagement in Adaptive Video Streaming

Today’s video service providers all desire to deeply understand the ever changing factors on user QoE to attract more users. In this paper, we study the user engagement with respect to video quality metrics and improve user engagement in adaptive video streaming systems. We conduct a comprehensive study of the real data from iQIYI, covering 700K users and 150K videos. We find bitrate switch becomes the new dominant factor on user engagement instead of rebuffering events. We also observe the impact of rate of rebuffering is more dominant than rebuffering time. We examine novel interdependencies between quality metrics in the system, e.g., the positive correlation between bitrate switch and average bitrate, which is due to the context system strategy, i.e., conservative bitrate enhancing strategy adopted by iQIYI. To improve user engagement, we propose a new engagement centric QoE function based on real data and design server side ABR algorithm which leverages our new QoE function. We evaluate our method for online test in iQIYI, with 490K real users viewing 666K streams. The results show our approach outperforms existing approaches by significantly improving the viewing time, i.e., 2.8 minutes longer viewing time per user.
Digital Repository Legacies: A Case Study in Assessing Organizational Trustworthiness

Academic libraries rarely discuss cases of digital repositories that do not meet the standards expected of trusted digital repositories. Implications from inconsistent adherence to technical and professional criteria often surface during migration projects. In 2020, Stony Brook University Libraries began migrating assets to a mono-repository environment. Persistent historical factors presented challenges to repository trustworthiness. This case study discusses a survey project to evaluate legacy repository statuses in the contexts of infrastructure, documentation, and staff capacity. It considers a paradigm of organizational accountability in digital asset stewardship and offers insights for reconciling inherited legacies with aspirations to be a trusted repository.
Spread-Spectrum Watermarking Model Using a Parity-Check Code for Simultaneous Restoration of Message and Image

In this study, we propose a spread-spectrum watermarking model using a parity-check code for simultaneous restorations of the message and image. In our model, in addition to the stego image, the parity-ckeck code is sent to the receiver as supplementary information. To restore both the message and image simultaneously, we assume that the parity-check code comprises both the message and image, one selected from the message and the other from the image. We apply the replica method to obtain equations of self-consistent order parameters, and compare the theoretical results with the Monte Carlo simulations.
A Control Plane for WireGuard

WireGuard is a VPN protocol that has gained significant interest recently.Its main advantages are: (i) simple configuration (via pre-shared SSH-like public keys), (ii) mobility support, (iii) reduced codebase to ease auditing, and (iv) Linux kernel implementation that yields high performance.However, WireGuard (intentionally) lacks a control plane.This means that each peer in a WireGuard network has to be manually configured with the other peers' public key and IP addresses, or by other means.In this paper we present an architecture based on a centralized server to automatically distribute this information.In a nutshell, first we manually establish a WireGuard tunnel to the centralized server, and ask all the peers to store their public keys and IP addresses in it.Then, WireGuard peers use this secure channel to retrieve on-demand the information for the peers they want to communicate to.Our design strives to: (i) offer a key distribution scheme simpler than PKI-based ones, (ii) limit the number of public keys sent to the peers, and (iii) reduce tunnel establishment latency by means of an UDP-based protocol.We argue that such automation can help the deployment in enterprise or ISP scenarios.We also describe in detail our implementation and analyze several performance metrics.Finally, we discuss possible improvements regarding several shortcomings we found during implementation.
Question-Guided Semantic Dual-Graph Visual Reasoning with Novel Answers

Visual Question Answering (VQA) has gained increasing attention as being the cross-disciplinary research of computer vision and natural language understanding. However, recent advances mostly treated it as a closed-set classification problem, by limiting the possible outputs to some fixed frequent answers available in a training set. Although effective on benchmark datasets, this paradigm is inherently defective---the VQA model would always fail on a question whose correct answer is out of the answer set, which severely hampers its generalization and flexibility. To try to close the gap, we explore an open-set VQA setting, where models are evaluated using novel samples with unseen answers given dynamic candidate answers from some candidate-generation module. For experimental purposes, two oracle candidate-sampling strategies are proposed to serve as a proxy for the candidate-generation module and generate dynamic candidate answers for testing samples. The conventional classification-based paradigm is no longer applicable in our setting. To this end, we design a matching based VQA model, in which a novel Single-Source Graph Convolutional Network (SSGCN) module is designed to jointly leverage question guidance and dual semantic answer-graphs to produce more discriminative and relevant answer embeddings. Extensive experiments and ablation studies by re-purposing two benchmark datasets demonstrate the effectiveness of our proposed model.
Application of Data Augmentation Techniques for Hate Speech Detection with Deep Learning

In the past decade, there has been a great increase in the usage of social media, and with it also an increase on dissemination of online hate-speech. Some of the most advanced techniques for online hate-speech detection are based on deep learning. Unfortunately, this kind of technique requires a large amount of labeled data, which is not so easy to find. One way of trying to overcome this problem is with the use of data augmentation techniques. The present paper explores data augmentation in order to improve the performance of deep neural networks on the task of hate speech detection on a small dataset in Portuguese.
Towards A Privacy-Preserving Voting System Through Blockchain Technologies

The electing framework or casting a ballot framework is the arrangement of decides that decide how decisions and political race decision are directed in the method of the outcomes that must be resolved. Political race plays an essential occasion during a cutting edge popular government yet huge areas of society all throughout the planet don't confide in a political decision framework, which would be a significant worry for the majority rules system. Besides the world's biggest majority rule governments like India, the US Japan actually experience the ill effects of misrepresentation casting a ballot framework. Political race control, polling form gear and hacking of electronic democratic machine are the fundamental issues inside the current democratic framework. Building and electronic democratic framework that fulfills the lawful necessities of gathering has been a tested for quite a while. The disseminated record innovation is an energizing mechanical in this innovative world. These blockchain advancements offers an endless scope of utilizations taking benefits of sharing political economy. In this paper , the proposed framework is to store the voting form data as a blockhub for every single elector and move the citizens vote to all or some other ''hub''. Our proposed framework is cost productive in contrast with the typical electronic democratic machines.
Style-Guided Image-to-Image Translation for Multiple Domains

The cross-domain image translation has drawn more and more attention. It aims to translate images from a source domain into target domains, such that images can appear in multiple styles. The most popular approaches are using encoders to extract style features from the source domain and then pushing them into a generator to produce new images. However, these methods usually only suit for two domains translation, and present low diversity in multiple domains since the extracted features are roughly used as input for the generator, instead of making full use of them. In this paper, we design a novel loss function, style-guided diversity loss (Sd loss), which utilizes the extracted style features to encourage our model exploring the image space and discovering diverse images. It is proved theoretically that the proposed loss is better than the diversity sensitive loss in the state-of-the-art approaches. In addition, qualitative and quantitative experiments demonstrate the superiority of the proposed approach against several state-of-the-art approaches in terms of the quality and the diversity of translated images.
SmaQ: Smart Quantization for DNN Training by Exploiting Value Clustering

Advancements in modern deep learning have shown that deeper networks with larger datasets can achieve state of the art results in many different tasks. As networks become deeper, the memory requirement of neural network training proves to be the primary bottleneck of single-machine training. In this letter, we first study the characteristics of neural network weight, gradient, feature map, gradient map, and optimizer state distributions for some popular neural network architectures. Our investigation shows that the majority of the data structures used by neural networks can have their value distributions be approximated with normal distributions. We then introduce Smart Quantization (SmaQ), a quantization scheme that exploits this observed normal distribution to quantize the data structures. Our dynamic quantization method calculates the sampled mean and standard deviation of tensors and quantizes each tensor element to 6 or 8 bits based on the z-score of that value. Our scheme reduces the memory usage during training by up to 6.7x with minor losses in accuracy.
Semantic Hilbert Space for Interactive Image Retrieval

The paper introduces a model for interactive image retrieval utilising the geometrical framework of information retrieval (IR). We tackle the problem of image retrieval based on an expressive user information need in form of a textual-visual query, where a user is attempting to find an image similar to the picture in their mind during querying. The user information need is expressed using guided visual feedback based on Information Foraging which lets the user perception embed within the model via semantic Hilbert space (SHS). This framework is based on the mathematical formalism of quantum probabilities and aims to understand the relationship between user textual and image input, where the image in the input is considered a form of visual feedback. We propose SHS, a quantum-inspired approach where the textual-visual query is regarded analogously to a physical system that allows for modelling different system states and their dynamic changes thereof based on observations (such as queries, relevance judgements). We will be able to learn the input multimodal representation and relationships between textual-image queries for retrieving images. Our experiments are conducted on the MIT States and Fashion200k datasets that demonstrate the effectiveness of finding particular images autocratically when the user inputs are semantically expressive.
The Experiences and Self-Efficacy of Faculty Members Using Distance Learning for the First Time

The purpose of this study was to understand faculty members' experience, self-efficacy, and sense-making processes after teaching via online distance learning for the first time in the Spring, Summer, and Fall 2020 academic terms (i.e., during the COVID-19 pandemic). The research also sought to understand how participants described their teaching and learning methods and approaches after having completed the three academic terms and received students' feedback. This study aimed to use the findings to develop and refine the preparation and procedural guidance offered to first-time distance learning teachers to improve their methods, approaches, and experiences. Using a phenomenological analysis, the researcher collected data from 70 interviews and seven focus groups. The results recommended school leadership to reform their current distance learning-based programs, and to first-time users of distance learning in teaching faculties to help them reflect on their teaching and learning methods and approaches based on a self-efficacy perspective.
Continuous Flow Measurement with SuperFlow

Flow-based network measurement enables operators to perform a wide range of network management tasks in a scalable manner. Recently, various algorithms have been proposed for flow record collection at very high speed. However, they all focus on processing traffic in a short time window, but overlook the fact that flow measurements are typically needed continuously for unlimited time. To this end, we propose a new algorithm named SuperFlow to support continuous and accurate flow record collection at very high speed by monitoring the flow activeness and exporting the inactive records from the data plane automatically. Our data structures and the corresponding algorithms are carefully designed and analyzed, so the above goal is achieved with limited memory and bandwidth consumption. We implement SuperFlow on both x86 CPU and state-of-the-art PISA target. Comprehensive experiments show that SuperFlow consistently outperforms its competitors significantly. Especially, compared with the best competitor, it records around 136.7% more flows, reduces the error in flow size estimation by 51.5%, and reduces the memory or bandwidth consumption by up to 71.0%, while bringing only negligible throughput degradation.
Quantized convolutional neural networks through the lens of partial differential equations

Quantization of convolutional neural networks (CNNs) is a common approach to ease the computational burden involved in the deployment of CNNs, especially on low-resource edge devices. However, fixed-point arithmetic is not natural to the type of computations involved in neural networks. In this work, we explore ways to improve quantized CNNs using PDE-based perspective and analysis. First, we harness the total variation (TV) approach to apply edge-aware smoothing to the feature maps throughout the network. This aims to reduce outliers in the distribution of values and promote piecewise constant maps, which are more suitable for quantization. Secondly, we consider symmetric and stable variants of common CNNs for image classification and graph convolutional networks for graph node classification. We demonstrate through several experiments that the property of forward stability preserves the action of a network under different quantization rates. As a result, stable quantized networks behave similarly to their non-quantized counterparts even though they rely on fewer parameters. We also find that at times, stability even aids in improving accuracy. These properties are of particular interest for sensitive, resource-constrained, low-power or real-time applications like autonomous driving.
Secure and Light Weight Elliptic Curve Cipher Suites in SSL/TLS

In the current circumstance, e-commerce through an online banking system plays a significant role.Customers may either buy goods from E-Commerce websites or use online banking to move money to other accounts.When a user participates in these types of behaviors, their sensitive information is sent to an untrustworthy network.As a consequence, when transmitting data from an internal browser to an external E-commerce web server using the cryptographic protocol SSL/TLS, the E-commerce web server ensures the security of the user's data.The user should be pleased with the confidentiality, authentication, and authenticity properties of the SSL/TLS on both the user's web browser and the remote E-commerce web server.E-Commerce web servers should choose the best SSL/TLS cipher suites for negotiating the user in order to attain such optimistic scenarios, as the cipher suite used in SSL/TLS plays an important role in securing E-Commerce web servers.The paper primarily focuses on analyzing the SSL/TLS cipher and elliptic curves.The paper also recommends the best elliptic curve cipher suites for E-Commerce and online banking servers, based on their power consumption, handshake execution time, and key exchange and signature verification time.
Extreme Maneuvering Control and Planning of Multi-Rotor UAV for High-Speed Invading Target Avoidance

Quadrotor UAVs are widely used in military, ecological, environmental, and other fields because of their many advantages, such as flexible movement, vertical takeoff and landing, quadrotor UAVs require the active obstacle avoidance capabilities for invading objects in complex scenarios, while current research is mainly focused on obstacle avoidance under smooth motion conditions, resolve the dynamic high-speed or sudden invading object avoidance problem is difficult. In this paper, we propose a novel active avoidance method for high-speed object invasion based on extreme maneuver controlling. First, design the hybrid control system for the quadrotor UAV to switch between the smooth flight mode and the extreme maneuvering mode respectively. Second, plan the UAV's trajectory and angular rate to ensure high-speed smooth flight and perform extreme maneuvers. Finally, we carried out three comparative experiments and the results demonstrate extreme maneuvering obstacle avoidance strategy is effective.
Head orientation control of projection area for projected virtual hand interface on wheelchair

A projected virtual hand interface, which visually extends the arms of users using projection images, enables wheelchair users to reach unreachable objects; however, its projection area is usually smaller than the reaching area for its users. In our previous research, we developed a wheelchair system with a projected virtual hand system in which users can move the projected area via finger movements on a touch panel. However, it requires some time to move the projected area to the desired position. To address this problem, we propose a wheelchair system enhanced with a projected virtual hand that allows controlling a projection area using a user's head orientation. The proposed system measures the current user's head orientation and distance between a user and a projection surface. Then, it adapts the suitable pan and tilt of the projector by considering its positional relationship with the projection plane. As users can operate the projection area simply by turning their head, this operation can be executed simultaneously with operating a virtual hand using their hands. We propose a control model for projector rotation according to the user's head orientation. We implemented a prototype based on the model and confirmed the system's latency. The usability study revealed that the proposed method enables users to perform pointing tasks in a shorter time compared with the existing method. Moreover, it has acceptable interface usability.
Detecting Electronic Banking Fraud on Highly Imbalanced Data using Hidden Markov Models

Recent researches have revealed the capability of Machine Learning (ML) techniques to effectively detect fraud in electronic banking transactions since they have the potential to detect new and unknown intrusions. A major challenge in the application of ML to fraud detection is the presence of highly imbalanced data sets. In many available datasets, majority of transactions are genuine with an extremely small percentage of fraudulent ones. Designing an accurate and efficient fraud detection system that is low on false positives but detects fraudulent activity effectively is a significant challenge for researchers. In this paper, a framework based on Hidden Markov Models (HMM), modified Density Based Spatial Clustering of Applications with Noise (DBSCAN) and Synthetic Minority Oversampling Technique Techniques (SMOTE) is proposed to effectively detect fraud in a highly imbalanced electronic banking dataset. The various transaction types, transaction amounts and the frequency of transactions are taken into consideration by the proposed model to enable effective detection. With different number of hidden states for the proposed HMMs, simulations are performed for four (4) different approaches and their performances compared using precision, recall rate and F1-Score as the evaluation metrics. The study revealed that, our proposed approach is able to detect fraudulent transactions more effectively with reasonably low number of false positives.