Reviewerly/exHarmony huggingface dataset:
Deep Bidirectional Long Short-Term Memory for Online Arabic Writer Identification Based on Beta-Elliptic Model

The considerable development of electronic document management system in these last years has led to a large increase in the number of documents must be controlled and archived, etc. Therefore, this is important in forensic investigations, because identifying the writer could assist in solving a crime. However, there is a lack of works done in the case of online Arabic writer identification. In this paper, we propose a novel system to text independent writer identification from online Arabic handwriting. Our proposed system is based on the use of Beta-elliptic model in feature extraction. Moreover, we explore the potential utility of Deep Bidirectional Long Short-Term Memory in classification. Experimental results admitted that the proposed system has a very good performance compared to the existing online Arabic writer identification systems.
Design of Extensible Non-Fungible Token Model in Hyperledger Fabric

This paper presents an extensible non-fungible token (NFT) model for supporting NFTs in Hyperledger Fabric (Fabric) with reference to ERC-721 defined as Ethereum standard NFT and Cosmos NFT. In this model, we defined the standard structure and interface for all Fabric NFTs. This model also supports the extensible structure and interface that accommodates different types of NFTs. To demonstrate how to take advantage of this model, we applied extensible NFTs such as document and signature tokens to a decentralized signature service.
Two Stream Deep Neural Network for Sequence-Based Urdu Ligature Recognition

Urdu text is a complex cursive script and poses a challenge for recognition by OCR systems due to its large number of ligatures and cursive style. In literature, several techniques have been proposed to recognize Urdu ligatures. However, we have investigated that, suitable challenging datasets and the consequently higher recognition rate is needed for ligature recognition. In this paper, a hybrid model based on the holistic approach is adopted for the recognition of Urdu ligatures (compound characters). More than 3800 unique ligatures were used to generate 46K (38K training, 7K testing) synthetic ligatures with 9 different kinds of transformations along with the normal ligatures. Each ligature is processed through two streams of Deep Neural Networks, namely Alexnet and Vgg16 to obtain a unique set of features corresponding to each net. These features are fused and then used as an input to double layer Bidirectional Long Short Term (BLSTM) network for learning a model. The learned model maps ligature images to their corresponding sequence of individual Urdu characters. In the proposed methodology output is in the editable Urdu-script format. The proposed model was evaluated and have shown an accuracy of 97% on the training dataset and 80% on more than 7K parametrically different query ligatures (test-set).
The Intersection of User Experience (UX), Customer Experience (CX), and Brand Experience (BX)

Regarding the definition of user experience, products cannot simply be regarded as isolated items, which users experience only by actively using them: As user experience can occur before, during, and after use, customer experience and brand perception are important factors, which need to be considered holistically. For optimizing user experience, designers have to define goals. While it is self-evident and generally expected that pragmatic aspects like functionality, usability, and user performance are taken into account, hedonic aspects have to be considered too: By defining goals based on the individual brand promise, products have their own identity following the personal values of the audience. By this, the hedonic quality of identification and stimulation can be achieved in all touchpoints of an experience (client support, advertisement, user interface design, interaction design, among others). Consequently, it is not possible to separate the disciplines of user experience (UX), customer experience (CX), and brand experience (BX). This chapter describes the theory of user experience and explains interrelationships between the co-disciplines of customer experience and brand experience, e.g., by involving the formal definition of user experience, brand experience, and customer experience, relevant scientific theories, as well as practical knowledge. These insights will provide the interested reader with valuable knowledge about what aspects they have to take into account when designing products for enjoyable experiences. Moreover, this chapter will provide an overview of technology-driven trends and give guidance on how to design these new touchpoints in order to create a holistic user-driven and brand-driven experience. Voice interfaces, smart objects, and the Internet of Things (IoT), as well as the intersections of virtual reality (VR) and augmented reality (AR), have to be regarded specifically with their very own characteristics in order to apply and design them in the best possible way.
A weak limit theorem for a class of long-range-type quantum walks in 1d

We derive the weak limit theorem for a class of long-range-type quantum walks. To do it, we analyze spectral properties of a time evolution operator and prove that modified wave operators exist and are complete.
CFRS: A Trends-Driven Collaborative Fashion Recommendation System

Fashion has a great impact in everyday life and therefore, people pay close attention to the way they dress. Fashion item recommendation is typically a manual, curated process, where experts recommend items and trends to large populations. However, there is increasing use of automated, personalized recommendation systems, which have valuable applications in e-commerce websites. In this paper, we propose a collaborative fashion recommendation system, called CFRS. Apart from classic features, we propose a new metric, called trend score. Trend score shows how trendy a product is and is calculated taking into account the ratings provided by CFRS users (fashion experts and registered users). In particular, users rate (like/ dislike scale) current trends about colors, prints and materials. Finally, trend score is used a) for sorting products of each category from trendiest options to classic ones and b) to recommend trendy products from different clothing categories.
Specializing FGPU for Persistent Deep Learning

Overlay architectures are a good way to enable fast development and debug on FPGAs at the expense of potentially limited performance when compared to fully customized FPGA designs. When used in concert with a hand-tuned FPGA solution, a performant overlay architecture can improve the time-to-solution and thus overall productivity of FPGA solutions. In this work, we tune and specialize FGPU, an open source OpenCL-programmable GPU overlay for FPGAs. We demonstrate that our PDL-FGPU architecture is able to maintain the ease-of-programming and generality of a software programmable soft GPU while achieving high performance due to specialization in the persistent deep learning domain. We also propose a easy method to specialize for different domains. PDL-FGPU includes new instructions, along with micro-architecture and compiler enhancements. We evaluate both the FGPU baseline and the proposed PDL-FGPU on a modern high-end Intel Stratix 10 2800 FPGA running a set of persistent DL applications (RNN, GRU, LSTM), as well as general non-DL applications to demonstrate generality. PDL-FGPU requires 1.5-3x more ALMs, 4.4-6.4x more M20ks, and 4.6-10x more DSPs than the FGPU baseline, but improves performance by 55-727x for persistent DL applications with an average 15% degradation on general non-PDL applications. We also demonstrate that the PDL-FGPU is only 4-7x slower than the Nvidia Volta V100 GPU.
Exploiting Bank Conflict-based Side-channel Timing Leakage of GPUs

To prevent information leakage during program execution, modern software cryptographic implementations target constant-time function, where the number of instructions executed remains the same when program inputs change. However, the underlying microarchitecture behaves differently when processing different data inputs, impacting the execution time of the same instructions. These differences in execution time can covertly leak confidential information through a timing channel. Given the recent reports of covert channels present on commercial microprocessors, a number of microarchitectural features on CPUs have been re-examined from a timing leakage perspective. Unfortunately, a similar microarchitectural evaluation of the potential attack surfaces on GPUs has not been adequately performed. Several prior work has considered a timing channel based on the behavior of a GPU’s coalescing unit. In this article, we identify a second finer-grained microarchitectural timing channel, related to the banking structure of the GPU’s Shared Memory. By considering the timing channel caused by Shared Memory bank conflicts, we have developed a differential timing attack that can compromise table-based cryptographic algorithms. We implement our timing attack on an Nvidia Kepler K40 GPU and successfully recover the complete 128-bit encryption key of an Advanced Encryption Standard (AES) GPU implementation using 900,000 timing samples. We also evaluate the scalability of our attack method by attacking an implementation of the AES encryption algorithm that fully occupies the compute resources of the GPU. We extend our timing analysis onto other Nvidia architectures: Maxwell, Pascal, Volta, and Turing GPUs. We also discuss countermeasures and experiment with a novel multi-key implementation, evaluating its resistance to our side-channel timing attack and its associated performance overhead.
Defining and measuring fairness in location recommendations

Location-based recommender systems learn from historical movement traces of users in order to make recommendations for places to visit, events to attend, itineraries to follow. As with other systems assisting humans in their decisions, there is an increasing need to scrutinize the implications of algorithmically made location recommendations. The challenge is that one can define different fairness concerns, as both users and locations may be subjects of unfair treatment. In this work, we propose a comprehensive framework that allows the expression of various fairness aspects, and quantify the degree to which the system is acting justly. In a case study, we focus on three fairness aspects, and investigate several types of location-based recommenders in terms of their ability to be fair under the studied aspects.
Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion

Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method.
Faster slab reassignment in memcached

Web applications, databases, and many datacenter services rely on in-memory key-value stores to cache frequently accessed data. In this work, we focus on a commonly used system, memcached, where even small performance improvements can result in large end-to-end speed ups in request latency. memcached organizes its memory into slabs that belong to different classes corresponding to object sizes. Many prior works have explored the problem of how many slabs should each class be assigned in the face of dynamic workloads, typically reassigning hundreds of slabs during a reassignment. However, we find that as workloads scale and applications use increasing amounts of memory, the current reassignment mechanism in memcached is inefficient. In fact, we measure that reassignments can take millions of requests to complete.
FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources

The FAIR principles articulate the behaviors expected from digital artifacts that are Findable, Accessible, Interoperable and Reusable by machines and by people. Although by now widely accepted, the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors. As different communities have their own, often well-established implementation preferences and priorities for data reuse, coordinating a broadly accepted, widely used FAIR implementation approach remains a global challenge. In an effort to accelerate broad community convergence on FAIR implementation options, the GO FAIR community has launched the development of the FAIR Convergence Matrix. The Matrix is a platform that compiles for any community of practice, an inventory of their self-declared FAIR implementation choices and challenges. The Convergence Matrix is itself a FAIR resource, openly available, and encourages voluntary participation by any self-identified community of practice (not only the GO FAIR Implementation Networks). Based on patterns of use and reuse of existing resources, the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services.
A discrete-time switched linear model of the particle swarm optimization algorithm

In this paper, the convergence issue of the Particle Swarm Optimization (PSO) algorithm is investigated. Most of the models of PSO algorithms are time-invariant linear models with the assumption the local and global best solutions do not change, i.e., the stagnation assumption. However, in this paper, a discrete-time switched linear model is introduced to study the stability and convergence of the PSO algorithm without the stagnation assumption. By considering the updates of local best positions and global best solutions, a sequence of state transform matrixes is generated during the searching process. The semistability of the proposed switched linear system is studied. The conditions of the convergence in mean and convergence in probability are derived by using the recently developed results in paracontraction. Moreover, numerical examples are provided to verify the results proposed in this paper.
Weak Moving Object Detection In Optical Remote Sensing Video With Motion-Drive Fusion Network

Object detection in optical remote sensing video (ORSV) is a new trend which makes it possible for obtaining richer information in more complicated and diverse situations. However, the small objects are blurred in the videos captured from optical sensor assembled in satellite, limited by the devices and natural weather. The concept of weak object is defined in this situation that the objects are extremely small and hardly detected with only one static image. Therefore, we propose a simple but efficient method for weak moving object detection in ORSV by combining the temporal information from neighbor frames and spatial features from image pixels. First, we compute the difference map between two adjacent frames, and stack it with original RGB channel so that a (1+3)-channel input data is made. Then, a motion-drive D-RGB (difference map with RGB image) fusion network is developed to obtain the feature map of this (1+3)-channel data. To adapt unusual scale in ORSV images, based on statistical prior objects size, we change the size of anchor box in original Faster R-CNN. The proposed method is demonstrated to improve the mean average precision on detecting weak moving remote sensing objects.
FollowMeUp Sports: New Benchmark for 2D Human Keypoint Recognition

Human pose estimation has made significant advancement in recent years. However, the existing datasets are limited in their coverage of pose variety. In this paper, we introduce a novel benchmark "FollowMeUp Sports" that makes an important advance in terms of specific postures, self-occlusion and class balance, a contribution that we feel is required for future development in human body models. This comprehensive dataset was collected using an established taxonomy of over 200 standard workout activities with three different shot angles. The collected videos cover a wider variety of specific workout activities than previous datasets including push-up, squat and body moving near the ground with severe self-occlusion or occluded by some sport equipment and outfits. Given these rich images, we perform a detailed analysis of the leading human pose estimation approaches gaining insights for the success and failures of these methods.
The influence of parents in the discursive construction of technology-mediated learning experiences

In the new learning ecology, Information and Communication Technologies (ICTs) promote the learning of children and young people in a variety of contexts and also shape their learning trajectories. However, empirical studies often ignore how learners construct narratives about their own learning trajectories and about what it means to learn with ICTs. Thus, we propose Subjective Learning Experiences (SLEs) as a unit of analysis for studying personal learning trajectories. SLEs allow us to investigate learning both across settings and over time. To illustrate our empirical approach, we present a brief study in which we explore how dominant discursive constructions, from parents, about what it means to learn with ICTs influence the learning experiences of children and young people. For this study, we conducted semi-structured interviews with 30 Spanish children and their parents, using thematic analysis to identify their discursive constructions. Our results provide evidence that discursive constructions about what it means to learn, present in the contexts of activity in which children and young people participate, influence their learning experiences. We conclude that our empirical approach advances the analysis of personal learning trajectories by allowing us to capture the fluid and boundless nature of learning across settings and time.
Volunteer Work

Research on the governance of online communities often requires exchanges and interactions between researchers and moderators. While a growing body of work has studied commercial content moderation in the context of platform governance and policy enforcement, only a small number of studies have begun to explore the work of unpaid, volunteer community moderators who manage the millions of different subcommunities that exist on platforms. This workshop will create a pathway for future scholars to tackle the challenges and opportunities of research on volunteer community moderators and establish best practices for engaging with volunteer moderators without disrupting their work. Through lightning talks, collaborative brainstorming exercises, and small-group activities applying principles to research practice, workshop participants will bring together their diverse experiences and perspectives to map the future of moderation research. Both industry and academic researchers as well as experienced moderators will lead this one-day workshop that may accommodate up to 20 participants.
Minimum Error Seam-Based Efficient Panorama Video Stitching Method Robust to Parallax

From stitching video sequences point of view, an existing image stitching technique may have limitations on object movement or loss in the stitched video sequence due to the different parallax of the individual video sequences. Also, an existing image stitching technique have higher computational complexity and have matching accuracy limitations in obtaining descriptors for many factors. Therefore, this paper proposes an edge-based weight minimum error seam method in order to overcome the limitations caused by parallax and video stitching and trigonometric ratio-based image match algorithm in order to reduce computational complexity and insensitivity to rotation, illumination, image size, or resolution.
Jointly Extract Entities and Their Relations From Biomedical Text

Entity recognition and relation extraction have become an important part of knowledge acquisition, and which have been widely applied in various fields, such as Bioinformatics. However, prior state-of-the-art extraction models heavily rely on the external features obtained from hand-craft or natural language processing (NLP) tools. As a result, the performance of models depends directly on the accuracy of the obtained features. Moreover, current joint extraction approaches cannot effectively tackle the multi-head problem (i.e. an entity is related to multiple entities). In this paper, we firstly present a novel tagging scheme and then propose a joint approach based deep neural network for producing unique tagging sequences. Our approach can not only simultaneously perform entity resolution and relation extraction without any external features, but also effectively solve the multi-head problem. Besides, since arbitrary tokens may provide important cues for two components, we exploit self-attention to explicitly capture long-range dependencies among them and character embeddings to learn the features of lexical morphology, which make our method less susceptible to cascading errors. The results demonstrate that the joint method proposed outperforms the other state-of-the-art joint models. Our work is beneficial for biomedical text mining, and the construction of the biomedical knowledge base.
Visual Prediction of Typhoon Clouds With Hierarchical Generative Adversarial Networks

We develop a hierarchical generative adversarial network (HGAN) for generating future typhoon cloud remote sensing images, which enables a visual means to typhoon cloud prediction. The HGAN consists of a global generator and a local discriminator. The global generator aims at producing the future typhoon cloud images as realistic as possible and accordingly reveals the structure and future location of the typhoon clouds. It is constructed in terms of a hierarchical architecture with multiple subnetworks, which capture the overall typhoon variations and favor generating clear future typhoon cloud images. The local discriminator tries its best to distinguish generated typhoon cloud images from ground-truth ones, based on the local patches. The local procedure encourages the discriminator to focus on characterizing the moving typhoon clouds rather than the still background. The global generator and the local discriminator are trained in an adversarial fashion with respect to historical typhoon cloud image sequences. The trained HGAN is capable of producing reliable visual predictions that are not only enabled by the global generator and but also examined by the local discriminator. Experiments validate the effectiveness of the HGAN for typhoon cloud prediction.