Reviewerly/exHarmony huggingface dataset:
An Interval-centric Model for Distributed Computing over Temporal Graphs

Algorithms for temporal property graphs may be time-dependent (TD), navigating the structure and time concurrently, or time-independent (TI), operating separately on different snapshots. Currently, there is no unified and scalable programming abstraction to design TI and TD algorithms over large temporal graphs. We propose an interval-centric computing model (ICM) for distributed and iterative processing of temporal graphs, where a vertex's time-interval is a unit of data-parallel computation. It introduces a unique time-warp operator for temporal partitioning and grouping of messages that hides the complexity of designing temporal algorithms, while avoiding redundancy in user logic calls and messages sent. GRAPHITE is our implementation of ICM over Apache Giraph, and we use it to design 12 TI and TD algorithms from literature. We rigorously evaluate its performance for diverse real-world temporal graphs - as large as 131M vertices and 5.5B edges, and as long as 219 snapshots. Our comparison with 4 baseline platforms on a 10-node commodity cluster shows that ICM shares compute and messaging across intervals to out-perform them by up to 25×, and matches them even in worst-case scenarios. GRAPHITE also exhibits weak-scaling with near-perfect efficiency.
Information-theoretic approach to atomic spin nonclassicality

Nonclassicality of light has been studied, both extensively and intensively, in terms of optical coherent states, and various quantifiers for nonclassicality of light have been introduced in recent decades. In contrast, the corresponding quantification issue for atomic spin systems is relatively less studied, though there has been some remarkable progress. In this paper, by virtue of the Wigner-Yanase skew information and working in the framework of atomic coherent states, we introduce a quantifier for nonclassicality of atomic spin states from an information-theoretic perspective and reveal its fundamental properties. This quantifier leads to a simple yet powerful criterion for detecting nonclassicality via convexity of the skew information. This criterion is sufficient, but not necessary. The concept is illustrated through several paradigms. We further elucidate some intrinsic relations between nonclassicality and entanglement and indicate that the nonclassicality quantifier may be transferred to an entanglement quantifier.
PSGraph: How Tencent trains extremely large-scale graphs with Spark?

Spark has extensively used in many applications of Tencent, due to its easy deployment, pipeline capability, and close integration with the Hadoop ecosystem. As the graph computing engine of Spark, GraphX is also widely deployed to process large-scale graph data in Tencent. However, when the size of the graph data is up to billion-scale, GraphX encounters serious performance degradation. Worse, Graphx cannot support the rising advancement of graph embedding (GE) and graph neural network (GNN) algorithms. To address these challenges, we develop a new graph processing system, called PSGraph, which uses Spark executor and PyTorch to perform calculation, and develops a distributed parameter server to store frequently accessed models. PSGraph can train extremely large-scale graph data in Tencent with the parameter server architecture, and enable the training of GE and GNN algorithms. Moreover, PSGraph still benefits from the advantages of Spark via staying inside the Spark ecosystem, and can directly replace GraphX without modification to the existing application framework. Our experiments show that PSGraph outperforms GraphX significantly.
Business Process Monitoring on Blockchains: Potentials and Challenges

The ability to enable a tamper-proof distribution of immutable data has boosted the studies around the adoption of blockchains also in Business Process Management. In this direction, current research work primarily focuses on blockchain-based business process design, or on execution engines able to enact processes through smart contracts. Although very relevant, less studies have been devoted so far on how the adoption of blockchains can be beneficial to business process monitoring. This work goes into this direction by providing an insightful analysis to understand the benefits as well as the hurdles of blockchain-enabled business process monitoring. In particular, this work considers the adoption of programmable blockchain platforms to manage the generation, distribution, and analysis of business process monitoring data.
Upper bounds on number fields of given degree and bounded discriminant

Let Nn(X) denote the number of degree n number fields with discriminant bounded by X. In this note, we improve the best-known upper bounds on Nn(X), finding that Nn(X)=O(Xc(logn)2) for an explicit constant c.
Effect of economic integration on sectorial value added in sub–saharan Africa: Does financial development matter?

The need for higher sectorial output for enhanced structural transformation has long been acknowledged in the literature, with economic integration touted as one of the ways of spurring sectorial value added. However, recent studies have been less informative in examining how integration influences sectorial output, given the narrow focus of trade and financial integration measures. More tellingly, whether countries domestic financial development mediates the impact of integration on sectorial value added is yet to be explored. This study, therefore, re-engages the literature by investigating the integration–sectorial output linkage using comprehensive measures of integration and financial development. By employing data from 28 sub–Saharan African countries from 1985 to 2015, we find that higher economic integration spurs sectorial value added with robust impact in the industrial sector. This effect holds albeit disproportionately when economic integration is disaggregated into its various forms with the effect of trade integration consistently higher relative to financial integration. Further evidence shows that improved financial development significantly increases the output–enhancing effect in the industrial sector with a weak magnifying role in the agricultural and service sectors.
ReGNN: A Repeat Aware Graph Neural Network for Session-Based Recommendations

Session-based recommendations have attracted significant attention because of their broad application scenarios. Recently, graph neural networks (GNNs) have been employed in session-based recommendation because of their superior performance in representation learning compared with recurrent neural networks (RNNs). Although most existing GNN-based methods have made great achievements in this field, none of them emphasizes the importance of repeat recommendations, which has been an important component in session-based recommendation (e.g., people tend to browse product information repeatedly or revisit websites in a period of time). In this paper, we propose a novel model called ReGNN to combine a graph neural network with a repeat-exploration mechanism for better recommendations. Specifically, we dynamically process the item sequence of a session as a graph structure and capture the complex transitions between items by a GNN. Then, we formulate an exact session representation with the attention mechanism. Finally, the repeat-exploration mechanism is incorporated into the ReGNN to model the user’s repeat-exploration behavior patterns and make more accurate predictions. We conduct extensive experiments on two public datasets. The experimental results show that our proposed model ReGNN consistently outperforms other state-of-the-art methods.
RecRisk: An enhanced recommendation model with multi-facet risk control

Recommender systems (RSs) play a crucial role in helping users quickly find their desired services and promoting sales of service providers in e-commerce. However, service providers intentionally upload cherry-picked service information to mislead RSs for greater profit. Such misleading recommendations pose the risk of degrading user experience and gradually undermine users' confidence in the whole service market over the long term. Worse still, most current expert recommendation methods are more susceptible to such risk because they are heavily dependent on this incomplete service information and assume it is trusted. Therefore, how to satisfy users' service requirements with risks minimized at the same time motivates our work. In this paper, we first discern two risky facets that pose significant challenges to expert recommendation: Sense Drop and Blue Joy. Then we propose a unified framework called RecRisk, which integrates trust, heat equation, and modern portfolio theory to address the above challenges. The main contributions of RecRisk are twofold: (1) To select the services which satisfy the users' preferences, we design a trust-aware heat equation model (TAHE) that combines heat flow theory with trust elements. (2) We develop a flexible model based on modern portfolio theory to weigh users' satisfaction and services' risk facets, and finally recommend a ranked service list to users. Our experimental results demonstrate that RecRisk simultaneously achieves higher recommendation precision and decreases risks when compared to state-of-the-art approaches.
Robust regularized extreme learning machine for regression with non-convex loss function via DC program

Extreme learning machine (ELM) is considered as a powerful data-driven modeling method and has been widely used to various practical fields. It relies on the assumption that samples are completely clean without noise or worst yet. However, this is often not the case in the real-world applications, and results in poor robustness. In this paper, we focus on addressing a key issue of inefficiency in ELM when confronting with outliers. Introducing the non-convex loss function, we propose a robust regularized extreme learning machine for regression by difference of convex functions (DC) program, denoted as RRELM. The proposed non-convex loss function sets a constant penalty on any large outliers to suppress their negative effects, and can be decomposed into the difference of two convex functions. The RRELM can be successfully solved by DC optimization. Numerical experiments were conducted on various datasets to examine the validity of RRELM. Each experiment was randomly contaminated with 0%, 10%, 20%, 30% and 40% outliers levels in the training samples. We also applied RRELM to the financial time series datasets prediction. The experimental results verify that the proposed RRELM can yield superior generalization performance. Moreover, it is less affected with the increasing proportions of outliers than the competing method.
Security-Aware Autonomic Allocation of Cloud Resources

Cloud computing has emerged as a dominant platform for computing for the foreseeable future. A key factor in the adoption of this technology is its security and reliability. Here, this article addresses a key challenge which is the secure allocation of resources. The authors propose a security-based resource allocation model for execution of cloud workloads called STARK. The solution is designed to ensure security against probing, User to Root (U2R), Remote to Local (R2L) and Denial of Service (DoS) attacks whilst the execution of heterogeneous cloud workloads. Further, this paper highlights the promising directions for future research.
Face recognition methods

Abstract Nowadays, a science as cybernetics is gaining popularity. World society is on the verge of digitalization a technological and social sectors of human life. One of the most popular areas of cybernetics is computer vision. Systems of video assistants that recognize images and objects are widely distributed, and social test and control systems are introduced in test mode. In this regard, the relevance of research in this area is extremely high. This paper provides an analysis of the available methods for recognizing faces in images and discusses the software implementation of one of them.
Designing resource-constrained neural networks using neural architecture search targeting embedded devices

Recent advances in the field of Neural Architecture Search (NAS) have made it possible to develop state-of-the-art deep learning systems without requiring extensive human expertise and hyperparameter tuning. In most previous research, little concern was given to the resources required to run the generated systems. In this paper, we present an improvement on a recent NAS method, Efficient Neural Architecture Search (ENAS). We adapt ENAS to not only take into account the network's performance, but also various constraints that would allow these networks to be ported to embedded devices. Our results show ENAS' ability to comply with these added constraints. In order to show the efficacy of our system, we demonstrate it by designing a Recurrent Neural Network that predicts words as they are spoken, and meets the constraints set out for operation on an embedded device, along with a Convolutional Neural Network, capable of classifying 32x32 RGB images at a rate of 1 FPS on an embedded device.
Attenuated Access: Accounting for Startup, Maintenance, and Affective Costs in Resource-Constrained Communities

The term "digital divide" indexes a body of research at the intersection of digital technology and social equity, including research on inequality that criticizes and recapitulates the original concept. Based on a qualitative study at a community literacy center serving resettled refugees and immigrants, we show that the digital divide framework rests on a distributive logic, one that implies that distributing access to digital technology constitutes a form of social equity. Because this framework only considers valorized goods, skills, and uses, research has frequently ignored the startup, maintenance, and affective costs we found accompanied digital access for our participants. To account for these costs, we propose a theoretical adjustment to the digital divide framework, one where design is an act of configuring both costs and benefits together. We argue that considering such costs enables HCI researchers to engage more effectively with host communities in the non-innocent work of confronting inequity.
Randpay: The technology for blockchain micropayments and transactions which require recipient's consent

Randpay is a technology developed in Emercoin for blockchain micropayments that can be more effective in some scenarios than the Lightning Network, as seen in the paper. The protocol is based on the concept of Ronald L. Rivest published in the paper 'Electronic Lottery Tickets as Micropayments' (1997). The "lottery ticket" was designed for centralized systems where a trusted third party is required to provide payments, and in some scenarios, is also a lottery facilitator. The existing blockchain protocol cannot accommodate peer-to-peer "lottery" micropayments at least, without creating payment channels. Therefore, the implementation requires the development of an upgrade to the blockchain core. In the result, RandpayUTXO was introduced – an infinitely spendable zero output that requires the payee's signature to be published in the blockchain. Randpay is considered to be the first blockchain protocol to require the payee to sign the transaction by their private key. This is a significant feature to improve, not only microtransactions but also extend the use of the blockchain for legal deeds that require a payee's consent to be recognized in legal applications. The second important innovation of this research is the implementation of Blum's 'coin flipping by telephone' problem to design a 'lottery ticket' that does not require any third party to facilitate the lottery. The paper offers an analysis of the mathematical model, and proof of how 'lottery' can be beneficial, an API description is also added. There is also an attack analysis and an overview of existing solutions.
IdleStripes shirt - wearable display of sedentary time

Prior work on clothing with integrated displays has typically presented catwalk-style garments, with light output from hundreds of LEDs. In contrast, we present the IdleStripes shirt, a garment for normal daily office wear, that encourages the wearer to avoid long sedentary periods through an integrated ambient display. Rather than adding the display functionality to an existing shirt, we present a process where a clothing designer designed the smart garment from scratch, in this way creating a more aesthetic and comfortable garment. A display element in the upper chest area of the shirt utilises cloth with embedded optical fibers, stripes of which illuminate based on the wearer's inactivity. Taking a short walk then resets the display. Participants in an in-the-wild user study wore the shirt during their normal working day, and, e.g., report positively on the design of the garment and their willingness to wear it. Those that were not already frequently active during their working day, reported that the shirt encouraged them to break their sedentary behaviour and take short walks.
Should We Rush to Implement Password-less Single Factor FIDO2 based Authentication?

Fast Identity Online (FIDO) Alliance and W3C have defined a set of specifications (called FIDO2) that allows a user to replace the password based authentication system. However, none of the high profile web sites have implemented FIDO2 yet as password-less single factor (SF) authentication (password-less SF). In this paper, we analyze the set of factors that make websites reluctant to adopt password-less FIDO SF authentication. We start by comparing the threat models of password-less FIDO SF authentication with password-based SF authentication. Our analysis shows that although password-based authentication is less secure than FIDO SF authentication, other factors related to the usability of FIDO security keys and FIDO based authentication system, the non-consideration of enterprise requirements and the lack of specifications regarding account recovery/deletion and suspension are the main obstacles to the adoption of password-less FIDO SF authentication.
Secure Hierarchical Processing and Logging of Sensing Data and IoT Events with Blockchain

Recently, we have seen increasing popularity of using the blockchain technology to secure sensing data generated by traditional wireless sensor networks and Internet of Things (IoT). One of the biggest obstacles for integrating the IoT and blockchain technologies is the limited throughput of the current distributed ledgers. In this paper, we propose a novel method for hierarchical processing and logging of potentially large amount of sensing data with the blockchain technology, which could drastically address the issue of limited throughput in blockchains. Depending on the scale of the sensing need, two or more levels of processing and logging could be involved, where only the highest level of summative sensor data are placed on the blockchain or a secure distributed ledger. By establishing a strong linkage between different level of data, all lower-level and raw data are also made immutable once the highest level of summative data are placed on the blockchain.
Pushing the (Visual) Narrative: The Effects of Prior Knowledge Elicitation in Provocative Topics

Narrative visualization is a popular style of data-driven storytelling. Authors use this medium to engage viewers with complex and sometimes controversial issues. A challenge for authors is to not only deliver new information, but to also overcome people's biases and misconceptions. We study how people adjust their attitudes toward (or away from) a message experienced through a narrative visualization. In a mixed-methods analysis, we investigate whether eliciting participants' prior beliefs, and visualizing those beliefs alongside actual data, can increase narrative persuasiveness. We find that incorporating priors does not significantly affect attitudinal change. However, participants who externalized their beliefs expressed greater surprise at the data. Their comments also indicated a greater likelihood of acquiring new information, despite the minimal change in attitude. Our results also extend prior findings, showing that visualizations are more persuasive than equivalent textual data representations for exposing contentious issues. We discuss the implications and outline future research directions.
People with Motor Disabilities Using Gaze to Control Telerobots

Telerobots may give people with motor disabilities access to education, events and places. Eye-gaze interaction with these robots is an option when hands are not functional. Gaze control of telerobots has not yet been evaluated by people from this target group. We conducted a field study with five users in a care-home to investigate their preferences and challenges when driving telerobots via our gaze-controlled robotic telepresence system. We used a Wizard of Oz method to explore gaze and speech interaction, and experience prototyping to consider robot designs and types of displays (e.i. monitors versus head-mounted displays).
Improving Adversarial Neural Machine Translation for Morphologically Rich Language

Generative adversarial networks (GAN) have great successes on natural language processing (NLP) and neural machine translation (NMT). However, the existing discriminator in GAN for NMT only combines two words as one query to train the translation models, which restrict the discriminator to be more meaningful and fail to apply rich monolingual information. Recent studies only consider one single reference translation during model training, this limit the GAN model to learn sufficient information about the representation of source sentence. These situations are even worse when languages are morphologically rich. In this article, an extended version of GAN model for neural machine translation is proposed to optimize the performance of morphologically rich language translation. In particular, we use the morphological word embedding instead of word embedding as input in GAN model to enrich the representation of words and overcome the data sparsity problem during model training. Moreover, multiple references are integrated into discriminator to make the model consider more context information and adapt to the diversity of different languages. Experimental results on German↔English, French↔English, Czech↔English, Finnish↔English, Turkish↔English, Chinese↔English, Finnish↔Turkish and Turkish↔Czech translation tasks demonstrate that our method achieves significant improvements over baseline systems.
Gaze-Net

Recent studies on appearance based gaze estimation indicate the ability of Neural Networks to decode gaze information from facial images encompassing pose information. In this paper, we propose Gaze-Net: A capsule network capable of decoding, representing, and estimating gaze information from ocular region images. We evaluate our proposed system using two publicly available datasets, MPIIGaze (200,000+ images in the wild) and Columbia Gaze (5000+ images of users with 21 gaze directions observed at 5 camera angles/positions). Our model achieves a Mean Absolute Error (MAE) of 2.84° for Combined angle error estimate within dataset for MPI-IGaze dataset. Further, model achieves a MAE of 10.04° for across dataset gaze estimation error for Columbia gaze dataset. Through transfer learning, the error is reduced to 5.9°. The results show this approach is promising with implications towards using commodity webcams to develop low-cost multi-user gaze tracking systems.