Reviewerly/exHarmony huggingface dataset:
Investigating online consumers’ responses to product presentation modes

Purpose With the proliferation of virtual reality (VR) applications in electronic commerce, investigations on the effects of VR on consumer responses are important. The purpose of this paper is to examine the effects of online presentation modes (i.e. situational VR, pure VR and picture) on consumer responses for three product types (i.e. geometric, material and mechanical). Design/methodology/approach This study conducted a 3×3 between-subjects experiment to validate the research model and hypotheses. Findings The results revealed that both the situational VR mode and the pure VR mode had a greater impact on product knowledge and purchase intention than the picture mode. The situational VR mode yielded a higher level of product knowledge and purchase intention than the pure VR mode although it was not statistically significant. Furthermore, the pattern of VR modes superiority was found to be consistent across geometric, material and mechanical product types. Originality/value This research study contributes to the VR literature by investigating a new type of VR: situational VR, and offering a more comprehensive picture of consumer responses to online product presentations. The authors then drew the implications from the findings to suggest guidelines for practitioners to efficiently allocate resources and maximize the effectiveness of online presentation modes.
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

Gaussian processes (GPs) provide a powerful framework for extrapolation, interpolation, and noise removal in regression and classification. This paper considers constraining GPs to arbitrarily-shaped domains with boundary conditions. We solve a Fourier-like generalised harmonic feature representation of the GP prior in the domain of interest, which both constrains the GP and attains a low-rank representation that is used for speeding up inference. The method scales as O(nm^2) in prediction and O(m^3) in hyperparameter learning for regression, where n is the number of data points and m the number of features. Furthermore, we make use of the variational approach to allow the method to deal with non-Gaussian likelihoods. The experiments cover both simulated and empirical data in which the boundary conditions allow for inclusion of additional physical information.
Identifying social roles using heterogeneous features in online social networks

Role analysis plays an important role when exploring social media and knowledge‐sharing platforms for designing marking strategies. However, current methods in role analysis have overlooked content generated by users (e.g., posts) in social media and hence focus more on user behavior analysis. The user‐generated content is very important for characterizing users. In this paper, we propose a novel method which integrates both user behavior and posted content by users to identify roles in online social networks. The proposed method models a role as a joint distribution of Gaussian distribution and multinomial distribution, which represent user behavioral feature and content feature respectively. The proposed method can be used to determine the number of roles concerned automatically. The experimental results show that the proposed method can be used to identify various roles more effectively and to get more insights on such characteristics.
Institutional Quality and Globalization in Developing Countries

The main purpose of this chapter is to analyze the theoretical relationship between globalization and institutional quality and the empirical analysis of this linking in developing countries. For this aim, this chapter seeks to answer three main questions: (1) How do institutions affect globalization (trade openness)? (2) Can the economic globalization and trade openness cause institutional changes? If the answer is positive, does globalization lead to an improvement in the institutional quality or its deterioration? (3) Is there any causal relationship between globalization and institutional quality in developing countries? To answer these questions, we use analytical-descriptive methods and econometric methods including Granger-type causality test based on panel vector error correction model (PVECM). The theoretical findings of this chapter show that the good institutional quality via various channels affects the volume, structure, and composition of the trade. Also, economic globalization may improve (or deteriorate) the quality of institutions, but the kind and the extent of its influence depend on the type of institutional system and institutional structure of countries. The descriptive analysis of data (status of globalization and institutional quality) in developing countries showed that the trend of economic globalization is not favorable in comparison with the world trend. In addition, compared to both three dimensions of globalization and the world as a whole, it presents an unfavorable situation. On the other hand, the position of institutional quality, in particular the quality of regulation and the effectiveness of governments (of the vital factors of trade expansion), has the worst situation. The results of Granger-type causality test showed that there is no causal relationship between economic globalization and legal-economic institutions (such as the rule of law and government effectiveness) in the short term, but there is at least one causal relationship in the long run. This relationship with the index of the rule of law is bidirectional and with other indexes is unidirectional. Also, the findings of this study show that in the short and long run, political globalization is the cause of political institutions (political stability and voice) and social globalization is the cause of social institutions. Therefore, the globalization view of institutional change can be cautiously supported.
Blockchain Development and Fiduciary Duty

This article is legal literature’s first detailed analysis of the operations and incentives involved in public blockchain development. We characterize the governance structures of public blockchain network participants and describe the cryptocurrency-based incentives aimed to align the motivations of network participants. We dissect the governance process of development to examine whether fiduciary obligations should be imposed on core developers for the benefit of owners of assets created through these networks. Our findings offer a foundational understanding of public blockchain and open source development for legal scholars, economists, and policymakers about the roles played by developers. This understanding will guide the creation of effective liability frameworks for public blockchain and open source innovation.
A joint model of extended LDA and IBTM over streaming Chinese short texts

With the prevalent of short texts, discovering the topics within them has become an important task. Biterm Topic Model (BTM) is more suitable to discover topics on short texts than traditional topic models. However, there are still some challenges that dealing short texts with BTM will always ignor e the document-topic semantic information and lack the true intentions of users. In addition, it is a static method and can not manage streaming short texts when a new one arrives immediately. In order to keep document-topic information and get the topic distribution of a new short text at once, we propose a joint model based on online algorithms of Latent Dirichlet Allocation (LDA) and BTM, which combines the merits of both models. Not only does it alleviate the sparsity when addressing short texts with the online algorithm of BTM, namely Incremental Biterm Topic Model (IBTM), but also keeps document-topic information with extended LDA. And considering the differences between English and Chinese text in writing, we use combined words in short texts as key words to extend the length of short texts and keep the true intensions of users. As shown in the experiment results on two real world datasets, our method is better than other baseline methods. In the end, we explain an application of our method in the task of discovering user interest tags.
A high capacity reversible data hiding scheme for encrypted covers based on histogram shifting

Reversible data hiding in encrypted domain has been discussed for years. However, the performance of the existing algorithms is generally not satisfactory for the difficulty in signal processing in encrypted domain. In this paper, a high capacity reversible data hiding scheme for encrypted covers is proposed. The cover is first partitioned into non-overlapping blocks and encrypted effectively with homomorphism but without data expansion. Then a histogram shifting algorithm based on prediction error extension is introduced to achieve high embedding capacity of the scheme. The parameters in embedding process can not only adjust the embedding rate but also provide pretty good support for the security of the embedded data. The algorithm can achieve full reversibility at high embedding rate without extra data, and the embedding rate can reach to 2 bpp (bit per pixel). The experimental results verify the effectiveness of our solution, and show that the proposed scheme has outstanding performance in both capacity and quality compared with similar algorithms.
Scene Context-aware Rapidly-exploring Random Trees for Global Path Planning

This paper introduces a global path planning method for autonomous systems. Global path planning finds a feasible and collision-free path in an environment in which various kinds of regions and objects exist. However, the most planning methods use information such as collision-free space and obstacles in the environment. Interactions at each region (e.g., sidewalk and pavement) would be different. In this paper, we propose a method for global path planning taking semantic scene context into account. In contrast to conventional path planning methods which use collision-free and obstacle regions, the proposed method represents an environment as a cost map. The cost map is estimated from demonstrated human behaviors and feature maps derived from semantic scene context. To find a path on the cost map, we define a path cost and leverage an optimal rapidly-exploring random tree (RRT*) algorithm. We evaluate the proposed method regarding accuracy and computational efficiency with two public datasets and our contributed dataset. Experimental results show that our method successfully reproduces paths like human behaviors in short computational time.
NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency

Images acquired by synthetic aperture radars are degraded by speckle that prevents efficient extraction of useful information from radar remote sensing data. Filtering or despeckling is a tool often used to improve image quality. However, depending upon image and noise properties, the quality of improvement can vary. Besides, a quality can be characterized by different criteria or metrics, where visual quality metrics can be of value. For the case study of discrete cosine transform (DCT)based filtering, we show that improvement of radar image quality due to denoising can be predicted in a simple and fast way, especially if one deals with particular type of radar data such as images acquired by Sentinel-1. Our approach is based on application of a trained neural network that, in general, might have a different number of inputs (features). We propose a set of features describing image and noise statistics from different viewpoints. From this set, that contains 28 features, we analyze different subsets and show that a subset of the 13 most important and informative features leads to a very accurate prediction. Test image generation and network training peculiarities are discussed. The trained neural network is then tested using different verification strategies. The results of the network application to test and real-life radar images are presented, demonstrating good performance for a wide set of quality metrics.
Coarse-to-Fine Grained Classification

Fine-grained image classification and retrieval become topical in both computer vision and information retrieval. In real-life scenarios, fine-grained tasks tend to appear along with coarse-grained tasks when the observed object is coming closer. However, in previous works, the combination of fine-grained and coarse-grained tasks was often ignored. In this paper, we define a new problem called coarse-to-fine grained classification (C2FGC) which aims to recognize the classes of objects in multiple resolutions (from low to high). To solve this problem, we propose a novel Multi-linear Pooling with Hierarchy (MLPH) model. Specifically, we first design a multi-linear pooling module to include both trilinear and bilinear pooling, and then formulate the coarse-grained and fine-grained tasks within a unified framework. Experiments on two benchmark datasets show that our model achieves state-of-the-art results.
On cost-effective software defect prediction: Classification or ranking?

Software defect prediction aims to identify defect-prone software modules in the purpose of allocating limited software reliability resources in a cost-effective manner. To identify these defect-prone modules, a classification model is usually built to classify software modules into defective or clean. However, a number of false alarms may be generated if the classifier is not so strong or the training modules are insufficient, which may waste many software reliability resources especially when the identified defect-prone modules are more than the available software reliability resources. A straight-forward way to prioritize the defect-prone modules to be checked is to rank the modules based on the predictive confidence generated by the classifier. Nevertheless, due to the ‘point-wise’ learning nature of the classifier, such predictive confidence may not be effective in generating the ordering of defect-proneness. To address this problem, we argue that the defect prediction model should be generated from a ‘pair-wise’ perspective instead of a ‘point-wise’ perspective, to explicitly model the pair-wise relations between software modules and help improve the prediction. We cast the software defect prediction as a learning to rank problem and address it by proposing a novel defect ranking model named DeRanken. Empirical studies on multiple open source software projects indicate that ranking model is able to provide better module prioritization than the classification model for Within-Project, Cross-Version and Cross-Project Defect Prediction, leading to a more cost effective software reliability.
Real-time Evasion Attacks with Physical Constraints on Deep Learning-based Anomaly Detectors in Industrial Control Systems

Recently, a number of deep learning-based anomaly detection algorithms were proposed to detect attacks in dynamic industrial control systems. The detectors operate on measured sensor data, leveraging physical process models learned a priori. Evading detection by such systems is challenging, as an attacker needs to manipulate a constrained number of sensor readings in real-time with realistic perturbations according to the current state of the system. In this work, we propose a number of evasion attacks (with different assumptions on the attacker's knowledge), and compare the attacks' cost and efficiency against replay attacks. In particular, we show that a replay attack on a subset of sensor values can be detected easily as it violates physical constraints. In contrast, our proposed attacks leverage manipulated sensor readings that observe learned physical constraints of the system. Our proposed white box attacker uses an optimization approach with a detection oracle, while our black box attacker uses an autoencoder (or a convolutional neural network) to translate anomalous data into normal data. Our proposed approaches are implemented and evaluated on two different datasets pertaining to the domain of water distribution networks. We then demonstrated the efficacy of the real-time attack on a realistic testbed. Results show that the accuracy of the detection algorithms can be significantly reduced through real-time adversarial actions: for the BATADAL dataset, the attacker can reduce the detection accuracy from 0.6 to 0.14. In addition, we discuss and implement an Availability attack, in which the attacker introduces detection events with minimal changes of the reported data, in order to reduce confidence in the detector.
HopsFS: Scaling Hierarchical File System Metadata Using NewSQL Databases

Recent improvements in both the performance and scalability of shared-nothing, transactional, in-memory NewSQL databases have reopened the research question of whether distributed metadata for hierarchical file systems can be managed using commodity databases. In this paper, we introduce HopsFS, a next generation distribution of the Hadoop Distributed File System (HDFS) that replaces HDFS' single node in-memory metadata service, with a distributed metadata service built on a NewSQL database. By removing the metadata bottleneck, HopsFS enables an order of magnitude larger and higher throughput clusters compared to HDFS. Metadata capacity has been increased to at least 37 times HDFS' capacity, and in experiments based on a workload trace from Spotify, we show that HopsFS supports 16 to 37 times the throughput of Apache HDFS. HopsFS also has lower latency for many concurrent clients, and no downtime during failover. Finally, as metadata is now stored in a commodity database, it can be safely extended and easily exported to external systems for online analysis and free-text search.
PILOT: Password and PIN information leakage from obfuscated typing videos1

This paper studies leakage of user passwords and PINs based on observations of typing feedback on screens or from projectors in the form of masked characters (∗ or ∙) that indicate keystrokes. To this end, we developed an attack called Password and Pin Information Leakage from Obfuscated Typing Vid eos (PILOT). Our attack extracts inter-keystroke timing information from videos of password masking characters displayed when users type their password on a computer, or their PIN at an ATM. We conducted several experiments in various attack scenarios. Results indicate that, while in some cases leakage is minor, it is quite substantial in others. By leveraging inter-keystroke timings, PILOT recovers 8-character alphanumeric passwords in as little as 19 attempts. When guessing PINs, PILOT significantly improved on both random guessing and the attack strategy adopted in our prior work (In European Symposium on Research in Computer Security (2018) 263–280 Springer). In particular, we were able to guess about 3% of the PINs within 10 attempts. This corresponds to a 26-fold improvement compared to random guessing. Our results strongly indicate that secure password masking GUIs must consider the information leakage identified in this paper.
Crowdsourcing the Paldaruo Speech Corpus of Welsh for Speech Technology

Collecting speech data for a low-resource language is challenging when funding and resources are limited. This paper describes the process of designing, creating and using the Paldaruo Speech Corpus for developing speech technology for Welsh. Specifically, this paper focuses on the crowdsourcing of data using an app on smartphones and mobile devices, allowing speakers from across Wales to contribute. We discuss the development of reading prompts: isolated words and full sentences, as well as the metadata collected from contributors. We also provide background on the design of the Paldaruo App as well as the main uses for the corpus and its availability and licensing. The corpus was designed for the development of speech recognition for Welsh and has been used to create a number of other resources. These methods can be extended to other languages, and suggestions for other low-resource languages are discussed.
Turkish Treebanking: Unifying and Constructing Efforts

In this paper, we present the current version of two different treebanks, the re-annotation of the Turkish PUD Treebank and the first annotation of the Turkish National Corpus Universal Dependency (henceforth TNC-UD). The annotation of both treebanks, the Turkish PUD Treebank and TNC-UD, was carried out based on the decisions concerning linguistic adequacy of re-annotation of the Turkish IMST-UD Treebank (Turk et. al., forthcoming). Both of the treebanks were annotated with the same annotation process and morphological and syntactic analyses. The TNC-UD is planned to have 10,000 sentences. In this paper, we will present the first 500 sentences along with the annotation PUD Treebank. Moreover, this paper also offers the parsing results of a graph-based neural parser on the previous and re-annotated PUD, as well as the TNC-UD. In light of the comparisons, even though we observe a slight decrease in the attachment scores of the Turkish PUD treebank, we demonstrate that the annotation of the TNC-UD improves the parsing accuracy of Turkish. In addition to the treebanks, we have also constructed a custom annotation software with advanced filtering and morphological editing options. Both the treebanks, including a full edit-history and the annotation guidelines, and the custom software are publicly available under an open license online.
Dictionary Based Amharic Sentiment Lexicon Generation

Sentiment analysis is a hot research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews. To employ rule based sentiment analysis, sentiment lexicon is required. However, manual construction of a sentiment lexicon is time consuming and costly for resource-limited languages. To reduce development time and costs, we propose an algorithm for constructing Amharic sentiment lexicons. The proposed approach transfers sentiment labels from a one language (e.g. English) to resource-limited language (e.g. Amharic) relying on Amharic-English dictionary. Using Bilingual/Monolingual dictionaries as a bridge, two Amharic sentiment lexicons are automatically generated the first based on SO-CAL polarity lexicon, the second on SentiWordNet 3.0. For each Amharic word, the algorithm finds the meaning of the corresponding English word(s). For these English words, sentiment information is searched from the aforementioned sentiment lexicon(s). The weighted average of returned sentiment values, part of speech and gloss information is assigned to the Amharic word. Lexicons of 5683 and 13679 words, respectively, are generated automatically and evaluated subsequently.
Multiagent Reinforcement Learning for Swarm Confrontation Environments

The swarm confrontation problem is always a hot research topic, which has attracted much attention. Previous research focuses on devising rules to improve the intelligence of the swarm, which is not suitable for complex scenarios. Multi-agent reinforcement learning has been used in some similar confrontation tasks. However, many of these works take centralized method to control all entities in a swarm, which is hard to meet the real-time requirement of practical systems. Recently, OpenAI proposes Multi-Agent Deep Deterministic Policy Gradient algorithm (MADDPG), which can be used for centralized training but decentralized execution in multi-agent environments. We examine the method in our constructed swarm confrontation environment and find that it is not easy to deal with complex scenarios. We propose two improved training methods, scenario-transfer training and self-play training, which greatly enhance the performance of MADDPG. Experimental results show that the scenario-transfer training accelerate the convergence speed by 50%, and the self-play training increases the winning rate of MADDPG from 42% to 96%.