Reviewerly/exHarmony huggingface dataset:
Study of the Usability Testing of E-Commerce Applications

Abstract Usability Testing determines the friendliness of the application to meet customers’ requirements. It must be adopted in the early stage of SDLC (Software Development Life Cycle) to avoid issues in a later stage. Today, E-commerce has revolutionized the mode of shopping, and thus, we need to focus on ways to improve the usability of e-commerce applications. We opted for survey questionnaires as our research methodology, along with testing websites using online tools based on the four parameters Connectivity, Readability, Accessibility, and Functional Performance. In the survey, the four most popular e-commerce applications were Amazon, Flipkart, Bigbasket, and Paytm. Out of which, Amazon was the most used e-commerce application by the consumers. Although, testing tools are depicted that applications need to improve in certain areas. From the analysis, we concluded that consumers are aware of the upcoming Jio-Mart, which can give tough competition to existing companies. Thus, all the existing e-commerce companies need to improve their web and mobile applications to retain their customer base.
Control Theory-Inspired Model Design for Single Image De-Raining

While existing image de-raining methods have obtained satisfactory performance, lacking theoretical basis prevents them from further improvement. In this brief, we propose an efficient image de-raining network empowered by control theory to address this issue. To the best of our knowledge, it is the first attempt to integrate the control theory into image de-raining model design. Different from previous methods that roughly construct complicated neural networks, our method is theoretical and provides a new perspective for model design. Specifically, by mimicking the signal processing flow of state observable and controllable standard form, we expand them to two network modules, named C-IM and O-IM with every component in the module one-to-one corresponding to each operation involved in state equation. Equipped with C-IM and O-IM, our proposed network could efficiently explore and exploit the features of rain streaks in a recursive fashion. Extensive experiments demonstrate that control theory equipped method is capable of obtaining promising performance and speeding up the model training.
Scheduling Distributed Deep Learning Jobs in Heterogeneous Cluster with Placement Awareness

Deep Neural Network models are integrated as parts of many real-world software applications. Due to the huge model size and complex computation, distributed deep learning (DDL) framework aims to provide a high-quality cluster scheduler to manage DDL training jobs from both resource allocation and job scheduling. However, existing schedulers either allocate a fixed amount of resources, or lack the control over task placement, which lead less efficient training. In this paper, we propose DeepSys, a GPU cluster scheduler tailored for DDL jobs. For single model, DeepSys builds a speed model to predict accurate training speed, and a memory model for high-quality resource utilization. For job scheduling, DeepSys considers resource allocation and task placement to provide efficient job scheduling in cluster. Experiments implemented on Kubernetes in two clusters show the advantage to the compared methods by 20% - 25% and 10% - 15% on average job completion time and makespan, respectively.
Track Association in Radars with Additional Multi-Scanning Observation Channels

In this paper, we analyze the association of track information obtained in the main and the additional channels of a surveillance radar. In the main channel of the radar, a two-stage radar information processing is implemented. The radar also includes an additional multi-scanning observation device whose functioning is based on combined processing of the results obtained within several scans. In each multi-scanning observation channel, the well-known "track-before-detect" procedure is implemented and it is assumed to process echo signals from several space scans stored in the buffer storage. We analyze the case when the main channel implements the procedure for detecting a trajectory of the sequential type, while in the additional channels, a block type trajectory detection is realized. We consider options for associating track information obtained from several channels in order to increase the radar efficiency.
DETECTION AND CLASSIFICATION OF SYMBOLS IN PRINCIPLE SKETCHES USING DEEP LEARNING

Abstract Data-driven methods from the field of Artificial Intelligence or Machine Learning are increasingly applied in mechanical engineering. This refers to the development of digital engineering in recent years, which aims to bring these methods into practice in order to realize cost and time savings. However, a necessary step towards the implementation of such methods is the utilization of existing data. This problem is essential because the mere availability of data does not automatically imply data usability. Therefore, this paper presents a method to automatically recognize symbols from principle sketches, which allows the generation of training data for machine learning algorithms. In this approach, the symbols are created randomly and their illustration varies with each generation. . A deep learning network from the field of computer vision is used to test the generated data set and thus to recognize symbols on principle sketches. This type of drawing is especially interesting because the cost-saving potential is very high due to the application in the early phases of the product development process.
Eye Tracking to Understand Impact of Aging on Mobile Phone Applications

Usage of smartphones and tablets has been increasing rapidly with multi-touch interaction and powerful configurations. Performing tasks on mobile phones become more complex as people age, thereby increasing their cognitive workload. In this context, we conducted an eye tracking study with 50 participants between the age of 20 to 60 years and above, living in Bangalore, India. This paper focuses on visual nature of interaction with mobile user interfaces. The study aims to investigate how aging affects user experience on mobile phones while performing complex tasks and estimate cognitive workload using eye tracking metrics. The study consisted of five tasks that were performed on an android mobile phone under naturalistic scenarios using eye tracking glasses. We recorded ocular parameters like fixation rate, saccadic rate, average fixation duration, maximum fixation duration, and standard deviation of pupil dilation for left and right eyes, respectively, for each participant. Results from our study show that aging has a bigger effect on performance of using mobile phones irrespective of any complex task given to them. We noted that participants aged between 50 and 60+ years had difficulties in completing tasks and showed increased cognitive workload. They took longer fixation duration to complete tasks which involved copy-paste operations. Further, we identified design implications and provided design recommendations for designers and manufacturers.
Scene recognition of road traffic accident based on an improved faster R-CNN algorithm

Traffic accident scene recognition is the first premise of accident analysis and scene reconstruction, so the research on traffic accident scene recognition technology is of great significance. In this paper, an improved Region Proposal Network based on Faster Region-based Convolutional Network (Faster R-CNN) is proposed. Because the network adopts four strategies to improve the Regional Proposal Network (RPN), the network structure in this paper is called Multi-strategy Region Proposal Network (MSRPN). The experimental results show that the mAP value of MSRPN algorithm surpasses the other two target recognition algorithms. At the same time, MSRPN only needs to generate 150 region proposals in each image to obtain the above experimental results. In addition, the algorithm has good performance in small target detection. Especially, the target recognition speed is 6 fps, which is faster than other target detection algorithms. In conclusion, the target recognition algorithm based on MSRPN can be effectively applied to target recognition system.
English–Welsh Cross-Lingual Embeddings

Cross-lingual embeddings are vector space representations where word translations tend to be co-located. These representations enable learning transfer across languages, thus bridging the gap between data-rich languages such as English and others. In this paper, we present and evaluate a suite of cross-lingual embeddings for the English–Welsh language pair. To train the bilingual embeddings, a Welsh corpus of approximately 145 M words was combined with an English Wikipedia corpus. We used a bilingual dictionary to frame the problem of learning bilingual mappings as a supervised machine learning task, where a word vector space is first learned independently on a monolingual corpus, after which a linear alignment strategy is applied to map the monolingual embeddings to a common bilingual vector space. Two approaches were used to learn monolingual embeddings, including word2vec and fastText. Three cross-language alignment strategies were explored, including cosine similarity, inverted softmax and cross-domain similarity local scaling (CSLS). We evaluated different combinations of these approaches using two tasks, bilingual dictionary induction, and cross-lingual sentiment analysis. The best results were achieved using monolingual fastText embeddings and the CSLS metric. We also demonstrated that by including a few automatically translated training documents, the performance of a cross-lingual text classifier for Welsh can increase by approximately 20 percent points.
User Experience: The Motivation and Promotion of Livestreaming Innovation in Chinese Marketing

China's e-commerce livestreaming economy was booming under the influence of the COVID-19, and competition for online users has become the main task of online marketing. To explore the relationship between user experience and livestreaming, this paper used document research, sentiment analysis and depth interview. The results showed that the integration of livestreaming and e-commerce enhanced user experience usability: livestreaming extended to social platform to provide users with positive emotional experience of interactive rituals: user value is an important factor in maintaining long-term and intimate relationships. The results of the present work implied that the rise of e-commerce livestreaming benefited from nice user experience in emergency situation, and the key to future development is also to enhance user experience from consumption scenarios, building interaction rituals and co-creating user value with private traffic.
Dynamic sampling for deep metric learning

Deep metric learning maps visually similar images onto nearby locations and visually dissimilar images apart from each other in an embedding manifold. The learning process is mainly based on the supplied image negative and positive training pairs. In this paper, a dynamic sampling strategy is proposed to organize the training pairs in an easy-to-hard order to feed into the network. It allows the network to learn general boundaries between categories from the easy training pairs at its early stages and finalize the details of the model mainly relying on the hard training samples in the later. Compared to the existing training sample mining approaches, the hard samples are mined with little harm to the learned general model. This dynamic sampling strategy is formulated as two simple terms that are compatible with various loss functions. Consistent performance boost is observed when it is integrated with several popular loss functions on fashion search and fine-grained image search.
A Knowledge Graph Based Approach to Social Science Surveys

Recent success of knowledge graphs has spurred interest in applying them in open science, such as on intelligent survey systems for scientists. However, efforts to understand the quality of candidate survey questions provided by these methods have been limited. Indeed, existing methods do not consider the type of on-the-fly content planning that is possible for face-to-face surveys and hence do not guarantee that selection of subsequent questions is based on response to previous questions in a survey. To address this limitation, we propose a dynamic and informative solution for an intelligent survey system that is based on knowledge graphs. To illustrate our proposal, we look into social science surveys, focusing on ordering the questions of a questionnaire component by their level of acceptance, along with conditional triggers that further customise participants' experience. Our main findings are: (i) evaluation of the proposed approach shows that the dynamic component can be beneficial in terms of lowering the number of questions asked per variable, thus allowing more informative data to be collected in a survey of equivalent length; and (ii) a primary advantage of the proposed approach is that it enables grouping of participants according to their responses, so that participants are not only served appropriate follow-up questions, but their responses to these questions may be analysed in the context of some initial categorisation. We believe that the proposed approach can easily be applied to other social science surveys based on grouping definitions in their contexts. The knowledge-graph-based intelligent survey approach proposed in our work allows online questionnaires to approach face-to-face interaction in their level of informativity and responsiveness, as well as duplicating certain advantages of interview-based data collection.
Phase unwrapping algorithm based on a rank information filter

A robust phase unwrapping algorithm based on a rank information filter is proposed to retrieve the unambiguous unwrapped phase from noisy wrapped phase images. First, a recursive phase unwrapping program, based on a rank information filter, is proposed to transform the problem of phase unwrapping for wrapped phase into the problem of the state estimation for state variables under the framework of a rank information filter, where a local phase gradient estimator based on the amended matrix pencil model (AMPM) is used to obtain phase gradient information required by the recursive phase unwrapping program. Second, an efficient path-following strategy based on heap-sort is used to guide the phase unwrapping path, which ensures that the recursive phase unwrapping program based on a rank information filter unwraps wrapped phase images along the path from high-quality pixels to low-quality pixels. Finally, the results obtained from synthetic data and experimental measured data demonstrate the effectiveness of the proposed method and show this method can obtain robust solutions from noisy wrapped phase images.
Creating an Inter-Hospital Resilient Network for Pandemic Response Based on Blockchain and Dynamic Digital Twins

Developing and using the rich data implied by dynamic digital twins and blockchain is relevant to manage both patients and medical resources (e.g., doctors/nurses, ventilators etc.) at the COVID-19 and post COVID period. This paper aims at exploring the blockchain solutions for preparing healthcare systems ready for both efficient operation daily and in pandemic through (1) information integration of patient and medical resource flow from healthcare and medical records; (2) optimizing the deployment of such resources based on hospitals, regions and local pandemic levels switching from normal to the outbreak. The main idea is to develop the concepts of the novel framework for creating an inter-hospital resilient network for pandemic response based on blockchain and dynamic digital twin, which will set up innovative ways to best care for patients, protect NHS staff, and support government scientific decisions to beat COVID-19 now and manage the crisis in the future.
FDR2-BD: A Fast Data Reduction Recommendation Tool for Tabular Big Data Classification Problems

In this paper, a methodological data condensation approach for reducing tabular big datasets in classification problems is presented, named FDR2-BD. The key of our proposal is to analyze data in a dual way (vertical and horizontal), so as to provide a smart combination between feature selection to generate dense clusters of data and uniform sampling reduction to keep only a few representative samples from each problem area. Its main advantage is allowing the model’s predictive quality to be kept in a range determined by a user’s threshold. Its robustness is built on a hyper-parametrization process, in which all data are taken into consideration by following a k-fold procedure. Another significant capability is being fast and scalable by using fully optimized parallel operations provided by Apache Spark. An extensive experimental study is performed over 25 big datasets with different characteristics. In most cases, the obtained reduction percentages are above 95%, thus outperforming state-of-the-art solutions such as FCNN_MR that barely reach 70%. The most promising outcome is maintaining the representativeness of the original data information, with quality prediction values around 1% of the baseline.
Graph Algorithms with Partition Transparency

Graph computations often have to be conducted in parallel on partitioned graphs. The choice of graph partitioning strategies, however, has strong impact on the design of graph computation algorithms. A graph algorithm developed under edge-cut partitions may not work correctly under vertex-cut, and vice versa. We often have to rewrite our algorithms when we switch from, e.g., edge-cut to vertex-cut. To cope with this, we propose a notion of partition transparency, such that graph algorithms are able to work correctly under different partitions without changes and moreover, benefit from recent hybrid partitions to speed up computations. Furthermore, we identify conditions under which graph algorithms are guaranteed to be partition-transparent, in graph-centric and vertex-centric models. We show that a variety of graph algorithms can be made partition-transparent. Using real-life and synthetic graphs, we experimentally verify that partition-transparent algorithms compute correct answers under different partitions; better still, under hybrid partitions these algorithms perform better than algorithms tailored for edge-cut and vertex-cut partitions in efficiency.
Linear sets on the projective line with complementary weights

Linear sets on the projective line have attracted a lot of attention because of their link with blocking sets, KM-arcs and rank-metric codes. In this paper, we study linear sets having two points of complementary weight, that is, with two points for which the sum of their weights equals the rank of the linear set. As a special case, we study those linear sets having exactly two points of weight greater than one, by showing new examples and studying their equivalence issue. Also, we determine some linearized polynomials defining the linear sets recently introduced by Jena and Van de Voorde [30].
On the accuracy of different neural language model approaches to ADE extraction in natural language corpora

The problem of extracting mentions of adverse events and reactions from text is especially relevant nowadays due to rapid emergence of datasets including such events, and progress in text analysis tools. This paper presents a comparison of existing methods for the task of automated extraction of adverse events from natural language texts. The considered methods are based on neural-network language models, pre-trained on different sets of unlabeled data. Experiments have been performed on the n2c2-2018 and CADEC corpora, using metrics coined within the CoNLL competition. Models of the aforementioned type show efficient solution of this task, provided sufficient amount of labeled training samples during.
Fractal modeling and relationship between thrust faults and carbonate-hosted Pb-Zn mineralization in Alborz Mountains, Northern Iran

Central Alborz Metallogenic Belt is a major mineral province of northern Iran placed at the northern margin of the Arabia-Eurasia collision zone. This study aimed to at recognition the affiliation between reverse/thrust faults and Pb-Zn carbonate-hosted mineral deposition using fractal modeling. Thrusting is the major mechanism of faulting in this region in terms of length, frequency and density. The carbonate-hosted Pb and Zn occurrence/deposit were classified using the concentration-number fractal method. All faults then are categorized based on fault density, and concentration-area fractal methods are utilized for the thrust faults. According to our analysis, main fault density zones are situated at the central and western parts of the Alborz Mountains. Relationship among the major fault density zones and the Pb-Zn carbonate-hosted occurrences/deposits using logratio matrix reveals that there is a proper connection between reverse/thrust faults and the Zn-Pb carbonate-hosted occurrences/deposits. Moreover, the results were controlled by four ore deposits/prospects in this region which shows high-grade ores directly relate to faults especially reverse/thrust faults based on their distribution. This research indicates that this method can be used for other various ore deposit types that have been distributed by faulting.
Do Judge an Entity by Its Name! Entity Typing Using Language Models

The entity type information in a Knowledge Graph (KG) plays an important role in a wide range of applications in Natural Language Processing such as entity linking, question answering, relation extraction, etc. However, the available entity types are often noisy and incomplete. Entity Typing is a non-trivial task if enough information is not available for the entities in a KG. In this work, neural language models and a character embedding model are exploited to predict the type of an entity from only the name of the entity without any other information from the KG. The model has been successfully evaluated on a benchmark dataset.
GRINCH: A Cache Attack against GIFT Lightweight Cipher

The National Institute of Standard and Technology (NIST) has recently started a competition with the objective to standardize lightweight cryptography (LWC). The winning schemes will be deployed in Internet-of-Things (IoT) devices, a key step for the current and future information and communication technology market. GIFT is an efficient lightweight cipher and it is used by one-fourth of the LWC candidates in the NIST LWC competition. Thus, its security evaluation is critical. One vital threat to the security are so-called logical side-channel attacks based on cache observations. In this work, we propose a novel cache attack on GIFT referred to as GRINCH. We analyzed the vulnerabilities of GIFT and exploited them in our attack. The results show that the attack is effective and that the full key could be recovered with less than 400 encryptions.
Success is not Final; Failure is not Fatal – Task Success and User Experience in Interactions with Alexa, Google Assistant and Siri

Speech assistants exhibit a high error rate with about one in three user requests resulting in an error. Nonetheless, speech assistants are adopted rapidly with about 1.8 billion users expected in 2021. Given the relatively high task failure rate of speech assistants this may be surprising and raises the question how much user experience (UX) is affected by task success in these devices. We measure user experience with four metrics of UX and evaluate task success in interactions with the speech assistants Alexa, Google Assistant, and Siri. We find that task success only explains between 13% and 28% of the variance of UX. This suggests that a majority of UX is not explained by whether an assistant successfully completes tasks. Moreover, we find that the three assistants do not significantly differ in task success rate, but differ in UX, which supports the conclusion that task success and UX possess limited alignment. We discuss our results and point out limitations and potential future work.