Reviewerly/exHarmony huggingface dataset:
PromotionLens: Inspecting Promotion Strategies of Online E-commerce Via Visual Analytics

Promotions are commonly used by e-commerce merchants to boost sales. The efficacy of different promotion strategies can help sellers adapt their offering to customer demand in order to survive and thrive. Current approaches to designing promotion strategies are either based on econometrics, which may not scale to large amounts of sales data, or are spontaneous and provide little explanation of sales volume. Moreover, accurately measuring the effects of promotion designs and making bootstrappable adjustments accordingly remains a challenge due to the incompleteness and complexity of the information describing promotion strategies and their market environments. We present PromotionLens, a visual analytics system for exploring, comparing, and modeling the impact of various promotion strategies. Our approach combines representative multivariant time-series forecasting models and well-designed visualizations to demonstrate and explain the impact of sales and promotional factors, and to support "what-if" analysis of promotions. Two case studies, expert feedback, and a qualitative user study demonstrate the efficacy of PromotionLens.
A Robust Selective Encryption Scheme for H.265/HEVC Video

To protect the information of video stream, many selective video encryption schemes have been proposed based on the H.265/HEVC video. However, most of the existing algorithms are not robust, thus failing to decrypt under packet loss. To further improve the robustness capability of video protection, a robust selective encryption scheme is proposed in this paper. In H.265/HEVC standard, video is encoded into multiple slices, and the slices are decoded independently. Inspired by the feature, each slice is individually encrypted using RC4 stream cipher. The pseudorandom binary sequence (PRBS) for one slice is related to encoding parameters and the SHA-256 hash value of the corresponding slice header, thus ensuring the real-time update of the PRBS and increasing the resistance to chosen-plaintext attack. Two-rounds shifting algorithm is designed to scramble non-zero coefficients of the transform units (TUs) and then motion vector difference (MVD) parameters, quantized transform coefficients (QTCs) and intra prediction modes (IPMs) are selected for encryption. The combination of coefficient scrambling and syntax elements encryption further improves the encryption performance. The proposed scheme is slice-synchronized. Hence, it decrypts normally in the case of packet loss, and supports online real-time interaction. Furthermore, the simulation and analysis results show that the proposed algorithm has format compatibility, high security and low time complexity, which is a promising tool for video cryptographic applications.
PointFilterNet: A Filtering Network for Point Cloud Denoising

Point clouds obtained by 3D scanning or reconstruction are usually accompanied by noise. Filtering-based point cloud denoising methods are simple and effective, but they are limited by the manually defined coefficients. Deep learning has shown excellent ability in automatically learning parameters. In this paper, a filtering network named PointFilterNet (PFN for short) is proposed to denoise point clouds by the combination of filtering and deep learning. Instead of directly outputting denoised points using networks, PFN generates the filtering denoised points through learned coefficients. Specifically, PFN outputs three coefficient vectors. These coefficient vectors are then used to filter the coordinates of points in the neighborhood of the noisy point. PFN consists of two parts: an outlier recognizer and a denoiser, both of which generate different but indispensable filtering coefficients. The outlier recognizer reduces the interference of outliers by assigning small coefficients to them. The denoiser is designed to progressively denoise point clouds which accords to the perception process of the human visual system. The experiments on synthetic and real scanned point clouds demonstrate that PFN outperforms state-of-the-art point cloud denoising works. Compared with DMR, the Chamfer distance of PFN is reduced by 22.07% on PCN-dataset.
A Query-Level Distributed Database Tuning System with Machine Learning

Knob tuning is important to improve the performance of database management system. However, the traditional manual tuning method by DBA is time-consuming and error-prone, and can not meet the requirements of different database instances. In recent years, the research on automatic knob tuning using machine learning algorithm has gradually sprung up, but most of them only support workload-level knob tuning, and the studies on query-level tuning is still in the initial stage. Furthermore, few works are focus on the knob tuning for distributed database. In this paper, we propose a query-level tuning system for distribute database with the machine learning method. This system can efficiently recommend knobs according to the feature of the query. We deployed our techniques onto CockroachDB, a distribute database, and experimental results show that our system achieves higher performance under typical OLAP workload. For all categories of queries, our system reduces the latency by 9.2% on average, and for some categories of queries, this system reduces the latency by more than 60%.
Utilization of Artificial Intelligence and Robotics Technology in Business

The purpose of this research is to thoroughly explain what artificial intelligence and robotics are and how they differ. Additionally, this research elucidates the approaches that various robotics may take and the difficulties they may encounter. Robotics is a branch of science that studies the interaction of thought patterns. To make the connection intelligent, Artificial Intelligence must play a significant role. Artificial Intelligence is concerned with the critical questions of what data is required for each aspect of thinking, how that knowledge should be represented, and how that knowledge should be used. Artificial intelligence (AI) and robotics advancements will have a significant impact on humanity's future in the near future. Artificial intelligence is commonly used to refer to any artificial computational framework that exhibits perceptive behavior, i.e., complex behavior that assists in achieving goals.
Natural Language Processing Based on Convolutional Neural Network and Semi Supervised Algorithm in Deep Learning

Natural language (neural network) is composed of a large number of human visual systems. Its research began with artificial perception and is used to describe things or phenomena involved in the process of human brain processing external information. At the same time, neural network is a nonlinear system description and analysis method. It is mainly used in manual calculation, pattern recognition and so on. Therefore, this paper studies natural language processing based on convolutional neural network and semi supervised algorithm in deep learning. Firstly, this paper expounds the definition of natural language processing, then studies the application of convolutional neural network and semi supervised algorithm, and tests the feature recognition of word vector in natural language to show the optimization results of deep learning algorithm. The test results show that the effect of word vector merging is relatively high. In the optimization process of convolutional neural network, after adding standard interval for semi supervised learning, the accuracy of prediction results also increases significantly, and with the increase of the number of labels, the accuracy also increases gradually.
Computer Vision Based Drone Detection Using Mask R-CNN

Security of private and military areas is very important, drones are becoming more available day by day even though they are used for different purposes. In no-fly zones like metros, airports, private properties and so on, unauthorized drones can be detected using computer vision models, in this study Mask-RCNN with two different backbones (ResNet-50 and MobileNet) was employed to detect drones. It was observed that the Mask-RCNN with ResNet-50 as backbone achieves mAP of 0.965, mAP-50 of 0.991, mAP-75 of 0.958 and recall of 0.961.
Video Summarization using Text Subjectivity Classification

Video summarization has attracted researchers' attention because it provides a compact and informative video version, supporting users and systems to save efforts in searching and understanding content of interest. Current techniques employ different strategies to select which video segments should be included in the final summary. The challenge is to process multimodal data present in the video looking for relevance clues (like redundant or complementary information) that help make a decision. A recent strategy is to use subjectivity detection. The presence or the absence of subjectivity can be explored as a relevance clue, helping to bring video summaries closer to the final user's expectations. However, despite this potential, there is a gap on how to capture subjectivity information from videos. This paper investigates video summarization through subjectivity classification from video transcripts. This approach requires dealing with recent challenges that are important in video summarization tasks, such as detecting subjectivity in different languages and across multiple domains. We propose a multilingual machine learning model trained to deal with subjectivity classification in multiple domains. An experimental evaluation with different benchmark datasets indicates that our multilingual and multi-domain method achieves competitive results, even compared to language-specific models. Furthermore, such a model can be used to provide subjectivity as a content selection criterion in the video summarization task, filtering out segments that are not relevant to a video domain of interest.
Learning discriminative representations via variational self-distillation for cross-view geo-localization

Cross-view geo-localization is to localize the same geographic target in images from different perspectives, e.g., satellite-view and drone-view. The primary challenge faced by existing methods is the large visual appearance changes across views. Most previous work utilizes the deep neural network to obtain the discriminative representations and directly uses them to accomplish the geo-localization task. However, these approaches ignore that the redundancy retained in the extracted features negatively impacts the result. In this paper, we argue that the information bottleneck (IB) can retain the most relevant information while removing as much redundancy as possible. The variational self-distillation (VSD) strategy provides an accurate and analytical solution to estimate the mutual information. To this end, we propose to learn discriminative representations via variational self-distillation (dubbed LDRVSD). Extensive experiments are conducted on two widely-used datasets University-1652 and CVACT, showing the remarkable performance improvements obtained by our LDRVSD method compared with several state-of-the-art approaches.
A leader-following paradigm based deep reinforcement learning method for multi-agent cooperation games

Multi-agent deep reinforcement learning algorithms with centralized training with decentralized execution (CTDE) paradigm has attracted growing attention in both industry and research community. However, the existing CTDE methods follow the action selection paradigm that all agents choose actions at the same time, which ignores the heterogeneous roles of different agents. Motivated by the human wisdom in cooperative behaviors, we present a novel leader-following paradigm based deep multi-agent cooperation method (LFMCO) for multi-agent cooperative games. Specifically, we define a leader as someone who broadcasts a message representing the selected action to all subordinates. After that, the followers choose their individual action based on the received message from the leader. To measure the influence of leader's action on followers, we introduced a concept of information gain, i.e., the change of followers' value function entropy, which is positively correlated with the influence of leader's action. We evaluate the LFMCO on several cooperation scenarios of StarCraft2. Simulation results confirm the significant performance improvements of LFMCO compared with four state-of-the-art benchmarks on the challenging cooperative environment.
Allocation of carbon quotas with local differential privacy

As a policy tool to limit greenhouse gas emissions using market-based instruments, carbon emissions trading is being adopted by an increasing number of countries and regions. By 2021, a total of 33 carbon trading systems have been put into operation worldwide, covering a wide range of industries such as power, industry, aviation, and construction. And by analyzing the data generated by the exchange, carbon quotas can be allocated more rationally, so that companies can use their quotas more fully, unfortunately, the analysis of the data may result in the leakage of company data. For example, since there is more than one carbon exchange, the analysis will be done after aggregating the transaction data of all exchanges, which may cause data leakage and create the problem of malicious competition between companies. Therefore, we searched for an efficient way to perform carbon allowance allocation analysis so that the data of trading companies are protected and do not lose their usability. We propose the locally differential privacy-carbon allocation model, which perturbs the data locally in the exchange to a certain extent, and the data center removes the impact of the perturbation as much as possible for data estimation, finally, uses it for carbon quota allocation share prediction. This allows for better protection of the trading data while still allowing for a reasonable allocation of carbon quotas. The final experimental results show that the allocation results are effective and reasonable under the premise of satisfying a certain degree of privacy protection.
A systematic literature review of the Design Critique method

The Design Critique (DC) method is becoming more common in Human–Computer Interaction (HCI) and User Experience (UX) studies as the need for new evaluation methods of emerging technologies is increasing. However, there is an clear lack of guidelines on how to conduct DC studies in the UX context. The goal of this paper is to provide an overview of the DC method in the fields of UX. In addition, this paper aims to propose a generic process of running DC studies in the same context. We present a systematic literature review of the DC method. Moreover, we conduct a course of thematic analysis on the selected papers to identify the various DC processes and explore the following attributes: participant categories, data collection methods, and data analysis methods in each process. We identified three different trends of DC processes: detailed, moderate and minimal. In addition, we proposed a generic DC process consisting of 10 steps divided into three main phases: preparation, conducting design critique, and pro-processing. We found that domain experts represent the majority of studies participants. Using interviews to collect qualitative data and using script coding analysis are the two most common methods of collecting and analyzing data. Conducting DC studies can improve overall systems usability by addressing design flaws at an early stage of development. The process of conducting a DC varies, depending on the project goals and states. The DC method aligns well with the small light-weight steps approach in Agile methods.
Empirical Formal Methods: Guidelines for Performing Empirical Studies on Formal Methods

Empirical studies on formal methods and tools are rare. In this paper, we provide guidelines for such studies. We mention their main ingredients and then define nine different study strategies (usability testing, laboratory experiments with software and human subjects, case studies, qualitative studies, surveys, judgement studies, systematic literature reviews, and systematic mapping studies) and discuss for each of them their crucial characteristics, the difficulties of applying them to formal methods and tools, typical threats to validity, their maturity in formal methods, pointers to external guidelines, and pointers to studies in other fields. We conclude with a number of challenges for empirical formal methods.
Shoulder-Surfing Resistant Authentication for Augmented Reality

Augmented Reality (AR) Head-Mounted Displays (HMD) are increasingly used in industry to digitize processes and enhance user experience by enabling real-time interaction with both physical and virtual objects. In this context, HMD provide access to sensitive data and applications which demand authenticating users before granting access. Furthermore, these devices are often used in shared spaces. Thus, shoulder-surfing attacks need to be addressed. As users can remember pictures more easily than text, we applied the recognition-based graphical password scheme "Things" from previous work on an AR HMD while placing the pictures for each authentication attempt in a random order. We implemented this scheme for the HMD Microsoft HoloLens and conducted a user study evaluating Things's usability. All participants could be successfully authenticated and the System Usability Scale (SUS) score is with 74 categorized as above average. We discuss as future work how to improve the SUS scores, e.g., by using different grid designs and input methods.
"I feel like I've never really achieved it"

This paper examines experiences of mindfulness and meditation applications for handling stress and emotional strain. We present a qualitative interview study. The analysis focuses particularly on the role of persuasive functions in the applications, such as notifications, history tracking, maintaining a daily streak, etc. The results suggest that persuasive design patterns can interfere with the original user needs. While providing some advantages for supporting the establishment of a meditation habit, persuasive design patterns also create friction between the application and the users' intentions to reduce stress. We critically examine how persuasive design patterns, intended for increasing performance and personal improvement, may affect the users of meditation apps. Finally, we discuss how persuasive design patterns in the major meditation apps have been instrumental in recontextualizing meditation from a spiritual practice to a productivity practice.
Exploring Data Intermediaries as Infrastructure for a Human-Centric Data Economy: Speculations &amp; Critical Reflections

Through participating in our contemporary digitally-mediated society individuals are surveilled, generating data about them that is analyzed and aggregated to create computational models of their everyday habits, behaviors, desires, and anxieties. The concept of data intermediaries has recently been proposed and gained purchase as digital services that could enable people to have more control over what data is collected about them and how it is used. Yet, data intermediaries currently exist as a high-level aspirational concept. Little is known about how they could or should be designed. In this pictorial, we propose a diverse range of concepts and scenarios that, taken together, offer a range of possibilities and consequences that personal data, shaped by data intermediaries, might hold for mediating data exchanges in people's everyday lives.
Co-imagining participatory design in religious and spiritual contexts

People with religious and/or spiritual interests have long been digitally connected and have engaged in joint prayer, worship, or meditation. However, the COVID pandemic has accelerated this trend as more religious and/or spiritual activities were technologically mediated. Many of these formats had to be implemented quickly, often with little participation from stakeholders. In this workshop, we aim to bring together scholars and practitioners from different backgrounds, such as religious studies, theology, HCI, and participatory design, to start a conversation about what collaborative technological design and research with religious and/or spiritual communities could look like in the future and what needs to be done to get to such futures. The outcomes of this workshop will be the formation of a transdisciplinary research and design community, the establishment of guiding principles and best practices that can inform research and design methodologies, and the dissemination of this knowledge in collaborative post-workshop publications.
From Concern to Care: A Transformative Reflection on Designing-with the Living

Anthropocentric perspectives are common in design practice, but also contribute to a damaged planet. Requirements for sustainable processes resulted in novel practices such as biodesign, which investigates how living and regenerative resources can be used instead of artificial and polluting. However, many biodesign practices still seek to benefit humans. In this critique we compare the journey of two designers in the development of a toolkit to setup the bacterial powered light installation Electric Life for exhibition at the Centre Pompidou in Paris. We reflect on the designers' considerations when designing with microorganisms through the lenses of materiality, temporality, ecology, and agency. Our critique offers a discussion on the revision of a post-human biodesign approach from concern to care when designing-with the living and the questions we confronted throughout.
Indigenous Knowledge Fairs: a new approach to pluralistic knowledge dissemination in HCI

Established knowledge dissemination processes in the HCI community, such as academic publications and conferences, promulgate a systemic exclusion of indigenous research partners. While few individuals from indigenous or marginalised communities have published, entire communities collaborating in research projects have not had the opportunity to present their work and the knowledge created in the process. Thus we argue for more inclusive events such as the bi-annual eBorneo and the inaugural Namibian knowledge fair following principles of open space technology and community means of knowledge production and dissemination. We share our concept, experiences and reflection of such indigenous knowledge fairs, as a new approach to pluralistic knowledge dissemination in HCI.
Phase-covariant mixtures of non-unital qubit maps

Abstract We analyze convex combinations of non-unital qubit maps that are phase-covariant. In particular, we consider the behavior of maps that combine amplitude damping, inverse amplitude damping, and pure dephasing. We show that mixing non-unital channels can result in restoring the unitality, whereas mixing commutative maps can lead to non-commutativity. For the convex combinations of Markovian semigroups, we prove that classical uncertainties cannot break quantum Markovianity. Moreover, contrary to the Pauli channel case, the semigroup can be recovered only by mixing two other semigroups.