Reviewerly/exHarmony huggingface dataset:
Evaluation of Text Selection Techniques in Virtual Reality Head-Mounted Displays

Text selection is an essential activity in interactive systems, including virtual reality (VR) head-mounted displays (HMDs). It is useful for: sharing information across apps or platforms, highlighting and making notes while reading articles, and text editing tasks. Despite its usefulness, the space of text selection interaction is underexplored in VR HMDs. In this research, we performed a user study with 24 participants to investigate the performance and user preference of six text selection techniques (Controller+Dwell, Controller+Click, Head+Dwell, Head+Click, Hand+Dwell, Hand+Pinch). Results reveal that Head+Click is ranked first since it has excellent speedaccuracy performance (2nd fastest task completion speed with 3rd lowest total error rate), provides the best user experience, and produces a very low workload—followed by Controller+Click, which has the fastest speed and comparable experience with Head+Click, but much higher total error rate. Other methods can also be useful depending on the goals of the system or the users. As a first systematic evaluation of pointing $\times$ selection techniques for text selection in VR, the results of this work provide a strong foundation for further research in this area of growing importance to the future of VR to help it become a more ubiquitous and pervasive platform.
Design-based science: Curriculum for architects

The article raises the problem of the relationship between design and scientific activities. The problem arises in the educational process of students of architectural specialties for whom the project (architectural design) is the traditional form of presenting the results. The article concludes that design and scientific activities can be considered within the dialectical contradiction "single" - "universal", where the project acts as a unit, and scientific theory acts as a general one. The antinomy “single” - “universal” requires the category of “special” for its resolution. For which the authors propose the following scheme: pre-project research, identifying a problem, formulating a hypothesis as a way to solve a problem, developing a project as a way to prove a hypothesis. The article presents evidence of the efficiency of the proposed scheme.
On the adoption of a TODO bot on GitHub

Bots support different software maintenance and evolution activities, such as code review or executing tests. Recently, several bots have been proposed to help developers to keep track of postponed activities, expressed by means of TODO comments: e.g., TODO Bot automatically creates a GitHub issue when a TODO comment is added to a repository, increasing visibility of TODO comments. In this work, we perform a preliminary evaluation of the impact of the TODO Bot on software development practice. We conjecture that the introduction of the TODO Bot would facilitate keeping track of the TODO comments, and hence encourage developers to use more TODO comments in their code changes.
PriVDT: An Efficient Two-Party Cryptographic Framework for Vertical Decision Trees

Privacy-preserving decision trees (DTs) in vertical federated learning are one of the most effective tools to facilitate various privacy-critical applications in reality. However, the main bottleneck of current solutions is their huge overhead, mainly due to the adoption of communication-heavy bit decomposition to realize complex non-linear operations, such as comparison and division. In this paper, we present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PriVDT</monospace> , an efficient two-party framework for private vertical DT training and inference in the offline/online paradigm. Specifically, we customize several cryptographic building blocks based on an advanced primitive, Function Secret Sharing (FSS). First, we construct an optimized comparison protocol to improve the efficiency via reducing the invocation of FSS evaluations. Second, we devise an efficient and privacy-enhanced division protocol without revealing the range of divisors, which utilizes the above comparison protocol and more importantly new designed FSS-based secure range and digital decomposition protocols. Besides, we further reduce the overhead of linear operations by employing lightweight pseudorandom function-based Beaver’s triple techniques. Building on the above efficient components, we implement the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PriVDT</monospace> framework and evaluate it on 5 real-world datasets on both LAN and WAN. Experimental results show that the end-to-end runtime of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PriVDT</monospace> outperforms the prior art by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$42 \sim 510\times $ </tex-math></inline-formula> on LAN and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16 \sim 70\times $ </tex-math></inline-formula> on WAN. Moreover, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PriVDT</monospace> provides comparable accuracy to the non-private setting.
Understanding the Enablers of Blockchain Technology Adoption in Sustainable Supply Chains: A DEMATEL-Based Analysis

In this paper, 20 enablers of blockchain adoption in Sustainable Supply Chain Management (SSCM) are identified and analyzed using DEMATEL methodology. Our analysis uncovers how the identified enablers interact with each other and impact blockchain adoption in SSCM. Results show the primary importance of management commitment and support for adopting and implementing blockchain technology in SSCM. The results also point out the need for the technology to evolve, so that clearer governance rules for blockchains are adopted, and the interoperability with other IT systems is enhanced. Insights from our study are provided to help more informed decisions at the level of the adopting firms, the industry as a whole, as well as at the level of governments and regulations authorities.
HolonCraft – An Architecture for Dynamic Construction of Smart Home Workflows

Smart home systems have developed rapidly and entered the daily lives of many people. However, it is difficult for products from different manufacturers to work together due to the interoperability barriers posed by divergent hardware, communication protocols and APIs. In this paper, we introduce HolonCraft as a tool to help the regular IoT user in building complex systems. HolonCraft builds on the Holon ontology which enables systems to share their descriptions so that devices can recognize and understand each other and their respective functions, and work together through composing a system of systems (SoS) at runtime to fulfill a given user workflow. HolonCraft extends the machine-readable information provided by the Holon ontology and uses open-source editors (namely Google Blockly and Microsoft Monaco) to implement a cross-platform graphical programming editor for smart home automation. HolonCraft automatically transforms device capabilities into visual programming elements, enabling users without programming backgrounds to design smart home automation through simple drag-and-drop operations while also supporting advanced programming features such as branches, loops and functions. It then type-checks the composed workflow and, accordingly, generates code to actualize it. In consequence, HolonCraft dramatically improves the abstraction and expressiveness of automation as indicated by our user experiments.
Moment-based Adversarial Training for Embodied Language Comprehension

In this paper, we focus on a vision-and-language task in which a robot is instructed to execute household tasks. Given an instruction such as "Rinse off a mug and place it in the coffee maker," the robot is required to locate the mug, wash it, and put it in the coffee maker. This is challenging because the robot needs to break down the instruction sentences into subgoals and execute them in the correct order. On the ALFRED benchmark, the performance of state-of-the-art methods is still far lower than that of humans. This is partially because existing methods sometimes fail to infer subgoals that are not explicitly specified in the instruction sentences. We propose Moment-based Adversarial Training (MAT), which uses two types of moments for perturbation updates in adversarial training. We introduce MAT to the embedding spaces of the instruction, subgoals, and state representations to handle their varieties. We validated our method on the ALFRED benchmark, and the results demonstrated that our method outperformed the baseline method for all the metrics on the benchmark.
Secure IoT Data Transmission at Physical Layer using RC6 Encryption Technique

In today’s society, the Internet of Things is the foremost important technology. The scope that incorporates all of the disciplines in which IoT is used is massive. On the other extreme, security is a massive challenge growing IoT. The Internet of Things is constructed on machine-to-machine communication. A machine gathers input, examines it, and then commands other machine to respond based on the findings. The information collected by these devices might be highly classified and confidential. As a result, security at each layer of the Internet of Things is critical, as a security breach could have disastrous impacts. For example, if the physical layer’s security is compromised, the entire concept of IoT is compromised, placing the security of the individuals or companies whose data is gathered at risk. As a result, this paper proposes a number of practical, moderate, and access control ways for protecting the physical layer of Internet of things (IoT). Data encryption is necessary to preserve data security by declaring it inaccessible and immutable using a secret key. The ongoing investigation focuses on the challenges surrounding encrypted data in particular. To accomplish this objective, a safe methodology at the physical layer is presented, that supplies cryptographic features for use in connection with an adaptable RC6 encryption/decryption strategy written in MATLAB, which addresses confidentiality and efficiency considerations for the real life data of video encryption and decryption. The suggested solution seemed to be a reasonable complement for establishing stable data transfer at the physical layer in operation. With the help of RC6 algorithm using MATLAB, the unauthorized users will not be able to decrypt the data. Furthermore, the proposed technique’s ciphertexts withstood all evaluations, confirming the ciphertext’s volatility. Moreover, the proposed research idea is examined in the simulated environment for evaluation. Future studies is also highlighted in enhancing the stability and pervasiveness of IoT-based physical layer security
Blockchain-based Secure Environment for Electronic Health Records

Electronic health records (EHRs) have become the de facto standard for storing patient data in hospitals because of the data technology revolution. Many hospitals use server-based systems to keep track of patient medical records, however, this limits the scalability of those systems because they require a lot of storage space. Interoperability and security and privacy concerns, as well as cyber-attacks on the centralized storage, are among the issues they are dealing with. Lab report downloads can be compromised by a poor authentication mechanism that can be easily shared with a third party. Highlighted issues will be addressed by the proposed system, a Blockchain-based private patient information management system. Using a distributed, immutable, and secure ledger, the solution promises efficient system access and retrieval. Consensus can be achieved without consuming a big amount of energy or causing network congestion thanks to an enhanced consensus technique. Because of their tight zero-knowledge requirement, near-perfect data interchange across many platforms is possible thanks to Non-Fungible Tokens, which encourage openness and immutability in the data flow. In addition, the proposed system uses a mix of a hybrid access control system and public key cryptography to ensure high levels of data protection. Additionally, it is a fantastic accomplishment when Lab Report Download Portal and the report generator for medical lab reports can be connected to the main system, which can dynamically modify the report template format with multi-factor authentication enabled. Know your customer verification is also used to authenticate the user to the system. Decentralizing the medical industry’s data storage, sharing, and record-keeping is the general goal of this solution; this method eliminates the need for paper records.
A GIS-based approach to assessing large-scale building-integrated photovoltaic generation: a case study of Milan, Italy

The paper aims at presenting a Geographic Information System approach to assessing large-scale building-integrated photovoltaic production. A hierarchical approach has been implemented to estimate the available rooftop surface feasible for photovoltaic generation starting from the building area. Some factors such as the type of rooftop, the area already occupied by other rooftop elements, shading effects, etc., are considered in the calculation. Moreover, the potential photovoltaic production is computed using solar radiation data and the expected PV energy production and power installed. Modules and system conversion efficiency, orientation etc., are considered. The methodology has been tested in the real case of Milan, Italy, using open-source data available on government websites and technical information related to the specific case study. The outputs of the analysis show a not negligible potential impact on the Milan distribution network and as well as on the bulk power system.
ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation

In this paper, we present a direct adaptation strategy (ADAS), which aims to directly adapt a single model to multiple target domains in a semantic segmentation task without pretrained domain-specific models. To do so, we design a multi-target domain transfer network (MTDT-Net) that aligns visual attributes across domains by transferring the domain distinctive features through a new target adaptive denormalization (TAD) module. Moreover, we propose a bi-directional adaptive region selection (BARS) that reduces the attribute ambiguity among the class labels by adaptively selecting the regions with consistent feature statistics. We show that our single MTDT-Net can synthesize visually pleasing domain transferred images with complex driving datasets, and BARS effectively filters out the unnecessary region of training images for each target domain. With the collaboration of MTDT-Net and BARS, our ADAS achieves state-of-the-art performance for multi-target domain adaptation (MTDA). To the best of our knowledge, our method is the first MTDA method that directly adapts to multiple domains in semantic segmentation.
Joint Token and Feature Alignment Framework for Text-Based Person Search

Text-based person search is a challenging crossmodal retrieval task. Existing works reduce the inter-modality and intra-class gaps by aligning local features extracted from image and text modalities, which easily lead to mismatching problems due to the lack of annotation information. Besides, it is sub-optimal to reduce two gaps simultaneously in the same feature space. This work proposes a novel joint token and feature alignment framework to reduce the inter-modality and intraclass gaps progressively. Specifically, we first build a dual-path feature learning network to extract features and conduct feature alignment to reduce the inter-modality gap. Second, we design a text generation module to generate token sequences using visual features, and then token alignment is performed to reduce the intra-class gap. Last, a fusion interaction module is introduced to further eliminate the modality heterogeneity using the strategy of multi-stage feature fusion. Extensive experiments on the CUHKPEDES dataset demonstrate the effectiveness of our model, which significantly outperforms previous state-of-the-art methods.
Scaling E-Textile Production: Understanding the Challenges of Soft Wearable Production for Individual Creators

Electronic textiles (e-textiles) incorporate functional capabilities into fabric forms, providing direct body contact not afforded by traditional hardware. While widely researched by the Wearable Computing community, e-textiles have yet to reach broad prevalence in the consumer market. A major obstacle lies in its scalable production. Specifically, moving from prototype to production remains elusive for individual researchers, makers, and startups. To better understand these challenges and pinpoint opportunities to reduce barriers, we conducted semi-structured interviews with 7 subject matter experts with experience in low volume production of e-textile products. We identified common challenges encountered during the prototype-to-production process and summarized our findings in four themes: (1) lack of production standards; (2) gaps between apparel and hardware manufacturing; (3) gaps in manufacturing costs vs. market expectations; and (4) lack of production-capable e-textile solutions. Our study revealed opportunities for future research on tools, materials, and community support specific to low-volume e-textile production. We envision that accessible low-volume e-textile production could enable more creators to bring their creations to the next step.
A Hierarchical Reinforcement Learning Algorithm Based on Attention Mechanism for UAV Autonomous Navigation

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. Meanwhile, UAV’s ability of autonomous navigation and obstacle avoidance becomes more and more critical. This paper focuses on filling up the gap between deep reinforcement learning (DRL) theory and practical application by involving attention mechanism and hierarchical mechanism to solve some severe problems encountered in the practical application of DRL. More specifically, in order to improve the robustness of DRL, we use averaged estimation function instead of the normal value estimation function. Then, we design a recurrent network and a temporal attention mechanism to improve the performance of the algorithm. Third, we propose a hierarchical framework to improve its performance on long-term tasks. Some realistic simulation environments, as well as the real-world, are used to evaluate the proposed UAV autonomous navigation method. The results demonstrate that our DRL-based navigation method performs well in different environments and outperforms the original DrQ algorithm.
YOLOX-DC: A Small Target Detection Network up to Underwater Scenes

The rapid expansion of underwater target detection technology enables underwater operation robots to better complete underwater tasks. Therefore, a kind of underwater target detection algorithm has been pursued with its high detection accuracy and fast detection speed. One-stage target detection algorithms based on deep learning have achieved marvelous detection speed, but their detection accuracy is poor. In addition to the complex and various underwater scenes, the underwater target is extremely small in the vast ocean background. These reasons lead to the lower detection accuracy of the one-stage detection algorithm in the underwater environment. In this paper, we propose a mechanism to dynamically select feature layer channels, named DC block, so that target features on feature layers with different sizes of receptive field can be better utilized. Moreover, YOLOX, an excellent one-stage target detection network, is called YOLOX-DC after being equipped with the DC block to ensure a faster detection speed and achieve better detection accuracy. On the real-world underwater dataset, for detection and recognition of marine organisms, compared with the advanced two-stage detector Cascade R-CNN, YOLOXDC improves the detection accuracy by 0.9% mAP and the detection time latency is only 3.4% of Cascade R-CNN.
Robust Blind Video Watermarking Against Geometric Deformations and Online Video Sharing Platform Processing

In recent years, online video sharing platforms have been widely available on social networks. To protect copyright and track the origins of these shared videos, some video watermarking methods have been proposed. However, their robustness performance is significantly degraded under geometric deformations, which destroy the synchronization between the watermark embedding and extraction. To this end, we propose a novel robust blind video watermarking scheme by embedding the watermark into low-order recursive Zernike moments. To reduce the time complexity, we give an efficient computation method by exploring the characteristics of video and moments. The moment accuracy is greatly improved due to the introduction of a recursive computation method. Furthermore, we design an optimization strategy to enhance visual quality and reduce distortion drift of watermarked videos by analyzing the radial basis function. The robustness of the proposed scheme is verified by different attacks, including geometric deformations, length-width ratio changes, temporal synchronization attacks, and combined attacks. In practical applications, the proposed scheme effectively resists processing from video sharing platforms and screenshots taken with smartphones and PC monitors. The watermark is extracted without the host video. Experimental results show that our proposed scheme outperforms other state-of-the-art schemes in terms of imperceptibility and robustness.
Jenga: Orchestrating Smart Contracts in Sharding-Based Blockchain for Efficient Processing

Sharding is a promising way to achieve blockchain scalability, increasing the throughput by partitioning nodes into multiple smaller groups, splitting the workload. However, when tackling the increasingly important smart contracts, existing blockchain sharding protocols do not scale well. They usually require complex multi-round cross-shard consensus protocols for contract execution and extensive cross-shard communication during state transmission, mainly because that each shard stores and executes an isolated, disjoint subset of contracts. In this paper, we present Jenga, a novel sharding-based approach for efficient smart contract processing. Its main idea is to break the isolation between shards by orchestrating the logic storage, state storage, and execution of smart contracts. In Jenga, all shards share the logic for all contracts. Therefore, multiple contracts involved in a smart contract transaction can be executed together by the same shard within one round. Moreover, different shards store distinct states (named state shards), several "orthogonal" execution channels are established based on the state shards, where each channel overlaps with all shards. Each node simultaneously belongs to a shard and an "orthogonal" channel, different channels execute different contracts. Therefore, via the overlapped nodes, the contract states can be directly broadcast between the state shards and the execution channels without additional cross-shard communication. We implement Jenga and evaluation results show that it provides outstanding performance gains in terms of throughput and transaction confirmation latency.