Reviewerly/exHarmony huggingface dataset:
ProvNet: Networked Blockchain for Decentralized Secure Provenance

Data sharing is increasingly popular especially for scientific research and business fields where large volume of datasets are usually required. People benefit from data sharing, but keeping track of the history of the shared data (i.e., provenance records) for monitoring and protecting them is not easy. On one hand, due to the decentralized nature of data sharing nowadays, it is impractical to have a centralized entity who collects all the provenance records. Some previous works that combined the data sharing with blockchain can store the provenance records of data sharing in a blockchain system. On the other hand, previous works focus on the scenario where malicious users will not modify the shared datasets and re-share them as the owner of these changed datasets. Besides, maintaining the correctness of collected records in the presence of malicious attackers is challenging. In this paper, we present ProvNet, a decentralized data sharing platform which can provide a secure and correct provenance record using a networked blockchain. All the valid sharing records will be collected and stored in a tamper-proof networked blockchain, named blocknet. Furthermore, ProvNet can also discover and detect the misbehaviors with the stored provenance records.
Dealing with Input Uncertainty in Automotive Voice Assistants

After their success in the smart home, voice assistants are becoming increasingly popular in automotive user interfaces. These voice assistants are traditionally designed to provide a human-like dialog with the user. Thus, when processing voice input, especially dealing with uncertainty is an important factor that needs to be considered when designing system responses. While state-of-the-art voice assistants offer responses based on their certainty of what they understood, these response-thresholds are largely under-explored. In this work, we close this gap by providing a user-centered approach to investigate which responses are acceptable for voice input users depending on input certainty. Through findings from semi-structured online interviews with 101 participants, we provide insights about designing voice user interface responses based on system certainty. Our findings reveal a sweet spot for executing a task versus requesting additional user input. Further, we provide data-driven guidelines for different in-car voice assistant behaviors.
AirTyping

In Human-Computer Interactions (HCI), to reduce the dependency of bulky devices like physical keyboards and joysticks, many gesture-based HCI schemes are adopted. As a typical HCI technology, text input has aroused much concern and many virtual or wearable keyboards have been proposed. To further remove the keyboard and allow people to type in a device-free way, we propose AirTyping, i.e., a mid-air typing scheme based on Leap Motion. During the typing process, the Leap Motion Controller captures the typing gestures with cameras and provides the coordinates of finger joints. Then, AirTyping detects the possible keystrokes, infers the typed words based on Bayesian method, and outputs the inputted word sequence. The experiment results show that our system can detect the keystrokes and infer the typed text efficiently, i.e., the true positive rate of keystroke detection is 92.2%, while the accuracy that the top-1 inferred word is the typed word achieves 90.2%.
My-AHA: Software Platform to Promote Active and Healthy Ageing

The population is getting old, and the use of technology has improved the quality of life of the senior population. This is confirmed by the increasing number of solutions targeting healthy and active ageing. Such solutions keep track of the daily routine of the elderly and combine it with other relevant information (e.g., biosignals, physical activity, social activity, nutrition) to help identify early signs of decline. Caregivers and elders use this information to improve their routine, focusing on improving the current condition. With that in mind, we have developed a software platform to support My-AHA, which is composed of a multi-platform middleware, a decision support system (DSS), and a dashboard. The middleware seamlessly merges data coming from multiple platforms targeting health and active ageing, the DSS performs an intelligent computation on top of the collected data, and the dashboard provides a user’s interaction with the whole system. To show the potential of the proposed My-AHA software platform, we introduce the My Personal Dashboard web-based application over a frailty use case to illustrate how senior well-being can benefit from the use of technology.
Mask R-CNN based segmentation method for satellite imagery of photovoltaics generation systems

There is increasing interest in getting the precise locations and the corresponding sizes of installed photovoltaics. Previous and existing methods have been confirmed to be time-consuming, not robust or incomplete. Thus, we proposed a new method based on the satellite imagery, deep learning and image processing which can be used to collect the precise information of installed photovoltaic automatically. The information can be utilized to support the adoption and management of solar electricity. The method includes three main parts: the overlap-tile strategy, Mask R-CNN model and right-angle polygon fit algorithm. The overlap-tile strategy used here to improve the image edge segmentation ability of Mask R-CNN. The right-angle polygon fit algorithm is proposed to better fit the mask area generated by Mask R-CNN, which helps us get the much more precise locations and sizes of photovoltaics. The training and testing of our method were based on a satellite dataset with 3904 images annotated with ground truth regions of photovoltaics. The numerical and visual results clearly demonstrate that the accuracy and efficiency of our method is better than the previous reports.
AlphaNet: An Attention Guided Deep Network for Automatic Image Matting

In this paper, we propose an end to end solution for image matting i.e high-precision extraction of foreground objects from natural images. Image matting and background detection can be achieved easily through chroma keying in a studio setting when the background is either pure green or blue. Nonetheless, image matting in natural scenes with complex and uneven depth backgrounds remains a tedious task that requires human intervention. To achieve complete automatic foreground extraction in natural scenes, we propose a method that assimilates semantic segmentation and deep image matting processes into a single network to generate detailed semantic mattes for image composition task. The contribution of our proposed method is two-fold, firstly it can be interpreted as a fully automated semantic image matting method and secondly as a refinement of existing semantic segmentation models.We propose a novel model architecture as a combination of segmentation and matting that unifies the function of upsampling and downsampling operators with the notion of attention. As shown in our work, attention guided downsampling and upsampling can extract high-quality boundary details, unlike other normal downsampling and upsampling techniques. For achieving the same, we utilized an attention guided encoder-decoder framework which does unsupervised learning for generating an attention map adaptively from the data to serve and direct the upsampling and downsampling operators. We also construct a fashion e-commerce focused dataset with high-quality alpha mattes to facilitate the training and evaluation for image matting.
Difference Verification with Conditions

Abstract Modern software-verification tools need to support development processes that involve frequent changes. Existing approaches for incremental verification hard-code specific verification techniques. Some of the approaches must be tightly intertwined with the development process. To solve this open problem, we present the concept of difference verification with conditions . Difference verification with conditions is independent from any specific verification technique and can be integrated in software projects at any time. It first applies a change analysis that detects which parts of a software were changed between revisions and encodes that information in a condition. Based on this condition, an off-the-shelf verifier is used to verify only those parts of the software that are influenced by the changes. As a proof of concept, we propose a simple, syntax-based change analysis and use difference verification with conditions with three off-the-shelf verifiers. An extensive evaluation shows the competitiveness of difference verification with conditions.
Multi-meter Intelligent Detection and Recognition Method under Complex Background

This paper proposes a deep learning based approach for multi- meter intelligent detection and recognition under complex background. Firstly, SSD is used to detect the meter images and get the category and boxes of meters. Then the key point detection model is used to get the center point, the zero point, full-scale point and the end point of the pointer. Finally the indication is calculated through those key points. The experimental results show that the method can simultaneously identify different pointer meters, the error of key point is 2.8252 pixels and the average relative error is only 1.37%, which has high robustness and recognition accuracy.
Tradies, Technology and Therapy: Towards Designing Gameful VR Environments for Burn Rehabilitation

Burn injuries are a significant public health challenge with the known consequences and long term problems including pain, permanent scarring and psychological trauma. Current available rehabilitation equipment is rudimentary, resource-intensive and lacks a human-centered design approach. Immersive Virtual Reality technologies may provide aid in this area, with prior studies showing positive effects that this technology has on reducing patient's physical pain, psychological trauma and improving movement. This study recognises the need for customised, measurable and goal-directed rehabilitation for burn patients. By conducting two ideation design sessions with health workers (session 1), and burn survivors and their carers (session 2), this study highlights the participants' perspectives on VR for burn rehabilitation such as the importance of patient agency, heat and healthy scarring. This work contributes core themes from survivors and health professionals to the field of design and computer human interaction that could inform future iterations of VR burn rehabilitation applications.
Online parameterized dictionary matching with one gap

We study the online Parameterized Dictionary Matching with One Gap problem (PDMOG) which is the following. Preprocess a dictionary D of d patterns, where each pattern contains a special gap symbol that can match any string, so that given a text T arriving online, a character at a time, we can report all the patterns from D that parameterized match to suffixes of the text that has arrived so far, before the next character arrives. Two equal-length strings are a parameterized match if there exists a bijection on the alphabets, such that one string matches the other under the bijection. The gap symbols are associated with bounds determining the possible lengths of matching strings. Online Dictionary Matching with One Gap (DMOG) captures the difficulty in a bottleneck procedure for cyber-security, as many digital signatures of viruses manifest themselves as patterns with a single gap. Parameterized match captures possible encryption of the patterns. We also define the strict PDMOG problem, in which subpatterns of the same dictionary pattern should be parameterized matched via the same bijection. This captures situations where subpatterns of a dictionary pattern are encoded simultaneously. We study this problem for special case called alphabet-saturated dictionairy, where every subpattern contains all characters of the dictionary alphabet Σ. We use the following parameters to describe our results: D is the total size of the dictionary (not including the gaps), plsc is the longest parameterized suffix chain of subpatterns in D, op is the number of parameterized patterns occurrences in T, α⁎ and β⁎ are the minimum left and maximum right gap borders in the non-uniformly bounded dictionary case, δ(GD) is the degeneracy of the graph GD representing dictionary D. This graph is classified as sparse or dense according the value of the δ(GD) and plsc parameters. We obtain: O˜(D) preprocessing time/space and O˜(δ(GD)⋅plsc+plsc⋅max⁡{|Σ|,M}+op) query time per text character algorithm for online PDMOG with sparse graph dictionaries. O˜(D+d(β⁎−α⁎)) preprocessing time/space and O˜(plsc⋅d⋅(β⁎−α⁎)+plsc⋅max⁡{|Σ|,M}+op) query time per text character algorithm for online PDMOG with dense graph dictionaries. O˜(D) preprocessing time/space and O˜(δ(GD)⋅plsc+op) query time per text character algorithm for strict PDMOG with alphabet-saturated dictionaries. These results are parallel to the ones obtained for the Dictionary with One Gap (DMOG) problem almost matching the lower bounds achieved for this problem [7]. While the parameter δ(GD) can be as large as d and much lager if the dictionary has non-uniform gap boundaries, and the parameter plsc could theoretically be as large as d, in many practical situations these parameters are actually small. The strength of our work is in achieving results that explore and exploit small values for these parameters, thus supplying algorithms that are suitable for some practical cyber security needs.
Unmanned Aerial Vehicle remote sensing image dehazing via global parameters

Unmanned Aerial Vehicle (UAV) aerial mapping technology is becoming more and more widely used for its specific advantages. But in some areas, where there is fog or haze, the visibility of images degrades severely. To solve this problem, we propose a formation model for UAV remote sensing hazed images by converting the dehazing problem into the computation of two global parameters, including airlight vector and transmission. Specifically, we first get the airlight vector by a statistical law of variation in local image brightness, but only use its precise magnitude; secondly, we estimate the accurate airlight orientation based on the geometric property of pixels (projected in RGB space) of the patches in which the transmission and surface reflectance are approximately constant. Thirdly, we calculate a mean transmission from the top 20% darkest pixels in the dark channel and use it as the global transmission. The dehazing experiments based on synthetic images show that our algorithm can get a more accurate airlight vector and global transmission. The experiments based on actual UAV images show that our algorithm has a good histogram shape preservation, which can effectively enhance the clarity of the hazed images. For UAV hazed remote sensing images of mapping, our algorithm can effectively improve the visual effect and enhance visibility.
Improving Fake Product Detection with Aspect-Based Sentiment Analysis

With the development of e-commerce, the number of counterfeit products is increasing and the rights and interests of customers have been seriously infringed. A product can be evaluated by reviews and ratings objectively. However, the topics of reviews are diverse while customers tend to focus on only a few aspects, and many reviews have wrong scores that are inconsistent with the content. Natural language processing (NLP) is helpful in mining the opinion of reviews automatically. In this paper, the goal is to improve fake product detection through text classification technology. Precisely, we use CNN and LSTM models to judge whether the review is quality related or not, which can remove useless reviews, and aspect-based sentiment analysis with an attention mechanism to determine the sentiment polarity of the concerning aspect to get ratings for different aspects. We experiment on the Self-Annotated datasets and results show that by using text classification technology, the performance of fake product detection can be greatly improved.
Indoor Query System for the Visually Impaired

Scene query is an important problem for the visually impaired population. While existing systems are able to recognize objects surrounding a person, one of their significant shortcomings is that they typically rely on the phone camera with a finite field of view. Therefore, if the object is situated behind the user, it will go undetected unless the user spins around and takes a series of pictures. The recent introduction of affordable, panoramic cameras solves this problem. In addition, most existing systems report all “significant” objects in a given scene to the user, rather than respond to a specific user-generated query as to where an object located. The recent introduction of text-to-speech and speech recognition capabilities on mobile phones paves the way for such user-generated queries, and for audio response generation to the user. In this paper, we exploit the above advancements to develop a query system for the visually impaired utilizing a panoramic camera and a smartphone. We propose three designs for such a system: the first is a handheld device, and the second and third are wearable backpack and ring. In all three cases, the user interacts with our systems verbally regarding whereabouts of objects of interest. We exploit deep learning methods to train our system to recognize objects of interest. Accuracy of our system for the disjoint test data from the same buildings in the training set is 99%, and for test data from new buildings not present in the training data set is 53%.
Designing External Automotive Displays: VR Prototypes and Analysis

Our work extends contemporary research into visualizations and related applications for automobiles. Focusing on external car bodies as a design space we introduce the External Automotive Displays (EADs), to provide visualizations that can share context and user-specific information as well as offer opportunities for direct and mediated interaction between users and automobiles. We conducted a design study with interaction designers to explore design opportunities on EADs to provide services to different road users; pedestrians, passengers, and drivers of other vehicles. Based on the design study, we prototyped four EADs in virtual reality (VR) to demonstrate the potential of our approach. This paper contributes our vision for EADs, the design and VR implementation of a few EAD prototypes, a preliminary design critique of the prototypes, and a discussion of the possible impact and future usage of external automotive displays.
Access Control for Smart Manufacturing Systems

In the ongoing $$4^{th}$$ industrial revolution, a new paradigm of modular and flexible manufacturing factories powered by IoT devices, cloud computing, big data analytics and artificial intelligence is emerging. It promises increased cost efficiency, reduced time-to-market and extreme customization. However, there is a risk that technical assets within such systems will be targeted by cybersecurity attacks. A compromised device in a smart manufacturing system could cause a significant damage, not only economically for the factory owner, but also physically on humans, machinery and the environment. Strict and granular Access Control is one of the main protective mechanisms against compromised devices in any system. In this paper we discuss the requirements and implications of Access Control within the context of Smart Manufacturing. The contributions of this paper are twofold: first we derive requirements on an Access Control Model in the context of smart manufacturing, and then asses the Attribute Based Access Control model against these requirements in the context of a use case scenario.
Using Jason Framework to Develop a Multi-agent System to Manage Users and Spaces in an Adaptive Environment System

Manage user preferences and local specifications on an IoT adaptive system is a actual problem. This paper uses Jason framework to develop a multi agent system to achieve a Smart Environment System, and supports interaction between persons and physical spaces, that users want to smartly adapt to their preferences in a transparent way. This work proposes a new approach, that has been developed using a multi agent system architecture with different layers to achieve a solution that entails all the proposed objectives.
Statistical watermarking approach for 3D mesh using local curvature estimation

In this study, an oblivious 3D mesh watermarking scheme is represented utilising local curvature estimation and statistical characteristics of 3D mesh to provide robustness as well as retaining the imperceptibility of the 3D model. The proposed method estimates the local curvature of 3D model by finding the difference between the average normal and the surface normal of all the faces in a 1-ring neighbourhood of a vertex under consideration. Feature vector of all vertices is then measured and used to select vertices for watermark insertion. Distributions of vertex norms are transformed statistically to hide the watermark as statistical parameters are more robust and less prone to attacks. The robustness and imperceptibility of the proposed method against various attacks are analysed through simulations.
Learning to Navigate for Mobile Robot with Continual Reinforcement Learning

Autonomous navigation of mobile ground robots is a promising research topic due to its extensive applications. Existing works are difficult to deal with obstacle-cluttered problems and often do not generalize well because of the differences between training scenarios and practical environments. In this paper, we present an end-to-end motion planning model that has the ability to navigate the mobile robot to the given destination safely in an unknown environment without any prior knowledge. Specifically, with the application of continual reinforcement learning, our approach is able to learn subtasks in a sequential fashion by decomposing the arbitrarily complex navigating task. The experiments show that our approach is more effective and can be directly transferable to other unseen environments.
Efficient fall activity recognition by combining shape and motion features

Abstract This paper presents a vision-based system for recognizing when elderly adults fall. A fall is characterized by shape deformation and high motion. We represent shape variation using three features, the aspect ratio of the bounding box, the orientation of an ellipse representing the body, and the aspect ratio of the projection histogram. For motion variation, we extract several features from three blocks corresponding to the head, center of the body, and feet using optical flow. For each block, we compute the speed and the direction of motion. Each activity is represented by a feature vector constructed from variations in shape and motion features for a set of frames. A support vector machine is used to classify fall and non-fall activities. Experiments on three different datasets show the effectiveness of our proposed method.
Post Synthesis-Optimization of Reversible Circuit using Template Matching

In this work, we propose an optimization technique for reversible circuits. The involved steps in this optimization are divided in two stages: First, we define templates and, second, we execute an optimization algorithm, where templates are applied on a specific order over the input circuit and exhaustively search through the circuit for possible replacements. The proposed optimization approach has been tested over a wide spectrum of benchmarks and improvement over state-of-the-art design approaches is registered.