Reviewerly/exHarmony huggingface dataset:
Dynamic Gesture Recognition Based on 3D Separable Convolutional LSTM Networks

Dynamic gesture recognition, as an important component of human-computer interaction (HCI), has attracted the attention of many researchers. However, there are still many challenges in the actual gesture recognition task, such as the non-rigid properties of the hand, the acquisition of different semantic information through spatial and motion information at different locations, and the changes of light and background in the surrounding environment can ultimately affect the extraction of gesture features. Since the essence of dynamic gesture recognition is classification and recognition, how to extract the spatio-temporal features of dynamic gestures will have a significant impact on the final recognition and classification of gestures. In recent years, three-dimensional convolutional neural networks have achieved excellent performance in motion recognition, but traditional three-dimensional convolution is redundant in the feature extraction process. In order to reduce the resource consumption, this paper adopts 3D separable convolution as an alternative solution. To further extract semantic and action information of gestures, we combine attentional mechanisms and long- and short-term memory. To further extract semantic and action information of gestures, we combine attentional mechanisms and long- and short-term memory (LSTM) networks for gesture recognition in this paper. We conducted experiments on the ChaLearn Large-Scale Gesture Recognition Dataset (IsoGD), and the experimental results validate the effectiveness of our method.
Identity through Social Wearables: Designing with Finnish University Students

Wearable devices are often expressive and part of our social interactions. Fashion and ethnographic studies have argued for a perspective on socio-cultural practices in wearable design, while recent work in HCI only began to formalise social requirements for wearable design. Our work aimed to understand how cultural practices can inform wearable design. We conducted a ten-month field study on the practice of customising and wearing boiler suits (fi: opiskeljhaalarit) in the Finnish university student culture and co-designed concepts for social wearables using the dialogue-labs method. The most popular designs supported self-expression and belonging to a group, which comply with the students' cultural practices of constructing social identity. We conclude that social wearables need to let users negotiate between differentiation and belonging to become part of everyday cultural practices.
A Novel Repair-Based Multi-objective Algorithm for QoS-Constrained Distributed Data-Intensive Web Service Composition

Distributed Data-intensive Web service composition (DWSC) implements new applications by using existing Web services distributed over the Internet. DWSC has multiple conflicting objectives, e.g. minimising response time and cost, and often needs to satisfy user-defined QoS constraints. While various approaches are proposed to handle this NP-hard problem, they either ignore the impact of the distributed nature of services on QoS, or treat DWSC as a single-objective optimisation problem assuming users can provide quantified preferences for each QoS, or do not consider QoS constraints while searching for composite solutions. To solve QoS-constrained multi-objective distributed DWSC problems, we propose a knowledge-based repair method for NSGA-II algorithm to effectively search for Pareto-optimal service compositions that satisfy all QoS constraints. This repair method facilitates the construction of constraint-obeying composite services. Experimental results verify that our algorithm outperforms existing NSGA-II based approaches for this problem on standard benchmark datasets.
(MBSE)<sup>2</sup>: Using MBSE to Architect, Implement, and Operate the MBSE System

Model-Based Systems Engineering (MBSE) is being applied in a new way at the enterprise level for the benefit of all its major programs and business units. For an enterprise to successfully implement MBSE, it must integrate models, datasets, tools, and infrastructure with appropriate processes, methods, and standards into a comprehensive, integrated MBSE “System”. This paper describes an approach being put to practice, which leverages MBSE principles and methods to architect this MBSE System, focusing on the descriptive models that serve as the foundation for MBSE. The approach is named (MBSE)2 since it uses MBSE to architect and implement the MBSE System. This (MBSE)2 approach is illustrated with example models of the MBSE System, showing how these models are used to inform the architecting process, monitor agile implementation of models, facilitate model-based execution of SE reviews and audits, and support other Government acquisition applications.
HDGI: A Human Device Gesture Interaction Ontology for the Internet of Things

Gesture-controlled interfaces are becoming increasingly popular with the growing use of Internet of Things (IoT) systems. In particular, in automobiles, smart homes, computer games and Augmented Reality (AR)/Virtual Reality (VR) applications, gestures have become prevalent due to their accessibility to everyone. Designers, producers, and vendors integrating gesture interfaces into their products have also increased in numbers, giving rise to a greater variation of standards in utilizing them. This variety can confuse a user who is accustomed to a set of conventional controls and has their own preferences. The only option for a user is to adjust to the system even when the provided gestures are not intuitive and contrary to a user’s expectations. This paper addresses the problem of the absence of a systematic analysis and description of gestures and develops an ontology which formally describes gestures used in Human Device Interactions (HDI). The presented ontology is based on Semantic Web standards (RDF, RDFS and OWL2). It is capable of describing a human gesture semantically, along with relevant mappings to affordances and user/device contexts, in an extensible way.
Blood Cells Detection Using Faster-RCNN

There has been an increase in the number of health issues in the recent decade with the evolution of various kinds of microorganisms and increasing pollution. The answer to prevent and protect ourselves from the diseases threatening our life lies at the microscopic level in our own blood. Blood consists of erythrocytes (RBCs), lymphocytes (WBCs) and platelets. Object detection is one of the many technologies which can help determine the different constituents of blood. It is a computer technology which deals with object occurrences of pre-defined classes such as cars, humans, buildings, etc. And has far-reaching applications such as self-driven cars, face recognition, face detection, etc. For object detection to be applicable in real-life situations, it should be fast and efficient in determining various entities in an image. There are many object detection techniques developed to make it more efficacious such as CNN and the family of R-CNN. In our research, we apply Faster R-CNN to the Blood Cell Count and Detection dataset to detect RBCs, WBCs, and Platelets. The number, shape and other meta-information or in other words any anomaly in the different constituents of blood can help in early detection of a wide array of issues and/or diseases such as leukemia, lymphoma, anemia, thrombocytopenia, leukopenia, sickle cell disease, etc. Our model detects a bounding box on the three blood constituents with a justifiable accuracy. It identifies all three constituents in the blood smear slide and forms a bounding box around them with their respective labels.
A Countermeasure for Traffic Analysis Attacks: Covert Communications With Digital Modulation

In this letter, we provide a physical layer supported defense mechanism against traffic analysis attacks by exploring the covert communication capability of digital modulation schemes. We analyze and compare the performances of M-PSK, M-QAM, M-ASK and M-APSK modulations in terms of the probability of detection by a warden and the maximum achievable throughput at the receiver. We obtain a lower bound for the covert transmission probability at Willie, which can be utilized as a covertness limit to maximize the throughput. Numerical studies highlight the tightness of the presented lower bound and show the maximum achievable throughput limits without being detected by a traffic analysis attacker.
Towards a User Feedback Approach for Smart Homes: An Explorative Interview Study

Smart homes are permeating our everyday lives. Different fast-evolving smart home technologies, various usage contexts, and heterogeneous users make it challenging to anticipate all requirements for smart homes in advance. User feedback gathered from the crowd of smart home technologies users is a valuable information source for verifying and eliciting requirements for system evolution. However, research on user feedback currently does not consider the smart home domain. Thus, we know only little about whether and how users of smart home technologies could provide feedback to support the elicitation of requirements and hence system evolution. In this paper, we present preliminary findings from our explorative interview study with smart home users on their attitudes towards feedback communication. Early results suggest that users are willing to provide feedback about their smart homes but have different needs regarding the feedback communication process. We assume the characteristics of smart home technologies (e.g., ubiquity, multimodality, connectivity) provide opportunities but also raise challenges for developing a user feedback approach in the context of smart homes.
Conflicting Bundles: Adapting Architectures Towards the Improved Training of Deep Neural Networks

Designing neural network architectures is a challenging task and knowing which specific layers of a model must be adapted to improve the performance is almost a mystery. In this paper, we introduce a novel theory and metric to identify layers that decrease the test accuracy of the trained models, this identification is done as early as at the beginning of training. In the worst-case, such a layer could lead to a network that can not be trained at all. More precisely, we identified those layers that worsen the performance because they produce conflicting training bundles as we show in our novel theoretical analysis, complemented by our extensive empirical studies. Based on these findings, a novel algorithm is introduced to remove performance decreasing layers automatically. Architectures found by this algorithm achieve a competitive accuracy when compared against the state-of-the-art architectures. While keeping such high accuracy, our approach drastically reduces memory consumption and inference time for different computer vision tasks.
Joint Feature Learning Network for Visible-Infrared Person Re-identification

Visible-infrared person re-identification (VI-ReID) is a significant technology in night-time surveillance applications. Compared to traditional person re-identification that focuses on only visible imaging system, the modality discrepancy between infrared and visible images brings an additional challenge to VI-ReID. Taking the shortcomings of existing VI-ReID works in terms of feature representation into account, in this paper, we propose an end-to-end network named Joint Feature Learning Network (JFLN) to jointly utilize global and local features, high-level and middle-level features, as well as shared and specific features. In addition, based on these extracted features, we introduce a multi-loss supervision strategy in the training process. Extensive experiments conducted on public SYSU-MM01 dataset demonstrate that the proposed method outperforms the state-of-the-arts, with relatively less computational cost.
The notion of immersion in virtual reality literature and related sources

The paper gives an overview of how virtual reality (VR) literature and related sources describe the notion of immersion, its sub-types and similar terms. There are two main schools giving definitions for immersion in VR, and their definitions are quite different. One school, associated with B. Witmer and M. Singer, regards immersion as a psychological state of the user. The other school, based on the works of M. Slater, sees immersion as an objective characteristic of a VR system. Some authors call the first type of immersion “psychological immersion” and the second type “technological immersion”. The authors of the present paper bring up the possibility of using alternative terms: “immersedness” for the immersion concept of the first school, and, for that of the second school, “immersingness” (denoting the actively immersing quality of the system) and “immersing” when referring to the act of immersing the user. The term “immersedness” is unambiguous, contrary to the term “psychological immersion” which could also mean a system characteristic when the system is created with the ability to try to manipulate the user with psychological means.
Robot Path Planning Algorithm based on Bi-RRT and Potential Field

To improve the search probability and search efficiency, the path planning method combining Bi-RRT and improved potential field is proposed. By introducing the resultant force of the improved potential field as the heuristic information of random tree expansion and changing the target point of the bidirectional random expansion trees, the gradient descent method and the target sampling method were adopted to enable the bidirectional random expansion tree to rapidly grow to the target point, avoiding the random tree from exploring useless areas, which improved the path searching ability and planning efficiency. Matlab software was used to simulate the algorithm, RRT-connect algorithm and artificial potential field method in different environments. The simulation results verified the effectiveness and superiority of the hybrid algorithm.
ISS: Intelligent Security System Using Facial Recognition

Security is mandatory and of utmost importance for all organizations. While the legitimate people should be allowed inside enterprises, the illegal ones should be barred from entering and this can be achieved using face recognition techniques. In this work, we have come up with a robust architecture using artificial intelligence and Internet of things that can be used across different enterprises. We have also derived the methodology and solution for implementing a more advanced security system. For proper demonstration, we have also considered one of the business use cases along with proposed processing work flow.
Monitoring temporal variations in the geothermal activity of Miocene Lesvos volcanic field using remote sensing techniques and MODIS – LST imagery

Many islands of the Aegean Sea show strong geothermal activity due to volcanism in the area. In this paper, Robust Satellite Techniques (RST) are used to isolate, from other known possible sources, those thermal anomalies possibly related to geothermal activity in the Miocene volcanic field of Lesvos Island (Northern Aegean). For this purpose, 12 years (2003–2014) of daily Night-time Land Surface Temperature (LST) products, from Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions were analyzed. The final dataset contained 770 thermal anomalies whose spatial correlation with geological and structural data of Lesvos - such as caldera rims, ring faults, major tectonic lineaments and hydrothermal alterations mapped by processing SENTINEL-2 MSI satellite images – has been particularly investigated. In the approximately 20 ma geothermal field of Lesvos, geothermal activity seems to be also associated with the extensional regime of the broader area that leads to lithosphere thinning and consequent heat transfer in the multi-fractured terrain of Lesvos through volcanic and tectonic faults. Achieved results seem to confirm the possibility to use RST-based thermal anomalies to identify temporal variations in the geothermal activity probably due to the uplifting and circulation of the hydrothermal waters.
SPEKS: Forward Private SGX-Based Public Key Encryption with Keyword Search

Public key encryption with keyword search (PEKS) enables users to search over encrypted data outsourced to an untrusted server. Unfortunately, updates to the outsourced data may incur information leakage by exploiting the previously submitted queries. Prior works addressed this issue by means of forward privacy, but most of them suffer from significant performance degradation. In this paper, we present a novel forward private PEKS scheme leveraging Software Guard Extension (SGX), a trusted execution environment provided by Intel. The proposed scheme presents substantial performance improvements over prior work. Specifically, we reduce the query processing cost from O(n) to O(1), where n is the number of encrypted data. According to our performance analysis, the overall computation time is reduced by 80% on average. Lastly, we provide a formal security definition of SGX-based forward private PEKS, as well as a rigorous security proof of the proposed scheme.
Robustification of Segmentation Models Against Adversarial Perturbations in Medical Imaging

This paper presents a novel yet efficient defense framework for segmentation models against adversarial attacks in medical imaging. In contrary to the defense methods against adversarial attacks for classification models which widely are investigated, such defense methods for segmentation models has been less explored. Our proposed method can be used for any deep learning models without revising the target deep learning models, as well as can be independent of adversarial attacks. Our framework consists of a frequency domain converter, a detector, and a reformer. The frequency domain converter helps the detector detects adversarial examples by using a frame domain of an image. The reformer helps target models to predict more precisely. We have experiments to empirically show that our proposed method has a better performance compared to the existing defense method.
Manipulating Player Performance via Music Tempo in Tetris

In this paper we study the possibility of influencing player performance in Tetris by varying the tempo of the music. To keep a player in the flow, it is useful to be able to vary the difficulty of a game. It has been shown that music influences human behavior and cognition (memory, reflexes). By synchronizing or desynchronizing the tempo with the fall of the tetriminos and varying the tempo in steps or more gradually, we found that a synchronous and steadily increasing tempo makes the game difficult for novice players and even more so for those with a neutral evaluation of the music. We also discovered that the tempo setting in one session can positively or negatively prepare the performance in the next session for all the players. These results pave the way for the creation of an AI capable of varying the difficulty of a game through music.
Modular Security Analysis of OAuth 2.0 in the Three-Party Setting

OAuth 2.0 is one of the most widely used Internet protocols for authorization/single sign-on (SSO) and is also the foundation of the new SSO protocol OpenID Connect. Due to its complexity and its flexibility, it is difficult to comprehensively analyze the security of the OAuth 2.0 standard, yet it is critical to obtain practical security guarantees for OAuth 2.0. In this paper, we present the first computationally sound security analysis of OAuth 2.0. First, we introduce a new primitive, the three-party authenticated secret distribution (3P-ASD for short) protocol, which plays the role of issuing the secret and captures the token issue process of OAuth 2.0. As far as we know, this is the first attempt to formally abstract the authorization technology into a general primitive and then define its security. Then, we present a sufficiently rich three-party security model for OAuth protocols, covering all kinds of authorization flows, providing reasonably strong security guarantees and moreover capturing various web features. To confirm the soundness of our model, we also identify the known attacks against OAuth 2.0 in the model. Furthermore, we prove that two main modes of OAuth 2.0 can achieve our desired security by abstracting the token issue process into a 3P-ASD protocol. Our analysis is not only modular which can reflect the compositional nature of OAuth 2.0, but also fine-grained which can evaluate how the intermediate parameters affect the final security of OAuth 2.0.
GPU accelerated stratified squamous epithelium biopsy image segmentation for OSCC detector and classifier

Automatic cancer detection and classification is one of the open research problems. Oral squamous cell carcinoma (OSCC) is prevalent among oral cancer patients. Traditional procedures of detection and classification of biopsy specimen are tedious and clinico pathological acumen. Most of the computer aided biomedical image analysis algorithms suffer from slow speed due to central processing unit (CPU) based sequential implementation. Adapting parallel processing for such algorithms can improve operational speed of such algorithms. In this work, NVIDIA graphical processing unit (GPU) GeForce GTX 1050Ti is used to offload segmentation process and part of Laws texture feature calculations in stratified squamous epithelium biopsy image classifier (SSE-BIC) from CPU. SSE-BIC detects and classifies oral SSE images either normal or one of the three grades of malignancy. Image segmentation of SSE-BIC is implemented using GPU which includes 2-D convolution, principal component analysis (PCA) and k-means clustering. 2-D convolution employed for Laws texture features also implemented using GPU. In this way, CPU based serial executable classifier SSE-BIC is altered to accommodate parallel processing and compared with the CPU implementation. Results showed that parallel implementation is about 13.04X times faster than the serial CPU implementation of SSE-BIC.
ABSNet: Aesthetics-Based Saliency Network Using Multi-Task Convolutional Network

As a smart visual attention mechanism to analyze visual scenes, visual saliency has been shown to closely correlate with semantic information such as faces. Although many semantic-information-guided saliency models have been proposed, to the best of our knowledge, no semantic information in affective domain has been employed for saliency detection. Aesthetic, the affective perceptual quality that integrates factors like scene composition and contrast, can certainly benefit visual attention that highly depends on these visual factors. In this letter, we propose an end-to-end multi-task framework called aesthetics-based saliency network (ABSNet). We use three commonly-used shared backbones and design two distinct branches for each task. Mean square error (MSE) loss and Earth Mover's Distance (EMD) loss are jointly adopted to alternately train the shared network and individual branch for different tasks, facilitating the proposed model to extract more effective features for visual perception. Moreover, our model is resolution-friendly to predict saliency for images of arbitrary size. It has been shown that the proposed multi-task method is superior over single-task version and outperforms state-of-the-art saliency methods.