Reviewerly/exHarmony huggingface dataset:
Investigation of Visual Self-Representation for a Walking-in-Place Navigation System in Virtual Reality

Walking-in-place (WIP) is one of the techniques for navigation in virtual reality (VR), and it can be configured in a limited space with a simple algorithm. Although WIP systems provide a sense of movement, it is important to deliver immersive VR experiences by providing information as similar as possible to walking in the real world. There have been many studies on the WIP technology, but it has rarely been done on visual self-representation of WIP in the virtual environment (VE). In this paper, we describe our investigation of virtual self-representation for application to a WIP navigation system using a HMD and full body motion capture system. Our system is designed to move in the pelvis direction by calculating the inertial sensor data, and a virtual body that is linked to the user's movement is seen from the first-person perspective (1PP) in two ways: (i) full body, and (ii) full body with natural walking. In (ii), when a step is detected, the motion of the lower part of the avatar is manipulated as if the user is performing real walking. We discuss the possibility of visual self-representation for the WIP system.
Parallel cryptographic hashing: Developments in the last 25 years

Parallel computing has led to fundamental changes in almost all research areas associated with computer science; cryptography is no exception. Besides improved speed and resource utilization, parallel cryptography is also promising from the security point of view. In particular, cryptographic hash functions (CHFs) – “the swiss knife” of the cryptography toolkit – stand to gain a lot from parallelism. Consequently, there has been an increasing interest in research towards the development of parallel CHFs for faster, more secure and more efficient hashing, particularly since the competition for the SHA-3 standard. This paper attempts to consolidate the research efforts in the field of parallel hashing, exploring various approaches in this direction. Within each approach discussed in the article, CHFs have been classified on the basis of construction and their design, implementation and performance have been explained, with the primary focus on parallelism. Finally, a broad temporal overview of the developments in the field of parallel hashing has been presented.
Recommending related functions from API usage-based function clone structures

Developers need to be able to find reusable code for desired software features in a way that supports opportunistic programming for increased developer productivity. Our objective is to develop a recommendation system that provides a developer with function recommendations having functionality relevant to her development task. We employ a combination of information retrieval, static code analysis and data mining techniques to build the proposed recommendation system called FACER (Feature-driven API usage-based Code Examples Recommender). We performed an experimental evaluation on 122 projects from GitHub from selected categories to determine the accuracy of the retrieved code for related features. FACER recommended functions with a precision of 54% and 75% when evaluated using automated and manual methods respectively.
Simulating Water Resistance in a Virtual Underwater Experience Using a Visual Motion Delay Effect

In this paper, we propose a new visual motion delay effect to enhance the presence of a user in a virtual underwater experience. To do this, we simulate the resistance in the underwater environment by delaying the hand and head movements of the user's avatar. The motion delay effect is implemented using two components: a drag force and a recovery force. The experimental results show that the combination of a drag force and a recovery force creates a realistic illusion of an underwater experience and enhances the user's presence, satisfaction, and immersion in the virtual underwater environment.
Blind Quality Assessment of Multiply Distorted Images Using Deep Neural Networks

In real-world visual content acquisition and distribution systems, a vast majority of visual content undergoes multiple distortions between the source and the end user. However, traditional image quality assessment (IQA) algorithms are usually validated and at times trained on image databases with a single distortion stage. Existing IQA methods for multiply distorted images remain limited in their scope and performance. In this work we design a first-of-its-kind blind IQA model for multiply distorted visual content based on a deep end-to-end convolutional neural network. The network is trained on a newly developed dataset which is composed of millions of multiply distorted images annotated with synthetic quality scores. Our tests on three publicly available subject-rated multiply distorted image databases show that the proposed model outperforms state-of-the-art blind IQA methods in terms of both accuracy and speed.
Texture-driven parametric snakes for semi-automatic image segmentation

We present a texture-driven parametric snake for semi-automatic segmentation of a single and closed structure in an image. We propose a new energy functional that combines intensity and texture information. The two types of image information are balanced using Fisher’s linear discriminant analysis. The framework can be used with any filter-based texture features. The parametric representation of the snake allows for easy and friendly user interaction while the framework can be trained on-the-fly from pixel collections provided by the user. We demonstrate the efficiency of the snake through an extensive validation on synthetic as well as on real data. Additionally, we show that the proposed snake is robust to noise and that it improves the segmentation performance when compared to an intensity-only scheme.
Design and Parameter Optimization of a 3-PSR Parallel Mechanism for Replicating Wave and Boat Motion

We present a low-cost, three-degree-of-freedom (3-DOF) prismatic-spherical-revolute (PSR) parallel mechanism used as a testing platform for an unmanned aerial vehicle (UAV) tethered to an unmanned surface vehicle (USV). The mechanism has three actuated linear rails kinematically linked to a platform which replicates boat motion up to 2.5 m vertical heave (sea state 4, Douglas Sea Scale). A lookup table relating relative slider heights to platform roll and pitch was developed numerically leveraging geometric constraints. A design parameter study optimized the arm length, platform size, and ball joint mounting angle relative to the overall radius to maximize the workspace. For this design, a maximum roll and pitch range from -32° to 32° and -25° to 35°, respectively, is achievable. A prototype was manufactured to carry the tethered UAV winch payload. Experimental testing confirmed the workspace and demonstrated boat motion replication, validated using an inertial measurement unit (IMU).
Comparing Concept Drift Detection with Process Mining Tools

Organisations have seen a rise in the volume of data corresponding to business processes being recorded. Handling process data is a meaningful way to extract relevant information from business processes with impact on the company's values. Nonetheless, business processes are subject to changes during their executions, adding complexity to their analysis. This paper aims at evaluating currently available Process Mining tools that handle concept drifts, i.e. changes over time of the statistical properties of the events occurring in a process. We provide an in-depth analysis of these tools briefly comparing their differences, advantages, and disadvantages.
The usefulness of multimedia surrogates for making relevance judgments about digital video objects

Large collections of digital video are increasingly accessible. The large volume and range of available video demands search tools that allow people to browse and query easily and to quickly make sense of the videos behind the result sets. This study focused on the usefulness of several multimedia surrogates, in terms of effectiveness, efficiency, and user satisfaction. Three surrogates were evaluated and compared: a storyboard, a 7-second segment, and a fast forward. Thirty-six experienced users of digital video conducted searches on each of four systems: three incorporated one of the surrogates each, and the fourth made all three surrogates available. Participants judged the relevance of at least 10 items for each search based on the surrogate(s) available, then re-judged the relevance of two of those items based on viewing the full video. Transaction logs and post-search and post-session questionnaires provided data on user interactions, including relevance judgments, and user perceptions. All of the surrogates provided a basis for accurate relevance judgments, though they varied (in expected ways) in terms of their efficiency. User perceptions favored the system with all three surrogates available, even though it took longer to use; they found it easier to learn and easier to use, and it gave them more confidence in their judgments. Based on these results, we can conclude that it's important for digital video collections to provide multiple surrogates, each providing a different view of the video.
Development of a 4.0 industry application for increasing occupational safety: guidelines for a correct approach

The Wearable Augmented Reality for employee safety in Manufacturing systems (W-Artemys) project aims to create a new technological support to increase the employee safety during operations on machines. Within the framework of this project, this paper will show the development's path of an easy-to-use software to enhance the operator's safety, highlighting the main difficulties encountered especially during the development of the database needed to support the application. In line with the concept of Industry 4.0, the W-Artemys software has been built to handle all the pieces of information coming from machines manuals and allowing the operator to use the software on a mobile device, coupled with an augmented reality solution. Due to the long work necessary to develop the various software elements, continuous evaluations have been made with the aim to highlight potential critical issues and compare the different ways to use the W-Artemys platform (mobile device vs. augmented reality). Different and intermediate tests will finally allow to evaluate whether the use of augmented reality will lead to improvement in the employee safety compared to the classic manual way and or to an easier software. To simulate the development of the database and the use of the software, this latter has been applied to an aseptic filling machine, which consists of different components and involves different maintenance operations. Appropriate data were retrieved to this end.
Augmenting Good Behaviour

In this paper, we present a case study inspired by sustainable initiatives carried out by the University of Bologna, such as decreasing the amount of paper used in favor of the digital form, and planting new trees in two Campus green areas to make sustainability tangible to communities. In this scenario, we envisioned the possibility to investigate how to increase awareness of sustainability issues in a specific community who is not directly involved in the decision process, with the final aim to convey sustainable behaviour. To accomplish this objective, we designed and developed an intervention mixing digital and reality experiences exploiting i) web-based technologies to create an interactive infographic, ii) mobile-based technologies to provide augmented reality (AR) features), iii) a sensors infrastructure to gather data about environmental conditions, and iv) an in-situ public installation to visualize the data collected by the sensors.
SPINE (SPIN Emulator) - A Quantum-Electronics Interface Simulator

A quantum computer comprises a quantum processor and the associated control electronics used to manipulate the qubits at the core of a quantum processor. CMOS circuits placed close to the quantum bits and operating at cryogenic temperatures offer the best solution for the control of millions of qubits. The performance requirements of the electronics are very stringent and its design requires the simultaneous optimization of both the circuits and the quantum system. This paper presents the SPINE (SPIN Emulator) toolset for the co-design and co-optimization of electronic/quantum systems. It comprises a SPICE simulator enhanced with a Verilog-A model based on a Hamiltonian solver emulating the quantum behavior of single-electron spin qubits. A co-design methodology is proposed to derive on the one hand the specifications of the electrical signals to be applied to and captured from the qubits, and to ensure on the other hand, the compliance of the electronics in generating the required signals. This methodology results in an optimized qubit performance while considering practical trade-offs in the control circuits, such as power consumption, complexity and cost as proven by a practical design example.
Host Hypervisor Trace Mining for Virtual Machine Workload Characterization

The efficient operation and resource management of multi-tenant data centers hosting thousands of services is a demanding task, that requires precise and detailed information regarding the behaviour of each and every virtual machine (VM). Often, coarse measures such as CPU, memory, disk and network usage by VMs are considered in grouping them onto the same physical server, as detailed measures would require access to the guest operating system (OS), which is not feasible in a multi-tenant setting. In this paper, we propose host-level hypervisor tracing as a non-intrusive means to extract useful features, that can provide for fine grain characterization of VM behaviour. In particular, we extract VM blocking periods as well as virtual interrupt injection rates to detect multiple levels of resource intensiveness. In addition, we consider the resource contention rate due to other VMs and the host, along with reasons for exit from non-root to root privileged mode, revealing useful information about the nature of the underlying VM workload. We also use tracing to get information about the rate of process and thread preemption in each VM, extracting process and thread contention as another feature set. We then employ various feature selection strategies and assess the quality of the resulting workload clustering. Notably, we adopt a two-stage feature selection approach in addition to a one shot clustering scheme. Moreover, we consider inter-cluster and intra-cluster similarity metrics, such as the silhouette score, to discover distinct groups of workloads as well as workload groups with significant overlap. This information can be used by 1) data center administrators to gain deeper visibility into the nature of various VMs running on their infrastructure, 2) performance engineers to assist root cause analysis of VM issues and 3) IaaS providers to help in resource management based on VM behavior.
OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras

Pedestrian detection is one of the most explored topics in computer vision and robotics. The use of deep learning methods allowed the development of new and highly competitive algorithms. Deep Reinforcement Learning has proved to be within the state-of-the-art in terms of both detection in perspective cameras and robotics applications. However, for detection in omnidirectional cameras, the literature is still scarce, mostly because of their high levels of distortion. This paper presents a novel and efficient technique for robust pedestrian detection in omnidirectional images. The proposed method uses deep Reinforcement Learning that takes advantage of the distortion in the image. By considering the 3D bounding boxes and their distorted projections into the image, our method is able to provide the pedestrian’s position in the world, in contrast to the image positions provided by most state-of-the-art methods for perspective cameras. Our method avoids the need of pre-processing steps to remove the distortion, which is computationally expensive. Beyond the novel solution, our method compares favorably with the state-of-the-art methodologies that do not consider the underlying distortion for the detection task.
Analysis of Cellular Network Latency for Edge-Based Remote Rendering Streaming Applications

Recent advances in low-latency streaming cloud technology have enabled remote rendering applications such as Google Stadia and Occulus VR. Edge-clouds located close to the end-user are expected to play an important part in improving user experience for remote rendering application. In this paper, we investigate network latency in edge-based remote rendering over LTE networks. We quantify network latency and identify the roadblocks in deploying remote rendering on existing cellular networks with extensive measurements. We show that crossing traffic could severely deteriorate latency performance and network side resource management can solve the issue. We also find the impact of various radio configurations and scheduling algorithm in LTE networks. Our results shed light on how the network side and client side can cooperatively reduce network latency for emerging applications.
Two-photon absorption in a two-level system enabled by noise

We address the textbook problem of dynamics of a spin placed in a dc magnetic field and subjected to an ac drive. If the drive is polarized in the plane perpendicular to the dc field, the drive photons are resonantly absorbed when the spacing between the Zeeman levels is close to the photon energy. This is the only resonance when the drive is circularly polarized. For linearly polarized drive, additional resonances corresponding to absorption of three, five, and multiple odd numbers of photons is possible. Interaction with the environment causes the broadening of the absorption lines. We demonstrate that the interaction with environment enables the forbidden two-photon absorption. We adopt a model of the environment in the form of random telegraph noise produced by a single fluctuator. As a result of the synchronous time fluctuations of different components of the random field, the shape of the two-photon absorption line is non-Lorentzian and depends dramatically on the drive amplitude. This shape is a monotonic curve at strong drive, while, at weak drive, it develops a two-peak structure reminiscent of an induced transparency on resonance.
Neural network pruning based on input importance

In this paper we present a new algorithm called Neural Network Pruning Based on Input Importance (NNPII) that prunes the neural network based on the input importance. The algorithm depends on the frequency of using a certain value of an attribute in all the given instances in the dataset. Pruning w ill include only links between input layer and hidden layer. The algorithm has three phases, the first phase is the preprocessing phase, where the data inputs are replaced with their importance. The second phase is a forward pass, which is similar to forward pass in the backpropgation algorithm, but instead of using the real inputs as inputs, we use the input importance obtained in the preprocessing stage. The third pass is the backward phase which is again as backpropgation algorithm, but in this stage we use the input importance instead of real inputs, and β factor that measures the value changing for every input attribute, β will be incorporated in the formula in updating the weights between the input layer and the hidden layer. The elimination process is performed based on criterion that depends on Ω factor that represents a threshold value for a certain input attribute for all instances. It is worth mentioning that the pruning is performed within the usual training phases. The proposed algorithm has been tested through three types of experiments, a comparison between backpropgation and NNPII, Applying NNPII with various parameter values and finally comparing NNPII with other various pruning algorithms. Results show that NNPII performs well and compete with other pruning algorithms. NNPII outperforms all other algorithms when the classes are fairly distributed in the datasets.
Anomaly Detection and Cognizant Path Planning for Surveillance Operations using Aerial Robots

In this paper we address the problem of unsupervised anomaly detection and cognizant path planning for surveillance operations using aerial robots. Through one-class classification exploiting deep learned features on image data and a Bayesian technique to fuse, encode and update anomaly information on a real-time reconstructed occupancy map, the robot becomes capable of detecting and localizing anomalies in its environment. Provided this information, path planning for autonomous exploration of unknown areas and simultaneous maximization of the entropy of sensor observations over abnormal regions is developed. The method is verified experimentally through field deployments above a desert-like environment and in a parking lot. Furthermore, analysis results on the suitability of different deep learning-based and hand-engineered features for anomaly detection tasks are presented.
Automated web development

A website helps a business to grow by using different marketing strategies. This paper describes a novel approach to develop a website by just providing the text (description of the website) or an image as input. Using Text Input it will suggest template (screenshots) after identifying the theme of the site inferred from the input. Those templates are converted into code for further customizations for their personal use. Current problem was that a web developer will take more than 15 days only to just make the basic structure of a website. This issue is resolved by our work which will generate the complete code of the webpage/ website in less amount of time. In this paper, it will tokenize each word to find their synonyms and then mapped it with root words for the theme identification and uses deep learning model to convert templates into code.
Learning Primitive Skills for Mobile Robots

Achieving effective task performance on real mobile robots is a great challenge when hand-coding algorithms, both due to the amount of effort involved and manually tuned parameters required for each skill. Learning algorithms instead have the potential to lighten up this challenge by using one single set of training parameters for learning different skills, but the question of the feasibility of such learning in real robots remains a research pursuit. We focus on a kind of mobile robot system - the robot soccer “small-size” domain, in which tactical and high-level team strategies build upon individual robot ball-based skills. In this paper, we present our work using a Deep Reinforcement Learning algorithm to learn three real robot primitive skills in continuous action space: go-to-ball, turn-and-shoot and shoot-goalie, for which there is a clear success metric to reach a destination or score a goal. We introduce the state and action representation, as well as the reward and network architecture. We describe our training and testing using a simulator of high physical and hardware fidelity. Then we test the policies trained from simulation on real robots. Our results show that the learned skills achieve an overall better success rate at the expense of taking 0.29 seconds slower on average for all three skills. In the end, we show that our policies trained in simulation have good performance on real robots by directly transferring the policy.