Reviewerly/exHarmony huggingface dataset:
Multiple-image optical encryption based on phase retrieval algorithm and fractional Talbot effect

A new multiple-image optical encryption scheme based on phase retrieval algorithm and fractional Talbot effect is proposed. In encryption process, by using Fresnel domain phase retrieval algorithm, each image is encoded into a pure phase distribution with different spatial constraints. The spatial constraints are chosen as complementary square aperture array. A Talbot illuminator is designed according to the fractional Talbot effect, which is treated as a decoder to obtain the designed square aperture array. Then all obtained phase distributions are combined into the final ciphertext image. In decryption process, the images can be decoded from the ciphertext by using the Talbot illuminator and keys. Benefit from the use of fractional Talbot effect, the demultiplexing setup of the cryptosystem is very simple and straightforward, with no need of lens. The simulation results prove that the multiplexing capability is considerably enhanced. At the same time, the security analysis proves that the system can resist various attacks.
Underwater Virtual Reality System for Neutral Buoyancy Training: Development and Evaluation

During terrestrial activities, sensation of pressure on the skin and tension in muscles and joints provides information about how the body is oriented relative to gravity and how the body is moving relative to the surrounding environment. In contrast, in aquatic environments when suspended in a state of neutral buoyancy, the weight of the body and limbs is offloaded, rendering these cues uninformative. It is not yet known how this altered sensory environment impacts virtual reality experiences. To investigate this question, we converted a full-face SCUBA mask into an underwater head-mounted display and developed software to simulate jetpack locomotion outside the International Space Station. Our goal was to emulate conditions experienced by astronauts during training at NASA's Neutral Buoyancy Lab. A user study was conducted to evaluate both sickness and presence when using virtual reality in this altered sensory environment. We observed an increase in nausea related symptoms underwater, but we cannot conclude that this is due to VR use. Other measures of sickness and presence underwater were comparable to measures taken above water. We conclude with suggestions for improved underwater VR systems and improved methods for evaluation of these systems based on our experience.
Data Integrity Threats and Countermeasures in Railway Spot Transmission Systems

Modern trains rely on balises (communication beacons) located on the track to provide location information as they traverse a rail network. Balises, such as those conforming to the Eurobalise standard, were not designed with security in mind and are thus vulnerable to cyber attacks targeting data availability, integrity, or authenticity. In this work, we discuss data integrity threats to balise transmission modules and use high-fidelity simulation to study the risks posed by data integrity attacks. To mitigate such risk, we propose a practical two-layer solution: At the device level, we design a lightweight and low-cost cryptographic solution to protect the integrity of the location information; at the system layer, we devise a secure hybrid train speed controller to mitigate the impact under various attacks. Our simulation results demonstrate the effectiveness of our proposed solutions.
Serious Games as innovative tools in HR policy

Abstract In the life of modern society information and communication technology occupy a significant place, being an active principle of social changes. Practically all processes, one way or another, go along with technologies. The total penetration of information and communication technologies into all spheres of life initiates the transformation of established social relations and norms. Particular attention we pay to the study of social norms in virtual reality. We observe the main forms of deviant behavior in the network, and the mechanism of social control, aimed at their regulation. At the same time, we prove that the areas of regulation are quite mobile and do not have a clear framework. Although we try to define three main areas of this type: area of law regulation, area of moral regulation, unregulated field. All the presented areas describe the visible phenomena. The essence of the virtualization process, however, turns out to be undetected. But after all, society itself can set a socially acceptable framework for the functioning of information and communication technologies, which can vary depending on religion, national circumstances or level of economic development. As a result, it can be noted that in the modern world the social landscape is changing dramatically.
TensorFlow to Cloud FPGAs: Tradeoffs for Accelerating Deep Neural Networks

We present the first open-source TensorFlow to FPGA tool capable of running state-of-the-art DNNs. Running TensorFlow on the Amazon cloud FPGA instances, we provide competitive performance and higher accuracy compared to a proprietary tool, thus providing a public framework for research exploration in the DNN inference space. We also detail the optimizations needed to map modern DNN frameworks to FPGAs, provide novel analysis of design tradeoffs for FPGA DNN accelerators and present experiments across a range of DNNs.
Feature dependencies in automotive software systems: Extent, awareness, and refactoring

Many automotive companies consider their software development process to be feature-oriented. In the past, features were regarded as isolated system parts developed and tested by developers from different departments. However, in modern vehicles, features are more and more connected and their behavior depends on each other in many situations. In this article, we describe how feature-oriented software development is conducted in automotive companies and which challenges arise from that. We present an empirical analysis of feature dependencies in three real-world automotive systems. The analysis shows that features in modern vehicles are highly interdependent. Furthermore, the study reveals that developers are not aware of these dependencies in most cases. For the three examined cases, we show that less than 12% of the components in the system architecture are responsible for more than 90% of the feature dependencies. Finally, we propose a refactoring approach for implicit communal components, which makes them explicit by moving them to a dedicated platform component layer.
ConceptMap: A Conceptual Approach for Formulating User Preferences in Large Information Spaces

In a large information space a user needs to iteratively investigate the data to formulate her preferences for IR systems. In recent years several visualization techniques have been proposed to help a user to better formulate her preferences. However, using these solutions a user needs to explicitly specify her preferences for IR systems in forms of keywords or phrases. In this paper we present ConceptMap, a system that takes the advantage of deep learning and a knowledge lake to provide a conceptual summary of the information space. ConceptMap allows a user to specify her preferences implicitly as a set of concepts without the need to iteratively investigate the information space. It provides a 2D Radial Map of concepts where a user can rank items relevant to her preferences through dragging and dropping. Our experiment results shows that ConceptMap can help users to better formulate their preferences when they need to retrieve varied and comprehensive list of information across a large amount of data.
Capturing high-level requirements of information dashboards' components through meta-modeling

Information dashboards are increasing their sophistication to match new necessities and adapt to the high quantities of generated data nowadays. These tools support visual analysis, knowledge generation, and thus, are crucial systems to assist decision-making processes. However, the design and development processes are complex, because several perspectives and components can be involved. Tailoring capabilities are focused on providing individualized dashboards without affecting the time-to-market through the decrease of the development processes' time. Among the methods used to configure these tools, the software product lines paradigm and model-driven development can be found. These paradigms benefit from the study of the target domain and the abstraction of features, obtaining high-level models that can be instantiated into concrete models. This paper presents a dashboard meta-model that aims to be applicable to any dashboard. Through domain engineering, different features of these tools are identified and arranged into abstract structures and relationships to gain a better understanding of the domain. The goal of the meta-model is to obtain a framework for instantiating any dashboard to adapt them to different contexts and user profiles. One of the contexts in which dashboards are gaining relevance is Learning Analytics, as learning dashboards are powerful tools for assisting teachers and students in their learning activities. To illustrate the instantiation process of the presented meta-model, a small example within this relevant context (Learning Analytics) is also provided.
Audio Legends: Investigating Sonic Interaction in an Augmented Reality Audio Game

Augmented Reality Audio Games (ARAG) enrich the physical world with virtual sounds to express their content and mechanics. Existing ARAG implementations have focused on exploring the surroundings and navigating to virtual sound sources as the main mode of interaction. This paper suggests that gestural activity with a handheld device can realize complex modes of sonic interaction in the augmented environment, resulting in an enhanced immersive game experience. The ARAG “Audio Legends” was designed and tested to evaluate the usability and immersion of a system featuring an exploration phase based on auditory navigation, as well as an action phase, in which players aim at virtual sonic targets and wave the device to hit them or hold the device to block them. The results of the experiment provide evidence that players are easily accustomed to auditory navigation and that gestural sonic interaction is perceived as difficult, yet this does not affect negatively the system’s usability and players’ immersion. Findings also include indications that elements, such as sound design, the synchronization of sound and gesture, the fidelity of audio augmentation, and environmental conditions, also affect significantly the game experience, whereas background factors, such as age, sex, and game or music experience, do not have any critical impact.
Ethereum Behavior Analysis with NetFlow Data

Ethereum is a blockchain platform that can run smart contracts and implement decentralized applications over a peer-to-peer(P2P) network. As the second-largest cryptocurrency in the world, Ethereum has attracted a lot of attention from industry and academia. Researches on Ethereum and other blockchain platforms focus on application, smart contracts, and P2P networks. Most of works use the data collected by active crawling, and rarely obtained data from the passive monitoring perspective, especially in the blockchain P2P network analysis. In this work, we propose a passive method using traffic association and machine learning to conduct online Ethereum node detection in NetFlow data, and monitor the Ethereum nodes in NetFlow traffic to gather nodes connection dataset. Based on the dataset, multi-dimensional measurement and analysis is made in order to reveal the true performance of Ethereum network.
An Improved Geometric Approach for Palette‐based Image Decomposition and Recoloring

Abstract Palette‐based image decomposition has attracted increasing attention in recent years. A specific class of approaches have been proposed basing on the RGB‐space geometry, which manage to construct convex hulls whose vertices act as palette colors. However, such palettes do not guarantee to have the representative colors which actually appear in the image, thus making it less intuitive and less predictable when editing palette colors to perform recoloring. Hence, we proposed an improved geometric approach to address this issue. We use a polyhedron, but not necessarily a convex hull, in the RGB space to represent the color palette. We then formulate the task of palette extraction as an optimization problem which could be solved in a few seconds. Our palette has a higher degree of representativeness and maintains a relatively similar level of accuracy compared with previous methods. For layer decomposition, we compute layer opacities via simple mean value coordinates, which could achieve instant feedbacks without precomputations. We have demonstrated our method for image recoloring on a variety of examples. In comparison with state‐of‐the‐art works, our approach is generally more intuitive and efficient with fewer artifacts.
Stormy

Tor is a tool for Internet privacy with millions of daily users. The Tor system benefits in many ways from information gathered about the operation of its network. Measurements guide operators in diagnosing problems, direct the efforts of developers, educate users about the level of privacy they obtain, and inform policymakers about Tor's impact. However, data collection and reporting can degrade user privacy, contradicting Tor's goals. Existing approaches to measuring Tor have limited capabilities and security weaknesses. We present Stormy, a general-purpose, privacy-preserving measurement system that overcomes these limitations. Stormy uses secure multiparty computation (MPC) to compute any function of the observations made by Tor relays, while keeping those observations secret. Stormy makes use of existing efficient MPC protocols that are secure in the malicious model, and in addition it includes a novel input-sharing protocol that is secure, efficient, and fault tolerant. The protocol is non-interactive, which is consistent with how relays currently submit measurements, and it allows the relays to go offline after input submission, even while ensuring that an honest relay will not have its input excluded or modified. The input-sharing protocol is compatible with MPC protocols computing on authenticated values and may be of independent interest. We show how Stormy can be deployed in two realistic models: (1) run primarily by a small set of dedicated authorities, or (2) run decentralized across the relays in the Tor network. Stormy scales efficiently to Tor's thousands of relays, tolerates network churn, and provides security depending only on either Tor's existing trust assumption that at least one authority is honest (in the first model) or the existing assumption that a large fraction of relay bandwidth is honest (in the second model). We demonstrate how to use the system to compute two broadly-applicable statistics: the median of relay inputs and the cardinality of set-union across relays. We implement Stormy and experimentally evaluate system performance. When Stormy is run among authorities we can perform 151 median computations or 533 set-union cardinalities over 7,000 relay inputs in a single day. When run among the relays themselves, Stormy can perform 36 median computations or 134 set union cardinalities per day. Thus, both deployments enable non-trivial analytics to be securely computed in the Tor network.
An empirical evaluation of teaching–learning-based optimization, genetic algorithm and particle swarm optimization

Metaheuristic algorithms have probable to solve global optimization problems in various fields of engineering and industry. To find a global solution by exploring irregular or non-linear surfaces, classical optimization techniques are not able. To overcome the limitation of the classical approach, in the recent study, a large number of metaheuristic methods have been investigated to improve the solution quality concerning convergence and accuracy on the complex problems. Nowadays, a popular metaheuristic algorithm is introduced called teaching–learning-based optimization (TLBO). It is recently being used as an innovative, and robust method to solve the global optimization problem, inspired by the teaching–learning phenomenon. On the other hand, particle swarm optimization (PSO) algorithm is one of the most utilized algorithms in the current scenario, which has indicated acceptable efficiency. Genetic algorithm (GA) is a valuable component of metaheuristic which has been applied in various research applications. In this article, the performance of TLBO algorithm is estimated on 25 numerical test suites against other metaheuristic algorithms such as PSO and GA. The results of our experimental study show that TLBO outperforms the PSO and GA algorithm regarding convergence solution.
Automated Geometric Registration for Multi-Projector Displays on Arbitrary 3D Shapes Using Uncalibrated Devices

In this article we present a completely automated and scalable multi-projector registration system that allows multiple uncalibrated projectors and cameras on arbitrary shape surfaces. Our method estimates the parameters of multiple uncalibrated tiled or superimposed projectors, the extrinsic parameters of the observing cameras (with known intrinsic parameters), the shape of the illuminated 3D geometry and geometrically registers the projectors on it. This is achieved without using any fiducials, even if part of the surface is visible to only one camera. The method uses a completely automatic approach for cross-correlation and cross-validation of the device parameters and the surface geometry resulting in an accurate registration on the arbitrary unknown geometry that does not need an accurate prior calibration of each of the uncalibrated devices using physical patterns or fiducials. Estimating projector parameters allows for quick recalibration of the system in the face of projector movements, by re-estimating only the parameters of the moved projector and not the entire system. Thus, our work can enable easy deployment of spatially augmented reality environments of different sizes (from small table top objects to large immersive environments), different shapes (inside-looking-out or outside-looking in), and different configurations (tiled or superimposed) using the same proposed method.
Filtered Pseudo-parallel Corpus Improves Low-resource Neural Machine Translation

Large-scale parallel corpora are essential for training high-quality machine translation systems; however, such corpora are not freely available for many language translation pairs. Previously, training data has been augmented by pseudo-parallel corpora obtained by using machine translation models to translate monolingual corpora into the source language. However, in low-resource language pairs, in which only low-accurate machine translation systems can be used, translation quality degrades when a pseudo-parallel corpus is naively used. To improve machine translation performance with low-resource language pairs, we propose a method to effectively expand the training data via filtering the pseudo-parallel corpus using quality estimation based on sentence-level round-trip translation. For experiments with three language pairs that utilized small, medium, and large size parallel corpora, BLEU scores significantly improved for low-resource language pairs. Additionally, the effects of iterative bootstrapping on translation performance quality is investigated; resultingly, it is confirmed that bootstrapping can further improve the translation performance.
Multiple Instance Learning with Emerging Novel Class

Diverse applications involving complicated data objects such as proteins and images are solved by applying multi-instance learning (MIL) algorithms. However, few MIL algorithms can deal with problems in an open and dynamic environment, where new categories of samples emerge. In this type of emerging novel class setting, algorithms should be able to not only classify the samples from the observed classes accurately, but also recognize the samples from the novel class. In this paper, we focus on the Multi-Instance learning with Emerging Novel class (MIEN) problem, and formulate MIEN from a metric learning perspective. We extract key instances to form the “super-bag” for each observed class, and non-key instances from all the observed classes to form a “meta super-bag”. Based on these super-bags, we propose the MIEN-metric method to learn discriminative metrics for classifying MIL bags from the observed classes and recognizing bags from the novel class. Experimental results of diverse domains, e.g., biological function annotation, text categorization, and object-centric/scene-centric image classification, show MIEN-metric outperforms other baseline methods significantly when the novel class emerges. Meanwhile, MIEN-metric is comparable with state-of-the-art MIL algorithms for binary classification in the traditional MIL setting.
Online cost optimization algorithms for tiered cloud storage services

The new generation multi-tiered cloud storage services offer various tiers, such as hot and cool tiers, which are characterized by differentiated Quality of Service (QoS) (i.e., access latency, availability and throughput) and the corresponding storage and access costs. However, selecting among these storage tiers to efficiently manage data and improve performance at reduced cost is still a core and difficult problem. In this paper, we address this problem by developing and evaluating algorithms for automated data placement and movement between hot and cool storage tiers. We propose two practical online object placement algorithms that assume no knowledge of future data access. The first online cost optimization algorithm uses no replication (NR) and initially places the object in the hot tier. Then, based on read/write access pattern following a long tail distribution, it may decide to move the object to the cool tier to optimize the storage service cost. The second algorithm with replication (WR) initially places the object in the cool tier, and then replicates it in the hot tier upon receiving read/write requests to it. Additionally, we analytically demonstrate that the online algorithms incur less than twice the cost in comparison to the optimal offline algorithm that assumes the knowledge of exact future workload on the objects. The experimental results using a Twitter Workload and the CloudSim simulator confirm that the proposed algorithms yield significant cost savings (5%–55%) compared to the no-migration policy which permanently stores data in the hot tier.
Comparison of Subjective Quality Test Methods for Omnidirectional Video Quality Evaluation

The test methods recommended by the International Telecommunication Union (ITU) for assessing 2D video quality are often used for evaluating omnidirectional / 360° videos. In this paper, we compare the performance of three different test methods, Absolute Category Rating (ACR), a modified version of ACR (M-ACR) with double presentation of the test stimulus, and DSIS (Double Stimulus Impairment Scale), based on the statistical reliability, assessment time and simulator sickness. Different settings were used for HEVC encoding of five 360° source videos of 10 s duration. Results indicate that DSIS is statistically more reliable with higher resolving power, followed by M-ACR and ACR. We found that simulator sickness increases with time, but can be reduced by taking breaks in-between the test sessions. The results for simulator sickness are compared across test methods and with similar tests conducted under different contextual conditions. We also recorded and analyzed the exploration behaviour of the users. Apart from the methodological findings, the test results provide insights into video quality for different resolution and encoding settings (“bitrate ladders”). These may be useful for choosing appropriate representations in the context of HTTP-based adaptive streaming in case of full-frame streaming.