Reviewerly/exHarmony huggingface dataset:
Preserving Chain-of-Evidence in Surveillance Videos for Authentication and Trust-Enabled Sharing

Surveillance video recording is a powerful method of deterring unlawful activities.A robust data protection-by-design solution can be helpful in terms of making a captured video immutable, as such recordings cannot become a piece of evidence until proven to be unaltered.Similarly, video sharing from closed-circuit television video recording or in social media interaction requires self-authentication for responsible and reliable data sharing.This article presents a computationally inexpensive method of preserving a chain-of-evidence in surveillance videos by means of hashing and steganography.The method conforms to the data protection regulations, which are increasingly adopted by governments, and is applicable to network edge storage.Encryption keys are stored in a hardware wallet independently of the video capture device itself, while evidential information is stored steganographically within video frames themselves, independently of the content.Added protection is provided by hiding information within the two least-valued of pixel bitplanes, using a newly introduced technique that randomizes the pixel storage locations on a per video frame and video-capture device basis.Overall, the proposed method has turned out to not only preserve the integrity of stored video data but also results in minimal degradation of the video data resulting from steganography.Despite the inclusion of hidden information, video frames will still be available for common image-processing tasks such as tracking and classification, as their objective video quality is almost unchanged.
On the Fairness of Distributed Ledger Technologies for the Internet of Things

Distributed networks have been widely studied in literature. However, the blockchain paradigm has inspired to revisit some of the results under a different point of view. In this paper, we analyze the "classic" spam protection problem applied to the IOTA Tangle, a distributed ledger technology which addresses Bitcoin's (monetary and energy) efficiency issues through the absence of mining pools. However, the lack of miners makes the network vulnerable to denial of service attacks. We propose an anti spam mechanism based on the solution of a cryptographic puzzle: When a node wants to generate a new transaction, it dynamically adapts the difficulty of the puzzle depending on its target throughput and on its reputation score. Specifically, the adaptive difficulty property guarantees that any node, even with low hashing power, can achieve similar throughput for a given reputation. In the paper, we prove this claim both analytically and through simulations, and we show that fairness between low- and high-power nodes is indeed reached.
Disease Classification Based on Synthesis of Multiple Long Short-Term Memory Classifiers Corresponding to Eye Movement Features

Medical research confirms that eye movement abnormalities are related to a variety of psychological activities, mental disorders and physical diseases.However, as the specific manifestations of various diseases in terms of eye movement disorders remain unclear, the accurate diagnosis of diseases according to eye movement is difficult.In this paper, a deep neural network (DNN) method is employed to establish a disease discrimination model according to eye movement.First, multiple eye-tracking experiments are designed to obtain eye images.Second, pupil characteristics, including position and size, are extracted, and the feature vectors of eye movement are obtained from the normalized pupil information.Based on a long short-term memory (LSTM) network, a classifier that corresponds to each feature, which is referred to as a weak classifier, is built.The experimental samples are preclassified, and the classification ability of each weak classifier for different diseases is also calculated.Last, a strong classifier is achieved for disease discrimination by synthesizing all the weak classifiers and their classification abilities.By classification testing for three categories of healthy controls, brain injury patients and vertigo patients, the experimental results demonstrated the efficiency of this method.With the deep learning method, more medical information can be excavated from eye movement to improve the values in disease diagnosis.
DOMAIN ADAPTATION WITH CYCLEGAN FOR CHANGE DETECTION IN THE AMAZON FOREST

Abstract. Deep learning classification models require large amounts of labeled training data to perform properly, but the production of reference data for most Earth observation applications is a labor intensive, costly process. In that sense, transfer learning is an option to mitigate the demand for labeled data. In many remote sensing applications, however, the accuracy of a deep learning-based classification model trained with a specific dataset drops significantly when it is tested on a different dataset, even after fine-tuning. In general, this behavior can be credited to the domain shift phenomenon. In remote sensing applications, domain shift can be associated with changes in the environmental conditions during the acquisition of new data, variations of objects’ appearances, geographical variability and different sensor properties, among other aspects. In recent years, deep learning-based domain adaptation techniques have been used to alleviate the domain shift problem. Recent improvements in domain adaptation technology rely on techniques based on Generative Adversarial Networks (GANs), such as the Cycle-Consistent Generative Adversarial Network (CycleGAN), which adapts images across different domains by learning nonlinear mapping functions between the domains. In this work, we exploit the CycleGAN approach for domain adaptation in a particular change detection application, namely, deforestation detection in the Amazon forest. Experimental results indicate that the proposed approach is capable of alleviating the effects associated with domain shift in the context of the target application.
Enabling Open-Set Person Re-Identification for Real-World Scenarios

Person re-identification (re-ID) is a significant problem of computer vision with increasing scientific attention.To date, numerous studies have been conducted to improve the accuracy and robustness of person re-ID to meet the practical demands.However, most of the previous efforts concentrated on solving the closed-set variant of the problem, where a query is assumed to always have a correct match within the set of known people (the gallery set).However, this assumption is usually not valid for the industrial re-ID use cases.In this study, we focus on the open-set person re-ID problem, where, in addition to the similarity ranking, the solution is expected to detect the presence or absence of a given query identity within the gallery set.To determine good practices and to assess the practicality of the person re-ID in industrial applications, first, we convert popular closed-set person re-ID datasets into the open-set scenario.Second, we compare performance of eight state-of-the-art closed-set person re-ID methods under the open-set conditions.Third, we experimentally determine the efficiency of using different loss function combinations for the open-set problem.Finally, we investigate the impact of a statistics-driven gallery refinement approach on the open-set person re-ID performance in the low false-acceptance rate (FAR) region, while simultaneously reducing the computational demands of retrieval.Results show an average detection and identification rate increase of 8.38% and 3.39% on the DukeMTMC-reID and Market1501 datasets, respectively, for a FAR of 1%.Index Terms-person re-identification, open
Task Parameters Analysis in Schedule-Based Timing Side-Channel Attack

Recent work has shown that the timing behavior of a real-time system can be utilized by attackers for various adverse purposes via schedule-based timing side-channel attacks. An important assumption in this type of attacks is the prior knowledge of attackers about the task parameter information, including the number of tasks in the system and the period and execution time of each task. The attackers can use such information, together with the execution sequence of the task system, to reconstruct the exact schedule of the tasks and perform various subsequent attacks. In this paper, we show that the schedule-based timing side channel attacks can actually be performed even without knowing the task parameter information in prior. We develop methods to infer the number of tasks in the system and the period and execution time of each task directly from the execution sequence. This removes the task parameter prior knowledge requirement of the attackers and shows greater threats of the schedule-based timing side channel attacks. Both simulation experiments with synthetic task sets and a Zedboard-based evaluation with control system for a 3DOF helicopter are conducted to evaluate the proposed task parameter analysis method.
An Approach of Securing Data using Combined Cryptography and Steganography

The recent advance in information technology field forcing us to ensure the privacy of the digital data.It is very important to develop the method that may satisfy the needs.Many methods/techniques applied to reach that goal.One of efficient way to reach that secrecy can be achieved by combining Cryptography and Steganography.In this paper, a new RGB shuffling method proposed.The concept of encryption using RGB Shuffling is shuffling all of RGB element to distort the image.RGB Shuffling method will shuffle the RGB each pixel of image depends on the input password from user.The basic step of RGB shuffling is adding RGB element with ASCII password, invers and shuffle it.
Hubble

Recently, in order to take a preemptive opportunity in the mobile economy, the Internet companies conduct thousands of marketing campaigns every day, to promote their mobile products and services. In the mobile marketing scenario, one of the fundamental issues is the audience expansion task for marketing campaigns. Given a set of seed users, audience expansion aims to seek more users (audiences), who are similar to the seeds and will finish the business goal of the targeted campaign (ie convert). However, the problem is challenging in three aspects. First, a company will run hundreds of campaigns to serve massive users every day. The requirements of scalability and timeliness make training model for each campaign extremely resource-consuming thus impractical. Therefore, we proposed to solve the problem in a two-stage manner, in which the offline stage employs heavyweight user representation learning and the online stage performs embedding-based lightweight audience expansion. Second, conventional two-stage audience expansion systems neglect the high-order user-campaign interactions and usually generate entangled user embeddings, thus fail to achieve high-quality user representation. Third, the seeds, which are usually provided by experts or collected from users' feedbacks, could be noisy and cannot cover the entire actual audiences, thus introduce coverage bias. Unfortunately, to our best knowledge, none of the related literatures tackle this crucial issue of audience expansion.
Clustering Ensemble Model Based on Self-Organizing Map Network

This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem of the poor performance of a single clusterer. Cascaded SOM is an extension of classical SOM combined with the cascaded structure. The method combines the outputs of multiple SOM networks in a cascaded manner using them as an input to another SOM network. It also utilizes the characteristic of high-dimensional data insensitivity to changes in the values of a small number of dimensions to achieve the effect of ignoring part of the SOM network error output. Since the initial parameters of the SOM network and the sample training order are randomly generated, the model does not need to provide different training samples for each SOM network to generate a differentiated SOM clusterer. After testing on several classical datasets, the experimental results show that the model can effectively improve the accuracy of pattern recognition by 4%∼10%.
How to Boost the Impact of Scientific Conferences

<h2>Summary</h2> We can maximize the impact of scientific conferences by uploading all conference presentations, posters, and abstracts to highly trafficked public repositories for each content type. Talks can be hosted on sites like YouTube and Youku, posters can be published on Figshare, and papers and abstracts can become open access preprints.
Multipurpose Watermarking Algorithm for Medical Images

Considering the existing medical image watermarking algorithms, a single function often has limitations, and a multipurpose watermarking algorithm for medical images is proposed. First, medical images are divided into regions of interest (ROIs) and regions of noninterest (RONIs). Then, the authentication watermark produced for each subblock of the ROI is embedded into the corresponding mapping subblock. The visible watermark is embedded into the RONI, and, finally, the watermark information and constructed authentication information in each subblock of the ROI are embedded into the corresponding RONI subblock. Simulation results show that the embedded visible watermark can protect and facilitate medical image management. In addition, the proposed algorithm has strong robustness and very good visual quality. It can simultaneously realize copyright protection and content authentication and also has high tamper localization capability.
A Permutational Boltzmann Machine with Parallel Tempering for Solving Combinatorial Optimization Problems

Boltzmann Machines are recurrent neural networks that have been used extensively in combinatorial optimization due to their simplicity and ease of parallelization. This paper introduces the Permutational Boltzmann Machine, a neural network capable of solving permutation optimization problems. We implement this network in combination with a Parallel Tempering algorithm with varying degrees of parallelism ranging from a single-thread variant to a multi-threaded system using a 64-core CPU with SIMD instructions. We benchmark the performance of this new system on Quadratic Assignment Problems, using some of the most difficult known instances, and show that our parallel system performs in excess of 100 $$\times $$ faster than any known dedicated solver, including those implemented on CPU clusters, GPUs, and FPGAs.
OPPORTUNITIES WITH UNCERTAINTIES: THE OUTLOOK OF VIRTUAL REALITY IN THE EARLY STAGES OF DESIGN

Adopting immersive Virtual Reality (VR) technology in the early stages of design appeals to designers, as research has shown that immersing people in a virtual environment can efficiently elicit empathy and facilitate deeper understanding of out-group members.However, after exploring and synthesis literature across the fields of design, psychology, and neuroscience, the present study found the opportunities VR opens to be accompanied by uncertainties.In this study, we (1) identified the benefits of adopting immersive VR in the early design stage, such as enhancing empathy and promoting design equity, (2) discovered previously unrealised problems that VR may bring to the design process, especially potential biases owing to emotional connections, and (3) determined a future direction for relevant research: gaining deeper knowledge about operators' mental activities to mitigate biases and uncertainties.
Circuit-based digital adiabatic quantum simulation and pseudoquantum simulation as new approaches to lattice gauge theory

Gauge theory is the framework of the Standard Model of particle physics and is also important in condensed matter physics. As its major non-perturbative approach, lattice gauge theory is traditionally implemented using Monte Carlo simulation, consequently it usually suffers such problems as the Fermion sign problem and the lack of real-time dynamics. Hopefully they can be avoided by using quantum simulation, which simulates quantum systems by using controllable true quantum processes. The field of quantum simulation is under rapid development. Here we present a circuit-based digital scheme of quantum simulation of quantum $\mathbb{Z}_2$ lattice gauge theory in $2+1$ and $3+1$ dimensions, using quantum adiabatic algorithms implemented in terms of universal quantum gates. Our algorithm generalizes the Trotter and symmetric decompositions to the case that the Hamiltonian varies at each step in the decomposition. Furthermore, we carry through a complete demonstration of this scheme in classical GPU simulator, and obtain key features of quantum $\mathbb{Z}_2$ lattice gauge theory, including quantum phase transitions, topological properties, gauge invariance and duality. Hereby dubbed pseudoquantum simulation, classical demonstration of quantum simulation in state-of-art fast computers not only facilitates the development of schemes and algorithms of real quantum simulation, but also represents a new approach of practical computation.
The Review of the International Voices on the Responses of the Worldwide School Closures Policy Searching during Covid-19 Pandemic

This article was to determine the voice of the international community in responding to the challenges of school closure policies during the 2019 Covid-19 pandemic. Effort to break the chain transmission of Covid-19 was believed to be closely related to the closure of all business activities including schools around the world. In order to understand the impacts and challenges of school closure, a series of critical searches were undertaken on various online sources, for instance news updates publications, practitioners, and school reactions to unplanned global policies to obligate students learning from home. The collecting data was analyzed using a qualitative phenomenological approach and in-depth interpretation to at the collective voice fulfills the reliability and validity of the research problem. The findings indicated that 16 international voices from different perspectives show relatively deep concern about school closures. Voices from these discussions were matched with existing questions and knowledge about difficulties resulting from school closures. The findings of this study can be categorized into three votes; First are voices in support of the national school closure policy. Second, questioning national policies without prior data. Third, there is concern that families face serious challenges in supporting children learning from home with low facilities and the ability to handle student needs. Therefore, these voices would provide essential insights for further decision-making regarding community school participation in supporting of government in fighting the coronavirus epidemic. Hence, a similar study was needed as additional research evidence that helps the government anticipate the spread of this deadly virus, especially in countries and regions experiencing different impacts.&#x0D; Keywords: School Closure Policy in Covid-19, Learning from Home Policy, Covid-19
Variational preparation of the thermofield double state of the Sachdev-Ye-Kitaev model

We provide an algorithm for preparing the thermofield double (TFD) state of the Sachdev-Ye-Kitaev (SYK) model without the need for an auxiliary bath. Following previous work, the TFD can be cast as the approximate ground state of a Hamiltonian, ${H}_{\text{TFD}}$. Using variational quantum circuits, we propose and implement a gradient-based algorithm for learning parameters that find this ground state, an application of the variational quantum eigensolver. Concretely, we find shallow quantum circuits that prepare the ground state of ${H}_{\text{TFD}}$ for the $q=4$ SYK model for $N=8$ Majoranas per side. For $N=12$, we achieve a variational energy within 1% of the true ground-state energy.
Fermionic State Discrimination by Local Operations and Classical Communication

We consider the problem of local operations and classical communication (LOCC) discrimination between two bipartite pure states of fermionic systems. We show that, contrary to the case of quantum systems, for fermionic systems it is generally not possible to achieve the ideal state discrimination performances through LOCC measurements. On the other hand, we show that an ancillary system made of two fermionic modes in a maximally entangled state is a sufficient additional resource to attain the ideal performances via LOCC measurements. The stability of the ideal results is studied when the probability of preparation of the two states is perturbed, and a tight bound on the discrimination error is derived.
Photonics for computing and computing for photonics

The liaison between photonics and computing is a pillar of modern optics and subject of cutting-edge research for more than half a century. As in many scientific disciplines, high-performance computational methods have become essential for describing, designing, interpreting and ultimately predicting an optical system’s behaviour, and today the wide availability of high-performance photonic components is testimony of how computing has boosted the field of photonics. At the same time, photonic architectures offer fascinating possibilities for carrying out computations scaling beyond today’s computing hardware. This establishes an almost uniquely reciprocal relationship between photonics and computing.
Interactive Exploration of the In-Situ Visualization of a Magnetohydrodynamic Simulation

We propose a new visualization method for large-scale computer simulations called “4D Street View”. This method uses omnidirectional in-situ visualization cameras to record a simulation from multiple viewpoints. The omnidirectional video dataset produced by the simulation is then analyzed interactively. In this paper, we apply the method to a magnetohydrodynamics turbulence simulation with 64 viewpoints to prove that it is possible to achieve interactive in-situ visualization for supercomputer simulations. Three types of in-situ visualization by omnidirectional cameras (with 4π steradian solid angle) from each viewpoint are applied. At the end of the simulation, a video dataset with 192 omnidirectional video files is produced. We demonstrate that we can change the viewing position, angle, and applied visualization method interactively using a specially designed application program.
APPLICATION OF NI MULTISIM ENVIRONMENT IN THE PRACTICAL SKILLS BUILDING FOR STUDENTS OF 125 "CYBERSECURITY" SPECIALTY

The article is devoted to the problem of practical skills building and development of students majoring in 125 "Cybersecurity". An analysis of the professional competencies of future cybersecurity professionals, in particular, related to technical protection of information. It is proved that the use of virtual laboratories in the educational process helps to increase the efficiency of the educational process and allows to form and improve the professional competencies of the future cybersecurity engineer. Education becomes practice-oriented. The possibilities of a virtual laboratory workshop based on the NI Multisim environment are considered in the study of disciplines "Theory of circles and signals in information and cyberspace", "Component base and elements of circuitry in information security systems", "Signals and processes in information security systems". The Multisim system is used as part of the training of future cybersecurity professionals at Borys Grinchenko Kyiv University and has proven its effectiveness in practice.