Reviewerly/exHarmony huggingface dataset:
Biological Principles of Integration Information at Big Data Processing

The article is devoted to the principles of integration of heterogeneous information in order to obtain generalized assessments of the state of various objects and systems. A characteristic feature of cyber-physical systems is the generation and availability of large volumes of heterogeneous information. The value of this information is entirely determined by its processing mechanisms. So for monitoring problems, the depth of processing and the semantic context of the processed data are crucial for diagnosing and predicting the state. The mechanisms of information integration are particularly important when building a digital twin, which is a virtual image of an object or system. In the paper, from the standpoint of the Bernstein's theory of movement construction levels, an analysis of the mechanisms of information integration in the formation of internal behavior patterns is carried out. The modern hypotheses of the formation of polysensory models and the mechanisms of their invariant representations are separately considered. The principles of the integration of heterogeneous information in the big data processing are formulated. The possibility of implementing these principles using the apparatus of cellular automata is shown.
ZeroKernel: Secure Context-isolated Execution on Commodity GPUs

In the last decade, the dedicated graphics processing unit (GPU) has emerged as an architecture for high-performance computing workloads. Recently, researchers have also focused on the isolation property of a dedicated GPU and suggested GPU-based secure computing environments with several promising applications. However, despite the security analysis conducted by the prior studies, it has been unclear whether a dedicated GPU can be leveraged as a secure processor in the presence of a kernel-privileged attacker. In this paper, we first demonstrate the security of dedicated GPUs through comprehensive studies on context information for GPU execution. The paper shows that a kernel-privileged attacker can manipulate the GPU contexts to redirect memory accesses or execute arbitrary GPU codes on the running GPU kernel. Based on the security analysis, this paper proposes a new on-chip execution model for the dedicated GPU and a novel defense mechanism supporting the security of the on-chip execution. With comprehensive evaluation, the paper assures that the proposed solutions effectively isolate sensitive data in on-chip storages and defend against known attack vectors from a privileged attacker, supporting that the commodity GPUs can be leveraged as a secure processor.
Marker‐less real‐time intra‐operative camera and hand‐eye calibration procedure for surgical augmented reality

Accurate medical Augmented Reality (AR) rendering requires two calibrations, a camera intrinsic matrix estimation and a hand-eye transformation. We present a unified, practical, marker-less, real-time system to estimate both these transformations during surgery. For camera calibration we perform calibrations at multiple distances from the endoscope, pre-operatively, to parametrize the camera intrinsic matrix as a function of distance from the endoscope. Then, we retrieve the camera parameters intra-operatively by estimating the distance of the surgical site from the endoscope in less than 1 s. Unlike in prior work, our method does not require the endoscope to be taken out of the patient; for the hand-eye calibration, as opposed to conventional methods that require the identification of a marker, we make use of a rendered tool-tip in 3D. As the surgeon moves the instrument and observes the offset between the actual and the rendered tool-tip, they can select points of high visual error and manually bring the instrument tip to match the virtual rendered tool tip. To evaluate the hand-eye calibration, 5 subjects carried out the hand-eye calibration procedure on a da Vinci robot. Average Target Registration Error of approximately 7mm was achieved with just three data points.
A Simple and Secured Cryptography System of Cloud Computing

Cloud computing is a vast growing technology in the IT industries. Cloud Service Providers (CSP) are offering various suitable and flexible services to the users. Users of cloud computing are growing in an equal rhyme. As cloud computing is becoming an essential part of human life, users and CSP are noticing for suitable security feathers. Many researchers are working in the security aspects of cloud computing. In this paper, we present a suitable cryptography and secure authentication system for cloud computing. This procedure includes auto encryption and KEYs changing process in the cloud end. New generated KEYs will not be sent to users initially. Users will be checked in three steps for authentication. CSP can trigger encryption process at any time manually or it will be performed automatically after logged out by the users. This procedure will ensure extra security for data/files as well as prevent hackers from getting original files/data even they got valid credential. Proposed protocol is simulated by CloudSim and we observed that one more step needs for user's credential, it required a bit of time to use data/files for only the first time. This bit of time is negligible and we can exclude it from consideration but security increase more and more.
Declarative assembly of web applications from predefined concepts

A new approach to web application development is presented, in which an application is constructed by configuring and composing concepts drawn from a catalog developed by experts.
A Noise-tolerant Fuzzy c-Means based Drift Adaptation Method for Data Stream Regression

Concept drift referring to the changes of data distributions has been one critical challenge typically associated with mining data streams. Current drift detection and adaptation methods focus on how to immediately detect the distribution changes once the concept drift occurs and swiftly update the model to be applicable to the newly arrived data instances. Most of those methods assume the data does not have noise or the noise is too weak to affect the modeling procedure. However, realworld data are normally contaminated, and denoise techniques are highly preferred as a necessary preprocess. This issue is more complex for a data stream with concept drift because the noise is very likely to be confused with drift. Motivated by that, this paper proposes a Noise-tolerant Fuzzy c-means based drift Adaptation method (NFA) which can adapt to the changing distributions and is suitable for noisy data streams. The concept drift problem is solved by using a fuzzy c-means based regression model to continuously include the most relevant data instances to the latest pattern in the training set. In addition, a denoise technique is designed in NFA to remove noise, and the ability of incremental updating enables it to be embedded in the incremental drift adaptation process, and therefore NFA can solve concept drift and noise problems at the same time. Experimental evaluation results also show good performance of our method on handling data streams with concept drift and noise.
Coastal Visibility Distance Estimation Using Dark Channel Prior and Distance Map Under Sea-Fog: Korean Peninsula Case

Commercial visibility sensors among meteorological sensors estimate the visibility distance based on transmission, backward scattering, and forward scattering principle. These optical visibility sensors yield comparatively accurate local visibility distance. However, it is still difficult to obtain comprehensive visibility information for a wide area, such as the coast or harbor due to the sensor structure using straightness and scattering properties of light. In this paper, we propose a novel visibility distance estimation method using dark channel prior (DCP) and distance map based on a camera image. The proposed method improves the local limit of optical visibility sensor and detects the visibility distance of a wide area more precisely. First, the dark channel for an input sea-fog image is calculated. The binary transmission image is obtained by applying a threshold to the estimated transmission from the dark channel. Then, the sum of the distance values of pixels, corresponding to the sea-fog boundary, is averaged, in order to derive the visibility distance. This paper also proposes a novel air-light and transmission estimation technique in order to extract the visibility distance for an abnormal sea-fog image, including any light source, such as sunlight, reflection light, and illumination light, etc. The estimated visibility distance was compared with optical visibility distance of an optical visibility sensor and their agreement was evaluated.
Weakly Supported Plane Surface Reconstruction via Plane Segmentation Guided Point Cloud Enhancement

Most of the widely used multi-view 3D reconstruction algorithms assume that object appearance is predominantly diffuse and full of good texture. For the objects that violate this restriction, the surface can hardly be reconstructed because such area lacks sufficient support from dense point clouds. To tackle this problem, we introduce a novel two-stage prior-guided method based on point clouds enhancement to enable the application of multi-view reconstruction approaches in such scenes. In the first stage, we optimize the original PlaneNet plane segmentation priors by taking advantage of the estimated depth map and confidence map from multi-view stereo. In the second stage, we correct and supply 3D point clouds for the weakly supported plane surface on the basis of the upgraded priors. Furthermore, we utilize a slight disturbance of the enhanced point clouds to facilitate the subsequent mesh reconstruction. The proposed point cloud enhancement approach is evaluated on the large-scale $DTU$ dataset. Our method significantly outperforms previous multi-view stereo state-of-the-arts. We also demonstrate weakly supported plane surface reconstruction results from real-world photos that are unachievable with either the methods aiming at preserving weakly supported surfaces or the traditional state-of-the-art 3D reconstruction systems.
Amnesiac DRAM: A Proactive Defense Mechanism Against Cold Boot Attacks

DRAMs in modern computers or hand-held devices store private or often security-sensitive data. Unfortunately, one known attack vector, called a cold boot attack, remains threatening and easy-to-exploit, especially when attackers have physical access to the device. It exploits the fundamental property of current DRAMs: remanence effects that retain the stored contents for a certain period of time even after powering off. To magnify the remanence effect, cold boot attacks typically freeze the victim DRAM, thereby providing a chance to detach, move, and reattach it to an attacker's computer. Once power is on, attackers can steal all the security-critical information from the victim's DRAM, such as a master decryption key for an encrypted disk storage. Two types of defenses were proposed in the past: 1) CPU-bound cryptography, where keys are stored in CPU registers and caches instead of in DRAMs, and 2) full or partial memory encryption, where sensitive data are stored encrypted. However, both methods impose non-negligible performance or energy overheads to the running systems, and worse, significantly increase the hardware and software manufacturing costs. We found that these proposed solutions attempted to address the cold boot attacks passively: either by avoiding or by indirectly addressing the root cause of the problem, the remanence effect. In this article, we propose and evaluate a proactive defense mechanism, Amnesiac DRAM, that comprehensively prevents the cold boot attacks. The key idea is to discard the contents in the DRAM when attackers attempt to retrieve (i.e., power on) them from the stolen DRAM. When Amnesiac DRAM senses a physical separation, it locks itself and deletes all the remaining contents, making it amnesiac. The Amnesiac DRAM causes neither performance nor energy overhead in ordinary operations (e.g., load and store) and can be easily implemented with negligible area overhead in commodity DRAM architectures.
Toys That Mobilize: Past, Present and Future of Phygital Playful Technology

This exploratory paper focuses on the technological development of the toy medium with an interest in toys’ capacity to mobilize homo ludens, the playing human. By conducting an extensive literary review on the history and the present of mobile play objects, the study demonstrates how toys have developed from early moving and mechanical automata to playthings that move by themselves through in-built computerized components. The interest is two-fold: By analyzing the historical trajectory of mobile toys, the authors highlight the role of both toys and their players as participants in technologically mediated, phygital play. This hybrid form of playing combines the physicality of playthings with both mechanical and digital features. The results of the review show how toys—character toys in particular—have transformed from entertaining, self-moving spectacles to educational machines that mobilize the player both physically and geographically. Based on the results of the literary review, the authors suggest a continuum that visualizes the development of the types of toys that afford mobility in play of the past, present, and future. The paper concludes with the observation that phygital, playful technologies, such as character toys, have the capacity to influence human well-being in its various dimensions. By making them mobile, toys as a medium invite engagement with phygital playful technologies to enhance physical, cognitive, and social well-being.
As Easy as 1, 2...4? Uncertainty in Counting Tasks for Medical Imaging

Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions. Estimating the uncertainty in the measurement is thus vital to making definite, informed conclusions. In this paper, we first compare a range of existing methods to perform counting in medical imaging and suggest ways of deriving predictive intervals from these. We then propose and test a method for calculating intervals as an output of a multi-task network. These predictive intervals are optimised to be as narrow as possible, while also enclosing a desired percentage of the data. We demonstrate the effectiveness of this technique on histopathological cell counting and white matter hyperintensity counting. Finally, we offer insight into other areas where this technique may apply.
Deep learning methods for video understanding

Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this tutorial aims to present the grounds and ways to develop applications that uses methods based on Deep Learning for video analysis tasks. Likewise, this tutorial is an opportunity for students and information technology professionals to qualify themselves. The main focus of this short course is to present fundamentals and technologies to develop such methods of DL.
Automatic rectification of warped <i>Bangla</i> document images

In this study, a robust algorithm for dewarping of camera-captured document images, mainly in Bangla script, is proposed. The algorithm can handle various types of warped document images and they are generated due to different types of document surfaces (convex, concave or multi-folded). The proposed algorithm is independent of font type, font size, font style and camera view angle. After initial preprocessing, the method first demarcates the text lines present in the document image. Then, the headline (shirorekha) position of each text line is estimated. Based on the headline position and shape, each text line is dewarped. If the document is highly warped, distorted text (e.g. thinner and shorter characters) is generated after dewarping. Special care has been taken to minimise this distortion based on most undistorted character information. Exhaustive testing shows the robustness and shape improvement of the proposed algorithm. Finally, for shape quality evaluation, some new measures are defined.
Sentiment Analysis using Partial Textual Entailment

The internet is an ocean of raw data in the form of social text messages, tweets, blogs etc. - arbitrary and unconnected (at the first glance). Only after processing, organizing and structuring of online text snippets, the data becomes information. From the Literature survey, we observed that the present sentiment analysis tools are slow, bulky and computationally heavy which makes the task at hand inefficient. Therefore, to overcome the aforementioned problem of analyzing sentiments efficiently, a new method is proposed in the present paper to drive the task of Sentiment Analysis by exploiting the idea of Partial Textual Entailment. We propose to use Partial Textual Entailment for measuring semantic similarity between the tweets so as to group similar tweets together. The method is anticipated to reduce the burden of sentiment analyzer and makes the processing faster. Moreover, we also propose a modification in an existing method of Partial Textual Entailment which can be further adopted for many Natural Language Processing applications.
Privacy-Preserving Artificial Intelligence: Application to Precision Medicine

Motivated by state-of-the-art performances across a wide variety of areas, over the last few years Machine Learning has drawn a significant amount of attention from the healthcare domain. Despite their potential in enabling person-alized medicine applications, the adoption of Deep Learning based solutions in clinical workflows has been hindered in many cases by the strict regulations concerning the privacy of patient health data. We propose a solution that relies on Fully Homomorphic Encryption, particularly on the MORE scheme, as a mechanism for enabling computations on sensitive health data, without revealing the underlying data. The chosen variant of the encryption scheme allows for the computations in the Neural Network model to be directly performed on floating point numbers, while incurring a reasonably small computational overhead. For feasibility evaluation, we demonstrate on the MNIST digit recognition task that Deep Learning can be performed on encrypted data without compromising the accuracy. We then address a more complex task by training a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) model. These results underline the potential of the proposed approach to outperform current solutions by delivering comparable results to the unencrypted Deep Learning based solutions, in a reasonable amount of time. Lastly, the security aspects of the encryption scheme are analyzed, and we show that, even though the chosen encryption scheme favors performance and utility at the cost of weaker security, it can still be used in certain practical applications.
Improving Wireless Network Security Based On Radio Fingerprinting

With the rapid development of the popularity of wireless networks, there are also increasing security threats that follow, and wireless network security issues are becoming increasingly important. Radio frequency fingerprints generated by device tolerance in wireless device transmitters have physical characteristics that are difficult to clone, and can be used for identity authentication of wireless devices. In this paper, we propose a radio frequency fingerprint extraction method based on fractional Fourier transform for transient signals. After getting the features of the signal, we use RPCA to reduce the dimension of the features, and then use KNN to classify them. The results show that when the SNR is 20dB, the recognition rate of this method is close to 100%.
A Study on Data Security and Query privacy in Cloud

A lot of organizations need effective resolutions to record and evaluate the existing enormous volume of information. Cloud computing as a facilitator offers scalable resources and noteworthy economic assistances as the decreased operational expenditures. This model increases a wide set of security and privacy problems that have to be taken into reflexion. Multi-occupancy, loss of control, and confidence are the key issues in cloud computing situations. This paper considers the present know-hows and a comprehensive assortment of both previous and high-tech tasks on cloud security and confidentiality. The paradigm shift that supplements the usage of cloud computing is progressively enabling augmentation to safety and privacy contemplations linked with the different facades of cloud computing like multi-tenancy, reliance, loss of control and responsibility. So, cloud platforms that deal with big data that have sensitive information are necessary to use technical methods and structural precautions to circumven t data defence failures that might lead to vast and costly harms.
Cooperative Avoidance Control With Velocity-Based Detection Regions

Guaranteed collision avoidance control laws for multi-agent systems typically rely on constant detection regions. This constraint tends to generate conservative and slower agents' trajectories. To reduce the conservatism of avoidance control laws, this letter presents two decentralized, cooperative strategies for arbitrarily large groups of agents that decrease the vehicles' effective detection regions by using velocity information. The vehicles are modeled as a class of nonlinear Lagrangian systems which full state vector represents absolute position. The control laws are proven to guarantee collision avoidance at all times and are shown to be more energy-efficient and to generate faster and smoother trajectories than traditional methods. Moreover, by decreasing the detection regions, the agents are able to converge to destinations closer to each other's avoidance regions, a feature not possible with traditional avoidance control.
Training approach using the shallow model and hard triplet mining for person re‐identification

Multi-target tracking in a non-overlapping camera network is an active research field, and one of the important problems in it is the person re-identification problem. In this study, the authors propose an approach to improve the performance of the backbone model in the person re-identification. Their approach focuses on training a fusion model with a shallow model and making hard triplets with relationship matrices quickly and efficiently. The proposed approach is simple, but it improves the performance of the backbone. In addition, the hard triplet mining in their process is much faster than the conventional approach. Experimental evaluation shows that the proposed approach can improve the performances of the backbone model. The proposed approach improves rank-1 and mean average precision (mAP) performance by more than 12.54 and 15.44%, respectively, over the backbone models in the Market1501 and DukeMTMC-reID dataset. The approach also achieves competitive performances compared with state-of-the-art approaches.