Reviewerly/exHarmony huggingface dataset:
Türkçe Karakter Destekli Genişletilmiş Kayan Polybius Karesi ile Şifrelenmiş Veriyi Gömme

The security of a message transmitted from one peer to another is a matter of many studies over centuries. Today, with digital communication, billions of data are transmitted from one point to another every milisecond. That is why data security has become one of the most studied topics. In this study, an ancient encryption method called Polybius square which has been a source of inspiration for today's encryption methods is introduced. In addition an expanded version of Polybius square was developed adding Turkish character support. An encryption key has also been added to the Polybius cipher. The entire message can be encrypted with a key or with different keys in each character of the message. The encrypted message is hidden within the pixels of the picture files using the LSB algorithm. Thus, when the message is transmitted to the other side, it is encrypted with a private key, hidden inside a cover data, and has become unsuspicious. To measure the change in cover data; MSE, PSNR and SSIM measurement methods are used. The method proposed in the study is coded with a visual programming language. The developed software has been introduced and its results are shown.
Merab Mamardashvili: Event of Thought and Pedagogic Practice

The concept of event has been widely disseminated in various fields of human sciences, including the psychology of education and pedagogics. If one seeks to explore the full heuristic potential of the concept, it is necessary to take into account its essential affiliation with non-classical ontology. The article presents an attempt to analyze the inner structure of Merab Mamardashvili’s oral philosophical speech, that can be considered a distinctive feature of the series of lection courses, which he held in the late 70-ies – beginning of 80-ies mainly for the audience of non-philosophers. The article deals with the research of essential connection between form and content of Mamardashvili’s philosophy. In the light of this connection the article reviews the project of the non-classical ontology of cognition, that Mamardashvili develops in some of his main works. The first part of the article contains an exposition of the problem field of “event” in the context of history and ontology of cognition, as well as reconstruction of the genesis of this theme in Mamardashvili’s philosophy. Mamardashvili describes the classical type of rationality and its “blind spots” – that is, historicity and “evental” character of phenomena of cognition, which cannot be seen in the objectifying gaze of the classical observer. An attempt is made to analyze Mamardashvili’s concept of conscience in the context of his phenomenology of event. The term “epiphany” is interpreted as a key characteristic of events of cognition. The second part of the article deals with the uniqueness of Merab Mamardashvili’s oral philosophical speech, that is interpreted as one of the strategies of indirect communication. Indirect communication is distinguished from direct communication of knowledge, the central model of classical pedagogics. The article studies several different approaches to interpretation of Merab Mamardashvili’s teaching strategy and proposes the approach that is based on the concept of indirect communication of event of cognition.
PoLPer

Setuid system calls enable critical functions such as user authentications and modular privileged components. Such operations must only be executed after careful validation. However, current systems do not perform rigorous checks, allowing exploitation of privileges through memory corruption vulnerabilities in privileged programs. As a solution, understanding which setuid system calls can be invoked in what context of a process allows precise enforcement of least privileges. We propose a novel comprehensive method to systematically extract and enforce least privilege of setuid system calls to prevent misuse. Our approach learns the required process contexts of setuid system calls along multiple dimensions: process hierarchy, call stack, and parameter in a process-aware way. Every setuid system call is then restricted to the per-process context by our kernel-level context enforcer. Previous approaches without process-awareness are too coarse-grained to control setuid system calls, resulting in over-privilege. Our method reduces available privileges even for identical code depending on whether it is run by a parent or a child process. We present our prototype called PoLPer which systematically discovers only required setuid system calls and effectively prevents real-world exploits targeting vulnerabilities of the setuid family of system calls in popular desktop and server software at near zero overhead.
Exploring Body Texture From mmW Images for Person Recognition

Imaging using millimeter waves (mmWs) has many advantages including the ability to penetrate obscurants such as clothes and polymers. After having explored shape information retrieved from mmW images for person recognition, in this work we aim to gain some insight about the potential of using mmW texture information for the same task, considering not only the mmW face, but also mmW torso and mmW wholebody. We report experimental results using the mmW TNO database consisting of 50 individuals based on both hand-crafted and learned features from Alexnet and VGG-face pretrained Convolutional Neural Networks (CNN) models. First, we analyze the individual performance of three mmW body parts, concluding that: i) mmW torso region is more discriminative than mmW face and the whole body, ii) CNN features produce better results compared to hand-crafted features on mmW faces and the entire body, and iii) hand-crafted features slightly outperform CNN features on mmW torso. In the second part of this work, we analyze different multi-algorithmic and multi-modal techniques, including a novel CNN-based fusion technique, improving verification results to 2% EER and identification rank-1 results up to 99%. Comparative analyses with mmW body shape information and face recognition in the visible and NIR spectral bands are also reported.
Edge Chaining Framework for Black Ice Road Fingerprinting

Detecting and reacting efficiently to road condition hazards are challenging given practical restrictions such as limited data availability and lack of infrastructure support. In this paper, we present an edge-cloud chaining solution that bridges the cloud and road infrastructures to enhance road safety. We exploit the roadside infrastructure (e.g., smart lampposts) to form a processing chain at the edge nodes and transmit the essential context to approaching vehicles providing what we refer as road fingerprinting. We approach the problem from two angles: first we focus on semantically defining how an execution pipeline spanning edge and cloud is composed, then we design, implement and evaluate a working prototype based on our assumptions. In addition, we present experimental insights and outline open challenges for next steps.
Composing Bidirectional Programs Monadically

Software frequently converts data from one representation to another and vice versa. Naïvely specifying both conversion directions separately is error prone and introduces conceptual duplication. Instead, bidirectional programming techniques allow programs to be written which can be interpreted in both directions. However, these techniques often employ unfamiliar programming idioms via restricted, specialised combinator libraries. Instead, we introduce a framework for composing bidirectional programs monadically, enabling bidirectional programming with familiar abstractions in functional languages such as Haskell. We demonstrate the generality of our approach applied to parsers/printers, lenses, and generators/predicates. We show how to leverage compositionality and equational reasoning for the verification of round-tripping properties for such monadic bidirectional programs.
Online learning for supervised dimension reduction

Online learning has attracted great attention due to the increasingdemand for systems that have the ability of learning and evolving. When thedata to be processed is also high dimensional and dimension reduction is necessary for visualization or prediction enhancement, online dimension reductionwill play an essential role. The purpose of this paper is to propose a new onlinelearning approach for supervised dimension reduction. Our algorithm is motivated by adapting the sliced inverse regression (SIR), a pioneer and effectivealgorithm for supervised dimension reduction, and making it implementable inan incremental manner. The new algorithm, called incremental sliced inverseregression (ISIR), is able to update the subspace of significant factors with intrinsic lower dimensionality fast and efficiently when new observations come in.We also refine the algorithm by using an overlapping technique and develop anincremental overlapping sliced inverse regression (IOSIR) algorithm. We verifythe effectiveness and efficiency of both algorithms by simulations and real dataapplications.
VERID

Ensuring the authenticity and integrity of the sensing data that are stored in a third-party cloud is a crucial task for the correctness and safety of many IoT applications. Although verifiable data outsourcing has been studied for over a decade, current solutions are not fully suitable for IoT systems, due to the hardware constraints, deployment features, and application requirements of IoT. This paper presents VERID, a verifiable data management system designed for IoT applications. VERID enables important ranged selection and aggregate queries of sensing data while imposing minimal overhead for resource-constraint IoT devices. Our important innovation is a computational and space-efficient authentication data structure called PrefixMHT which fits into resource-constrained IoT devices and supports both range and aggregate queries. We design a new signature aggregation scheme called Condensed Bilinear Pairing to further improve the efficiency. The experiments using real IoT datasets show that VERID is able to provide authenticity, integrity, and completeness of data queries while achieving substantial advantages in computation, memory, and communication efficiency than possible methods.
DSL: Discriminative Subgraph Learning via Sparse Self-Representation

The goal in network state prediction (NSP) is to classify the global state (label) associated with features embedded in a graph.This graph structure encoding feature relationships is the key distinctive aspect of NSP compared to classical supervised learning.NSP arises in various applications: gene expression samples embedded in a protein-protein interaction (PPI) network, temporal snapshots of infrastructure or sensor networks, and fMRI coherence network samples from multiple subjects to name a few.Instances from these domains are typically "wide" (more features than samples), and thus, feature sub-selection is required for robust and generalizable prediction.How to best employ the network structure in order to learn succinct connected subgraphs encompassing the most discriminative features becomes a central challenge in NSP.Prior work employs connected subgraph sampling or graph smoothing within optimization frameworks, resulting in either large variance of quality or weak control over the connectivity of selected subgraphs.In this work we propose an optimization framework for discriminative subgraph learning (DSL) which simultaneously enforces (i) sparsity, (ii) connectivity and (iii) high discriminative power of the resulting subgraphs of features.Our optimization algorithm is a single-step solution for the NSP and the associated feature selection problem.It is rooted in the rich literature on maximal-margin optimization, spectral graph methods and sparse subspace self-representation.DSL simultaneously ensures solution interpretability and superior predictive power (up to 16% improvement in challenging instances compared to baselines), with execution times up to an hour for large instances.
Faster Convolutional Neural Networks in Low Density FPGAs Using Block Pruning

Convolutional Neural Networks (CNNs) are achieving promising results in several computer vision applications. Running these models is computationally very intensive and needs a large amount of memory to store weights and activations. Therefore, CNN typically run on high performance platforms. However, the classification capabilities of CNNs are very useful in many applications running in embedded platforms close to data production since it avoids data communication for cloud processing and permits real-time decisions turning these systems into smart embedded systems. In this paper, we improve the inference of large CNN in low density FPGAs using pruning. We propose block pruning and apply it to LiteCNN, an architecture for CNN inference that achieves high performance in low density FPGAs. With the proposed LiteCNN optimizations, we have an architecture for CNN inference with an average performance of 275 GOPs for 8-bit data in a XC7Z020 FPGA. With our proposal, it is possible to infer an image in AlexNet in 5.1 ms in a ZYNQ7020 and in 13.2 ms in a ZYNQ7010 with only 2.4% accuracy degradation.
Exploring Data Size to Run Convolutional Neural Networks in Low Density FPGAs

Convolutional Neural Networks (CNNs) obtain very good results in several computer vision applications at the cost of high computational and memory requirements. Therefore, CNN typically run on high performance platforms. However, CNNs can be very useful in embedded systems and its execution right next to the source of data has many advantages, like avoiding the need for data communication and real-time decisions turning these systems into smart sensors. In this paper, we explore data quantization for fast CNN inference in low density FPGAs. We redesign LiteCNN, an architecture for real-time inference of large CNN in low density FPGAs, to support hybrid quantization. We study the impact of quantization over the area, performance and accuracy of LiteCNN. LiteCNN with improved quantization of activations and weights improves the best state of the art results for CNN inference in low density FPGAs. With our proposal, it is possible to infer an image in AlexNet in 7.4 ms in a ZYNQ7020 and in 14.8 ms in a ZYNQ7010 with 3% accuracy degradation. Other delay versus accuracy ratios were identified permitting the designer to choose the most appropriate.
Image Compression Based on Block SVD Power Method

Abstract In recent years, the important and fast growth in the development and demand of multimedia products is contributing to an insufficiency in the bandwidth of devices and network storage memory. Consequently, the theory of data compression becomes more significant for reducing data redundancy in order to allow more transfer and storage of data. In this context, this paper addresses the problem of lossy image compression. Indeed, this new proposed method is based on the block singular value decomposition (SVD) power method that overcomes the disadvantages of MATLAB’s SVD function in order to make a lossy image compression. The experimental results show that the proposed algorithm has better compression performance compared with the existing compression algorithms that use MATLAB’s SVD function. In addition, the proposed approach is simple in terms of implementation and can provide different degrees of error resilience, which gives, in a short execution time, a better image compression.
Research on detection and integration classification based on concept drift of data stream

As a new type of data, data stream has the characteristics of massive, high-speed, orderly, and continuous and is widely distributed in sensor networks, mobile communication, financial transactions, network traffic analysis, and other fields. However, due to the inherent problem of concept drift, it poses a great challenge to data stream mining. Therefore, this paper proposes a dual detection mechanism to judge the drift of concepts, and on this basis, the integration classification of data stream is carried out. The system periodically detects data stream with the index of classification error and uses the features of the essential emerging pattern (eEP) with high discrimination to help build the integrated classifiers to solve the classification mining problems in the dynamic data stream environment. Experiments show that the proposed algorithm can obtain better classification results under the premise of effectively coping with the change of concepts.
Indoor location method of interference source based on deep learning of spectrum fingerprint features in Smart Cyber-Physical systems

The intensity acquisition and fluctuation of the signal intensity of the interference source caused by the indoor multipath effect are very great, and there is a problem that the best eigenvalue is difficult to choose. A kind of unsupervised machine learning algorithm is proposed, which can independently identify and select the optimal eigenvalue without relying on the prior information. First, the wave signal filtering is reduced and processed by kernelized principle component analysis (KPCA) algorithm. Then, the eigenvalues are selected and the redundant features are eliminated by adaptive parameter adjustment denoising auto-encoder (APADAE) algorithm. Finally, the feature vectors are classified and identified by Softmax algorithm and the classification process are optimized by the particle swarm optimization (PSO) algorithm. Experimental results of the Smart Cyber-Physical systems show that the algorithm can indirectly improve the accuracy of the source location based on improving the classification accuracy.
Dependency graph for short text extraction and summarization

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Multipurpose Quantum Simulator Based on a Hybrid Solid-State Quantum Device

This paper proposes a scheme to enhance the fidelity of symmetric and asymmetric quantum cloning using a hybrid system based on nitrogen-vacancy (N-V) centers. By setting different initial states, the present scheme can implement optimal symmetric (asymmetric) universal (phase-covariant) quantum cloning, so that the copies with the assistance of a Current-biased Josephson junction (CBJJ) qubit and four transmission-line resonators (TLRs) can be obtained. The scheme consists of two stages: cjhothe first stage is the implementation of the conventional controlled-phase gate, and the second is the realization of different quantum cloning machines (QCM) by choosing a suitable evolution time. The results show that the probability of success for QCM of a copy of the equatorial state can reach 1. Furthermore, the | W 4 ± ⟩ entangled state can be generated in the process of the phase-covariant quantum anti-cloning. Finally, the decoherence effects caused by the N-V center qubits and CBJJ qubit are discussed.
Auto-calibration of depth camera networks for people tracking

We address the problem of calibrating an embedded depth camera network designed for people tracking purposes. In our system, the nodes of the network are responsible for detecting the people moving in their view, and sending the observations to a centralized server for data fusion and tracking. We employ a plan-view approach where the depth camera views are transformed to top-view height maps where people are observed. As the server transforms the observations to a global plan-view coordinate system, accurate geometric calibration of the sensors has to be performed. Our main contribution is an auto-calibration method for such depth camera networks. In our approach, the sensor network topology and the initial 2D rigid transformations that map the observations to the global frame are determined using observations only. To distribute the errors in the initial calibration, the transformation parameters and the estimated positions of people are refined using a global optimization routine. To overcome inaccurate depth camera parameters, we re-calibrate the sensors using more flexible transformations, and experiment with similarity, affine, homography and thin-plate spline mappings. We evaluate the robustness, accuracy and precision of the approach using several real-life data sets, and compare the results to a checkerboard-based calibration method as well as to the ground truth trajectories collected with a mobile robot.
Compact LEA and HIGHT Implementations on 8-Bit AVR and 16-Bit MSP Processors

In this paper, we revisited the previous LEA and HIGHT implementations on the low-end embedded processors. First, the general purpose registers are fully utilized to cache the intermediate results of delta variable during key scheduling process of LEA. By caching the delta variables, the number of memory access is replaced to the relatively cheap register access. Similarly, the master key and plaintext are cached during key scheduling and encryption of HIGHT block cipher, respectively. Second, stack storage and pointer are fully utilized to store the intermediate results and access the round keys. This approach solves the limited storage problem and saves one general purpose register. Third, indirect addressing mode is more efficient than indexed addressing mode. In the decryption process of LEA, the round key pair is efficiently accessed through indirect addressing with minor address modification. Fourth, 8-bit word operations for HIGHT is efficiently handled by 16-bit wise instruction of 16-bit MSP processors. Finally, the proposed LEA implementations on the representative 8-bit AVR and 16-bit MSP processors are fully evaluated in terms of code size, RAM and execution timing. The proposed implementations over the target processors (8-bit AVR processor, 16-bit MSP processor) are faster than previous works by (13.6%, 9.3%), (0.6%, 8.5%), and (3.4%, 1.5%) for key scheduling, encryption, and decryption, respectively. Similarly, the proposed HIGHT implementations on the 16-bit MSP processors are faster than previous works by 38.6%, 33.7%, and 33.6% for key scheduling, encryption, and decryption, respectively.
Computational Healthcare System With Image Analysis

The quickly extending field of huge information examination has begun to assume a crucial part in the advancement of human services practices and research. In this chapter, challenges like gathering information from complex heterogeneous patient sources, utilizing the patient/information relationships in longitudinal records, understanding unstructured clinical notes in the correct setting and efficiently dealing with expansive volumes of medicinal imaging information, and removing conceivably valuable data is shown. Healthcare and IoT and machine learning along with data mining are also discussed. Image analysis and segmentation methods comparative study is given for the examination of computer vision, imaging handling, and example acknowledgment has gained considerable ground amid the previous quite a few years. Examiners have distributed an abundance of essential science and information reporting the advance and social insurance application on medicinal imaging.