Reviewerly/exHarmony huggingface dataset:
The Improvement of Quine-McCluskey Method Using Set Covering Problem for Safety Systems

When designing complex safety systems which consist of large scale logical circuits, the basic requirement is to minimise the number of elements that will implement the given logical functions. This will increase the reliability, and thus potentially the security of the devices. For logical functions with a number of variables of no more than 4, Karnaugh maps are preferred. However, in practice, we encounter much more complex functions, either directly applying Boolean algebra laws or using the Quine-McCluskey method, which is based on their systematic use. However, because this method does not provide a minimal form of logical function, and as a result, there may be redundant expressions, we will show that the additional phase of minimisation means solving the problem of covering all inputs by the obtained output expressions. For the purpose of clear representation and implementation process of post-processing method, the genetic algorithms and simulated annealing were implemented on OR-Library benchmarks.
Smart Contracts

The chapter focuses, in the area of property and security rights, on the interface between the virtual and transnational dimension of transactions on the blockchain on one hand and the “real” world of compulsory and public policy rules applying on a given territory and on given property on the other. The author takes the perspective of a continental lawyer analyzing the validity of transactions, the validity and integrity of the property title electronically created and transferred. In doing so, he relies not only on the traditional legal tools but also on the most recent legislative initiatives, especially in France, introducing a legal framework encompassing new ways of transferring property (based on blockchain technologies) and new titles (e.g., tokens in the context of initial coin offerings). Through this analysis, the core issue is to determine whether these new transactions and these new property titles can be effective, in a national and international context. The author raises questions and concerns about the actual legal uncertainty and the best (local and global) regulatory responses to the technological challenges.
The Kalman Filter

The Kalman filter uses linear quadratic estimation to produce estimates of unknown variables from a series of uncertain measurements. It can therefore be used to fit and filter the track candidates produced by the Hough transform track finder. In addition, a simple duplicate removal algorithm is described. A mathematical derivation of the algorithm is provided, alongside a description of the FPGA firmware design. FPGA resource utilisation and latency are provided.
An Improved Correlation Electromagnetic Analysis Based On Genetic Algorithm Optimization

Correlation electromagnetic analysis(CEMA) is one of the most powerful ways in side channel analysis. Because it don't need to modify the target device and even can be measured at distance. But in the real scenarios, only those interesting points relevant to the target intermediate value is helpful to recover the secret key. In this paper, we proposed a novel method based on genetic algorithm to select the interesting points, and the hamming distance between S-Box input and S-Box output in first round is chosen as the target intermediate value. We perform our experiment on Sakura-G board against AES-128 algorithm. Our experiment result shows our method is more effective which decreases about 9.1% in trace number than traditional CEMA for AES crypto algorithm to recover the secret key.
A novel data ciphering method for secure cloud storage

Humans deeply rely on technologies for storing their personal data. Technologies play a major role in function of industries, governments and so on. Simultaneously technologies need to fight against the problems like malware, hacking, intruders, eavesdroppers and data collisions etc., The most important problem is data breaching by attackers. So we have to give a strongest and unbreakable cryptography algorithm for protecting the data from the hackers. Cryptography encode the data and decode it only by the authentic user. There is so many encryption algorithm available like AES, DES, 3DES and more on. But these algorithms are breakable. So we devised the new ciphering algorithm called Deep Substitution Encryption Method. In this method, we are hiding the text using continuous substitution process for secure the cloud data.
Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting

The growing edge computing paradigm, notably the vision of the internet-of-things (IoT), calls for a new epitome of lightweight algorithms. Currently, the most successful models that learn from temporal data, which is prevalent in IoT applications, stem from the field of deep learning. However, these models evince extended training times and heavy resource requirements, prohibiting training in constrained environments. To address these concerns, we employ deep stochastic neural networks from the reservoir computing paradigm. These networks train quickly with no need for backpropagation and we further accelerate training by employing Tucker decomposition. We demonstrate that such networks benefit from both tensorization and compression, and achieve a reduction of FLOPs up to ~95% while outperforming the uncompressed counterparts.
A Tool to Solve Nonlinear Algebraic Equations Systems

This article explains the development of a tool (application) to solve nonlinear algebraic equations systems (NAES). Systems of nonlinear equations are usually complex and can not be solved analytically or in an exact way, therefore numerical methods are used to solve this type of systems. This paper shows the implementation of four numerical algorithms: conjugate gradient method (CGM), Newton-Raphson (N-R), Ostrowski's method (OM) and homotopic continuum methods (HCM). These methods are capable of solving systems of nonlinear algebraic equations of n×n. In addition, the GiNaC library is used, which is completely written in C++ and allows the user to perform algebraic operations and interact within this language. The application has been programmed considering the following attributes: easy to use, multi-platform, that is, it must be able to operate in different operating systems (S.O.), fast in obtaining the solution, able to configure some fields to try to find the solution.
Caution: GALS-ification as a Means against SCA Attacks

The following topics are dealt with: fault diagnosis; logic testing; cryptography; logic design; railways; program testing; combinational circuits; probability; field programmable gate arrays; error statistics.
Neural Net as Pseudo-Inverse Filter in Speech Coding Problem

In the general case, a finite impulse response (FIR)filter has no inverse filter. To solve the inverse filtering problem, we propose an approximate method that restores the initial sequence. We analyze the sequence obtained by filtering the source sequence with an arbitrary FIR filter. The analysis made with the help of a deep neural network. The results of the study are applied to speech coding. We show experimentally, that our approach provides lowering bit rate in the transmission of speech data through a channel.
Mention Detection Method for Entity Linking

This paper proposes a method for detecting entity mentions in the given text. The method consists of two sequential labelling steps. The first sequential labelling is responsible for identifying a chunk of words (morphemes) mentioning an entity. The second sequential labelling verifies whether or not each chunk (or un-chunked word) is really an entity. The paper also investigates the relation between granularity of categories of named entities and mention detection performance. Our experimental results have shown that the second sequential labeling step for checking slightly improved recall and the eleven categories is the best granularity.
Automatic Identification of Conodonts Based on Deep Learning

The study of conodonts can promote people's understanding of the major events of extinction and resuscitation in geological history. The identification of conodonts requires rich expertise. The identification time is long, the efficiency is low, and sometimes the accuracy is difficult to guarantee, which brings a lot of inconvenience to the in-depth study and application of conodonts. In this paper, we propose a deep learning model based on CNN for automatic identification of conodonts. In order to optimize the CNN model, we put forward a hierarchical classification method to solve the species relation problem among different categories. Also, we use softmax classification loss function to reduce the impact of class imbalance and class overlap. In order to prevent model over-fitting, we also use some data enhancement and anti-over-fitting methods. This work provides a reference for the realization of intelligent and automatic identification of conodonts.
Design of wireless smart chair system for people with cognitive deficiency

This article describes a sensor-based wireless system which monitors sitting postures of a person with cognitive impairment, assesses risks and alarms both the person and his caregiver if the risk of falling is high. Unlike existing smart chairs, the proposed system identifies postures that might cause a person to fall from chair by using minimal number of sensors and a simple posture classification algorithm. Experimental evaluation shows that the system operates in real-time, is robust yet inexpensive.
Design of a service oriented architecture for a geodesic monitoring system

Geodesic monitoring systems are used for the surveillance of modern constructions. They have evolved significantly over the past years due to several breakthroughs in engineering. One of the significant issues for such systems is the difficulty of developing new systems based on the existing ones. The solution presented in this paper relies on a service oriented architecture having an enterprise service bus as its core component. By using this approach, the three main components of the monitoring system (measurement system, data integration and cleaning, and deformation analysis) become loosely coupled, and, thus, more independent. The paper focuses on the data integration and cleaning component for which three main chains of sequential or parallel services have been developed. The first chain processes the observation files provided by the three tachymeters which monitor the object, the second one processes the data provided by the temperature and humidity sensors, and the third chain provides preliminary analyses functions, and publishes the results on a website. A fourth chain of the ESB focuses on the error management at bus level, which handles the issues of language level constructions and provides greater transparency. Thus, the ESB decouples the main components of the system, it provides data transformation and validation, and it inserts the data into the data warehouse. The loosely coupled nature of the system facilitates parallel development of the components, enhanced reusability, and greater flexibility, all of which are key aspects for a service oriented architecture. Another advantage of our approach is the varying granularity of reusability, which can be adopted at service level, but also at chain level.
TwinPorter - An Architecture For Enabling the Live Migration of VMI-Based Monitored Virtual Machines

Virtual machine introspection has evolved into a valuable tool for several purposes such as incident analysis and intrusion detection. In addition to that, there has been a lot of work that aims at enabling cloud tenants to do VMI on their virtual machines in IaaS-based clouds. Migration can be a required operation in cloud environments, for example, to keep service level agreements or to minimize power consumption. Common tools for migration of virtual machines do not provide support for coordinating VMI-based monitoring with migration. TwinPorter introduces a mechanism that migrates in parallel both the analyzing system and the analyzed system to the same target host. It ensures that the analyzed virtual machine never runs without being actively monitored and keeps the downtime of the analyzed system minimal by extending the live pre-copy based migration protocol of Xen. In our TwinPorter prototype, we measured an additional downtime of about 2 s of the monitored system during migration.
Hardware-Accelerated Similarity Search with Multi-Index Hashing

Similarity search plays a major role in a large variety of database applications where high-dimensional data, usually derived from images and videos, are mapped onto indexed structures. Its essence relies on a k-nearest-neighbors (kNN) algorithm to find similar contents given a specific input data. An efficient way to store and speedup such searches is to apply kNN in Hamming space by encoding the data as binary and splitting it into multiple hash tables. This paper proposes an efficient Hardware-Accelerated Similarity Search co-processor architecture using Multi-Index Hashing as storage structure. The accelerator is specified in Verilog hardware description language and implemented in a Xilinx low-cost Zynq FPGA. Performance, circuit-area and power consumption results are presented, with the accelerator being up to 13x and 18x faster than the corresponding C/C++ code on the ARM host processor when running the SIFT and GIST datasets, respectively, while also requiring less power.
Patterns of Life Visualiations for Intelligence Analysis: Exploring Animation with POLAR

POLAR is a prototype data visualisation system developed for use by Intelligence Analysts conducting Patterns-of-Life (PoL) analysis. PoL analysis involves exploring data to identify behaviour patterns of individuals or groups. POLAR began with a series of requirements interviews with an ex-military analyst. The design process revealed a series of design alternatives that were explored. One of these related to whether, contrary to suggestions in the literature, animation might provide an advantage to the analyst. We report the design of the system and an evaluation study to assess the effect of animation on users. Whilst animation didn't result in significant improvements in performance, the use of animation with trails did result in significantly better usability assessments and higher user-engagement ratings. Post task interviews with analysts suggested where animation might offer a performance advantage.
Extracting Inclusion Graphs of API Member Sets to Improve Searchability

Search tools for API usage patterns extracted from open-source repositories could provide useful information for application developers. However, API member sets on search results are not necessarily easy to utilize if the results are shown by simple lists because API member sets tend to be similar to each other. We propose a method to improve the searchability of API member sets by utilizing inclusion graphs among API member sets that are automatically extracted from source code. The proposed method incorporates the frequent pattern mining to obtain inclusion graphs and offers the user a way to search API member sets smoothly and intuitively by using a GUI. We describe the details of our method and the design and implementation of the prototype. We discuss the usability of the proposed tool.
Using Physical Activity Monitors in Smart Environments and Social Networks: Applications and Challenges

The use of smart watches and fitness wrists has been increasing in recent years. On the one hand, their cost has become cheaper and their performance has improved. On the other hand, the increase in the number of people who practice sports such as running and cycling is another factor to consider. The increase in the number of popular athletes has meant that these devices are no longer considered intended for a minority or an elite, but are even used in people’s daily movements or in simpler activities such as walking. Some of these devices simply count the number of steps taken, while more advanced devices include energy consumption, distance travelled, speed, GPS tracking position, altimetry or heart rate. Moreover, there are social networks that allow athletes to share information gathered by their own activities, especially track and altimetry. This opens up a wider range of possibilities in sports training. However, the gathering of this rich information makes many more applications possible. For example, city planners can analyze the movements of people to detect possible shortcomings in public transport systems, deficiencies in urban pathways, and so on. This research work shows a taxonomy of different applications that use the data gathered by physical activity monitors and shared in social networks. The opportunities and drawbacks regarding the use of such applications in intelligent environments are also discussed.
Ant Colony Algorithm for Determining Dynamic Travel Routes Based on Traffic Information from Twitter

Combining the search method for fire suppression routes with ant colony algorithms and methods of analyzing twitter events on the highway is the basis of the problems to be studied. The results of the twitter data feature extraction are classified with Support Vector Machine after it is implemented in the Simple Additive Weighting method in calculating path weights with criteria of distance, congestion, multiple branching, and many holes. Line weights are also used as initial pheromone values. The C-means method is used to group the weights of each path and distance so that the path with the lowest weight and the shortest distance that will be simulated using the Ant Colony. The validation results with cross fold on SVM with linear kernels produce the greatest accuracy value is 97.93% for training data distribution: test data 6: 4. The simulation of the selection of the damkar car path from Feather to Pleburan with Ant Colony obtained 50 seconds of computation time, whereas with Ant Colony with Clustering the computation time was 15, resulting in a reduction in computing of 35. Ant colony with MinMax optimization gives the best computation time of 14.47 seconds with 100 iterations and 10 nodes.
Light-Weight Novel View Synthesis for Casual Multiview Photography

Traditional view synthesis for image-based rendering requires various processes: camera synchronization with professional equipment, geometric calibration, multiview stereo, and surface reconstruction, resulting in heavy computation, in addition to manual user interactions throughout these processes. Therefore, view synthesis has been available exclusively for professional users. In this paper, we address these expensive costs to enable view synthesis for casual users even with mobile-phone cameras. We assume that casual users take multiple photographs using their phone-cameras, which are used for view synthesis. First, without relying on any expensive synchronization hardware, our method can capture synchronous multiview photographs by utilizing a wireless network protocol. Second, our method provides light-weight image-based rendering on the mobile phone, where heavy computational processes, such as estimating geometry proxies, alpha mattes, and inpainted textures, are processed by a server to be shared in an interactable time. Finally, it allows us to render novel view synthesis along a virtual camera path on the mobile devices, enabling bullet-time photography from casual multiview captures.
Deep Convolutional Center-Based Clustering

Deep clustering utilizes deep neural networks to learn feature representation which is suitable for clustering. One popular category of deep clustering algorithms combines stacked autoencoder and k-means clustering by defining objectives including both clustering loss and reconstruction loss so that the feature representation and the cluster assignment could be learned simultaneously by the networks. However, we observe that the optimization direction of two terms are not consistent. To address this issue, in this paper, we propose a model called Deep Convolutional Center-Based Clustering (DCCBC), which replaces the usual reconstruction loss by a novel reconstruction loss based on cluster centers. The model can produce compact feature representation and capture the salient features of instances in different clusters. The optimization problem can be efficiently solved by mini-batch stochastic gradient descent and back propagation. Experiments on several benchmark datasets showed that the proposed model could achieve competitive results and outperformed some popular state-of-the-art clustering algorithms.
Mineral chemistry and physical conditions of crystallization in Filshour and Goft dioritic intrusions, Southwest of Sabzevar

Mineral chemistry and physical conditions of crystallization in Filshour and Goft dioritic intrusions, Southwest of Sabzevar
Statistical Analysis of Discriminators under the Influence of Additive Correlated non-Gaussian Noise Described by Markov Processes

To improve the accuracy of the evaluation of information parameters of the signal allows the function of the probability density of noise affecting the processed signal and errors of mismatch. The paper presents a statistical analysis of discriminators of nonlinear blocks of motion parameters meters under the influence of additive mixture of useful signal and correlated non-Gaussian noise. The synthesis of a discriminator, exposed to additive noise described by singly connected and multiply connected Markov processes and noise with independent values, is performed. Structural diagrams of the discriminators are shown, and the mathematical relationships describing the main characteristics of the discriminators, including the generalized discriminator, are given. The efficiency of the discriminator in the conditions of shifted hypotheses is analyzed. The main characteristics of the discriminator exposed to correlated additive noise with the finite mismatch error are obtained. It is noted that in the case of exposure to non-Gaussian noise, its reduction occurs due to decorrelation and nonlinear amplitude suppression, while, as a rule, it must be consistent with the spectral power density of the influencing noise.