Reviewerly/exHarmony huggingface dataset:
Bloom Filter Cascade Application to SQL Query Implementation on Spark

A new method for SQL query implementation in a parallel execution environment Apache Spark in a package mode was developed on the basis of Bloom Filter Cascade Application (BFCA). It includes representation of the original query in a form of a few subqueries and intermediate tables, where the Bloom filters are created and applied. Then the representation is transformed into Scala code. Theoretical justification of the developed method is provided. The theoretical estimation of the network data transmission volume reduction (shuffle) is shown on an example. The efficiency of the method is demonstrated on the example of Q3 (three tables join) and Q17 (correlated subquery) queries from the TPC-H test. The developed BFCA method is more than 2 times faster and more than 10 times better on shuffle volume (as compared to Spark SQL) on Q3 at a Scale Factor SF=500. The BFCA is 8 times faster on Q17 than Hive (Spark SQL cannot implement Q17).
Adaptive Natural Gradient Method for Learning Neural Networks with Large Data set in Mini-Batch Mode

Natural gradient learning, which is one of gradient descent learning methods, is known to have ideal convergence properties in the learning of hierarchical machines such as layered neural networks. However, there are a few limitations that degrades its practical usability: necessity of true probability density function of input variables and heavy computational cost due to matrix inversion. Though its adaptive approximation have been developed, it is basically derived for online learning mode, in which a single update is done for a single data sample. Noting that the on-line learning mode is not appropriate for the tasks with huge number of training data, this paper proposes a practical implementation of natural gradient for mini-batch learning mode, which is the most common setting in the real application with large data set. Computational experiments on benchmark datasets shows the efficiency of the proposed methods.
Optimized Power Consumption Model for Multiplication in Galois Field of AES

Side-channel analysis (SCA) is an analysis method to extract secret information using power consumption, electromagnetic waves, sound, etc., which occur when an encryption algorithm operates in a device. In [2], Lee et al. conducted SCA into the 8-bit based AES-128 algorithm operating on the ChipWhisperer-Lite board. As a result, it was found that Correlation Power Analysis (CPA) using only the most significant bit (MSB) of an 8-bit intermediate value is better than CPA using an 8-bit Hamming weight of the value. In this paper, we demonstrate that CPA using only a specific bit has the superior performance because of the modulo operation in Galois field of the 8-bit based AES-128 encryption algorithm. We performed CPA on the xtime operation part. As a result, we show that power consumption related with the MSB occurs while performing the modulo operation in Galois field.
VLSI Implementation and Software Co-Simulation of Digital Image Watermarking with Increased Security

Background: Security is one of the fundamental and essential factors, which has to be addressed in the field of communication. Communication refers to the exchange of useful information between two or more nodes. Sometimes it is required to exchange some of the confidential information such as a company’s logo, which needs to be hidden from the third person. The data that is being exchanged between these nodes has to be kept confidential and secured from unintended users. The three fundamental components of security are confidentiality, integrity and authentication. The data that is being exchanged has to be confidential, and only the authorized party should have access to the information that is being exchanged. One of the key methods for securing the data is encryption. Objective: The main objective of this paper was to address the problem of data hiding and security in communication systems. There is a need for having hardware resources for having high speed data security and protection. Methods: In this paper, we implemented image watermarking using LSB technique to hide a secret image, and employed encryption using Advanced Encryption Standard, to enhance the security of the image. An image is a two dimensional signal, with each pixel value representing the intensity level. The secure transmission of the image along the channel is a challenging task, because of the reason that, any individual can access it, if no security measures are taken. Conclusion: An efficient method of digital watermarking has been implemented with increased security and performance parameters are presented. Results: In this paper, hardware realization of image watermarking/encryption and dewatermarking/ decryption is implemented using Very Large Scale Integration. The design is verified by means of co-simulation using MATLAB and Xilinx. The paper also presents the performance parameters of the design, with respect to speed, area and power.
Collaborative Filtering Recommendation Algorithm Based on AdaBoost-Naïve Bayesian Algorithm

With the rapid development of social network and E-commence, collaborative filtering (CF) has been widely applied and studied as an effective way to alleviate the pressure of information overloading. However, the CF-based algorithms often degrade significantly in their recommendation performance due to the sparse history rating data. In view of this sparse problem, this paper presented a collaborative filtering recommendation algorithm based on AdaBoost-Naïve Bayesian algorithm. In this model, we transform the traditional algorithms of predicting ratings directly to classify unrated data. Considering that Bayesian theory is a good machine learning method that is often used for text class. Considering that machine learning method can be used for text classification, we utilize it to learn user’s preferences for a certain item characteristic as a based classify. Ensemble learning, Adaboost, is also adopted to optimize and adjust adaptively the weights of the results of Bayesian classify. Finally, all of the predicted results of unrated data would be filled into the user-item matrix. The experimental results show that the sparseness of rating data has been alleviated and our proposed algorithm can improve the quality of recommender system.
Models and Methods of Computer-Aided Design of the User Interface of Software Systems

The article deals with the design, mathematical modeling and development of the prototype of the interface of the information system of the audit fund.In this paper, the study of the design of the user interface, methods for creating interfaces, user interaction types, a list of the functionality of the system, a mathematical model, nondeterministic finite automaton (NFA) was built user interaction with system elements and a prototype of the system have been created.According to the authors, this system will automate and simplify the process of managing the audit fund, joint use of the NFA and one of the methods of creating interfaces, namely, ontology will allow describing domain concepts, edit domain concepts, generate databases and knowledge bases, broadcast from one language to another, get rid of information redundancy, reduce time for database development as well as simplify the process of scheduling the university.
From Smart Concept to User Experience Practice a Synthetic Model of Reviewed and Organized Issues to Conceive Qualified Interactions

Nowadays, we live in environments where smart products enable new processes of human and non-human interaction. Smart concepts push functions beyond traditional levels of expectation. It is widely appraised that smart concepts enhance products to make them respond to human needs and expectations. However, regardless of the level of complexity and intelligence of those products (regardless of scale, from home environment to an object), if the environment is non-user-friendly, users will reject them and eventually abandon them. Conceptualizing intelligent solutions is more often centered on the requirements of functions and overlooks the profile of users, a critical aspect to outline the experience. Present-day interaction between users and smart products should be enabled by their physical model, finishing, interface features and functional capabilities. The quality of experience is not only grounded on the type and efficiency of the task performed. The quality of the experience depends on: the physical experience (dimension, height, shape, finishing); the sensorial experience (how does the artifact stimulate the sensorial human system); the cognitive experience (how easy it is to interact with technology) and the emotional experience (is this a pleasant experience? Would we repeat it?) Moreover, outlining the users’ demands and expectations from environments, products and technology is also critical to reach qualified and appealing solutions. The aim of this paper is to establish the need of taking a different approach when conceptualizing smart environments and products, arguing that users can only understand inherent intelligence if the parameters that sponsor an emotional appealing experience are considered throughout the design process.
Segmentation of Magnetic Anomalies in the Conduct of Archaeological Excavations

There are different ways to identify sites for future excavation in archaeology. One of these methods is based on magnetic properties of hidden underground objects and is carried out by means of magnetometric survey. In the pre-selected points the magnetic induction module of geomagnetic field is measured. Then the data is preprocessed (for example, changes in the Earth’s total magnetic field are taken into account). After that, the researchers build a map of the area and estimate the probability that there are objects of archaeological value in the selected area. Based on this map the decision is taken whether it is worth to conduct excavations. However, both useful anomalies caused by the presence of archaeological sites and anomalies of a random nature can be simultaneously presented on the map. Unfortunately, sometimes it is quite difficult for a human to detect with one’s eyes what anomalies are useful in the picture. In this article a process of automatic allocation of segments of archaeological objects is described. For this purpose specific smoothing and adaptive threshold binarization methods as well as clustering algorithms are considered.
Quantum Algorithm for Distribution-Free Junta Testing

Inspired by a recent classical distribution-free junta tester by Chen, Liu, Serverdio, Sheng, and Xie (STOC'18), we construct a quantum tester for the same problem with complexity $$O(k/\varepsilon )$$ , which constitutes a quadratic improvement. This result was obtained independently from the algorithm for this problem by Bshouty.
Reactive navigation of autonomous vehicle

Research on dynamic path planning for autonomous vehicle has been increased significantly in recent years because of the percentage of car accidents reduced by autonomous driving. The functions of Artificial Potential Fields (APF) as mentioned in previous studies, particularly nonlinear constraint, speed and safe distance, are seldom discussed or mentioned. In this paper, we focus on the need for vehicle safety. Thus, this study proposes a fuzzy artificial potential field path planning algorithm (FZ-APF) for local path planning in vehicle. FZ-APF introduces the distance factor to solve the well-known drawbacks of the traditional APF like goal non-reachable with obstacle nearby (GNRON) problem. However, FZ-APF not only optimizes path but also deals with the constraints of vehicle like the limit of steering angle at different speeds. Therefore, FZ-APF introduces longitudinal potential function to make the planned path accord with vehicle characteristics. In addition, to achieve more safe and efficient FZ-APF for vehicle, the fuzzy technique which adjusts the parameters at any moment according to the current speed and safety distance of the vehicle is added into FZ-APF. Numerical simulations are presented and validate the proposed algorithm under some complicated test scenarios. Experiment eventually illustrates the efficiency of this FZ-APF algorithm using an autonomous vehicle prototype.
Distributed control system for mobile medical complexes

Currently, high-tech service robotics is increasingly developing. Service robots perform various tasks: patient care, condition monitoring and alert medical personnel and patient registration. In connection with the urgency of the task, this article will consider a distributed control system for mobile complexes, which task is to control, manage, and distribute tasks for complexes for the operation of re-equipping hospital wards and transporting mobile equipment in a medical institution. For more accurate route building, a local positioning system can be used. Now there are no ready-made algorithms for solving such problems. There are algorithms for managing single complexes, complexes in a closed space, and the problems of transition from the initial state to the final state have not been considered either. In this connection, a number of problems arise, which are considered in this article: transfer of medical complexes from the initial state to the final state, determination of initial parameters and conditions, determination of final parameters and conditions. During the task creation with a distributed control system, the following factors should be taken into account: the availability of the resource required for delivery on the mobile complex, the priority of the task being performed. The practical significance of the work will be determined by the result of the development of a mathematical model that will take into account all current problems and factors. As a result, a theory was determined by which the theoretical aspects were analysed and the closest toolkit of interactions within the control system in a distributed environment was selected.
Literature Review of Evolutionary Hardware Application Based on Nios II and Neural Network

The development and research status of the evolutionary hardware technology are analyzed and summarized. The characteristics of the existing evolutionary circuit design methods are analyzed and compared. The genetic algorithm and other evolutionary control algorithms are analyzed, and the design method of the virtual reconfigurable circuit is studied. An evolutionary circuit design method of "Nios II+VRC array" is proposed, which has a wide range of applications and can be evolved from PC to online evolution.
Presentation methodologies: an assessment for forensic signature analysis

This study introduces a new methodology – the Line-Up method – for the presentation of signatures for forensic comparison. This method gives one approach to reduce the potential for contextual bias that is present using the traditional Target methodology. The findings of the research showed no significant difference in the number of inconclusive opinions, or level of accuracy, between the two presentation methods when used by the forensic handwriting examiners. However, lay participants made significantly fewer correct calls using the Line-Up method than the Target method. The findings reported here also show that forensic handwriting examiners exhibit greater levels of accuracy of authorship opinion utilizing the Line-Up method compared with lay participants. An investigation of the levels of inconclusive opinion using the Line-Up method found that forensic handwriting examiners were significantly more likely to offer an inconclusive opinion than lay participants (lay participants mean 21.3%; forensic handwriting examiners mean 56.3%). This study has established a baseline that will serve as the platform for longitudinal studies and is the first study to involve forensic experts from different countries. It will enable more globally collaborative studies to be undertaken.
IntelliSoC: A System Level Design and Conception of a System- on-a-Chip (SoC) to Cognitive Agents Architecture

This chapter presents a system level design and conception of a System-on-a-Chip (SoC) for the execution of cognitive agents. The computational architecture of this SoC will be presented using the cognitive model of the concurrent autonomous agent (CAA) as a reference. This cognitive model comprises three levels that run concurrently, namely the reactive level, the instinctive level and the cognitive level. The reactive level executes a fast perception-action cycle. The instinctive level receives perceptions from and sends the active behavior to the reactive level, and using a Knowledge Based System (KBS) executes plans by selecting reactive behaviors. The cognitive level receives symbolic information from the instinctive level to update its logical world model, used for planning and sends new local goals to instinctive level. Thus, this work proposes a novel SoC whose architecture fits the computational demands of the aforementioned cognitive model, allowing for fast, energy-efficient, embedded intelligent applications.
Personal Book Management Application on Blockchain

Using a blockchain, an application that guarantees reliability, can be realized by managing a distributed ledger of agreements. A blockchain’s usefulness in applications requiring trust, such as virtual currency, has been confirmed, and it is being used in actual services. However, the time taken to add data to the distributed ledger and the cost of this operation may be too high. As a result, other real applications for blockchains have not been found yet. In this research, we design and implement an application for lending and borrowing books on a distributed ledger. The application is implemented as a distributed application on Ethereum, which is a platform used for building blockchain-based smart contracts. Users of this application can lend and borrow books through a web application that functions as the user interface of the application. The usage logs show that the time and fees required for adding data to the distributed ledger are acceptable.
SOGN: novel generative model using SOM

Generative models such as variational autoencoder (VAE) and generative adversarial network (GAN) have been widely applied to many areas including image synthesis and voice generation. However, they have some problems that VAE makes blur images and GAN is difficult to learn due to mode collapsing. A novel generative model is proposed using a self-organising map (SOM) termed a self-organising generative network (SOGN). In the SOGN, training images are first mapped to SOM and then the output space of SOM is transformed into 2D vector spaces. These vector values are used as latent vectors to train the generative network such as artificial neural networks or convolutional neural networks. Experimental results with MNIST and CIFAR-10 datasets showed that their generative model was easy to train without mode collapsing and made more clean images than VAE. It was also confirmed that the manifold is well-observed without generating by the average effect of multiple images.
Dynamic Guest Memory Resizing - Paravirtualized Approach

Nowadays cloud-computing systems take a great advantage of virtualization for the benefits of workload isolation and flexible resources partitioning. It is expected that the same functionalities will be available also on disaggregated architectures, proposed recently as next generation approach for building data-centers. In this publication, we are presenting the design and prototype of an enhanced virtualization layer, enabling runtime memory balancing between virtual machines on a section granularity. Guests' RAM is backed by isolated chunks of host memory, coming from independent physical banks, not necessarily a local one. It can be dynamically resized without requiring any support for the ACPI emulation in the virtualization framework, as we exemplified by implementing the prototype on ARMv8 platform.
Symmetric Quantization Scheme and Approximate Batch Normalization for Quantized Neural Networks Training

With the advancements of circuit design and manufacturing processes of hardware, Deep Neural Networks (DNN) have been developed to be deeper and larger to achieve remarkable results in many aspects. Despite this, problems such as excessive memory usage and severe resource consumption are still unavoidable. We proposed a symmetric quantization scheme, which quantizes the weights from a certain kernel to be symmetric to zero along with a scaling factor and bias term in order to make full use of the whole quantization space and reduce the multiplication operations when convolution happens. We also came up with an Approximate Batch Normalization (ABN) method, which simplifies the traditional BN to linear operations lightly. Besides, activations and gradients are quantized in our scheme with fine distinctions, so that most floating-point calculations can be converted into fixed-point calculations and bit-shift operations in both training and inference phase. Our proposed scheme greatly alleviates the problems mentioned above theoretically with fewer computations and smaller storage theoretically. We carried out various experiments to prove its feasibility.
Design of Data Interchange Platform for Digital Highway

A digital highway data exchange platform of Foshan city has been planned. Through analyzing of the status quo, The real demand of building a data exchange platform is proposed. By via of building a series of data exchange standards and constructing data center. The feasible system frame has been built for data exchange platform.
A spectral decomposition for a simple mutation model

We consider a population of $N$ individuals. Each individual has a type belonging to some at most countable type space $K$. At each time step each individual of type $k\in K$ mutates to type $l\in K$ independently of the other individuals with probability $m_{k,l}$. It is shown that the associated empirical measure process is Markovian. For the two-type case $K=\{0,1\}$ we derive an explicit spectral decomposition for the transition matrix $P$ of the Markov chain $Y=(Y_n)_{n\ge 0}$, where $Y_n$ denotes the number of individuals of type $1$ at time $n$. The result in particular shows that $P$ has eigenvalues $(1-m_{0,1}-m_{1,0})^i$, $i\in \{0,\ldots ,N\}$. Applications to mean first passage times are provided.
Weighted Histogram of Oriented Uniform Gradients for Moving Object Detection

With the growth of the automotive electronics technology, the advanced driver assistance system (ADAS) becomes more and more important. Especially, the moving object detection (MOD) is an important issue in the ADAS in intelligent vehicles. In realistic systems, there exist two critical challenges including computing time and detection rate for MOD. To overcome these problems, we propose a novel moving object detection system which contains pre-processing, feature extraction, classification and state machine. The pre-processing contains ROI extraction and skipping low busyness windows, which accelerates the computing time to solve the mentioned problem. To improve the performances, in this paper, the weighted histogram of oriented uniform gradient (WHOUG) with support vector machine (SVM) is proposed to promote the detection accuracy. Besides, the finite state machine could further improve the robustness of the proposed system. The results demonstrate that the proposed system achieves better performance than the traditional one, and also maintains real time computation.