Reviewerly/exHarmony huggingface dataset:
New Approach to Constructing Noise Source Based on Race Conditions

A noise source is an essential component of random bit generator, and is either an application or a device to provide entropy from analog noise. In 2008, Colesa et al. first proposed two software strategies for constructing noise source based on race conditions. However, Colesa et al.'s designs require a lot of threads and even suffer from a low bit rate. Moreover, setting a parameter for each system is complicated since the parameter is related to the entropy and the bit rate at the same time. In this paper, we propose new constructions of noise source based on race conditions. We call them NSRC-1 and NSRC-2. The bit rate of our designs is improved by up to 819 times higher on multi-core systems with high entropy. The parameter adjustment becomes straightforward by removing the relation between the parameter and the entropy. Additionally, since NSRC-1 and 2 require only two threads at once, they are more available software-based methods for harvesting entropy not only on general devices but also on mobile devices.
Variable-Length Quantization Strategy for Hashing

Hashing is widely used to solve fast Approximate Nearest Neighbor (ANN) search problems, involves converting the original real-valued samples to binary-valued representations. The conventional quantization strategies, such as Single-Bit Quantization and Multi-Bit quantization, are considered ineffective, because of their serious information loss. To address this issue, we propose a novel variable-length quantization (VLQ) strategy for hashing. In the proposed VLQ technique, we divide all samples into different regions in each dimension firstly given the real-valued features of samples. Then we compute the dispersion degrees of these regions. Subsequently, we attempt to optimally assign different number of bits to each dimensions to obtain the minimum dispersion degree. Our experiments show that the VLQ strategy achieves not only superior performance over the state-of-the-art methods, but also has a faster retrieval speed on public datasets.
Robust Quadratic Programming for MDPs with uncertain observation noise

The problem of Markov decision processes (MDPs) with uncertain observation noise has rarely been studied. This paper proposes a Robust Quadratic Programming (RQP) approach to approximate Bellman equation solution. Besides efficiency, the proposed algorithm exhibits great robustness against uncertain observation noise, which is essential in real world applications. We further represent the solution into kernel forms, which implicitly expands the state-encoded feature space to higher or even infinite dimensions. Experimental results well justify its efficiency and robustness. The comparison with different kernels demonstrates its flexibility of kernel selection for different application scenarios.
Multi-Scale Fusion of Enhanced Hazy Images Using Particle Swarm Optimization and Fuzzy Intensification Operators

Dehazing from a single image is still a challenging task, where the thickness of the haze depends on depth information. Researchers focus on this area by eliminating haze from the single image by using restoration techniques based on haze image model. Using haze image model, the haze is eliminated by estimating atmospheric light, transmission, and depth. A few researchers have focused on enhancement based methods for eliminating haze from images. Enhancement based dehazing algorithms will lead to saturation of pixels in the enhanced image. This is due to assigning fixed values to the parameters used to enhance an image. Therefore, the enhancement based methods fail in the proper tuning of the parameters. This can be overcome by optimizing the parameters that are used to enhance the images. This paper describes the research work carried to derive two enhanced images from a single input hazy image using particle swarm optimization and fuzzy intensification operators. The two derived images are further fused using multi-scale fusion technique. The objective evaluation shows that the entropy of the haze eliminated images is comparatively better than the state-of-the-art algorithms. Also, the fog density is measured using an evaluator known as fog aware density evaluator (FADE), which considers all the statistical parameters to differentiate a hazy image from a highly visible natural image. Using this evaluator we found that the density of the fog is less in our proposed method when compared with enhancement based algorithms used to eliminate haze from images.
Incremental Learning with Unlabeled Data in the Wild.

Deep neural networks are known to suffer from catastrophic forgetting in class-incremental learning, where the performance on previous tasks drastically degrades when learning a new task. To alleviate this effect, we propose to leverage a continuous and large stream of unlabeled data in the wild. In particular, to leverage such transient external data effectively, we design a novel class-incremental learning scheme with (a) a new distillation loss, termed global distillation, (b) a learning strategy to avoid overfitting to the most recent task, and (c) a sampling strategy for the desired external data. Our experimental results on various datasets, including CIFAR and ImageNet, demonstrate the superiority of the proposed methods over prior methods, particularly when a stream of unlabeled data is accessible: we achieve up to 9.3% of relative performance improvement compared to the state-of-the-art method.
Invariance and identifiability issues for word embeddings

Word embeddings are commonly obtained as optimisers of a criterion function f of a text corpus, but assessed on word-task performance using a different evaluation function g of the test data. We contend that a possible source of disparity in performance on tasks is the incompatibility between classes of transformations that leave f and g invariant. In particular, word embeddings defined by f are not unique; they are defined only up to a class of transformations to which f is invariant, and this class is larger than the class to which g is invariant. One implication of this is that the apparent superiority of one word embedding over another, as measured by word task performance, may largely be a consequence of the arbitrary elements selected from the respective solution sets. We provide a formal treatment of the above identifiability issue, present some numerical examples, and discuss possible resolutions.
Performance comparison of various probability gate assisted binary lightning search algorithm

&lt;div style="’text-align: justify;"&gt;Recently, many new nature-inspired optimization algorithms have been introduced to further enhance the computational intelligence optimization algorithms. Among them, lightning search algorithm(LSA) is a recent heuristic optimization method for resolving continuous problems. It mimics the natural phenomenon of lightning to find out the global optimal solution around the search space. In this paper, a suitable technique to formulate binary version of lightning search algorithm(BLSA) is presented. Three common probability transfer functions, namely, logistic sigmoid, tangent hyperbolic sigmoid and quantum bit rotating gate are investigated to be utilized in the original LSA. The performances of three transfer functions based BLSA is evaluated using various standard functions with different features and the results are compared with other four famous heuristic optimization techniques. The comparative study clearly reveals that tangent hyperbolic transfer function is the most suitable function that can be utilized in the binary version of LSA.&lt;/div&gt;
Video Temporing Detection Assessment in No-Reference Mode using Machine Learning

Information System Security is an integral part of almost all modern communication system.The requirement for ensuring security varies with the application under consideration.Some applications require encryption so that no-one, except the intended receiver, should have the access of the digital data.This form of information hiding comes under cryptography.The encryption can be combined with covert communication in which the encrypted file is embedded using suitable techniques in some other media so that no-one could think of its presence.One the other hand, there are applications in which we need copyright protection.One large class of applications is the one in which some form of watermarking is required so as to later provide a proof of ownership of the document.All the three techniques; viz, Cryptography, Steganography and Watermarking shares common attributes and are related with Information System Security.Despite of the purpose which differentiates Steganography and Watermarking, the tools and techniques used to change the cover media are equally applicable to both these techniques.In this paper, video tempering detection techniques are proposed for detection of spatial and temporal tempering in the video frames of mpeg encoded video.The tempering detection has been investigated under No Reference Mode.In this mode, correlation properties between image segments in adjacent frames and statistical feature set is used to train machine learning classifier for tempering detection and the type and location of tempering.Particular emphasis has been given on MPEG-2 (Moving Picture Expert Group-2) as it is a widely accepted standard.Simulation of the proposed techniques is done using MATLAB and R. Standard Quality metrics are derived and it turns out that the proposed techniques outperform those of the benchmark techniques.
Explicit Planning for Efficient Exploration in Reinforcement Learning

Efficient exploration is crucial to achieving good performance in reinforcement learning. Existing systematic exploration strategies (R-MAX, MBIE, UCRL, etc.), despite being promising theoretically, are essentially greedy strategies that follow some predefined heuristics. When the heuristics do not match the dynamics of Markov decision processes (MDPs) well, an excessive amount of time can be wasted in travelling through already-explored states, lowering the overall efficiency. We argue that explicit planning for exploration can help alleviate such a problem, and propose a Value Iteration for Exploration Cost (VIEC) algorithm which computes the optimal exploration scheme by solving an augmented MDP. We then present a detailed analysis of the exploration behaviour of some popular strategies, showing how these strategies can fail and spend O(n^2 md) or O(n^2 m + nmd) steps to collect sufficient data in some tower-shaped MDPs, while the optimal exploration scheme, which can be obtained by VIEC, only needs O(nmd), where n, m are the numbers of states and actions and d is the data demand. The analysis not only points out the weakness of existing heuristic-based strategies, but also suggests a remarkable potential in explicit planning for exploration.
La questione del dialetto in Giuseppe Lombardo Radice

Since Unification in 1861, the linguistic issue has been in Italy a topic of fundamental importance in the schooling-nationalization process. For a long time refused in school and considered as a “bad” and “dirty” language, it is only since the first decades of the twentieth century that dialect acquires a new dimension in the classroom. Among the main supporters of this new development is Giuseppe Lombardo Radice, author, in particular, of the programmes for elementary school of 1923. The essay reconstructs his thought on the matter and the new elements he introduced in the didactic field.
Determination of Optimal Parameters for Bilateral Filter for Medical Images

Recent camera can take a bright picture even in dark field. The Processor of camera gain signal. However noise is amplified when signal amplified. In this article, we propose an easily technique bilateral filter for noise reduction. The proposed technique can efficient noise reduction in 4 times of filtering and the optimized parameter searching time is drastically shortened.
An Enhanced University Registration Model Using Distributed Database Schema

A big database utilizes the establishing network technology, and it became an emerging trend in the computing field.Therefore, there is a necessity for an optimal and effective data distribution approach to deal with this trend.This research presents the practical perspective of designing and implementing distributed database features.The proposed system has been establishing the satisfying, reliable, scalable, and standardized use of information.Furthermore, the proposed scheme reduces the vast and recurring efforts for designing an individual system for each university, as well as it is effectively participating in solving the course equivalence problem.The empirical finding in this study shows the superiority of the distributed system performance based on the average response time and the average waiting time than the centralized system.The system throughput also overcomes the centralized system because of data distribution and replication.Therefore, the analyzed data shows that the centralized system thrashes when the workload exceeds 60%, while the distributed system becomes thrashes after 81% workload.
A Analysis Of Information Technology Governance In Department of Communication And Informatics of Salatiga Using COBIT 5 Framework DSS Domain

Technological advances and strategic environmental changes encourage the government sector to improve bureaucratic performance and services. Salatiga's Department of Communication and Informatics (Diskominfo Kota Salatiga) is a service that provides communication and informatics, statistics and coding services. However, in carrying out their duties and functions, there are still problems which indicate that the IT governance process is still not optimal, so that there is a need for identification and assessment efforts. This study aims to analyze the maturity level of IT governance using the COBIT 5 framework domain Deliver, Service and Support (DSS). The results of this study indicate that the level of capability of the DSS01, DSS03 and DSS06 processes is at level 2 (Managed process). While the DSS02 and DSS04 processes are at level 1 (Performed process). The recommendations in each process may be an input for Diskominfo in improving the quality of public services and for realizing good governance.
Illuminating musical code: Program an electronic music performance in real time - [Resources_Hands On]

Live coding is a type of performance art in which the performer creates music by programming and reprogramming a synthesizer as the composition plays. The synthesizer code is typically projected onto walls or screens for the audience to inspect as they listen to the unfolding sound. Live coding events are sometimes known as algoraves, and at these events it's common to see visualizations of the evolving music projected alongside the code. Often, these visualizations are created by a second performer manipulating graphics software in tandem with the live coder. After attending a few algoraves in New York City (musically, the results tend to fall along a spectrum from ambient soundscapes to pounding electronic dance music, with a few detours into more experimental domains), I decided to look a little closer at the software the performers were using. I wanted to see if I could come up with my own hardware spin on creating visualizations. While I'm not yet ready to take to the stage, the results have been fun. I'd recommend that any reader interested in music or sound art should try live coding, even if they have no experience playing any traditional musical instrument. The most popular software for live coding appears to be Sonic Pi. This is an open source project originally created by Sam Aaron for the Raspberry Pi, although it is also available for Windows and macOS. Sonic Pi's basic interface is a text editor. Apart from some performance-specific buttons, such as for starting and stopping a piece of music, it looks pretty much like any integrated development environment (IDE), in this case for a version of the Ruby language. Like Python, Ruby is an interpreted language that can run interactively. The Ruby-powered Sonic Pi IDE provides a friendly front end to the powerful SuperCollider sound-synthesis engine, which has been used for over two decades as the basis of many electronic music and acoustic research projects.
Regional mapping of geophysical and geological data in the process of their integrated analysis and interpretation

Summary A joint mapping of two type of data - geophysical gravity data in terms of Total Horizontal Gradient (THG) and geological data for the distribution of known metal ore deposits, occurrences and mineralization in Bulgaria, is presented in a regional scale. For the purpose the whole territory of Bulgaria is divided in 1270 areal elements E of size 10×10 km. The number V1 of metal ore objects, and the length V2 (km) of gravity lineaments (THG, mGal/km), that appear in every single element E, are used as input data. Thus, a space index of coincidence, Qi = [(V1+V2)/√(V1 2+V2 2+C)]i, can be calculated in the areal elements Ei, i=1,2, …, N. The space distribution of this index gives us the integrated geological-geophysical information which can be used for revealing of different characteristics such as common origin, similar tectonic environment, etc.
Measurement Selection for Autonomous Satellite Constellation Navigation Using Parallel Extended Kalman Filters

The autonomous navigation method for a constellation with several Earth satellites and a lunar satellite using inter-satellite range measurements is studied. The presented PEKF algorithm is suitable for autonomous satellite constellation navigation. As the inter-satellite range measurements are susceptible to the exterior disturbance, this paper focuses on the measurement selection problem with noise statistic uncertainty. To select the appropriate measurements adaptively, a parallel extended Kalman filters (PEKF) is presented, where each extended Kalman filter (EKF) is designed to process a subset of the measurements, and the estimation results of the parallel filters are combined on the basis of the individual residual sequences. The derivation of the Cramer-Rao lower bound (CRLB) shows that introducing more measurements could leads to an improvement in the theoretical filtering performance. The performance advantage is illustrated in comparison with the EKF and the traditional multiple-model adaptive estimation (MMAE). An effective PEKF algorithm is developed to deal with the measurement selection problem for an uncertain system, such that the available measurements which have the potential to improve the estimation accuracy are utilized appropriately.
Training Optimization of Feedforward Neural Network for Binary Classification

In this paper, we present a heuristic based technique to reduce the training time of a feedforward neural network by intuiting some of the parameters involved in construction and initialization of the network. These estimated parameters include the number and size of the hidden layers along with the weights related to the neurons. The weights and network architecture is estimated before training by formulating a geometric approximation of the target function we want the network to learn. This specific configuration will allow the network to learn the optimum weights in less iterations than in the case of random initialization of weights.
Real-Time Recognition of Motor Vehicle Whistle with Convolutional Neural Network

This paper proposes a method based on convolutional neural network (CNN) to recognition of motor vehicle whistle, which is used to monitor illegal whistle. The convolutional neural network architecture takes the spectrum as input and infers through the trained convolutional network to determine whether whistled. We achieve a recognition accuracy of 99% on the whistle data collected by China Orient Institute of Noise & Vibration. The convolutional neural network consists of two layers of convolution and two layers of full connections. The time of single inference is less than 3 ms, which can used to monitor the whistle in real time.
Neighbors-Based Graph Construction for Dimensionality Reduction

Dimensionality reduction is a fundamental task in the field of data mining and machine learning. In many scenes, examples in high-dimensional space usually lie on low-dimensional manifolds; thus, learning the low-dimensional embedding is important. Some well-known methods, such as LPP and LE, adopt a locality-preserving strategy by constructing an adjacent graph and using the graph Laplacian to project raw examples into subspace in order to obtain the low-dimensional representation. Accordingly, in this paper, we propose a novel neighbors-based distance that measures the distance of two examples through their neighbors. To be more specific, we create a virtual bridge point from the neighbors of each example and use it to link with others. Instead of computing their direct Euclidean distance, we derive the distance of any two examples using their bridge points. We note that the introduced metric shows a high discriminative ability for the examples on the boundary, which known to be infamously hard examples. Extensive experiments on classification and clustering demonstrate that our proposed graph construction method can achieve a large margin improvement in spite of its simple form.
A Robust 3 Tier DNA blended chaotic framework for Grayscale images

In this digital era, information plays a vital role in ensuring the secured transmission of images over potentially unsecure channels. For the purposes of this study, a particular focus toward image security had been seeded. Millions of images are shared on a daily basis between people all over the world, with the underlying issue being the extent of privacy that channels can offer whilst sharing images, potentially harboring highly sensitized data, very well extending to even military transmissions. As such the first and perhaps the last line of defense is the securing of information itself, in this case pertaining to images. To that effect, a 2-D chaotic map sequence with DNA encoding is proposed. This is to be implemented in a three-layer encryption architecture. Experimental testing had been carried out in order to validate the strength of encryption architecture. Resistance toward typical attacks, including noise attacks, differential attacks and chosen and plain-text attacks demonstrate the potential for an encryption scheme to succeed in providing high security to encrypted images.
Design of rolling ball control system based on OpenMV

Abstract The ball-on-plate system, a non-linear control system with a 2-degree of freedom, is crucial for understanding the control system applications. As increase of the non-linearity with the degree of freedoms, the system can be used to test and identify different aspects of controls. This paper describes the design and implementation of a ball-on-plate system based on OpenMV that includes machine vision modules. Camera OV7725 with chip model of STM32F765 in OpenMV detects and calculates ball position on the plate in real time, and then the OpenMV sends the position data of the ball to the main Micro Controller Unit through a serial port. The MCU calculates the data with a cascade PID controller, and outputs the PWM wave to control the rotation angle of the steering engine. The steering engine drives two vertical parallelogram linkage mechanisms to manipulate the motion of the plate. Thus, the system can exactly control the motion state and position of the ball on the plate in real time.