Reviewerly/exHarmony huggingface dataset:
Polynomial values in affine subspaces of finite fields

We introduce a new approach and obtain new results for the problem of studying polynomial images of affine subspaces of finite fields. We improve and generalise several previously known results, and also extend the range of such results to polynomials of degrees higher than the characteristic of the field. Our approach is based on estimates for a certain new type of exponential sums. The results we obtain have a wide scope of applications similar to those associated with their counterparts studying consecutive intervals over prime fields instead of affine subspaces. Here we give only two immediate consequences: to bounding the size of the intersection of orbits of polynomial dynamical systems with affine subspaces and to the Waring problem over affine subspaces.
A New Approach Toward Invariant Shape Descriptor Tools for Shape Classification Through Morphological Analysis of Image

The study of image classification is based on various aspects. One of the dimension-based image classifications is shape description technique. This region-based multidimensional descriptor acts as a morphometric tool for contour analysis of close convex images. However, in some cases, we may get same information from extrinsic structural feature components of different cluster-based regions as well as different hidden information from several intrinsic structural components of the same cluster region. At the same time, the problem becomes severe if the image undergoes homogeneous transformations, such as translation, rotation and shearing. Analyzing the image object using an invariant shape descriptor may alleviate such problems. In this paper, we introduce a new class of invariant shape descriptor tool known as circularity which works locally and globally throughout the image texture pattern. A comparative study on variant and invariant transformations which work on multiresolution, multilateral and multicluster image texture has also been discussed.
A conjecture on permutation trinomials over finite fields of characteristic two

In this paper, by analyzing the quadratic factors of an $ 11 $-th degree polynomial over the finite field $ {\mathbb F}_{2^n} $, a conjecture on permutation trinomials over $ {\mathbb F}_{2^n}[x] $ proposed very recently by Deng and Zheng is settled, where $ n = 2m $ and $ m $ is a positive integer with $ \gcd(m,5) = 1 $.
Display methods of projection augmented reality based on deep learning pose estimation

In this paper, we propose three display methods for projection-based augmented reality. In spatial augmented reality (SAR), determining where information, objects, or contents are to be displayed is a difficult and important issue. We use deep learning models to estimate user pose and suggest ways to solve the issue based on this data. Finally, each method can be appropriately applied according to various the applications and scenarios.
Periodicity for the Fourier quantum walk on regular graphs

Quantum walks determined by the coin operator on graphs have been intensively studied. The typical examples of coin operator are the Grover and Fourier matrices. The periodicity of the Grover walk is well investigated. However, the corresponding result on the Fourier walk is not known. In this paper, we present a necessary condition for the Fourier walk on regular graphs to have the finite period. As an application of our result, we show that the Fourier walks do not have any finite period for some classes of regular graphs such as complete graphs, cycle graphs with selfloops, and hypercubes.
The algorithm that changed quantum machine learning

A college student discovered a classical computing algorithm that experts overlooked. It promises to change both classical and quantum machine learning.
SSD Forensics: Evidence Generation and Analysis

In this chapter, we forensically examine three different solid state drives (SSDs) from three different manufacturers using three commercial digital forensic tools and one open source forensic tools, and report on the findings of the analysis.
Line Segmentation in Persian Handwritten Documents Based on a Novel Projection Histogram Method

The present paper introduces an efficient algorithm to detect and extract lines in Persian handwritten text images. The proposed approach first calculates the projection profiles (image histogram) of the binary image along with all the rows in the image. Document histograms are noisy functions and there are many spurious local maxima and minima. To simplify measuring the variations in these histograms, a technique based on cubic spline smoothing is applied to smooth the noises. Afterward, by an efficient search of the smoothed histogram, the skew angle (θ) is estimated and the image is de-skewed around the skew angle (θ). Finally, the de-skewed image text is segmented into lines by an efficient linear algorithm with a θ (n). The experimental results in the handwritten Persian database show the superiority of the proposed method in extracting lines in the text images.
Fully Homomorphic Encryption with Table Lookup for Privacy-Preserving Smart Grid

Smart grids are indispensable applications in smart connected communities (SCC). To construct privacy-preserving anomaly detection systems on a smart grid, we adopt fully homomorphic encryption (FHE) to protect users' sensitive data. Although FHE allows a third party to perform calculations on encrypted data without decryption, FHE only supports addition and multiplication on encrypted data. In anomaly detection, we must calculate both harmonic and arithmetic means consisting of logarithms. A naïve implementation of such arithmetic operations with FHE is a bitwise operation; thus, it requires huge computation time. To speed up such calculations, we propose an efficient protocol to evaluate any functions with FHE using a lookup table (LUT). Our protocol allows integer encoding, i.e., a set of integers is encrypted as a single ciphertext, rather than using bitwise encoding. Our experimental results in a multi-threaded environment show that the runtime of our protocol is approximately 51 s when the size of the LUT is 448,000. Our protocol is more practical than the previously proposed bitwise implementation.
Hybrid Control-Based ABR: Towards Low-Delay Live Streaming

Video content providers are increasingly interested in interactive live streaming since user engagement increases their revenues. To provide high quality of experience (QoE), it is critical to design a low delay adaptive bitrate (ABR) algorithm, but which is lacked in existing studies. The low delay constraint poses much more challenges to achieve high bitrate and low rebuffering. For example, low delay requires the player to maintain a small playback buffer, which, however, increases the risk of rebuffering. This work designs a low delay ABR algorithm called HCA which provides good QoE by controlling the buffer occupancy at a low but non-empty level. To achieve accurate control, HCA uses the hybrid of feedback and feedforward control to regulate the buffer dynamic (buffer occupancy and its variation) based on predictions of future network condition (throughput and its variation). Trace-driven experiments show that HCA achieves zero rebuffering for 98% of all traces while ensuring high bitrate.
In a Corrupt Emerging Economy, Who Would Bribe Less and Then Innovate More?

This paper examines in a highly corrupt emerging economic context, how do founder-CEOs’ future orientation affect its firm’s engagement in bribery, which further influences the firm’s innovation. By connecting research on firm bribery and innovation with research on the individual differences in temporal orientation, we submit that despite operating in a corrupt environment where bribery is prevalent, firms with founder-CEOs characterized with a future orientation are more likely to engage in innovation than those with founder-CEOs with an immediate orientation. Yet such effect is largely achieved via CEOs' time orientation in reducing the firms’ engagement in bribery. Furthermore, we posit that this mediation relationship between founder-CEOs’ future orientation, firm bribery and innovation is stronger for firms that face more local market competition. A longitudinal survey-based data from 236 firms in Iran-, as one of the most corrupt economies in the world,- supported our arguments.
Decentralized Multi-authority Anonymous Authentication for Global Identities with Non-interactive Proofs

We propose a decentralized multi-authority anonymous authentication scheme in which a prover and a verifier are non-interactive. When a prover wants authorities to issue private attribute certificates, the authorities simply generate digital signatures on her global identity string. We give two security definitions; resistance against collusion attacks that cause misauthentication, and anonymity for privacy protection. Then we give a concrete construction by employing the structure-preserving signature scheme and the Groth-Sahai non-interactive proof system, the both of which are based on bilinear groups. We give security proofs in the standard model, which reduce to the security of the building blocks.
A Simple, Accurate and Highly Secure Method to Encrypt-Decrypt Digital Images

The digital image may be important and has a secret character, which requires not understanding it when looking at the naked eye or not understanding the contents. So seeking a method of digital image encryption-decryption is a very important task. In this paper we will introduce a new method of digital image encryption-decryption, which will be very simple, highly secure and accurate and highly efficient.
Unsupervised Video Summarization with Independently Recurrent Neural Networks

Video summarization, which is one of the research topics that has gained significant acceleration over the past few years, is producing shorter and more concise videos that can represent the content of large videos as diversely as possible. It is observed that the hyperbolic tangent and the sigmoid action function used in long short-term memory (LSTM) and gated recurrent unit (GRU) models which are used in the recent studies on video summarization may cause gradient decay over layers. Besides, entanglement of neurons on recurrent neural network (RNN) can make it troublesome to interpret and develop these networks. In order to solve these issues, in this study, a method that uses deep reinforcement learning together with independently recurrent neural networks (IndRNN) is proposed for unsupervised video summarization problem. In this way, the model can be trained with more steps and has more layers without having any problem related to gradients. Based on the experiments conducted on two benchmark datasets, it is observed that compared to the state-of-the-art techniques on video summarization, better results are obtained.
Incrementing Adversarial Robustness with Autoencoding for Machine Learning Model Attacks

Nowadays, machine learning is being used widely. There have also been attacks towards machine learning process. In this study, robustness against machine learning model attacks which cause many results such as misclassification, disruption of decision mechanisms and avoidance of filters has been shown by autoencoding and with non-targeted attacks to a model trained with Mnist dataset. In this work, the results and improvements for the most common and important attack method, non-targeted attack are presented.
Functional Classification of Web Pages with Deep Learning

Automatic processing of websites is of great importance for applications such as search engine that extract information from web pages. Search engines use meta tag values when classifying pages of websites. Meta tag names can change for different languages. For example, for login page, entries such as login, login page or giris, giris sayfasi may change from language to language. When the websites are examined, it can be seen that each of the pages created for the same purpose has similar designs. In this study, a deep learning based model was proposed for functional classification of web pages, regardless of language. Transfer learning was used to reduce the cost during the feature extraction process from recorded web page images. Finally, the results of two different experiments are presented for show the effectiveness of the proposed method in the classification of web pages according to their functions.
Impact of Repetitive Text Messages on Retention of Knowledge

Background: Because of frequent changes in health care, nursing requires lifelong learning. Information learned in nursing school is not enough. To deliver quality patient care throughout their career, nurses need to keep up with these changes. Method: This quantitative study explored using repetitive text messaging to improve retention of information to help nurses keep up with the perpetual changes in nursing. Participants attended a class, took an examination, and then were placed randomly in either a control or an experimental group. The experimental group received text messages with key details from the class, whereas the control group did not receive text messages. Participants took the examination again and group pretest–posttest scores were compared. Results: The experimental group showed statistically significant improvement in the final examination scores, whereas the control group showed no significant difference. Conclusion: The results demonstrated that repetitive text messaging can be used to enhance retention of information. [ J Contin Educ Nurs. 2019;50(9):399–403.]
Velocity and transportation ability of vulnerable people during a community tsunami evacuation drill: Outdoor evacuation using a rollator, transport chair, wheelchair, and cart in Shinyo Bokomi, Kobe, Japan

Abstract In this study, a method is established for planning the practical evacuations of residential areas. The emergency workers and caregivers in a community may use various instruments to transport vulnerable people while evacuating during a tsunami to minimize the number of fatalities. An evacuation drill was conducted in Shinyo Bokomi, Kobe, Japan, by simulating the occurrence of a tsunami. During this drill, the evacuation volunteers evacuated vulnerable persons to a safe destination using rollators, transport chairs, wheelchairs, or carts. Furthermore, all evacuation trials were completed before the estimated tsunami impact time. This study quantitatively analyzed the capacity and quantity of the evacuation transportation. The average evacuation transportation capacities of the rollators, transport chairs, wheelchairs, and carts were 3.7 persons/km/h, 5.1 persons/km/h, 5.4 persons/km/h, and 4.6 persons/km/h, respectively. The debriefing meetings following the drills suggested that the written rescue instructions worked effectively in the drill. We recommend that communities incorporate the usage of such instruments into their evacuation plans and implement the same in their tsunami response.
Using a smartphone to investigate classroom acoustics

Abstract We present a proposal for active learning about environmental acoustics and room acoustics for high school and undergraduate students. Students work on the concepts of sound pressure level and reverberation time through a set of activities centred on speech communication in the school. For experimental work, they use smartphone applications for sound measurement. At the end of the work, students would be able to suggest some actions to improve the acoustic comfort in school. The students’ interest increases as they focus on their own experience. Moreover, they can apply and test the solutions that they propose.
Safety Verification for Deep Neural Networks with Provable Guarantees (Invited Paper).

Computing systems are becoming ever more complex, increasingly often incorporating deep learning components. Since deep learning is unstable with respect to adversarial perturbations, there is a need for rigorous software development methodologies that encompass machine learning. This paper describes progress with developing automated verification techniques for deep neural networks to ensure safety and robustness of their decisions with respect to input perturbations. This includes novel algorithms based on feature-guided search, games, global optimisation and Bayesian methods.
An Efficient Crossing-Line Crowd Counting Algorithm with Two-Stage Detection

Crowd counting is a challenging task in crowded scenes due to heavy occlusions, appearance variations and perspective distortions. Current crowd counting methods typically operate on an image patch level with overlaps, then s... | Find, read and cite all the research you need on Tech Science Press
Deploy Chatbots in Your Business

This chapter is dedicated to different business scenarios in which chatbots can be implemented to gain business benefits. In this chapter, we look at various business challenges that can be resolved by implementing chatbots and then examine the benefits of chatbots. Last, we see how chatbots enhance customer experience.
Approximate Feature Collisions in Neural Nets

Work on adversarial examples has shown that neural nets are surprisingly sensitive to adversarially chosen changes of small magnitude. In this paper, we show the opposite: neural nets could be surprisingly insensitive to adversarially chosen changes of large magnitude. We observe that this phenomenon can arise from the intrinsic properties of the ReLU activation function. As a result, two very different examples could share the same feature activation and therefore the same classification decision. We refer to this phenomenon as feature collision and the corresponding examples as colliding examples. We find that colliding examples are quite abundant: we empirically demonstrate the existence of polytopes of approximately colliding examples in the neighbourhood of practically any example.
Book Detection Using Deep Learning

This paper deals with library automation and book detection using learning which comes under field of computer vision [1]. In order to automate a large Library where finding a book is a tough task, this paper shows a solution for it. Here we detect the required book with the help of learning concept through MATLAB Neural network toolbox which can pick the particular book with the help of a mobile manipulator. Convolutional neural network used is Alex Net which can detect around 1000 classes.Deep learning is a part of machine learning in which a network learns how to classify objects from texts, Images, and sound. The first part in the name Deep Learning i.e. deep means number of layers in the network—A network is said to be deeper when it has many layers. Generally neural networks contain 2 or 3 layers, while deeper networks contain hundreds of layers.The device used is a rover with capability of holding a book with its fingers. It also consists of a camera which scans through the books whose output is then analysed by a neural network.
Yarn-Dyed Fabric Image Retrieval Using Colour Moments and the Perceptual Hash Algorithm

Due to the variety of yarn colours and arrangement, it is a challenging problem to retrieve a yarn-dyed fabric image. In this paper, yarn-dyed fabric samples are captured by the DigiEye system first, and then pattern images of the fabric images captured are simulated by pattern design software based on extracted structure parameters of the yarn-dyed fabric. For the simulated pattern image, an effective algorithm is proposed to retrieve these kinds of images by combining the colour moments method and perceptual hash algorithm. Then the pattern images retrieved are mapped back to the yarn-dyed fabric image so as to realise the yarn-dyed fabric image retrieval. In the algorithm proposed, the colour moments method is adopted to extract the colour features, and the perceptual hash algorithm is utilised to calculate the spatial features of the simulated pattern images. Then the two kinds of image features are used to compute the similarity between the input original image and each target image based on the Euclidean distance and Hamming distance. Relevant images can be retrieved in dependence on the similarity value, which is determined by calculating the optimum weighted value of the colour features’ similarity and spatial features’ similarity. In order to measure the retrieval efficiency of the method proposed, the accuracy rate and retrieval rate of image retrieval were computed in experiments using a PATTERN image database with 300 images. The experimental results show that the average accuracy rate of the method proposed is 85.30% and the retrieval rate - 53.51% when the weighted value of the colour feature similarity is fixed at 0.45 and the spatial feature similarity is 0.55. It is shown that the method presented is effective to retrieve pattern images of yarn-dyed fabric.
Workflow Variability for Autonomic IoT Systems

Autonomic IoT systems require variable behaviour at runtime to adapt to different system contexts. Building suitable models that span both design-time and runtime is thus essential for such systems. However, existing approaches separate the variability model from the behavioural model, leading to synchronization issues such as the need for dynamic reconfiguration and dependency management. Some approaches define a fixed number of behaviour variants and are therefore unsuitable for highly variable contexts. This paper extends the semantics of the DX-MAN service model so as to combine variability with behaviour. The model allows the design of composite services that define an infinite number of workflow variants which can be chosen at runtime without any reconfiguration mechanism. We describe the autonomic capabilities of our model by using a case study in the domain of smart homes.
Robust RLS Wiener State Estimators In Linear Discrete-Time Stochastic Systems With Uncertain Parameters

This paper proposes the robust estimation technique for the signal and the state variables with respect to the state-space model having the general types of system matrices in linear discrete-time stochastic systems with the uncertain parameters. It is assumed that the signal and degraded signal processes are fitted to the finite order autoregressive (AR) models. By fitting the signal process to the AR model, the system matrix for the signal is transformed into the controllable canonical form. By using the system matrix, the existing robust RLS Wiener filter and fixed-point smoother are adopted to estimate the signal. Concerning the state estimation, the existing robust RLS Wiener filter calculates the filtering estimate of the signal. By replacing the observed value with the robust filtering estimate of the signal in the existing RLS Wiener filtering and fixed-point smoothing algorithms, the robust filtering and fixed-point smoothing estimates of the signal and the state variables are calculated.The simulation result shows the superior estimation characteristics of the proposed robust estimation technique for the signal and the state variables in comparison with the existing  RLS Wiener filter, the robust Kalman filter, and the existing robust RLS Wiener filter and fixed-point smoother.