Reviewerly/exHarmony huggingface dataset:
What Syntactic Structures block Dependencies in RNN Language Models

Recurrent Neural Networks (RNNs) trained on a language modeling task have been shown to acquire a number of non-local grammatical dependencies with some success. Here, we provide new evidence that RNN language models are sensitive to hierarchical syntactic structure by investigating the filler--gap dependency and constraints on it, known as syntactic islands. Previous work is inconclusive about whether RNNs learn to attenuate their expectations for gaps in island constructions in particular or in any sufficiently complex syntactic environment. This paper gives new evidence for the former by providing control studies that have been lacking so far. We demonstrate that two state-of-the-art RNN models are are able to maintain the filler--gap dependency through unbounded sentential embeddings and are also sensitive to the hierarchical relationship between the filler and the gap. Next, we demonstrate that the models are able to maintain possessive pronoun gender expectations through island constructions---this control case rules out the possibility that island constructions block all information flow in these networks. We also evaluate three untested islands constraints: coordination islands, left branch islands, and sentential subject islands. Models are able to learn left branch islands and learn coordination islands gradiently, but fail to learn sentential subject islands. Through these controls and new tests, we provide evidence that model behavior is due to finer-grained expectations than gross syntactic complexity, but also that the models are conspicuously un-humanlike in some of their performance characteristics.
Speech Encryption on FPGA Using a Chaotic Generator and S-Box Table

In this paper, we proposed a new technique for designing a dynamic S-box depended on the idea of DNA module and Chaotic system to increase its security. Lorenz chaotic generator is utilized as the chaos part of the proposed design. This design is Tested on the Field Programmable Gate Array (FPGA) for the use of offline speech encryption and decryption in real time. The experimental results are presented on the oscilloscope. The security of the system is also validated through tests on Matlab.
Hybrid Unified Deep Learning Network for Highly Precise Gleason Grading of Prostate Cancer

Prostate cancer is one of the leading causes of death around the world. The manual Gleason grading of prostate cancer after histological analysis of stained tissue slides is rigorous, time-consuming and also suffers from subjectivity among experts. Image-based computer-assisted diagnosis can serve pathologists to efficiently diagnose cancer in early stages. We have proposed a Hybrid Unified Deep Learning Architecture to grade the prostate cancer accurately and quickly. For the feature analysis technique, we have implemented the shearlet transform in addition to original RGB images. We have introduced saliency maps of images using a Deep Convolutional Generative Adversarial Network (DCGAN) by applying semantic segmentation technique with the salient maps provided by pathology experts. Our proposed architecture is a combination of Convolutional Neural Netowork (CNN), Recurrent Neural Netowrk (RNN) and fine-tuned VGGnet. We have introduced a novel approach of utilizing LSTM-RNN for the sequential subband images of the shearlet coefficients. Our hybrid framework is a computationally high-cost architecture to train but proved to be highly accurate and faster in the testing phase. With our approach, we have achieved an accuracy of 0.98 ± 0.02 for Gleason grading of prostate cancer on the dataset provided by Jafari-Khouzani and Soltanian-Zadeh which is used in successive research work.
Argumentation-Based Reasoning about Plans, Maintenance Goals, and Norms

In a normative environment, an agent’s actions are directed not only by its goals but also by the norms activated by its actions and those of other actors. The potential for conflict between agent goals and norms makes decision making challenging, in that it requires looking ahead to consider the longer-term consequences of which goal to satisfy or which norm to comply with in face of conflict. We therefore seek to determine the actions an agent should select at each point in time, taking account of its temporal goals, norms, and their conflicts. We propose a solution in which a normative planning problem is the basis for practical reasoning based on argumentation. Various types of conflict within goals, within norms, and between goals and norms are identified based on temporal properties of these entities. The properties of the best plan(s) with respect to goal achievement and norm compliance are mapped to arguments, followed by mapping their conflicts to attack between arguments, all of which are used to identify why a plan is justified.
A forensic examination of web browser privacy-modes

Private browsing facilities are part of many mainstream Internet browsing applications and arguably, there is now more awareness of their function and purpose by the average Internet user. As a result the potential for those engaging in malicious and/or illegal browsing behaviours, to do so in a ‘privatised’ way is increased. Many private browsing modes are designed to be ‘locally private’, preventing data denoting a user’s browsing actions from being stored on their device. Such actions, potentially compromise the availability of any evidential data, provide an investigatory headache. This work documents the examination of 30 web browsers to determine the presence of a ‘private mode’, and where available, the ‘privateness’ of said mode. Our test methodology is documented and results and limitations described for the purpose of open, transparent scrutiny and evaluation from those operating in this area.
Breaking an image encryption scheme based on Arnold map and Lucas series

Recently, a novel image encryption based on Arnold scrambling and Lucas series has been proposed in the literature. The scheme design is based on permutation-substitution operations, where Arnold map is used to permute pixels of the image for T rounds, and Lucas sequence is used to mask the image and substitute pixel's values. The authors of the cryptosystem have already tested their system against several statistical tests to show its robustness from a statistical view. This cryptanalytic paper scrutinizes the security of this cryptosystem in order to evaluate its immunity against conventional attacks. The results and methods adopted in this paper show that besides the statistical tests, we may need a deep cryptanalysis of every newly proposed cryptosystem before possible adoption in real applications. Those security results should be considered in further proposals.
Can We Trust Autonomous Systems? Boundaries and Risks

Can we trust autonomous systems? This question arises urgently with the perspective of massive use of AI-enabled techniques in autonomous systems, critical systems intended to replace humans in complex organizations. We propose a framework for tackling this question and bringing reasoned and principled answers. First, we discuss a classification of different types of knowledge according to their truthfulness and generality. We show basic differences and similarities between knowledge produced and managed by humans and computers, respectively. In particular, we discuss how differences in the system development process of knowledge affect its truthfulness. To determine whether we can trust a system to perform a given task, we study the interplay between two main factors: (1) the degree of trustworthiness achievable by a system performing the task; and (2) the degree of criticality of the task. Simple automated systems can be trusted if their trustworthiness can match the desired degree of criticality. Nonetheless, the acceptance of autonomous systems to perform complex critical tasks will additionally depend on their ability to exhibit symbiotic behavior and allow harmonious collaboration with human operators. We discuss how objective and subjective factors determine the balance in the division of work between autonomous systems and human operators. We conclude emphasizing that the role of autonomous systems will depend on decisions about when we can trust them and when we cannot. Making these choices wisely goes hand in hand with compliance with principles promulgated by policy-makers and regulators rooted both in ethical and technical criteria.
Linguistic Versus Latent Relations for Modeling Coherent Flow in Paragraphs

Generating a long, coherent text such as a paragraph requires a high-level control of different levels of relations between sentences (e.g., tense, coreference).We call such a logical connection between sentences as a (paragraph) flow.In order to produce a coherent flow of text, we explore two forms of intersentential relations in a paragraph: one is a human-created linguistical relation that forms a structure (e.g., discourse tree) and the other is a relation from latent representation learned from the sentences themselves.Our two proposed models incorporate each form of relations into document-level language models: the former is a supervised model that jointly learns a language model as well as discourse relation prediction, and the latter is an unsupervised model that is hierarchically conditioned by a recurrent neural network (RNN) over the latent information.Our proposed models with both forms of relations outperform the baselines in partially conditioned paragraph generation task.Our codes and data are publicly available 1 .
A Robust Face Recognition System for One Sample Problem

Most of the practical applications have limited number of image samples of individuals for face verification and recognition process such as passport, driving licenses, photo ID etc. So use of computer system becomes challenging task, when image samples available per person for training and testing of system are limited. We are proposing a robust face recognition system based on Tetrolet, Local Directional Pattern (LDP) and Cat Swam Optimization (CSO) to solve this problem. Initially, the input image is pre-processed to extract region of interest using filtering method. This image is then given to the proposed descriptor, namely Tetrolet-LDP to extract the features of the image. The features are subjected to classification using the proposed classification module, called Cat Swarm Optimization based 2-Dimensional Hidden Markov Model (CSO-based 2DHMM) in which the CSO trains the 2D-HMM. The performance is analyzed using the metrics, such as accuracy, False Rejection Rate (FRR), & False Acceptance Rate (FAR) and the system achieves high accuracy of 99.65%, and less FRR and FAR of 0.0033 and 0.003 for training percentage variation and 99.65%, 0.0035 and 0.004 for k-Fold Validation.
Construction of marine ship automatic identification system data mining platform based on big data

In order to process massive Marine Ship Automatic Identification System (AIS) data in real time and extract the required information from it used for subsequent data mining and rule extraction, based on big data background, cloud computing and distributed technology are applied, and combined with d ata mining methods, the massive AIS data mining platform is designed and realized, and the ship trajectory is analyzed with big data. The research results show that the cloud computing distributed technology AIS data mining platform studied is feasible, which has practical value and significance.
Using AutoEncoders for Radio Signal Denoising

We investigated the use of a Deep Learning approach to radio signal de-noising. This data-driven approach has does not require explicit use of expert knowledge to set up the parameters of the denoising procedure and grants great flexibility across many channel conditions. The core component used in this work is a Convolutional De-noising AutoEncoder, known to be very effective in image processing. The key of our approach consists in transforming the radio signal into a representation suitable to the CDAE: we transform the time-domain signal into a 2D signal using the Short Time Fourier Transform. We report about the performance of the approach in preamble denoising across protocols of the IEEE 802.11 family, studied using simulation data. This approach could be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A perspective advantage of using the AutoEncoders in that pipeline is that they can be co-trained with the downstream classifier, to optimize the classification accuracy.
Large-Scale Automatic K-Means Clustering for Heterogeneous Many-Core Supercomputer

This article presents an automatic k-means clustering solution targeting the Sunway TaihuLight supercomputer. We first introduce a multilevel parallel partition approach that not only partitions by dataflow and centroid, but also by dimension, which unlocks the potential of the hierarchical parallelism in the heterogeneous many-core processor and the system architecture of the supercomputer. The parallel design is able to process large-scale clustering problems with up to 196,608 dimensions and over 160,000 targeting centroids, while maintaining high performance and high scalability. Furthermore, we propose an automatic hyper-parameter determination process for k-means clustering, by automatically generating and executing the clustering tasks with a set of candidate hyper-parameter, and then determining the optimal hyper-parameter using a proposed evaluation method. The proposed autoclustering solution can not only achieve high performance and scalability for problems with massive high-dimensional data, but also support clustering without sufficient prior knowledge for the number of targeted clusters, which can potentially increase the scope of k-means algorithm to new application areas.
Laser triangulation measurement model based on least square polynomial fitting method

激光三角法具有非接触测量、测量范围大、相对测量精度高、结构简单、环境适应性强等多种优点, 得到了广泛应用。但是三角测量的理论公式具有非线性特征, 而且光学结构参数(<i>a</i>、<i>b</i>、<i>θ</i>)等在现实工程中具有不可测性。研究了三角测量中数学模型的建立方法, 选用多项式展开方法建立数学模型。通过应用最小二乘法拟合多项式的方法求解模型系数, 提出了根据最大相对拟合残差要求、结合相关系数用于控制拟合多项式阶数的评价方法, 并通过实际光学系统验证了该方法的可行性, 达到了0.01%的相对误差。最小二乘法拟合多项式的方法对于激光三角位移传感器的标定和系统误差消除具有实际的指导意义。
Fast template matching method in white-light scanning interferometry for 3D micro-profile measurement

White-light scanning interferometry (WLSI) is an important measurement technique that has been widely used in three-dimensional profile reconstruction. Because of the effects of environmental noise and phase changes caused by surface reflection, existing WLSI algorithms have problems in measurement accuracy and measurement speed. Addressing these problems, this paper proposes a fast template matching method to determine precisely the zero optical path difference (ZOPD) position in the WLSI. Due to the uniform shape of the interference signals, a template interference signal can be obtained in advance by performing a least-square fitting or Fourier interpolation on an interference signal of one pixel. In the method, the ZOPD position is initially obtained by the centroid method. Then, the ZOPD position is determined by a precise matching process through moving the template interference signal on the measured interference signal. Through the two-step processes, the ZOPD position can be obtained precisely with much less time. The method was simulated and verified through the measurement of a spherical surface, a 1.8-µm-height standard step and a flip-chip substrate. The experimental results show that the proposed algorithm can achieve both high precision and fast measurement.
Structured Object-Aware Physics Prediction for Video Modeling and Planning

When humans observe a physical system, they can easily locate components, understand their interactions, and anticipate future behavior, even in settings with complicated and previously unseen interactions. For computers, however, learning such models from videos in an unsupervised fashion is an unsolved research problem. In this paper, we present STOVE, a novel state-space model for videos, which explicitly reasons about objects and their positions, velocities, and interactions. It is constructed by combining an image model and a dynamics model in compositional manner and improves on previous work by reusing the dynamics model for inference, accelerating and regularizing training. STOVE predicts videos with convincing physical behavior over hundreds of timesteps, outperforms previous unsupervised models, and even approaches the performance of supervised baselines. We further demonstrate the strength of our model as a simulator for sample efficient model-based control, in a task with heavily interacting objects.
Perceptual Image Quality Assessment for Various Viewing Conditions and Display Systems

From complete darkness to direct sunlight, real-world displays operate in various viewing conditions often resulting in a non-optimal viewing experience. Most existing Image Quality Assessment (IQA) methods, however, assume ideal environments and displays, and thus cannot be used when viewing conditions differ from the standard. In this paper, we investigate the influence of ambient illumination level and display luminance on human perception of image quality. We conduct a psychophysical study to collect a novel dataset of over 10000 image quality preference judgments performed in illumination conditions ranging from 0 lux to 20000 lux. We also propose a perceptual IQA framework that allows most existing image quality metrics (IQM) to accurately predict image quality for a wide range of illumination conditions and display parameters. Our analysis demonstrates strong correlation between human IQA and the predictions of our proposed framework combined with multiple prominent IQMs and across a wide range of luminance values.
How Can The Virtual Reality Help In Implementation Of The Smart City?

To meet the challenges of the city of tomorrow: efficient service, transport and intelligent mobility, sustainable environment, responsible urbanization and intelligent housing. This has triggered the use of technology as a solution to all these issues and to address them in a smarter way. New technologies, in particular, virtual reality (VR), can play a decisive role in developing different aspects that contribute to making smart sustainable cities. VR is first mentioned as a new paradigm of interaction in which the user is an actor actively participating in the construction of three-dimensional virtual world. A discussion is proposed with particular regard to the characteristics, requirements, and devices of VR. The reconstruction of the real environment in the virtual is then discussed through the aspects of immersion and interaction. The conclusion evokes perspectives for research in the field.
Ontology‐lexicon–based question answering over linked data

Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD-5 benchmark and exhibits promising results.
A Brief Survey on Contemporary Methods for Anomaly Detection in Videos

Anomaly detection has stayed among the core topics of research in machine learning and computer vision for decades. Due to its complex nature, limitless possibilities of abnormal cases and the bounded amount of normal examples available, it is still among the popular topics of research. With the increase in surveillance cameras, it is becoming important to have systems which can learn from the vastly available normal data to identify any unusual happenings in the videos. In this paper, an attempt has been made to evaluate and compare the recently introduced such systems. In order to evaluate the methods for implementation as well as their possible applications, we have classified the major papers introduced in last 5 years by the network architectures and the datasets that are used to evaluate these methodologies.
Developing a quantitative mudrock calibration for a handheld energy dispersive X-ray fluorescence spectrometer

Handheld energy dispersive X-ray fluorescence (hhEDXRF) spectrometers enable rapid and non-destructive means of quantifying the elemental composition of rocks. Advances in hhEDXRF technology have resulted in their widespread usage in the geological sciences. Currently, most popular hhEDXRFs lack lithology-specific calibrations and in turn acquire datasets with high precision but in many cases with unknown or low accuracy. This study derives a methodology to create an internal regression-based mudrock calibration for a hhEDXRF analyzer, with the aim of increasing the analytical quality of elemental data collected from core and outcrop samples. For this study we utilize 64 compositionally diverse Middle Devonian mudrock samples from four cored locations within the central Appalachian basin. Samples were analyzed on both a hhEDXRF and a quantitatively superior bench-top wavelength-dispersive XRF (WDXRF) spectrometer. Utilizing linear regressions, hhEDXRF values were calibrated to the values of the WDXRF for individual elements. Measurements from the two XRF modes record the following coefficients of determination: Zn, Mo, and Ca (r2 > 0.99), Al, K, Ti, Fe, Ni, V, Cu, and Pb (r2 > 0.92), Zr and Si (r2 > 0.78), and Cr (r2 = 0.56). To assess precision, relative standard deviations (RSD) were calculated for each element based on repeated measurements collected on internal reference samples. All elements recorded RSD values of <8.8%, excluding Cr, which recorded values of 14–25%. Magnesium (Mg), Cl, and Ag were below the limit of detection by the hhEDXRF and thus RSD values could not be computed. To assess hhEDXRF accuracy, we propose a novel “iterative blind test” approach, which quantifies accuracy through deriving the percentage difference between the calibrated hhEDXRF value, derived from the blind test, and WDXRF values for each element. Si, Ti, Al, Fe, Ni, Cu, Zn, and Zr record percentage difference values of <10%. For Mn, Ca, Cr, V, Mo and Pb, a minority of samples (<13% on average) record concentrations below their reliable detection limit (RDL). Excluding measurements below the RDL, the elements record percent difference values between 6 and 15%. Potassium yielded measurements outside of the RDL and was the only element that returned percentage difference values of >20%, outside of the acceptable limit. The internal mudrock calibration creates high analytical quality datasets for more robust analyses when compared to previous approaches.
Cloud Task Scheduling Algorithm Based on Three Queues and Dynamic Priority

In view of the current situation that the heterogeneity of cloud computing cluster is increasing and the load difference of system is obvious, this paper proposes a cloud task scheduling TQ (Three Queues) algorithm based on three queues and dynamic priority. TQ algorithm first puts jobs into the waiting queue according to the priority of jobs, and then divides jobs into job types according to the data input amount of Map phase, data output amount of Map phase, total number of current node running tasks, completion time of Map tasks and disk I/O rate. Jobs are put into corresponding queues to improve hardware utilization. The experimental results show that the algorithm can effectively improve the performance of cloud task scheduling under the coexistence of I/O-intensive jobs and CPU-intensive jobs, and shorten the completion time of the total task.