Reviewerly/exHarmony huggingface dataset:
Real Versus Fake 4k - Authentic Resolution Assessment

In recent years, the native 4K/Ultra High Definition (UHD) resolution has been trending towards the new normal of video content creation and distribution, but the practical pipelines of video acquisition, production and delivery often involve downscaling stages where the spatial resolution drops below the 4K level. Even though the video may be upscaled back to 4K/UHD resolution later, the content has lost its authentic resolution. This work aims at authentic resolution assessment (ARA). We first construct a database of over 10,000 real and fake 4K/UHD images. We then develop a two-stage ARA (TSARA) approach that classifies a video frame to have real or fake 4K resolution, where the first stage classifies local patches using a convolutional neural network (CNN), and the second stage aggregates local assessment into a global image level decision using logistical regression. Experimental results show that the proposed approach achieves high accuracy at low computational cost, and outperforms state-of-the-art no-reference (NR) image quality assessment (IQA) and image sharpness assessment (ISA) models. The built database and the proposed method are made publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Gaussian Kernelized Self-Attention for Long Sequence Data and its Application to CTC-Based Speech Recognition

ISelf-attention (SA) based models have recently achieved significant performance improvements in hybrid and end-to-end automatic speech recognition (ASR) systems owing to their flexible context modeling capability. However, it is also known that the accuracy degrades when applying SA to long sequence data. This is mainly due to the length mismatch between the inference and training data because the training data are usually divided into short segments for efficient training. To mitigate this mismatch, we propose a new architecture, which is a variant of the Gaussian kernel, which itself is a shift-invariant kernel. First, we mathematically demonstrate that self-attention with shared weight parameters for queries and keys is equivalent to a normalized kernel function. By replacing this kernel function with the proposed Gaussian kernel, the architecture becomes completely shift-invariant with the relative position information embedded using a frame indexing technique. The proposed Gaussian kernelized SA was applied to connectionist temporal classification (CTC) based ASR. An experimental evaluation with the Corpus of Spontaneous Japanese (CSJ) and TEDLIUM 3 benchmarks shows that the proposed SA achieves a significant improvement in accuracy (e.g., from 24.0% WER to 6.0% in CSJ) in long sequence data without any windowing techniques.
The effect of edge sharpness on phase-step diffractometry

Fresnel diffractometry using a phase step, comparable to interferometry, has led to several high-precision metrological applications. Either in theoretical investigations or in the applications, the phase step is considered to be sharp. However, practically, fabrication of such elements is impossible and a level of bluntness is unavoidable. In this paper, we address the effect of edge sharpness on phase-step diffractometry. We show, theoretically and with simulations, that if the conjunction length is less than approximately 10% of the phase-step height, the bluntness does not alter the visibility of the diffraction patterns. Therefore, since the measurements in phase step diffractometry is mostly performed by calculating the visibility, this level of bluntness is allowed without considerable effect on the measurements. We corroborate the simulations with experiments.
Smart Contract Security: A Practitioners' Perspective: The Artifact of a Paper Accepted in the 43rd IEEE/ACM International Conference on Software Engineering (ICSE 2021)

Smart contracts have been plagued by security incidents, which resulted in substantial financial losses. Given numerous research efforts in addressing the security issues of smart contracts, we wondered how software practitioners build security into smart contracts in practice. We performed a mixture of qualitative and quantitative studies with 13 interviewees and 156 survey respondents from 35 countries across six continents to understand practitioners' perceptions and practices on smart contract security. Our study uncovers practitioners' motivations and deterrents of smart contract security, as well as how security efforts and strategies fit into the development lifecycle. We also find that blockchain platforms have astatistically significant impact on practitioners' security perceptions and practices of smart contract development. Based on our findings, we highlight future research directions and provide recommendations for practitioners.
Hardware Implementation For Fast Block Generator Of Litecoin Blockchain System

The development of blockchain-based cryptocurren-cies has attracted a lot of interest in many fields, especially in academic research. Besides Bitcoin, Litecoin is one of the most popular cryptocurrency based on blockchain currently. The Litecoin mining process uses the Scrypt algorithm as a hash function in Proof-of-Work (PoW) consensus mechanism. The yield of cryptocurrency mining depends a lot on the processing performance of the hardware. Therefore, this paper proposes a hardware design of the Scrypt algorithm in the Litecoin mining system using the Verilog hardware description language (HDL). In the proposed design, the pipeline technique is applied at two levels: the overall Scrypt level and the PBKDF2 level. Additionally, the design is divided into several stages to reduce the latency of each clock cycle and increase the operating frequency. The simulation and verification of the design are performed based on Atlys Spartan-6 FPGA Board and Xilinx Virtex-7 FPGA VC707. The performance of the design evaluated on Atlys Spartan-6 has the hash rate of 2209 hashes/s. The maximum operating frequency of the system is 147.059MHz. On Xilinx Virtex-7, the hash rate of the design reaches 5332 hashes/s and the maximum operating frequency reaches 355.051 MHz.
Multi-Entity Collaborative Relation Extraction

Multi-entity collaborative relationship extraction is an important but challenging task, which has been attracting a lot of interest and poses significant issues in front of systems aimed at natural language understanding. Instead of designing specific models for single relationship extraction tasks, this paper aims to propose a general framework to extract multiple relations among multiple entities in unstructured text by taking advantage of existing models. Based on performing named entity recognition and relation extraction collaboratively, the framework exploits correlations and information propagation among words and relations in a graph network to grasp fundamental features for final classification. The experimental results on two real-world datasets demonstrate that our framework has remarkable applicability and generalizability, and consistently outperforms the strong competitors by a noticeable margin for multi-entity relation extraction.
Selective Combination based on Diversity-Accuracy Balance in Outlier Ensembles

In the outlier detection task, unsupervised detection methods have been widely applied due to the absence of ground truth. Ensemble method is an emerging topic that has been studied recently and applied to more and more scenarios. The main challenges in this field include the selection of ensemble members and the combination of them to get final results. Most of the existing outlier ensemble methods combine all the underlying detectors without considering selection for the most appropriate ones, which may adversely affect the final result since some base detectors that are not proficient at identifying all outlier instances may be combined. Therefore, in this paper, we propose a new approach for outlier detection by taking the balance between diversity and accuracy in unsupervised ensembles into account. Specifically, clustering algorithm is adopted to induce the diversity of ensemble, and the most competent detector in each cluster is picked to improve the accuracy of ensemble, then all these selected detectors are combined to get final result. Our proposed approach is experimentally compared with other nine popular methods in the literature on fifteen real-world datasets, and the results demonstrate that the proposed method outperforms these compared methods on the majority of datasets. Moreover, our approach is also applied in a practical case for data quality control of breast cancer community cohort, and its effectiveness is further validated.
Generative Adversarial Network with Masking Bits Based Image Augmentation Technique for Nuclei Image Classification

In the domain of medical image analytics, especially in nuclei cell image analysis, the major challenges associated with it is the availability of smaller amount of interpreted samples of images and the inability to construct a machine learning model using deep neural network. The classification performance of the deep neural network model trained with smaller number of images is very low. To improve the classification performance of the model, the dataset size is increased by using the Generative Adversarial Network (GAN) with the integration of masking bits. It generates the images that remain more similar to the original nuclei cell images present in the dataset with various logical operations along with masking bits. Moreover, GAN contains two layers such as generator layer and the discriminator layer. The generator layer takes the image as an input and produces the images that remain most comparable to the unique Nuclei images. The discriminator layer takes input as original data and the generated Nuclei cell images from the generator to predict whether the generator nuclei cell images belong to original dataset or not. Finally, the classification performance of the machine learning model is measured by using the convolution neural network (CNN) architecture.
An Improved News Recommendation Algorithm Based on Text Similarity

With the advent of the data age, the public has been facing the problem of information overload. Recommendation algorithms are an effective way to solve this problem. At present, a large number of recommended algorithms adopt the following two ideas: content-based text similarity algorithm and user-based collaborative filtering algorithm. Researchers have developed a distributed collaborative recommendation protocol based on blockchain. However, these algorithms ignore the characteristics of the news industry itself. Just adopting the above ideas will inevitably lead to many internet public opinion problems. Therefore, this paper proposes an improved N-TF-IDF algorithm, which is more suitable for the news industry, and can control the outbreak of negative public opinion, and has a positive effect on stabilizing internet public opinion. Through the verification of the experimental data set, the algorithm is superior to the traditional information retrieval and text mining technology TF-IDF in both the time dimension and the emotional dimension, and this algorithm is not affected by citizens' privacy rights.
Noise resistance in communication: Quantifying uniformity and optimality

Over the past few decades, several lines of research have investigated how information is distributed in language. In particular, research has suggested that speakers distribute the information in linguistic utterances as evenly as possible, in order to make the utterance more robust against noise for the hearer. Several studies have provided evidence for this hypothesis in limited linguistic domains, showing that speakers manipulate acoustic and syntactic features by using redundancy to avoid drastic spikes or troughs in information content. However, this previous work doesn't consider information density across entire utterances, and only rarely has the proposed function of noise resistance been directly explored in detail. Here, we introduce a new descriptive statistic (DORM) that quantifies the uniformity of information across an entire utterance. We present this alongside an algorithm (UIDO) that allows us to quantify the contribution made to an utterance's uniformity by the order of its elements, independent of its level of redundancy. Using a simple simulation with data from an English corpus, we show that utterances whose order results in more uniform distributions of information are, in fact, more robust against noise.
Evaluating Initialization of Nelder-Mead Method for Hyperparameter Optimization in Deep Learning

In deep learning, hyperparameters can severely affect the learning model performance. The Nelder-Mead (NM) method is known for showing a superior performance for hyperparameter optimization in deep learning. An initial simplex, one of the initial NM method's values, is usually determined randomly while the search performance strongly depends on the shape of the initial simplex. Therefore, it is necessary to determine a proper initial simplex as previous researchers have proposed methods to construct an initial simplex from one starting point in the bounded search space. In this study, we verified how these methods for constructing an initial simplex contribute to improving the result of hyperparameter optimization in deep learning, by using a simple model and a complicated model. A smaller initial simplex may fail to optimization by bad local minima because there are some bad local minima in both learning models. We concluded that the starting point is not necessarily located close to the origin, and that a larger initial simplex contributes to improving the result of hyperparameter optimization in deep learning.
Long-step path-following algorithm for quantum information theory: Some numerical aspects and applications

&lt;p style='text-indent:20px;'&gt;We consider some important computational aspects of the long-step path-following algorithm developed in our previous work and show that a broad class of complicated optimization problems arising in quantum information theory can be solved using this approach. In particular, we consider one difficult optimization problem involving the quantum relative entropy in quantum key distribution and show that our method can solve problems of this type much faster in comparison with (very few) available options.&lt;/p&gt;
Semantic-Guided Inpainting Network for Complex Urban Scenes Manipulation

Manipulating images of complex scenes to reconstruct, insert and/or remove specific object instances is a challenging task. Complex scenes contain multiple semantics and objects, which are frequently cluttered or ambiguous, thus hampering the performance of inpainting models. Conventional techniques often rely on structural information such as object contours in multi-stage approaches that generate unreliable results and boundaries. In this work, we propose a novel deep learning model to alter a complex urban scene by removing a user-specified portion of the image and coherently inserting a new object (e.g. car or pedestrian) in that scene. Inspired by recent works on image inpainting, our proposed method leverages the semantic segmentation to model the content and structure of the image, and learn the best shape and location of the object to insert. To generate reliable results, we design a new decoder block that combines the semantic segmentation and generation task to guide better the generation of new objects and scenes, which have to be semantically consistent with the image. Our experiments, conducted on two large-scale datasets of urban scenes (Cityscapes and Indian Driving), show that our proposed approach successfully address the problem of semantically-guided inpainting of complex urban scene.
Securing of Cloud Data with Duplex Data Encryption Algorithm

Data Outsourcing and Data Backups is controlled solely by means of external agencies so as to decrease the cost for information administration. Data Protection is the major challenge in public clouds where there is no proper security mechanism followed, so to access data or store data in cloud, the administrator first checks the authentication of user. Proposed system provides Cryptographic tool by utilizing a combination of RSA, AES algorithm and set of rules to accomplish a secure sharing of information. Our convention is based totally upon an association of cryptographic key additives which is exempt from any external cloud administrations.
Exclusive lasso-based k-nearest-neighbor classification

Conventionally, the k nearest-neighbor (kNN) classification is implemented with the use of the Euclidean distance-based measures, which are mainly the one-to-one similarity relationships such as to lose the connections between different samples. As a strategy to alleviate this issue, the coefficients coded by sparse representation have played a role of similarity gauger for nearest-neighbor classification as well. Although SR coefficients enjoy remarkable discrimination nature as a one-to-many relationship, it carries out variable selection at the individual level so that possible inherent group structure is ignored. In order to make the most of information implied in the group structure, this paper employs the exclusive lasso strategy to perform the similarity evaluation in two novel nearest-neighbor classification methods. Experimental results on both benchmark data sets and the face recognition problem demonstrate that the EL-based kNN method outperforms certain state-of-the-art classification techniques and existing representation-based nearest-neighbor approaches, in terms of both the size of feature reduction and the classification accuracy.
Study on the sensitivities and damage mechanisms of ultra-low permeability sandstone reservoirs: taking Chang 6 reservoir in Jingbian oilfield as an example

This paper takes the ultra-low permeability sandstone reservoir of Jingbian oilfield in Ordos Basin as the research object, analyzes the petrological characteristics, diagenesis, physical characteristics and pore structure characteristics of the reservoir, and carries out reservoir sensitivity evaluation by using rock casting thin sections, X-ray diffraction, and sensitive flow experiments. The research results show that the ultra-low permeability Chang 6 sandstone reservoir has weak velocity sensitivity, medium-weak water sensitivity, weak salt sensitivity, weak alkali sensitivity and strong acid sensitivity; the damage mechanism of reservoir sensitivity mainly depends on the composition of clay minerals and pore structure after diagenesis. The clay mineral content from high to low is chlorite, illite, a small amount of illite / smectite layer, and kaolinite, of which the chlorite content is as high as 75 %; the reservoir has poor physical properties, the types of small hole-thin throat and small hole-fine throat. The reservoir is prone to blockage such as bridge plugging. Therefore, ultra-low permeability sandstone reservoirs are prone to different degrees of sensitivity. The reservoir characteristics are consistent with the reservoir sensitivity evaluation results.
Logic Synthesis of Binarized Neural Networks for Efficient Circuit Implementation

Neural networks (NNs) are key to deep learning systems. Their efficient hardware implementation is crucial to applications at the edge. Binarized NNs (BNNs), where the weights and output of a neuron are of binary values {−1,+1} (or encoded in {0, 1}), have been proposed. As no multiplier required, BNNs are particularly attractive and suitable for hardware realization. Most prior NN synthesis methods target on hardware architectures with neural processing elements (NPEs), where the weights of a neuron are loaded and the output of the neuron is computed. The load-and-compute method, though area efficient, requires expensive memory access, which deteriorates energy and performance efficiency. In this work we aim at synthesizing BNN layers into dedicated logic circuits. We formulate the corresponding model pruning problem and matrix covering problem to reduce the area and routing cost of BNNs. For model pruning, we propose and compare three strategies at the BNN training stage. For matrix covering, we propose a scalable logic-sharing algorithm. By combining these two methods, experimental results justify the effectiveness of the method in terms of area and net savings on FPGA implementation. Our method provides an alternative implementation of BNNs, and can be applied in combination with NPE-based implementation for area, speed, and power tradeoffs.
User-Independent Gaze Estimation by Extracting Pupil Parameter and Its Mapping to the Gaze Angle

Since gaze estimation plays a crucial role in recognizing human intentions, it has been researched for a long time, and its accuracy is ever increasing. However, due to the wide variation in eye shapes and focusing abilities between the individuals, accuracies of most algorithms vary depending on each person in the test group, especially when the initial calibration is not well performed. To alleviate the user-dependency, we attempt to derive features that are general for most people and use them as the input to a deep network instead of using the images as the input. Specifically, we use the pupil shape as the core feature because it is directly related to the 3D eyeball rotation, and thus the gaze direction. While existing deep learning methods learn the gaze point by extracting various features from the image, we focus on the mapping function from the eyeball rotation to the gaze point by using the pupil shape as the input. It is shown that the accuracy of gaze point estimation also becomes robust for the uncalibrated points by following the characteristics of the mapping function. Also, our gaze network learns the gaze difference to facilitate the re-calibration process to fix the calibration-drift problem that typically occurs with glass-type or head-mount devices.
Assessing the Quality of Online Reviews Using Formal Argumentation Theory

Review scores collect users’ opinions in a simple and intuitive manner. However, review scores are also easily manipulable, hence they are often accompanied by explanations. A substantial amount of research has been devoted to ascertaining the quality of reviews, to identify the most useful and authentic scores through explanation analysis. In this paper, we advance the state of the art in review quality analysis. We introduce a rating system to identify review arguments and to define an appropriate weighted semantics through formal argumentation theory. We introduce an algorithm to construct a corresponding graph, based on a selection of weighted arguments, their semantic similarity, and the supported ratings. We provide an algorithm to identify the model of such an argumentation graph, maximizing the overall weight of the admitted nodes and edges. We evaluate these contributions on the Amazon review dataset by McAuley et al. [], by comparing the results of our argumentation assessment with the upvotes received by the reviews. Also, we deepen the evaluation by crowdsourcing a multidimensional assessment of reviews and comparing it to the argumentation assessment. Lastly, we perform a user study to evaluate the explainability of our method. Our method achieves two goals: (1) it identifies reviews that are considered useful, comprehensible, truthful by online users and does so in an unsupervised manner, and (2) it provides an explanation of quality assessments.