Reviewerly/exHarmony huggingface dataset:
AI and Administrative Law

Algorithmically-driven decision-making tools have long existed in Canadian administrative agencies, but administrative law largely fails to address such technologies. This chapter uses the basic principles enunciated in administrative law doctrines to demonstrate how algorithmically-driven decisions fundamentally challenge administrative law notions of fairness, responsibility, and justice. In doing so, it identifies which common law principles, such as notice requirements or prohibitions on sub-delegation, might best apply to algorithmic tools. Other doctrines, it argues, must adapt to meaningfully address the problems algorithmic technologies raise for procedural fairness, responsible and responsive decision makers, and substantive justice. A central dilemma, and an area for future evolution, is how administrative law ought to conceptualize algorithmic tools: should it approach them as a forceful policy, a uniquely persuasive source of evidence, or as though they are an additional decision maker? This chapter illustrates how different answers to this question may require varied doctrinal shifts. It then pinpoints opportunities for legislators and policymakers to develop new governance frameworks that address the dilemmas of algorithmically-driven decision-making. When crafting statutes, policies, and institutional arrangements, legislators and policymakers must meaningfully address the interests of marginalized communities, as algorithmically-driven tools tend to impact members of these communities most harshly. This proposal marks a new path for this area of law, as administrative law principles typically protect individual rights and interests but are less adept at confronting systemic injustice.
An inductive abstract semantics for coFJ

We describe an inductive abstract semantics for coFJ, a Java-like calculus where, when the same method call is encountered twice, non-termination is avoided, and the programmer can decide the behaviour in this case, by writing a codefinition. The proposed semantics is abstract in the sense that evaluation is non-deterministic, and objects are possibly infinite. However, differently from typical coinductive handling of infinite values, the semantics is inductive, since it relies on detection of cyclic calls. Whereas soundness with respect to the reference coinductive semantics has already been proved, we conjecture that completeness with respect to the regular subset of such semantics holds as well. This relies on the fact that in the proposed semantics detection of cycles is non-deterministic, that is, does not necessarily happens the first time a cycle is found.
Efficient Abnormal Motion Change Detection Based on Translational Velocity Indicator

The translational velocity indicator(TVI) estimation is a simple and convenient way of solving for objects' motion direction without segmentation or tracking. Based on this, we defined and distinguished the change level of image sequences by defining Abnormality Coefficient (AC). An abrupt increase in AC can indicate the happening of abnormal motion change. To gauge the accuracy of judgment, we also introduced the width of S-band which is related to the size of the field of view. TVI, AC, and S-band can be estimated from videos captured by a single camera and do not require the detection of image information in detail. The proposed method is computationally efficient and simple to implement. Our motivation stems from the limited number of provided screens and a large number of monitoring cameras require the videos or image sequences ordered by change level. We demonstrate the TVI method by applying it on two video sequences, and the results show that the method can distinguish the different change levels for sequences with and without abnormal motion change; the S-band method is applied on another two video sequences and it successfully shows different sizes of the field of view.
Privacy-Preserving Scheme Based on Group Signature in Smart Grid

Smart grid is a typical cyber-physical fusion system, which combines power system engineering technology and communication information technology. Compared with traditional power grids, smart grids involve more complex interaction processes. The interaction of information poses a certain threat to the privacy of users. However, most of the existing privacy-preserving schemes are not secure and cannot resist internal or external attacks well. In order to solve the existing challenges, this article proposes a privacy-preserving scheme based on group signatures, applying elliptic curve cryptography to ensure user privacy and data integrity and authenticity. We use the ProVerif verification tool to formally analyze and verify the scheme, and compare it with other schemes to prove the safety and correctness of the proposed scheme. At the same time, our solution has good scalability and low computing and storage costs.
Detection-Based Measurements for Quantum Emulation Devices

Quantum emulation devices use analog electronic signals to mimic precisely the behavior of a gate-based quantum computer. These devices also introduce the flexibility to choose different detection rules to generate measurement outcomes. Such rules may depend on deterministic decisions rather than the randomized decisions occurring in Born rule-based measurements of true quantum systems. In this paper, we leverage this flexibility in an emulation device to consider implementations of measurement detection rules with better performance than the Born rule. We visit physically-inspired detection strategies such as maximum-energy and threshold detectors, as well the optimal maximum-likelihood detector. We gauge the detectors by how often they successfully produce the correct outcome of a quantum algorithm in the presence of noise, deriving computationally efficient implementations and comparing their probability of error performance to the Born rule detectors. We find that efficient detectors may be defined with significantly better performance than the Born rule detector at comparable signal-to-noise ratios. We also introduce a theoretical performance comparison to a true quantum computing system operating in a generalized depolarizing channel.
Performance Evaluation of Region-Based Segmentation Algorithms for Brain MR Images

Segmentation of an image is crucial for dividing it into different classes so that it can be made useful for information extraction for image classification and dissemination in medical imaging and diagnosis. There are various region-based segmentation algorithms that are used to detect the required region of interest in MR image. The hard and soft clustering algorithm along with an unsupervised classification method based on expectation maximization for image segmentation is illustrated in this paper. These methods are used on the brain MR image to confer the result for medical diagnosis purposes. The best method is chosen by computing certain performance evaluating parameters such as rand index, variation of information and global consistency error. As per the performance measure, expectation maximization result proved to be the best when it was compared with the ground truth image. Thus, it propels to be used in medical diagnosis.
Navigation Method for Pioneer P3-DX Ground Wheeled Robot in V-REP Platform Using Type-2 Fuzzy Neural Network (T2FNN) Architecture

This article presents the navigational architecture for a Pioneer P3-DX Ground Wheeled Robot (PGWR) by applying the minimum rule-based type-2 fuzzy neural network (T2FNN) architecture to control the motion, direction and orientation of PGWR between obstacles and help the PGWR to reach the goal. The real-time obstacle distance information received from the ring of ultrasonic sensors of PGWR is feed to the T2FNN as inputs, and necessary wheel velocity control commands for obstacle avoidance are obtained as outputs form rule-based system architecture of T2FNN. The experimental results in the Virtual Robot Experimentation Platform (V-REP) software show that the T2FNN method has successfully guided the PGWR under unknown scenarios. Also, the comparison analysis has been done with the previous benchmark neural network (NN) approach and found smoother trajectory during obstacle avoidance because the T2FNN provided an additional degree of freedom over NN to handle uncertainty situations.
Progressive face super-resolution via learning prior information

Abstract Face super-resolution is a branch of the field of super-resolution. It is mainly aimed at the reconstruction of face images and distinguishing SR from general images. Face geometry prior information is used to optimize the face super-resolution network, which can generate high-resolution face images with better visual quality from low-resolution face images. In order to further improve the visual quality of reconstructed images, an improved face super-resolution network is proposed. In this paper, the key module of FSRNet is improved and new loss function is added to achieve a better face super-resolution network. Specifically, our job is to:(1) In the generated rough SR face image, we input low-resolution(16 x16) face image, then use the Deconv convolution enlarge images.(2)By introducing heatmap loss, facial attention loss and adversarial training to reduce the artifacts of FSRNet network. (3) We divide the network into two-step training, first train coarse SR network, get SR images quickly, and then we train the rest of the network. The final output is a super-resolution face with high visual quality.
FINE ARTS LESSON IN A MODERN SCHOOL: MOSCOW ELECTRONIC SCHOOL AND TRADITIONS IN TEACHING DRAWING

The article deals with the digital educational environment of the Moscow Electronic School in the context of its use in art lessons at school. The authors emphasize the positive impact of forming a single library of electronic resources in all academic subjects. The article analyzes the specific features of the scenario of the Moscow Electronic School lesson in fine arts, offers a universal model of such a scenario and describes its key aspects.
Script Identification of Multilingual Document Images Based on Block Finite Ridgelet Transform and Discrete Curvelet Transform

In recent years, many script recognition methods have emerged since they were studied as a front-end technique of OCR. These methods generally have a pleasing effect on a particular script, but they are not suitable for all languages. In this paper, we utilize the block finite ridgelet transform(BFRT) and discrete curvelet transform(DCT) and propose a fusion method in series for a total of 10,000 document images of 10 scripts including English, Chinese, Uyghur, Tibetan, Arabic, Turkish, Mongolian, Russian, Kazakhstan, Kyrgyzstan. The experimental results show that average accuracy is 99.35% in the classifier of linear discriminant analysis. Comparative experiments showed that the recognition rates of single BFRT and DCT were 89.03% and 86.3%, respectively. It demonstrates the effectiveness of the proposed method than the sole method. The validity of this method is proved by comparing it with some existing methods.
Dataflow Design of Overlap-Add FFT on the Convolution Neural Networks

As the computation demand of CNNs is dominated by convolution layers, many researches exploit the duality between spatial domain and frequency domain through fast Fourier transform (FFT) to replace convolutions with pointwise multiplications. Also, some of them apply the overlap-add FFT algorithm to resolve the large zero padding problem in full FFT model. However, none of them discuss the dataflow problem in the overlap-add FFT technique. In this paper, we propose an integration of output stationary and filter reuse dataflow to minimize output buffer size and amount of data migration between internal buffer and external memory. Experiments on ResNet-34 shows that the proposed integration of output stationary and filter reuse dataflow reduces almost 50% of data migration in comparison with the output stationary and input reuse dataflow.
Mathematical model of quantum channel for teleportation through atmosphere

The algorithm of quantum teleportation in the turbulent atmosphere is studied. The approach of fluctuating loss channels in applied. The entanglement preserving for qubits coding by Gaussian beam modes is investigated. It was shown which modes are more appropriate for transfer quantum information through the atmosphere with weak turbulence.
Distorted Perspective for the Forward Camera Dolly

We present a method to add distorted perspective effects for scenes with a forward camera dolly (the camera moves in the depth direction). Our target scene is a first-person view of the main character traversing a long path. A distorted perspective has been applied to such scenes in hand-drawn animation films to create more dynamic and dramatic motions. Unfortunately, it is difficult to create a perspective for the camera direction in 3D animation. Therefore, we created an interactive tool to design cartoon-like perspectives for 3D computer-generated animations. Users can control the affine transformation, including translation, rotation, and scaling, in the depth direction of the camera coordinate system on a 2D screen. We implemented the proposed deformer on a vertex shader to ensure the real-time performance of our system.
Feasibility Analysis of Openstack and Dynamips Application Fusion

In recent years, with the continuous development of information technology, the use of cloud computing is more and more extensive, among which openstack cloud platform is one of the most popular open-source cloud platforms. This paper mainly studies the feasibility of the integration of openstack and Dynamips software based on its basic characteristics and network structure.
Masked Word Prediction with Statistical and Neural Language Models

Language modeling is one of the main tools used in most of the natural language processing problems. Neural language models became popular with the approach of transfer learning (using a model trained for one task to another task). However, studies with neural language models on Turkish have not yet reached the desired levels. In this study, statistical and neural language models were compared using the prediction of a word that was closed randomly in a sentence. Neural language models were found to have higher success.
Edge-Angle Features for Modern Biometric System Design

Phase congruency is a significant feature detection and measurement method. It is a robust approach against variation in image contrast and illumination. This paper proposed three novel techniques for developing face recognition software based human identification system. First, the complexity is highly reduced by utilizing the detected phase congruency features rather than extracted features in the system design. Secondly, the long matching time resulted from the large feature vectors is fixed by introducing an averaging-vectors technique in the matching process. Finally, the gradient-edges and their associated angles are used separately for classifying 130 subjects taken from AR, ORL, and VIS-CH databases. Furthermore, for the accurate evaluation of recognition rates, a comparison amongst three competitive distance measurements is applied in this work. The system performance is so satisfactory, where the experimental results show promising accuracies with a lowest matching time.
Asking Graphs "How Did I Play?" Generating Graphs through Images Via Signals

Cricket is a game that requires players to constantly adapt to situations and customize their game depending on opponents and playing conditions. Players and coaching staff often watch video clips to understand the strategies of opponents. Iterating through multiple matches over many years across various leagues and formats, and extracting clips is a tiring process. In this paper, we propose a computer vision framework to segment cricket matches into clips based on context and construct real-time graphs using meta-data from segmented clips. We discuss various queries on the generated graphs and also evaluate our segmentation and querying model based on the accuracy and quality of the retrieved data.
Visual Surveillance using Deep Reinforcement Learning

Visual surveillance aims a robust detection of foreground objects, and traditional algorithms usually use a background model image. A current is compared with the background model image. In this paper, we present a visual surveillance algorithm, which determines the parameters in Vibe using deep reinforcement learning. We apply DQN to determine three parameters in Vibe algorithm. We present a policy model which is composed of encoder and decoder type network. Experimental results shows the feasibility of the presented algorithm.
Algorithm-Architecture Optimization for Linear and Quadratic Regression on Reconfigurable Platforms

Linear and quadratic regressions are techniques widely used in digital signal processing applications. This paper proposes a procedure and hardware architecture for the implementation of both regression methods and their mean square error (MSE) on FPGAs. Efficient computation of the bit widths of the coefficients of the regressions is carried out by finding their maxima and minima. Based on this optimization, a low-latency memory-less implementation for the computation of the MSE is proposed. Additionally, we have implemented the proposed architecture as part of a signal modulation classifier with hard real-time constraints.
An Error Correction Method of Nanopore Sequencing Data Using Deep Learning

The small-sized MinlON nanopore sequencing device provided by Oxford Nanopore Technologies (ONT) could generate ultra-long reads with a user-friendly DNA library building process, which would facilitate the studies of genomics into a new stage. However, nanopore sequencing reads currently do show a relatively high error rate, which significantly limits the application of Nanopore sequencing. In this study, we took advantage of the information from both the raw electrical signals and the basecalled sequences from the basecallers to develop a deep learning method for correcting the nanopore sequencing basecalling errors introduced by current basecallers provided by default. Specifically, we first re-segmented the raw electrical signals based on the basecalled sequences to extract the input representations. Then, the preprocessed input will pass through several convolution neural network (CNN) layers and bidirectional long short-term memory (Bi-LSTM) network layers to generate sophisticated multi-dimensional features. Moreover, we recruited center loss function in our model with the traditional SoftMax cross-entropy loss function to deal with the extremely unbalanced data. Our results showed that our post-basecalling correction method significantly improved the basecalling quality and could correct the errors in the homopolymer regions of human genome.
Differential Private Spatial Decomposition for Mobile Crowdsensing Using Staircase Mechanism

Although mobile crowdsensing (MCS) has become a new paradigm of collecting, analyzing, and exploiting massive amounts of sensory data, sharing the sensory data with users’ sensitive location data may expose them to potential privacy. Differential privacy is a popular privacy preservation approach, which could realize strong privacy protection in various scenarios, ranging from data collection, data releasing, to data analysis. In this paper, we focus on the noise adding mechanism in constructing differentially private spatial decomposition. The noise adding mechanism, as the standard approach to preserving differential privacy, directly affects the utility of differentially private data. To improve the accuracy of counting query on the private two-dimensional spatial datasets, we propose a Staircase mechanism based differentially private Uniform grid partition method, namely Staircase_Ugrid. We first investigate the relationship between non-uniform error and query intersection area, and utilize the linear least square to fit the linear relation between them. Then we deduce the optimal partition granularity by minimizing non-uniform error and noise error. In the experiments, we use two real world datasets to evaluate the performance of the proposed method. Experiments show that the proposed two-dimensional spatial publishing method makes a good trade-off between data privacy and utility.
Prediction Model of Blast Furnace Gas Flow Distribution Based on GWO-ELM

The distribution of blast furnace gas flow has an important influence on the use of chemical and thermal energy. The temperature of the cross measuring points of blast furnace can directly reflect the distribution of gas flow. In order to predict the temperature of the cross measuring points, a prediction model based on GWO-ELM is established in this paper. The bias of the hidden layer and the weights between the input layer and hidden layer of extreme learning machine (ELM) are generated randomly, which results in a decrease in the accuracy of the model. To solve this problem, Grey Wolf Optimizer (GWO) with good global optimization capability is utilized to optimize the ELM prediction model. The experiment results show that this method improves the accuracy of temperature prediction model and the operation of blast furnace might be well guided by this prediction model.
A Literature Review on the Determinants of Human Development

Globalization is the main engine of the today’s economic and social universe. It has emerged with the promise of a more livable, more prosperous, richer and more developed world by providing the accumulation of capital. However, contrary to the general beliefs, accumulation of capital is not enough to ensure and increase in well-being and quality of life. It is not a sign of human development alone and there are different social and economic dimensions of human development. Thus, it is important to understand the relationship between different economic indicators and human development and welfare. In this context, this study aims to find out the effect of FDI, import, export, which are commonly associated indicators in economic globalization, and corruption on human development for developing countries in the existing literature.
Reforming Architecture and Loss Function of Artificial Neural Networks in Binary Classification Problems

Artificial neural networks (ANNs), implied models of the biological human brain's neuron, have now a long history as prime techniques in machine learning and computational intelligence with a wide range of applications, and are of great interest thanks to their great success. Classification is one of the most populous realms of research in ANNs with a vast and growing literature. Our innovation is to revolutionize loss function for ANNs, in accordance with a novel architecture for the last layer of the neural net (NN), that empowers them to apply dynamic thresholding while deciding the label of a sample based on its probability-of-belonging values; hence, to model complexities of the data more discriminatingly and attain better quantitative results. Although we established our approach through mathematical argument particularly for binary classification, the concept and formulation are entirely and purposefully generalizable to multiclass classification problems.
Autonomous chess-playing robotic arm using Raspberry PI

Chess is one of the most ancient and challenging board games. Recently many chess programs have been developed for computers. In this project, an autonomous chess-playing robotic arm which uses OpenCV for computer vision is implemented and an open source engine is used to predict the optimal moves. A raspberry PI and a camera module are used for computer vision to detect the chess board, and a robotic arm controlled by the PI is designed to pick and place chess pieces and make moves predicted by the engine.
PaperTracker: A Gamified Music &amp; Tech Teaching Tool

Papertracker is an interactive educational platform that engages audiences of all ages with music and technology. The focus is on providing fun and inexpensive challenges that promote creative problem solving, collaborative work, and programming using a system of placeable tiles.