Reviewerly/exHarmony huggingface dataset:
Students Formative Assessment Framework (Faus) Using the Blockchain

Colleges should digitize to improve and enhance their services and make them more accessible to beneficiaries. Universities can become more productive, efficient, adaptable, and comfortable by implementing new technologies. This consideration has grown significantly due to smart universities, a concept representing higher education in the digital age, and blockchain is one of the fastest-growing technologies of this era. This article defines blockchain and smart universities and provides a conceptual framework for how smart universities can use blockchain technology to assist and provide better knowledge of their students&#x0027; interactions with universities. To support this research, the researcher uses two case studies where the main problem is the impact of the assessment on the quality of education and the assessment of academic supervision in the thesis. To improve the quality of education, a seven-level formative assessment framework will be used in smart universities.
Reproducible learning in large-scale graphical models

Learning the conditional dependence structures through high-dimensional graphical models is of fundamental importance in many contemporary applications. Despite the fast growing literature on graphical models, a practical issue of reproducibility remains largely unexplored as most of existing methods for graph recovery do not guarantee the false discovery rate (FDR) control. In this paper, we propose a new procedure, called the high-dimensional graphical knockoff filter, to control the overall FDR for large-scale graph recovery. The proposed procedure enjoys not only theoretical guarantees and high power but also the robustness of FDR control even when the population precision matrices of predictors are replaced by consistent estimates. Furthermore, a scalable implementation approach is developed such that all knockoff variables can be generated through one single estimation of the overall graphical structure. Our new methodology and results are evidenced by numerical studies.
Implementation and Evaluation of Ring Oscillator-based True Random Number Generator

A true random number generator (TRNG) is suitable for generating secure keys and nonces. TRNGs implemented in IoT devices must be small in scale and have low power consumption. The random number sequence generated by TRNG needs to have high entropy immediately after startup and a stable state. In this paper, three types of ring oscillator type TRNGs, TERO-based, COSO-based, and STR-based TRNG, are implemented on Zynq-7010. When these TRNGs are implemented as a single entropy source, it is challenging to implement them because it is necessary to evaluate the layout and wiring for each FPGA. This paper proposes a TRNG configuration, which exclusively ORs the outputs of multiple entropy sources. We show that this configuration can reduce the implementing difficulty and realize high entropy. For the random number sequence evaluation, we use the statistical test of NIST SP800-90B and BSI AIS 20/31. In addition, the random number sequence immediately after the startup is also statistically evaluated. As a result, our proposed TRNGs generate high entropy random numbers and are easy to implement on FPGA when we implement TRNGs with eight single entropy sources for TERO-based TRNG, 48 for COSO-based TRNG, and two for STR-based TRNG, respectively.
SCMNet: Shared Context Mining Network for Real-time Semantic Segmentation

Different architectures have been adopted for realtime scene segmentation. A popular design is the multi-branch approach in which multiple independent branches are deployed at the encoder side to filter input images at different resolutions. The main purpose is to reduce the computational cost and handle high resolution. However, independent branches do not contribute in the learning process. To address this issue, we introduce a novel approach in which two branches at the encoder share their knowledge whilst generating the global feature map. At each sharing point, the shared features will go through a new effective feature scaling module, called the Context Mining Module (CMM), which will refine the shared knowledge before passing it to the next stage. Finally, we introduce a new multidirectional feature fusion module which fuses deep contextual features with shallow features successively with accurate object localization. Our novel scene parsing model, termed SCMNet, produces 66.5% validation mIoU on the Cityscapes dataset and 78.6% on the Camvid dataset whilst having only 1.2 million parameters. Furthermore, the proposed model can efficiently handle higher resolution input images whilst having low computational cost. Our proposed model produces state-of-the-art results on Camvid.
Hierarchy-based semantic embeddings for single-valued &amp; multi-valued categorical variables

Abstract In low-resource domains, it is challenging to achieve good performance using existing machine learning methods due to a lack of training data and mixed data types (numeric and categorical). In particular, categorical variables with high cardinality pose a challenge to machine learning tasks such as classification and regression because training requires sufficiently many data points for the possible values of each variable. Since interpolation is not possible, nothing can be learned for values not seen in the training set. This paper presents a method that uses prior knowledge of the application domain to support machine learning in cases with insufficient data. We propose to address this challenge by using embeddings for categorical variables that are based on an explicit representation of domain knowledge (KR), namely a hierarchy of concepts. Our approach is to 1. define a semantic similarity measure between categories, based on the hierarchy—we propose a purely hierarchy-based measure, but other similarity measures from the literature can be used—and 2. use that similarity measure to define a modified one-hot encoding. We propose two embedding schemes for single-valued and multi-valued categorical data. We perform experiments on three different use cases. We first compare existing similarity approaches with our approach on a word pair similarity use case. This is followed by creating word embeddings using different similarity approaches. A comparison with existing methods such as Google, Word2Vec and GloVe embeddings on several benchmarks shows better performance on concept categorisation tasks when using knowledge-based embeddings. The third use case uses a medical dataset to compare the performance of semantic-based embeddings and standard binary encodings. Significant improvement in performance of the downstream classification tasks is achieved by using semantic information.
Unsupervised Traffic Scene Generation with Synthetic 3D Scene Graphs

Image synthesis driven by computer graphics achieved recently a remarkable realism, yet synthetic image data generated this way reveals a significant domain gap with respect to real-world data. This is especially true in autonomous driving scenarios, which represent a critical aspect for overcoming utilizing synthetic data for training neural networks. We propose a method based on domain-invariant scene representation to directly synthesize traffic scene imagery without rendering. Specifically, we rely on synthetic scene graphs as our internal representation and introduce an unsupervised neural network architecture for realistic traffic scene synthesis. We enhance synthetic scene graphs with spatial information about the scene and demonstrate the effectiveness of our approach through scene manipulation.
Interactive Cohort Analysis and Hypothesis Discovery by Exploring Temporal Patterns in Population-Level Health Records

It is challenging to visualize temporal patterns in electronic health records (EHRs) due to the high volume and high dimensionality of EHRs. In this paper, we conduct a formative study with three clinical researchers to understand their needs of exploring temporal patterns in EHRs. Based on those insights, we develop a new visualization interface that renders medical event trajectories in a holistic timeline view and guides users towards interesting patterns using an information scent based method. We demonstrate how a clinical researcher can use our tool to discover interesting sub-cohorts with unique disease progression and treatment trajectories in a case study.
Multi-label Emotion Classification of Tweets Using Machine Learning

Twitter is one of the biggest social media network in the world. It has 330 million active monthly users. Users of Twitter use micro-blogs called tweets to express their opinions. This generates a huge amount of textual data every minute. Analyzing this data to search for emotions in it will lead us to understand the emotions currently presiding over the Internet. Emotion classification has a long history. Based on the approach, three ways of solving the emotion classification problem are devised. Binary classification detects whether an emotion is present or not. Multi-class classification classify tweets into one of the many available classes. In this paper, we proposed multi-label emotion classification of tweets. In multi-label classification, it is possible to label a tweet with more than one emotion. Multi-label classification methods follow two approaches to solve emotion classification problems. A total of 13 advanced multi-label classification methods was used to train and evaluate a tweet dataset containing 8501 tweets. 10 of them were problem transformation methods, and 3 were algorithm adaptation methods. We found that, although all the classifiers performance are pretty close, problem adaptation method like binary relevance and label powerset performs better that other multi-label classifiers. We have also found that random forest classifier works better than support vector machine as base classifier in problem transformation methods for multi-label classification. We achieved micro F-score up to 0.91 and subset 0/1 loss of 0.28. We also showed that, Senticnet5 can be used to improve the accuracy of the models. Considering our dataset contains tweets from various incidents, this represents a statistically significant improvement.
Functionally-defined recurrent multi-word units in English-to-Polish translation

Abstract This study uses both parallel and comparable reference corpora in the English-Polish language pair to explore how translators deal with recurrent multi-word items performing specific discoursal functions. We also consider whether the observed tendencies overlap with those found in native texts, and the extent to which the discoursal functions realised by the multi-word items under scrutiny are “preserved” in translation. Capitalizing on findings from earlier research ( Granger, 2014 ; Grabar &amp; Lefer, 2015 ), we analyzed a pre-selected set of phrases signaling stance-taking and those functioning as textual, discourse-structuring devices originally found in the European Parliament proceedings corpus ( Koehn, 2005 ) and included in the English-Polish parallel corpus Paralela ( Pęzik, 2016 ). Since our goal was to explore whether and to what extent English functionally-defined phrases reflect the same level of formulaicity and regularity in both Polish translations and native Polish texts, the findings provided insights into the translation tendencies of such items, and revealed – using inter-rater agreement metrics – that the discoursal functions of recurrent n-grams may change in translation.
Collaborative Coverage Path Planning of UAV Cluster based on Deep Reinforcement Learning

With the continuous application of unmanned aerial vehicles (UAV) in the field of national defense and civil use, the UAV cluster system in which multiple UAVs cooperate to perform tasks has become a key research in many countries. This paper focuses on the problem of multi-UAV's Coverage Path Planning (mCPP) to exploit all points of interests within an area based on Reinforcement Learning (RL), where each UAV starts with a random position and carries a camera during the mission. There have been a number of optimal algorithms proposed for the coverage path planning of single UAV, however, it is under-explored for multiple UAVs. As such, we leverage Deep reinforcement learning with Double Q-learning Networks (DDQN) to learn a global optimal control policy for a team of UAVs under certain power constraints to cooperate effectively to explore a wider area. Regarding the task area as a 2D plane, we divide it into a collection of uniform grid cells, which represent a section of the environment. The camera field of view of each UAV covers a cell area underneath the UAV. Simulation results demonstrate that wherever the start positions are, the UAV cluster can fully cover the whole task area under the energy constraints and achieve autonomous collaboration. The proposed method has great potential in applying to dynamic environment.
Study the accuracy of the correlation fractal dimension estimation

Fractals are widely used in different fields of science. The main characteristic of a fractal set is its fractal dimensions. The presented work addresses the accuracy of estimation of the correlation fractal dimension D2 and corresponding confidence interval. For example, in geosciences D2 is applied for characterizing surfaces or the spatial distribution of fracture systems. Two qualitatively different problems are studied. The first problem is related to the analysis of the fractal sets assigned on regular grids. The fractals consisting of a family of irregular points are considered in the second problem. The Monte Carlo method is proposed to estimate the confidence intervals of correlation fractal dimension for the first problem. This approach is based on the statistical generation of the fractal sets realizations with a given D2. Numerical testing using synthetic models showed the reliability of this method. A comparison with PBM and BCA bootstrap methods is performed. The paper also investigates the accuracy of the D2 and the corresponding confidence intervals estimate for the second studied problem. An algorithm employing the jackknife method is suggested. Its accuracy has been studied and proved numerically.
CommanderGabble: A Universal Attack Against ASR Systems Leveraging Fast Speech

Automatic Speech Recognition (ASR) systems are widely used in various online transcription services and personal digital assistants. Emerging lines of research have demonstrated that ASR systems are vulnerable to hidden voice commands, i.e., audio that can be recognized by ASRs but not by humans. Such attacks, however, often either highly depend on white-box knowledge of a specific machine learning model or require special hardware to construct the adversarial audio. This paper proposes a new model-agnostic and easily-constructed attack, called CommanderGabble, which uses fast speech to camouflage voice commands. Both humans and ASR systems often misinterpret fast speech, and such misinterpretation can be exploited to launch hidden voice command attacks. Specifically, by carefully manipulating the phonetic structure of a target voice command, ASRs can be caused to derive a hidden meaning from the manipulated, high-speed version. We implement the discovered attacks both over-the-wire and over-the-air, and conduct a suite of experiments to demonstrate their efficacy against 7 practical ASR systems. Our experimental results show that the over-the-wire attacks can disguise as many as 96 out of 100 tested voice commands into adversarial ones, and that the over-the-air attacks are consistently successful for all 18 chosen commands in multiple real-world scenarios.
A Resource Allocation Strategy for Edge Services Based on Intelligent Prediction

Artificial intelligence is one important technologies for industrial applications, but it requires a lot of computing resources and data to support it. With the development of edge computing and the Internet of Things, artificial intelligence is playing an increasingly important role in the field of edge services. Therefore, how to make intelligent algorithms to provide better services for the development of the Internet of Things has become an increasingly important topic. This paper focuses on the application of edge service distribution strategy and proposes a novel edge service distribution strategy based on intelligent prediction, which reduces the bandwidth consumption of edge service providers and minimizes the cost of edge service providers. In addition, this article uses the real data provided by the Wangsu Technology Company and an improved long short-term memory (LSTM) prediction method to dynamically change the bandwidth and achieves better optimization of resources allocation comparing with actual industrial applications. The simulation results show that our intelligent prediction can achieve good results, and the mechanism can achieve higher resource utilization.
A Low-Power Hardware Accelerator for ORB Feature Extraction in Self-Driving Cars

Simultaneous Localization And Mapping (SLAM) is a key component for autonomous navigation.SLAM consists of building and creating a map of an unknown environment while keeping track of the exploring agent's location within it.An effective implementation of SLAM presents important challenges due to real-time inherent constraints and energy consumption.ORB-SLAM is a state-of-the-art Visual SLAM system based on cameras that can be used for self-driving cars.In this paper, we propose a high-performance, energy-efficient and functionally accurate hardware accelerator for ORB-SLAM, focusing on its most time-consuming stage: Oriented FAST and Rotated BRIEF (ORB) feature extraction.We identify the BRIEF descriptor generation as the main bottleneck, as it exhibits highly irregular access patterns to local on-chip memories, causing a high performance penalty due to bank conflicts.We propose a genetic algorithm to generate an optimal memory access pattern offline, which greatly simplifies the hardware while minimizing bank conflicts in the computation of the BRIEF descriptor.Compared with a CPU system, the accelerator achieves 8x speedup and 1957x reduction in power dissipation.
Implementation and verification of a dual-probe measurement system for geometric form evaluation of a ring-type cylinder

This paper presents the implementation and verification of a dual-probe measurement system with a novel method and easily integrated chromatic confocal probes, for synchronous evaluation of not only the inner and outer cylindricity but also the thickness distribution of a ring-type cylinder. A pair of measuring probes mounted on a four-axis scanning stage, which is composed of XYZ -slides and a spindle, is utilized to target the two sides of the ring-type cylinder. In the developed system, to reduce the influence of the positioning errors and tilt misalignments of the confocal probes and the cylinder, alignment methods are developed. The outputs of the measuring probes are employed to accurately evaluate the geometric errors of the ring-type cylinder through performing spiral scanning while separating the Z -slide and spindle motion errors of the scanning stage based on a reversal error separation method. To verify the performance of the proposed system and method, the geometric errors of a thin-walled cylinder which has a nominal outer diameter of 50 mm, an inner diameter of 40 mm, and a length of 100 mm are evaluated in experiments. The experimental results are further compared with those by a coordinate measuring machine (CMM), and the high correspondence of the comparison results well confirm the feasibility of the implemented measurement system. Finally, measurement uncertainty analysis is conducted to demonstrate the reliability of the proposed method for the evaluation of the geometric errors of ring-type cylinders. The measurement accuracy is comparable to that of a CMM. • A dual-probe measurement system was developed for geometrical evaluation of ring-type cylinders. • The inner, outer roundness and thickness distribution of a ring-type cylinder was simultaneously measured. • The measurement uncertainty was analyzed to confirm the reliability of the proposed method.
Frequency Domain Analysis and Convolutional Neural Network Based Modulation Signal Classification Method in OFDM System

Automatic Modulation Classification (AMC) is widely used in many aspects and occupies a critical position in non-cooperative communication. Recently, deep learning (DL) based AMC algorithms attract more and more attention due to the outstanding performance in modulation recognition. In this paper, we propose a novel convolutional neural network (CNN)-based AMC method that employs frequency domain analysis (FDA) pre-processing and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2}$</tex> regularization for the orthogonal frequency division multiplexing (OFDM) systems. Different from traditional algorithms, the proposed algorithm is superior in high accuracy of the classification even at low signal-to-noise ratios (SNRs) owing to pre-processing by FFT. Moreover, the adoption of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2}$</tex> regularization effectively suppresses overfitting. Simulation results are given to illustrate that our proposed method has an evidently advantage over traditional methods in classifying BPSK, 4PSK, 8PSK and 16QAM modulation techniques.
Differentially Privacy-Preserving Federated Learning Using Wasserstein Generative Adversarial Network

Artificial intelligence (AI) requires a large amount of data to train high-quality machine learning (ML) models. However, due to privacy issues, individuals or organizations are not willing to share data with others, which results in “data islands”. This motivates the emergence of Federated Learning (FL), a novel ML framework allowing clients to exchange model parameters rather than the raw data. Unfortunately, the private data may be reconstructed by malicious participants by exploiting the context of model parameters in FL. This poses further challenges to privacy protection. To address this issue, we propose to integrate Wasserstein Generative Adversarial Network (WGAN) and differential privacy (DP) to protect the model parameters. WGAN is used to generate controllable random noise, which is then injected into model parameters. The new mechanism satisfies DP requirements while the data utility is highly improved. We experimentally demonstrate superior performances from aspects of convergence, accuracy, and data utility.
Research on UAV Searching Path Planning Based on Improved Ant Colony Optimization Algorithm

Aiming at the problems of slow convergence speed and easy to fall into local optimum when traditional ant colony algorithm is used in 3D path planning, a path planning method based on improved ant colony algorithm is proposed. The algorithm adjusts the initial pheromone content according to the distance between the node and the connection between the initial point and the target point and adopts an adaptive pheromone update strategy during the search process. The globality of the algorithm is effectively improved, and introducing distance heuristics and path safety factors into transition probability can improve the search efficiency and convergence speed of ants. Finally, the improved ant colony algorithm, dynamic programming algorithm and traditional ant colony algorithm are numerically simulated. The experimental results show that the improved ant colony algorithm converges faster and has a stronger ability to find the optimal solution. The research provides a solution for the optimization plan of UAV path planning.