Reviewerly/exHarmony huggingface dataset:
Thread-Modular Analysis of Release-Acquire Concurrency

We present a thread-modular abstract interpretation (TMAI) technique to verify programs under the release-acquire (RA) memory model for safety property violations. The main contributions of our work are: we capture the execution order of program statements as an abstract domain, and propose a sound upper approximation over this domain to efficiently reason over RA concurrency. The proposed domain is general in its application and captures the ordering relations as a first-class feature in the abstract interpretation theory. In particular, the domain represents a set of sequences of modifications of a global variable in concurrent programs as a partially ordered set. Under the upper approximation, older sequenced-before stores of a global variable are forgotten and only the latest stores per variable are preserved. We establish the soundness of our proposed abstractions and implement them in a prototype abstract interpreter called PRIORI. The evaluations of PRIORI on existing and challenging RA benchmarks demonstrate that the proposed technique is not only competitive in refutation, but also in verification. PRIORI shows significantly fast analysis runtimes with higher precision compared to recent state-of-the-art tools for RA concurrency.
Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements

Multiple-target tracking algorithms generally operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field-of-view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field-of-view. To address this problem, we propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves and then re-enters the UAV’s field-of-view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, receding horizon and deep reinforcement learning, and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target’s entropy. In addition, we develop an algorithm that computes the upper bound on the filter’s performance, thus facilitating an estimate of the number of UAVs needed to achieve a desired performance threshold.
A Checklist Proposal to Evaluate the Quality of University Websites

Currently, internet is the most wanted medium of communication and most used by people or entities. Unquestionably, it has transformed significantly consumer behaviour, namely in the access to information or in the way a product or service is purchased. In this context, the evaluation of the effectiveness of websites has increased over time, following the evolution of the concepts of Human Machine Interface, Usability and User Experience Design. In the context of the methods and processes of usability evaluation, different proposals have been developed, from direct observation, to interviews and questionnaires, among other techniques and tools. Thus, this article is based on a case study of descriptive type, based on the literature review concerning the models of evaluation of the quality of websites. The results of the article consist in the definition of a set of parameters to analyse the quality of the interface of websites and in the proposal of a checklist concerning content, aesthetics and navigation.
LiDAR Data Noise Models and Methodology for Sim-to-Real Domain Generalization and Adaptation in Autonomous Driving Perception

In autonomous driving, object detection and semantic segmentation are critical tasks for path planning and control of an autonomous vehicle. Recent approaches are based on supervised learning methods, with large datasets sampled in the target domain. However, annotating training data for supervised learning methods is a high resource and time-consuming task. In this work, we propose to exploit artificial LiDAR data for object detection and semantic segmentation. We use the CARLA simulator [1] to generate artificial data of autonomous driving scenarios and propose ways to mitigate the differences between artificial and real-world data (domain generalization). We modeled both the noise and the missed reflections (denoted point dropout) that occur in real-world data collection, and show their effects in the detection and segmentation tasks. We assess the potential benefits of using pre-trained models on artificial data when fine-tuning with all, or a fraction, of the available real-world data (domain adaptation). We find clear improvements when using artificial data to pretrain a network, which allows to use a reduced amount of realworld data, and boost the performance of the trained models.
Facial Expression Recognition Based on Multi-scale Feature Fusion Convolutional Neural Network and Attention Mechanism

Facial expression recognition plays an important role in human-computer interaction. At present, the traditional algorithm applied to facial expression recognition is not accurate enough, and deep learning methods have problems such as a large number of network model parameters, insufficient generalization ability, and high requirements on hardware devices in actual deployment. To solve these problems, a lightweight facial expression recognition model based on multi-scale feature fusion and attention mechanism is proposed in this paper. Firstly, a multi-scale deeply separable densely connected convolutional neural network is constructed, which can greatly reduce the number of parameters and computation. The convolution kernel of different scales can obtain the receptive fields of different scales, and finally integrate the features of multiple scales, which is beneficial to the learning of the features of different scales. A dense connection reduces gradient disappearance and improves model generalization performance. Secondly, group convolution is introduced to further reduce the number of parameters. Finally, the attention mechanism and multi-scale were integrated to improve the classification accuracy of the model. The network model proposed in this paper has been trained and verified on RAF-DB, FER2013, CK+ and FERPlus datasets. Experimental results show that compared with other classical convolutional neural networks, the model proposed in this paper can significantly reduce the number of parameters and calculation amount while ensuring high accuracy. In the future, it is of great significance for the practical application of facial expression recognition in medical monitoring, education assistance, traffic warning and other fields.
Evaluation of Contribution Rate of Armaments to System-of-Systems Based on Complex Network and Improved Information Entropy

Aiming at the problem of complicated structures and correlations in operation system-of-systems under the information conditions, an evaluation method of equipment's contribution rate of system-of-systems based on complex network and improved information entropy is proposed. The armaments in system-of-systems are abstracted as nodes while the relationships among armaments are abstracted as edges in view of complex network theory. The metrics of edges are determined according to the tactical and technical index of armaments nodes, and the operation network model of system-of-systems is established. The improved information entropy is used to evaluate the operation effectiveness of system-of-systems based on information entropy theory. And the corresponding evaluation model is built. Taking an air defense anti-missile system as an example, the results indicate that the proposed method has fully taken the relationships and heterogeneity among armaments into consideration to evaluate the contribution rate of armaments to system-of-systems comprehensively and objectively.
Special Issue on Basics and Applications in Quantum Optics

Quantum technologies are advancing very rapidly and have the potential to innovate communication and computing far beyond current possibilities [...]
The Cost of IEEE Arithmetic in Secure Computation

Programmers are used to the rounding and error properties of IEEE double precision arithmetic, however in secure computing paradigms, such as provided by Multi-Party Computation (MPC), usually a different form of approximation is provided for real number arithmetic. We compare the two standard variants using for LSSS-based MPC, with an implementation of IEEE compliant double precision using binary circuit-based MPC. We compare the relative performance, and conclude that the addition cost of IEEE compliance maybe too great for some applications. Thus in the secure domain standards bodies may wish to examine a different form of real number approximations.
Cryptocurrency Mining Protocols: A Regulatory and Technological Overview

This chapter offers an overview of digital currencies beginning with a background to the digitalization of money. The chapter then proceeds to discuss central bank issued digital currencies, a highly topical area of recent development, and issues surrounding the regulation of digital currencies. The second half of the chapter begins by providing the reader with a short history of ledgers and explains the differences between centralized and decentralized ledgers. Next cryptocurrencies are discussed in detail including issues surrounding their adoptions, and how they work including the various consensus algorithms employed to secure and validate cryptocurrency transactions.
Neuro-Scientific Analysis of Weights in Neural Networks

Deep learning is a popular topic among machine learning researchers nowadays, with great strides being made in recent years to develop robust artificial neural networks for faster convergence to a reasonable accuracy. Network architecture and hyperparameters of the model are fundamental aspects of model convergence. One such important parameter is the initial values of weights, also known as weight initialization. In this paper, we perform two research tasks concerned with the weights of neural networks. First, we develop three novel weight initialization algorithms inspired by the neuroscientific construction of the mammalian brains and then test them on benchmark datasets against other algorithms to compare and assess their performance. We call these algorithms the lognormal weight initialization, modified lognormal weight initialization, and skewed weight initialization. We observe from our results that these initialization algorithms provide state-of-the-art results on all of the benchmark datasets. Second, we analyze the influence of training an artificial neural network on its weight distribution by measuring the correlation between the quantitative metrics of skewness and kurtosis against the model accuracy using linear regression for different weight initializations. Results indicate a positive correlation between network accuracy and skewness of the weight distribution but no affirmative relation between accuracy and kurtosis. This analysis provides further insight into understanding the inner mechanism of neural network training using the shape of weight distribution. Overall, the works in this paper are the first of their kind in incorporating neuroscientific knowledge into the domain of artificial neural network weights.
Development of physical education resources and design of college physical education teaching system from the perspective of data fusion

This paper expounds the auxiliary teaching function of computer network technology in physical education from the perspective of data fusion, and then combined with the specific application, analyzes the user needs in detail, and puts forward the development scheme and implementation method of physical education network auxiliary system. The system adopts three-tier architecture, pays attention to good scalability, adopts modular design method, divides it into four functional modules: teaching resources, online Q &amp. This system breaks the time and space limitation of traditional physical education teaching mode, and realizes the functions of students' online learning, answering students' questions, downloading materials and so on. The system has friendly interface, simple operation, and can be used by teachers and students conveniently. It can realize the information management of teaching resources and the sharing of teaching resources among teachers.
Simultaneous Bi-directional Structured Light Encoding for Practical Uncalibrated Profilometry

Profilometry based on structured light is one of the most popular methods for 3D reconstruction. It is widely used when high-precision and dense models, for a variety of different objects, are required. User-friendly procedures encode the scene in horizontal and vertical directions, which allows a unique description of points in the scene. The resulting encoding, can be used to auto-calibrate the devices used. Thus, any consumer or industrial cameras or projectors can be supported and the procedure is not limited to pre-calibrated setups. This approach is extremely flexible, but requires a large number of camera acquisitions of the scene with multiple patterns projected. This paper presents a new approach that encodes the scene simultaneously in horizontal and vertical directions using sinusoidal fringe patterns. This allows to almost halve the number of recorded images, making the approach attractive again for many practical applications with time aspects.
Conditional t-SNE: More informative t-SNE embeddings

Dimensionality reduction and manifold learning methods such as t-distributed Stochastic Neighbor Embedding (t-SNE) are frequently used to map high-dimensional data into a two-dimensional space to visualize and explore that data. Going beyond the specifics of t-SNE, there are two substantial limitations of any such approach: (1) not all information can be captured in a single two-dimensional embedding, and (2) to well-informed users, the salient structure of such an embedding is often already known, preventing that any real new insights can be obtained. Currently, it is not known how to extract the remaining information in a similarly effective manner. We introduce conditional t-SNE (ct-SNE), a generalization of t-SNE that discounts prior information in the form of labels. This enables obtaining more informative and more relevant embeddings. To achieve this, we propose a conditioned version of the t-SNE objective, obtaining an elegant method with a single integrated objective. We show how to efficiently optimize the objective and study the effects of the extra parameter that ct-SNE has over t-SNE. Qualitative and quantitative empirical results on synthetic and real data show ct-SNE is scalable, effective, and achieves its goal: it allows complementary structure to be captured in the embedding and provides new insights into data.
Adversarial training for named entity recognition of rail fault text

At present, most rail faults in metro systems are recorded in the form of text. Due to the lack of effective mining and analysis tools, information in the massive textual data is not fully utilized. Learning from past fault texts and identifying some key concepts are essential to analyze faults and help decision making. In this paper, a word-enhanced adversarial training model (AT-BiLSTM-CRF) is proposed to address this problem. In this model, the named entity recognition (NER) is achieved by bi-directional long short-term memory (BiLSTM) with conditional random field (CRF). At the same time, the Chinese word segmentation (CWS) task is introduced to conduct adversarial training with the NER task. The structure of adversarial training is to make full use of the boundary information and filter out the noise caused by introducing the CWS task. More importantly, the experiments on five different train fault datasets are conducted in the rail field. The results show that the model performs better than the state-of-the-art baselines, which indicates it has the potential to lay the foundation for textual data analysis in the rail field.
A magyar közoktatásban tanító pedagógusok tapasztalatai a digitális munkarendű oktatásról IKT tudásszintjük tükrében: egy kevert módszertanú kutatás eredményei a Covid-19 idején

The aim of this study was to find out more about the experiences of Hungarian K12 teachers with Covid-19-triggered emergency remote teaching in light of their existing technopedagogical skills. In this mixed-methods study, data was first collected through a large-scale questionnaire (N=216) consisting of 7 constructs, each measuring teachers' TPACK knowledge components. The results of the study allowed for the clustering of Hungarian K12 teachers based on their techno-pedagogical knowledge; it was found that 20% of them are Beginners, 41% are Intermediate and 39% are Advanced implementers of technology-inclusive teaching. The study also found that the techno-pedagogical groups are quite homogenous, there is no difference between the groups based on gender, age, qualification or teaching experience. Secondly, based on a further interview (N=8) study, Beginners could be described as those who had to use technology in their teaching practices for the first time, thus they struggled with the very operational levels of technology. Intermediate users tried to mirror their traditional, in-class methodology in the online space, while Advanced users experimented with the most progressive remote teaching methods such as project work, learner portfolios, gamification, and longitudinal projects. Besides techno-pedagogical levels, teachers' general motivation towards online teaching was greatly influenced by their school leadership; the more detailed rules were set about the requirements of distant education both towards teachers and learners, the more teachers felt supported and motivated throughout the emergency period. Teachers named the lack of personal contact, the generally unmotivated learners and the invalidity of digital testing as the biggest challenges of online education, while they perceived that their techno-pedagogical knowledge greatly benefited from remote teaching, and claimed that this improved knowledge would be incorporated in their everyday teaching practice in the future. Implications of this study center around suggestions for offering (more) subject-specific technological methodological courses in teacher education or in the form of teacher training programs, because Covid-19-triggered emergency remote teaching created new standards for what is conceptually meant by teachers' techno-pedagogical knowledge.
Comparing Holt-Winter and Multi Layer Perceptron in Forecasting The Amount of Rice Supply

Rice supply through the Cipinang Rice Main Market is volatile and seasonal with a total supply from all production centers in 2020 of 795179 tons of which 33&#x0025; or most of it is supplied from the Cirebon area. The existence of climate change associated with shifting planting seasons and shrinking paddy fields makes it necessary to make accurate supply forecasts so that the price control mechanism through rice market operations can run effectively. The supply forecast uses monthly supply data from the Cirebon area from 2011 to 2020 and is forecasted using the Holt-Winter and Multi Layer Perceptron (MLP) methods. The results show that forecasting using the MLP method with two hidden layer produces the most accurate values where the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values are 544.17 and 2.08, respectively.
Multimodal Region-level Clothing Re-identification in E-commerce Livestreaming

This is a technical report on the 1st Workshop on Multimodal Product Identification in Livestreaming and WAB Challenge. The technical pipeline includes, 1)object detection. 2)Product Reidentification. 3)Multimodal classification under multi-criteria supervision. 4)Smart sorting. The technical details and code documents will be shown in this report. Our team, "THU-TAOBAO MAN'', ranked 4th in the final season.
Recognizing Threat through Object and Gesture Recognition by Applying Convolutional Neural Network and Adaptive Boosting Algorithm Using Microsoft Kinect for Xbox One

The study aimed to apply the Microsoft Kinect for Xbox One with the use of Convolution Neural Network and Adaptive Boosting to be able to recognize the hazardous object and threatening gestures. Microsoft Kinect for XBOX One and Visual Gesture Builder were used for obtaining the threatening object and gesture within the range of the main sensor. Whereas, the depth sensor is used for acquiring the gesture, and RGB-D Image Processing is for the use of distinguishing objects. Defined threat objects and threat gestures, the ones which are most common, are trained using supervised machine learning in order to recognize proper threat object and threat gesture. The system integrates the recognition of the two in order to define a threat recognition system.
Collaborative Robot Grasping System Based on Gaze Interaction

The bionic robotic arm has the characteristics of high safety, lightweight and portability, which can assist humans in completing various daily activities in life. Based on the research background of human-machine collaboration and simulating its experimental environment, a vision-assisted grasping framework for collaborative robots serving humans is proposed, and a discrimination scheme based on the combination of the shortest path and gIOU is designed. We can interact with the robotic grasping system by directly gazing at the object of interest. The gaze coordinates are provided by an eye tracker, which is matched with the image of the target detection algorithm to obtain the grasping information, and the signal is transmitted to the grasping system. The grasping system executes the grasping operation through the robotic arm after calculating the target pose, and implements the automatic grasping target by gazing at the target object. The experimental results show that this framework has good positioning accuracy and can effectively complete daily grasping tasks.
Kernel optimization using conformal maps for the minimal complexity machine

The Minimal Complexity Machine (MCM) is a kernel-based learning model that can learn very sparse models that yield comparable or better performance than Support Vector Machines (SVMs). However, kernel optimization for the MCM has not yet been explored. It has been shown in prior work that a data dependent kernel helps improve generalization. We show results on data dependent optimized kernels for the MCM and a large-scale MCM variant. Results on benchmark datasets demonstrate both model sparsity and improved generalization.
CIGLI: Conditional Image Generation from Language &amp; Image

Multi-modal generation has been widely explored in recent years. Current research directions involve generating text based on an image or vice versa. In this paper, we propose a new task called CIGLI: Conditional Image Generation from Language and Image. Instead of generating an image based on text as in text-image generation, this task requires the generation of an image from a textual description and an image prompt. We designed a new dataset to ensure that the text description describes information from both images, and that solely analyzing the description is insufficient to generate an image. We then propose a novel language-image fusion model which improves the performance over two established baseline methods, as evaluated by quantitative (automatic) and qualitative (human) evaluations. The code and dataset is available at https://github.com/vincentlux/CIGLI.