Reviewerly/exHarmony huggingface dataset:
Development of AR Experiment on Electric-Thermal Effect by Open Framework with Simulation-Based Asset and User-Defined Input

Augmented reality (AR), an advanced information and communication technology, has gained widespread concerns in teaching physics due to its possibility for reducing cost of equipment, enhancing comprehension of abstract concepts, and overcoming tendency of diminishing interests of students. Although the fruitful applications have been created frequently with the aid of AR framework using standard devices, for example, head-mounted display, there is less likely for the image detection-based framework to integrate the virtual assets with the real objects with ability of supporting inputs of the invisible elements and thus makes it difficult to maintain evolutions of the augmented information over the physical quantity such as current in the AR-based physical experiment. In this paper, an open AR framework with simulation-based assets triggered by user-defined inputs is proposed to allow physics teachers to create their own AR teaching materials. Considering the teaching of electric-thermal effects by exploring the thermoelectric cooler-based thermal management of power device, furthermore, the proposed framework is illustrated from the perspective of the three paramount tasks: (1) a simple and accurate generation of assets from the finite element simulation; (2) a low-cost and convenient measurement of physical quantities by the microcomputer unit with Bluetooth connectivity; and (3) a real-time and changeable controlment of simulation-based assets by the measured data using the thread-based Python script. Experimental results show that students are excited in the AR application interacted with the real operation of current, and moreover, physic teachers are easy to design and deploy the simulation-based framework with user-defined inputs to create their own AR learning materials for sparking growing interests of students.
Acting Like an Algorithm: Digital Farming Platforms and The Trajectories They (Need Not) Lock-in

This paper contributes to our understanding of farm data value chains with assistance from 54 semi-structured interviews and field notes from participant observations. Methodologically, it includes individuals, such as farmers, who hold well-known positionalities within digital agriculture spaces—platforms that include precision farming techniques, farm equipment built on machine learning architecture and algorithms, and robotics—while also including less visible elements and practices. The actors interviewed and materialities and performances observed thus came from spaces and places inhabited by, for example, farmers, crop scientists, statisticians, programmers, and senior leadership in firms located in the U.S. and Canada. The stability of “the” artifacts followed for this project proved challenging, which led to me rethinking how to approach the subject conceptually. The paper is animated by a posthumanist commitment, drawing heavily from assemblage thinking and critical data scholarship coming out of Science and Technology Studies. The argument’s understanding of “chains” therefore lies on an alternative conceptual plane relative to most commodity chain scholarship. To speak of a data value chain is to foreground an orchestrating set of relations among humans, non-humans, products, spaces, places, and practices. The paper’s principle contribution involves interrogating lock-in tendencies at different “points” along the digital farm platform assemblage while pushing for a varied understanding of governance depending on the roles of the actors and actants involved.
HINT: Hierarchical Neuron Concept Explainer

To interpret deep networks, one main approach is to associate neurons with human-understandable concepts. However, existing methods often ignore the inherent connections of different concepts (e.g., dog and cat both belong to animals), and thus lose the chance to explain neurons responsible for higher-level concepts (e.g., animal). In this paper, we study hierarchical concepts inspired by the hierarchical cognition process of human beings. To this end, we propose HIerarchical Neuron concepT explainer (HINT) to effectively build bidirectional associations between neurons and hierarchical concepts in a low-cost and scalable manner. HINT enables us to systematically and quantitatively study whether and how the implicit hierarchical relationships of concepts are embedded into neurons. Specifically, HINT identifies collaborative neurons responsible for one concept and multimodal neurons pertinent to different concepts, at different semantic levels from concrete concepts (e.g., dog) to more abstract ones (e.g., animal). Finally, we verify the faithfulness of the associations using Weakly Supervised Object Localization, and demonstrate its applicability in various tasks, such as discovering saliency regions and explaining adversarial attacks. Code is available on https://github.com/AntonotnaWang/HINT.
Elements of semantic analysis based on lambda-calculus

This paper considers the applicative computing technology (ACT), within the framework of which the semantic analysis of a number of natural language constructs is performed. The necessary elements of grammatical analysis are involved. Much first-order logical means is used, and predicate variables are necessarily used for analysis. At the same time, the advantages of higher-order systems, which include ACT, are extracted. The fact is that the semantics of a natural language is characterized by a multi-tiered nesting of grammatical structures, which conflicts with first-order logical systems.
An Embedded Feature Whitening Approach to Deep Neural Network Optimization

Compared with the feature normalization methods that are widely used in deep neural network (DNN) training, feature whitening methods take the correlation of features into consideration, which can help to learn more effective features. However, existing feature whitening methods have several limitations, such as the large computation and memory cost, inapplicable to pre-trained DNN models, the introduction of additional parameters, etc., making them impractical to use in optimizing DNNs. To overcome these drawbacks, we propose a novel Embedded Feature Whitening (EFW) approach to DNN optimization. EFW only adjusts the gradient of weight by using the whitening matrix without changing any part of the network so that it can be easily adopted to optimize pre-trained and well-defined DNN architectures. The momentum, adaptive dampening and gradient norm recovery techniques associated with EFW are consequently developed to make its implementation efficient with acceptable extra computation and memory cost. We apply EFW to two commonly used DNN optimizers, i.e., SGDM and Adam (or AdamW), and name the obtained optimizers as W-SGDM and W-Adam. Extensive experimental results on various vision tasks, including image classification, object detection, segmentation and person ReID, demonstrate the superiority of W-SGDM and W-Adam to state-of-the-art DNN optimizers. The code are publicly available at https://github.com/Yonghongwei/W-SGDM-and-W-Adam .
A Low-Cost Prototyping Approach for Autonomous Unmanned Ground Vehicle for Real-Time Surveillance

The Unmanned Ground Vehicle (UGV) system architecture is a versatile platform for surveillance. The use of such technology improves the reach of security personnel in remote patrolling areas. The main objective of this paper is to design and develop a low-cost autonomous Unmanned Ground Vehicle prototype using open-source platforms for surveillance. The system consists of Wireless Sensor Network (WSN) for real-time data acquisition, an Ardupilot system for the UGV control, and LoRa (Long Range) transceiver system for internet-free sensor data transmission for security. The GPS and the inertial sensor are interfaced with the Ardupilot system to achieve autonomous operation with preset waypoints mapping. The real-time video obtained from the UGV is processed with OpenCV to detect the human face and eyes. Different electrical test data of the UGV under various operating conditions are presented. Integration of such systems results in an effective autonomous UGV to provide better patrolling in remote and harsh environmental conditions.
Feature Envy Detection with Deep Learning and Snapshot Ensemble

Code Smell is a code symptom of deep-seated quality problems caused by design defects or improper coding habits in software. It may not directly affect the operation of program, but it affects the readability, understandability and maintainability of code. Therefore, the identification, location and reconstruction of code smell are becoming increasingly significant. Combined with the deep learning methods, based on the existing model for detecting Feature Envy, this paper optimizes it from three aspects: introducing the attention mechanism, expanding and modifying the model structure, and applying the snapshot ensemble. The experimental results show that compared with the standard results, the model proposed in this paper gets a better performance on four evaluation metrics: precision, recall, F1 measure and AUC. Based on the research results of this experiment, we can see the effectiveness of deep learning in the field of code smell detection and the prospect of theories in natural language processing to be utilized in code smell detection, which provides a practical cornerstone for the research of deep learning based smell detection methods in the future.
Security Requirements for Vehicle-to-Everything (V2X) Communications Integrated with Blockchain

With the advancement of fifth-generation cellular systems (5G) and the already provoked research on the sixth-generation cellular systems (6G), stringent functional requirements are growing to facilitate more reliable vehicular communication systems. The progressive needs of V2X communications include obtaining cooperative sensing data in real-time, then analyze and use them in the decision-making process that greatly enhances driving security, traffic quality, infotainment facilities, etc. However, the traditional V2X communications are not capable of providing all these necessities. Moreover, the lack of trust among heterogeneous, massive sets of participants in V2X networks at present may hamper those activities with frequent privacy violations and other irreversible consequences. Hence, the blockchains are applied to assure the accountability and integrity among network participants. On the other hand, the integrated edge layer today consists of Macro BS (MBSs), Small BS (SBSs), Road Side Units (RSUs), and 5G Radio Access Networks (RANs) which are geo-distributed and installed with independent storage facilities. As a result, revolutionary blockchain platforms can be easily accommodated to 5G Mutiaccess Edge Computing (MEC). This allows to apply fast and efficient consensus mechanism for block verification while preventing backhaul congestion by offloading computation at the cloud. Therefore, efficient New Radio (NR) access technology, core network capabilities, and edge-cloud services are needed to reach the full convergence of V2X networks while blockchain can be used to solve major related security vulnerabilities ranging from data privacy leakage, cyber-attacks, and access control to security breaches leading to extremely advanced data theft. Hence, in this research paper, an attempt was made to discuss some existing blockchain integrated architectures in diverse V2X communications by paving the path to develop more robust security mechanisms with 5G enabling technologies,
Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs

Entity alignment is a basic and vital technique in knowledge graph (KG) integration. Over the years, research on entity alignment has resided on the assumption that KGs are static, which neglects the nature of growth of real-world KGs. As KGs grow, previous alignment results face the need to be revisited while new entity alignment waits to be discovered. In this paper, we propose and dive into a realistic yet unexplored setting, referred to as continual entity alignment. To avoid retraining an entire model on the whole KGs whenever new entities and triples come, we present a continual alignment method for this task. It reconstructs an entity’s representation based on entity adjacency, enabling it to generate embeddings for new entities quickly and inductively using their existing neighbors. It selects and replays partial pre-aligned entity pairs to train only parts of KGs while extracting trustworthy alignment for knowledge augmentation. As growing KGs inevitably contain non-matchable entities, different from previous works, the proposed method employs bidirectional nearest neighbor matching to find new entity alignment and update old alignment. Furthermore, we also construct new datasets by simulating the growth of multilingual DBpedia. Extensive experiments demonstrate that our continual alignment method is more effective than baselines based on retraining or inductive learning.
3D-FM GAN: Towards 3D-Controllable Face Manipulation

3D-controllable portrait synthesis has significantly advanced, thanks to breakthroughs in generative adversarial networks (GANs). However, it is still challenging to manipulate existing face images with precise 3D control. While concatenating GAN inversion and a 3D-aware, noise-to-image GAN is a straight-forward solution, it is inefficient and may lead to noticeable drop in editing quality. To fill this gap, we propose 3D-FM GAN, a novel conditional GAN framework designed specifically for 3D-controllable Face Manipulation, and does not require any tuning after the end-to-end learning phase. By carefully encoding both the input face image and a physically-based rendering of 3D edits into a StyleGAN’s latent spaces, our image generator provides high-quality, identity-preserved, 3D-controllable face manipulation. To effectively learn such novel framework, we develop two essential training strategies and a novel multiplicative co-modulation architecture that improves significantly upon naive schemes. With extensive evaluations, we show that our method outperforms the prior arts on various tasks, with better editability, stronger identity preservation, and higher photo-realism. In addition, we demonstrate a better generalizability of our design on large pose editing and out-of-domain images.
READINESS OF VOCATIONAL EDUCATION TEACHERS TO IMPLEMENT THE EDUCATIONAL PROCESS IN THE CONTEXT OF DIGITALIZATION OF EDUCATION

The issues of the readiness of vocational education teachers to implement the educational process in the context of digitalization of education are especially relevant at the present time, since the use of technology solves many problems faced by teachers. The article presents the results of the study conducted among teachers in general educa- tion schools and secondary vocational institutions of the city of Ekaterinburg. The results of the study were used to organize methodological work with teachers in general educa- tion schools and secondary vocational institutions and to organize advanced training courses in order to overcome professional deficits of education personnel in connection with the use of distance-learning mode in the educational process.
The Missing Link: Finding Label Relations Across Datasets

Computer vision is driven by the many datasets available for training or evaluating novel methods. However, each dataset has a different set of class labels, visual definition of classes, images following a specific distribution, annotation protocols, etc. In this paper we explore the automatic discovery of visual-semantic relations between labels across datasets. We aim to understand how instances of a certain class in a dataset relate to the instances of another class in another dataset. Are they in an identity, parent/child, overlap relation? Or is there no link between them at all? To find relations between labels across datasets, we propose methods based on language, on vision, and on their combination. We show that we can effectively discover label relations across datasets, as well as their type. We apply our method to four applications: understand label relations, identify missing aspects, increase label specificity, and predict transfer learning gains. We conclude that label relations cannot be established by looking at the names of classes alone, as they depend strongly on how each of the datasets was constructed.
Towards a Model Checking Framework for a New Collector Framework

Garbage collectors provide memory safety, an important step toward program correctness. However, correctness of the collector itself can be challenging to establish, given both the style in which such systems are written and the weakly-ordered memory accesses of modern hardware. One way to maximize benefits is to use a framework in which effort can be focused on the correctness of small, modular critical components from which various collectors may be composed. Full proof of correctness is likely impractical, so we propose to gain a degree of confidence in collector correctness by applying model checking to critical kernels within a garbage collection framework. We further envisage a model framework, paralleling the framework nature of the collector, in hope that it will be easy to create new models for new collectors. We describe here a prototype model structure, and present results of model checking both stop-the-world and snapshot-at-the-beginning concurrent marking. We found useful regularities of model structure, and that models could be checked within possible time and space budgets on capable servers. This suggests that collectors built in a modular style might be model checked, and further that it may be worthwhile to develop a model checking framework with a domain-specific language from which to generate those models.
Hand Gesture Signatures Acquisition and Processing by Means of a Novel Ultrasound System

Hand gestures represent a natural way to express concepts and emotions which are peculiar to each culture. Several studies exploit biometric traits, such as fingerprint, iris or face for subject identification purposes. Within this paper, a novel ultrasound system for person identification that exploits hand gestures is presented. The system works as a sonar, measuring the ultrasonic pressure waves scattered by the subject's hand, and analysing its Doppler information. Further, several transformations for obtaining time/frequency representations of the acquired signal are computed and a deep learning detector is implemented. The proposed system is cheap, reliable, contactless and can be easily integrated with other personal identification approaches allowing different security levels. The performances are evaluated via experimental tests carried out on a group of 25 volunteers. Results are encouraging, showing the promising potential of the system.
Accurate and flexible calibration method for a 3D microscopic structured light system with telecentric imaging and Scheimpflug projection

3D imaging and metrology of complex micro-structures is a critical task for precision manufacturing and inspection. In this paper, an accurate and flexible calibration method for 3D microscopic structured light system with telecentric imaging and Scheimpflug projector is proposed. Firstly, a fringe projection 3D microscopy (FP-3DM) system consisting of a telecentric camera and a Scheimpflug projector is developed, which can take full advantage of the depth of field (DOF) and increase the measurement depth range. Secondly, an accurate and flexible joint calibration method is proposed to calibrate the developed system, which utilizes the established pinhole imaging model and Scheimpflug distortion model to calibrate telecentric imaging, and fully considers the correction and error optimization of the Scheimpflug projection model. Meanwhile, the optimized local homography is calculated to obtain more accurate sub-pixel correspondence between the camera and the projector, and the perspective-n-point (PnP) method make the 3D coordinate estimation of the feature point more accurate. Finally, a prototype and a dedicated calibration program are developed to realize high-resolution and high-precision 3D imaging. The experimental results demonstrate that the re-projection error is less than 1 µm , and the 3D repeated measurement error based on feature fitting is less than 4 µm , within the calibrated volume of 10(H) mm × 50(W) mm × 40(D) mm .
A Cost-efficient and Scalable Framework for E-Voting System based on Ethereum Blockchain

The importance of democratic voting systems in any nation has increased the mistrust of conventional voting methods. Other digital voting methods have been challenged due to a lack of transparency, security, rigging and scalability. Blockchain (BC) is a decentralized, distributed ledger technology for recording and keeping safe transactions. Unfortunately, BC implementation in large-scale elections is still being challenged because the amount of information in the Electronic Voting (EV) system increased, and storage space and processing costs are also growing. Moreover, existing BC technologies have scaling problems. Off-chain (OC) and Directed Acyclic Graph (DAG) solutions, among others, have been suggested to alleviate the scaling problem. Throughput and latency are crucial performance metrics for a BC system. Sharding is a viable option for addressing BC's scalability and performance issues. Therefore, this paper proposes a model to shard the current BC to scale the main chain to achieve performance securely. The proposed system is distributed using Ethereum and works as an OC that is in sync with the district-level databases, updated the main-chin periodically by authorities, and is faster in terms of cost-effectiveness, scalability and data storage. Some limitations are involved, such as the authorities should be trusted to avoid malicious attacks.
CLCD-I: Cross-Language Clone Detection by Using Deep Learning with InferCode

Source code clones are common in software development as part of reuse practice. However, they are also often a source of errors compromising software maintainability. The existing work on code clone detection mainly focuses on clones in a single programming language. However, nowadays software is increasingly developed on a multilanguage platform on which code is reused across different programming languages. Detecting code clones in such a platform is challenging and has not been studied much. In this paper, we present CLCD-I, a deep neural network-based approach for detecting cross-language code clones by using InferCode which is an embedding technique for source code. The design of our model is twofold: (a) taking as input InferCode embeddings of source code in two different programming languages and (b) forwarding them to a Siamese architecture for comparative processing. We compare the performance of CLCD-I with LSTM autoencoders and the existing approaches on cross-language code clone detection. The evaluation shows the CLCD-I outperforms LSTM autoencoders by 30% on average and the existing approaches by 15% on average.
The use of COVID-19 contact tracing app data as evidence of a crime

This commentary draws attention to the introduction of data collected by COVID-19 tracing apps as evidence in criminal proceedings and the novel considerations this evidence presents for criminal justice agents and digital forensics professionals.