Reviewerly/exHarmony huggingface dataset:
The conception of the digital platform of independent assesment of the future teachers

Introduction. Modern processes of digitalization of education put forward new requirements for the procedures for evaluating the results of research results of research results of teachers. Of particular relevance are approaches to the design of digital research tools, including electronic resources that allow consolidating the consumption and evaluation of the educational result by experts with the possibility of implementing an integrative and differentiated evaluation procedure. Materials and Methods. Methods of analysis, systematization and generalization of scientific literature on problematic studies were used. The purpose of the article is to analyze and substantiate the conceptual foundations for creating a digital platform for an independent assessment of the level of achievement of research results by future teachers. Results. The article analyzes approaches to the protection of electronic resources in the fields of education, substantiates the advantages and functionality of the phenomenon of electronic assessment. The conceptual foundations for creating digital platforms are the methodological unity of the platform and ecosystem approach, extended methodological assessments of the foundations for creating an independent educational result system, including systemic, activity-based, criteria-based, competency-based, technological, level-based approaches, objectivity, objectivity, reliability and openness of assessment procedures. The digital platform for independent assessment of the level of achievement of the educational result gives its idea based on the evaluation of the effectiveness of the study of results, the effectiveness of data and their use on the basis of broad procedures and algorithms for assessing the educational result of the observation of teachers, the modularity of the organization of procedures for assessing the educational result, non-linear functionality, partisipatory analytics and evaluation of results , popular results at the lowest cost of resources. The digital platform for independent assessment of the level of achievement of the educational result performs an analytical, predictive, evaluation, information and aggregation function. Discussion and Conclusions. As part of the study of the conceptual and practice-oriented statistical foundations for independent evaluation of the results of teacher research, promising areas for long-term research have been identified: – adding a digital platform for independent evaluation of the results of teachers' observation in the system of teachers' professional growth, accompanying young professionals in the life cycle of the profession; – improvement of the university accreditation system based on the use of a digital platform; – determination of approaches to the combination of emotional, psychological comfort and personality verification processes in the procedures for implementing the evaluation of research results.
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders

Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequency of the user’s interactions with the item. However, taking the PIF as an explicit feature incurs bias towards frequent items. Items that a user purchases frequently are assigned higher weights in PIF-based recommender system and appear more frequently in the personalized recommendation list. As a result, the system will lose the fairness and balance between items that the user frequently purchases and items that the user never purchases. We refer to this systematic bias on personalized recommendation lists as frequency bias, which narrows users’ browsing scope and reduces the system utility. We adopt causal inference theory to address this issue. Considering the influence of historical purchases on users’ future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram. In this paper, we propose a deconfounder model named FENDER (Frequency-aware Deconfounder for Next-basket Recommendation) to mitigate the frequency bias. With the deconfounder theory and the causal diagram we propose, FENDER decomposes PIF with a neural tensor layer to obtain substitute confounders for users and items. Then, FENDER performs unbiased recommendations considering the effect of these substitute confounders. Experimental results demonstrate that FENDER has derived diverse and fair results compared to ten baseline models on three datasets while achieving competitive performance. Further experiments illustrate how FENDER balances users’ historical purchases and potential interests.
Self-Supervised Person Re-Identification with Channel-Wise Transformer

Unsupervised domain adaptive (UDA) person Re-Identification aims to improve the model’s generalization capability from labeled source domain to unlabeled target domain. To this end, a strong and robust method is required to extract discriminative features of pedestrians. Recently, transformer-based method achieves great performance on person Re-Identification (ReID). However, due to the domain gap between ImageNet and ReID datasets, it requires a large pre-training dataset to boost performance on Vision Transformer (ViT). To this end, we first investigate self-supervised learning methods with ViTs pretrained on LUPerson datasets, and find it significantly outperforms ImageNet supervised pre-training models on ReID tasks. A Catastrophic Forgetting Score (CFS) is also used to select a subset of LUPerson, which reduces the training time and improves performance. We then proposed a channel-wise self-attention module to reduce the computing cost on the class token. A dual prototype contrastive learning is proposed to fully exploit the hard feature on memory bank under unsupervised domain adaptation. Finally, we achieve state-of-the-art performance on Market-1501 and MSMT17. Our model achieves 91.5%/69.6% mAP accuracy on Market-1501/MSMT17 for supervised ReID, and 90.7%/57.4% mAP for MS2MA/MA2MS UDA ReID.
Real-time testing of vision-based systems for AGVs with ArUco markers

Automated Ground Vehicles (AGVs) use deep-learning-based vision systems to perceive the surrounding environment and extract relevant information about it. Although deep learning models offer high capabilities, they require large amounts of data to be properly trained and tested. Testing is especially important when off-the-shelf models are used by the AGVs - to examine whether they can meet the demands of complex environments such as the production halls of automated factories. One area of such perception algorithms is object recognition. To test such systems, we propose a solution based on ArUco fiducial markers used for automatic labeling of objects. Our solution can be used to test deep learning systems in real time directly on a robot. Our solution requires minimal interference with the environment and additional infrastructure - the desired objects only need to be marked with a marker printed on a home printer. Therefore, the presented testing procedure can be used for testing of AGVs in real-life environments during a real ride from an actual robot perspective. Data gathered during the online testing can be used for the offline comparison of the accuracy of different deep learning models. Although we focus on the online and offline testing in our study, we also incorporated a marker masking procedure. Therefore, the resulting datasets may also be used for training.
Recurrent LSTM-based UAV Trajectory Prediction with ADS-B Information

Recently, unmanned aerial vehicles (UAVs) are gathering increasing attentions from both the academia and industry. The ever-growing number of UAV brings challenges for air traffic control (ATC), and thus trajectory prediction plays a vital role in ATC, especially for avoiding collisions among UAVs. However, the dynamic flight of UAV aggravates the complexity of trajectory prediction. Different with civil aviation aircrafts, the most intractable difficulty for UAV trajectory prediction depends on acquiring effective location information. Fortunately, the automatic dependent surveillance-broadcast (ADS-B) is an effective technique to help obtain positioning information. It is widely used in the civil aviation aircraft, due to its high data update frequency and low cost of corresponding ground stations construction. Hence, in this work, we consider leveraging ADS-B to help UAV trajectory prediction. However, with the ADS-B information for a UAV, it still lacks efficient mechanism to predict the UAV trajectory. It is noted that the recurrent neural network (RNN) is available for the UAV trajectory prediction, in which the long short-term memory (LSTM) is specialized in dealing with the time-series data. As above, in this work, we design a system of UAV trajectory prediction with the ADS-B information, and propose the recurrent LSTM (RLSTM) based algorithm to achieve the accurate prediction. Finally, extensive simulations are conducted by Python to evaluate the proposed algorithms, and the results show that the average trajectory prediction error is satisfied, which is in line with expectations.
Multi-Signal Classification Using Deep Learning and Sparse Arrays

In uncoordinated spectrum scenarios, such as shared spectrum, accurate spectral analysis is challenging if devices are interfering with one another. This paper examines wireless standard classification for spatially separated emitters that are interfering in time and frequency. The proposed approach involves estimating the number of interfering signals and their directions of arrival, spatial isolation, and then wireless standard classification using a deep learning-based convolutional neural network (CNN). The CNN is trained to classify commercial wireless standards, including 4G LTE, 5G, IEEE 802.11ax, Bluetooth Low Energy 5.0, and Narrowband Internet-of-Things, using a synthetic dataset impaired with typical channel and receiver impairments, including AWGN and Rayleigh/Rician fading. When a single emitter is transmitting, spatial filtering using a four element uniform linear array (ULA) increases the classification accuracy of low SNR signals by up to 26% compared to a single antenna system. When multiple emitters are transmitting, especially when there are three or more, a four-element ULA cannot spatially filter well enough to yield high classification accuracies. In these cases, we show that switching to a sparse array can improve classification accuracy without adding more array elements. Sparse arrays can improve classification accuracy by up to 33% compared to ULAs in scenarios with multiple emitters or closely spaced emitters.
Deep learning for intra-hour solar forecasting with fusion of features extracted from infrared sky images

The increasing penetration of solar energy leaves power grids vulnerable to fluctuations in the solar radiation that reaches the surface of the Earth due to the projection of cloud shadows. Therefore, an intra-hour solar forecasting algorithm is necessary to reduce power instabilities caused by the impact of moving clouds on energy generation. The most accurate intra-hour solar forecasting methods apply convolutional neural networks to a series of visible light sky images. Instead, this investigation uses data acquired by a novel infrared sky imager on a solar tracker, which is capable of maintaining the Sun in the center of the images throughout the day and, at the same time, reducing the scattering effect produced by the Sun’s direct radiation. In addition, infrared sky images allow the derivation and extraction of physical cloud features. The cloud dynamics are analyzed in sequences of images to compute the probability of the Sun intercepting air parcels in the sky images (i.e., voxels). The method introduced in this investigation fuses sky condition information from multiple sensors (i.e., pyranometer, sky imager, solar tracker, weather station) and feature sources using a multi-task deep learning architecture based on recurrent neural networks. The proposed deterministic and Bayesian architectures reduce computation time by avoiding convolutional filters. The proposed intra-hour solar forecasting algorithm reached a forecast skill of 18.6% with a forecasting horizon of 8 min. Consequently, the proposed intra-hour solar forecasting method can potentially reduce the operational costs of power grids with high participation of solar energy.
Characterization of Rayfield-Jos Columbite Deposit for Efficient Beneficiation and Recovery of Niobium and Tantalum

The study primarily focuses on the characterizationCharacterization and physicochemicalPhysicochemical investigations of columbite samples from Rayfield-JosRayfield-Jos, Plateau state, Nigeria. Samples were prepared and characterized by particle size, microstructural, physical and chemicalChemical analyses. Further, density/specific gravity, pH, moisture content and water absorption were determined. ChemicalChemical analyses showed reasonable compositions of Nb/Ta penta-oxides with certain degree of impurities/gangue mineralsMineral including radioactive materials. Results of analyses conducted strongly depict the columbite deposit as a somewhat promising mineralMineral source of Nb and Ta but however of low-grade, and with the potential in developing appropriate, (cost) effective beneficiationBeneficiation routes for the metal(s) recovery. Thus, the study will create baseline information on choice selection as well as designing suitable, feasible extraction process routes/techniques to efficiently harness the refractory metals and other value metals from the study area. This will also serve as a tool in the advancement of mineralMineral upgrade/beneficiationBeneficiation routes prior to hydrometallurgical and thermal treatments.
YORO - Lightweight End to End Visual Grounding

We present YORO - a multi-modal transformer encoder-only architecture for the Visual Grounding (VG) task. This task involves localizing, in an image, an object referred via natural language. Unlike the recent trend in the literature of using multi-stage approaches that sacrifice speed for accuracy, YORO seeks a better trade-off between speed an accuracy by embracing a single-stage design, without CNN backbone. YORO consumes natural language queries, image patches, and learnable detection tokens and predicts coordinates of the referred object, using a single transformer encoder. To assist the alignment between text and visual objects, a novel patch-text alignment loss is proposed. Extensive experiments are conducted on 5 different datasets with ablations on architecture design choices. YORO is shown to support real-time inference and outperform all approaches in this class (single-stage methods) by large margins. It is also the fastest VG model and achieves the best speed/accuracy trade-off in the literature. Code released (Code available at https://github.com/chihhuiho/yoro ).
Development Framework Using 360VR Cameras and Lidar Scanners for Web-Based XR Educational Materials Supporting VR Goggles

This paper proposes a development framework using 360VR cameras and lidar scanners for web-based XR educational materials that supports VR goggles. Recently, the demand of XR (VR/AR/MR) applications has become increased due to the advancement of XR technologies, e.g., high specification VR goggles, 360VR cameras and lidar scanners have become available at a cheaper price rather than ever. Although more than ten years ago, many entertainment XR applications have been produced, XR applications in other fields have not been so. Besides the entertainment field, educational materials using XR have become in great demand. Especially, XR seems useful for educational materials of experiment and exercise subjects. However, the development of such educational materials is not easy and needs much time. Therefore, in this paper, the authors propose the development framework for them. In this paper, the authors also show several material examples to justify the usefulness of the proposed framework.
Blockchain-assisted data sharing supports deduplication for cloud storage

Offloading data to cloud storage has changed the way people live and work, which reduces data management costs and increases productivity. People can use their devices to download data from cloud storage regardless of location and time. It means that data can be shared across domains through cloud storage. However, offloading data to cloud storage brings convenience as well as various challenges, such as how to solve the performance bottleneck of cloud storage and how to achieve secure data sharing in cloud storage. Blockchain has played an important role in secure data sharing based on its tamper-evident nature. In this paper, a blockchain-assisted data sharing scheme is proposed which implements the detection of deduplicated data and completes the ciphertext combination after deduplication. In order to verify the legitimacy of the ciphertext, blockchain is used to assist in verifying the ciphertext based on its tamper-evident nature. Furthermore, partial ciphertext parameters are calculated at free time to improve server operation efficiency. Finally, we simulated all algorithm of the proposed scheme on the PBC library and the results show that it achieves a balance between usability and performance.
An Adaptive, Reconfigurable, Tethered Aerial Grasping System for Reliable Caging and Transportation of Packages

Aerial robot development has gathered steam in recent years for applications such as package delivery and transportation of arbitrary payloads, both in academia and business. However, current solutions for Unmanned Aerial Vehicles (UAVs) based transportation of large objects and/or parcels rely on some form of standardization of packaging. This design constraint greatly limits the applicability of the autonomous package delivery drone concepts. In this paper, we propose a reconfigurable, tethered aerial gripping system that can allow for the execution of a more diverse range of package handling and transportation tasks, employing autonomous aerial robots. The system combines a reconfigurable, telescopic, rectangular frame that is used to conform to the parcel geometry and lift it, and a net system that is used to secure the parcel from the bottom, facilitating the execution of caging grasps. This combination provides reliable aerial grasping and transportation capabilities to the package delivery UAV. The grasping and transportation process used by the proposed concept system can be divided into three stages: i) the reconfigurable, telescopic frame conforms to the parcel geometry securing it, ii) the package is lifted or tilted by the frame's lifting mechanism, exposing its bottom part, and iii) the net is closed, caging and securing the package for transportation. A series of airborne gripping and transportation trials have experimentally validated the system's effectiveness, confirming the viability and usefulness of the proposed concept. Results demonstrate that the prototype can successfully secure and transport a package box. Furthermore, the complete system can be tethered to any type of aerial robotic vehicle.
A Cognitive Solver with Autonomously Knowledge Learning for Reasoning Mathematical Answers

Reasoning answers to mathematical problems requires machines to think and operate like a human to learn knowledge from mathematical data, which is one of the fundamental tasks for exploring general artificial intelligence. Most solutions focus on mimicking how humans understand problems, which generate the necessary expressions for answers. However, they are still far from enough since they ignore the core ability of humans to acquire knowledge from experience. In this paper, we propose a Cognitive Solver (CogSolver) that is capable of autonomously learning knowledge from scratch to solve mathematical problems, inspired by two cognitive science theories. Specifically, we draw one insight from the dual process theory to establish an intelligent BRAIN-ARM framework, and refer to another information processing theory to summarize the knowledge learning process into Store-Apply-Update steps. In CogSolver, the BRAIN system stores three types of mathematical knowledge, including semantics knowledge, relation knowledge, and mathematic rule knowledge. Then, the ARM system applies the knowledge in BRAIN to answer the problems. Specifically, we design a knowledge-aware module and a commutative module in ARM to improve its reasoning ability, where the knowledge is organically integrated into answer reasoning process. After solving the problems, BRAIN updates the stored knowledge according to the feedback of ARM, where we develop knowledge filters to eliminate the redundant ones and further form a more reasonable knowledge base. Our CogSolver carries out the above three steps iteratively, which behaves more like a human. We conduct extensive experiments on real-world math word problem datasets. The experimental results demonstrate the improvement in answer reasoning and clearly show how CogSolver gains knowledge from the problems, leading to superior interpretability. Our codes are available at https://github.com/bigdata-ustc/CogSolver.
Image De-noising and Edge Segmentation using Bilateral Filtering and Gabor-cut for Edge Representation of a Breast Tumor

Breast cancer is the globe’s initial highest death-causing cancer in women which is given to the international agency of analysis on cancer-the World Health organization and the American cancer society. This paper expounds on the classification of breast cancer from the mammogram images MLO view. This paper proposes a technique to de-noise the mammogram Images using the improved bilateral filter and improved canny edge detection for the breast tumor. The region from the image is then selected using the New Gabor cut algorithm. The Mean Square Error and Structural Similarity are proposed to be improved using the improved Bilateral filter. The approximation in the background and foreground extraction for the Region of Interest is performed proposing the New Gabor cut algorithm and so the Edges were drawn predominantly by using the improved Canny Edge Detection. The Mean Squared Error and Similarity Index is 15.65 and 0.91. Doing This Pre-processing will facilitate further research in the Feature Extraction process to detect breast cancer in an efficient way.
EM-Fault It Yourself: Building a Replicable EMFI Setup for Desktop and Server Hardware

Electromagnetic fault injection (EMFI) has become a popular fault injection (FI) technique due to its ability to inject faults precisely considering timing and location. Recently, ARM, RISC- V, and even x86 processing units in different packages were shown to be vulnerable to EMFI attacks. However, past publications lack a detailed description of the entire attack setup, hindering researchers and companies from easily replicating the presented attacks on their devices. In this work, we first show how to build an automated EMFI setup with high scanning resolution and good repeatability that is large enough to attack modern desktop and server CPUs. We structurally lay out all details on mechanics, hardware, and software along with this paper. Second, we use our setup to attack a deeply embedded security co-processor in modern AMD systems on a chip (SoCs), the AMD Secure Processor (AMD-SP). Using a previously published code execution exploit, we run two custom payloads on the AMD-SP that utilize the SoC to different degrees. We then visualize these fault locations on SoC photographs allowing us to reason about the SoC's components under attack. Finally, we show that the signature verification process of one of the first executed firmware parts is susceptible to EMFI attacks, undermining the security architecture of the entire SoC. To the best of our knowledge, this is the first reported EMFI attack against an AMD desktop CPU.