Reviewerly/exHarmony huggingface dataset:
Human Pose Estimation from Ambiguous Pressure Recordings with Spatio-temporal Masked Transformers

Despite the impressive performance of vision-based pose estimators, they generally fail to perform well under adverse vision conditions and often don't satisfy the privacy demands of customers. As a result, researchers have begun to study tactile sensing systems as an alternative. However, these systems suffer from noisy and ambiguous recordings. To tackle this problem, we propose a novel solution for pose estimation from ambiguous pressure data. Our method comprises a spatio-temporal vision transformer with an encoder-decoder architecture. Detailed experiments on two popular public datasets reveal that our model outperforms existing solutions in the area. Moreover, we observe that increasing the number of temporal crops in the early stages of the network positively impacts the performance while pre-training the network in a self-supervised setting using a masked auto-encoder approach also further improves the results.
Semantic-Preserving Augmentation for Robust Image-Text Retrieval

Image text retrieval is a task to search for the proper textual descriptions of the visual world and vice versa. One challenge of this task is the vulnerability to input image and text corruptions. Such corruptions are often unobserved during the training, and degrade the retrieval model decision quality substantially. In this paper, we propose a novel image text retrieval technique, referred to as robust visual semantic embedding (RVSE), which consists of novel image-based and text-based augmentation techniques called semantic preserving augmentation for image (SPAugI) and text (SPAugT). Since SPAugI and SPAugT change the original data in a way that its semantic information is preserved, we enforce the feature extractors to generate semantic aware embedding vectors regardless of the corruption, improving the model robustness significantly. From extensive experiments using benchmark datasets, we show that RVSE outperforms conventional retrieval schemes in terms of image-text retrieval performance.
Upper Bound of Real Log Canonical Threshold of Tensor Decomposition and its Application to Bayesian Inference

Tensor decomposition is now being used for data analysis, information compression, and knowledge recovery. However, the mathematical property of tensor decomposition is not yet fully clarified because it is one of singular learning machines. In this paper, we give the upper bound of its real log canonical threshold (RLCT) of the tensor decomposition by using an algebraic geometrical method and derive its Bayesian generalization error theoretically. We also give considerations about its mathematical property through numerical experiments.
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems

Automatic speech recognition (ASR) systems based on deep neural networks are weak against adversarial perturbations. We propose mixPGD adversarial training method to improve the robustness of the model for ASR systems. In standard adversarial training, adversarial samples are generated by leveraging supervised or unsupervised methods. We merge the capabilities of both supervised and unsupervised approaches in our method to generate new adversarial samples which aid in improving model robustness. Extensive experiments and comparison across various state-of-the-art defense methods and adversarial attacks have been performed to show that mixPGD gains 4.1% WER of better performance than previous best performing models under white-box adversarial attack setting. We tested our proposed defense method against both white-box and transfer based black-box attack settings to ensure that our defense strategy is robust against various types of attacks. Empirical results on several adversarial attacks validate the effectiveness of our proposed approach.
Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection

This paper presents a novel framework, named Global-Local Correspondence Framework (GLCF), for visual anomaly detection with logical constraints. Visual anomaly detection has become an active research area in various real-world applications, such as industrial anomaly detection and medical disease diagnosis. However, most existing methods focus on identifying local structural degeneration anomalies and often fail to detect high-level functional anomalies that involve logical constraints. To address this issue, we propose a two-branch approach that consists of a local branch for detecting structural anomalies and a global branch for detecting logical anomalies. To facilitate local-global feature correspondence, we introduce a novel semantic bottleneck enabled by the visual Transformer. Moreover, we develop feature estimation networks for each branch separately to detect anomalies. Our proposed framework is validated using various benchmarks, including industrial datasets, Mvtec AD, Mvtec Loco AD, and the Retinal-OCT medical dataset. Experimental results show that our method outperforms existing methods, particularly in detecting logical anomalies.
Learning to Select Camera Views: Efficient Multiview Understanding at Few Glances

Multiview camera setups have proven useful in many computer vision applications for reducing ambiguities, mitigating occlusions, and increasing field-of-view coverage. However, the high computational cost associated with multiple views poses a significant challenge for end devices with limited computational resources. To address this issue, we propose a view selection approach that analyzes the target object or scenario from given views and selects the next best view for processing. Our approach features a reinforcement learning based camera selection module, MVSelect, that not only selects views but also facilitates joint training with the task network. Experimental results on multiview classification and detection tasks show that our approach achieves promising performance while using only 2 or 3 out of N available views, significantly reducing computational costs. Furthermore, analysis on the selected views reveals that certain cameras can be shut off with minimal performance impact, shedding light on future camera layout optimization for multiview systems. Code is available at https://github.com/hou-yz/MVSelect.
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution

In recent years, image and video delivery systems have begun integrating deep learning super-resolution (SR) approaches, leveraging their unprecedented visual enhancement capabilities while reducing reliance on networking conditions. Nevertheless, deploying these solutions on mobile devices still remains an active challenge as SR models are excessively demanding with respect to workload and memory footprint. Despite recent progress on on-device SR frameworks, existing systems either penalize visual quality, lead to excessive energy consumption or make inefficient use of the available resources. This work presents NAWQ-SR, a novel framework for the efficient on-device execution of SR models. Through a novel hybrid-precision quantization technique and a runtime neural image codec, NAWQ-SR exploits the multi-precision capabilities of modern mobile NPUs in order to minimize latency, while meeting user-specified quality constraints. Moreover, NAWQ-SR selectively adapts the arithmetic precision at run time to equip the SR DNN's layers with wider representational power, improving visual quality beyond what was previously possible on NPUs. Altogether, NAWQ-SR achieves an average speedup of 7.9×, 3× and 1.91× over the state-of-the-art on-device SR systems that use heterogeneous processors (MobiSR), CPU (SplitSR) and NPU (XLSR), respectively. Furthermore, NAWQ-SR delivers an average of 3.2× speedup and 0.39 dB higher PSNR over status-quo INT8 NPU designs, but most importantly mitigates the negative effects of quantization on visual quality, setting a new state-of-the-art in the attainable quality of NPU-based SR.
Innovative educational technologies in the system of specialist’s professional training

The process of education development with its innovative component begins with the influence of the need for changes in the arena of the educational process in higher education. It is created during the analytical work of specialized scientific organizations, education management bodies, heads of higher education institutions, scientific and pedagogical teams. It is highlighted the main principles that show the innovative work of modernization of higher education carried out by the Bologna process. The structure of readiness for innovative pedagogical work is outlined as a combination of motivational, cognitive, creative, reflective components. These components are interconnected. The article considered the levels of formation of a specialist's readiness for innovative work. The formation of specialists' readiness for innovative work outlines the presence of various models. Attention is focused on the model of training specialists for the use of innovative technologies, which includes various interconnected components. It was found that when training future specialists for innovative activities, the relationship between the teacher and students should be based on certain principles. The parameters of the specialist's innovative activity are determined: the specialist's readiness to carry out innovative activity, the specialist's innovative activity, the result of the innovative work.
Development of Low-Cost Biosignal Acquisition System for ECG, EMG, and EOG

The use of bio-signal is very crucial, providing enormous information concerning health and well-being of the individual. such signals can be measured and monitored by specialized devices to each bio-signal, for instance, the electrocardiogram (ECG), electromyography (EMG), electroencephalogram (EEG), and electrooculogram (EOG). Due to use of such devices, these signals could be utilized for several objectives. As it is observed in the devices of medical detection and Human to Machine Interactions (HCI). This paper presents a low-cost bio-signal collection device which is having the ability to record ECG, EMG, and EOG signals. Furthermore, STM32F103C8 system is used in Analog to Digital Conversion (ADC), with its particular application. An application has been developed in order to allow admins to observe and save the data signal simultaneously. This application has been developed by using C++ programming language and MATLAB’s code. The data signal is recorded in a format of mat file, which can be studied in details in the proposed system. This system is capitalized on Universal Serial Bus (USB) wired communication link, which is used to transmit the bio-signal through, that guarantees the safety ,avoid noise and interference. The system shows its compatiblity with various operating systems, such as, Windows, Linux, and Mac.
Flight test data processing and analysis platform based on new generation information technology Design and Application

Flight test data is the core product of flight test, which has the characteristics of many parameters, large data volume, diverse data types, random data changes, high demand for data timeliness, and high data value. It serves aircraft design, manufacturing, flight test, operation, etc. stage. In the traditional data management mode, the flight test data management is mainly in the form of text files, which is difficult to quickly respond to the application requirements of PB-level stored flight test data and give full play to the value of flight test data. Based on the characteristics of flight test data, introduce cloud computing technology and big data technology, design flight test data processing and analysis platform, build a spark-based flight test data processing system, MPP-based flight test data warehouse, cloud native flight test data service system, and B/S-based flight test data analysis software and other functional modules have been successfully applied in the flight test of a domestic civil aircraft. Provide services for flight test engineers, aircraft designers and project engineers, improving the efficiency of flight test data management and the application value of flight test data.
A Reinforcement Learning Approach Based on Parameter Prediction for Multi-AUV Task Assignment in Ocean Current

In this paper, the reinforcement learning (RL) is used to study task assignment problem of multiple autonomous underwater vehicles (AUVs) in ocean current. To solve the problem that traditional RL is prone to trap into non-optimal policy in training, a RL algorithm based on parameter prediction is proposed. The algorithm consists of four parts: First, we design an objective function with four constraints, including ocean current, task emergency, energy consumption and collision detection. The AUVs need to balance these constraints to find optimal strategy. Second, the reward distribution matrix (RDM) is constructed by the team cumulative reward generated by multiple episodes. Third, the features in RDM are extracted by truncated singular value decomposition, and the features are used to predict the policy feature. Forth, the prediction feature is mapped into policy parameters by its surrounding features, and we design a recovery mechanism to avoid the prediction errors. Finally, we analyze the effectiveness of the algorithm, and the simulation results show that compared with DDQN, our algorithm have a better performance.
Acquisition Method of Container Lock Pin Model Based on Point Cloud Data

In order to improve the automation degree and container loading and unloading efficiency of quayside container cranes, an automatic container lock pin unloading method based on 3D point cloud data processing -was proposed. The target object is scanned by the line laser sensor to obtain the point cloud structure, and the optimized filtering algorithm is used to denoise the point cloud data. The actual test data shows that the algorithm can analyze the key point coordinates of the locking point cloud in about 2s, and the key point coordinates of the locking point cloud can be analyzed in about 1.5s. The center coordinates of the keyhole at the bottom of the container are analyzed left and right, and the accuracy of the analysis is 98%. It is proved that the scheme has certain advancedness and practicability, and provides ideas for the specific implementation of the fully automated quayside container crane.
Construction and application of network security knowledge graph for power monitoring system

Facing the network security problem of power monitoring system, this paper studies the knowledge graph-based network security knowledge extraction and management method. In the entity recognition module, an entity recognition algorithm based on the idea of multi task learning is adopted, which decomposes the entity recognition process into entity boundary perception and entity classification to reduce the impact of error accumulation. Meanwhile, the introduction of comprehensive description information of entity categories into entity classification tasks can not only be widely used in general fields, but also be targeted to specific scenes in the field of power monitoring system. In the relation extraction module, based on the idea of multi-instance learning, the method of piecewise convolution neural network is employed, which reduces the impact of some instance annotation errors. The relation extraction method can extract text feature expression and inter entity structure information, which has a good effect on specialized field knowledge. Finally, based on the established knowledge graph, this paper studies the application problems of network security knowledge management and intelligent question answering of power monitoring system, which shows the effectiveness of knowledge graph technology in the field of network security management.
Research of Gesture Recognition Based on Improved Convolutional Neural Network and Support Vector Machine

Aiming at the cumbersome feature extraction of gesture recognition, time-consuming network training, and low network recognition accuracy, an optimization algorithm based on improved convolutional neural network (CNN) and support vector machine (SVM) was proposed. The idea of multi-layer convolution in series is used to construct the neural network model, so as to obtain better ability of gesture feature extraction. Batch normalization (BN) layer is added after relu function to prevent over fitting. The fully connected layer is replaced by an adaptive global average pool (GAP) layer to reduce the network parameters. SVM classifier in machine learning is used to replace Softmax classifier, which improves the classification ability of the model. Through experimental verification, compared with the traditional CNN, the proposed model greatly improves the accuracy of gesture recognition and effectively shortens the convergence time of the model.
Trajectory Planning Method of Autonomous Vehicle in Road Scene

In this paper, a trajectory planning algorithm based on the integration of a sampling and an optimization is proposed in autonomous vehicle. Unlike other planning methods, this algorithm introduces a three-dimensional sampling scheme. A coarse behavioral trajectory is predicted by the evaluating the sampled state points. The trajectory optimization scheme is designed on the basis of the behavioral trajectory. For the efficiency of trajectory optimization, the search range is restricted to local optimal solutions. The simulation results show that the method has good obstacle avoidance performance.
RANCANGAN SISTEM MONITORING PENGIRIMAN BARANG BERBASIS WEBSITE PADA PT. DENSO CENTRA

The purpose of this research is to design a delivery information system for Denso Centra company. PT. Denso Centra is a company engaged in the sale of auto spare parts. In the delivery process to the customer's location, couriers often forget the customer's location and in reporting shipments there are often queues. Based on the analysis using the PIECES framework, it can be concluded that the main problem of PT. Denso Centa is the accuracy and efficiency of information that is less than the maximum on the running system so by making this web-based system it is hoped that it can produce better information. With the design of a website-based application program at PT. Denso Centra, is expected to help simplify the management of data on the delivery of goods and speed up the process of making reports to the leadership. The results of the modeling are in the form of use case diagrams, class diagrams, activity diagrams, and sequence diagrams. While the software used is the PHP and MySql Web programming languages and Micro Dreamweaver. The results of the research are in the form of a web-based delivery system which is expected to overcome the problems that exist in the delivery of goods at PT Denso Centra
Investigation of Quantum Adder Error Rate on IBM Quantum Systems

This paper reports the results of an investigation into the performance of quantum addition operations carried out by two quantum adders. These algorithms were run on IBM quantum computers and simulated with a noise model on Qiskit. Following this experimentation, we studied the rates of errors and the differences between the two adders. This study illuminated the benefits and drawbacks of the adders as well as the capabilities of a noise simulation consisting of coupling and basis gates noise to emulate real hardware. Our work provides additional data to inform the development of more efficient addition algorithms aimed at helping minimize error rates.
Monitoring of soil pollution in the area affected by the production of phosphorus fertilizers

The soils of the Kingiseppsky district of the Leningrad region were studied in the zone of influence of the production of phosphorus fertilizers at a distance of up to 5 km from the pollution source. In 43 soil samples, the total content and mobile forms of heavy metals, sulfur, fluorine, phosphorus, calcium, magnesium, carbon, actual and potential acidity were determined. Three types of territories have been identified – conditionally background, reclaimed quarries, large man-made objects - phosphogypsum dumps and tailings. Exceedances of the MPC of mobile forms of elements in the conditionally background territory were revealed for copper, and for man-made objects - for manganese, copper, lead, nickel and zinc. The maximum gross content (in mg/kg) of barium (877), cadmium (1.5), copper (63), nickel (105), strontium (17,000), vanadium (218), zinc (107), antimony (1 ,86), chromium (103), phosphorus (13904), sulfur (1100) are typical for soils of technogenic objects. Barium, cobalt, manganese, lead, scandium, iron are evenly distributed over the study area. The average gross content of barium is 530, cadmium - 0.19, cobalt - 6, chromium - 32, copper - 14, iron - 22834, nickel - 16, manganese - 510, lead - 21, antimony - 0.36, scandium - 7 , vanadium - 48, zinc - 49, strontium - 627 mg/kg. At the same time, barium and lead are characterized by low variation; medium - cobalt, chromium, iron, manganese, scandium, zinc; high - copper, nickel, antimony, vanadium; abnormally high - cadmium and strontium. Medium mobility is characteristic of barium, increased mobility is characteristic of lead and nickel, manganese, copper and zinc are characterized by high mobility. Concentration coefficients of chemical elements in the soils of large technogenic objects calculated from the zonal background for podzolic soils: V(1.03); Zn(1,2); Ni(1,2); Cu(1.4); Sb(1.5); Pb(1.7); F(7,3); Ba(8); P(11.6); Sr(12.9); Ca(38.6). On the conditionally background territory: Zn (1,2); P(1,3); pb(2,2); F(1.5); Ba(12). Significant soil pollution and a decrease in the amount of organic matter are observed at man-made objects. In the conditionally background area, the amount and concentration of pollutants are decreasing. It is necessary to constantly monitor the ecological state of soils, especially their chemical pollution.
The Effects of Different Types of Task Implementation and the PPP Framework on Teaching L2 Interactional Sequences

The present study investigates the effect of different types of task implementation on teaching L2 interactional sequences. 83 EFL learners were randomly assigned to one control and two experimental groups. In the first experimental group (T1-EG, N = 21), implicit instruction appeared during the pre-task phase, while the post-task phase included explicit focus on forms. The second group (T2-EG, N = 32) received implicit instruction in the target structures and a reactive focus on form during task performance. The control group (CG, N = 30) followed a PPP lesson framework. Groups’ pragmatic production was measured using written discourse completion tasks. Results showed that in the current study, all three groups reported gains, yet the implicit-explicit condition (T1-EG) appeared to be more beneficial for teaching the interactional sequences than the implicit-only (T2-EG) or the PPP (CG) framework.
UNICODE AS AN ANALOGUE OF THE GENCODE

The structure of artificial intelligence as a multilevel symmetry is built by association with natural intelligence, being its digitized analogue. The principle of reduction of multivalent truth values is formulated. A metaphysical clock with two hands describing two levels of genesis, which consists of orthogonal phases of the rhythm of becoming, is constructed. There is an easy way to directly comprehend the truth. These are two level coordinates based on a Cartesian square.
IS NOT IT A. EINSTEIN WAS MISTAKEN 100 YEARS AGO?

Abstracts of the report “Relational Foundations of Nonsingular Cosmology”, presented at the conference “Foundations of Fundamental Physics and Mathematics” (OFFM-2020) (ed. by Yu. S. Vladimirov, V. A. Panchelyuga. Moscow: PFUR, 2020), are being developed. The universality of the application of the relational paradigm for various areas of fundamental physics and the fallacy of considering singularities as models of real phenomena are emphasized. The role of singularities is pointed out only as a means of covering up the mysterious properties of the universe.