Reviewerly/exHarmony huggingface dataset:
Hybrid Optimization for DNN Model Compression and Inference Acceleration

Deep Neural Networks are known for their applications in the domains like computer vision, natural language processing, speech recognition, pattern recognition etc. Though these models are incredibly powerful they consume a considerable amount of memory bandwidth, storage and other computational resources. These heavy models can be successfully executed on machines with CPU/GPU/TPU support. It becomes difficult for the embedded devices to execute them as they are computationally constrained. In order to ease the deployment of these models onto the embedded devices we need to optimize them. Optimization of the model refers to the decrease in model size without compromising with the performance such as model accuracy, number of flops, and model parameters. We present a hybrid optimisation method to address this problem. Hybrid optimization is a 2-phase technique, pruning followed by quantization. Pruning is the process of eliminating inessential weights and connections in order to reduce the model size. Once the unnecessary parameters are removed, the weights of the remaining parameters are converted into 8-bit integer value and is termed quantization of the model. We verify and validate the performance of this hybrid optimization technique for image classification task on the CIFAR-10 dataset. We performed hybrid optimization process for 3 heavy weight models in this work namely ResNet56, ResNet110 and GoogleNet. On an average, the difference in number of flops and parameters is 40%. The reduction in number of parameters and flops has negligible effect on model performance and the variation in accuracy is less than 2%. Further, the optimized model is deployed on edge devices and embedded platform, NVIDIA Jetson TX2 Module.
Faster R-CNN Transmission Line Multi-target Detection Based on BAM

Aiming at the problems that the existing aerial inspection algorithm in the transmission line is affected by the environment and complex background and other factors, resulting in poor detection effect, low accuracy and poor real-time performance. A recognition algorithm for multi-target detection of transmission lines based on BAM's Faster R-CNN is proposed. Firstly, the structure of the traditional Fast-R-CNN feature extraction network is optimized by using the residual network ResNet50 as its backbone feature extraction network and adding BAM (bottleneck attention module) to the network to improve the visibility and accuracy of the target region in the image. Secondly, we use Softer NMS instead of NMS to improve the non-maximal suppression of the images taken during the inspection. Then combined with the K-means++ clustering algorithm to optimize the anchor parameters. Finally, the FPN feature pyramid network is integrated to further improve the detection accuracy of the algorithm. The experimental results show that the detection speed and accuracy of the improved Faster R-CNN have been effectively improved, achieving a gain of +4.3 mAP.
Toward Explainable Dialogue System Using Two-stage Response Generation

In recent years, neural networks have achieved impressive performance on dialogue response generation. However, most of these models still suffer from some shortcomings, such as yielding uninformative responses and lacking explainable ability. This article proposes a Two-stage Dialogue Response Generation model (TSRG), which specifies a method to generate diverse and informative responses based on an interpretable procedure between stages. TSRG involves a two-stage framework that generates a candidate response first and then instantiates it as the final response. The positional information and a resident token are injected into the candidate response to stabilize the multi-stage framework, alleviating the shortcomings in the multi-stage framework. Additionally, TSRG allows adjusting and interpreting the interaction pattern between the two generation stages, making the generation response somewhat explainable and controllable. We evaluate the proposed model on three dialogue datasets that contain millions of single-turn message-response pairs between web users. The results show that, compared with the previous multi-stage dialogue generation models, TSRG can produce more diverse and informative responses and maintain fluency and relevance.
How a Parachute Accident Helped Jump-Start Augmented Reality: In 1992, Hardware for the First Interactive AR System Literally Fell from the Skies

I climb into an upper-body exoskeleton that's covered in sensors, motors, gears, and bearings, and then lean forward, tilting my head up to press my face against the eyepieces of a vision system hanging from the ceiling. In front of me, I see a large wooden board, painted black and punctuated by a grid of metal holes. The board is real. So is the peg in my hand that I'm trying to move from one hole to another, as fast as I can. When I begin to move the peg, a virtual cone appears over the target hole, along with a virtual surface easing toward it. I can feel the surface as I slide the peg along it toward the cone and into the hole.
Augmenting the Social Presence of Interactive Characters Using Real-time Speech Recognition

Recently, in addition to games, interaction with real virtual agents has been used for job training. Furthermore, multimodal dialog using both voice and gesture recognition is used. However, there is a challenge with conventional batch processing speech recognition: it is difficult to make the virtual agent respond while the user is speaking. Therefore, in this study, we confirm the possibility of enhancing social presence by generating responses using real-time speech recognition in spoken dialog with characters. While the user speaks, by extracting keywords from the speech content in real-time, we generate the emotional response of the virtual agent. The experimental results demonstrate that our approach improves social presence compared with conventional spoken dialog.
Multi-tone Morlet wavelet-based high-frequency resonance parameter estimation

Accurate estimations of high-frequency resonance (HFR) parameters are useful in HFR mitigation, early warning, and status monitoring. This paper proposes a novel multi-tone Morlet wavelet (MTMW) that consists of multiple Morlet wavelets (MWs) with different center frequencies and can be truncated for HFR parameter estimation. The MTMW parameters, i.e., scale factor and center frequency combination, are optimally selected for HFR parameter estimation. Simulation test results indicate that the proposed MTMW-based estimator has higher accuracy than the MW-based estimator and the interpolated discrete Fourier transform. The response times of the MTMW-based estimator for HFR phasor estimation are always shorter than 30 ms.
Digital Ecosystem in Public Services of the Northeastern Local Administrative Organizations: Initial Findings

Due to the rapid change in technology and the epidemic of COVID-19, the private and public sectors have adjusted more readiness to use digital technology services, but public services of local government organizations are not yet concrete. Hence, this work aims to explain the current state and supporting factors of the digital ecosystem in public services of local administrative organizations, The data were collected through semi-structured in-depth interviews and group meetings from 80 key informants directly involved in digital public services at both policy and operational levels. In this phase, the data were collected in 5 research units with 20 key informants and were analyzed using the thematic analysis method under the research framework. From the preliminary results, the organizational structure and knowledge and skills of personnel dimensions are the major factors that are the lowest availability. However, all research units report no problem with the readiness of service recipients and infrastructure in both hardware and software. The results of this research are only the initial findings, and still need to be analyzed and explained further.
Challenges for Electronic Access Control Systems amid pandemic: A Survey

To meet the demands for highest level security of today’s world, a sophisticated security management system is essential. An access control system generally categorized into biometric and non-biometric types based upon contact or contactless in operation. This research work aims to survey the preferences of people, for understanding the role and need of access control systems during the difficult pandemic situation through an online survey. This survey finds that various access control solutions fail to provide the required security during this worldwide pandemic due to their contact-based operations. Henceforth, a feasible integrated electronic access control system requires to be adopted to fulfill the expectations of users amid global pandemic.
Presaging Software Maintainability Using Collaborative Techniques and Arranged Broad View

It has become clear that predicting software maintainability is an important research area in light of the industry&#x2019;s need to reduce overall costs, and maintenance costs in particular. Predicting software maintainability has been the subject of numerous research over the last few decades, but no one is certain which technique is the most effective at doing so. Recently, ensemble techniques, which integrate two or more techniques, have been explored. Maintainability in terms of line code modifications is the focus of this study, which analyses ensemble approaches (homogeneous and heterogeneous). Homogeneous ensembles such as Bagging, Boosting and Random Forest are examined initially because of their well-known homogeneity. This method builds heterogeneous ensembles from the dataset&#x2019;s most accurate members using the stacking generalization technique. A look at the data shows that Gradient Boosting and Extra Trees are the most effective ensembles for all datasets in terms of performance. According to results from the examination of layered generalization-based heterogeneous ensembles, they outperformed all homogeneous ensembles in terms of prediction accuracy
Establishing a Connection between Programming and Proof Writing

This talk introduces an ongoing study which began Summer 2021. We emulate proof writing in novice programmers by having them explain their solution to logic puzzles in the first week of an intro course, as this contains many of the skills used in proof writing without requiring any prerequisite knowledge of mathematical structures. An isomorphic question is asked at the conclusion and checked for improvement. We correlate with their performance in the cs class. To ensure that the improvement is related to the cs course material, other factors (prior math courses, GPA et al) are controlled. Computer science majors typically cover material in logic and proof, either as part of programming course, or as a separate computer class. Conventional wisdom would hold that there are a variety of overlapping skill sets between the subject of logic/proof and writing efficient error-free code, and so instructors rely on this intuition to justify introducing (and in fact frequently requiring) this content to introductory programmers. Our study checks for transfer of skills between computer science and proof writing, as no approach to these logic problems is ever directly a part of the content matter in the computer class. Early results of isomorphic questioning from the start and end of the summer quarter indicate a highly statistically significant correlation, even controlling for other factors such as mathematical background and academic strength.
GhostSiamRPN++: Optimizing SiamRPN++ via GhostModule

SiamRPN++ is a Siamese network based tracker in object tracking and it has excellent performance. However, SiamRPN++ is a ResNet-driven tracker with huge amounts of parameters and calculations. It has extremely strict requirements for hardware and devices without professional GPU are difficult to meet the requirements. So SiamRPN++ should become more lightweight by optimization. In this paper, we propose a lightweight tracker named GhostSiamRPN++ by optimizing SiamRPN++ and replacing the ResNet backbone with a lightweight backbone build by GhostModule. When compared to SiamRPN++, GhostSiamRPN++ reduce 84&#x0025; parameters, 92&#x0025; calculations and 52&#x0025; memory usage and it boost the FPS by 55&#x0025; on GPU and by 545&#x0025; on CPU. Large scale optimization does not bring great loss of performance. When tested on VOT and OTB datasets, GhostSiamRPN++ shows excellent performance in all videos and leading performance in some specific videos and it has 4.5&#x0025; to 13.6&#x0025; lower accuracy than SiamRPN++.
A Block-Based Adaptive Decoupling Framework for Graph Neural Networks

Graph neural networks (GNNs) with feature propagation have demonstrated their power in handling unstructured data. However, feature propagation is also a smooth process that tends to make all node representations similar as the number of propagation increases. To address this problem, we propose a novel Block-Based Adaptive Decoupling (BBAD) Framework to produce effective deep GNNs by utilizing backbone networks. In this framework, each block contains a shallow GNN with feature propagation and transformation decoupled. We also introduce layer regularizations and flexible receptive fields to automatically adjust the propagation depth and to provide different aggregation hops for each node, respectively. We prove that the traditional coupled GNNs are more likely to suffer from over-smoothing when they become deep. We also demonstrate the diversity of outputs from different blocks of our framework. In the experiments, we conduct semi-supervised and fully supervised node classifications on benchmark datasets, and the results verify that our method can not only improve the performance of various backbone networks, but also is superior to existing deep graph neural networks with less parameters.
University website as an Internet marketing tool: a case study of Kazakhstan

Today, more than ever, a university’s website is critical for the promotion of its mission, academic programs and services. Its communications with its target markets must be organized, professional and creative. A successfully created website design attracts visitors and, of course, should increase enrollment. A well-designed and clearly written university website will allow readers to receive all the needed and required information for them to make the proper decisions. Accordingly, research aimed at analyzing various issues of creating an effective university website is gaining significant relevance. The article discusses both the theoretical and practical issues of website activities. A selection of university websites has been analyzed and the issues and challenges discussed.
Exploration on Informatization Reform of C Language Programming Based on Mobile Platform

The informatization of education and teaching based on mobile platform is the trend of teaching development in colleges and universities. With the research background of "C language programming" course, this paper presents a mature teaching scheme with the help of a mobile platform to carry out the exploration and practice of information teaching reform. The introduction of scientific computing, mathematical physics disciplines or engineering technology fields as information cases in the information teaching is designed. In order to guide students to complete information modeling, grammar structure selection, program design, effectiveness test, etc., Internet + technology to build an information teaching mode is fully applied, the students&#x2019; participation, independent learning and active exploration ability are improved, and the improvement and promotion of information teaching level in colleges and universities are realized. A series of digital information resources have been constructed along with the exploration of information teaching reform. At the same time, intelligent teaching, open teaching and information teaching are gradually integrated into the teaching of basic courses.
Rolling Motion: Augmented Reality Animations and Multiplatform Simulation

In the past decade, we have witnessed the emergence of a large number of different computer-based animations and simulations that have the goal to foster better learning of different physics topics. Past studies have shown many benefits of animations and simulations, but for their efficient usage it is very important that teachers are well educated in the teaching material and usage of selected visualizations. Furthermore, studies have proven that augmented reality technology has a potential to reduce cognitive load and improve the quality of physics lectures. Many of these visualizations are generally designed for targeted physics phenomena, and sometimes it is not easy to address specific students’ misconceptions. In this paper, we will present augmented reality animations and a simulation that can generally be useful for teaching about counterintuitive aspects of rolling motion, and specifically address students’ misconceptions about rolling friction and velocity in contact with the ground.
A Deep Learning Network for Action Recognition Incorporating Temporal Attention Mechanism

Although motion recognition is widely used in various research fields, the performance of traditional motion recognition methods is poor in complex environments. In this paper a method for pedestrian action recognition in complex environments is proposed. A network for action recognition incorporating temporal attention mechanism is proposed. The main improvement of the method is as follows: firstly, RCNN network is used for pedestrian detection to get the locations of all pedestrians in videos. Secondly, long and short term memory network (LSTM) is used to extract temporal features. On one hand, the network uses a residual part incorporating a spatial attention mechanism to extract the spatial features, which could reduce the interference from the image background. On the other hand, the Temporal Attention Mechanism (TAM) is introduced, which dynamically allocates video frame sequence weights according to the importance of LSTM output. Finally, experiments are conducted on the UCF101 dataset to verify the improvement of the accuracy and precision of the method.
Generation of Compatible Queries

In order to study query processing efficiency and effectiveness using a simulated text corpus, it is necessary to obtain a set of compatible queries and judgments. The text generation methods implemented in Syntha Corpus make no pretence of being able to generate meaningful natural language. Consequently, it is presently out of the question that ad hoc queries along the lines of those in the TREC1 ad hoc task, could be devised or judged.
Modeling Document Lengths

In some applications, very approximate modeling of the distribution of document lengths will suffice. However, there are many scenarios in which accurate modeling is desirable. For example, significant gains in retrieval effectiveness were achieved at TREC-3 through better normalization of document length [74, 81]. Those effects could not have been observed on a simulated collection in which all documents had the same length. Furthermore, distributions of term co-occurrences, distributions of TFs, and topic mixtures are all affected by the distribution of document lengths.
Tutorial on robotic olfaction via gas-sensitive UAV: technologies and methodologies

When used in a wide range of environments for sniffing tasks, gas-sensitive drones will perform better than common ground-based mobile robots due to their flexibility. In recent years, with the improvement of various related theories, gas-sensitive unmanned aerial vehicle (UAV) has gradually become a hot research direction. Generally speaking, gas-sensitive UAV platform consists of electronic nose and commercial UAV, which can be divided into different application directions according to the configuration of electronic nose module. The research on its application mainly focuses on gas localisation and gas distribution mapping. In this paper, the design and application of gas-sensitive UAV are reviewed, the electronic nose applicable to gas-sensitive UAV is summarised, and the design methods, existing problems and its solutions of gas-sensitive UAV are introduced. After that, the research on gas localisation and gas distribution mapping is summarised and analysed, and the possible research directions in the future are proposed.
A neural network enhanced detection method for GPS with slowly growing error

The slowly growing error in GPS positioning is the difficult type of failure to detect. To solve the problem, an improved detection method based on the neural network has been proposed and implemented in the study. It is found that there is a certain mapping relationship between the constructed test statistics and the fault. A synthesis method of the back propagation neural network model for fault detection and the robust estimation theory for data fusion has been proposed and implemented in this study. The simulation results and the field test show that the method proposed has a better detection performance compared with other algorithms. It can shorten the time of fault detection so as to improve the accuracy and integrity of the INS/GPS system in required navigation performance.
Geophysical well logs analyses for mineralogy and pore-typing determinations of Barra Velha Formation carbonates, Santos Basin

The pre-salt lacustrine reservoirs from Barra Velha Formation (BVF), Santos Basin, were deposited during the sag phase of opening South Atlantic Ocean.Small variations in climate or tectonic can induce large variations in the lake level and chemical composition of waters, resulting in heterogeneous deposition composed by carbonate particles with a wide range of sizes and shapes mixed to form heterogeneous depositional textures.Understand and predict their mineralogical and petrophysical heterogeneity is not a simple task.Several works describe the genesis and tectonic of pre-salt carbonates, however, few ones show and interpret petrophysical and log wells.Here, we use the public geophysical well logs dataset to estimate the lithology and pore types from four wells ̶ 3-BRSA-883 (3-RJS-680), 3-BRSA-496 (3-RJS-646), 9-BRSA-716 (9-RJS-660) and 9-BRSA-908D (9-RJS-686D) ̶ located in south-southeast of Tupi Oilfield, throughout the BVF.Results indicate that the BVF is formed by calcitic and dolomitic matrix, where the firsts 10-20 m are characterized by dolomitization of rudstones and grainstones in well 3-BRSA-496, while in well 9-BRSA-716 it occurs in laminites and stromatolites.Regarding the petrophysical results, the vuggy porosity was characterized using T1LM X T2LM, which T2LM around 300 ms.This kind of porosity was observed in all four studied wells.The interpreted data contributes to a better understanding the variability of matrix and porosity through geophysical results.Hence, this model can help us to predict the mineralogy to other close wells and provide important parameters to the reservoir.