Reviewerly/exHarmony huggingface dataset:
Domain Knowledge-Informed Self-supervised Representations for Workout Form Assessment

Maintaining proper form while exercising is important for preventing injuries and maximizing muscle mass gains. Detecting errors in workout form naturally requires estimating human’s body pose. However, off-the-shelf pose estimators struggle to perform well on the videos recorded in gym scenarios due to factors such as camera angles, occlusion from gym equipment, illumination, and clothing. To aggravate the problem, the errors to be detected in the workouts are very subtle. To that end, we propose to learn exercise-oriented image and video representations from unlabeled samples such that a small dataset annotated by experts suffices for supervised error detection. In particular, our domain knowledge-informed self-supervised approaches (pose contrastive learning and motion disentangling) exploit the harmonic motion of the exercise actions, and capitalize on the large variances in camera angles, clothes, and illumination to learn powerful representations. To facilitate our self-supervised pretraining, and supervised finetuning, we curated a new exercise dataset, Fitness-AQA ( https://github.com/ParitoshParmar/Fitness-AQA ), comprising of three exercises: BackSquat, BarbellRow, and OverheadPress. It has been annotated by expert trainers for multiple crucial and typically occurring exercise errors. Experimental results show that our self-supervised representations outperform off-the-shelf 2D- and 3D-pose estimators and several other baselines. We also show that our approaches can be applied to other domains/tasks such as pose estimation and dive quality assessment.
An Equivalent Time-Optimal Problem to find Energy-Optimal Paths for Skid-Steer Rovers

A skid-steer rover's power consumption is highly dependent on the turning radius of its path. For example, a point turn consumes a lot of power compared to a straight-line motion. Thus, in path planning for this kind of rover, turning radius is a factor that should be considered explicitly. There is a lack of any analytical approach in literature for finding energy-optimal paths for skid-steer rovers. The key contribution of this work is an energy-time equivalency theorem, for skid-steer rovers on obstacle-free hard ground. The theorem converts the energy-optimal problem into an equivalent time-optimal problem. This non-intuitive result stems from the fact that with this model of the system the total energy is fully parameterized by the geometry of the path alone. Hence, instead of directly solving the energy-optimal path planning problem, which is highly nonlinear, the equivalent time-optimal problem can be solved. Furthermore, experimental results are provided to experimentally prove the equivalency theorem while using Husky UGV skid-steer rover.
Customer satisfaction in service delivery with artificial intelligence: A meta-analytic study

ABSTRACT Purpose: This study intends to identify the main background and consequent constructs that form consumer satisfaction in providing services using artificial intelligence (AI) and their magnitudes. Originality/value: This work seeks to fill a gap arising from the scarcity of meta-analytic research on service delivery with AI and also its relationship to consumer satisfaction. Design/methodology/approach: The study adopted the meta-analytic method, and its development followed three phases: 1. research; 2. collection; and 3. coding and data analysis. We analyzed 19 articles published in journals of international relevance from January 2000 to December 2020, present on the Web of Science and Science Direct platforms, totaling 128 observations and 28 topic-related. Findings: Five background constructs and one consequent construct were identified, from which an integrated model was built to illustrate the relationships between consumer satisfaction in intelligent services. The results show that consumer satisfaction in the provision of services is significantly correlated to the adoption of artificial intelligence. Then, the integrated quantitative evaluation that was performed in this study aims to contribute to future empirical evidence in such a way that an increase in the scope of studies on artificial intelligence and consumer satisfaction occurs, based on the analysis of the following constructs: perceived value, perceived features, perception of quality, marketing orientation, identification with the service and behavior of using AI in services.
Social media marketing adoption by agriculturists: A TAM based study

Purpose : This research is intended to identify and analyse the underlying factors in the adoption of social media among agriculturists in South India.&#x0D; Theoretical Framework: Marketing through social media such as Whatsapp, Facebook and YouTube, particularly in India, is not well understood due to the lack of research in agricultural marketing. This paper bridges the research gap by applying the Theory of Planned Behaviour (TPB) and TAM.&#x0D; Design/ Methodology/Approach: A structured questionnaire is adopted for data collection. Primary data was collected from 320 agriculturalists in Tamilnadu, South India.&#x0D; Findings: Multiple regression is used to test the significance of the research model. It demonstrates that the perceived credibility, reference group, infotainment, and perceived usefulness had a significant positive impact on the adoption of social media marketing. At the same time, perceived ease of use has a negative effect on attitude towards the adoption of social media marketing.&#x0D; Research, Practical &amp; Social Implications: The implication drawn from this study helps the researchers and agriculturalists use social media to increase farm product marketability.&#x0D; Originality/ Value: This research adds value to the agricultural marketing literature by exploring the novel determinants of the adoption of social media.
Knowledge-Enriched Attention Network With Group-Wise Semantic for Visual Storytelling

As a technically challenging topic, visual storytelling aims at generating an imaginary and coherent story with narrative multi-sentences from a group of relevant images. Existing methods often generate direct and rigid descriptions of apparent image-based contents, because they are not capable of exploring implicit information beyond images. Hence, these schemes could not capture consistent dependencies from holistic representation, impairing the generation of reasonable and fluent stories. To address these problems, a novel knowledge-enriched attention network with group-wise semantic model is proposed. Three main novel components are designed and supported by substantial experiments to reveal practical advantages. First, a knowledge-enriched attention network is designed to extract implicit concepts from external knowledge system, and these concepts are followed by a cascade cross-modal attention mechanism to characterize imaginative and concrete representations. Second, a group-wise semantic module with second-order pooling is developed to explore the globally consistent guidance. Third, a unified one-stage story generation model with encoder-decoder structure is proposed to simultaneously train and infer the knowledge-enriched attention network, group-wise semantic module and multi-modal story generation decoder in an end-to-end fashion. Substantial experiments on the visual storytelling datasets with both objective and subjective evaluation metrics demonstrate the superior performance of the proposed scheme as compared with other state-of-the-art methods. The source code of this work can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://mic.tongji.edu.cn</uri> .
The Digital Big Bang in the Metaverse Era

Since the Internet's widespread adoption in the 1990s, cyberspace has evolved by leveraging diversified mediums like text, images, 3D worlds, virtual reality, and mobile AR. Several computer-mediated virtual environments have demonstrated successful examples in cyberspace evolution, such as Twitter, Zoom, Meta Horizon, Pokémon Go, and Axie Infinity. As mentioned earlier, the non permanent and disconnected virtual worlds have afforded us varying degrees of digital change. In contrast, the term "metaverse" was invented to continuously allow the digital transformation of all aspects of our physical life. As such, the metaverse is the concept of an immersive Internet that is vast, unified, permanent, and shared. Emerging technologies such as Extended Reality, 5G, and Human-centric Artificial Intelligence may make the metaverse seem futuristic, yet cyberspace's digital "big bang" is not far off. This talk discusses the metaverse concept and building blocks of the metaverse. Furthermore, we illustrate various metaverse applications and the critical challenges of building metaverse cities.
Open-Set Semi-Supervised Object Detection

Recent developments for Semi-Supervised Object Detection (SSOD) have shown the promise of leveraging unlabeled data to improve an object detector. However, thus far these methods have assumed that the unlabeled data does not contain out-of-distribution (OOD) classes, which is unrealistic with larger-scale unlabeled datasets. In this paper, we consider a more practical yet challenging problem, Open-Set Semi-Supervised Object Detection (OSSOD). We first find the existing SSOD method obtains a lower performance gain in open-set conditions, and this is caused by the semantic expansion, where the distracting OOD objects are mispredicted as in-distribution pseudo-labels for the semi-supervised training. To address this problem, we consider online and offline OOD detection modules, which are integrated with SSOD methods. With the extensive studies, we found that leveraging an offline OOD detector based on a self-supervised vision transformer performs favorably against online OOD detectors due to its robustness to the interference of pseudo-labeling. In the experiment, our proposed framework effectively addresses the semantic expansion issue and shows consistent improvements on many OSSOD benchmarks, including large-scale COCO-OpenImages. We also verify the effectiveness of our framework under different OSSOD conditions, including varying numbers of in-distribution classes, different degrees of supervision, and different combinations of unlabeled sets.
K-centered Patch Sampling for Efficient Video Recognition

For decades, it has been a common practice to choose a subset of video frames for reducing the computational burden of a video understanding model. In this paper, we argue that this popular heuristic might be sub-optimal under recent transformer-based models. Specifically, inspired by that transformers are built upon patches of video frames, we propose to sample patches rather than frames using the greedy K-center search, i.e., the farthest patch to what has been chosen so far is sampled iteratively. We then show that a transformer trained with the selected video patches can outperform its baseline trained with the video frames sampled in the traditional way. Furthermore, by adding a certain spatiotemporal structuredness condition, the proposed K-centered patch sampling can be even applied to the recent sophisticated video transformers, boosting their performance further. We demonstrate the superiority of our method on Something–Something and Kinetics datasets.
Free Hand Text Displaying Through Hand Gestures Using MediaPipe

Over years, presenting the content on chalk boards or through power point presentations have been following in many sectors. Digitalization of things across the world can be seen these days but having an idea to make model which operates on gestures, that doesn’t require any physical contact with keyboard, mouse or any kind of hardware devices drives the attention of many users across the globe. Interaction with computers using gestures can be implemented for text displaying which helps people who are having physical disability of joints can use this model for their presentation. This idea also helps for electronic waste management and provides a healthy environment for future generations. The aim of this research is to brief about the idea to mount digitalization using gestures in educational and IT sectors.
ConCAD: A Tool for Interactive Detection of Code Anomalies

Code anomalies are indicators of software design can potentially decrease software maintainability and they are associated with an explicit set of refactoring actions. However, Detection of code anomalies is traditionally supported by Non-Interactive Detection (NID) techniques. These techniques encourage developers to reveal anomalies in later revisions or versions of a program, implying in counter-productive or even prohibitive refactoring actions. In this context we created ConCAD as an eclipse plug-in that enable Interactive Detection (ID) of code anomalies. This tool provide developers’ support to reveal anomalies when code fragments are still being edited, encouraging early and continuous detection of code anomalies.
Training university students for the development of innovative products and technologies

This article describes a way to solve the priority task of training personnel who is able to develop vital products and technologies. Based on the analysis and study of international and Russian practices and tools, the advantages and disadvantages of the project approach in training students are revealed. Identified and described projects aimed at solving the strategic problems of the Caspian macro-region, including the Astrakhan region. The methodological basis for the application of the patterns of the activity theory of learning for the stage formation of implementing the project activities among students of natural science and technical areas of training is described, since their future professional activity is aimed at solving various practically objects. The process of training students is aimed at mastering the sequence of actions for developing projects. It is emphasized mastering the content of project activities, provided that students form actions related to goal-setting of the project; putting forward the idea of developing an object with given properties; determining the physical phenomena and processes transforming the object properties to the required ones; designing and implementing the planned final product. The results of teaching students project activities through their implementation at the initial stage of educational projects using the knowledge of some subjects and in the future – mutual interdisciplinary projects aimed at solving the real applied regional problems are presented.
A Possible Classification for Metaheuristic Optimization Algorithms in Engineering and Science

A Possible Classification for Metaheuristic Optimization Algorithms in Engineering and Science
IntelliTC

Developers often change types of program elements. Such refactoring often involves updating not only the type of the element itself, but also the API of all type-dependent references in the code, thus it is tedious and time-consuming. Despite type changes being more frequent than renamings, just a few current IDE tools provide partially-automated support only for a small set of hard-coded types. Researchers have recently proposed a data-driven approach to inferring API rewrite rules for type change patterns in Java using code commits history. In this paper, we build upon these recent advances and introduce IntelliTC --- a tool to perform Java type change refactoring. We implemented it as a plugin for IntelliJ IDEA, a popular Java IDE developed by JetBrains. We present 3 different ways of providing support for such a refactoring from the standpoint of the user experience: Classic mode, Suggested Refactoring, and Inspection mode. To evaluate these modalities of using IntelliTC, we surveyed 22 experienced software developers. They positively rated the usefulness of the tool.
A named entity recognition method towards product reviews based on BiLSTM-attention-CRF

Named entity recognition (NER) towards product review intends to identify domain dependent named entities (e.g., organisation name, product name, etc.) from product reviews. Due to the fragmentation and non-construction of product reviews, traditional methods are difficult to capture the domain feature information and dependencies precisely. To solve the problem, we proposed a NER method towards product reviews based on BiLSTM-attention-CRF. Firstly, three kinds of features (character, word and part of speech) are integrated into the feature representation of texts. The final feature vector is obtained through training, mapping and linking the selected features. Then, the BiLSTM network is built to extract text features, and the attention mechanism is adopted to strengthen the capture of local features. Finally, CRF is applied to annotate and identify the entity. Compared with existing models, it is demonstrated that the proposed method can effectively recognise named entities from product reviews.
Data-Free Backdoor Removal Based on Channel Lipschitzness

Recent studies have shown that Deep Neural Networks (DNNs) are vulnerable to the backdoor attacks, which leads to malicious behaviors of DNNs when specific triggers are attached to the input images. It was further demonstrated that the infected DNNs possess a collection of channels, which are more sensitive to the backdoor triggers compared with normal channels. Pruning these channels was then shown to be effective in mitigating the backdoor behaviors. To locate those channels, it is natural to consider their Lipschitzness, which measures their sensitivity against worst-case perturbations on the inputs. In this work, we introduce a novel concept called Channel Lipschitz Constant (CLC), which is defined as the Lipschitz constant of the mapping from the input images to the output of each channel. Then we provide empirical evidences to show the strong correlation between an Upper bound of the CLC (UCLC) and the trigger-activated change on the channel activation. Since UCLC can be directly calculated from the weight matrices, we can detect the potential backdoor channels in a data-free manner, and do simple pruning on the infected DNN to repair the model. The proposed Channel Lipschitzness based Pruning (CLP) method is super fast, simple, data-free and robust to the choice of the pruning threshold. Extensive experiments are conducted to evaluate the efficiency and effectiveness of CLP, which achieves state-of-the-art results among the mainstream defense methods even without any data. Source codes are available at https://github.com/rkteddy/channel-Lipschitzness-based-pruning.
Deep Quantified Visibility Estimation for Traffic Image

Image-based quantified visibility estimation is an important task for both atmospheric science and computer vision. Traditional methods rely largely on meteorological observation or manual camera calibration, which restricts its performance and generality. In this paper, we propose a new end-to-end pipeline for single image-based quantified visibility estimation by an elaborate integration between meteorological physical constraint and deep learning architecture design. Specifically, the proposed Deep Quantified Visibility Estimation Network (abbreviated as DQVENet) consists of three modules, i.e., the Transmission Estimation Module (TEM), the Depth Estimation Module (DEM), and the Extinction coEfficient Estimation Module (E3M). Casting on these modules, the meteorological prior constraint can be combined with deep learning. To validate the performance of DQVENet, this paper also constructs a traffic image dataset (named QVEData) with accurate visibility calibration. Experimental results compared with many state-of-the-art methods on QVEData demonstrate the effectiveness and superiority of DQVENet.
DIA-TTS: Deep-Inherited Attention-Based Text-to-Speech Synthesizer

Text-to-speech (TTS) synthesizers have been widely used as a vital assistive tool in various fields. Traditional sequence-to-sequence (seq2seq) TTS such as Tacotron2 uses a single soft attention mechanism for encoder and decoder alignment tasks, which is the biggest shortcoming that incorrectly or repeatedly generates words when dealing with long sentences. It may also generate sentences with run-on and wrong breaks regardless of punctuation marks, which causes the synthesized waveform to lack emotion and sound unnatural. In this paper, we propose an end-to-end neural generative TTS model that is based on the deep-inherited attention (DIA) mechanism along with an adjustable local-sensitive factor (LSF). The inheritance mechanism allows multiple iterations of the DIA by sharing the same training parameter, which tightens the token-frame correlation, as well as fastens the alignment process. In addition, LSF is adopted to enhance the context connection by expanding the DIA concentration region. In addition, a multi-RNN block is used in the decoder for better acoustic feature extraction and generation. Hidden-state information driven from the multi-RNN layers is utilized for attention alignment. The collaborative work of the DIA and multi-RNN layers contributes to outperformance in the high-quality prediction of the phrase breaks of the synthesized speech. We used WaveGlow as a vocoder for real-time, human-like audio synthesis. Human subjective experiments show that the DIA-TTS achieved a mean opinion score (MOS) of 4.48 in terms of naturalness. Ablation studies further prove the superiority of the DIA mechanism for the enhancement of phrase breaks and attention robustness.
UAV Path Planning Method Based on Modified White Shark Optimization

Path planning of Unmanned Aerial Vehicle (UAV) in the three-dimensional environment is an important and complex work. In this paper, a UAV path planning method based on modified WSO (White Shark Optimization) is proposed. Aiming at the problem that traditional WSO has slow convergence speed and is difficult to get away local optimal, the proposed method modifies the probability parameters of WSO to adapt to the optimization process and further coordinate all stages of the algorithm search process. In addition, referring to the wave theory, the trajectory motion equation of fluid particles in the micro amplitude wave theory is introduced, and the exploration process of the algorithm is improved combined with the spiral search method of the whale optimization algorithm. The experimental results demonstrate that the modified algorithm which studied in this paper has faster convergence speed and better ability to get away from local optimization. Using the modified algorithm for path planning, an optimal path avoiding obstacles and threat areas can be obtained, which proves the effectiveness of the proposed method in UAV path planning.
Pix2Pix Generative Adversarial Network with ResNet for Document Image Denoising

Noise degrades quality of scanned document images and adversely affects the accuracy of document digitization and text extraction tasks such as optical character recognition (OCR). Denoising and quality enhancement is the preprocessing stage of the processing pipeline in OCR. This research work proposes an effective end-to-end framework that uses the pre-trained pix2pix Generative Adversarial Network (GAN) to denoise degraded electronic document images. To increase the capacity of the generator network, a variation of the baseline model is developed by replacing the U-net architecture with ResNet6. Along with the discriminator patchGAN in pix2pix model, a pipeline has been developed to extract the patches from input images, predict clean patches using the trained model and finally merge the output patches smoothly. For training, a noisy scanned document dataset created by synthetically adding noises has been utilized to clean the images. Finally, the proposed model is tested by performing quantitative analysis based on different metrics - Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) as well as qualitative analysis by using OCR test on test dataset and real-time documents.