Reviewerly/exHarmony huggingface dataset:
Multiple Pedestrians and Vehicles Tracking in Aerial Imagery Using a Convolutional Neural Network

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for more accurate and stable tracking. Moreover, we investigate the influence of the Squeeze-and-Excitation layers and Online Hard Example Mining on the performance of AerialMPTNet. To the best of our knowledge, we are the first to use these two for regression-based Multi-Object Tracking. Additionally, we studied and compared the L1 and Huber loss functions. In our experiments, we extensively evaluate AerialMPTNet on three aerial Multi-Object Tracking datasets, namely AerialMPT and KIT AIS pedestrian and vehicle datasets. Qualitative and quantitative results show that AerialMPTNet outperforms all previous methods for the pedestrian datasets and achieves competitive results for the vehicle dataset. In addition, Long Short-Term Memory and Graph Convolutional Neural Network modules enhance the tracking performance. Moreover, using Squeeze-and-Excitation and Online Hard Example Mining significantly helps for some cases while degrades the results for other cases. In addition, according to the results, L1 yields better results with respect to Huber loss for most of the scenarios. The presented results provide a deep insight into challenges and opportunities of the aerial Multi-Object Tracking domain, paving the way for future research.
On clustering categories of categorical predictors in generalized linear models

We propose a method to reduce the complexity of Generalized Linear Models in the presence of categorical predictors. The traditional one-hot encoding, where each category is represented by a dummy variable, can be wasteful, difficult to interpret, and prone to overfitting, especially when dealing with high-cardinality categorical predictors. This paper addresses these challenges by finding a reduced representation of the categorical predictors by clustering their categories. This is done through a numerical method which aims to preserve (or even, improve) accuracy, while reducing the number of coefficients to be estimated for the categorical predictors. Thanks to its design, we are able to derive a proximity measure between categories of a categorical predictor that can be easily visualized. We illustrate the performance of our approach in real-world classification and count-data datasets where we see that clustering the categorical predictors reduces complexity substantially without harming accuracy.
TranSmart: A Practical Interactive Machine Translation System

Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed. This technique report introduces TranSmart, a practical human-machine interactive translation system that is able to trade off translation quality and efficiency. Compared to existing publicly available interactive translation systems, TranSmart supports three key features, word-level autocompletion, sentence-level autocompletion and translation memory. By word-level and sentence-level autocompletion, TranSmart allows users to interactively translate words in their own manners rather than the strict manner from left to right. In addition, TranSmart has the potential to avoid similar translation mistakes by using translated sentences in history as its memory. This report presents major functions of TranSmart, algorithms for achieving these functions, how to use the TranSmart APIs, and evaluation results of some key functions. TranSmart is publicly available at its homepage (https://transmart.qq.com).
Towards Robust Vision Transformer

Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most vision tasks. However, existing ViTs focus on the standard accuracy and computation cost, lacking the investigation of the intrinsic influence on model robustness and generalization. In this work, we conduct systematic evaluation on components of ViTs in terms of their impact on robustness to adversarial examples, common corruptions and distribution shifts. We find some components can be harmful to robustness. By using and combining robust components as building blocks of ViTs, we propose Robust Vision Transformer (RVT), which is a new vision transformer and has superior performance with strong robustness. We further propose two new plug-and-play techniques called position-aware attention scaling and patch-wise augmentation to augment our RVT, which we abbreviate as RVT*. The experimental results on ImageNet and six robustness benchmarks show the advanced robustness and generalization ability of RVT compared with previous ViTs and state-of-the-art CNNs. Furthermore, RVT-S* also achieves Top-1 rank on multiple robustness leaderboards including ImageNet-C and ImageNet-Sketch. The code will be available at \url{https://github.com/alibaba/easyrobust}.
High-speed multimode fiber imaging system based on conditional generative adversarial network

The multimode fiber (MMF) has great potential to transmit high-resolution images with less invasive methods in endoscopy due to its large number of spatial modes and small core diameter. However, spatial modes crosstalk will inevitably occur in MMFs, which makes the received images become speckles. A conditional generative adversarial network (GAN) composed of a generator and a discriminator was utilized to reconstruct the received speckles. We conduct an MMF imaging experimental system of transmitting over 1 m MMF with a 50 μm core. Compared with the conventional method of U-net, this conditional GAN could reconstruct images with fewer training datasets to achieve the same performance and shows higher feature extraction capability.
FedScale: Benchmarking Model and System Performance of Federated Learning

We present FedScale, a diverse set of challenging and realistic benchmark datasets to facilitate scalable, comprehensive, and reproducible federated learning (FL) research. FedScale datasets are large-scale, encompassing a diverse range of important FL tasks, such as image classification, object detection, language modeling, speech recognition, and reinforcement learning. For each dataset, we provide a unified evaluation protocol using realistic data splits and evaluation metrics. To meet the pressing need for reproducing realistic FL at scale, we have also built an efficient evaluation platform to simplify and standardize the process of FL experimental setup and model evaluation. Our evaluation platform provides flexible APIs to implement new FL algorithms and includes new execution backends with minimal developer efforts. Finally, we perform indepth benchmark experiments on these datasets. Our experiments suggest fruitful opportunities in heterogeneity-aware co-optimizations of the system and statistical efficiency under realistic FL characteristics. FedScale is open-source with permissive licenses and actively maintained,1 and we welcome feedback and contributions from the community.
University as an analogue of the neural network

A mathematical model is proposed, which allows to estimate the number of successful university graduates based on parameters characterizing the effectiveness of vertical (lectures, seminars) and horizontal (peer education) training. It is shown that with low effectiveness of vertical learning, an effective means of improving the quality of education in general is the targeted formation of horizontal groups within which information is exchanged. It is shown that with extremely low quality of vertical learning, the behavior of the “university” system is characterized by phase transitions: with a smooth increase in the parameter characterizing the intensity of horizontal learning, there is an abrupt increase in the number of successful graduates. It has been established that with the existence of pronounced links between individual lecture courses, the “university” system becomes an analogue of a neural network.
MindReader

Knowledge Graphs (KGs) have been integrated in several models of recommendation to augment the informational value of an item by means of its related entities in the graph. Yet, existing datasets only provide explicit ratings on items and no information is provided about users' opinions of other (non-recommendable) entities. To overcome this limitation, we introduce a new dataset, called the MindReader dataset, providing explicit user ratings both for items and for KG entities. In this first version, the MindReader dataset provides more than 102 thousands explicit ratings collected from 1,174 real users on both items and entities from a KG in the movie domain. This dataset has been collected through an online interview application that we also release as open source. As a demonstration of the importance of this new dataset, we present a comparative study of the effect of the inclusion of ratings on non-item KG entities in a variety of state-of-the-art recommendation models. In particular, we show that most models, whether designed specifically for graph data or not, see improvements in recommendation quality when trained on explicit non-item ratings. Moreover, for some models, we show that non-item ratings can effectively replace item ratings without loss of recommendation quality. This finding, in addition to an observed greater familiarity from users towards certain descriptive entities than movies, motivates the use of KG entities for both warm and cold-start recommendations.
Real-Time Collection Method of Athletes’ Abnormal Training Data Based on Machine Learning

Real-time collection of athletes’ abnormal training data can improve the training effect of athletes. This paper studies the real-time collection method of athletes’ abnormal training data based on machine learning. The main motivation of this paper is to collect the athletes’ abnormal training data in time, which can help to evaluate and improve the training effect. Four sensor nodes are arranged in the upper and lower limbs of athletes to collect the angular velocity, acceleration, and magnetic field strength data of athletes in training state. The data are sent to the data transmission base station through wireless sensors, and the data transmission base station transmits the data to the data processing terminal. The data processing terminal calculates the difference between the sample values of each sensor to obtain the data dispersion of each sensor. The features of each dimension data in a time domain and frequency domain are obtained by using the dispersion degree to construct 32-dimensional feature vectors, and the extracted feature vectors are input into the hidden Markov model. The forward algorithm is used to obtain the probability of the final observation sequence, so as to realize the final collection of athletes’ abnormal training data. The experimental results show that the accuracy and recall rate of the abnormal data collected by this method is higher than 98%, which requires less time.
Exploratory and exploitative innovation influenced by contextual leadership, environmental dynamism and innovation climate

Purpose Organisations have to be ambidextrous to survive in modern times. This study, therefore, aims to investigate the influence of contextual leadership on exploratory and exploitative innovation. Environmental dynamism was the moderator in this relationship, and innovation climate was the mediator. Design/methodology/approach The research design was a quantitative study, using a Web-based survey questionnaire, which consisted of valid and reliable scales. There were 1,204 respondents who completed the survey. Analyses included reliability, validity tests and structural equation modelling to test the hypothesised relationships among the variables. Findings The results show that exploitative and exploratory innovation is predicted by the innovation climate, which in turn is predicted by contextual leadership. The findings include a slight moderating effect of environmental dynamism on these relationships. The results suggest that contextual leadership is a significant predictor for improving innovation climate. Practical implications As contextual leadership explains 33% of the variance in organisational climate, companies can benefit from developing their leaders to create climates that promote innovation. At increased levels of environmental dynamism, innovation efforts should increase. Originality/value Contextual leadership is a crucial element to build innovation-friendly workplaces. The study addresses the gap in research on the influence of contextual leadership on exploitative and exploratory innovation with the mediating and moderator effect on this relationship.
The Potential of The Electronic Information-Educational Environment of A University In Professional Education: Trends And Prospects

At present, the educational paradigm of professional education shows a tendency to change towards the creation of electronic information-educational environments of universities. This trend is determined by the global conditions of the COVID-19 pandemic, as well as national state interests. The problem of the study lies in substantiating the potential of the electronic information-educational environment of a university in professional education in the context of global changes. The goal of the study is to reveal the potential of university electronic information-educational environments in professional education and identify the trends and perspectives of creating electronic information-educational environments of higher education institutions. Results: A characteristic of the electronic information-educational environment of a university is provided, and its components and resources are identified. The predominant tendencies in the development of e-learning in modern professional education are revealed. The advantages of electronic information-educational environments of universities in professional education are allocated. The prospects for the development of electronic information environments of universities are substantiated.
Addressing virtual reality misclassification: A hardware‐based qualification matrix for virtual reality technology

Abstract Through its unique sensory synchronized design, virtual reality (VR) provides a convincing, user‐centred experience of highly controllable scenarios. Importantly, VR is a promising modality for healthcare, where treatment efficacy has been recognized for a range of conditions. It is equally valuable across wider research disciplines. However, there is a lack of suitable criteria and consistent terminology with which to define VR technology. A considerable number of studies have misclassified VR hardware (e.g. defining laptops as VR), hindering validity and research comparisons. This review addresses these limitations and establishes a standardized VR qualification framework. As a result of a comprehensive theoretical and literature review, the hardware‐based VR qualification matrix is proposed. The matrix criteria consist of (1) three‐dimensional (3D) synchronized sensory stimulation; (2) degrees of freedom tracking; and (3) visual suppression of physical stimuli. To validate the model and quantify the current scale/diversity of VR misclassification, a 2019 sectional review of health‐related studies was conducted. Of the 115 studies examined against standardized criteria, 35.7% utilized VR, 31.3% misclassified VR, 18.3% were considered quasi‐VR, and 14.8% omitted critical specifications. The proposed model demonstrates good validity and reliability for qualifying and classifying VR. Key Practitioner Messages Virtual reality (VR) therapy has gained rapid empirical support, although many practitioners do not understand the difference between genuine and less‐realistic VR variations. That has resulted from an evident lack of suitable criteria to define VR across a range of studies and protocols. Our proposed hardware‐based virtual reality qualification matrix addresses issues to do with misclassification, via the introduction of standardised criteria. Applying the matrix to existing literature has revealed that more than 30% of VR studies use hardware that does not fit the high standards of rigour required for immersion in a simulated space. The model is a practical tool researchers and practitioners can use to quality and verify VR standards across research studies.
How do consumers use emoji in a food‐related context? Insights for the design and interpretation of emoji questionnaires

Abstract The present research contributed to the growing body of research regarding the use of emoji in sensory and consumer research by filling an acknowledged knowledge gap. Its specific aim was to explore the meaning of emoji in a food‐related context. The empirical approach involved 357 consumers who were tasked with writing a social media post or text message relating to a situation involving eating/drinking which was to be centered around a specific emoji. A total of 21 facial emoji were included in the research. As expected, emoji were used to convey different meanings, including degree of liking/disliking, emotional associations, gestures and expressions. The emoji were also used as part of messages describing negative and positive disconfirmation of expectations, which points to the importance of measuring expectations in a food‐related context. Two emoji were identified as unsuitable for future research due to their nonconsensual meaning ( and ). Practical Applications Emoji have significant potential as a research tool, stretching beyond cross‐cultural research or studies involving children, to general applications in food‐related consumer research. This is further enhanced by their versatility which includes use in emotion questionnaires, open‐ended questions and web‐scraped content. The present research provides recommendations for the design of emoji questionnaires, as well as insights on emoji meanings in a food‐related context. Results also highlight the relevance of negative and positive disconfirmation of expectations in shaping emotional associations with eating/drinking experiences, and, as such, serve as a reminder to include these important determinants of consumer behavior in future emotion‐focused research.
Innovation in banking: fusion of artificial intelligence and blockchain

Purpose This study aims to analyze the importance of disruptive technological innovations on qualitative service delivery and their impact on the investment banks’ employee performance. Design/methodology/approach The cluster sampling method has been used to collect the primary data from the 250 respondents from foreign investment banks. Variables used are employee performance, service delivery, technology, security, operations, strategy and quality through chi-square, linear stepwise multiple regression analysis and correlation. Findings Storage network, operating cost, client reporting, cloud system and money laundering are the highest and most significant predictors of employee performance. Employee performance multiplies every unit with a strategic solution owing to positive and robust correlation (0.944). Fusion technology-based banks offer quality service to their clients. Originality/value A combination of artificial intelligence and blockchain ensures increasing automation to improve efficiency and reduce the operating cost creating a seamless integration in fraud detection, customer support, risk management, security, digitization and automation process, algorithmic trading, wealth management, etc.
A brief comparative study of the potentialities and limitations of machine-learning algorithms and statistical techniques

Machine learning is a popular way to find patterns and relationships in high complex datasets. With the nowadays advancements in storage and computational capabilities, some machine-learning techniques are becoming suitable for real-world applications. The aim of this work is to conduct a comparative analysis of machine learning algorithms and conventional statistical techniques. These methods have long been used for clustering large amounts of data and extracting knowledge in a wide variety of science fields. However, the central knowledge of the different methods and their specific requirements for the data set, as well as the limitations of the individual methods, are an obstacle for the correct use of these methods. New machine learning algorithms could be integrated even more strongly into the current evaluation if the right choice of methods were easier to make. In the present work, some different algorithms of machine learning are listed. Four methods (artificial neural network, regression method, self-organizing map, k-means al-algorithm) are compared in detail and possible selection criteria are pointed out. Finally, an estimation of the fields of work and application and possible limitations are provided, which should help to make choices for specific interdisciplinary analyses.
Auto Block IoT: A Forensics Framework for Connected Vehicles

Abstract The increase in the number of vehicles has led to the increase in number of accidents. The rise in connected vehicles and autonomous vehicles will increase the level of forensics and investigations required in case of an accident. A hindrance to the investigation is the reliability of data. This paper proposes a blockchain framework to ensure that the vehicular data at the time of accident is stored in a tamper-proof and reliable way for further analysis by various stakeholders. A test bed using IoT, IPFS and Etherum test network is proposed and evaluated to study the feasibility of the proposed approach.
Convex Variational Inference for Multi-Hypothesis Fractional Belief Propagation Based Data Association in Multiple Target Tracking Systems

The success of Multi-hypothesis tracking(MHT) lies in the use of multiple scans, which can often yield improved tracking performance over single scan based data association methods in radar-centric multi-target tracking(MTT) systems. However, with the increase of the number of targets associated with measurements, there is an exponentially increasing need for formulating potential hypotheses, of which the computational cost may be prohibitively expensive. In this paper, a multi-hypothesis fractional belief propagation (MHFBP) based data association algorithm is proposed by the use of a probabilistic graph model for both the previous trajectories and the current measurements. To achieve this idea, there are two main steps. First, the trajectory-related indicators associated state variables are created by making use of a convex fractional free energy (FFE)function. Second, the convex optimization algorithm is used for the objective function and the fractional belief propagation (FBP) is exploited to obtain the best marginal belief of measurement for target association. In this way, incorrect hypotheses with extremely low probability can be eliminated. Finally, the proposed method is applied to multiple scenarios for MTT by indoor radar system. From the results, we can observe that it provides higher tracking performance compared with the classical MHT, feature-aided MHT(FA-MHT) and MHT-belief propagation (MHT-BP). In addition, we see that the computational burden of the proposed method is reduced significantly to avoid the phenomenon of exponential growth with increasing the number of targets.