Reviewerly/exHarmony huggingface dataset:
Transfer Deep Learning Along With Binary Support Vector Machine for Abnormal Behavior Detection

Today, machine learning and deep learning have paved the way for vital and critical applications such as abnormal detection.Despite the modernity of transfer learning, it has proved to be one of the crucial inventions in the field of deep learning because of its promising results.For the purpose of this study, transfer learning is utilized to extract human motion features from RGB video frames to improve detection accuracy.A convolutional neural network (CNN) based on Visual Geometry Group network 19 (VGGNet-19) pre-trained model is used to extract descriptive features.Next, the feature vector is passed into Binary Support Vector Machine classifier (BSVM) to construct a binary-SVM model.The performance of the proposed framework is evaluated by three parameters: accuracy, area under the curve, and equal error rate.Experiments performed on two different datasets comprising highly different context abnormalities accomplished an accuracy of 97.44% and an area under the curve (AUC) of 0.9795 for University of Minnesota (UMN) dataset and accomplished an accuracy of 86.69% and an AUC of 0.7987 for University of California, San Diego Pedistrain1 (UCSD-PED1) dataset.Moreover, the performance of the pre-trained network VGGNet-19 with handcrafted feature descriptors and with other CNN pre-trained networks, respectively, has been investigated in this study for abnormal behavior detection.The results demonstrated that VGGNet-19 has better performance than histogram of oriented gradients, background subtraction, and optical flow.In addition, the VGGNet-19 shows higher detection accuracy than other pre-trained networks: GoogleNet, ResNet50, AlexNet, and VGGNet-16.
Bot in the Bunch: Facilitating Group Chat Discussion by Improving Efficiency and Participation with a Chatbot

Although group chat discussions are prevalent in daily life, they have a number of limitations. When discussing in a group chat, reaching a consensus often takes time, members contribute unevenly to the discussion, and messages are unorganized. Hence, we aimed to explore the feasibility of a facilitator chatbot agent to improve group chat discussions. We conducted a needfinding survey to identify key features for a facilitator chatbot. We then implemented GroupfeedBot, a chatbot agent that could facilitate group discussions by managing the discussion time, encouraging members to participate evenly, and organizing members' opinions. To evaluate GroupfeedBot, we performed preliminary user studies that varied for diverse tasks and different group sizes. We found that the group with GroupfeedBot appeared to exhibit more diversity in opinions even though there were no differences in output quality and message quantity. On the other hand, GroupfeedBot promoted members' even participation and effective communication for the medium-sized group.
The effects of security and traceability of blockchain on digital affordance

Purpose This study takes an affordance approach to explain how users perceive the affordance of user action within blockchain and examines how it influences the subsequent user experience. Focusing on the effect of trust on cognitive processes, the authors analyze how affordances in blockchains affect the user experience. Design/methodology/approach The blockchain affordances are examined through a two-stage process. The authors employ a qualitative analysis based on insights gained from the current literature and interviews. The authors then apply a quantitative survey to examine the role of trust in interactions with blockchain services. A structural user model was tested in which their appreciation of affordances of blockchain predicted the trust and satisfaction. Findings Users' appreciation for transparency and reliability explained to what extent they trust and are satisfied, thereby suggesting the heuristic roles of trust in blockchains. The study findings indicate a heuristic role for trust regarding underlying links to technological and affective affordances. A user's cognitive heuristics affect their attitudes toward blockchain, in which technological features are processed through users' perceptions and experience. Research limitations/implications The model contributes to the conceptualization of security, privacy and traceability along with trust, which is then linked to transparency and reliability. The findings show how the frame of affordances gains explanatory power by being linked to the concepts of affect and emotion. The heuristics of direct perception of security–traceability–privacy (STP) can be used to understand the trajectory of heuristics and ongoing choices of blockchain. Practical implications The study results offer a lens through which to address the technology's most common problems by pairing user experience principles and heuristics to blockchain technologies. This study offers insights into the understanding of user actions related to blockchains and into practical implications for developing trust-based services. The results guide the application and tailoring of motivational affordances in blockchain. Originality/value While blockchain technology has gained popularity and momentum, there has been little research on how specific features of blockchain technology create value. This study contributes to the research gap by highlighting the role and dimension of trust in relation to STP in blockchains and provides meaningful implications for theory and practice.
More Than Just Privacy: Using Contextual Integrity to Evaluate the Long-Term Risks from COVID-19 Surveillance Technologies

The global coronavirus pandemic has raised important questions regarding how to balance public health concerns with privacy protections for individual citizens. In this essay, we evaluate contact tracing apps, which have been offered as a technological solution to minimize the spread of COVID-19. We argue that apps such as those built on Google and Apple’s “exposure notification system” should be evaluated in terms of the contextual integrity of information flows; in other words, the appropriateness of sharing health and location data will be contextually dependent on factors such as who will have access to data, as well as the transmission principles underlying data transfer. We also consider the role of prevailing social and political values in this assessment, including the large-scale social benefits that can be obtained through such information sharing. However, caution should be taken in violating contextual integrity, even in the case of a pandemic, because it risks a long-term loss of autonomy and growing function creep for surveillance and monitoring technologies.
A Dynamic Artificial Potential Field (D-APF) UAV Path Planning Technique for Following Ground Moving Targets

Path planning is a vital and challenging component in the support of Unmanned Aerial Vehicles (UAVs) and their deployment in autonomous missions, such as following ground moving target.Few attempts are reported in the literature on multirotor UAV path planning techniques for following ground moving targets despite the great improvement in their control dynamics, flying behaviors and hardware specifications.These attempts suffer several drawbacks including their hardware dependency, high computational requirements, inability to handle obstacles and dynamic environments in addition to their low performance regarding the moving target speed variations.In this paper, a novel dynamic Artificial Potential Field (D-APF) path planning technique is developed for multirotor UAVs for following ground moving targets.The UAV produced path is a smooth and flyable path suitable to dynamic environments with obstacles and can handle different motion profiles for the ground moving target including change in speed and direction.Additionally, the proposed path planning technique effectively supports UAVs following ground moving targets while maneuvering ahead and at a standoff distance from the target.It is hardware-independent where it can be used on most types of multirotor UAVs with an autopilot flight controller and basic sensors for distance measurements.The developed path planning technique is tested and validated against existing general potential field techniques for different simulation scenarios in ROS and gazebo-supported PX4-SITL.Simulation results show that the proposed D-APF is better suited for UAV path planning for following moving ground targets compared to existing general APFs.In addition, it outperforms the general APFs as it is more suitable for UAVs flying in environments with dynamic and unknown obstacles.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization

Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. We introduce BoTorch, a modern programming framework for Bayesian optimization that combines Monte-Carlo (MC) acquisition functions, a novel sample average approximation optimization approach, auto-differentiation, and variance reduction techniques. BoTorch's modular design facilitates flexible specification and optimization of probabilistic models written in PyTorch, simplifying implementation of new acquisition functions. Our approach is backed by novel theoretical convergence results and made practical by a distinctive algorithmic foundation that leverages fast predictive distributions, hardware acceleration, and deterministic optimization. We also propose a novel one-shot formulation of the Knowledge Gradient, enabled by a combination of our theoretical and software contributions. In experiments, we demonstrate the improved sample efficiency of BoTorch relative to other popular libraries.
Quantum-Inspired Support Vector Machine

Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points. To tackle the big data challenge, a quantum SVM algorithm was proposed, which is claimed to achieve exponential speedup for least squares SVM (LS-SVM). Here, inspired by the quantum SVM algorithm, we present a quantum-inspired classical algorithm for LS-SVM. In our approach, an improved fast sampling technique, namely indirect sampling, is proposed for sampling the kernel matrix and classifying. We first consider the LS-SVM with a linear kernel, and then discuss the generalization of our method to nonlinear kernels. Theoretical analysis shows our algorithm can make classification with arbitrary success probability in logarithmic runtime of both the dimension of data space and the number of data points for low rank, low condition number, and high dimensional data matrix, matching the runtime of the quantum SVM.
Blockchain-Based Secure Data Storage Protocol for Sensors in the Industrial Internet of Things

The Industrial Internet of Things (IIoT) that introduces Internet of Things (IoT) technology into industrial environments is beneficial to construct smart factories. It utilizes various sensors to collect the data of industrial devices. These data are analyzed to improve the manufacturing efficiency and product quality. Cloud storage provides a solution for storing data outsourced, especially for sensors that have limited local storage and computational capacity. To ensure the privacy preserving of devices, the collected data should be stored in the formal ciphertext. Therefore, encrypted data sharing should be implemented to analyze the devices’ data. In this article, the cloud storage solution for sensors is considered. To achieve a secure and efficient data storage and sharing, a novel group signature scheme, which has less computation overhead and communication overhead, is designed to realize anonymous authentication first. And then, a novel blockchain-based cloud storage protocol for sensors in IIoT is constructed on basis of the proposed group signature scheme. Smart contract and proxy re-encryption are utilized in this protocol to realize secure data sharing with a less computational overhead. Furthermore, security proofs and performance evaluations demonstrate that this protocol is secure, privacy-preserving, and has at least 40% and 20% performance improvement in data storage and sharing phase, respectively.
The Design of Social Drones

The design space of social drones, where autonomous flyers operate in close proximity to human users or bystanders, is distinct from use cases involving a remote human operator and/or an uninhabited environment; and warrants foregrounding human-centered design concerns. Recently, research on social drones has followed a trend of rapid growth. This paper consolidates the current state of the art in human-centered design knowledge about social drones through a review of relevant studies, scaffolded by a descriptive framework of design knowledge creation. Our analysis identified three high-level themes that sketch out knowledge clusters in the literature, and twelve design concerns which unpack how various dimensions of drone aesthetics and behavior relate to pertinent human responses. These results have the potential to inform and expedite future research and practice, by supporting readers in defining and situating their future contributions. The materials and results of our analysis are also published in an open online repository that intends to serve as a living hub for a community of researchers and designers working with social drones.
Weight Agnostic Neural Networks

Not all neural network architectures are created equal, some perform much better than others for certain tasks. But how important are the weight parameters of a neural network compared to its architecture? In this work, we question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task. We propose a search method for neural network architectures that can already perform a task without any explicit weight training. To evaluate these networks, we populate the connections with a single shared weight parameter sampled from a uniform random distribution, and measure the expected performance. We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. On a supervised learning domain, we find network architectures that achieve much higher than chance accuracy on MNIST using random weights. Interactive version of this paper at this https URL
Write, Execute, Assess: Program Synthesis with a REPL

We present a neural program synthesis approach integrating components which write, execute, and assess code to navigate the search space of possible programs. We equip the search process with an interpreter or a read-eval-print-loop (REPL), which immediately executes partially written programs, exposing their semantics. The REPL addresses a basic challenge of program synthesis: tiny changes in syntax can lead to huge changes in semantics. We train a pair of models, a policy that proposes the new piece of code to write, and a value function that assesses the prospects of the code written so-far. At test time we can combine these models with a Sequential Monte Carlo algorithm. We apply our approach to two domains: synthesizing text editing programs and inferring 2D and 3D graphics programs.
Artificial Intelligence Marketing: Chatbots

Artificial Intelligence is a tool that enables marketers to create highly personalized customer experiences, increases organization's responsiveness and solve customers' problems. In this paper, the chatbot is analyzed as an artificial intelligence tool in marketing, its today's application, as well as its future potential in the above-mentioned field. A survey of respondents' behaviors, habits, and expectations when using different communication channels was conducted, with particular emphasis on chatbots, their advantages and disadvantages in relation to other communication channels, in total sum of 60 survey respondents. The results showed that the greatest advantage of using chatbots in the marketing service was when providing simple, fast obtained information, but also showed respondents' fear of chatbots giving them the wrong information. Organizations should consider using chatbots, especially if challenges in communication with customers are reality, but also if they intend to keep up with the growing number of consumers' lifestyle.
Sparse Sinkhorn Attention

We propose Sparse Sinkhorn Attention, a new efficient and sparse method for learning to attend. Our method is based on differentiable sorting of internal representations. Concretely, we introduce a meta sorting network that learns to generate latent permutations over sequences. Given sorted sequences, we are then able to compute quasi-global attention with only local windows, improving the memory efficiency of the attention module. To this end, we propose new algorithmic innovations such as Causal Sinkhorn Balancing and SortCut, a dynamic sequence truncation method for tailoring Sinkhorn Attention for encoding and/or decoding purposes. Via extensive experiments on algorithmic seq2seq sorting, language modeling, pixel-wise image generation, document classification and natural language inference, we demonstrate that our memory efficient Sinkhorn Attention method is competitive with vanilla attention and consistently outperforms recently proposed efficient Transformer models such as Sparse Transformers.
A spatial-temporal attention model for human trajectory prediction

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory (LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention (ST-Attention) model, which studies spatial and temporal affinities jointly. Specifically, we introduce an attention mechanism to extract temporal affinity, learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.
Real-Time Semantic Stereo Matching

Scene understanding is paramount in robotics, self-navigation, augmented reality, and many other fields. To fully accomplish this task, an autonomous agent has to infer the 3D structure of the sensed scene (to know where it looks at) and its content (to know what it sees). To tackle the two tasks, deep neural networks trained to infer semantic segmentation and depth from stereo images are often the preferred choices. Specifically, Semantic Stereo Matching can be tackled by either standalone models trained for the two tasks independently or joint end-to-end architectures. Nonetheless, as proposed so far, both solutions are inefficient because requiring two forward passes in the former case or due to the complexity of a single network in the latter, although jointly tackling both tasks is usually beneficial in terms of accuracy. In this paper, we propose a single compact and lightweight architecture for real-time semantic stereo matching. Our framework relies on coarse-to-fine estimations in a multi-stage fashion, allowing: i) very fast inference even on embedded devices, with marginal drops in accuracy, compared to state-of-the-art networks, ii) trade accuracy for speed, according to the specific application requirements. Experimental results on high-end GPUs as well as on an embedded Jetson TX2 confirm the superiority of semantic stereo matching compared to standalone tasks and highlight the versatility of our framework on any hardware and for any application.
A Survey of Text Data Augmentation

When natural language processing (NLP) technology is applied to industrial internet, problems such as lack of data and imbalance of data are often encountered. In order to improve the accuracy and robustness of the model, text data augmentation was proposed to expand data. Data augmentation is widely used in computer vision. For example, the semantics of the image will not be changed if the image is rotated several degrees or converted to gray level. However, augmentation of text data in NLP is pretty rare. Data augmentation is a low-cost means to expand the amount of data and improve the effect of the model, which has a wide range of applications.
Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification

This paper presents an extensive research carried out for enhancing the performances of convolutional neural network (CNN) object detectors applied to municipal waste identification. In order to obtain an accurate and fast CNN architecture, several types of Single Shot Detectors (SSD) and Regional Proposal Networks (RPN) have been fine-tuned on the TrashNet database. The network with the best performances is executed on one autonomous robot system, which is able to collect detected waste from the ground based on the CNN feedback. For this type of application, a precise identification of municipal waste objects is very important. In order to develop a straightforward pipeline for waste detection, the paper focuses on boosting the performance of pre-trained CNN Object Detectors, in terms of precision, generalization, and detection speed, using different loss optimization methods, database augmentation, and asynchronous threading at inference time. The pipeline consists of data augmentation at the training time followed by CNN feature extraction and box predictor modules for localization and classification at different feature map sizes. The trained model is generated for inference afterwards. The experiments revealed better performances than all other Object Detectors trained on TrashNet or other garbage datasets with a precision of 97.63% accuracy for SSD and 95.76% accuracy for Faster R-CNN, respectively. In order to find the optimal higher and lower bounds of our learning rate where the network is actually learning, we trained our model for several epochs, updating the learning rate after each epoch, starting from 1 × 10−10 and decreasing it until reaching 1 × 10−1.