Reviewerly/exHarmony huggingface dataset:
Analyzing embedded pose estimation solutions for human behaviour understanding

This work represents the first phase of a more complete work that has the goal of using RGB images as information to make analyses of human behavior. In this phase, we developed a prototype of hardware/software, capable of estimating human pose using only RGB information. The equipment chosen was the NVIDIA Jetson Nano, known for having have a better computational performance compared to Raspberry pi and Arduino microcontoller alternatives. In the search for important algorithms for pose estimation, applied to the limited platform as the Jetson Nano, we found important works such as HyperPose, TensorRT Pose Estimation, and the used on the project, tf-poseestimation. The results show a low FPS performance of the Jetson Nano, using the chosen algorithm, compared to related hardware, such as the NVIDIA Jetson TX2 and NVIDIA Jetson Xavier.
Social Engineering—The Hidden Control

The former energy-wasting lifestyles of developed societies can no longer be sustained. In our age, efficiency is the key to continued sustainability. Increasing efficiency requires the use of infocommunication systems and their regulation. Regulatory modeling is based on the cybernetic loops model. The systems are not closed, so they are constantly suffering from environmental disturbances. External interference can also come from a human resource that covertly exploits the technological and psychic elements of the system to achieve its own goals. Social engineering is also such an intervention. The aims of the present study are to draw a parallel between cybernetic loops and social engineering, then to define social engineering on the cybernetic base.
Dynamics of the Quantum Discord with Weak Measurement for a Two-atom System in Thermal Reservoirs

Weak measurement is a kind of state partial collapse measurement developed on the basis of von Neumann measurement and positive operator value measurement, which allows us to explore the quantum world which has the least influence on the research system.Based on the weak measurement theory, the dynamics of quantum discord for two isolated atoms in their own thermal reservoirs is presented.We examine the time evolution of both standard quantum discord and quantum discord with weak measurement for the two-atom system, and analyzes the differences between the standard quantum discord and quantum discord with weak measurement in the evolution process with time, as well as the general role of the strength parameter in determing the discord and affecting its dynamic evolution.We show that quantum discords depend on how weak or strong one perturbs the quantum system.We also show that the difference of the standard quantum discord and the quantum discord with weak measurements increases as the strength parameter decreases.This means that the weak measurements can capture more quantum discord of a bipartite system.Our results show that the weak measurement performed on one of the subsystems can lead to the quantum discord that is a more natural measure of quantum correlations than the standard quantum discord captured by the projective measurements.
Pathwise Approximations for the Solution of the Non-Linear Filtering Problem

We consider high order approximations of the solution of the stochastic filtering problem, derive their pathwise representation in the spirit of the earlier work of Clark [2] and Davis [10, 11] and prove their robustness property. In particular, we show that the high order discretised filtering functionals can be represented by Lipschitz continuous functions defined on the observation path space. This property is important from the practical point of view as it is in fact the pathwise version of the filtering functional that is sought in numerical applications. Moreover, the pathwise viewpointwill be a stepping stone into the rigorous development ofmachine learning methods for the filtering problem. This work is a cotinuation of [5] where a discretisation of the solution of the filtering problem of arbitrary order has been established. We expand the work in [5] by showing that robust approximations can be derived from the discretisations therein.
Privacy and Security in Augmentation Technologies

In this chapter, we present a privacy and security framework for designers of technologies that augment humans’ cognitive and perceptive capabilities. The framework consists of several groups of questions, meant to guide designers during the different stages of the design process. The objective of our work is to support the need for considering implications of novel technologies with regard to privacy and security early in the design process rather than post-hoc. The framework is based on a thorough review of the technologies presented earlier on in this book as well as of prior research in the field of technology augmentation. From this review, we derived several themes that are not only valuable pointers for future work but also serve as a basis for the subsequent framework. We point out the need to focus on the following aspects: data handling, awareness, user consent, and the design of the user interface.
Option Predictive Clustering Trees for Multi-label Classification

In this work, we focus on the task of multi-label classification (MLC), where every example is associated with a set of labels.We present an algorithm for learning option predictive clustering trees (OPCTs) for MLC, based on the predictive clustering framework.The algorithm addresses the myopia of the standard tree induction algorithm by considering alternative splits in the internal nodes of the tree and introducing option nodes where appropriate.An option tree can be viewed as a compact representation of an ensemble, as well as, used as a pool of candidates from which a single tree can be extracted.This broadens the space of trees that is searched and reduces the myopia, compared to the standard tree induction.We evaluate the proposed OPCTs on 12 benchmark MLC datasets from different domains.Results show that OPCTs as ensembles can achieve performance similar to the bagging ensembles of PCTs, while the single trees extracted from OPCTs can outperform standard PCTs.We also perform parameter sensitivity analysis and provide avenues for future work.
Online System Identification for Nonlinear Uncertain Dynamical Systems Using Recursive Interval Type-2 TS Fuzzy C-means Clustering

This paper presents a novel online system identification technique based on a recursive interval type-2 Takagi-Sugeno fuzzy C-means clustering technique (IT2-TS-FC) for modeling nonlinear uncertain dynamics of autonomous systems. The construction of the fuzzy antecedent parameters is based on the type-2 fuzzy C-means clustering (IT2FCM) technique, while the Weighted Least Square (WLS) algorithm is utilized to determine the upper and lower fuzzy consequent parameters. Moreover, a scaling factor to represent the footprint of uncertainties (FoU) is introduced to convert type-l and type2 fuzzy systems. The efficiency of our proposed algorithm has been validated using two benchmark system datasets, flight test data from a quadcopter and Mackey-Glass time series data. We also compare our proposed technique with a type-l fuzzy C-means technique. The robustness of our proposed identification is investigated by means of a noisy dataset.
BERT Algorithms Explained

This chapter takes a deep dive into the BERT algorithm for sentence embedding along with various training strategies, including MLM and NSP. We will also see an implementation of a text classification system using BERT.
Application of Hybrid Ant Colony Algorithm in Urban Traffic Path Planning

To plan the urban traffic path using the ant colony algorithm, the composition and functional division of the mobile robot are analyzed. The TSP (Traveling Salesman Problem) is used to deeply understand the traditional ant colony algorithm. Then, based on this, the improvement scheme of the traditional ant colony algorithm is analyzed. The results showed that the artificial potential field method and the A* algorithm improved the performance of the ant colony algorithm. At the initial stage of the search path, the blindness and randomness of the ant colony algorithm due to insufficient pheromone concentration in each path were solved. The local optimal path is avoided with the development of algorithm iteration. Therefore, the improved ant colony algorithm is superior to the traditional ant colony algorithm.
Front Obstacle Detection System based on YOLOv3

Abstract The task of front obstacle detection in unmanned driving is to allow the model to accurately detect types and corresponding position of each object. Complex true driving environment, multiple types of objects within range of visibility and multiple examples are challenges for detection. Particularly in unmanned driving field, not only the detection precision shall be pursued, but also the detection speed shall be guaranteed. This paper puts forward the front obstacle detection method based on YOLOv3. It establishes a Tensor Flow+Keras framework on GPU server cluster, designs and realizes YOLOv3, and realizes calibration and classification of front obstacles through training and optimization of the model on the data set. This method supports over 10 object types. Experimental results show validation set Loss (VAL Loss) reaches 21.206, mAP reaches 78.64, and detection speed reaches 30 FPS. The study provides theoretical basis for obstacles detection of automated vehicle in complex environment.
Temporally guided articulated hand pose tracking in surgical videos

Abstract Purpose Articulated hand pose tracking is an under-explored problem that carries the potential for use in an extensive number of applications, especially in the medical domain. With a robust and accurate tracking system on surgical videos, the motion dynamics and movement patterns of the hands can be captured and analyzed for many rich tasks. Methods In this work, we propose a novel hand pose estimation model, CondPose , which improves detection and tracking accuracy by incorporating a pose prior into its prediction. We show improvements over state-of-the-art methods which provide frame-wise independent predictions, by following a temporally guided approach that effectively leverages past predictions. Results We collect Surgical Hands , the first dataset that provides multi-instance articulated hand pose annotations for videos. Our dataset provides over 8.1k annotated hand poses from publicly available surgical videos and bounding boxes, pose annotations, and tracking IDs to enable multi-instance tracking. When evaluated on Surgical Hands , we show our method outperforms the state-of-the-art approach using mean Average Precision, to measure pose estimation accuracy, and Multiple Object Tracking Accuracy, to assess pose tracking performance. Conclusion In comparison to a frame-wise independent strategy, we show greater performance in detecting and tracking hand poses and more substantial impact on localization accuracy. This has positive implications in generating more accurate representations of hands in the scene to be used for targeted downstream tasks.
Efficient Data Aggregation for Human Activity Detection with Smart Home Sensor Network Using K-Means Clustering Algorithm

Smart home sensor network utilizes various sensors to measure physical and send data to a base station. The pattern of measured data in each room can be considered as an active pattern when activity occurrence in that room and a irrelevant pattern when no activity in the room. In order to improve data aggregation in smart home, we propose human activity pattern-based data aggregation, which applies K-means clustering algorithm based on human activity into cluster heads of cluster-based sensor network. The result of simulation shows that the clustering algorithm can detect active event by calculating the similarity between the active pattern of collected data and human activity according to room usage.
Assisted Technology for Cognitive Comprehension in the Differently Abled

This paper is a preliminary study on what Assisted Technology (AT) and Information and Communication Technology (ICT) are and the kinds of disabilities in learners. It examines what Assisted Technology Tools (ATT) / Softwares are and how they can help in cognitive comprehension in differently abled individuals/learners. It presents the types of Assistive Technology, its benefits and provides an overview of the opportunities offered by the internet and ICT (Information and Communication Technology) for the active and wholesome participation of persons with disabilities, in the learning process. Terms and keywords related to the topic have been explained in the course of the article. In addition, the paper provides a list of the Assisted Technology Tools, Softwares and Systems which give learners an opportunity to choose a relevant and user friendly tool. The new technologies highlight the benefit and opportunities that learners with disability can avail as other learners—turning yesterday’s disabled students into today’s enabled students.
Utilizing Reinforcement Learning to Continuously Improve a Primitive-Based Motion Planner

This paper describes how the performance of motion primitive based planning algorithms can be improved using reinforcement learning. Specifically, we describe and evaluate a framework for policy improvement via the discovery of new motion primitives. Our approach combines the predictable behavior of deterministic planning methods with the exploration capability of reinforcement learning. The framework consists of three phases: evaluation, exploration, and extraction. This framework can be iterated continuously to provide successive improvement. The evaluation step scores the performance of a motion primitive library using value iteration to create a cost map. A local difference metric is then used to identify regions that need improvement. The exploration step utilizes reinforcement learning to examine new trajectories in the regions of greatest need. The extraction step encodes the agent’s experiences into new primitives. The framework is tested on a point-to-point navigation task using a 6-DOF nonlinear F-16 model. One iteration of the framework discovered 17 new primitives and provided a maximum planning time reduction of 96.91%. After 3 full iterations, 123 primitives were added with a maximum time reduction of 97.39%. The proposed framework is easily extensible to a range of vehicles, environments, and cost functions.
DWT-QR based blind image watermarking method using firefly algorithm

Digital watermarking is a method used to protect intellectual property rights and copyrights. This study represents a blind method based on Discrete Wavelet Transform (DWT) and QR decomposition. In this method, the cover image is firstly divided into non-overlapping blocks, and then the blocks with low standard deviation are selected to embed the watermark. After each of the selected blocks is decomposed by DWT, low frequency sub-bands are subjected to QR decomposition. The first row of the R matrix resulting from QR decomposition is used to embed the watermark bits. Firefly Algorithm (FA) is utilized to optimize the gain factor of embedding, which is effective in determining the robustness and imperceptibility of the watermarking scheme. To appraise the performance of the study, Peak Signal to Noise Ratio (PSNR) is used to measure the perceptual quality and Normalized Correlation (NC) is used to evaluate robustness. According to experimental results, the perceptual quality of the proposed method is found to be higher than that of similar studies in the literature. In addition, it is established that the robustness of the method against attacks such as noise, cropping, compression, and filtering is better.
Counting and Tracking of Vehicles and Pedestrians in Real Time Using You Only Look Once V3

Machine based image and video processing are at the heart of computer vision. Here the tracking of objects from videos is done using correlation filter. Detection of objects involves detecting the presence and location of a target object or entity within an image or video frame. Here deep learning approaches have been used to perform the object detection. When utilizing Deep Learning techniques, the concept of Transfer learning is utilized and use a pre-trained network that’s trained on large datasets. The object detection technique used here is YOLOv3. YOLO stands for You Only Look Once. Darknet is the framework used to train neural networks, with 53 convolutional layers. Object tracking is identifying the path, an object takes in the concurrent frames. Tracking using the dlib correlation filter provides the accurate localization of the target from the scene. The approach is tested by various video sequences.
Introduction to Word Embeddings

NLP tasks such as document classification, sentiment analysis, clustering, and document summarization require processing and understanding of textual data. Implementation of these tasks depends on how data are being processed and understood by AI systems. One way of doing this is to convert textual representation to a numerical form using some statistical methods such as term frequency-inverse document frequency (TF-IDF), count vector, and so on, but these methods do not consider the meaning of a sentence and only deal with the occurrence of words in sentences.
Introduction to Natural Language Processing

With recent advances in technology, communication is one of the domains that has seen revolutionary developments. Communication and information have formed the backbone of modern society and it is language and communication that has led to such advances in human knowledge in all spheres. Humans have been fascinated by the idea of machines or robots having human-like abilities to converse in our language. Numerous science fiction books and media have dealt with this topic. The Turing test was designed for this purpose, to test whether a human being is able to decipher if the entity on the other end of a communication channel is a human being or a machine.
High-Speed 3D Optical Sensing and Information Processing for Automotive Industry

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;This paper explains the basic principles behind two platform technologies that my research team has developed in the field of optical metrology and optical information processing: 1) high-speed 3D optical sensing; and 2) real-time 3D video compression and streaming. This paper will discuss how such platform technologies could benefit the automotive industry including in-situ quality control for additive manufacturing and autonomous vehicle systems. We will also discuss some of other applications that we have been working on such as crime scene capture in forensics.&lt;/div&gt;&lt;/div&gt;
Learning Generalizable Visual Representations via Interactive Gameplay

A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the neural flexibility for creative problem solving, decision making, and socialization. Comparatively little is known regarding the impact of embodied gameplay upon artificial agents. While recent work has produced agents proficient in abstract games, these environments are far removed the real world and thus these agents can provide little insight into the advantages of embodied play. Hiding games, such as hide-and-seek, played universally, provide a rich ground for studying the impact of embodied gameplay on representation learning in the context of perspective taking, secret keeping, and false belief understanding. Here we are the first to show that embodied adversarial reinforcement learning agents playing Cache, a variant of hide-and-seek, in a high fidelity, interactive, environment, learn generalizable representations of their observations encoding information such as object permanence, free space, and containment. Moving closer to biologically motivated learning strategies, our agents' representations, enhanced by intentionality and memory, are developed through interaction and play. These results serve as a model for studying how facets of vision develop through interaction, provide an experimental framework for assessing what is learned by artificial agents, and demonstrates the value of moving from large, static, datasets towards experiential, interactive, representation learning.
A Siamese CNN Architecture for Learning Chinese Sentence Similarity.

This paper presents a deep neural architecture which applies the siamese convolutional neural network sharing model parameters for learning a semantic similarity metric between two sentences. In addition, two different similarity metrics (i.e., the Cosine Similarity and Manhattan similarity) are compared based on this architecture. Our experiments in binary similarity classification for Chinese sentence pairs show that the proposed siamese convolutional architecture with Manhattan similarity outperforms the baselines (i.e., the siamese Long Short-Term Memory architecture and the siamese Bidirectional Long Short-Term Memory architecture) by 8.7 points in accuracy.
Novel multi-party quantum key agreement protocols under collective noise

Based on the logic Bell states, we present two novel multi-party quantum key agreement (QKA) protocols under collective noise. The proposed protocols make full use of four-qubit logic Bell states as quantum resources and perform the novel encoding operation to generate the shared key. The security analysis shows that these two protocols can resist against both participant and outsider attacks. Furthermore, compared with the other existing multi-party QKA protocols over collective noise, our protocols have higher qubit efficiency. Finally, we perform the simulation of the relationship between efficiency and security, which is completely consistent with the conclusion of the security analysis of the protocols.
Cardiac MR Image Sequence Segmentation with Temporal Motion Encoding

The segmentation of cardiac magnetic resonance (MR) images is a critical step for the accurate assessment of cardiac function and the diagnosis of cardiovascular diseases. In this work, we propose a novel segmentation method that is able to effectively leverage the temporal information in cardiac MR image sequences. Specifically, we construct a Temporal Aggregation Module (TAM) to incorporate the temporal image-based features into a backbone spatial segmentation network (such as a 2D U-Net) with negligible extra computation cost. In addition, we also introduce a novel Motion Encoding Module (MEM) to explicitly encode the motion features of the heart. Experimental results demonstrate that each of the two modules enables clear improvements upon the base spatial network, and their combination leads to further enhanced performance. The proposed method outperforms the previous methods significantly, demonstrating the effectiveness of our design.
Distinctive Slogan Generation with Reconstruction

E-commerce sites include advertising slogans along with information regarding an item. Slogans can attract viewers’ attention to increase sales or visits by emphasizing advantages of an item. The aim of this study is to generate a slogan from a description of an item. To generate a slogan, we apply an encoder–decoder model which has shown effectiveness in many kinds of natural language generation tasks, such as abstractive summarization. However, slogan generation task has three characteristics that distinguish it from other natural language generation tasks: distinctiveness, topic emphasis, and style difference. To handle these three characteristics, we propose a compressed representation–based reconstruction model with refer–attention and conversion layers. The results of the experiments indicate that, based on automatic and human evaluation, our method achieves higher performance than conventional methods.