Reviewerly/exHarmony huggingface dataset:
Simultaneous 3D hand detection and pose estimation using single depth images

In this paper, we investigate 3D hand pose estimation using single depth images. On the one hand, accurate hand localization is a crucial factor for pose estimation. On the other hand, multi-task learning methods have achieved great success in visual recognition tasks. Therefore, in this paper we proposed to simultaneously detect the hand location and estimate its 3D pose in a multi-task learning framework. We used 3D region proposal for 3D pose estimation, which searches possible hand locations in the 3D space. In the experimental part, the proposed method is evaluated on several benchmark datasets and shown it is comparable to most existing 3D hand pose estimation methods.
Large scale quality transformation in hybrid development organizations – A case study

As the software industry transitions to a subscription-based software-as-a-service (SaaS) model, software development companies are transforming to hybrid development organizations with increased adoption of Agile and Continuous Integration/ Continuous Delivery (CI/CD) development practices for newer products while continuing to use Waterfall methods for older products. This transformation is a huge undertaking impacting all aspects of the software development life cycle (SDLC), including the quality management system. This paper presents a case study of a large-scale transformation of a legacy quality management system to a modern system developed and implemented at Cisco Systems. The framework for this transformation is defined by six distinct areas: metrics, process, measurement, reporting, quality analytics, and culture & leadership. Our implementation leveraged recent advances in Machine Learning (ML), Artificial Intelligence (AI), connected data, integrated operations, and big data technologies to solve the challenges created by a hybrid software development organization. We believe this case study will help researchers and industry leaders understand the benefits and potential challenges of such sizeable transformations.
Planning and implementing an automated storage and retrieval system at the University of Limerick

The case study introduces the automated storage and retrieval system (ASRS) implemented in the new library at the University of Limerick (UL), Ireland. A quarter of the Library’s print collections were moved into this robotic system that has a capacity of 500,000 books and takes up oneninth of the space of conventional shelving. This is the first on-site automated storage system located within a university library in Europe. The technology provides an opportunity for a radical and game-changing shift away from providing space for collections to providing new and innovative learning spaces for library users. The case study outlines the drivers that influenced the decision to include an ASRS in the building, and describes and reflects on the significant change management challenges of implementing a complex storage solution that affected all staff and had a transformative impact on library services.
Study of total suspended solid concentration based on Doxaran algorithm using Landsat 8 image in coastal water between Bodri River estuary up to east flood canal Semarang City

Abstract The water quality in the northern coastal waters estuary of Central Java has been polluted at a moderate to severe level. Water quality monitoring efforts for evaluating the condition of dissolved sedimentation can be done through total suspended solid methods by analising the remote sensing imagery. Total suspended solid (TSS) is measured by suspended material (diameter&gt; 1 µm) which retained in the millopore filter with a 0.45 µm pore diameter. The purpose of this research is to assess the distribution of TSS concentrations in the research location. This study was conducted using Landsat 8 image data on August 9th, 2018. TSS concentration values were obtained through linear regression tests Doxaran algorithm which is the most suitable algorithm with the field condition. The results showed that: 1) The appropriate empirical algorithm for mapping TSS concentrations is the Doxaran algorithm with the TSS equation = 1.0248x - 10.503, 2) Distribution of TSS with the range &gt;75 mg/l mostly concentrated in center and west of the study area, while the concentration with range &lt;75 mg/l is concentrated in the east, and 3) Distribution of TSS is affected by the movement of currents driven by the wind towards the west and southwest and partly collected in the western part of the study area.
Steering Direction for a Powered-Wheelchair Using the Analytical Hierarchy Process

For the first time, the Analytical Hierarchy Process (AHP) is employed to recommend a steering course for a powered-wheelchair. A safe route for a wheelchair is proposed by the decision-making system that tends to evade obstacles. Two ultrasonic transceivers are mounted onto a wheelchair. The resulting route is a blend between a provided direction from a user's joystick and a proposed direction from the decision making system that steers a powered-wheelchair to safely avoid obstacles in the way of the wheelchair. The procedure assists a disabled user to steer their wheelchair by choosing a direction that is a compromise between a direction suggested by the sensors and a direction requested by the driver. Sensitivity analysis explores directions amid uncertainty and risk. An appropriate direction is chosen but a human driver can over-ride the decision if necessary.
Detecting Entities of Works for Chinese Chatbot

Chatbots such as Xiaoice have gained huge popularity in recent years. Users frequently mention their favorite works such as songs and movies in conversations with chatbots. Detecting these entities can help design better chat strategies and improve user experience. Existing named entity recognition methods are mainly designed for formal texts, and their performance on the informal chatbot conversation texts may not be optimal. In addition, these methods rely on massive manually annotated data for model training. In this article, we propose a neural approach to detect entities of works for Chinese chatbot. Our approach is based on a language model (LM) long-short term memory (LSTM) convolutional neural network (CNN) conditional random value (CRF), or LM-LSTM-CNN-CRF, framework, which contains a language model to generate context-aware character embeddings, a Bi-LSTM network to learn contextual character representations from global contexts, a CNN to learn character representations from local contexts, and a CRF layer to jointly decode the character label sequence. In addition, we propose an automatic text annotation method via quote marks to reduce the effort of manual annotation. Besides, we propose an iterative data purification method to improve the quality of the automatically constructed labeled data. Massive experiments on a real-world dataset validate that our approach can achieve good performance on entity detection for Chinese chatbots.
Accelerating the AES Algorithm using OpenCL

Nowadays, cryptography plays an important role in the field of information security. The most common symmetric cryptographic algorithm is Advanced Encryption Standard (AES), which is based on the well-known Rijndael algorithm and is used worldwide in every domain. In this document, we present the implementation of the AES algorithm in two parallel modes of operation (CTR and XTS) with the OpenCL programming language. We used OpenCL because it is designed for parallel computing on heterogeneous platforms and ensures portability. Furthermore, we applied 128, 192 and 256 bit cryptographic key size and a file size ranging from 512B to 8MB to evaluate the performance of the kernel runtime and throughput (Gbps). The results have shown that, the performance of the CTR mode is better than the XTS mode. CTR mode speeds up the process of encryption with 128 bit key over 10.15%, 192 bit key over 10.09% and 256 bit key over 10.05%. The decryption process shows 128 bit key acceleration over 10.11%, 192 bit key over 10.05% and 256 bit key over 10.02%. Finally, comparing the results of our implementation to other similar parallel models, we have achieved better throughput performance.
MRMRP: Multi-source Review-Based Model for Rating Prediction

Reviews written by users often contain rich semantic information which can reflect users' preferences for different attributes of items. For the past few years, many studies in recommender systems take user reviews into consideration and achieve promising performance. However, in daily life, most consumers are used to leaving no comments for products purchased and most reviews written by consumers are short, which leads to the performance degradation of most existing review-based methods. In order to alleviate the data sparsity problem of user reviews, in this paper, we propose a novel review-based model MRMRP, which stands for Multi-source Review-based Model for Rating Prediction. In this model, to build multi-source user reviews, we collect supplementary reviews from similar users for each user, where similar users refer to users who have similar consuming behaviors and historical rating records. MRMRP is capable of extracting useful features from supplementary reviews to further improve recommendation performance by applying a deep learning based method. Moreover, the supplementary reviews can be incorporated into different neural models to boost rating prediction accuracy. Experiments are conducted on four real-world datasets and the results demonstrate that MRMRP achieves better rating prediction accuracy than the state-of-the-art methods.
Factor Analysis of XRF and XRPD Data on the Example of the Rocks of the Kontozero Carbonatite Complex (NW Russia). Part II: Geological Interpretation

Numerical comparison of mineralogical and geochemical data, which is required in a variety of geological applications, is a challenging task, especially when analyzing extensive sample collections. Herein, we apply factor analysis (FA) to a collection of 198 diffraction patterns of bulk rock samples from the Kontozero carbonatite complex. The mineralogical information hidden in the X-ray powder diffraction (XRPD) data is thereby squeezed down to a set of two dozen variables represented by factor scores (FS). The values of these FSs show a functional relationship with the contents of the minerals composing the rocks. Therefore, factor scores can be considered as a beneficial tool for rapid qualitative and semiquantitative analysis of the mineral composition of rocks. Supplementing principal component analysis (PCA) with FSs as independent variables characterizing the mineral content of rocks allows for the numerical comparison of mineralogical and geochemical data. By PCA, we reveal the main trends in the mineralogical and geochemical evolution of the investigated rocks of the Kontozero complex. Furthermore, the results are obtained in the very first stages of the research. This fact elucidates the potential use of the proposed technique in geological studies and mining.
Core components of blockchain

In the blockchain network, there are two types of nodes such as miner nodes and normal nodes. The miner nodes are used for authentication, auditing, and validating the transactions and normal nodes are used to verify those transactions. Similarly, based upon several criteria, blockchain systems are classified as public and private blockchains. A public blockchain provides an open platform for people from various organizations and backgrounds to join, transact, and mine whereas private blockchain is used to facilitate private sharing and exchange of data among a group of individuals. Along with this, we have discussed the various blockchain platforms that can be used to execute and run smart contracts on the blockchain network.
Huber Loss Function Based on Variable Step Beetle Antennae Search Algorithm with Gaussian Direction

Huber Loss function is a regression type loss function, mainly used for optimization problems.In order to make the predicted value close to the true value and minimize the distance between them, the traditional loss function generally uses the gradient descent method to find the minimum value. However, the gradient descent method takes too long and consumes a lot of memory. It may not necessarily find the global optimal value, and it is easy to fall into the problem of local optimization. The data of the unconstrained function is complicated and huge, and the Huber Loss function sets the hyper-parameter δ to optimize MSE and MAE, we must iterate continuously to train the hyper-parameters to obtain more accurate parameter values, which will cause a problem of large amount of calculations. The beetle antennae search algorithm is a simple biological intelligence optimization algorithm that can provide the best solution based on huge data, with fast convergence speed, small amount of calculation, simple operation, and no need to know the specific function. In order to improve the accuracy of finding the global optimal value, this paper changed the traditional fixed-step antennae search algorithm to variable-step beetle antennae search algorithm. This article will discuss the application of the gradient descent method in the regression loss function, and then use the variable step size beetle antennae search algorithm to optimize Huber Loss function.
The Circuit Design of Voltage-controlled Color Changing Lamp Based on Multisim

This paper introduces the circuit design of voltage-controlled color changing lamp based on Multisim. The circuit mainly comprises the reference signal generating circuit and voltage comparator. With Multisim software simulating and debugging, the simulation results show the circuit function can be achieved.
Gated dynamic convolutions with deep layer fusion for abstractive document summarization

We present a novel abstractive document summarization based on the recently proposed dynamic convolutional encoder-decoder architectures. We address several aspects of summarization that are not well modeled by the basic architecture, by integrating multiple layers of the encoder, controlling information flow in the hierarchy, and exploiting external knowledge. First, we propose a simple and efficient deep layer fusion to extract salient information from the encoder layers. Second, we propose a gating mechanism to control and maintain important contextual information through the encoder-decoder layers into dynamic convolutions. Lastly, we put part-of-speech information into the model as external knowledge to better predict filters for dynamic convolutions. We evaluate our model using ROUGE metrics on three different datasets: CNN-DM, NEWSROOM-ABS, and XSUM. Experimental results show that the proposed model outperforms the state-of-the-art abstractive models on NEWSROOM-ABS and XSUM and shows comparable scores on CNN-DM.
Nonlinear von Mises–Fisher Filtering Based on Isotropic Deterministic Sampling

We present a novel deterministic sampling approach for von Mises–Fisher distributions of arbitrary dimensions. Following the idea of the unscented transform, samples of configurable size are drawn isotropically on the hypersphere while preserving the mean resultant vector of the underlying distribution. Based on these samples, a von Mises–Fisher filter is proposed for nonlinear estimation of hyperspherical states. Compared with existing von Mises–Fisher-based filtering schemes, the proposed filter exhibits superior hyperspherical tracking performance.
Exploring the Participant-Related Determinants of Simulator Sickness in a Physical Motion Car Rollover Simulation as Measured by the Simulator Sickness Questionnaire

Physical motion driving simulators serve as a valuable research and training tool. Since many simulator participants suffer from simulator sickness (SS), we aimed to gain a better understanding of participant-related variables that may influence its incidence and severity. The study involved a 2-min mobile-platform car rollover simulation conducted in a group of 100 healthy adult participants. SS was measured with the Simulator Sickness Questionnaire immediately before and after the simulation. We investigated how the symptomatology of SS varies with gender, as well as with participants’ previous experiences such as extra driving training or car accidents. Although many SS symptoms occurred already before the simulation, all the symptoms except burping had a significantly greater incidence and severity after the simulation. Before the simulation, men reported disorientation symptoms more often than women, while participants with prior experiences of extra driving training or car accidents scored significantly higher in three out of four Questionnaire components: nausea symptoms, oculomotor symptoms, and the total score. The study offers interesting insights into associations between SS and prior experiences observed by means of high-fidelity real-motion simulations. More research is needed to determine the nature of these associations and their potential usefulness, for example, in helping accident survivors to cope with the distressing or even potentially disabling psychological consequences of accidents.
Latent Dirichlet allocation based generative adversarial networks

Generative adversarial networks have been extensively studied in recent years and powered a wide range of applications, ranging from image generation, image-to-image translation, to text-to-image generation, and visual recognition. These methods typically model the mapping from latent space to image with single or multiple generators. However, they have obvious drawbacks: (i) ignoring the multi-modal structure of images, and (ii) lacking model interpretability. Importantly, the existing methods mostly assume one or more generators can cover all image modes even if we do not know the structure of data. Thus, mode dropping and collapse often take place along with GANs training. Despite the importance of exploring the data structure in generation, it has been almost unexplored. In this work, aiming at generating multi-modal images and interpreting model explicitly, we explore the theory on how to integrate GANs with data structure prior, and propose latent Dirichlet allocation based generative adversarial networks (LDAGAN). This framework is extended to combine with a variety of state-of-the-art single-generator GANs and achieves improved performance. Extensive experiments on synthetic and real datasets demonstrate the efficacy of LDAGAN for multi-modal image generation. An implementation of LDAGAN is available at https://github.com/Sumching/LDAGAN.
An Integrated Design of World-in-Miniature Navigation in Virtual Reality

Navigation is considered one of the most fundamental challenges in Virtual Reality (VR) and has been extensively researched [11]. The world-in-miniature (WIM) navigation metaphor allows users to travel in large-scale virtual environments (VEs) regardless of available physical space while maintaining a high-level overview of the VE. It relies on a hand-held, scaled-down duplicate of the entire VE, where the user’s current position is displayed, and an interface provided to introduce his/her next movements [17]. There are several extensions to deal with challenges of this navigation technique, e.g. scaling and scrolling [23]. In this work, a WIM is presented that integrates state-of-the-art research insights and incorporates additional features that became apparent during the integration process. These features are needed to improve user interactions and to provide both look-ahead and post-travel feedback. For instance, a novel occlusion handling feature hides the WIM geometry in a rounded space reaching from the user’s hand to his/her forearm. This allows the user to interact with occluded areas of the WIM such as buildings. Further extensions include different visualizations for occlusion handling, an interactive preview screen, post-travel feedback, automatic WIM customization, a unified diegetic UI design concerning WIM and user representation, and an adaptation of widely established gestures to control scaling and scrolling of the WIM. Overall, the presented WIM design integrates and extends state-of-the-art interaction tasks and visualization concepts to overcome open conceptual gaps and to provide a comprehensive practical solution for traveling in VR.
Toward Court-Admissible Sensor Systems to Fight Domestic Violence

Gender-based violence is an important problem in nowadays society. A huge number of women are killed every year, and those who survive often live in constant danger because of potential retaliation. In an effort to deal with this problem from a technological perspective, the UC3M4Safety task force has developed an innovative solution to detect stress or violence based on data obtained from smart wearable sensors. The goal is to use that information to trigger alarms and to be used as an admissible evidence in a court of law. The goal of this paper is to design a security architecture for the sensor system addressing all relevant security issues according to the European regulation in terms of data privacy and data protection (General Data Protection Regulation). The design tries to preserve the chain of custody of the data in order to facilitate its consideration as admissible evidence in court.
Expecting the unexpected

When developing autonomous systems, engineers and other stakeholders make great efforts to prepare the system for all foreseeable circumstances. However, such systems are still bound to encounter situations that were not considered at design time. For reasons like safety, cost, or ethics it is often highly desired that these new cases be handled correctly upon first encounter. In this paper, we first justify our position that there will always exist unpredicted events and conditions, driven by, e.g., new inventions in the real world, the diversity of world-wide system deployments and uses, and the possibility that multiple events that were unforeseen at design time (or overlooked, or knowingly abandoned following cost-benefit-risk calculations) will not only occur, but will occur together. We then argue that despite the unpredictability, handling such situations is indeed possible. Hence, we offer and exemplify design principles, which, when applied in advance, can improve the system's ability to deal with unpredicted situations. We conclude with a discussion of how this work and a much-needed thorough study of the unexpected can contribute toward a foundation of engineering principles for developing trustworthy next-generation autonomous systems.