Reviewerly/exHarmony huggingface dataset:
Human Motion Prediction Based on Visual Tracking

The following topics are dealt with: learning (artificial intelligence); mobile robots; motion control; path planning; robot vision; feature extraction; autonomous aerial vehicles; SLAM (robots); neural nets; computer vision.
Remote Collaboration in a Complex Environment

A remote collaboration system simplifies a collaboration task among multiple users at a distance. It allows local users see the real world with virtual guidance/annotations (provided by remote experts) seamlessly rendering over target objects in the real world, so they can easily follow the remote guidance to complete complicated tasks. An effective collaboration system often utilizes (1) computer vision algorithms (e.g. 3D reconstruction, and visual tracking and localization algorithms) to analyze objects and scenes in the real world, as well as (2) augmented reality (AR) to accurately superimpose virtual guidance/annotations over the target object in a real environment. However, some complex working environments with deformable linear objects (DLO), e.g. wire harness, prevents remote experts from easily annotating objects in the video, virtual scenes, or clear non-verbal communication due to the difficulty of DLO tracking. In this project, we proposed a remote guidance system, which is able to identify and track multiple DLOs, starting with minimal user input, so it allows remote experts to provide local users with simple and clear instructions/guidance in a complex environment.
Comprehensive Management and Control Method of Library Lending Information Based on Fuzzy Analytic Hierarchy Process

Carry on the comprehensive control to the library loan information, improve the library loan information management and the retrieval efficiency. In order to improve the comprehensive control and control ability of library loan information, this paper puts forward a comprehensive management and control model of library loan information based on load balance and scheduling of book information resources and fuzzy analytic hierarchy process. Firstly, the time series analysis of library loan information flow is carried out, and the fuzzy information clustering method is used to extract and retrieve the characteristic information of library loan information. Under the condition of load balance constraint, the comprehensive control of library loan information is carried out, and the fuzzy analytic hierarchy process (AHP) method is used to reduce the storage cost of library loan information registration, and the comprehensive management and control model of library loan information is improved. The simulation results show that the model is superior to the traditional method in the allocation and scheduling of library borrowing information, which improves the retrieval efficiency of library information, reduces the storage cost of library borrowing information base, the information precision rate of library borrowing information, and the calculation cost. This method has good application value in the comprehensive management and control of library borrowing information and the optimal dispatching of book resources.
Perceptually Inspired Normalized Conditional Compression Distance

Image similarity measurement is a common issue in a broad range of applications in image processing, recognition, classification and retrieval. Conventional image similarity measures are often limited to specific applications and cannot be applied in general scenarios. The theory of Kolmogorov complexity provides a universal framework for a generic similarity metric based on information distance between objects. Normalized Information Distance (NID) has been shown to be a valid and universal distance metric applicable in measurement of similarity of any two objects, and has been successfully applied to a wide range of applications in the past. The difficulty of NID lies in the non-computable nature of the Kolmogorov complexity, and thus approximation has to be applied in practice. Here we propose a perceptually-inspired Normalized Conditional Compression Distance (NCCD) measure by using the Divisive Normalization Transform (DNT) as a means to model the non-linear behavior of the Human Visual System (HVS) in reducing statistical dependencies of visual signals for efficient representation, and show that this perceptual extension of NID can be used in a wide range of image processing applications, including texture classification and face recognition.
Proposed Fuzzy Harmony Search Method

The proposed method is based on the HS algorithm, and the HMR and PArate parameters.
ICT Curriculum Requirements in the Service Area of Champasak University, Laos

This feasibility study investigated the needs of ICT training courses to be used as the primary data for developing an ICT curriculum in the service area, 4 provinces, of Champasak University, Lao PDR. Quantitative methodology was employed using a questionnaire comprising of 51 questions under 24 items. 600 questionnaires were distributed in four provinces and 530 were returned. Data were analyzed using percentage, average value, standard deviations and the One-Way ANOVA analysis. The result illustrated four main topics including the target group characteristics, ICT behavior, ICT skills, ICT course requirements and needs of ICT professionals. Additionally, it was found that 1) the most three preferable ICT courses among respondents are spreadsheet, word processing and website developing, 2) the most ten popular ICT professionals are IT supporter, network administrator, programmer, database administrator, computer aid designer, multimedia creator, website developer, system analyst, CRM / ERP operator and ICT consultant and 3) as many as 82.30% of executives from various organizations had expressed their needs to encourage their staff to increase ICT skill via attending trainings.
Measuring Documents Similarity using Finite State Automata

In this paper, the similarity between a pair of text documents is measured by identifying three sets of words: words that exist in the first but not in the second, in the second but not in the first and those words that exist in both. Two deterministic finite automata are built to recognize the words of the two documents. These finite state automata are used to identify these three sets of words. An experimental study was conducted to present the proposed approach and to compare it with other peer text similarity measures: Jaccard and cosine distance. The proposed approach gave more reasonable results than Jaccard but more inflated than cosine distance approach.
Unsupervised Translation Quality Estimation for Digital Entertainment Content Subtitles

We demonstrate the potential for using aligned bilingual word embeddings in developing an unsupervised method to evaluate machine translations without a need for parallel translation corpus or reference corpus. We explain different aspects of digital entertainment content subtitles. We share our experimental results for four languages pairs English to French, German, Portuguese, Spanish, and present findings on the shortcomings of Neural Machine Translation for subtitles. We propose several improvements over the system designed by Gupta et al. [P. Gupta, S. Shekhawat and K. Kumar, Unsupervised quality estimation without reference corpus for subtitle machine translation using word embeddings, IEEE 13th Int. Conf. Semantic Computing, 2019, pp. 32–38.] by incorporating custom embedding model curated to subtitles, compound word splits and punctuation inclusion. We show a massive run time improvement of the order of [Formula: see text] by considering three types of edits, removing Proximity Intensity Index (PII) and changing post-edit score calculation from their system.
Single Camera Measurement of Out of Plane (OoP) Displacement

In this paper we propose a method which is useful to measure out of plane displacement of beams. The proposed method is targeted to be effective in micro fabrication applications where a speckle pattern is impossible to make on the microactuators. However, microfabrication techniques where etch holes are used to release actuator beams can make use of this method. The paper presents the application of image processing techniques that can be exploited to measure effective in-plane pixel displacement for a corresponding out of plane (vertical) beam displacements. Firstly, a calibration measurement is taken at known beam deflections. Secondly, beam deflection at various unknown points can be extracted. Lastly, a profile of the beam deflection along its length can be achieved for any deflection.
d'Artagnan: A Trusted NoSQL Database on Untrusted Clouds

Privacy sensitive applications that store confidential information such as personal identifiable data or medical records have strict security concerns. These concerns hinder the adoption of the cloud. With cloud providers under the constant threat of malicious attacks, a single successful breach is sufficient to exploit any valuable information and disclose sensitive data. Existing privacy-aware databases mitigate some of these concerns, but sill leak critical information that can potently compromise the entire system's security. This paper proposes d'Artagnan, the first privacy-aware multi-cloud NoSQL database framework that renders database leaks worthless. The framework stores data as encrypted secrets in multiple clouds such that i) a single data breach cannot break the database's confidentiality and ii) queries are processed on the server-side without leaking any sensitive information. d'Artagnan is evaluated with industry-standard benchmark on market-leading cloud providers.
Dynamic Balance Testing System for Micro-Roller Based on MEMS

In order to address the problems such as heavy weight, high cost and poor sensitivity related to the swing frame of the existing roller test system, a micro-roller dynamic balance test system based on Micro-Electro-Mechanical Systems (MEMS) acceleration sensor is proposed. The MEMS accelerometer is symmetrically embedded acting as the support of the swing frame to measure the vibration acceleration. The circuit board with perforated processing is directly used as the support of the swing frame. Two wires are used to connect the sensor and the data acquisition system to avoid the interference of cable elasticity on the vibration characteristics of the swing frame. The data obtained from this test system has been processed. The influence of various interference signals in the system on the vibration measurement is identified. Vibration signals generated by imbalances are extracted from complex measurement signals. The test data show that the optimized swing frame design has smaller weight, lower cost, higher sensitivity, simpler operation and much broad application range.
Understanding the Structure of Streaming Documents based on Neural Network

Document structure understanding can obtain the structural information of long-form documents, which plays a key role in the automatic layout of document formats. In the main document format, the structure of streaming documents is difficult to understand, and the current recognition effect is not ideal. According to the characteristics of streaming documents, neural network has obvious advantages for sequence labeling problems, and then a neural network based streaming document structure identification method is proposed. First, the format features, content features and semantic features are extracted, and deep and cross network model is introduced to apply the feature intersections in an automated manner. Secondly, using Long- and short-term memory(LSTM) neural network to construct the recognition model can identify eighteen types of logical labels and document structures more accurately. Finally, using the migration learning method, the structure identification of reports and other types of documents is initially realized. Experiments show that the recognition model proposed in this paper has better document structure recognition ability than other machine learning-based models or methods, and the effect is better than the current best products.
Reconstructing Obfuscated Human Faces with Conditional Adversarial Network

In today’s era of advanced forensic and security technologies, the problem of identifying a human face from a low-quality image obtained from low-quality hardware or other reasons is a major challenge. Trying to extract meaningful information from these images is very difficult. These low-quality images have mainly two kinds of distortion in it, i.e. blurring and pixelation. Prior attempts have been done using different machine learning and deep learning techniques, but the desired high-quality images are not obtained. In this paper, we have used the conditional adversarial network to reconstruct highly obfuscated human faces. Various previous works on the conditional adversarial network have suggested it as a general-purpose solution for image-to-image translation problems. The conditional adversarial network is able to learn mapping from the provided input image to resulting output image. It is also able to learn the loss function to train the mapping. We have examined the result of this model using pixel loss function which gives the exact mapping of obtained high-quality human face with ground truth; furthermore, we have examined the capabilities of this model with very high-level obfuscated images.
A Quantum Heat Exchanger for Nanotechnology

In this paper, we design a quantum heat exchanger which converts heat into light on relatively short quantum optical time scales. Our scheme takes advantage of heat transfer as well as collective cavity-mediated laser cooling of an atomic gas inside a cavitating bubble. Laser cooling routinely transfers individually trapped ions to nano-Kelvin temperatures for applications in quantum technology. The quantum heat exchanger which we propose here might be able to provide cooling rates of the order of Kelvin temperatures per millisecond and is expected to find applications in micro- and nanotechnology.
“Human-Vehicle-Tunnel-Environment” Based Study on Key Issues and Solutions of Operation and Maintenance Risk Evaluation for Urban Road Tunnels

Abstract This paper provides analyses of operating environment features of urban tunnels in building, traffic, hydrology, vertical alignment, social environment and examination of key issues in operation and maintenance risk evaluation for urban tunnels: diversity and complexity of evaluation factors; timeliness of evaluation result; uncertainty of rating, etc. Relevant countermeasures are proposed for the above-mentioned key issues, including information collection technology based on the Internet of big data technology and risk dynamic assessment and management platform, etc., to provide a solution path for the real-time, intelligent and informatization of urban tunnel risk management and control.
A Diversity-Based Adaptive Synchronous-Asynchronous Switching Simulated Kalman Filter Optimizer

The original Simulated Kalman Filter (SKF) is an optimizer that employs synchronous update mechanism. The agents in SKF update their solutions after all fitness calculations, prediction process, and measurement process are completed. An alternative to synchronous update is asynchronous update. In asynchronous update, only one agent does fitness calculation, prediction, measurement, and estimation processes at one time. Recent study found that the original SKF is subjected to premature convergence. Thus, synchronous and asynchronous mechanisms are combined in SKF to address the premature convergence problem in SKF. At first, the SKF starts with synchronous update. If no improved solution is found, the SKF changes its update mechanism. The decision to switch from synchronous to asynchronous or vice versa is made based on the information of the population. In this paper, population’s diversity is used as switching indicator. Using the CEC2014 benchmark test suite, experimental results indicate that the proposed diversity-based adaptive switching synchronous-asynchronous SKF outperforms the original SKF significantly.
Evaluation Method of Drivers Vision Impacts from Green Belts of Arterial

As an important part of the cross section of road, the green belt of urban road has heavy impacts on the drivers’ visual environment. Green belt directly relates to traffic safety. But the research on this area is only carried out from the aspects of color and monotonicity. Few research can be found which is about impacts indexes. In this article, the impacts model of green belt is established. The green belts impact value of the different level and different section is obtained by expert method. In order to validate the model, an eye movement instrument (SmartEye) is adopted. The data of impacts indexes can be obtained in SmartEye. Then, ErgoLAB package is used to identify the drivers’ gaze points during the course of the experiment and to generate the view distribution map of the gaze area. After this, the experiment in driving simulator dome under variable traffic flow condition is carried out to modify the model built above. At last, a recommended rating grade standard is presented.
P ≈ NP, at least in Visual Question Answering

In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets. One of the most widely-used of these is the VQA 2.0 dataset, consisting of polar (“yes/no”) and non-polar questions. Looking at the question distribution over all answers, we find that the answers “yes” and “no” account for 38% of the questions (19% per class), while the remaining 62% are spread over the remaining 3127 answers (0.02% per class). While several sources of biases have been investigated in the field, the effects of such an over-representation of polar questions remain unclear. In this paper, we measure the potential confounding factors when polar and non-polar samples are used jointly to train a baseline VQA classifier, and compare it to an upper bound where the over-representation of polar questions is excluded from the training. Further, we perform cross-over experiments to analyze how well the feature spaces of polar and non-polar samples align. Contrary to expectations, we find no evidence of counterproductive effects in the joint training of unbalanced classes. In fact, by exploring the intermediate feature space of visual-text embeddings, we find that the feature space of polar questions already encodes sufficient structure to answer many non-polar questions. Our results indicate that the polar (P) and the non-polar (NP) feature spaces are strongly aligned, hence the expression P ≈ NP.
Human Pose Refinement for Reliable Robotic Teleoperation

Three-dimensional human pose estimation can be effectively applied to teleoperation of humanoids, thanks to its intuitive and easy usability. However, the human pose wrongly inferred can cause critical and dangerous situations for robotic operation. In this paper, a reliable human pose estimation method is presented. Initially, 3-D joint positions are inferred by a conventional pose estimation method using stereo images. The joint positions are refined by Kalman filtering followed by median filtering. The motor control value for each joint is smoothed with an IIR filter to perform stable and flexible operations.
A Survey of Probabilistic Resource Space Model

This paper surveys research on the Probabilistic Resource Space Model (P-RSM), which is a probabilistic, multidimensional classification space model. It can effectively manage various uncertain resources with a complete theoretical basis. This paper introduces the basic definition, paradigms, operations, constraints of this model, and discusses its usefulness through application instances.
Learning Parsers for Technical Drawings

From a set of technical drawings, we learn a parser program to interpret the tabular data contained in such a drawing. This enables automatic reasoning and learning on top of a database of technical drawings. For example to help designers find or complete designs more easily.
How to develop hydrocarbon e-textbook of chemistry based knowledge building environment with 4S TMD models?

Abstract This study aims to describe how to develop hydrocarbon e-textbook of chemistry based knowledge building environment with 4S TMD models. 4S TMD models consists of four stages i.e selection, structuring, characterization and didactic reduction. Hydrocarbon there are many applications in daily life. Knowledge building environment is a theory pioneered by Carl Bereiter and Marlene Scardamalia to construct knowledge. Knowledge building environment contains values that can build knowledge through the environment related to teaching material. The values contained in the knowledge building environment include attentiveness, careness, curiosity, critical, moderation, respect for environment, respect for health and wisdom. Developmental Research was used as method of the study consist of design, develop, and evaluation. In this article, we explain result of developed e-textbook of chemistry in selection and structuring stages. Selection stage consist of the curriculum based on the selection steps, the interrelationships between certain skill value and subject matter. Structuring stage consist of make a concept maps and macro structure. This research only to structuring stage characterization and didactic reduction stages will be carried out.
Parsing Indoor Scenes from RGB-D Image Using Superpixel and Region Merging

This paper presents a new method of parsing indoor scene from an RGB-D image using superpixel of the RGB image and region merging of depth information. The goal of parsing indoor scene is to reconstruct the interior wall and floor scene based on an RGB-D image, the architecture of Manhattan structure. Firstly, an original RGB image of an indoor scene is segmented into superpixels using SLIC method, and a region merging procedure is used to iteratively merge adjacent superpixels possessing some identical properties in RGB-and depth-channel, which assures that the pixels in a merged region have basically identical normal, which is essential for reconstructing indoor wall and floor plane from a RGB-D image. Secondly, the obtained regions of the scene are translated into plane surface using normal information of regions, and then wall segments and walls are extracted to remove the influence of furniture and persons in the indoor scene on the reconstruction of indoor wall and floor plane. Finally, the Manhattan structure wall and floor scene of an indoor scene can be reconstructed by using dynamic programming method on candidate wall segments. The experiments show that the proposed method obtains better 3D structures than the ones the state-of-the-art produced.
Aspect-Based Sentiment Analysis with the Multiple-Element Attention and Part of Speech

Aspect-based Sentiment analysis (ABSA) is a rapidly growing field of research in natural language processing. ABSA is a fine-grained task of Sentiment analysis. How to capture precise sentiment expressions in a sentence towards the specific aspect remains a challenge. In this paper, we propose a novel neural network, named Multiple-element Attention LSTM (MEA-LSTM) to alleviate the problem of self-attention or binary-element attention used in the ABSA task. These attention mechanisms mentioned above are weak attention, they ignore the information of aspect target or sentence representation. To capture the precise sentiment expressions, we make use of multiple-element attention to assign different importance degrees of different words in a sentence. To store these informative aspect-dependent representations, extra representation memory is designed. Part of speech (POS) is an important feature in identifying the sentiment expressions in the ABSA task. We combine POS with the LSTM in the proposed MEA-LSTM. Experimental results show that our proposed model acquires state-of-the-art accuracy at both restaurant and laptop datasets. Besides, a rule of thumb about choosing the number of hops is given on both datasets.