Reviewerly/exHarmony huggingface dataset:
A multitask model for person re-identification and attribute recognition using semantic regions

In recent years, more and more video surveillance cameras are being used both in military and civilian applications. This trend results in large amounts of available image and video footage. An effective manual search and evaluation of this data is difficult due to the large data volume and limited human attention span. This is why automatic algorithms are required to aid in data analysis. A key task in this context is search for persons of interest, i.e., person re-identification. Based on a query image, re-identification methods retrieve further occurrences of the depicted person in large data volumes. The prevailing success of convolutional neural networks (CNNs) in computer vision did not spare person re-identification and has recently led to significant improvements. Current state-of-the-art approaches mostly rely on features extracted from CNNs trained with person images and corresponding identity labels. However, person re-identification still remains a challenging problem due to many task-specific influences such as, e.g., occlusions, incomplete body parts, background clutter, varying camera perspectives, and pose variation. Unlike conventional CNN features, descriptive person attributes represent higher-level semantic information that is more robust to many of these influences. Therefore, person re-identification can be improved by integrating attributes into the algorithms. In this work we investigate approaches for attribute-based person re-identification using deep learning methods with the goal of developing efficient models with the best possible re-identification accuracy. We show that best practices in person re-identification approaches can be transferred to the task of pedestrian attribute recognition to achieve strong baseline results for both tasks. Moreover, we show that leveraging information about semantic clothing and body regions during training of the networks improves the results further. Finally, we combine pedestrian attribute recognition and person re-identification models in a multi-task architecture to build our attribute-based person re-identification approach. We develop our attribute model on the large RAP dataset, which currently offers the largest available number of persons and attributes and thus allows for a differentiated analysis. The final combined attribute and re-identification model is trained on the Market-1501 dataset, which provides person identities and attribute annotations simultaneously. Our results show that baseline re-identification results are surpassed, thus indicating that complementary information from the two different tasks is leveraged.
Machine Learning Based Seismic Region Classification

It has become increasingly common in academic and industrial environments the necessity to process huge amounts of seismic signals. Several researchers have been seeking for ways to improve and optimize the processing of these enormous amounts of data that are related to routine demands of geophysicists. One of these demands is the classification of distinct seismic regions captured by the same seismograph, a task that could take up to months of manual data processing. In this paper, we propose the usage of machine learning techniques to the task of the classification of seismic regions, in order to achieve accurate results with better performance and speed. The algorithms K-NN, MLP, Naive Bayes and Decision Tree were used for tests as base classifiers and also combined on ensemble methods. We also employed Deep Learning techniques, namely, a pure RNN network, and a variation of RNN called LSTM. The best results were achieved when using heterogeneous classifiers, showing accuracy rates of up to 98.52%. The results show that one can build an efficient seismic region classification system even when few classified data are already available for a specific seismograph setting.
Deep Learning Creativity in EDA

Computing power brings new technologies into human life. NVIDIA devoted to accelerating parallel computing through CPU-GPU cooperating architecture since we invented CUDA in 2007. In the last decade, one of the most important technologies, deep learning, became feasible and realistic because GPU accelerates the optimizing process of neural network over 60x, which means that the model training time shortens from weeks to hours. DL not only performed superior to human in some computer vision tasks like ImageNet but also made significant progress of many fields like medical, autonomous, manufacturing, finance, electronic design automation (EDA) etc.In this paper, we introduced two deep learning innovations in EDA field including (1) Graph Convolutional Network in Testability Analysis and (2) DREAMPlace. In (1), graph convolutional network can predict observation point candidates in a netlist more efficiently compared to commercial tools. In (2), DREAMPlace significantly accelerates the VLSI placement process by using deep learning framework of GPU for optimizing process.
Two Novel Approaches for Automatic Labelling in Semi-Supervised Methods

In real world classification problems, the amount of labelled data is usually limited (very hard or expensive to manually label the instances). However, a natural limitation of a classification algorithm is that it needs to have a set of labelled instances with a reasonable size in order to achieve a reasonable performance. Therefore, one solution to smooth out this problem is the use of semi-supervised learning. Several semi-supervised approaches (e.g. self training) have been proposed in the literature, aiming to use only a few labelled instances, to train a classifier, and to apply a labelling process in which a high number of unlabelled instances is labelled and included in the labelled set. However, this approach can include unreliable instances to the labelled set, impairing the performance of a semi-supervised method. In other words, the selection criterion to include newly labelled instances in the labelled set as well as the labelling step have an important effect in the performance of a semi-supervised method. In this paper, we propose two new approaches for automatic labelling in semi-supervised methods based on the prediction agreement of a pool of classifier as selection criterion. In addition, we compare them to the standard self-training method, and one variation of it called Flexible Confidence Classifier as baselines. In general, both methods obtained significantly better predictive results than the other two methods over 40 classification datasets.
Use of an automatic device for the determination of mechanical properties on photoelastic samples of small dimensions

Photoelastic materials and Photoelasticity have been very effective in the study of the effects of stress and strain in materials such as iron, steel, concrete, etc. However, mainly because of human interaction, systematic errors are introduced during the measurement process, which directly affects the final result. When using small photoelastic samples, these types of errors can escalate due to the increased difficulty of handling. Intending to minimize systematic errors in the process of data collection, this work presents the use of an automatic loading device, developed by us, to apply mechanical stresses in small photoelastic samples. The device created has shown great effectiveness in photoelastic applications that uses a transmission polariscope, but it can also be used in other photoelastic techniques, such as reflection photoelasticity, as well as in other areas of optics such as Holography and Speckle. An important benefit is the ease and speed of handling that can improve the organization and quality of the activities in labs of optics.
A Smart Contract Oracle for Approximating Real-World, Real Number Values

A key challenge of smart contract systems is the fact that many useful contracts require access to information that does not natively live on the blockchain. While miners can verify the value of a hash or the validity of a digital signature, they cannot determine who won an election, whether there is a flood in Paris, or even what is the price of ether in US dollars, even though this information might be necessary to execute prediction market, insurance, or financial contracts respectively. A number of promising projects and research developments have provided a better understanding of how one might construct a decentralized, binary oracle - namely an oracle that can respond by one of two possibilities, typically yes or no, even while not requiring the interaction of a trusted third party. In this work, we extend these ideas to construct a general-purpose, decentralized oracle that can estimate the value of a real-world quantity that is in a dense totally ordered set, such as R. In particular, this proposal can be used to estimate real number valued quantities, such as required for a price oracle. We will establish a number of desirable properties about this proposal. Particularly, we will see that the precision of the output is tunable to users' needs.
The Importance Of The Discussion Method In The Development Of Argumentative Competence In Students

This article discusses the importance of using debate as a teaching method in mastering key technologies to improve students’ argumentative competence. At the same time, the ability to argue constructively is interpreted as the ability to perform various argumentative actions to substantiate or refute certain points of view. The article presents the characteristics of argumentative skills, as well as the conditions for the formation of logical and psychological components of debate, which is one of the specific manifestations of argumentation.
Comparative Analysis of Image Steganography using SLT, DCT and SLT-DCT Algorithm

Steganography is an interesting science to be studied and researched at this time, because steganography is the science of hiding messages on other digital media so that other parties are not aware of the existence of information in the digital media. Steganography is very effective in maintaining information security, because the existence of this information is obscured so that it is difficult to know where it is. This paper discusses hiding text into images using the Slantlet Transform (SLT) method, Descreate Cosine Transform (DCT) and Hybrid of SLT and DCT. The three methods are implemented in the frequency domain where steganographic imagery is transformed from the spatial domain to the frequency domain and the message bit is inserted into the cover image frequency component. The comparison parameters of these three techniques are based on MSE, PSNR, Capacity &amp; Robustness. From the results of the tests that have been done, it is obtained that the highest PSNR value is generated using the SLT-DCT method, the largest storage capacity is the SLT method while the resistance, SLT-DCT method and DCT method are more resistant to attack than the SLT method.
A Respiratory Monitoring System in Surgical Environment Based on Fiducial Markers Tracking

Breathing is an important indicator of human life. Respiratory monitoring is needed in many surgical environments. To build a respiratory monitoring system for data acquisition requires the aid of acoustic or infrared sensor devices. Adding relevant equipment to the existing surgical environment not only adds the corresponding technical costs, but also may conflict with the original surgical equipment. In this paper, a respiratory monitoring system based on Aruco, an open source reality enhancement library for fiducial marker tracking, was proposed. This system has the advantages of low cost and convenient construction. The test of this monitoring system in the environment of simulated pancreatic lithotripsy verified the feasibility of its respiratory monitoring capability. Based on this system, we further explored the functions of multi-camera calibrations, breathing data extraction and posture analysis.
VR/AR head-mounted display system based measurement and evaluation of dynamic visual acuity

&#x0D; &#x0D; &#x0D; This study evaluated the dynamic visual acuity of candidates by implementing a King–Devick (K-D) test chart in a virtual reality head-mounted display (VR HMD) and an augmented reality head-mounted display (AR HMD). Hard-copy KD (HCKD), VR HMD KD (VHKD), and AR HMD KD (AHKD) tests were conducted in 30 male and female candidates in the age of 10S and 20S and subjective symptom surveys were conducted. &#x0D; In the subjective symptom surveys, all except one of the VHKD questionnaire items showed subjective symptoms of less than 1 point. In the comparison between HCKD and VHKD, HCKD was measured more rapidly than VHKD in all tests. In the comparison between HCKD and AHKD, HCKD was measured more rapidly than AHKD in Tests 1, 2, and 3. In the comparison between VHKD and AHKD, AHKD was measured more rapidly than VHKD in Tests 1, 2, and 3. In the correlation analyses of test platforms, all platforms were correlated with each other, except for the correlation between HCKD and VHKD in Tests 1 and 2. There was no significant difference in the frequency of errors among Tests 1, 2, and 3 across test platforms. &#x0D; VHKD and AHKD, which require the body to be moved to read the chart, required longer measurement time than HCKD. In the measurements of each platform, AHKD was measured closer to HCKD than VHKD, which may be because the AHKD environment is closer to the actual environment than the VHKD environment. The effectiveness of VHKD and AHKD proposed in this research was evaluated experimentally. The results suggest that treatment and training could be performed concurrently through the use of clinical test and content development of VHKD and AHKD. &#x0D; &#x0D; &#x0D;
Hyperspectral VIS/SWIR wide-field imaging for ink analysis

Hyperspectral imaging can reveal the slightest difference in the spectral response of seemingly identical inks. We have studied the reflectivity of the fundamental colors used in inkjet printers, and found distinctive spectral features depending on the supplier of the ink. We analysed discontinued bank notes, and identified discrepancies in the SWIR response of allegedly genuine notes of different ages. This may either reveal information on the history of the note as a legal tender, or reveal the fake nature of a note sold as genuine. These observations may be useful in the identification of high-fidelity counterfeit documents, or bank notes.
MirAIProjection

Allowing projections on moving objects is associated with a problem that a projection might shift due to the delay between tracking and projection. In the present paper, we proposed a new prediction model based on deep neural networks that can be used to predict both pose and position of the target object. As a result, we developed a real-time tracking and projection system named"MirAIProjection that employs motion-capture cameras and common projectors. We conducted several experiments to evaluate the effectiveness of the proposed system and demonstrated that the proposed system could reduce the slipping and increase the accuracy and robustness of the projection.
Missing Load Situation Reconstruction Based on Generative Adversarial Networks

The completion and the correction of measurement data are the foundation of the ubiquitous power internet of things construction. However, data missing may occur during the data transporting process. Therefore, a model of missing load situation reconstruction based on the generative adversarial networks is proposed in this paper to overcome the disadvantage of depending on data of other relevant factors in conventional methods. Through the unsupervised training, the proposed model can automatically learn the complex features of loads that are difficult to model explicitly to fill the incomplete load data without using other relevant data. Meanwhile, a method of online correction is put forward to improve the robustness of the reconstruction model in different scenarios. The proposed method is fully data-driven and contains no explicit modeling process. The test results indicate that the proposed algorithm is well-matched for the various scenarios, including the discontinuous missing load reconstruction and the continuous missing load reconstruction even massive data missing. Specifically, the reconstruction error rate of the proposed algorithm is within 4% under the absence of 50% load data.
Image-Based Real-Time Path Generation Using Deep Neural Networks

We propose an image-based real-time path planner for the self-driving car IARA, named DeepPath. DeepPath uses a CNN for inferring paths from images. During the self-driving car operation, DeepPath receives an image and the current car pose. Then, it sends the image to a CNN trained to infer a model of the path. After that, DeepPath generates the path in the IARA's coordinate system using the path model. Subsequently, given the current IARA's pose, DeepPath transforms each pose of the path in the IARA's coordinate system into another pose in the world coordinate system. Finally, it sends the path to the IARA's Behavior Selector subsystem, the next subsystem in the IARA's Decision-Making system. We evaluated the performance of DeepPath in real world scenarios. Our results showed that DeepPath is able to correctly generate paths for IARA that differ only slightly from those defined by humans.
IoEduc - Bring Your Own Device to the Classroom

The evolution of technology brings new challenges to teaching platforms. Students are demanding and want to have dynamic and interactive environments. Classrooms are much more than a simple place to study. Students can work and learn anywhere and anytime they want. Having this reality in consideration emerges the concept of Bring Your Own Device to classrooms. So, in the first instance, it is essential to understand the concept and which tasks can be transported to students devices. After that, it is time to design a new tool capable of answering new teaching demands. ioEduc arises as a platform able to support a unique and interactive way of teaching. This emerging platform was tested during a semester, and the assessment results of utility are promising (0% of negative answers and more than 60% of students consider this tool indispensable to the future).
Archetypes of delay: An analysis of online developer conversations on delayed work items in IBM Jazz

A widely adopted methodology, agile software development provides enhanced flexibility to actively adjust a project scope. In agile teams, particularly in distributed environment, developers interact, manage requirements knowledge, and coordinate primarily in online collaboration tools. Developer conversations become invaluable sources to track and understand developers’ interactions around implementation of requirements, as well as the progress of implementation relative to the project scope and the planned iterations in agile projects. Although extensive research around iteration planning exists, there is a lack of research that leverages developer conversation data to understand delays in implementing planned requirements in agile projects. By using developer conversations in a large agile project at IBM, this work aims to analyze conversation in work items (WIs) that are delayed and derive patterns that suggest reasons for delay in the project. We conducted a case study of the IBM Jazz project, and used thematic analysis to code the developer conversations as time-series, and cluster analysis to identify patterns that differentiated the evolution of discussions in WIs that were late vs. not late in the project. We identified six main patterns of WI delay. Through semantic analysis of developer conversations within particular clusters we were able to explain the reasons for delays in each pattern. In comparison to non-late WIs, we find that the major reason for delay is a lack of frequent communication associated with a poor project management of WIs. Similarly, non-late tasks more often delegate to children tasks to accelerate the implementation of requirements, in addition to processing requests quickly to resolve bottlenecks in implementation. Our study complements existing research in bringing evidence that developer conversations are a useful resource that can highlight delays in requirement implementation, as well as recommend patterns in the dynamics of developers interactions relevant to such delays.
Towards DiArg : An Argumentation-based Dialogue Reasoning Engine

This paper presents ongoing work on the implementation of the DiArg argumentation-based reasoning engine that focuses on automating sequential argumentation for inquiry and deliberation dialogues. ...
Deep High-order Asymmetric Supervised Hashing for Image Retrieval

Deep hashing has recently been attracting more and more attentions for large-scale image retrieval task owing to its superior performance of search efficiency and less storage space requirements. Among deep hashing models, asymmetric deep hashing performs feature learning on query dataset and directly generates hash code on database images, significantly improving the retrieval performance of deep hashing models. Meanwhile, recently works also establish that high-order statistic of deep features are helpful to obtain more discriminant representations of images. Therefore, to boost the retrieval capability of deep hashing, this work tries to integrate merits of the high-order statistic module and the asymmetric deep hashing architecture, and it further proposes a novel deep high-order asymmetric supervised hashing (DHoASH) for image retrieval. More specifically, we utilize a powerful global covariance pooling module based on matrix power normalization to compute the second-order statistic features of input images, which is fluently embedded into an asymmetric hashing architecture in an end-to-end manner, leading to the generation of more discriminant binary hashing code. Experiment results on two benchmarks illuminates the effectiveness of the proposed DHoASH, which also achieves very competitive retrieval accuracy compared to the state-of-the-art methods.
Discrepancy-Aware Collaborative Representation for Unsupervised Domain Adaptation

Domain adaptation aims at learning from the la-beled source domain to build an accurate classifier for a related but different target domain. Existing methods attempt to reduce domain discrepancy explicitly by means of statistical properties yet ignore the inherent differences among samples. In this paper, we present a novel solution for domain adaptation based on collaborative representation, named Discrepancy-Aware Collaborative Representation (DACR). Inspired by the success of nearest regularization, DACR develops a novel indicator to measure the discrepancy among every source sample and target domain. Then the indicator is employed in sparse regularization thus ensure that samples with small discrepancy have larger weights in the learned representation. Extensive experiments verify that DACR is able to achieve comparable performance with existing methods while significantly reducing computing complexity.