Reviewerly/exHarmony huggingface dataset:
Improving Multiperson Pose Estimation by Mask-aware Deep Reinforcement Learning

Research on single-person pose estimation based on deep neural networks has recently witnessed progress in both accuracy and execution efficiency. However, multiperson pose estimation is still a challenging topic, partially because the object regions are selected greedily from proposals via class-agnostic nonmaximum suppression (NMS), and the misalignment in the redundant detection yields inaccurate human poses. Therefore, we consider how to obtain the optimal input in human pose estimation under conditions in which intermediate label information is not available. As supervised learning–based alignment does not generalize well to unseen samples in the human pose space, in this article, we present a mask-aware deep reinforcement learning approach to modify the detection result. We use mask information to remove the adverse effects from the cluttered background and to select the optimal action according to the revised reward function. We also propose a new regularization term to punish joints that are outside of the silhouette region in the human pose estimation stage. We evaluate our approach on the MPII Multiperson dataset and the MS-COCO Keypoints Challenge. The results show that our approach yields competing inference results when it is compared to the other state-of-the-art approaches.
The Effectiveness of Fuzzy-SAW Method for the Selection of New Student Admissions in Vocational High School

New student admission activities are routine activities carried out by vocational high schools every year to get the best students. A method is needed that can be used as a means of screening to select students based on their abilities and fields that are in accordance with the characteristics of students. This study uses the Fuzzy-SAW Method which is carried out through 3 stages, namely preparation of the components of the situation, the analysis and synthesis of information. The use of this method is expected to be able to help and provide the best decisions in the acceptance of new students.
Intrusion detection on robot cameras using spatio-temporal autoencoders: A self-driving car application

Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self-driving cars industry. However, this framework is far from being totally secure, and the existing security breaches do not have robust solutions. In this paper we focus on the camera vulnerabilities, as it is often the most important source for the environment discovery and the decision-making process. We propose an unsupervised anomaly detection tool for detecting suspicious frames incoming from camera flows. Our solution is based on spatio-temporal autoencoders used to truthfully reconstruct the camera frames and detect abnormal ones by measuring the difference with the input. We test our approach on a real-word dataset, i.e. flows coming from embedded cameras of self-driving cars. Our solution outperforms the existing works on different scenarios.
MotionHub

There is a substantial number of body tracking systems (BTS), which cover a wide variety of different technology, quality and price range for character animation, dancing or gaming. To the disadvantage of developers and artists, almost every BTS streams out different protocols and tracking data. Not only do they vary in terms of scale and offset, but also their skeletal data differs in rotational offsets between joints and in the overall number of bones. Due to this circumstance, BTSs are not effortlessly interchangeable. Usually, software that makes use of a BTS is rigidly bound to it, and a change to another system can be a complex procedure. In this paper, we present our middleware solution MotionHub, which can receive and process data of different BTS technologies. It converts the spatial as well as the skeletal tracking data into a standardized format in real time and streams it to a client (e.g. a game engine). That way, MotionHub ensures that a client always receives the same skeletal-data structure, irrespective of the used BTS. As a simple interface enabling the user to easily change, set up, calibrate, operate and benchmark different tracking systems, the software targets artists and technicians. MotionHub is open source, and other developers are welcome to contribute to this project.
Practice of Gesture Recognition Based on Resnet50

Absrtact This paper mainly describes and analyzes the common network models of deep learning. By comparing the existing network structure in the industry, this paper focuses on the static gesture recognition algorithm based on the residual network resnet50. In this paper, we choose the depth residual network resnet50 to realize the gesture control of web browser, and apply the gesture recognition method proposed in this paper.
A Novel Classification Method of Medical Image Segmentation Algorithm

Medical image segmentation is a relevant and active research field of medical image processing. The proposal of various algorithms not only enriches the means to solve the problem of medical image segmentation but also makes the algorithm classification and summary urgent. At present, a variety of classification methods are mostly based on the characteristics of the algorithm itself. If the classification principle of the algorithm is determined according to the essential elements of the organ plane space, such as point, line, and surface, a new classification method will be formed, and it is more in line with people’s intuitive feelings. Using this new segmentation principle to classify medical image segmentation algorithms is helpful to clarify the relationship between various algorithms.
Optimizing the Topology and Energy-Aware VM Migration in Cloud Computing

The advancements in virtual machine migration (VMM) have been trending due to its effective load balancing features in cloud infrastructure. Previously, data centers were used for handling VMs organized in racks. These racks are arranged in a spanning tree topology with a high bandwidth. Thus, the cost for moving the data between servers is highest when the racks are far from each other. This work addresses this issue and proposed VMM strategy based on self-adaptive D-Crow algorithm (S-DCrow) that incorporates adaptive constants in Dragonfly-based Crow (D-Crow) optimization algorithm based on the proposed topology model. The proposed S-DCrow describes a migrating model, which is based on topology, energy consumption, load, and migration cost. Here, the network is organized in a spanning tree topology and is adapted by proposed S-DCrow for optimal VMM. The performance of the proposed S-DCrow shows superior performance in terms of load, energy consumption, and migration cost with the values of 0.1417, 0.1009, and 0.1220, respectively.
Feature Extraction and Enhancement Of Breast Cancer Mammogram Noisy Image Using Image Processing

In recent days, breast cancer [1] is gradually becoming a significant cause of death in female cancer patients in developing countries. Approximately 2.1 million women are dying globally due to this fatal endemic. The diversified image processing techniques are applied in malignancy research to extract the pragmatic attributes from mammographic images for effective decision making or diagnosis. Mammograms are the X-rays scanning of the breast used by the oncologist to visualize the early appearance of cancer with the aid of various images processing techniques. Oncologists investigate the micro-calcifications using image processing approaches that detect white specks (microscopic calcium substance) on the mammogram(s). The lumps on breast do not cause cancer, but if it appears in a cluster or certain pattern may lead as early stages of cancer. The aim of our research is to enhance the image and detect the carcinogenic cells by using different existing image filtering approaches with a comparative analysis of their output performance(s). The detection analysis helps in predictions [2] of breast cancer at the beginning stage, which can save many precious lives.
Who, When and Why: The 3 Ws of Code-Switching

With the rise of globalization, the use of mixed languages in daily conversations, referred to as "code-switching" (CS) has become a common linguistic phenomenon among bilingual/multilingual communities. It has become common for people to alternate between distinct languages or "codes" in daily conversations. This has placed a high demand on Natural Language Processing (NLP) applications to be able to deal with such mixed-language input. Researchers have lately achieved advancements in multilingual NLP applications, however, few work has been done to adapt these applications to users' CS behaviour. In this work, we take the first steps towards this goal by investigating the CS behavior and its correlation with the users' profiles in our case study on Egyptian Arabic-English code-switching. Although these factors have been investigated by linguists, the findings have been mostly made through theoretical studies. We provide empirical evidence based on a user study with 50 participants showing initial correlations between user traits and the CS frequency, which can be used to predict users' CS. Our findings imply that in the scope of our study, people (who) code-switch in specific discourse domains more than others (when) and depending on their background and social factors (why). The study also shows that to be able to properly investigate who code-switches more data needs to be collected and further analysis is required.
Neural machine translation of Hindi and English

Translation has been one of the oldest problems in natural language processing. Despite its age, it is still one where there is a tremendous scope for improvement and creativity; the quantity and quality of research in it is testament to that fact. The subfield of primarily using deep neural networks for translation has recently started to gain traction. Many techniques have been developed using deep encoder-decoder networks for bilingual translation using both parallel as well as non-parallel corpora. There is a lot of potential in applying concepts such as bilingual embeddings to create generic translation architecture, which doesn’t need huge parallel corpora to train. These ideas are particularly pertinent in the case of Indic languages, where it is generally difficult to obtain such corpus. In this paper, we try to adapt some of newest techniques in autoencoder networks and bilingual embeddings to the task of translating between English and Hindi. The models considerably outperform state of the art translating systems for these languages.
TIGER Algorithm

TIGER Algorithm (TA) is a metaheuristic algorithm that has been designed for finding global and some noticeable local optimization points. TIGER algorithm is the abbreviation of “Target Identify with Gross Error Reduction algorithm” but it follows the Tigers' life and behaviors which include; Birth, Migration, Migration of cubs (children), Death, Suicide, Prowl, Rivalry mating and signing. Optimization problems would be solved in a noticeable time if they are so complicated, but this algorithm tries to solve them as fast as possible and the main difference between TA and other metaheuristic algorithms is that TIGER algorithm doesn't need to initialize the population and it adjusts the population by producing new tigers or deleting them for solving optimization problems efficiently. In this article some definitions of this algorithm are described but, just some of them are used in optimization for simplicity. TIGER algorithm could be used for engineering design or business planning and other cases.
Smart contracts applied to a functional architecture for storage and maintenance of digital chain of custody using blockchain

• Smart contracts applied to legal rules. • Blockchain platform applied to procedural transactions in court. • Blockchain applied to rogatory letters. • Blockchain guaranteeing integrity of the chain of custody. • Specific chaincodes for Digital Evidence and Chain of Custody.
Fast and incremental algorithms for exponential semi-supervised discriminant embedding

In various pattern classification problems, semi-supervised learning methods have shown its effectiveness in utilizing unlabeled data to yield better performance than some supervised and unsupervised learning methods. Semi-supervised discriminant embedding (SDE) is a semi-supervised extension of local discriminant embedding (LDE). However, when dealing with high dimensional data, SDE often suffers from the small-sample-size (SSS) problem. In order to settle this problem, an exponential semi-supervised discriminant embedding (ESDE) method was proposed in [F. Dornaika, Y. EI Traboulsi. Matrix exponential based semi-supervised discriminant embedding for image classification, Pattern Recognition, 61 (2017): 92–103], which makes use of the tool of matrix exponential. Despite its high discriminative ability, the computational overhead of ESDE is very large for high dimensional data. In order to cure this drawback, the first contribution of this paper is to propose a fast implementation on the ESDE method. The key is to equivalently transform the large matrix problem of size d into a much smaller one of size n, where d is the data dimension and n is the number of training samples, with d ≫ n in practice. On the other hand, in many real world applications, it is likely that whole labeled training set is unavailable beforehand, and the training data is obtained incrementally. Many incremental semi-supervised learning methods have been proposed to deal with this problem, to the best of our knowledge, however, there are no incremental algorithms for matrix exponential discriminant methods till now. To fill in this gap, the second contribution of this paper is to propose incremental ESDE algorithms for incremental learning problems. Numerical experiments on some real-world data sets show the numerical behavior of the proposed algorithms.
End-to-end indonesian speech recognition with convolutional and gated recurrent units

Abstract Automatic Speech Recognition has penetrated deeply into our life. For well-resourced language, it can be considered as solved, but that’s not the case for under-resourced language like Bahasa. Although it’s the 7th most spoken language in the world, the research of speech recognition for Bahasa was still extremely limited, with setting still inconvenient for the real world and industry. This research is an attempt to make a speech recognition model that has applicability to the real world and industry, specifically that supports sentence level input with variable character length with end-to-end training. We built the model using the deep learning approach, specifically utilizing the residual networks and Bi-Directional Gated Recurrent Unit (Bi-GRU). To the best of our knowledge, this is the first Indonesian ASR model that can be trained in an end-to-end manner. Our model surpassed the baseline model on all metrics and achieve competitiveness with the current best result, which used the visual modal, for the dataset even with a more difficult and prone to noise modality like sound.
Managing Human and Artificial Knowledge Bearers

As part of the digitization, the role of artificial systems as new actors in knowledge-intensive processes requires to recognize them as a new form of knowledge bearers side by side with traditional knowledge bearers, such as individuals, groups, organizations. By now, artificial intelligence (AI) methods were used in knowledge management (KM) for knowledge discovery, for the reinterpreting of information, and recent works focus on the studying of different AI technologies implementation for knowledge management, like big data, ontology-based methods and intelligent agents [1]. However, a lack of holistic management approach is present, that considers artificial systems as knowledge bearers. The paper therefore designs a new kind of KM approach, that integrates the technical level of knowledge and manifests as Neuronal KM (NKM). Superimposing traditional KM approaches with the NKM, the Symbiotic Knowledge Management (SKM) is conceptualized furthermore, so that human as well as artificial kinds of knowledge bearers can be managed as symbiosis. First use cases demonstrate the new KM, NKM and SKM approaches in a proof-of-concept and exemplify their differences.
Blockchain-Based Traceable Management System for Entry and Exit of Cultural Relics

As a country with rich culture and a long history, China has a large number of cultural relics. The current management system for entry and exit of cultural relics is inefficient and untrusted, which contains a lot of offline operations. What's worse, the cultural relic data is distributed geographically and can be tampered with. In order to solve these problems, this paper proposes a blockchain-based system for managing entry and exit of cultural relics, to improve data traceability, data immutability and workflow efficiency. We use blockchain technology to monitor and trace the whole process of entry and exit of cultural relic. Smart contracts are designed to maintain an immutable access control list and grant privileges to authorized entities. We also combined the blockchain storage with distributed file system to facilitate the scalability and efficiency of our system. We provide the design aspects of system architecture and our technological concerns in system implementation. We also evaluate the computational cost, data transferring cost and storage cost to show that our system is efficient and practical. Our aim is to replace the traditional management system for entry and exit of cultural relics with a blockchain-based one.
No Trouble Found (NTF) Customer Return Analysis

This paper analyzes NTF customer returns using production test data to verify if they can be screened. A total of 70 NTF customer returned units from multiple lots spanning three different product families across different customer applications and market segments were examined using three statistical methods - Principal Component Analysis (PCA), Mahalanobis Distance, and binary Support Vector Machine (SVM) in search of potential outliers. The data shows the NTF customer returns are not marginal units and cannot be screened without significant yield penalty.
Understandable Collaborating Robot Teams

As robots become increasingly complex and competent, they will also become increasingly more difficult to understand for interacting humans. In this paper, we investigate understandability for teams of robots collaborating to solve a common task. While such robots do not need to communicate verbally with each other for successful coordination, human bystanders may benefit from overhearing verbal dialogues between the robots, describing what they do and plan to do. We present a novel and flexible solution based on Cooperating Distributed Grammar Systems and a multi-agent algorithm for coordination of actions. The solution is implemented and evaluated on three Pepper robots collaborating to solve a task while commenting on their own and other robots' current and planned actions.
Recommendation Algorithm in Double-Layer Network Based on Vector Dynamic Evolution Clustering and Attention Mechanism

The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.
Adaptive Weighted High Frequency Iterative Algorithm for Fractional-Order Total Variation with Nonlocal Regularization for Image Reconstruction

We propose an adaptive weighted high frequency iterative algorithm for a fractional-order total variation (FrTV) approach with nonlocal regularization to alleviate image deterioration and to eliminate staircase artifacts, which result from the total variation (TV) method. The high frequency gradients are reweighted in iterations adaptively when we decompose the image into high and low frequency components using the pre-processing technique. The nonlocal regularization is introduced into our method based on nonlocal means (NLM) filtering, which contains prior image structural information to suppress staircase artifacts. An alternating direction multiplier method (ADMM) is used to solve the problem combining reweighted FrTV and nonlocal regularization. Experimental results show that both the peak signal-to-noise ratios (PSNR) and structural similarity index (SSIM) of reconstructed images are higher than those achieved by the other four methods at various sampling ratios less than 25%. At 5% sampling ratios, the gains of PSNR and SSIM are up to 1.63 dB and 0.0114 from ten images compared with reweighted total variation with nuclear norm regularization (RTV-NNR). The improved approach preserves more texture details and has better visual effects, especially at low sampling ratios, at the cost of taking more time.
A Secure Enhanced Key-Policy Attribute-Based Temporary Keyword Search Scheme in the Cloud

Attribute-based keyword search (ABKS), as an important type of searchable encryption, has been widely utilized for secure cloud storage.In a key-policy attribute-based temporary keyword search (KP-ABTKS) scheme, a private key is associated with an access policy that controls the search ability of the user, while a search token is associated with a time interval that controls the search time of the cloud server.However, after a careful study, we uncover that the only existing KP-ABTKS construction [1] is not secure.Through two carefully designed attacks, we first show that the cloud server can search the ciphertext in any time.As a result, their scheme cannot support temporary keyword search.To address this problem, we present an enhanced KP-ABTKS scheme and prove that it is selectively secure against chosen-keyword attack in the random oracle model.The proposed scheme achieves both fine-grained search control and temporary keyword search simultaneously.In addition, the performance evaluation indicates that our scheme is practical.