Reviewerly/exHarmony huggingface dataset:
MV-Net

Recently the development of deep learning has been propelling the sheer growth of vision and speech applications on lightweight embedded and mobile systems. However, the limitation of computation resource and power delivery capability in embedded platforms is recognized as a significant bottleneck that prevents the systems from providing real-time deep learning ability, since the inference of deep convolutional neural networks (CNNs) and recurrent neural networks (RNNs) involves large quantities of weights and operations. Particularly, how to provide quality-of-services (QoS)-guaranteed neural network inference ability in the multitask execution environment of multicore SoCs is even more complicated due to the existence of resource contention. In this article, we present a novel deep neural network architecture, MV-Net, which provides performance elasticity and contention-aware self-scheduling ability for QoS enhancement in mobile computing systems. When the constraints of QoS, output accuracy, and resource contention status of the system change, MV-Net can dynamically reconfigure the corresponding neural network propagation paths and thus achieves an effective tradeoff between neural network computational complexity and prediction accuracy via approximate computing. The experimental results show that (1) MV-Net significantly improves the performance flexibility of current CNN models and makes it possible to provide always-guaranteed QoS in a multitask environment, and (2) it satisfies the quality-of-results (QoR) requirement, outperforming the baseline implementation significantly, and improves the system energy efficiency at the same time.
Real-Time Depth Estimation with an Optimized Encoder-Decoder Architecture on Embedded Devices

As a strategy to obtain dense depth maps from a single image, sparse depths, or the fusion of both (RGBd), depth estimation receives much attention. Usually relying on high-performance workstations, existing depth prediction methods, are not particularly suitable for embedded devices. Moreover, the accuracy of depth estimation needs to be improved. Towards real-time and reliable depth prediction on portable embedded platforms, we propose an encoder-decoder architecture (EDA). Specifically, these are the three contributions of this work. (1) We present a light-weight encoder, which takes three modalities of data as inputs, namely, RGB images, sparse depths, or RGBd. The encoder is efficient to extract features on portable devices. (2) A novel decoder is designed to obtain accurate and high-resolution depth maps. (3) We use the existing TVM to deploy, compile, and optimize EDA on embedded platforms, for further improvements in the computational speed and power of depth estimation. In real scenarios, the optimized EDA achieves remarkable results with low latency. Through extensive experimental evaluation on two public datasets, we show that the optimized EDA outperforms the state of the arts in accuracy and computational resources. To the best of our knowledge, this is the first paper that provides an efficient encoder, learnable decoder, and end-to-end optimized encoder-decoder architecture for precise depth prediction.
Wrapper for Building Classification Models Using Covering Arrays

Wrapper methods are a type of feature selection method that finds a subset of variables to improve the performance of a classifier by removing redundant and irrelevant variables. The use of a wrapper implies that each time a candidate solution is explored, the classifier is evaluated on the quality measures selected (e.g. accuracy or precision). Though robust, this iteration across several candidate solutions can become computationally intensive and time-consuming. In this paper we propose a wrapper, that is based on binary Covering Arrays (CAs), and binary Incremental Covering Arrays (ICAs), that have been widely used for experimental design and fault detection in software and hardware testing. The new wrapper was evaluated with six classifiers on seven data sets. The results show that the CAs and ICAs with strength 6 significantly improve the performance and reduces the number of variables required by the classifier. A comparative analysis of the proposed method against wrappers based on other search approaches such as genetic algorithms (GA) and particle swarm optimization (PSO), shows that the proposed method yields results similar to GA, but not to PSO, with differences to PSO, in accuracy, which in the majority of cases is below 0.04. This lack of accuracy, by which the new wrapper fails to match PSO, is offset by the fact that the user does not need to fine tune algorithm parameters, such as velocity ranges, timing, cognitive coefficient, and social coefficient, while it is also much easier to program in parallel.
Query recommendation based on improved query flow graph

Search engines contain many user search behaviours. Based on the idea of group wisdom, analysis of the search log in a search engine to recommend a query has attracted significant attention from researchers. Mining valid information from a search log has shown good utility for constructing a query flow graph for query recommendation. In a query flow graph, nodes representing queries are connected if they have the same search intention. In the process of query recommendation, user intention is associated both with current queries and with the clicked URL. The URL can serve as important information to locate user search intentions. However, a query flow graph only considers query information in the search log to determine the relation between the queries. Accordingly, a novel method based on the improved query flow graph is proposed, which expands the query flow graph by adding the clicked URL information and semantic information. The clicked URL information and semantic information in the query flow graph can make the next query closer to user search intentions. Empirical experiments are performed in accordance with AOL log, and the results confirm the effectiveness of our approach in suggesting queries. The results demonstrate that the performance of our query recommendation algorithm is superior to those of other algorithms.
Comparative Analysis of Text Mining Classification Algorithms for English and Indonesian Qur’an Translation

Qur'an, As-Sunnah, and Islamic old book have become the sources for Islam followers as sources of knowledge, wisdom, and law. But in daily life, there are still many Muslims who do not understand the meaning of the sentence in the Qur'an even though they read it every day. It becomes a challenge for Science and Engineering field academicians especially Informatics to explore and represent knowledge through intelligent system computing to answer various questions based on knowledge from the Qur'an. This research is creating an enabling computational environment for text mining the Qur'an, of which purpose is to facilitate people to understand each verse in the Qur'an. The classification experiment uses Support Vector Machine (SVM), Naive Bayes, k-Nearest Neighbor (kNN), and J48 Decision Tree classifier algorithms with Al-Baqarah verses translated to English and Indonesian as the dataset which was labeled by three most fundamental aspects of Islam: 'Iman' (faith), 'Ibadah' (worship), and 'Akhlaq' (virtues). Indonesian translation was processed by using the sastrawi package in Python to do the pre-processing and StringToWord Vector in WEKA with the TF-IDF method to implement the algorithms. The classification experiments are determined to measure accuracy, and f-measure, it tested with a percentage split 66% as the data training and the rest as the data testing. The decision from an experiment that was carried out by the classification results, SVM classifier algorithms have the overall best accuracy performance for the Indonesian translation of 81.443% and the Naïve Bayes classifier has the best accuracy for the English translation, which achieved 78.35%.
Cross-project Defect Prediction via ASTToken2Vec and BLSTM-based Neural Network

Cross-project defect prediction (CPDP) as a means to focus quality assurance of software projects was under heavy investigation in recent years. In this paper, we propose a novel CPDP approach via deep learning. In particular, we model each program module via simplified abstract syntax tree (S-AST). For each node in S-AST, only the project-independent node type is remained and other project-specific information (such as name of variable and method) is ignored, so that the modeling method is project-independent and suitable for CPDP issue. Then we extract token sequences from program modules modeled as S-AST. In addition, to construct meaningful vector representations for token sequences, we propose a novel unsupervised embedding method ASTToken2Vec, which learns semantic information from S-AST's natural structure. Finally, we use BLSTM (bi-directional long short-term memory) based neural network to automatically learn semantic features from vectorized token sequences and construct CPDP models. In our empirical studies, 10 real large-scale open source Java projects are chosen as our empirical subjects. Final results show that our proposed CPDP approach can perform significantly better than 5 state-of-the-art CPDP baselines in terms of AUC.
Automated Enriched Medical Concept Generation for Chest X-ray Images

Decision support tools that rely on supervised learning require large amounts of expert annotations. Using past radiological reports obtained from hospital archiving systems has many advantages as training data above manual single-class labels: they are expert annotations available in large quantities, covering a population-representative variety of pathologies, and they provide additional context to pathology diagnoses, such as anatomical location and severity. Learning to auto-generate such reports from images present many challenges such as the difficulty in representing and generating long, unstructured textual information, accounting for spelling errors and repetition/redundancy, and the inconsistency across different annotators. We therefore propose to first learn visually-informative medical concepts from raw reports, and, using the concept predictions as image annotations, learn to auto-generate structured reports directly from images. We validate our approach on the OpenI [2] chest x-ray dataset, which consists of frontal and lateral views of chest x-ray images, their corresponding raw textual reports and manual medical subject heading (MeSH ) annotations made by radiologists.
A Robotic Writing Framework–Learning Human Aesthetic Preferences via Human–Machine Interactions

Intelligent robots are required to fully understand human intentions and operations in order to support or collaborate with humans to complete complicated tasks, which is typically implemented by employing human-machine interaction techniques.This paper proposes a new robotic learning framework to perform numeral writing tasks by investigating human-machine interactions with human preferences.In particular, the framework implements a trajectory generative module using a generative adversarial network (GAN)-based method and develops a human preference feedback system to enable the robot to learn human preferences.In addition, a convolutional neural network, acting as a discriminative network, classifies numeral images to support the development of the basic numeral writing ability, and another convolutional neural network, acting as a human preference network, learns a human user's aesthetic preference by taking the feedback on two written numerical images during the training process.The experimental results show that the written numerals based on the preferences of ten users were different from those of the training data set and that the writing models with the preferences from different users generate numerals in different styles, as evidenced by the Fréchet inception distance (FID) scores.The FID scores of the proposed framework with a preference network were noticeably greater than those of the framework without a preference network.This phenomenon indicates that the human-machine interactions effectively guided the robotic system to learn different writing styles.These results prove that the proposed approach is able to enable the calligraphy robot to successfully write numerals in accordance with the preferences of a human user.
Hands-Free User Interface for AR/VR Devices Exploiting Wearer’s Facial Gestures Using Unsupervised Deep Learning

Developing a user interface (UI) suitable for headset environments is one of the challenges in the field of augmented reality (AR) technologies. This study proposes a hands-free UI for an AR headset that exploits facial gestures of the wearer to recognize user intentions. The facial gestures of the headset wearer are detected by a custom-designed sensor that detects skin deformation based on infrared diffusion characteristics of human skin. We designed a deep neural network classifier to determine the user's intended gestures from skin-deformation data, which are exploited as user input commands for the proposed UI system. The proposed classifier is composed of a spatiotemporal autoencoder and deep embedded clustering algorithm, trained in an unsupervised manner. The UI device was embedded in a commercial AR headset, and several experiments were performed on the online sensor data to verify operation of the device. We achieved implementation of a hands-free UI for an AR headset with average accuracy of 95.4% user-command recognition, as determined through tests by participants.
Artificial Neural Network Pada Industri Non Migas Sebagai Langkah Menuju Revolusi Industri 4.0

The research conducted aims to make predictions with artificial neural metwork (backpopagation) and sensitivity analysis in the non-oil processing industry for the value of industrial exports. Data was obtained from the Badan Pusat Statistik (BPS) in collaboration with the Ministry of Industry of the Republic of Indonesia in the last 7 years (2011-2017). The process is carried out by dividing the data into 2 parts (training and testing) to obtain the best architectural model. The data processing uses the help of Matlab 6.0 software. Model selection is done by try and try to get the best architectural model. In this study using 7 architectural models (15-2-1; 15-5-1; 15-10-1; 15-15-1; 15-2-5-1; 15-5-10-1 and 15- 10-5-1) who have been trained and tested. By using the help of Matlab 6.0 software, the best architectural model is obtained 15-2-1 with an accuracy rate of 93%, epoch training = 189,881, MSE testing = 0.001167108 and MSE training = 0,000999622. The best architecture will be continued to predict the non-oil industry based on the most dominant export value using sensitivity analysis. From the architectural model a prediction of 5 out of 15 non-oil and gas industries contributes: Food &amp; Beverage Industry, Textile &amp; Apparel Industry, Basic Metal Industry, Rubber Industry, Rubber and Plastic Goods and Metal Goods Industry, Not Machines and Equipment , Computers, Electronics and Optics.
Finding Sami Cognates with a Character-Based NMT Approach

We approach the problem of expanding the set of cognate relations with a sequence-to-sequence NMT model. The language pair of interest, Skolt Sami and North Sami, has too limited a set of parallel data for an NMT model as such. We solve this problem on the one hand, by training the model with North Sami cognates with other Uralic languages and, on the other, by generating more synthetic training data with an SMT model. The cognates found using our method are made publicly available in the Online Dictionary of Uralic Languages.
Fast Classification and Detection of Marine Targets in Complex Scenes with YOLOv3

In order to meet the needs of fast detection and classification of different marine targets during intelligent unmanned surface vehicle (USV) operations, In this paper, I introduce a convolutional neural network based on one of the most effective object detection algorithms, named YOLOv3, to classify and detect images of different marine targets. Firstly, I showed the network structure of the algorithm in this paper. Then, I explained how I got the optimal anchor box parameter of the algorithm. Finally, I improved the activation function to make the algorithm more robust to noise. The final results show that the MAP of the detector in this paper is 91.83%,and we reach a detection rate of 58.3 fps by improving the YOLOV3 algorithm.
A Literature Review on the Impact of Games on Learning English Vocabulary to Children

Digital games play a significant role in the life of the new generation. Although there are many criticisms, many studies focus on the importance of digital games in improving learner’s vocabulary in the target language. Researchers have begun conductingseveral researches on how using games in the class can foster vocabulary learning. The aim of this paper is to investigate the impacts of digital games on children’s vocabulary learning depending on a literature review. Many studies focus on the impacts of digital games on different aspects of education. This conceptual paper aims to shed light on some games' benefits, and challenges which educators and children face in the use of digital games. The findings of this paper show that Games are used not only for making children successful in EFL classes but more importantly, for motivating them and increasing the cooperation among children. In addition,the findings state that there are advantages as well as disadvantages in using games for learning English vocabulary.
Cross-domain person re-identification using Dual Generation Learning in camera sensor networks

Cross-domain person re-identification (Re-ID) in camera sensor networks is a challenge task due to large domain-style and camera-style variances. In this work, we propose a novel deep learning method called Dual Generation Learning (DGL) for cross-domain person Re-ID, which simultaneously considers domain and camera styles by expanding training samples. Correspondingly, we design a three-branch deep model with different losses. We further propose Hybrid Triplet Loss (HTL) to deal with the combination of the source dataset, the target dataset and their expansions. Thus, the learned features are robust to domain shifts and camera differences. The experimental results prove that DGL achieves the promising generalization ability and accuracy compared with the state-of-the-art methods.
Impact of Internet of Thing in Developing Country: Systematic Review

The idea to connect everything to anything and at any point of time is what vaguely defines the concept of the Internet of Things (IoT).The IoT is not only about providing connectivity but also facilitating interaction among these connected things.One of the most significant concerns of IoT is to provide security assurance for the data exchange because data is vulnerable to some attacks by the attackers at each layer of IoT.The IoT has a layered structure where each layer provides a service.The security needs vary from layer to layer as each layer serves a different purpose.This paper aims to analyze the various security and privacy threats related to IoT.Some attacks have been discussed along with some existing and proposed counter measures.In developing countries, IoT technologies have brought increased efficiency and effectiveness to existing processes.For instance, farmers are using remote sensors to monitor moisture levels and soil conditions in the fields to avoid crop failure.Similar sensors are providing remote control of micro-irrigation pumps for example in India and water pumps in Rwanda, improving functionality and reducing repair intervals.And in Haiti, healthcare professionals are using "smart" thermometers to better track vaccine delivery and storage.Totally the paper presents an overview of the enablement of IoT in developing countries and discusses the visions, general applications, opportunities and challenges of IoT in these countries.It also touches on issues relating to security, privacy, trust and the accompanied complexity.The potential applications for IoT are countless and can optimize processes with a direct impact on society.Certain challenges have to be addressed to make IoT deployment successful.It will be shown that some of the challenges can be turned into opportunities and that IoT has good chances to succeed in developing countries.This report explores the current use and potential of Internet of Things (IoT) technologies in tackling global development challenges, highlighting a number of specific instances where IoT interventions are helping to solve some of the world's most pressing issues it presents summary conclusions on what is required for the IoT to reach billions of people living in the developing world, and also to accelerate income growth and social development as a result.
Early Prediction of Sepsis From Clinical Data via Heterogeneous Event Aggregation

Sepsis is a life-threatening condition that seriously endangers millions of people over the world.Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and take early preventive treatment.However, the early prediction is challenging because patients' sequential data in EHR contains temporal interactions of multiple clinical events.And capturing temporal interactions in the long event sequence is hard for traditional LSTM.Rather than directly applying the LSTM model to the event sequences, our proposed model firstly aggregates heterogeneous clinical events in a short period and then captures temporal interactions of the aggregated representations with LSTM.Our proposed Heterogeneous Event Aggregation can not only shorten the length of clinical event sequence but also help to retain temporal interactions of both categorical and numerical features of clinical events in the multiple heads of the aggregation representations.In the PhysioNet/Computing in Cardiology Challenge 2019 [1], with the team named PKU_DLIB, our proposed model, in high efficiency, achieved utility score (0.321) in the full test set .1.
CSID: Center, Scale, Identity and Density-aware Pedestrian Detection in a Crowd.

Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusion and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion. Besides the typical semantic features, center position, target's scale and offset, we introduce a pedestrian-oriented attribute feature to encode the high-level semantic differences among the crowd. Moreover, a novel attribute-feature-based Non-Maximum Suppression~(NMS) is proposed to distinguish the person from a highly overlapped group by adaptively rejecting the false-positive results in a very crowd settings. Furthermore, a novel ground truth target is designed to alleviate the difficulties caused by the attribute configuration and extremely class imbalance issues during training. Finally, we evaluate our proposed attribute-aware pedestrian detector on two benchmark datasets including CityPersons and CrowdHuman. The experimental results show that our approach outperforms state-of-the-art methods at a large margin on pedestrian detection.