Reviewerly/exHarmony huggingface dataset:
Learning to Reconstruct and Understand Indoor Scenes From Sparse Views

This paper proposes a new method for simultaneous 3D reconstruction and semantic segmentation for indoor scenes. Unlike existing methods that require recording a video using a color camera and/or a depth camera, our method only needs a small number of (e.g., 3~5) color images from uncalibrated sparse views, which significantly simplifies data acquisition and broadens applicable scenarios. To achieve promising 3D reconstruction from sparse views with limited overlap, our method first recovers the depth map and semantic information for each view, and then fuses the depth maps into a 3D scene. To this end, we design an iterative deep architecture, named IterNet, to estimate the depth map and semantic segmentation alternately. To obtain accurate alignment between views with limited overlap, we further propose a joint global and local registration method to reconstruct a 3D scene with semantic information. We also make available a new indoor synthetic dataset, containing photorealistic high-resolution RGB images, accurate depth maps and pixel-level semantic labels for thousands of complex layouts. Experimental results on public datasets and our dataset demonstrate that our method achieves more accurate depth estimation, smaller semantic segmentation errors, and better 3D reconstruction results over state-of-the-art methods.
AN OPENCV BASED EFFICIENT FACE RECOGNIZATION APPROACH FOR AUTOMATED ATTENDANCE UPDATION

Traditional way of taking attendance can lead to various anomalies such as proxy, misallocation of attendance register. The proposed method overcomes the defects that exists in the traditional method. The latest digital register tracks the student’s presence by identifying and recognizing their details by using Machine learning algorithms. KEYWORDS- digital register, identification, recognizing, attendance, machine learning
Implementasi Algoritma Neural Network dalam Memprediksi Tingkat Kelulusan Mahasiswa

Higher education institutions are demanded to be quality education providers. One of the instruments used by the government to measure the quality of education providers is the number of graduates. The higher the graduation level, the better the quality of education and this good quality will positively influence the value of accreditation given by BAN-PT. Therefore, in this study the researchers provided input for research conducted at Bhayangkara Jakarta Raya University to predict student graduation rates using the Neural Network algorithm. Neural Network is one method in machine learning developed from Multi Layer Perceptron (MLP) which is designed to process two-dimensional data. Neural Network is included in the Deep Neural Network type because of its deep network level and is widely implemented in image data. Neural Network has two methods; namely classification using feedforward and learning stages using backpropagation. The way Neural Network works is similar to MLP, but in Neural Network each neuron is presented in two dimensions, unlike MLP where each neuron is only one dimensional in size. The prediction accuracy obtained is 98.27%.
Sistem Pendukung Keputusan Penerima Beasiswa Berbasis WEB Menggunakan Metode Simple Additive Weighting (SAW) pada Pondok Pesantren Daarul Ahsan

Scholarships are a work program in every school or madrasa. The scholarship program is held to ease the burden on students in pursuing their studies especially in financial costs. Scholarships are given to students in a selective manner in accordance with the type of scholarships held. Distribution of scholarships is carried out by several institutions to help someone who is less capable or achievers during his studies. Based on this to help determine in determining a student to get a scholarship, then a decision support system is needed. The model used in this decision support system is simple additive weighting, this method is often also known as the weighted sum method, the basic concept of this method is to find the weighted sum of the ranking of performance on each alternative on all attributes. This method was chosen because it is able to select the best alternative from a number of alternatives, in this case the intended alternative is the one who is entitled to receive a scholarship based on specified criteria.
Leveraging N-1 Queues to Improve the Energy Efficiency of Scalable Computing

The clusters in a blockchain computing system can be constructed to be elastic, thus supporting scalable computing and improving energy efficiency.To form an elastic cluster, the service nodes are dynamically divided into the working nodes and the reserved nodes.Specifically, the working nodes are active to meet the computing requirements of workloads, while the reserved nodes are switched to a low-power state for energy saving.Traditionally, workloads are distributed to working nodes in the mode of N-N service queues.But in this mode, the Quality of Service (QoS) of different working nodes may be diverse, because the requirements are various for the accumulated requests in different working nodes.As a result, the overall system capability is not sufficiently utilized, and the overall system QoS is dragged down.In this paper, we propose an N-1 queueing and on-demand resource provisioning method to process workloads in the mode of N-1 service queues.Different from N-N service queues, N-1 service queues prohibit the accumulation of requests in working nodes.Thereby, once there are idle working nodes, waiting requests can immediately be delivered to them.As a result, all the working nodes are sufficiently utilized, and the overall QoS is improved.Accordingly, after using the N-1 service queues, fewer working nodes are enough to meet the same Service Level Agreement (SLA) on same workloads.In addition, by using a resource demand monitor module, our method dynamically readjusts the number of working nodes to match workload demand.Finally, the energy efficiency of an elastic cluster can be measurably improved, due to that fewer working nodes are powered on while the same SLA can be met.
Enhancing breast cancer detection with recurrent neural network

Early-stage breast cancers are very challenging for computer-aided detection (CAD) because they are small and often blend in with surrounding tissues. One reason for the current CAD limitations may be the lack of temporal analysis. A radiologist usually uses the current and prior mammograms side by side to evaluate changes over time. We propose a CAD method for breast cancer screening using a recurrent neural network (RNN), a convolutional neural network (CNN) with follow-up scans. First, mammographic images are examined by three cascading object detectors to detect suspicious cancerous regions. This is similar to generating a region proposal. Then all regional images (one channel) are scaled to 224×224×3 and fed to a pre-trained CNN (ResNet-50 model) to extract features. The image features are extracted from a registered prior scan, a current scan, and their difference image, each of which has a dimension of 2048 prior to the fully-connected layer. Finally the features from the three images are combined to train a RNN classifier. The RNN functions as a temporal analysis, which can factor in multiple follow-up scans. Our digital mammographic database includes 102 cancerous masses, architecture distortion, and 27 healthy subjects, each of which includes two scans: current (cancerous or healthy), and prior scan (healthy typically one year before). Our experimental results show that the performance of the proposed CAD method is very promising.
Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments

GidaBot is an application designed to setup and run a heterogeneous team of robots to act as tour guides in multi-floor buildings. Although the tours can go through several floors, the robots can only service a single floor, and thus, a guiding task may require collaboration among several robots. The designed system makes use of a robust inter-robot communication strategy to share goals and paths during the guiding tasks. Such tours work as personal services carried out by one or more robots. In this paper, a face re-identification/verification module based on state-of-the-art techniques is developed, evaluated offline, and integrated into GidaBot’s real daily activities, to avoid new visitors interfering with those attended. It is a complex problem because, as users are casual visitors, no long-term information is stored, and consequently, faces are unknown in the training step. Initially, re-identification and verification are evaluated offline considering different face detectors and computing distances in a face embedding representation. To fulfil the goal online, several face detectors are fused in parallel to avoid face alignment bias produced by face detectors under certain circumstances, and the decision is made based on a minimum distance criterion. This fused approach outperforms any individual method and highly improves the real system’s reliability, as the tests carried out using real robots at the Faculty of Informatics in San Sebastian show.
Research on Improvement of the Combination Method for Conflicting Evidence Based on Historical Data

In a multi-sensor system, due to the difference of performance of sensors and the environment in which the sensor collects evidence, evidence collected will be highly conflicting, which leads to the failure of D-S evidence theory. The current research on combination methods of conflicting evidence focuses on eliminating the problem of "Zadeh paradox" brought by conflicting evidence, but do not distinguish the evidence from different sources effectively. In this paper, the credibility of each piece of evidence to be combined is weighted based on historical data, and the modified evidence is obtained by weighted average. Then the final result is obtained by combining the modified evidence using D-S evidence theory, and the improved decision rule is used for the final decision. After the decision, the system updates and stores the historical data based on actual results. The improved decision rule can solve the problem that the system cannot make a decision when there are two or more propositions corresponding to the maximum support in the final combination result. This method satisfies commutative law and associative law, so it has the symmetry that can meet the needs of the combination of time-domain evidence. Numerical examples show that the combination method of conflict evidence based on historical data can not only solve the problem of “Zadeh paradox”, but also obtain more reasonable results.
MAOD: An Efficient Anchor-Free Object Detector Based on MobileDet

For real-time object detectors, accuracy and efficiency are two important considerations. In this paper, we propose a lightweight anchor-free detector, MAOD, to better balance efficiency and accuracy. Our object detector contains three components: an efficient backbone network (MobileDet), a lightweight feature pyramid structure (L-FPN) and an anchor-free per-pixel prediction method. MobileDet and L-FPN provide more accurate and faster multi-scale feature extraction. Our anchor-free per-pixel prediction method achieves efficient classification and location regression tasks. On the benchmark MS-COCO dataset, MAOD achieves 46.1% AP at the speed of 68 FPS with the input size 512 × 512. When the input size is 800 × 800, MAOD achieves 47.1% AP at the speed of 43 FPS. The fast version of MAOD (320 × 320 input size) can run at 91 PS with 43.3% AP. Compared with other state-of-the-art object detectors, our detector has similar accuracy while maintaining extremely fast inference speed. MAOD achieves an optimal efficiency-accuracy tradeoff.
Hierarchical Aggregation for Reputation Feedback of Services Networks

Product ratings are popular tools to support buying decisions of consumers, which are also valuable for online retailers. In online marketplaces, vendors can use rating systems to build trust and reputation. To build trust, it is really important to evaluate the aggregate score for an item or a service. An accurate aggregation of ratings can embody the true quality of offerings, which is not only beneficial for providers in adjusting operation and sales tactics, but also helpful for consumers in discovery and purchase decisions. In this paper, we propose a hierarchical aggregation model for reputation feedback, where the state-of-the-art feature-based matrix factorization models are used. We first present our motivation. Then, we propose feature-based matrix factorization models. Finally, we address how to utilize the above modes to formulate the hierarchical aggregation model. Through a set of experiments, we can get that the aggregate score calculated by our model is greater than the corresponding value obtained by the state-of-the-art IRURe; i.e., the outputs of our models can better match the true rank orders.
RecycleTrashNet: Strengthening Training Efficiency for Trash Classification via Composite Pooling

In this paper, we propose RecycleTrashNet to classify house trash based on deep neural networks. In our model, we use 3Ã—3 filter in convolution layer instead of 7Ã—7 filter, which is a smaller filter tends to learn more features. Since single max pooling or average pooling in pooling layer canâ€™t achieve good performance, we present composite pooling to preserve as many image features as possible. Experiments on trash dataset from Stanford University demonstrate good performance of RecycleTrashNet over other neural network models in speed and accuracy. It can improve training efficiency, which achieves classification results with 88% test accuracy at only 80 epochs.
SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

The recognition of activities of daily living (ADL) in smart environments is a well-known and an important research area, which presents the real-time state of humans in pervasive computing. The process of recognizing human activities generally involves deploying a set of obtrusive and unobtrusive sensors, pre-processing the raw data, and building classification models using machine learning (ML) algorithms. Integrating data from multiple sensors is a challenging task due to dynamic nature of data sources. This is further complicated due to semantic and syntactic differences in these data sources. These differences become even more complex if the data generated is imperfect, which ultimately has a direct impact on its usefulness in yielding an accurate classifier. In this study, we propose a semantic imputation framework to improve the quality of sensor data using ontology-based semantic similarity learning. This is achieved by identifying semantic correlations among sensor events through SPARQL queries, and by performing a time-series longitudinal imputation. Furthermore, we applied deep learning (DL) based artificial neural network (ANN) on public datasets to demonstrate the applicability and validity of the proposed approach. The results showed a higher accuracy with semantically imputed datasets using ANN. We also presented a detailed comparative analysis, comparing the results with the state-of-the-art from the literature. We found that our semantic imputed datasets improved the classification accuracy with 95.78% as a higher one thus proving the effectiveness and robustness of learned models.
Is the Recovery of Transition Countries Possible on the New Global Stage and How?

When it comes to finding a new development formula in transition countries, it is worrisome that a well-trodden development paradigm lacks in today’s multipolar world. Therefore, overcoming the apparent deadlock in the development of transition countries is possible only through the “new” order, or, in other words, through acceptance of the “new” economic paradigm of development. This paper aims to answer the key question regarding the development perspectives of the transition countries, “Can the transition countries (region) ever catch up with the living standards of the world’s most advanced market economies?” In this sense, the research of a new development pattern involving reactivation of the entrepreneurial economy is the answer to the second question: given the omnipresent globalization, is it possible to reverse the concept of development in the transition to greater equality of wealth in the world; therefore, this paper openly advocates for the re-industrialization of transition countries.
Pyramid Spatial Context Features for Salient Object Detection

Salient object detection aims to obtain the most attractive objects from the input images, which severs as a pre-processing step for many image processing tasks. This paper presents a novel deep neural network design for salient object detection by formulating a pyramid spatial context module, PSC module for short, to capture the spatial context information at multiple scales. To achieve this, we first adopt convolutional operations with different dilated rates to generate the feature maps with different respective fields, and then use the two-round recurrent translations to explore multiple types of spatial context features on these feature maps. By further inserting this module in a deep network, namely PSCNet, we are able to optimize the network in an end-to-end manner for salient object detection. We evaluate the proposed method on six public benchmark datasets by comparing it with 25 salient object detection methods. The experimental results demonstrate that our PSCNet performs favorably against all the other methods.
RSSD: Object Detection via Attention Regions in SSD Detector

This paper designs a module of attention regions in SSD detector for accurate and efficient object detection (RSSD). Different from previous one-stage detection method like SSD which just simply applied the multi-scale head-features and directly extracted from backbone network, for classification and regression, our method aims to strengthen the characterization of head-features further. The parallel encode-to-decode structure is constructed and a computation method of regional distribution on features (R-Softmax) is proposed. What’s more, in order to reduce time-costs, the down-sampling layers are shared with the multi-scale layers from backbone network. Our detector performs better on PASCAL VOC datasets (e.g., 78.4% mAP V.S. SSD 76.4% on VOC 07test) and costs 0.001s per image more than SSD.
Video denoising and moving object detection by rank-1 and total variation regularization on robust principal component analysis framework

With the complexity of the video environment and the problem of possible noise during data transmission, traditional robust principal component analysis (RPCA) failed to obtain the lowest rank representation from corrupted data. A method of video denoising and an object detection algorithm based on the RPCA model with total variation and rank-1 constraint (TVR1-RPCA) is proposed; it employs the more refined prior representations for the static and dynamic components of the video sequences. The proposed method is based on RPCA under the framework of low-rank sparse decomposition; the rank-1 constraint is exploited to describe the strong low-rank property of the background layer, TV regularization is combined with l1 regularization to constrain the sparsity and spatial continuity of the foreground component, and l2 norm regularization is combined to constrain the noise to make up for the deficiencies of the existing RPCA model. In addition, an efficient algorithm based on the alternating direction method of multipliers is designed to solve the proposed video denoising and moving object detection issues. Our experiments on static and moving camera videos demonstrate that the proposed method is superior to the state-of-the-art methods in terms of denoising capability and detection accuracy.
AI Driven Methodology for Anomaly Detection in Apache Spark Streaming Systems

Cloud computing, Artificial Intelligence, and Big Data technologies have recently become one of the most impactful forms of technology innovation. It is common to have multiple users share the same computing resources. This practice noticeably leads to performance anomalies. For instance, some applications can feature variability in processing time due to interference from other applications, or software contention from the other users, which may lead to unexpectedly long execution time and be considered anomalous. There is an urgent need for an automated effective performance anomaly detection method that can be used within the production environment for the streaming system to avoid any late detection of unexpected system failures. To address this challenge, we introduce a new black-box training workload configuration optimization with a neural network driven methodology to identify anomalous performance in an in-memory Spark streaming Big Data platform. The proposed methodology effectively uses Bayesian Optimization to find the ideal training dataset size and Spark streaming workload configuration parameters to train the anomaly detection model. The proposed model is validated on the Apache Spark streaming system. The results demonstrate that the proposed solution succeeds and accurately detects many types of performance anomalies. In addition, the training time for the machine learning model is reduced by more than 50%, which offers a fast anomaly detection deployment for system developers to utilize more efficient monitoring solutions.
Estimation of solar energy potential over Mongolia based on satellite data

Abstract —We aim to estimate solar resource of Mongolia using satellite data in combination with limited ground measurements. Visible channel images provided by Japanese Geostationary Meteorological Satellite (GMS) Himawari 8 are correlated with ground-based measurements of solar irradiation to derive parameters of the semi-physical model.
Digital Image Steganography

Steganography is the practice of concealing the truth that communication is taking place.Intruders can easily attack information within a system if it is readily available.It can be read, modified, misused, and revealed to others.Therefore, the goal is to hide the existence of the secret data.Using steganography techniques, secret data can be concealed within an appropriate multimedia carrier, such as, image, audio, text files; among which digital image is the most widely used carrier format.Different steganography techniques are available, some are more advanced than the others, having strong and weak points respectively.The suitable technique can be selected as per the application requirement.The main objectives of steganography are undetectability, untraceable, robustness, and capacity of secret data.These are the factors that make it different from techniques such as cryptography.This paper analysis the uses of steganography and its techniques.It additionally aims to determine which steganography technique is more suitable for a certain application.