Reviewerly/exHarmony huggingface dataset:
Large-Truck Safety Warning System Based on Lightweight SSD Model

Transportation is an important link in the mining process, and large trucks are one of the important tools for mine transportation. Due to their large size and small driving position, large trucks have a blind spot, which is a hidden danger to the safe transportation of mines and has a great impact on production efficiency and economic loss. The traditional large truck safety warning system mainly uses the ultrasonic short-distance ranging method, radar ranging method, GPS (Global Positioning System) technology, and so on. The disadvantage of these methods is that they are affected by the environment and weather, and they cannot display the object status in real time. Therefore, it is becoming increasingly important to realize the large truck safety warning system based on machine vision. Therefore, this paper proposes a lightweight SSD (Single Shot MultiBox Detector) model and an atrous convolution to build a large-truck object recognition model. First, the training images are collected and marked. Then, the object recognition model is established by using the lightweight SSD model. The atrous convolutional layer is introduced to improve small object detection accuracy. In the end, the objectness prior method is used to improve the classification speed. Experimental results show that, compared with the original SSD model, the lightweight SSD model occupies less space and runs faster. The lightweight SSD model with the atrous convolutional layer is more sensitive to small objects and improves detection accuracy. The objectness prior method further improves the identification speed. Compared with the traditional large truck safety warning, the system is not affected by the environment and realizes the visualization of large truck safety warning.
AcSIS: Authentication System Based on Image Splicing

Text-based passwords are widely used for the authentication of digital assets. Typically, password security and usability is a trade-off, i.e. easy-to-remember passwords have higher usability that makes them vulnerable to brute-force and dictionary attacks. Complex passwords have stronger security but poor usability. In order to strengthen the security in conjunction with the improved usability, we hereby propose a novel graphical authentication system. This system is a picture-based password scheme which comprises of the method of image splicing. Authentication data were collected from 33 different users. The usability of the method was evaluated via a comparison between the number of correct and incorrect authentication attempts and time taken. Additionally, a comparison was made between our proposed method and a complex text-based password authentication method using the authentication success rate. Authentication using image splicing proved to be resilient to brute-force attacks since the processing of images consumes a voluminous password space. The evaluation of the usability revealed that graphical passwords were easy-to-remember, resulting in a higher number of correct attempts. The proposed method produced 50% higher success rate compared to the text-based method. Findings motivate the use of the proposed method for securing digital assets.
Two-Stage Decomposition Method Based on Cooperation Coevolution for Feature Selection on High-Dimensional Classification

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of data and improve the performance. However, feature selection is still a challenge task especially for the large-scale problems with small sample size and extremely large number of features. A variety of methods have been applied to solve the feature selection problems, in which evolutionary algorithm has recently attracted increasing attention and made great progress. In this study, a two-stage decomposition cooperating coevolution strategy for feature selection (CCFS/TD) is proposed. In the first stage, the proposed algorithm decomposes evolutionary process into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -level and evolves by cooperating coevolution for each level. Then, in the second stage, evolution process of each level is further decomposed into several independent processes. The selected subset of features are determined by the results of all independent processes through majority voting. Experiments on ten benchmark datasets are carried out to verify the effectiveness of the proposed method. The results demonstrate that the proposed CCFS/TD can obtain better classification performance with a smaller number of features in most cases in comparison to some existing methods.
A Novel Neutrosophic Subsets Definition for Dermoscopic Image Segmentation

Dermoscopic images suffer from irregular and vague boundaries. New directions established the neutrosophic set (NS) approaches for clustering, and segmenting the dermoscopic images. In this work, an accurate segmentation process was developed by mapping initially the dermoscopic images to the NS domain. Thus, the neutrosophic image was defined by three subsets, namely True (T), Indeterminacy (I) and False (F). For accurate boundary detection and segmentation, different high pass (HP) filter types were used in the definition of I subset and low pass (LP) filter types in the definition of T. These filters form a new way to obtain an NS image for segmenting dermoscopy images. A comparative study was carried on the ISIC2016 skin lesion dermoscopic images dataset using different combinations of NS filter types and sizes. The results depicted the superiority of using an unsharp filter in implementing the I subset and an average filter for the T subset. 96% segmentation accuracy was reported using the proposed design compared to 92% accuracy using the default NS definition.
CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels

This work tackles the problem of generating a medical report for multi-image panels. We apply our solution to the Renal Direct Immunofluorescence (RDIF) assay which requires a pathologist to generate a report based on observations across eight different whole slide images (WSI) in concert with existing clinical features. To this end, we propose a novel attention-based multi-modal generative recurrent neural network (RNN) architecture capable of dynamically sampling image data concurrently across the RDIF panel. The proposed methodology incorporates text from the clinical notes of the requesting physician to regulate the output of the network to align with the overall clinical context. In addition, we found the importance of regularizing attention weights for the word generation processes. This is because the system can ignore the attention mechanism by assigning equal weights for all members. Thus, we propose two regularizations to encourage efficient use of the attention mechanism. Experiments on our novel collection of RDIF WSIs provided by Sullivan Nicolaides Pathology demonstrate that our framework offers significant improvements over existing methods.
Determinants of Economic Development: A Case of Gulf Cooperation Council (GCC) Countries

The main objective of this research is to identify the determinants of economic development in Gulf Cooperation Council (GCC) countries over the period of 1996-2016. The economic growth of GCC countries has slowed down due to a sharp drop in oil prices as GCC countries are depending on oil exportation for their economies. The GCC countries preferred to diversify their economies through the strategic plans called Vision 2030. The Vision 2030 for Gulf countries started in Saudi Arabia in 26 April 2016 when the Crown Prince (Mohammad bin Salman Al-Saud) declared that Saudi Arabia has to not depend on oil exportation substantially and that the diversification of oil is a must. The economic growth can be measured through the gross domestic production (GDP). Higher GDP indicates a better economy and higher standards of lives (welfare). Based on this, this research is finding the main indicators of economic development through regressions of fixed-effects model (FEM), random-effects model (REM), generalized methods of moments (GMM) and generalized least squares (GLS) models. The results show that production and rule of law strongly support the economy. In contrast, political instability and a larger population impact economic growth significantly and negatively. In addition, the global financial crisis (GFC) also decreased the economic strength significantly. This study helps the policymakers in economics sector to focus on the positive determinants and to avoid (or reduce) the implementation of the negative factors. In addition, the researcher on economics can be benefited from this study.
The Effect of Total Quality Management on University Performance in Jordan

Total Quality Management (TQM) a functioning idea to accomplish incessant performance improvement-is the word of mouth of the 1990s, attracting deep interest among organizations and educational institutions in many countries. Numerous policy-makers truly believe that TQM can improve the performance of their organizations but they did not test it to make sure the effect of TQM. Hence, the purpose of this study is to examine the effect of TQM on university performance in Jordan. This relationship obtains a substantial scholarly attention and several researches have been conducted in the western countries, but none has been conducted in Jordan in the recent year using this variable in a model. A structured survey was conducted and selected 10 public and 10 private universities in Jordan via cluster random sampling. The hypotheses were tested using SEM-AMOS package 22.0 based on resource based theory. Based on the statistical results, TQM has statistically significant effect on university performance in Jordan. Consequently, the findings evoked that there is a dire need to focus on TQM for boosting university performance and its sustainability in Jordan.
Evaluation of 4D Light Field Compression Methods

Light field data records the amount of light at multiple points in space, captured e.g. by an array of cameras or by a light-field camera that uses microlenses.Since the storage and transmission requirements for such data are tremendous, compression techniques for light fields are gaining momentum in recent years.Although plenty of efficient compression formats do exist for still and moving images, only a little research on the impact of these methods on light field imagery is performed.In this paper, we evaluate the impact of state-of-the-art image and video compression methods on quality of images rendered from light field data.The methods include recent video compression standards, especially AV1 and XVC finalised in 2018.To fully exploit the potential of common image compression methods on four-dimensional light field imagery, we have extended these methods into three and four dimensions.In this paper, we show that the four-dimensional light field data can be compressed much more than independent still images while maintaining the same visual quality of a perceived picture.We gradually compare the compression performance of all image and video compression methods, and eventually answer the question, "What is the best compression method for light field data?".
Research on Heterogeneous Terminal Security Access Technology in Edge Computing Scenario

Edge computing real-time processing of requests for massive amounts of terminal equipment brought a feasible solution, but the edge of the distributed calculation, real time characteristics as well as information security protection has brought more limited, identity authentication and privacy for edge computing applications and data security protection needs to face the challenge of problem, this paper expounds the current edge computing terminal security access to the information security requirements, analyzes the possible means of the information security threats, put forward a kind of edge computing scenarios "cloud - edge - end" three-tier system of heterogeneous terminal access authentication mechanism, solutions to support access to huge amounts of terminal authentication request, And through the anonymous identity to protect the privacy of the terminal equipment.
Scalenet: A Convolutional Network to Extract Multi-Scale and Fine-Grained Visual Features

Many convolutional neural networks have been proposed for image classification in recent years.Most tend to decrease the plane size of feature maps stage-by-stage, such that the feature maps generated within each stage show the same plane size.This concept governs the design of most classification networks.However, it can also lead to semantic deficiency of high-resolution feature maps as they are always placed in the shallow layers of a network.Here, we propose a novel network architecture, named ScaleNet, which consists of stacked convolution-deconvolution blocks and a multipath residual structure.Unlike most current networks, ScaleNet extracts image features by a cascaded deconstruction-reconstruction process.It can generate scale-variable feature maps within each block and stage, thereby realizing multiscale feature extraction at any depth of the network.Based on the CIFAR-10, CIFAR-100, and ImageNet datasets, ScaleNet demonstrated competitive classification performance compared to state-of-the-art ResNet.In addition, ScaleNet exhibited a powerful ability to capture strong semantic and fine-grained features on its high-resolution feature maps.The code is available at https://github.com/zhjpqq/scalenet.
Verifying quantum computations at scale: A cryptographic leash on quantum devices

Rapid technological advances point to a near future where engineered devices based on the laws of quantum mechanics are able to implement computations that can no longer be emulated on a classical computer.Once that stage is reached, will it be possible to verify the results of the quantum device?Recently, Mahadev introduced a solution to the following problem: Is it possible to delegate a quantum computation to a quantum device in a way that the final outcome of the computation can be verified on a classical computer, given that the device may be faulty or adversarial and given only the ability to generate classical instructions and obtain classical readout information in return?Mahadev's solution combines the framework of interactive proof systems from complexity theory with an ingenious use of classical cryptographic techniques to tie a "cryptographic leash" around the quantum device.In these notes I give a self-contained introduction to her elegant solution, explaining the required concepts from complexity, quantum computing, and cryptography, and how they are brought together in Mahadev's protocol for classical verification of quantum computations.
Learning Coarse and Fine Features for Precise Temporal Action Localization

Temporal action localization from untrimmed videos is a fundamental task for real-world computer vision applications such as video surveillance systems. Even though a great deal of research attention has been paid to the problem, precise localization of human activities at a frame level still remains as a challenge. In this paper, we propose CoarseFine networks that learn highly discriminative features without loss of time granularity with two streams: the coarse and fine networks. The coarse network aims to classify the action category based on the global context of a video by taking advantage of the description power of successful action recognition models. On the other hand, the fine network does not deploy temporal pooling constrained with a low channel capacity. The fine network is specialized to identify the per-frame location of actions based on local semantics. This approach enables CoarseFine networks to learn find-grained representations without any temporal information loss. Our extensive experiments on two challenging benchmarks, THUMOS14 and ActivityNet-v1.3, validate that our proposed method provides a higher accuracy compared to the state-of-the-art by a remarkable margin in per-frame labeling and temporal action localization tasks while the computational cost is significantly reduced.
Pengaruh Metode Pembelajaran SQ3R terhadap Hasil Belajar Bahasa Indonesia

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; Learning to read in Indonesian language material in grade IV SDN 1 Sumberagung is still dominated by the teacher as the delivery material using the lecture method (teacher centered) so that students are not optimal in the learning process because students are less interested in learning and result in many student learning outcomes under the KKM. This study aims to determine the effect on the SQ3R learning method on class IV Indonesian language learning outcomes at SDN 1 Sumberagung. This study uses a research method that is quantitative with the type of research that is quasi experimental design with the research design is a non equivalent control group design. The subjects of this study consisted of 50 students. The results of the implementation of the sq3r learning method show an increase in learning outcomes which can be seen from the results of the t test based on the hypotheses performed showing the results of t count 6.866&amp;gt; t table 2.011 and the significance value of 0.000 &amp;lt;alpha 0.050. The conclusion of the data is that there is a difference between the experimental class and the control class.&lt;/p&gt;&lt;strong&gt;Abstrak: &lt;/strong&gt;&lt;strong&gt; &lt;/strong&gt;Pembelajaran membaca pada materi bahasa Indonesia di kelas IV SDN 1 Sumberagung masih didominasi guru sebagai penyampai materi dengan menggunakan metode ceramah (&lt;em&gt;teacher centered&lt;/em&gt;) sehingga siswa belum optimal dalam proses pembelajaran dikarenakan siswa kurang tertarik dengan pembelajaran dan berakibat pada hasil belajar siswa banyak yang masih dibawah KKM. Penelitian ini memiliki tujuan untuk mengetahui pengaruh pada metode pembelajaran SQ3R terhadap hasil belajar bahasa indonesia kelas IV di SDN 1 Sumberagung. Penelitian ini menggunakan metode penelitian yakni kuantitatif dengan jenis penelitiannya yaitu &lt;em&gt;quasi experimental design&lt;/em&gt; dengan design penelitiannya adalah &lt;em&gt;non-equivalent control group design&lt;/em&gt;&lt;em&gt;.&lt;/em&gt; Subjek penelitian ini terdiri dari 50 siswa. Hasil pelaksanaan metode pembelajaran SQ3R menunjukkan peningkatan pada hasil belajar yang terlihat dari hasil uji t berdasarkan hipotesis yang dilakukan menunjukkan hasil nilai t hitung 6,866 &amp;gt; t tabel 2,011 dan nilai signifikasnsi 0,000 &amp;lt;alpha 0,050. Kesimpulan dari data adalah ada perbedaan antara kelas eksperimen dan kelas kontrol.
Implementation of Digital Literacy to Achieve 21st Century Skills in The 2013’s Curriculum

The 21st century is a century where all movements use technology.The rapid development of technology and the use of digital media as technological developments demand good and positive abilities.The scope of school is one of the targets in technological development, therefore the teacher accompanies students in digital literacy.Digital literacy forms students and teachers able to access various information, collaborate, communicate, think critically and be able to create.This is in accordance with the objectives of the 2013 curriculum that requires children to be able to cooperate, communicate, think critically and be able to create.Digital literacy is important to be mastered by students, namely information, media and communication.This study aims to describe the advantages of digital literacy in achieving 21st century skills in the 2013 curriculum.This study used a descriptive qualitative research method with interview research techniques.The research site was in SDN 03 Jaten, Karanganyar Regency.The results of this study are that students are able to access various information, think critically about information obtained and manage information obtained into information that is easily accepted by other students of their age and make digital literacy a place for children to have a work.
Person re-identification using Hybrid Task Convolutional Neural Network in camera sensor networks

This paper proposes a new framework called Hybrid Task Convolutional Neural Network (HTCNN) which combines the advantages of ranking and classification tasks for person re-identification (re-ID) in camera sensor networks. As for the ranking task, we propose Weighted Triplet Loss (WTL) to optimize global features of pedestrians, and meanwhile WTL emphasizes the foreground of pedestrian image and weakens the background in order to enhance the feature discrimination. As for the classification task, we evenly divide the convolutional activation map into several horizontal parts and utilize average pooling to obtain local features of pedestrians. We evaluate our method on public person re-ID datasets, and the results indicate HTCNN exceeds the state-of-the-art re-ID methods.
Mammographic Criteria for Determining the Diagnostic Accuracy of Microcalcifications in the Detection of Malignant Breast Lesions

Background Breast cancer is a progressive disease, with conditions secondary to primary breast cancer being among the more common causes of malignancy-related deaths in women. Early diagnosis can halt disease progression and significantly improve patient's survival. Microcalcifications detected on mammograms may be an indicator of breast cancer. This study assessed the diagnostic accuracy of microcalcifications seen on mammograms for the detection of malignant breast disease when compared with histopathology. Materials and methods This study enrolled 144 women referred to the Radiology Department of Aga Khan University Hospital in Karachi, Pakistan, for mammograms and who were found to have suspicious microcalcifications, for which they underwent subsequent biopsy with histopathology over one year. The accuracy of microcalcifications, along with their sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV), were assessed relative to histopathology results. Results Compared with histopathology results, microcalcifications had a sensitivity of 88%, and specificity of 62.8%, a PPV of 55.7%, and an NPV of 90.8%. The overall accuracy of microcalcifications was 71.5%. Conclusions The presence of microcalcifications on mammograms may predict breast malignancy. Studies with larger numbers of patients are required to determine whether microcalcifications have higher specificity and PPV relative to breast histopathology.
Integration of Blockchain and IoT in Satellite Monitoring Process

The Internet of Things is evolving as a complete matured technology to be used in all the Smart applications and it establishes itself in the future generations of internet. As like Internet of Things, Blockchain is the blooming technology in which each node involved in the blockchain contains the distributed ledger which enhances the security and data transparency. Illegal users are not able to perform any fault transaction in the blockchain network due to its ability of performing smart contract and consensus. The Internet of Things can be merged with the blockchain to improve the performance of the application in real time. However, managing the devices connected to the sensors in IoT environment and mining the block chain remains the technical challenge forever. With this background, an attempt is made to survey the core details of blockchain technology and its features. The design architecture by merging IoT and BlockChain for Satellite Monitoring and ended up with some new architectural framework has been proposed in this paper.