Reviewerly/exHarmony huggingface dataset:
Airport and Ship Target Detection on Satellite Images Based on YOLO V3 Network

Airplane and ship play a very important role in both civil life and military operations. It is a meaningful to detect airplane and ship around the world through remote sensing images. Target recognition algorithms based on deep learning technology are proven to be effective and gradually replacing traditional algorithms. This paper builds a target detection system based on NVIDIA TX2 development platform and YOLO V3 algorithm and focuses on both the ship and airplane targets. The training data comes from image fragments generated by satellites such as Jilin No. 1, DigitalGlobe, and Planet. The label of each target includes a bounding box and category information. The image processing method such as rotation and noise is added to increase the robustness of the trained YOLO V3 network for different sensors and atmosphere. The training of the input image takes about 3 days on an NVIDIA Titan X GPU. At test time, we partition testing images of arbitrary size into cutouts with a fixed size of 1 k × 1 k and run each cutout through our trained model to find ships and airplanes. The experimental results show that the F1-score values of the airport and the ship are 91.48% and 93.89%, respectively, and the detection speed of one cutout on NVIDIA TX2 development platform is about 0.56 s.
Efficient Full Higher-Order Unification

We developed a procedure to enumerate complete sets of higher-order unifiers based on work by Jensen and Pietrzykowski. Our procedure removes many redundant unifiers by carefully restricting the search space and tightly integrating decision procedures for fragments that admit a finite complete set of unifiers. We identify a new such fragment and describe a procedure for computing its unifiers. Our unification procedure is implemented in the Zipperposition theorem prover. Experimental evaluation shows a clear advantage over Jensen and Pietrzykowski’s procedure.
Search-Guidance Mechanisms for Numeric Planning Through Subgoaling Relaxation

Recently, a new decomposition based relaxation has been proposed for numeric planning problems. Roughly, this relaxation is grounded on the identification of regression-based necessary conditions for the satisfaction of sets of numeric subgoals. So far, it has been used to define novel heuristics that are able to provide great guidance in problems exhibiting a pronounced numeric structure. This paper investigates how to further exploit this relaxation; it does so by introducing the notion of the multi-repetition relaxed plan. The multi-repetition plan annotates actions with the number of times such actions need to be executed. We use this structure for different purposes: extraction of a concrete relaxed plan based heuristic, definition of subgoaling based helpful actions, and definition of what we call up-to-jumping actions. Up-to-jumping actions allow us to deeply leverage from the metric structure of the problem and devise an informed search strategy that can collapse several decision steps. We experimentally analyze a forward state space planner equipped with these novel mechanisms across several planning benchmarks, showing the benefit of the ideas presented in the paper.
Identifying Forensic Interesting Files in Digital Forensic Corpora by Applying Topic Modelling

The cyber forensics is an emerging area, where the culprits in a cyber-attack are identified. To perform an investigation, investigator needs to identify the device, backup the data and perform analysis. Therefore, as the cybercrimes increase, so the seized devices and its data also increase, and due to the massive amount of data, the investigations are delayed significantly. Till today many of the forensic investigators use regular expressions and keyword search to find the evidences, which is a traditional approach. In traditional analysis, when the query is given, only exact searches that are matched to particular query are shown while disregarding the other results. Therefore, the main disadvantage with this is that, some sensitive files may not be shown while queried, and also additionally, all the data must be indexed before performing the query which takes huge manual effort as well as time. To overcome this, this research proposes two-tier forensic framework that introduced topical modelling to identify the latent topics and words. Existing approaches used latent semantic indexing (LSI) that has synonymy problem. To overcome this, this research introduces latent semantic analysis (LSA) to digital forensics field and applies it on author’s corpora which contain 29.8 million files. Interestingly, this research yielded satisfactory results in terms of time and in finding uninteresting as well as interesting files. This paper also gives fair comparison among forensic search techniques in digital corpora and proves that the proposed methodology performance outstands.
University of Tsukuba’s Machine Translation System for IWSLT20 Open Domain Translation Task

In this paper, we introduce University of Tsukuba's submission to the IWSLT20 Open Domain Translation Task.We participate in both Chinese→Japanese and Japanese→Chinese directions.For both directions, our machine translation systems are based on the Transformer architecture.Several techniques are integrated in order to boost the performance of our models: data filtering, large-scale noised training, model ensemble, reranking and postprocessing.Consequently, our efforts achieve 33.0 BLEU scores for Chinese→Japanese translation and 32.3 BLEU scores for Japanese→Chinese translation.
Vessel Segmentation and Stenosis Quantification from Coronary X-Ray Angiograms

Angiography is the gold standard for diagnosis and interventional treatment of vascular pathologies, especially for stenosis, in many hospitals around the world. Still, the physicians complain of visual burdens due to its rather low spatial resolution and artefacts. Fluoroscopic angiography series from the dataset are obtained with standard clinical protocol. In the following, there is proposed an algorithm for vessel segmentation and edge detection. It is related to gradient operator applied on a pre-processed image with the Frangi’s vesselness filtering for removing the equipment acquisition noises, followed by morphological operations for removing the spurs and adaptive threholding. The contour tracking along the vessel is done using Dijkstra’s smallest path algorithm. The severity of the stenosis for a vessel segment can be assessed visually by a medical imagistic expert. The angiograph software can provide a graphic containing on the X axis the vessel segment length and on Y axis its corresponding cross-sectional areas. More objectively, the percentage of area stenosis can be computed.
Community for data integration 2018 funded project report

First posted June 30, 2020 For additional information, contact: Director, Science Analytics and SynthesisU.S. Geological Survey108 National Center12201 Sunrise Valley DriveReston, VA 20192 The U.S. Geological Survey Community for Data Integration annually funds small projects focusing on data integration for interdisciplinary research, innovative data management, and demonstration of new technologies. This report provides a summary of the 10 projects funded in fiscal year 2018, outlining their goals, activities, and accomplishments.
Arduino UNO and Android Based Digital Lock Using Combination of Vigenere Cipher and XOR Cipher

Data security becomes an important thing in the computer era nowadays, especially in digital lock. Because of easy to use, there is a lot of digital locks connected to Android smartphone. However, the security of digital lock is lower if commands and passwords exchange is unprotected. That is why algorithm combination concept is important to be applied. Vigenere Cipher and XOR Cipher algorithm combination is simple to be applied, where both are symmetric cryptography algorithm which has advantage in short calculation time, so the plaintext processing can run quickly. To implement the cryptography algorithm combination concept, a digital lock prototype is also needed. One of the best microcontroller for prototyping is Arduino UNO. It can communicate with other devices through Bluetooth connection especially Android smartphone if connected to Bluetooth module. The Arduino UNO based digital lock will communicate with Android smartphone via an application in the smartphone. It is found that Vigenere Cipher and XOR Cipher algorithm combination, and also other support algorithms can improve the secrecy of communication between devices in the digital lock system.
Test Fatigue

As the recent coronavirus outbreak has made painfully clear, the quantity and quality of our test efforts determine what defect rates we measure. If you don't test, or test poorly, you will discover few defects and may be tempted to draw the wrong conclusions about the quality of whatever it was that you were testing.
A Study on the Attitude of Middle and High School Students Toward Hanbok: Focusing on Hanbok Images, Types of Traditional Hanbok Choices, and the Route to Access Hanbok

The purpose of this study was to examine the attitude of middle and high school students toward hanbok, focusing on the image of hanbok, the types of choices of traditional hanbok, and the route to access hanbok.BR The results of the study are as follow:BR First, middle and high school students responded positively to the image of hanbok. But questions regarding comfort, stuffiness, and practical images were received negatively by students. Through factor analysis, three image factors were extracted: elegant image, rustic image, and colorful image. The image factors of hanbok differed significantly depending on gender and grade.BR Second, middle and high school students chose hanbok, which is close to the traditional hanbok, from the 18th century, 19th century, and 20th century hanbok. Also, 66.7% of respondents said that hanbok in the 20th century was more like the traditional hanbok. In particular, 78.5% of female respondents chose hanbok as their traditional hanbok in the 20th century. The choice of traditional hanbok differed significantly depending on gender and grade.BR Third, as a result of checking the route to access hanbok, more than 10% of respondents chose experience, textbooks, traditional villages, mass media, and SNS. Differences between gender and grade were partially significant.
A Knowledge Management Model for Enhancing Quality and Performance of Higher Education Institutions: Insights from Oman

The quality and performance of higher education institutions (HEIs) are crucial to the success of HEIs in the short and long run. In this paper, a knowledge management (KM) model for HEIs is developed and proposed as an appraising mechanism that can be used to identify and monitor the quality and performance areas that are important to HEIs. The conceptualisation and operationalisation of the KM model is also articulated extensively in this paper and tested rigorously using a sample of HEIs in the form of College of Applied Sciences (CASs) in Oman. The findings from the study, which produces important implications for the Ministry of Higher Education (MoHE) as well as for HEIs, generally supports the KM model as an appraising mechanism for identifying and monitoring the quality and performance of HEIs.
Semantic-driven bibliometric techniques for co-citation analysis

Co-citation analysis can be exploited as a bibliometric technique used for mining information on the relationships between scientific papers. Proposed methods rely, however, on co-citation counting techniques that slightly take the semantic aspect into consideration. The present study proposes a se mantic driven bibliometric techniques for co-citation analysis through measuring the semantic similarity (SS) between the titles of co-cited papers. Several computational measures rely on knowledge resources to quantify the semantic similarity, such as the WordNet “is a” taxonomy. Our proposal analyzes the SS between the titles of co-cited papers using word-based SS measures. Two major analytical experiments are performed: the first includes the benchmarks designed for testing word-based SS measures through the correlation coefficients for expressing the measures efficiency; the second exploits the dataset DBLP1 citation network. As a result, the semantic similarity measures shows good performance in relation with the human judgements compared to automatic provided estimated similarities. Therefore, the lexical similarity can be consequently used for the automatic assessment of similarity between co-cited papers. The analysis of highly repeated co-citations demonstrates that the different SS measures display almost similar behaviours, with slight differences due to the distribution of the provided SS values. Furthermore, we note a low percentage of similar referred papers into the co-citations.
A study of the development of AI mediated assessment tasks and their application into practice in primary English classrooms

The purpose of this study aims to develop AI mediated assessment tasks and put them into primary English teaching practice. Taking into account the teaching and assessment context of primary English teaching, and also the mainstream theories of process-oriented assessment, a model of AI chatbot mediated process-oriented assessment was developed. To conduct the research, five task types were selected; in line with this, AI chatbots were developed two times. After that, the chatbots were applied to primary English classes. According to the results of the study, the AI chatbots were operated properly after correcting operation errors that occurred at the first trial. The interaction between the chatbot and the students played a role as scaffolding, which could make the students progress in addressing the tasks; through this, especially some underachievers improved their English communication skills. However, there are still some issues that need to be addressed, such as designing AI chatbot tasks according to the English levels of the students as well as providing the teachers with in-service education programs regarding the chatbot platforms. Other issues needing further investigation were identified in the last part of the study.
Soil geochemistry study of the listvenite area of Ayvacik (Çanakkale, Turkey)

Objective of this study is to use soil geochemistry surveys using statistical methods for the exploration of possibly gold mineralization related to Alakeci listvenite area (Bayramic, Canakkale, Turkey). In the scope of soil geochemistry, about 350 soil samples were analyzed and evaluated for nine elements including gold. Element concentrations for Cu, Pb, Zn, As, Sb, Mo, Ni, Co and Au (all element except Au in ppm, Au in ppb) were 10-265, 10-90, 15-545, 5-800, 2-60, 4-8, 10-6300, 15-1255 and 40-300 respectively. The gold concentrations were comparatively detected in low value than important ore deposit and exhibited no correlation with other elements (except Mo). However, positive correlation was observed between Cu and Zn; Sb and As; Mo with As and Sb; Au and Mo; Ni and Co. In literature, Cu, Zn, As, Sb and Mo were widely used as pathfinder elements for exploration of gold mineralization. So, these elements were accepted as pathfinders in this study and their distribution maps were prepared. The element distribution maps for pathfinder elements and gold displayed remarkable zoning in the central to southeast site of the area connected with tectonic lines and lithological borders. So, in conclusion, this part in the area needs to be investigated in detailed with geophysics and drilling methods for blind gold mineralization.
Rubbing Image Retrieval Using Deep Convolutional Neural Network

With the widespread use of digital imaging data, the size of image collections about Bronze Inscriptions rubbing is increasing rapidly. It becomes difficult to manage and query a specific image from these large databases, which motivates the need for image retrieval. In this paper, we proposed an effective content-based rubbing image retrieval (CBRIR) framework based on deep convolutional neural network (DCNN) for Bronze Inscriptions rubbing. Specifically, we extract discriminative local features for image retrieval using the activations of convolutional neural networks. We use cosine metric, Euclidean metric, and Hamming metric to measure similarity in the CBRIR framework. Experimental results show that our framework has an excellent accuracy of rubbing retrieval.
Geochemical Explorations of Leached Fe in Tiga Warna Beach, Indonesia

To explore Fe minerals in Tiga Warna Beach, we investigated to assess the geochemical fraction of Fe in sediment. The samples were leached of Fe using the optimized BCR microwave method. The optimized of BCR microwave method revealed the high accuracy and precision before Fe assessment. The leached of Fe in each of fraction in sediment were measured by AAS. The same patterns of explored Fe in the geochemical fractions from the sediment fraction 1 (mobile fraction) to fraction 3 (oxidizable fraction) as stated nonresistant sediment fractions showed a lower portion of Fe compared with the Fe levels in sediment fraction 4 (residual fraction) or resistant sediment fraction. The Pollution Load Index (PLI) value and the geoaccumulation Index (I-geo) value of sediment samples in blue, green, and red waters indicating that the sediments of Tiga Warna Beach are still natural and has not been categorized as polluted.
A New Method of Data Encryption based on One to One Functions

<p class='IJASEITAbtract'>Due to the rapid growth of computer networks, critical highly confidential information shared across these networks. Accordingly, securing such information from unauthorized intruders has become a vital issue in the field of information technology. In this paper, we present a new algorithm for encrypting and decrypting English plain text based on the well-known Caesar's algorithm and a special type of functions called One to One function. The proposed algorithm is referred to as the One to One function algorithm (OtO). The OtO belongs to a symmetric key concept where the same key is used in both encryption and decryption processes. In the OtO algorithm, the triplet (<em>K, a, b</em>) represent the private keys. To speed up the proposed OtO computation, the value of <em>K</em> is calculated based on Fibonacci sequence, on eigenvalues, Leslie matrices and Markov chain. This private key <em>K</em> only knew to the transmitter and receiver and considered one of the private keys used in the encryption process. When the message arrives at the receiver, it uses the inverse function of the proposed one to one function that used at the transmitter. The proposed OtO algorithm is conducted using MATLAB and its efficiency is checked in terms of encrypted time, decrypted time, and Avalanche Effect. We think that the obtained results are acceptable compared to famous algorithms DES, 3DES, AES and RSA.
A Multi-Sampling Convolutional Neural Network-Based RF Fingerprinting Approach for Low-Power Devices

Radio frequency (RF) fingerprint-based identification and authentication can improve the security of the Internet of Things (IoT). However, with the ever-increasing scale of low-power devices in IoT, how to address the semi-steady behavior of low-power devices owing to sleep mode switching and improve the identification accuracy in large-scale scenario become a new challenge. To tackle the above problems, this paper presents a multi-sampling convolutional neural network (MSCNN) to extract RF fingerprint based on the adaptive region of interest (ROI) selection strategy. The proposed MSCNN use multiple downsampling transformations for multi-scale feature extraction and classification automatically. Extensive experiments with 54 CC2530 devices as targets are conducted to demonstrate the feasibility and reliability of this method. The classification accuracy is as high as 97.0% under the line-of-sight (LOS) scenarios around SNR=30 dB. Our scheme is robust over a wide range of SNRs under the LOS scenarios as well as under the non-line-of-sight (NLOS) scenarios.
Normalization of Facial Pose and Expression to Increase the Accuracy of Face Recognition System

Abstract Normalization of face poses and expressions is one of the critical steps to restore the raw appearance of faces in uncontrolled conditions (in the wild) and help improving the performance of face recognition systems. Researchers in the field of computer vision has for a long time trying to find methods to normalize of poses and expressions to gain accuracy improvement for the face recognition process. The process of normalization is related to the face alignment stage, which aims to find face landmarks (point markers of facial features). From this face landmarks, the poses and facial expressions that are different from canonical face obtained for later transformation. This paper discusses the method of normalizing poses and facial expressions that have been proposed by researchers from geometrical-based transformation to the current state of the art, which is a deep learning-based system.
Enhancement security AES algorithm using a modification of transformation ShiftRows and dynamic S-box

Abstract AES algorithm is a popular encryption algorithm and has a strong resistance to a variety of attacks, especially linear and differential attacks. However, it is necessary to modify the algorithm to improve the performance of the AES algorithm so that the encryption produced (ciphertext) is more resistant from various types of attacks. In this paper, the increased strength of encryption in the AES algorithm uses a modified ShiftRows transformation and dynamic S-box. Modifications to the transformation of ShiftRows are used to simplify the encryption process, while dynamic S-box is used to randomize S-box. The data used in this paper are image data sets. The results of image encryption were tested using histogram analysis, correlation analysis, information entropy, and sensitivity analysis. Based on the test results obtained the ideal range value is 26.76114. This value is better than the results of previous studies.
Development of Motion Platform-Based Exercise Equipment for Rehabilitation Training and Posture Balance

In this study, a four-axis platform capable of rolling, pitch, and yaw rotation was created for rehabilitation and exercise. Based on this, a virtual coach and a virtual reality game system were developed. The virtual coach is a 3D person model created with the Unity program that allows the user to exercise in the correct posture with the virtual coach, and recognizes the correct posture with the Kinect. Additionally, a virtual reality game has been developed, and in conjunction with this, the actual platform also moves to increase the rehabilitation effect. The upper body and lower body movement and plantar pressure measurement were used to associate with the four-axis platform to maintain strength and balance the body.
Multisensory Learning System Applying Augmented Reality

Mathematics is essential in our daily life. However, traditional teaching methods are mainly limited to the use of textbooks, generating demotivation and low interest in learning the subject. The present study proposes the development of an augmented reality system for the multi-sensory learning of students in the field of mathematics. For the creation of this proposal, we have used a human–computer interface tool called Makey Makey. Besides, the Singapore method, which has produced excellent results in the learning of mathematics, is applied. Scratch, a programming language that allows people to develop applications without having deep knowledge of the code, was used for the development of the application. Scratch allows us to combine Singapore, Makey Makey, and Augmented Reality optimally for learning.