Reviewerly/exHarmony huggingface dataset:
Exploit Direction Information for Remote Ship Detection

Ship detection in remote sensing has been achieving increasing significance recently. In remote sensing, ships are arbitrary oriented and the detector has to learn the object features of arbitrary orientation by rote, which demands a large amount of training data to prevent overfitting. In addition, plenty of ships have a distinct direction from the center point to the head point. However, little attention has been paid to the direction information of ships and previous studies just predict the bow directions of ships. In this paper, we propose to further exploit the ship direction information to solve the arbitrary orientation problem, including direction augmentation, direction prediction, and direction normalization. A Variable-Direction Rotated RoI Align module is designed for direction augmentation and normalization with an additional feature extraction direction as input. The direction augmentation method directly augments the features of ship RRoIs and brings great diversities to the training data set. The direction prediction introduces additional direction information for learning and helps to reduce noise. In the direction normalization method, the predicted ship directions are utilized to normalize the directions of ship features from stern to bow through the VDR RoI Align module, making the ship features present in one orientation and easier to be identified by the detector. On the L1 task of the HRSC2016 data set, the direction augmentation method and direction normalization method boost the RoI Transformer baseline from 86.2% to 90.4% and 90.6%, respectively, achieving the state-of-the-art performance.
E-Commerce product comparison portal for classification of customer data based on data mining

In this search paper scrapping the data such as product name, product price, product reviews and ratings related to suggested products from Flipkart and Amazon using Robotic Process Automation tool and data is cleaned in order to remove noisy values and based on the user request the products from Flipkart and Amazon is suggested on E-Commerce portal based on user preferences the user can purchase the product from any site which is completely dependent on user. The RPA tool i.e., UiPath is used to create software robot for both Flipkart and Amazon, this bot is used to scrape the data from Flipkart and Amazon and perform cleaning operation and dump the data to database which can be used for future use. And the bot can be triggered on daily basis for data collection for database updation. This E-Commerce portal helps to categorize and distinguish the product purchase behavior and satisfy the end customer requirements.
Code Smells and Detection Techniques: A Survey

Design and code smells are characteristics in the software source code that might indicate a deeper design problem. Code smells can lead to costly maintenance and quality problems, to remove these code smells, the software engineers should follow the best practices, which are the set of correct techniques which improve the software quality. Refactoring is an adequate technique to fix code smells, software refactoring modifies the internal code structure without changing its functionality and suggests the best redesign changes to be performed. Developers who apply correct refactoring sequences to remove code smells, improve the software maintenance and development time significantly. Many tools have been created to automatically or semi-automatically detect code smells and refactor them. This study discusses the code smells, detection techniques, detection and refactoring tools, in addition to observing the challenges and suggesting enhancements for better performance.
System application control based on Hand gesture using Deep learning

In general, the Human Computer Interaction progresses toward interfaces that seem to be natural and intuitive to use rather than the customary usage of keyboard and mouse. Hand gesture recognition system is one of crucial techniques to build user-friendly interfaces, because of its diversified application and the potential of interacting with machines proficiently. Hand gestures including the movement of hands, fingers or arms are considerable for interaction. The proof levels of the hand gesture is perceived from the level of static gesture to the dynamic gestures or intricate foundation through which the communication of human feeling with computers succeed to occur. The proposed solution is framed by the identification of hand gestures as it possesses the perk of being used effortlessly and does not require an intervening medium. The existing system for the application access is inflexible and arduous for people with blindness and hand deformity regarding the human-computer interaction. A deep convolutional neural network (DCNN) is put forward in this paper, to use hand gestures recognition and immediately classify them by preserving even the not-hand area without any detection or segmentation process. Hence the proposed objective is to use different hand gestures via integrated webcam with the aid of deep learning concept being beneficial for the visually impaired and people with hand disability.
Mixed Attention Densely Residual Network for Single Image Super-Resolution

Recent applications of convolutional neural networks (CNNs) in single image super-resolution (SISR) have achieved unprecedented performance.However, existing CNN-based SISR network structure design consider mostly only channel or spatial information, and cannot make full use of both channel and spatial information to improve SISR performance further.The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information.Specifically, we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network.This structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group, to enable the algorithm to fuse channel attention with laplacian spatial attention effectively, and thereby more adaptively focus on valuable feature learning.The qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.
Analyzing the Synergy between HCI and TRIZ in Product Innovation through a Systematic Review of the Literature

The boundary between tangible and digital products is getting more fused while rapidly evolving systems for interaction require novel processes that allow for rapidly developed designs, evaluations, and interaction strategies to facilitate efficient and unique user interactions with computer systems. Accordingly, the literature suggests combining creativity enhancement tools or methods with human-computer interaction (HCI) design. The TRIZ base of knowledge appears to be one of the viable options, as shown in the fragmental indications reported in well-acknowledged design textbooks. The goal of this paper is to present a systematic review of the literature to identify and analyze the published approaches and recommendations to support the synergy between HCI and TRIZ from the perspective of product innovation related to HCI, with the aim of providing a first comprehensive classification and discussing about observable differences and gaps. The method followed is the guidelines related to systematic literature review methods. As results, out of 444 initial results, only 17 studies reported the outcomes of the synergy between HCI and TRIZ. The 7 of these studies explored the feasibility of the combination of HCI and TRIZ. The 10 studies attempted to combine and derive approaches in these two fields, and the outcomes defined 3 different integration strategies between HCI and TRIZ. Some conclusions achieved are that the generic solutions to support the synergy between HCI and TRIZ are still rare in the literature. The extraction and combination of different tools caused the randomization of the evaluation criteria, and the performance of the proposals has not been comprehensively evaluated. However, the findings can help inform future developments and provide valuable information about the benefits and drawbacks of different approaches.
Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts

For most real-world data streams, the concept about which data is obtained may shift from time to time, a phenomenon known as concept drift. For most real-world applications such as nonstationary time-series data, concept drift often occurs in a cyclic fashion, and previously seen concepts will reappear, which supports a unique kind of concept drift known as recurring concepts. A cyclically drifting concept exhibits a tendency to return to previously visited states. Existing machine learning algorithms handle recurring concepts by retraining a learning model if concept is detected, leading to the loss of information if the concept was well learned by the learning model, and the concept will recur again in the next learning phase. A common remedy for most machine learning algorithms is to retain and reuse previously learned models, but the process is time-consuming and computationally prohibitive in nonstationary environments to appropriately select any optimal ensemble classifier capable of accurately adapting to recurring concepts. To learn streaming data, fast and accurate machine learning algorithms are needed for time-dependent applications. Most of the existing algorithms designed to handle concept drift do not take into account the presence of recurring concept drift. To accurately and efficiently handle recurring concepts with minimum computational overheads, we propose a novel and evolving ensemble method called Recurrent Adaptive Classifier Ensemble (RACE). The algorithm preserves an archive of previously learned models that are diverse and always trains both new and existing classifiers. The empirical experiments conducted on synthetic and real-world data stream benchmarks show that RACE significantly adapts to recurring concepts more accurately than some state-of-the-art ensemble classifiers based on classifier reuse.
Hierarchical Model for Goal Guided Summarization of Annual Financial Reports

Every year publicly listed companies file financial reports to give insights about their activities. These reports are meant for shareholders or general public to evaluate the company’s health and decide whether to buy or sell stakes in the company. However, these annual financial reports tend to be long, and it is time-consuming to go through the reports for each company. We propose a Goal Guided Summarization technique through which the summary is extracted. The goal, in our case, is the decision to buy or sell company’s shares. We use hierarchical neural models for achieving this goal while extracting summaries. By the means of intrinsic and extrinsic evaluation we observe that the summaries extracted by our approach can model the decision of buying and selling shares better compared to summaries extracted by other summarization techniques as well as the complete document itself. We also observe that the summary extractor model can be used to construct stock portfolios which give better returns compared to major stock index.
A Camera Identity-guided Distribution Consistency Method for Unsupervised Multi-target Domain Person Re-identification

Unsupervised domain adaptation (UDA) for person re-identification (re-ID) is a challenging task due to large variations in human classes, illuminations, camera views, and so on. Currently, existing UDA methods focus on two-domain adaptation and are generally trained on one labeled source set and adapted on the other unlabeled target set. In this article, we put forward a new issue on person re-ID, namely, unsupervised multi-target domain adaptation (UMDA). It involves one labeled source set and multiple unlabeled target sets, which is more reasonable for practical real-world applications. Enabling UMDA has to learn the consistency for multiple domains, which is significantly different from the UDA problem. To ensure distribution consistency and learn the discriminative embedding, we further propose the Camera Identity-guided Distribution Consistency method that performs an alignment operation for multiple domains. The camera identities are encoded into the image semantic information to facilitate the adaptation of features. According to our knowledge, this is the first attempt on the unsupervised multi-target domain adaptation learning. Extensive experiments are executed on Market-1501, DukeMTMC-reID, MSMT17, PersonX, and CUHK03, and our method has achieved very competitive re-ID accuracy in multi-target domains against numerous state-of-the-art methods.
Geological setting and mineralization characteristics of the Tick Hill Gold Deposit, Mount Isa Inlier, Queensland, Australia

The high grade (~22.5 g/t), gold-only Tick Hill deposit presents a unique mineralization style in the Mt Isa Inlier. The deposit was mined in the early 1990s, and is hosted in biotite schist, calc-silicate gneiss, quartzite and quartz-feldspar mylonite. These rocks were affected by D1 shearing and D2 upright folding at high-grade metamorphic conditions, followed by D3 events including normal faulting with widespread quartz-feldspar alteration and D4 strike-slip faulting. The Tick Hill ore body was linear in shape and paralleled the D1 mineral elongation lineation, and the intersection lineation between sets of D3 faults. Gold is dominantly hosted within intensely silicified units affected by D3 fracturing and associated alteration. However, some gold grains are contained within syn-D1, peak-metamorphic minerals indicating that mineralization involved two separate events; an early event predating or concomitant with D1 when gold was first introduced and concentrated, and a second event during D3 when gold was remobilized and/or further enriched to be deposited in its final configuration. Mineralization at Tick Hill is characterized by coarse grained gold associated with Bi-selenides, minor sulfides, and a general paucity of other metals except for a slight enrichment of Cu in the footwall to the ore zone. Alteration is characterized by early silicification and magnetite alteration that formed during the initial D1-2 events and may have coincided with the first stage of gold enrichment. During syn-D3 (re-)mobilization of gold, alteration involved the destruction of magnetite, the emplacement of abundant laminar quartz veins, and the deposition of proximal albite, hematite, chlorite, amphibole, epidote overprinted by later K-feldspar, sericite, clay minerals and minor calcite. The ore zone is bounded by a strongly silicified zone and surrounded by a chlorite-epidote shell. The low pressure D3 gold (re-)mobilization event involved an oxidized, S-under-saturated, acidic, saline aqueous fluid. In terms of alteration characteristics, the Tick Hill deposit shares similarities with IOCG deposits in the area. However, its high-grade gold-only nature, lack of Cu, and the presence of Bi-selenides makes it a unique deposit in the Mt Isa Inlier.
A new approach for physical human activity recognition based on co-occurrence matrices

In recent years, it has been observed that many researchers have been working on different areas of detection, recognition and monitoring of human activities. The automatic determination of human physical activities is often referred to as human activity recognition (HAR). One of the most important technology that detects and tracks the activity of the human body is sensor-based HAR technology. In recent days, sensor-based HAR attracts attention in the field of computers due to its wide use in daily life and is a rapidly growing field of research. Activity recognition (AR) application is carried out by evaluating the signals obtained from various sensors placed in the human body. In this study, a new approach is proposed to extract features from sensor signals using HAR. The proposed approach is inspired by the Gray Level Co-Occurrence Matrix (GLCM) method, which is widely used in image processing, but it is applied to one-dimensional signals, unlike GLCM. Two datasets were used to test the proposed approach. The datasets were created from the signals obtained from the accelerometer, gyro and magnetometer sensors. Heralick features were obtained from co-occurrence matrix created after 1D-GLCM (One (1) Dimensional-Gray Level Co-Occurrence Matrix) was applied to the signals. HAR operation has been carried out for different scenarios using these features. Success rates of 96.66 and 93.88% were obtained for two datasets, respectively. It has been observed that the new approach proposed within the scope of the study provides high success rates for HAR applications. It is thought that the proposed approach can be used in the classification of different signals.
qIoTAgriChain: IoT Blockchain Traceability Using Queueing Model in Smart Agriculture

Blockchain is an egressing digitally managed technology which allows ubiquitous monetary transactions with the untrusted entities, without involving the intermediary bodies, viz., banks. The literature reveals that IoT along with blockchain technology (BT) offers various benefits to the functioning of the agriculture supply chain (AgriChain). It is going to be implemented with which BT is going to perform a change in paradigm in which the transactions will be taken care of by reducing the number of intermediaries, making fast payment procedures among the parties. Developing economies with an ever-growing population country like India caters to food security demands and faces lots of challenges pretending agriculture supply chain sustainability. Therefore, it is crucial to follow BT in the agriculture supply chain to leverage multiple profits. In this chapter, we develop a queueing model which evaluates in detail the characteristics of an AgriChain system and measures the performance of the model such as the number of transactions expected that are waiting inside the queue to enter into the block, mean transactions waiting in the block, waiting for the time of a transaction, confirmation time of any transaction, etc.
Distant learning in extreme conditions: problems of readiness and pedagogical interaction among the subjects of the educational process

The transition of all Russian universities to distant learning at the end of March, 2020 revealed a number of organizational and pedagogical problems associated with identifying the readiness of university teachers and students for a new form of interaction. The purpose of the study is to determine the degree of readiness of the subjects of the educational process for the implementation of distant learning at the level of legal regulation, pedagogical qualifcations and pedagogical interaction. The research hypothesis is based on the assumption that pedagogical interaction in the course of distant learning has special properties that make increased demands on the professional qualifcations of university teachers, as well as on the competencies of both subjects of the educational process.The article analyses the degree of elaboration of the issue of pedagogical interaction in the legal and scientifc literature. The results of the empirical study conducted in order to identify the degree of readiness of university teachers and students to use distance learning are described and analysed. Recommendations are suggested to improve the quality of distant learning and the degree of satisfaction of participants in pedagogical interaction.
Atomic non-locality dynamics of two moving atoms in a hybrid nonlinear system: concurrence, uncertainty-induced non-locality and Bell inequality

The paper explores the non-locality dynamics of two moving atoms in a hybrid nonlinear system, which describes a nondegenerate parametric amplifier cavity containing two moving atoms with Stark shift interactions. Based on the Bell function, uncertainty-induced non-locality and concurrence, the two moving atomic non-locality is calculated numerically, when the cavity is filled initially by pair coherent fields. The results showed that the hybrid nonlinear qubit-cavity interactions have a high ability to generating quantum correlations. The amount of the generated quantum correlation can be improved and accelerated significantly with the increase of the time-dependent qubit location parameter. The increase of the Stark shift ratio and the difference between the mean-number photons of the two cavity fields leads to a clear deterioration of the generated quantum correlation.
Towards Correct Smart Contracts: A Case Study on Formal Verification of Access Control

Ethereum is a platform for deploying smart contracts, which due to their public nature and the financial value of the assets they manage are attractive targets for attacks. With asset management as a main task of smart contracts, access control aspects are naturally part of the application itself, but also of the functions implemented in a smart contract. Therefore, it is desirable to establish the correctness of smart contracts and their access control on application and single-function level through formal methods. However, there is no established methodology of formalising and verifying correctness properties of smart contracts. In this work, we make an attempt in this direction on the basis of a case study. We choose an existing smart contract application which aims to ascertain the integrity of binary files distributed over the Internet by means of decentralised identity management and access control. We formally specify and verify correctness at the level of single functions as well as temporal properties of the overall application. We demonstrate how to use verified low-level correctness properties for showing correctness at the higher level. In addition, we report on our experience with existing verification tools.
DigiNet: Prediction of Assamese handwritten digits using convolutional neural network

Summary Numerous work focusing on Indian Languages for automatically recognizing characters has been witnessed in literature in the last few decades. But it was observed that only a handful of them targeted the optical character recognition of the Assamese language despite the language being widely spoken and used in many official works and activities across North‐East India. The limited contribution in this field seems to be inadequate and insufficient in their ability to recognize the handwritten characters due to dissimilarity in the people's writing style. This has motivated us to device a computational model for automatic recognition of the handwritten Assamese digits belonging to 10 different classes of 0 to 9. This study employs a convolutional neural network model (DigiNet) to learn and understand the various styles of handwritten digits. Our model exercises the convolutional neural network composed of six alternative Convolution and Pooling layers and is able to attain state‐of‐the‐art performance on the Assamese handwritten digits, achieving a test accuracy of which is a pretty good success. The proficiency of the model architecture is also tested on the MNIST and the Bangla handwritten numeral datasets. Furthermore, our proposed model is compared with the pre‐trained VGG 19 architecture, where the performance of our model was better compared to the pre‐trained model on the Assamese as well as the Bangla handwritten numerals.