Reviewerly/exHarmony huggingface dataset:
R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images

Recently, convolutional neural network (CNN)-based methods have been extensively explored for ship detection in synthetic aperture radar (SAR) images due to their powerful feature representation abilities. However, there are still several obstacles hindering the development. First, ships appear in various scenarios, which makes it difficult to exclude the disruption of the cluttered background. Second, it becomes more complicated to precisely locate the targets with large aspect ratios, arbitrary orientations and dense distributions. Third, the trade-off between accurate localization and improved detection efficiency needs to be considered. To address these issues, this paper presents a rotate refined feature alignment detector (R 2 FA-Det), which ingeniously balances the quality of bounding box prediction and the high speed of the single-stage framework. Specifically, first, we devise a lightweight non-local attention module and embed it into the stem network. The recalibration of features not only strengthens the object-related features yet adequately suppresses the background interference. In addition, both forms of anchors are integrated into our modified anchor mechanism and thus can enable better representation of densely arranged targets with less computation burden. Furthermore, considering the shortcoming of the feature misalignment existing in the cascaded refinement scheme, a feature-guided alignment module which encodes both the position and shape information of current refined anchors into the feature points is adopted. Extensive experimental validations on two SAR ship datasets are performed and the results demonstrate that our algorithm has higher accuracy with faster speed than some state-of-the-art methods.
Garbage Recognition and Classification System Based on Convolutional Neural Network VGG16

To study the application of deep learning in the field of environmental protection, the convolutional neural network VGG16 model is used to solve the problem of identification and classification of domestic garbage. This solution first used the OpenCV computer vision library to locate and select the identified objects and preprocessed the images into 224×224 pixel RGB images accepted by the VGG16 network. Then after data enhancement, a VGG16 convolutional neural network based on the TensorFlow framework is built, by using the RELU activation function and adding BN layer to accelerate the model's convergence speed, while ensuring recognition accuracy. This project finally classifies domestic garbage into recyclable garbage, hazardous garbage, kitchen waste and other garbage. After actual tests, the correct classification rate of the garbage classification system based on VGG16 network proposed in this paper is 81.1%, the result meets the needs of daily use.
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness

The accuracy of deep learning, i.e., deep neural networks, can be characterized by dividing the total error into three main types: approximation error, optimization error, and generalization error. Whereas there are some satisfactory answers to the problems of approximation and optimization, much less is known about the theory of generalization. Most existing theoretical works for generalization fail to explain the performance of neural networks in practice. To derive a meaningful bound, we study the generalization error of neural networks for classification problems in terms of data distribution and neural network smoothness. We introduce the cover complexity (CC) to measure the difficulty of learning a data set and the inverse of the modulus of continuity to quantify neural network smoothness. A quantitative bound for expected accuracy/error is derived by considering both the CC and neural network smoothness. Although most of the analysis is general and not specific to neural networks, we validate our theoretical assumptions and results numerically for neural networks by several data sets of images. The numerical results confirm that the expected error of trained networks scaled with the square root of the number of classes has a linear relationship with respect to the CC. We also observe a clear consistency between test loss and neural network smoothness during the training process. In addition, we demonstrate empirically that the neural network smoothness decreases when the network size increases whereas the smoothness is insensitive to training dataset size.
ASTER and WorldView-3 satellite data for mapping lithology and alteration minerals associated with Pb-Zn mineralization

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing data were used for mapping lithological units and hydrothermal alteration zones associated with Pb-Zn mineralization in the Kerman–Kashmar Tectonic Zone (KKTZ), Iran. The visible near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) bands of ASTER were used to map iron oxide/hydroxides, Al-OH minerals, Fe,Mg-OH minerals, quartz and carbonate minerals. The VNIR bands of WV-3 were used to discriminate Fe3+ and Fe2+ absorption intensities. Selective Principal Component analysis (SPCA), Spectral Angle Mapper (SAM), Linear Spectral Unmixing (LSU) and Automatic Lineament Extraction techniques were implemented. Lithological units were discriminated based on Al/Fe-OH, Fe2+/Fe3+ and Mg-Fe-OH/CO3 absorption properties. The spatial distribution of hematite, goethite, jarosite, gypsum, calcite, dolomite, kaolinite and muscovite were comprehensively detected. Some prospective zones were identified in the intersection of N-S, NW-SE and NE-SW trending fault systems, gossan, argillic/phyllic and dolomitic units.
Double random forest

Random forest (RF) is one of the most popular parallel ensemble methods, using decision trees as classifiers. One of the hyper-parameters to choose from for RF fitting is the nodesize, which determines the individual tree size. In this paper, we begin with the observation that for many data sets (34 out of 58), the best RF prediction accuracy is achieved when the trees are grown fully by minimizing the nodesize parameter. This observation leads to the idea that prediction accuracy could be further improved if we find a way to generate even bigger trees than the ones with a minimum nodesize. In other words, the largest tree created with the minimum nodesize parameter may not be sufficiently large for the best performance of RF. To produce bigger trees than those by RF, we propose a new classification ensemble method called double random forest (DRF). The new method uses bootstrap on each node during the tree creation process, instead of just bootstrapping once on the root node as in RF. This method, in turn, provides an ensemble of more diverse trees, allowing for more accurate predictions. Finally, for data where RF does not produce trees of sufficient size, we have successfully demonstrated that DRF provides more accurate predictions than RF.
Changes in reading behaviour of periodicals on mobile devices: A comparative study

Mobile devices not only bring convenience to aspects of life but also change people’s behaviour in their daily lives, in particular reading. While most of the studies focus on reading books, there are few systematic publications primarily focusing on electronic periodicals, especially comparing the different needs of patrons from different faculties. Through an online questionnaire, the authors explored whether and how university patrons of the University of Hong Kong changed their reading behaviour of e-periodicals in the context of mobile devices. Six reading patterns were investigated: reading frequency, types of periodicals, preference of mobile devices, reading time spent, reading time slot and reading location. The authors purposefully compare subjects selected from three faculties (Education, Engineering and Science) to examine whether research and learning requirements affect their behaviours. The analysis found that reading patterns did indeed change after patrons adopted mobile devices to read periodicals. There are also some statistically significant differences among the faculties investigated, reflecting their different information needs. The findings can help academic libraries review their periodical subscription policies and reading promotion schemes to satisfy various patrons’ needs.
Feature selection based on chaotic binary black hole algorithm for data classification

With the advance of generating high-dimensional data, feature selection is the most significant procedure to guarantee selecting the most discriminative subset of features and to improve the classification performance. As a result, a binary black hole optimization algorithm (CBBHA) has been developed by getting inspired from natural phenomena. In this paper, the most discriminating features are selected by a new chaotic binary black hole algorithm (CBBHA) where chaotic maps embedded with movement of stars in the BBHA. Ten chaotic maps are employed. Experiments on three chemical datasets show the proposed algorithm, CBBHA, has an advantage over the standard BBHA in terms of selecting relevant features with a high classification performance. Additionally the performance of CBBHA is compared with BBHA in term of the computational time efficiency which is revealing that CBBHA outperforms the BBHA.
Tokens, Types, and Standards: Identification and Utilization in Ethereum

Tokens are often referred to as the killer application of blockchains and cryptocurrencies. Some even believe that basically everything can be tokenized, meaning that it can be represented by a tradable digital token. With decentralized P2P networks that hold and distribute the tokens, one can build various decentralized applications around them. As a digital asset on top of a cryptocurrency, crypto tokens are managed by a smart contract, most commonly on Ethereum. A widespread high-level categorization of tokens distinguishes between payment tokens, security tokens, and utility tokens. The need for clarifying the differences lies in the fact that in most jurisdictions, security tokens are more heavily regulated than other tokens. In this work, we contribute to the detection and classification of crypto tokens from bytecode. First, we examine how ideas on regulation are currently incorporated into respective standards. Then, we discuss methods for identifying deployed token contracts from bytecode. Furthermore, we analyze deployed contracts regarding the standards to which they comply, and the type of token they may represent. Moreover, we investigate the actual usage of tokens. Our empirical analysis uses the transaction data from the Ethereum main chain up to block 8750000, mined on Oct 16, 2019.
On Shooting Stars

Inactivity and a lack of engagement with exercise is a pressing health problem in the United States and beyond. Immersive Virtual Reality (iVR) is a promising medium to motivate users through engaging virtual environments. Currently, modern iVR lacks a comparative analysis between research and consumer-grade systems for exercise and health. This article examines two such iVR mediums: the Cave Automated Virtual Environment (CAVE) and the head-mounted display (HMD). Specifically, we compare the room-scale Mechdyne CAVE and HTC Vive Pro HMD with a custom in-house exercise game that was designed such that user experiences were as consistent as possible between both systems. To ensure that our findings are generalizable for users of varying abilities, we recruited 40 participants with and without cognitive disabilities with regard to the fact that iVR environments and games can differ in their cognitive challenge between users. Our results show that across all abilities, the HMD excelled in in-game performance, biofeedback response, and player engagement. We conclude with considerations in utilizing iVR systems for exergaming with users across cognitive abilities.
Automatic Waveform Recognition of Overlapping LPI Radar Signals Based on Multi-Instance Multi-Label Learning

In an ever-increasingly complex electromagnetic environment, multiple low probability of intercept (LPI) radar emitters may transmit their own signals simultaneously on similar bands, resulting in overlapping receiving signals in both time and frequency domain. In this letter, a novel Multi-Instance Multi-Label learning framework based on Deep Convolutional Neural Network (MIML-DCNN) is proposed to automatically recognize the overlapping LPI radar signals,which is trained by single type of signals only. The framework handles signals in an end-to-end manner that is integrated with a well-designed instance generation module, a sophisticated MIML classifier, and an adaptive threshold calibration. Through comprehensive experiments on simulated overlapping signals with four different modulation types, we prove that the proposed framework identifies each individual signal type precisely in the presence of overlapping signals, and is also robust to variation of the signal-to-noise ratio (SNR) and power ratio conditions.
Proactive Forensics in IoT: Privacy-Aware Log-Preservation Architecture in Fog-Enabled-Cloud Using Holochain and Containerization Technologies

Collecting and preserving the smart environment logs connected to cloud storage is challenging due to the black-box nature and the multi-tenant cloud models which can pervade log secrecy and privacy. The existing work for log secrecy and confidentiality depends on cloud-assisted models, but these models are prone to multi-stakeholder collusion problems. This study proposes ’PLAF,’ a holistic and automated architecture for proactive forensics in the Internet of Things (IoT) that considers the security and privacy-aware distributed edge node log preservation by tackling the multi-stakeholder issue in a fog enabled cloud. We have developed a test-bed to implement the specification, as mentioned earlier, by incorporating many state-of-the-art technologies in one place. We used Holochain to preserve log integrity, provenance, log verifiability, trust admissibility, and ownership non-repudiation. We introduced the privacy preservation automation of log probing via non-malicious command and control botnets in the container environment. For continuous and robust integration of IoT microservices, we used docker containerization technology. For secure storage and session establishment for logs validation, Paillier Homomorphic Encryption, and SSL with Curve25519 is used respectively. We performed the security and performance analysis of the proposed PLAF architecture and showed that, in stress conditions, the automatic log harvesting running in containers gives a 95% confidence interval. Moreover, we show that log preservation via Holochain can be performed on ARM-Based architectures such as Raspberry Pi in a very less amount of time when compared with RSA and blockchain.
Exploring Mental Models for Transparent and Controllable Recommender Systems: A Qualitative Study

While online content is personalized to an increasing degree, eg. using recommender systems (RS), the rationale behind personalization and how users can adjust it typically remains opaque. This was often observed to have negative effects on the user experience and perceived quality of RS. As a result, research increasingly has taken user-centric aspects such as transparency and control of a RS into account, when assessing its quality. However, we argue that too little of this research has investigated the users' perception and understanding of RS in their entirety. In this paper, we explore the users' mental models of RS. More specifically, we followed the qualitative grounded theory methodology and conducted 10 semi-structured face-to-face interviews with typical and regular Netflix users. During interviews participants expressed high levels of uncertainty and confusion about the RS in Netflix. Consequently, we found a broad range of different mental models. Nevertheless, we also identified a general structure underlying all of these models, consisting of four steps: data acquisition, inference of user profile, comparison of user profiles or items, and generation of recommendations. Based on our findings, we discuss implications to design more transparent, controllable, and user friendly RS in the future.
Software Defect Prediction Using Machine Learning Techniques

Software defect prediction provides development groups with observable outcomes while contributing to industrial results and development faults predicting defective code areas can help developers identify bugs and organize their test activities. The percentage of classification providing the proper prediction is essential for early identification. Moreover, software-defected data sets are supported and at least partially recognized due to their enormous dimension. This Problem is handled by hybridized approach that includes the PCA, randomforest, naïve bayes and the SVM Software Framework, which as five datasets as PC3, MW1, KC1, PC4, and CM1, are listed in software analysis using the weka simulation tool. A systematic research analysis is conducted in which parameters of confusion, precision, recall, recognition accuracy, etc Are measured as well as compared with the prevailing schemes. The analytical analysis indicates that the proposed approach will provide more useful solutions for device defects prediction.
Probing Spin Correlations in a Bose-Einstein Condensate Near the Single-Atom Level

Using parametric conversion induced by a Shapiro-type resonance, we produce and characterize a two-mode squeezed vacuum state in a sodium spin 1 Bose-Einstein condensate. Spin-changing collisions generate correlated pairs of atoms in the $m=\pm 1$ Zeeman states out of a condensate with initially all atoms in $m=0$. A novel fluorescence imaging technique with sensitivity $\Delta N \sim 1.6$ atom enables us to demonstrate the role of quantum fluctuations in the initial dynamics and to characterize the full distribution of the final state. Assuming that all atoms share the same spatial wave function, we infer a squeezing parameter of 15.3\,dB.
Development and Validation of a Deep-learning Model to Assist With Renal Cell Carcinoma Histopathologic Interpretation

To develop and test the ability of a convolutional neural network (CNN) to accurately identify the presence of renal cell carcinoma (RCC) on histopathology specimens, as well as differentiate RCC histologic subtype and grade.Digital hematoxylin and eosin stained biopsy images were downloaded from The Cancer Genome Atlas. A CNN model was trained on 100 um2 samples of either normal (3000 samples) or RCC (12,168 samples) tissue samples from 42 patients. RCC specimens included clear cell, chromophobe, and papillary histiotypes, as well as tissue of Fuhrman grades 1 through 4. Model testing was performed on an additional held-out cohort of benign and RCC specimens. Model performance was assessed on the basis of diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.The CNN model achieved an overall accuracy of 99.1% in the testing cohort for distinguishing normal parenchyma from RCC (sensitivity 100%, specificity 97.1%). Accuracy for distinguishing between clear cell, papillary, and chromophobehistiotypes was 97.5%. Accuracy for predicting Fuhrman grade was 98.4%.CNNs are able to rapidly and accurately identify the presence of RCC, distinguish RCC histologic subtypes, and identify tumor grade by analyzing histopathology specimens.
Detecting quantum attacks: a machine learning based defense strategy for practical continuous-variable quantum key distribution

Abstract The practical security of a continuous-variable quantum key distribution (CVQKD) system is compromised by various attack strategies. The existing countermeasures against these attacks are to exploit different real-time monitoring modules to prevent different types of attacks, which significantly depend on the accuracy of the estimated excess noise and lack a universal defense method. In this paper, we propose a defense strategy for CVQKD systems to address these disadvantages and resist most of the known attack types. We investigate several features of the pulses that would be affected by different types of attacks, derive a feature vector based on these features as the input of an artificial neural network (ANN) model, and show the training and testing process of the ANN model for attack detection and classification. Simulation results show that the proposed scheme can effectively detect most of the known attacks at the cost of reducing a small part of secret keys and transmission distance. It establishes a universal attack detection model by simply monitoring several features of the pulses without knowing the exact type of attack in advance.
Kratos

In a smart home system, multiple users have access to multiple devices, typically through a dedicated app installed on a mobile device. Traditional access control mechanisms consider one unique trusted user that controls the access to the devices. However, multi-user multi-device smart home settings pose fundamentally different challenges to traditional single-user systems. For instance, in a multi-user environment, users have conflicting, complex, and dynamically changing demands on multiple devices, which cannot be handled by traditional access control techniques. To address these challenges, in this paper, we introduce Kratos, a novel multi-user and multi-device-aware access control mechanism that allows smart home users to flexibly specify their access control demands. Kratos has three main components: user interaction module, back-end server, and policy manager. Users can specify their desired access control settings using the interaction module which are translated into access control policies in the backend server. The policy manager analyzes these policies and initiates negotiation between users to resolve conflicting demands and generates final policies. We implemented Kratos and evaluated its performance on real smart home deployments featuring multi-user scenarios with a rich set of configurations (309 different policies including 213 demand conflicts and 24 restriction policies). These configurations included five different threats associated with access control mechanisms. Our extensive evaluations show that Kratos is very effective in resolving conflicting access control demands with minimal overhead, and robust against different attacks.