Reviewerly/exHarmony huggingface dataset:
A Novel Method for Highly Imbalanced Classification with Weighted Support Vector Machine

In real life, the problem of imbalanced data classification is unavoidable and difficult to solve. Traditional SVMs based classification algorithms usually cannot classify highly imbalanced data accurately, and sampling strategies are widely used to help settle the matter. In this paper, we put forward a novel undersampling method i.e., granular weighted SVMs-repetitive under-sampling (GWSVM-RU) for highly imbalanced classification, which is a weighted SVMs version of the granular SVMs-repetitive undersampling (GSVM-RU) once proposed by Yuchun Tang et al. We complete the undersampling operation by extracting the negative information granules repetitively which are obtained through the naive SVMs algorithm, and then combine the negative and positive granules again to compose the new training data sets. Thus we rebalance the original imbalanced data sets and then build new models by weighted SVMs to predict the testing data set. Besides, we explore four other rebalance heuristic mechanisms including cost-sensitive learning, undersampling, oversampling and GSVM-RU, our approach holds the higher classification performance defined by new evaluation metrics including G-Mean, F-Measure and AUC-ROC. Theories and experiments reveal that our approach outperforms other methods.
Effectively Classify Short Texts with Sparse Representation Using Entropy Weighted Constraint

Short texts have become a kind of prevalent source of information, and the classification of these short texts in various forms is valuable to many applications. However, most existing short text classification approaches circumvent the sparsity problem by extending short texts (or their feature representations) or exploiting additional information to adapt short texts to traditional text classification approaches. In this paper, we try to solve the sparsity problem of short text classification in a different direction: adapting the classifier to short texts. We propose a sparse representation short text classification method based on entropy weighted constraint. The main idea behind this study is to consider that the short texts are similar in potentially specific subspace. Specifically, we first introduce word embedding to represent the initial sparse representation dictionary, and then a fast feature subset selection algorithm is used to filter the dictionary. Again, we design an objective function based on sparse representation of entropy weight constraint. The optimal value of the objective function is obtained by Lagrange multiplier method. Finally, the distance between the short text to be classified and the short text in each class is calculated under the subspace, and the short text is classified according to three classification rules. Experiments over five datasets show that the proposed approach can effectively alleviate the problem of sparse feature of short text and is more efficient and effective than the existing short text classification method.
Blockchain-Enabled Security-Aware Applications in Home Internet of Thing

Data security and privacy protection in the Internet of Things (IoT) have attracted wide attention, especially in private scenarios, such as Home Internet of Things (HIoT). Data de-centralization and mature encryption technology based on blockchain technology provide a proper security guarantee for HIoT. However, common blockchain technology consensus algorithm consumes a lot of computing resources of devices, and it is easy to cause network congestion in LAN due to the size of each transaction and transactions' frequently spread. Just Blockchain Storage Model (JBSM) means every blockchain node in the network stores all of the transaction data that launched in the whole blockchain network, there is no doubt that JBSM consumes a lot of storage resources. In this paper, we propose a Blockchain and Centralized Storage Model (BCSM) for HIoT, which reduces the network bandwidth occupied by transaction data and increases the speed of transaction verification by reducing the size of transaction data. The model consists of three parts: HIoT, Blockchain Network and Cloud Storage Server. This paper analyses how BCSM improves transaction efficiency. Finally, through the simulation experiment on Tencent BaaS, the model is compared with the JBSM, which shows the method in this paper not only has the data security characteristic endowed by the blockchain but also improves the transaction efficiency to a certain extent.
A theoretical investigation of solar radiation and heat transfer in a solar pond in Karbala city

This paper concerns the simulation study of a mathematical model of the solar pond performance for collecting and storing the incident solar energy on the Karbala city (32.616° N, 44.025°E) in a form of thermal energy in its lower layer. It was found that there are several parameters affecting the amount of stored energy. The most effective factor is the amount of incident global radiation on the pond's location. Therefore, a program in the FORTRAN language was established in the first stage to calculate incident solar radiation and the second stage to solve the One-Dimensional Finite Difference Method for heat transfer equations by using the arrays dimension. Various data for incident global solar radiation were in high agreement with NASA's published data for Karbala city. Additionally, the obtained data of temperatures inside the solar pond were very similar to the published physical and experimental data of solar ponds.
The Unified Object Detection Framework with Arbitrary Angle

Traditional horizontal bounding boxes are hard to locate targets with various directions. In the complex background, this method also cannot distinguish object from background very well and it is difficult to separate dense objects. Therefore, the traditional deep learning model is far from meeting the need for object detection in arbitrary orientation scenarios. In order to solve these problems, the unified object detection framework with arbitrary orientation is proposed in this paper. The angle is used as a variable for regression, which can accurately detect the rotating object, its position and direction information in complex background. The rotation region of interest (RRoI) align is proposed to replace region of interest(RoI) pooling, which can improve the precision of the detection model. Besides, a skew intersection over union (IoU) calculation method is adopted to solve the problem that densely placed objects are hard to detect and improve the recall of the detection model, which can reduce redundant detection regions. Experiments on remote sensing data set show that our method has better robustness and performance than other algorithms in detecting small targets. Compared with other algorithms, our algorithm is not only applicable to ship detection, but also to remote sensing images, document detection and other arbitrary-oriented object detection scenarios.
Anti-Shoulder Surfing Login Based on Multi-Entry Models on Onscreen Keyboard

Shoulder surfing is a common threat used to steal sensitive information, specifically credential and PIN number either by human or recorder cameras. The proposed solution is designed for both PCs and smartphone-based login interfaces. In this paper, we propose a new authentication scheme based on augmented misinformation existing techniques in addition to the introduction of a novel one for maximizing security level and keeping it simple to use. We reuse the crossing-based technique with keys layout randomisation support in addition to displacement factor consideration. Moreover, we introduce white keys-based technique that is used for increasing the security level when keeping the same difficulty-level of exploitation. The evaluation results show that the proposed scheme outperforms well known methods of the state of the art and gives a trade-off between the usability and the security levels against active shoulder surfing attacks using camera recorder.
Multi-Task Learning Based Joint Pulse Detection and Modulation Classification

In this work, a multi-task learning and recurrent neural network based new technique is proposed that performs joint SNR (singal-to-noise ratio) estimation and pulse detection over the pulse based signal samples coming from the digital receivers used in electronic warfare systems, and automatically classifies the modulation present on detected pulses. Proposed technique uses the raw IQ data as input, without any feature extraction. Moreover, usage of separately trained classifiers for different SNR regions is proposed for better classification accuracy. Most suitable classifier is selected based on the estimated SNR level from raw IQ data. Detailed tests show the proposed structure can achieve a detection with %90 accuracy, SNR estimation with 1.5 dB mean absolute error, and classification with 84% accuracy at a very low SNR level as -30 dB.
Uncertainty Based Adaptive Projection Selection Strategy for Binary Tomographic Reconstruction

The goal of binary tomography is to examine the inner structure of homogeneous objects based on their projections. The 2D slices of the objects can be represented by binary matrices and the aim is to recreate these matrices from a collection of their line sums. For cost-effectiveness and speed reasons it is worth to do the reconstruction from as few projections as possible while still maintaining an acceptable image quality. The key is to specify the most informative projection angles. In this paper we propose a reconstruction uncertainty based adaptive (online) projection selection method for binary tomographic reconstruction. We compare our algorithm to other already published methods.
Will Your Paper Get Promoted by a Citation? A Case Study of Citation Promoter in Computer Science Discipline

Reseachers have investigated numerous factors influencing citation counts of cited papers.One factor investigated has been the number of gained citations, as this could increase the visibility of cited papers and subsequently induce further citations.In this paper, aiming to identify a particular kind of citation that could trigger a rapid growth in the citation counts of cited papers, a concept of a "citation promoter" was proposed.We defined citation promoters based on the annual citation rates of the cited papers and the co-citation counts received by the pair of cited and citing papers.The comparative results showed that papers would obtain a sharp rise in citation counts shortly after they were cited by citation promoters.Papers that received citation promoters at an early age also outperformed other papers in long-term citation counts.In addition, we developed a classification model for predicting whether a citing paper would be a citation promoter for its cited paper.Since it was a class imbalanced problem (4% positive instances), and there was a lack of content and author features in our dataset, our preliminary models achieved moderate performance with an F 1 score slightly higher than 0.5, while the F 1 score obtained by random guessing was 0.07.
Evaluating Real-Time-Location Solar Irradiance Data Against SOLARGIS Ground Station Solar Irradiance For The South African Sasol Solar Challenge

The South African Solar Challenge is an international event where teams build solar powered challenger vehicles, to see who can travel furthest in 8 days. Rules stipulate that the main source of power for solar vehicles should only be from solar irradiance. Global Horizontal Irradiance data is measurements that give an indication of potential power for solar vehicles and could be used to predict the range of the solar vehicles. Success in the challenge is determined by the most distance traveled by the solar car in the set time. GHI measurements are predominantly done with expensive and scarce pyranometer sensors and equipment. A substitute for a pyranometer sensor could be to access free ground solar irradiation data through sites such as SOLARGIS. This paper explores the prospect for solar teams to use local ground weather stations in South Africa as a substitute or supplement for GHI data. Evaluation took place during the 2018 challenge for relevance towards a solar challenge team. Results and discussions are in the form of correlating data from a custom designed and constructed mobile GHI measurement station that includes a pyranometer for solar irradiance measurements with the closest on route SOLARGIS station. Results could be used by future solar teams, or any other relevant application, that want to substitute or supplement SOLARGSI ground weather stations with real-time-location solar irradiance measurements. In the nearby future all vehicles might be solar powered, and results show that an accessible grid of GHI ground sensors could be beneficial.
Data Management Law for the 2020s: The Lost Origins and the New Needs

In the data analytics society, each individual’s disclosure of personal information imposes costs on others. This disclosure enables companies, deploying novel forms of data analytics, to infer new knowledge about other people and to use this knowledge to engage in potentially harmful activities. These harms go beyond privacy and include difficult to detect price discrimination, preference manipulation, and even social exclusion. Currently existing, individual-focused, data protection regimes leave law unable to account for these social costs or to manage them. This Article suggests a way out, by proposing to re-conceptualize the problem of social costs of data analytics through the new frame of “data management law.” It offers a critical comparison of the two existing models of data governance: the American “notice and choice” approach and the European “personal data protection” regime (currently expressed in the General Data Protection Regulation). Tracing their origin to a single report issued in 1973, the article demonstrates how they developed differently under the influence of different ideologies (market-centered liberalism, and human rights, respectively). It also shows how both ultimately failed at addressing the challenges outlined already forty-five years ago. To tackle these challenges, this Article argues for three normative shifts. First, it proposes to go beyond “privacy” and towards “social costs of data management” as the framework for conceptualizing and mitigating negative effects of corporations’ data usage. Second, it argues to go beyond the individual interests, to account for collective ones, and to replace contracts with regulation as the means of creating norms governing data management. Third, it argues that the nature of the decisions about these norms is political, and so political means, in place of technocratic solutions, need to be employed.
Analysis of loss functions for fast single-class classification

We consider neural network training, in applications in which there are many possible classes, but at test time, the task is a binary classification task of determining whether the given example belongs to a specific class. We define the single logit classification (SLC) task: training the network so that at test time, it would be possible to accurately identify whether the example belongs to a given class in a computationally efficient manner, based only on the output logit for this class. We propose a natural principle, the Principle of Logit Separation, as a guideline for choosing and designing losses suitable for the SLC task. We show that the cross-entropy loss function is not aligned with the Principle of Logit Separation. In contrast, there are known loss functions, as well as novel batch loss functions that we propose, which are aligned with this principle. Our experiments show that indeed in almost all cases, losses that are aligned with the Principle of Logit Separation obtain at least 20% relative accuracy improvement in the SLC task compared to losses that are not aligned with it, and sometimes considerably more. Furthermore, we show that fast SLC does not cause any drop in binary classification accuracy, compared to standard classification in which all logits are computed, and yields a speedup which grows with the number of classes.
Orthogonal state of coherent state based on Hermite-excited superposition operator: Production and Wigner function

An orthogonal state of coherent state is produced by applying an orthogonalizer related with Hermite-excited superposition operator [Formula: see text]. Using some technique, we cleverly deal with the normalization and discuss the nonclassical and non-Gaussian characters of the orthogonal state. The analytical expressions for the Wigner functions of the orthogonal state are derived in detail. Numerical results show that the orthogonal state will exhibit its richly nonclassical and non-Gaussian character by changing the interaction parameters.
A Novel RRAM Based Watermark Technique Utilizing the Impact of Forming Conditions on Reset Distribution

Watermark is a very important technique for information security. In this work, a RRAM-based electronic watermark technique leveraging the impact of Forming conditions on Reset distribution is experimentally demonstrated. The Forming process is indispensable for almost all RRAM device. The Forming conditions can affect the shape and size of the conductance filament, and further affects the resistance distribution at Reset. The tested data shows that the stronger Forming condition can get smaller mean value of resistance, and the use of RRAM as memory is not affected. In this design, two different Forming conditions are used to write watermark data `0' and `1'. A resistance in parallel technique is applied to enhance reliability. The experimental results show that the bit error rate (BER) reduced from ~30% to ~7% after using the optimized method. And no reliability degradation occurs after 200 normal Reset/Set cycles.
Research and Implementation of ε-SVR Training Method Based on FPGA

Online training of Support Vector Regression (SVR) in the field of machine learning is a computationally complex algorithm. Due to the need for multiple iterative processing in training, SVR training is usually implemented on computer, and the existing training methods cannot be directly implemented on Field-Programmable Gate Array (FPGA), which restricts the application range. This paper reconstructs the training framework and implementation without precision loss to reduce the total latency required for matrix update, reducing time consumption by 90%. A general ε-SVR training system with low latency is implemented on Zynq platform. Taking the regression of samples in two-dimensional as an example, the maximum acceleration ratio is 27.014× compared with microcontroller platform and the energy consumption is 12.449% of microcontroller. From the experiments for the University of California, Riverside (UCR) time series data set. The regression results obtain excellent regression effects. The minimum coefficient of determination is 0.996, and running time is less than 30 ms, which can meet the requirements of different applications for real-time regression.
Drone-Assisted Inspection for Automated Accident Damage Estimation: A Deep Learning Approach

Drones have been used in many application domains nowadays including traffic congestion control, weather information collection, disaster and rescue interventions, and surveillance operations. The drone adoption lies on their capabilities to collect images, videos as well as other sensory data from the air, stream this data to the cloud for processing, and analytics in order to derive important real-time decisions. In this paper, we propose a drone assisted inspection for accident damage estimation based on deep learning approach. Drones are automatically scheduled to visit the accident locations, and data is retrieved for further processing and analytics. We developed a two-phases damage estimation approach, where in the first phase we use deep learning approach to identify and classify objects from accident's images, and in the second phase we measure the size of damaged objects and we estimate the overall cost of the accident's damages. We evaluated our two-phase approach using data of various accidents, and the classification accuracy we have obtained vary between 0.79 and 0.94 and the accident's damage cost estimation most of time is 100% accepted by the expert.
Using Semantic Markup to Boost Context Awareness for Assistive Systems

Considerable effort to manually configure the user's context and too coarse-grained activity recognition results often make it difficult to set up and run an assistive system. This chapter is the result of our experience with the Human Behavior Monitoring and Support (HBMS) assistive system, which monitors user's activities of daily life and supports the user in carrying out these activities based on his own behavior model. To achieve the required context awareness, we join assistive systems with the semantic web to (1) simplify the construction of a personalized context model and to (2) improve the system's activity recognition capabilities. We show how to semantically describe devices and web applications including their functionalities and user instructions and how to represent these descriptions in the web. The advantages of this semantic markup approach for the application of HBMS-System and beyond are discussed. Moreover, we show how personalized and adaptive HBMS user clients and the power of the context model of HBMS-System can be used to bridge an existing activity recognition gap.
Chinese Technical Communicators' Opinions on Cultural Differences between Chinese and Western User Manuals

One of the most significant challenges that technical communicators face in today's globalizing world is how to meet the requirements of target groups from different cultures. Translation and localization of user documentation are important activities in the field of technical communication. Previous studies have shown that there are differences between content, structure and style of user manuals from different cultures. Especially the differences between Chinese and Western manuals seem to be significant. It is not known, however, if and how technical communicators take cultural differences into account when designing manuals. To explore the opinions of Chinese technical communicators, twenty of them were interviewed. The results show that the majority of the interviewees assumed that Chinese and Western manuals differ from each other in many aspects (content, structure, style, visuals) and that Chinese and Western users have a different approach to manuals. Overall, the interviewees considered it important to target manuals to the culture of the users, but they experience several obstacles to do this, such as the obligation to use Western style sheets, the costs related to localizing and a lack of knowledge. Although the results may not be generalizable, because most interviewees worked in the software and telecommunication industry, they clearly show that there is a lack of knowledge about how to localize user manuals.