Reviewerly/exHarmony huggingface dataset:
Dual Channel Attention Networks

Abstract Channel attention is currently widely used in Computer Vision. Most existing channel attention networks are proposed based on Squeeze-and-Excitation Networks (SE- Net),which can obtain excellent performance by designing complex structures, however, they also has more additional network parameters and higher floating point operations per second (FLOPs). We propose a novel lightweight attention structure called Dual Channel Attention Networks (DCA-Net). By introducing the channel attention preprocessing module and using 1-D convolution with K=1, DCA-Net has a more straightforward and delicate structure. We add DCA-Net to ResNet and perform image classification experiments on CIFAR-100 dataset, object detection and instance segmentation experiments on MS-COCO dataset. Experimental results show that our DCA-Net achieves better results than the existing attention networks, such as SE-Net, ECA-Net and so on. For example, in the image classification task on CIFAR-100, the parameter amount of DCA-Net using ResNet-50 decreases by 50.03 compared to SE-Net using ResNet-101, and decreases by 44.48 compared to ECA-Net using ResNet-101, GFLOPs decreased by 48.41 and 48.21, respectively. At the same time, the Top-1 accuracy of DCA-Net using ResNet-50 is 0.3 higher than that of SE-Net using ResNet-101, and 0.86 higher than that of ECA-Net using ResNet-101.
Efficiency-Effectiveness Trade-offs in Recommendation Systems

Throughout the years, numerous recommendation algorithms have been developed to address the information filtering problem by leveraging users’ tastes through implicit or explicit feedback. In this paper, we present the work undertaken as part of a PhD thesis focused on exploring new evaluation dimensions centred around the efficiency-effectiveness trade-offs present in state-of-the-art recommendation systems. Firstly, we highlight the lack of efficiency-oriented studies and we formulate the research problem. Then, we propose a mapping of the design space and a classification of the recommendation algorithms/models with respect to salient attributes and characteristics. At the same time, we explain why and how assessing the recommendations on an accuracy versus training cost curve would advance the current knowledge in the area of evaluation, as well as open new research avenues for exploring parameter configurations within well-known algorithms. Finally, we make the case for a comprehensive methodology that incorporates predictive efficiency-effectiveness models, which illustrate the performance and behaviour of the recommendation systems under different recommendation tasks, while satisfying user-defined quality of service constraints and goals.
Floor Climbing Cleaning Robot Based on Slide Rail Lifting Structure

Existing cleaning robots, such as household circular cleaning robots and road cleaning vehicles, are applied to flat ground. However, whether it is indoor or outdoor, there are a large number of steps or stairs, such as in residential buildings, office buildings and many high-rise buildings. The cleaning of the steps in the stairwell cannot be ignored, but it requires labor and material resources, so a device that can replace manual cleaning is obviously very much needed. The robot can efficiently and stably clean the stair area within the specified range within a set time and any time period. The device is a robot for cleaning stairs and platforms, which can automatically go upstairs and clean the surface of the stairs. The device is equipped with two independent suspension systems at the front and rear to adapt to stairs with different height differences and to complete the movement of going upstairs or steps. The steering wheel controls the forward and backward movement of the four wheels to achieve the movement of the entire machine, and the wheels can be rotated horizontally to move left and right to achieve the entire sweep of all corners of a single staircase.
Digital Money: The Empowering New Currency

Digital forms of money have turned into the most discussed issue in the budgetary business due to probable transmission of COVID-19 through coins and bank notes. A digital money is a computerized or virtual cash that utilizes cryptography for security. Digital money is hard to follow considering its security. A characterizing highlight of a digital money is that it is not issued by any focal specialist, rendering it hypothetically safe to government impedance or control. Digital currencies have their advantages and disadvantages. This paper explains distinctive components of digital currencies, beginning with their initial advancement, difficulties and threats associated with them, points of interest and weaknesses, and their future managing issues identified with the down to earth capacity of cryptographic forms of money. It was presumed that it is difficult to anticipate the eventual fate of cryptographic forms of money as it is yet to be created. The banks and other monetary based organizations may analyze digital currencies as an option for the monetary exchanges later.
The sparse variance contamination model

We consider a Gaussian contamination (i.e., mixture) model where the contamination manifests itself as a change in variance. We study this model in various asymptotic regimes, in parallel with the work of Ingster YI. [Some problems of hypothesis testing leading to infinitely divisible distributions. Math Methods Stat. 1997;6(1):47–69] and Donoho D, Jin J. [Higher criticism for detecting sparse heterogeneous mixtures. Ann Stat. 2004;32(3):962–994], who considered a similar model where the contamination was in the mean instead. We show that the higher criticism test is (first-order) comparable to the likelihood ratio test in all sparsity regimes, while the chi-squared test and the extremes test are suboptimal.
Analysis of Cyber Incident Categories Based on Losses

The fact that “cyber risk” is indeed a collective term for various distinct risks creates great difficulty in communications. For example, policyholders of “cyber insurance” contracts often have a limited or inaccurate understanding about the coverage that they have. To address this issue, we propose a cyber risk categorization method using clustering techniques. This method classifies cyber incidents based on their consequential losses for insurance and risk management purposes. As a result, it also reveals the relationship between the causes and the outcomes of incidents. Our results show that similar cyber incidents, which are often not properly distinguished, can lead to very different losses. We hope that our work can clarify the differences between cyber risks and provide a set of risk categories that is feasible in practice and for future studies.
Reform and Practice of University Computer Basic Course Teaching Mode Based on Smart learning

With the continuous development and application of "Internet + education", the limitations of the traditional teaching model have been exposed. This paper constructs the intelligent learning teaching mode with the support of the intelligent learning theory and the intelligent learning environment, and applies it to the teaching practice of basic computer courses in universities. The teaching results show that this teaching mode can enhance students' active learning and innovative learning ability, and make students change from passive learning to active learning, eager learning, happy learning and wisdom learning, thus improving the teaching quality.
A Feature Selection Method using PSO-MI

Feature selection method is used for generating an optimal number of features to be used for a certain task like classification. Particle Swarm Optimization (PSO) is an algorithm influenced by the habit of bird flocking or fish schooling. The main goal of this paper is designing and evaluating a viable wrapper-based feature selection algorithm. Feature selection can also be viewed as a minimization problem for which PSO can be applied. In this paper, we attempt to introduce a PSO based feature selection method using mutual information (MI). Feature-class MI has been used to select a subset of features based on its relevancy. A wrapper-based method is used to find the productiveness of the method by evaluating with different classifiers in different datasets. The classification performances have been found promising when compared with classifications performed using normal classifiers and PSO method without using mutual information.
A Preliminary Systematic Mapping on Software Engineering for Robotic Systems

Robotic systems have been increasingly employed in everyday tasks. Considering that software plays a crucial point in robot systems, to investigate how software engineering concepts in a software quality perspective can improve robotic systems. In this work, we present a systematic mapping to identify and classify the state-of-art of software engineering for robotic systems in a quality software perspective. We selected and systematically analyzed a final set of 35 primary studies extracted from an automated search on Scopus digital library.
Coupled Graph Convolutional Neural Networks for Text-Oriented Clinical Diagnosis Inference

Text-oriented clinical diagnosis inference is to predict a set of diagnoses for a specific patient given its medical notes. Due to the great potential of automatic diagnosis inference, machine learning methods have began to be applied to this domain. However, existing approaches focus on performing either labor-intensive feature engineering or sequential modeling of each medical note separately, without considering the information sharing among similar patients, which is essential for evidence-based medicine, an emerging new diagnosis process. Motivated by this issue and the recently proposed graph convolutional network (GCN) for text classification, we propose to apply GCN for the text-oriented clinical diagnosis inference task. To encode the comorbidity of diagnoses into the GCN model and allow information sharing between patients, we devise a coupled graph convolutional neural networks (CGCN), where a note-dependent graph and a label-dependent graph are learned collaboratively with hyperplane projection to ensure they are in the same semantic space. The comprehensive results on two real datasets show that our method outperforms the state-of-art methods in text-oriented diagnosis inference.
Feature Based Sentimental Analysis for Prediction of Mobile Reviews Using Hybrid Bag-Boost algorithm

Sentiment analysis or opinion mining is one of the major challenge of NLP (natural language processing). Business Analytics plays a key role in the current scenario with a perception that people wants to enhance their enterprise. In particular, these people rely on feedback of their goods to withstand the competition and knowledge mining that can give them an outstanding view into what to expect in the future. Few words or phrases may decide results or outcomes. As a majority of these people seek to boost their company in order to achieve full benefit by providing premium goods. In this aspect, sentiment analysis has gained a lot of interest in the current years. SA is an area of research of NLP that is used to classify a specific feature's opinion or perspective within a text. This paper is based on the different methods used to identify a particular text according to the opinions conveyed by the user's i.e. whether the overall sentiment of a individual is negative or positive or neutral. We are also looking at the two advance approaches adopted (feature classification pursued by polarity classification) along with the experimental results. Finally in this paper we compared 3 ML classification techniques 1) Logistic Regression, 2) Hybid Bag-Boost algorithm 3) SVM in which hybrid algorithm provides more accuracy compared to the other 3 ML algorithms. The Main objective of the proposed method is to predict the user reviews for choosing a best mobile using several classification Algorithms.
Smart Contract Microservitization

A smart contract is a computable protocol that automatically enforces contract terms in a computer, transforming real-world contract terms into digital promises of the virtual world. Early smart contracts have been stuck in the theoretical phase due to the lack of a credible execution environment and the means to control digital assets. With the emergence of blockchain technology, it has solved the problems mentioned above. Smart contracts are stored on blockchain, ensuring the credibility of contract execution through the joint execution of contracts by the various nodes in the blockchain network. However, the current technology of blockchain-based smart contracts is still not mature enough and faces many major challenges. Among them, the extensibility and performance of smart contracts are the most important and most concerned ones. This paper studies the extensibility and performance of smart contracts by combining blockchain-based smart contracts with cloud technologies to address the extensibility and performance issues of smart contracts. Combined with micro-service technology, a new type of smart contract architecture is proposed, and then the key technologies in each layer of the architecture are further studied.
Boundary Spanning in a Digital World: The Case of Blockchain

Blockchain is a relatively new technology that is often described as “creating trust” or “removing intermediaries”. In this paper, we posit that blockchain is a new form of digitally-enabled boundary spanning that allows co-ownership models for the companies in question. Where companies have traditionally employed humans to act as interfaces to the external world, new digital technologies enable a digitised approach to many corporate operations that require interaction towards the external market and environment within which firms must operate. Blockchain is a special subset of digital technologies in this regard, enabling companies to co-operate to control parts of the market and to internalise transaction costs that until now have been a market function; using blockchain companies effectively create a new transaction boundary that means boundary spanning can be deeply embedded in core business, rather than kept to its periphery. This digitally-enabled boundary spanning is a key attribute of the emerging digital economy. Understanding its implications is of critical importance for economics, business and social science literature.
Applying probabilistic models to C++ code on an industrial scale

Machine learning approaches are widely applied to different research tasks of software engineering, but C/C++ code presents a challenge for these approaches because of its complex build system. However, C and C++ languages still remain two of the most popular programming languages, especially in industrial software, where a big amount of legacy code is still used. This fact prevents the application of recent advances in probabilistic modeling of source code to the C/C++ domain.
Cost-Sensitive Extreme Gradient Boosting for Imbalanced Classification of Breast Cancer Diagnosis

The clinical information can enhance the doctors for predicting and diagnosing the diseases also making the right decisions. Breast cancer is the most dangerous disease, early diagnosis can improve a chance of survival and can support clinical treatment. Detecting breast cancer takes a lot of time and it is hard to classification. However, the problem of the classification occurs when there is an unequal distribution of classes the dataset. This is caused by the low performance in the traditional machine learning models. For this reason, this work proposed the cost-sensitive XGBoost model, which is an improved version of the XGBoost model in conjunction with cost-sensitive learning. The models were applied to classify the four breast cancer datasets that contained the imbalanced data. In the experiment, this work determined the best parameters on each dataset by the hyperparameters optimization technique before configuring the models. The results indicated that the cost-sensitive XGBoost model had been skillful, and could improve classification accuracy in four datasets. In addition, this work evaluated the model performance by accuracy, ROC AUC, and k- Fold cross-validation to ensure that the new models is accurate.
Post‐filtering with surface orientation constraints for stereo dense image matching

Abstract Dense image matching (DIM) is a critical technique when computing accurate 3D geometric information for many photogrammetric applications. Most DIM methods adopt first‐order regularisation priors for efficient matching, which often introduce stepped biases (also called fronto‐parallel biases) into the matching results. To remove these biases and compute more accurate matching results, this paper proposes a novel post‐filtering method by adjusting the surface orientation of each pixel in the matching process. The core algorithm formulates the post‐filtering as the optimisation of a global energy function with second‐order regularisation priors. A compromise solution of the energy function is computed by breaking the optimisation into a collection of sub‐optimisations of each pixel in a local adaptive window. The proposed method was compared with several state‐of‐the‐art post‐filtering methods on indoor, aerial and satellite datasets. The comparisons demonstrate that the proposed method obtains the highest post‐filtering accuracies on all datasets.
A Drift Propensity Detection Technique to Improve the Performance for Cross-Version Software Defect Prediction

In cross-version defect prediction (CVDP), historical data is derived from the prior version of the same project to predict defects of the current version. Recent studies in CVDP focus on subset selection to deal with the changes of the data distributions. No prior study has focused on training data arriving in streaming fashion across the versions where the significant differences between versions make the prediction unreliable. We refer to this situation as Drift Propensity (DP). By identifying DP, necessary steps can be taken (e.g., updating or retraining the model) to improve the prediction performance. In this paper, we investigate the chronological defect datasets and identify DP in the datasets. The no-memory data management technique is employed to manage the data distributions and a DP detection technique is proposed. The idea behind the proposed DP detection technique is to monitor the algorithm's error-rate. The DP detector triggers DP, warning, and control flags to take necessary steps. The proposed technique is significantly superior in identifying the distribution differences (p-value <; 0.05). The DP's identified in the data distributions achieve large effect sizes (Hedges' g ≥ 0.80) during the pair-wise comparisons. We observe that if the error-rate exponentially increases, it causes DP, resulting in prediction performance deterioration. We thus recommend researches and practitioners to address DP in the chronological datasets. Due to its potential effects in the datasets, the prediction models could be enhanced to get the best results in CVDP.
Calcification segmentation based on a different scales superpixels saliency detection algorithm

Accurate detection of breast tumor calcifications is of great significance in assisting doctors’ diagnosis to improve the accuracy of breast cancer early detection. In this article, a different scale of superpixels saliency detection algorithm is used to segment calcifications in breast tumor ultrasound images based on a simple linear iterative cluster. First, a multi-scale saliency segmentation algorithm was used to divide the tumor region of different sizes and weak calcification (Wca) was extracted according to uneven gray distribution and texture contrast between regions. Second, based on single-scale superpixel segmentation of the original image, the strong calcification extraction map was calculated by measuring gray value difference and calcification gray distance features. Finally, the final calcification extraction map was obtained by combining the strong and weak calcification extraction maps. The detection algorithm proposed in this article could effectively detect calcifications in breast ultrasound images.
An Incremental Pruning Strategy for Fast Training of CNN Models

Deep Neural Networks have progressed significantly over the past few years and they are growing better and bigger each day. Thus, it becomes difficult to compute as well as store these over-parameterized networks. Pruning is a technique to reduce the parameter-count resulting in improved speed, reduced size and reduced computation power. In this paper, we have explored a new pruning strategy based on the technique of Incremental Pruning with less pre-training and achieved better accuracy in lesser computation time on MNIST, CIFAR-10 and CIFAR-100 datasets compared to previous related works with small decrease in compression rates. On MNIST, CIFAR-10 and CIFAR-100 datasets, the proposed technique prunes 10x faster than conventional models with similar accuracy.
ORSUM - Workshop on Online Recommender Systems and User Modeling

Modern online web-based systems continuously generate data at very fast rates. This continuous flow of data encompasses web content – e.g. posts, news, products, comments –, but also user feedback – e.g. ratings, views, reads, clicks, thumbs up –, as well as context information – device used, geographic info, social network, current user activity, weather. This is potentially overwhelming for systems and algorithms design to train in offline batches, given the continuous and potentially fast change of content, context and user preferences. Therefore it is important to investigate online methods to be able to transparently adapt to the inherent dynamics of online systems. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online, as data is generated.
Super-resolving Tiny Faces with Face Feature Vectors

Most of the current state-of-the-art tiny face super-resolution (SR) methods aim at learning a single one-to-one mapping to super-resolve low-resolution (LR) face images. In contrast with high-resolution (HR) faces images, LR faces images lack fine facial details, implying that an LR face image can be mapped to many HR candidates or vice versa. This ambiguity may lead that an HR face image super-resolved by one-to-one SR methods cannot preserve the accurate facial details. To alleviate this problem, we consider tiny face SR as an one-to-many mapping, and demonstrate that injecting reasonable additional facial prior knowledge can significantly reduce the ambiguity in face SR. Specifically, with the GAN architecture, we propose a novel face SR network consisting of an upsampling network and a discriminative network. The upsampling network is designed to embed facial prior knowledge (represented as a vector) into the residual features of LR inputs and super-resolve LR inputs (16 × 16 pixels) with up-scaling factor of 4 ×. The discriminative network aims at examining whether the generated HR face images matches the corresponding facial prior. Furthermore, we also propose a model which can map the samples of a standard normal distribution to the facial prior distribution. In this way, we can readily sample different facial prior to support us to super-resolve a single LR face image to abundant different HR faces images. Through conducting extensive evaluations on a large-scale dataset, we demonstrate that our method achieves encouraging results, outperforming the current competitive algorithms.