Reviewerly/exHarmony huggingface dataset:
A hybrid scalable collaborative filtering based recommendation system using ontology and incremental SVD algorithm

Recommendation system is a dynamic information filtering system based on machine learning algorithm. Recommendation engine filters large amounts of information generated through the interaction of users to web-portals generate the recommendation to fulfill the expectation of the online users. Collaborative filtering is most popular and efficient method to generate the proper recommendation. Current method of CF is unable to handle the most popular problem of recommendation like data sparsity, scalability, recommendation inaccuracy and big error in predication. To handle the such problem of recommendation this research develops a new hybrid CF approach to addresses the problem of CF like sparsity, scalability inefficiency in recommendation improve the accuracy prediction using the incremental approach of singular value decomposition and ontology approach. To enhance the prediction and recommendation accuracy ontology based approach is applied. This approach is decomposed into two parts offline phase and online phase. In offline phase of collaborative filtering method, use dimension reduction technique like singular value decomposition (SVD) and also use the clustering method to form the clusters of most similar users and items depends upon the preferences which significantly improve the scalability of collaborative filtering. In online phase of recommendation the incremental SVD is used to find out the most relevant item to the new item. The performance of the recommendation system is evaluated through evolution parameter likes Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) over the two real word dataset like Movielens and Flixster Dataset. The result shows that the implemented method effective is to solve the problem of user scalability, data sparsity and improve the performance of prediction and recommendation.
Estimating Downward Shortwave Solar Radiation on Clear-Sky Days in Heterogeneous Surface Using LM-BP Neural Network

Downward surface shortwave radiation (DSSR) plays an important role in the energy balance of the earth’s surface. Accurate estimate of DSSR is of great significance for the rational and effective use of solar energy. Some parameterization schemes were proposed to estimate DSSR using meteorological measurements given ground-based radiation observation sites are scare and uneven. With the development of remote sensing technique, remotely sensed data can be applied to obtain continuous DSSR in space. Commonly, the spatial resolution of most radiation products is relatively low and cannot meet the needs of certain fields. Moreover, some retrieval algorithms based on the radiation transfer models are complicated for non-professionals. In this study, a back-propagation (BP) neural network method with Levenberg–Marquardt (LM) algorithm (hereafter referred to as LM-BP) was applied to predict DSSR by building the relationship between measured DSSR and high-resolution remote sensing data from the Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER). The DSSR observations from the four-component radiation sensor installed at the land covered by vegetable, village, maize, orchard, Gobi, sandy desert, desert steppe, and wetland were used to validate the model estimates. The results showed that the estimates of DSSR from LM-BP agreed well with the site measurements, with the root mean square error (RMSE) and the mean bias error (MBE) values of 27.34 W/m2 and −1.59 W/m2, respectively. This indicates that by combining the LM-BP network model and ASTER images can obtain precise DSSR in heterogenous surface. The DSSR results of this study can provide accurate high-spatial resolution input data for hydrological, evapotranspiration, and crop models.
Secure federated learning with efficient communication in vehicle network

Internet of Vehicles (IoV) generates large amounts of data at the network edge. Machine learning models are often built on these data, to enable the detection, classification, and prediction of traffic events. Due to network bandwidth, storage, and especially privacy concerns, it is often impossible to send all the IoV data to the edge server for centralized model training . Federated learning is a promising paradigm for distributed machine learning, which enables edge nodes to train models locally. As vehicle usually has unreliable and relatively slow network connection, reducing the communication overhead is importance. In this paper, we propose a secure federated learning with efficient communication (SFLEC) scheme in vehicle network. To protect the privacy of local update, we upload the updated parameters of the model with local differential privacy. We further propose a client selection approach that identifies relevant updates trained by vehicles and prevents irrelevant updates from being uploaded for reduced network footprint to achieve efficient communication. Then we prove the loss function of the trained FL in our scheme exits a theoretical convergence. Finally, we evaluate our scheme on two datasets and compare with basic FL. Our proposed scheme improves the communication efficiency, while preserves the data privacy.
On the Decomposition of Abstract Dialectical Frameworks and the Complexity of Naive-based Semantics

Abstract dialectical frameworks (ADFs) are a recently introduced powerful generalization of Dung’s popular abstract argumentation frameworks (AFs). Inspired by similar work for AFs, we introduce a decomposition scheme for ADFs, which proceeds along the ADF’s strongly connected components. We find that, for several semantics, the decompositionbased version coincides with the original semantics, whereas for others, it gives rise to a new semantics. These new semantics allow us to deal with pertinent problems such as odd-length negative cycles in a more general setting, that for instance also encompasses logic programs. We perform an exhaustive analysis of the computational complexity of these new, so-called naive-based semantics. The results are quite interesting, for some of them involve little-known classes of the so-called Boolean hierarchy (another hierarchy in between classes of the polynomial hierarchy). Furthermore, in credulous and sceptical entailment, the complexity can be different depending on whether we check for truth or falsity of a specific statement.
Retrospective comparison between single reading plus an artificial intelligence algorithm and two-view digital tomosynthesis with double reading in breast screening

Objective To examine the breast cancer detection rate by single reading of an experienced radiologist supported by an artificial intelligence (AI) system, and compare with two-dimensional full-field digital mammography (2D-FFDM) double reading. Materials and methods Images (3D-tomosynthesis) of 161 biopsy-proven cancers were re-read by the AI algorithm and compared to the results of first human reader, second human reader and consensus following double reading in screening. Detection was assessed in subgroups by tumour type, breast density and grade, and at two operating points, referred to as a lower and a higher sensitivity threshold. Results The AI algorithm method gave similar results to double-reading 2D-FFDM, and the detection rate was significantly higher compared to single-reading 2D-FFDM. At the lower sensitivity threshold, the algorithm was significantly more sensitive than reader A (97.5% vs. 89.4%, p = 0.02), non-significantly more sensitive than reader B (97.5% vs. 94.4%, p = 0.2) and non-significantly less sensitive than the consensus from double reading (97.5% vs. 99.4%, p = 0.2). At the higher sensitivity threshold, the algorithm was significantly more sensitive than reader A (99.4% vs. 89.4%, p &lt; 0.001) and reader B (99.4% vs. 94.4%, p = 0.02) and identical to the consensus sensitivity (99.7% in both cases, p = 1.0). There were no significant differences in the detection capability of the AI system by tumour type, grading and density. Conclusion In this proof of principle study, we show that sensitivity using single reading with a suitable AI algorithm is non-inferior to that of standard of care using 2D mammography with double reading, when tomosynthesis is the primary screening examination.
Blockchain-based Fog Computing Applications in Healthcare

Recently, the use of blockchain technology in the field of healthcare has increased. Although blockchain technology brought several innovations to healthcare, still there are problems waiting to be resolved. In order to provide alternative solutions to these problems, the use of fog computing together with blockchain technology has been proposed. In this study, the applications of blockchain based fog computing technology in healthcare are investigated. The aim of this study is to provide the readers an idea about the interactive use of blockchain and fog computing in the field of healthcare. For this purpose, firstly, fog computing and blockchain technologies are introduced. Afterwards, the integration of these areas, the advantages and disadvantages of using these technologies in the field of healthcare is discussed and a new system architecture is proposed.
3D Geological Model of the Touro Cu Deposit, A World-Class Mafic-Siliciclastic VMS Deposit in the NW of the Iberian Peninsula

The Touro volcanogenic massive sulfide (VMS) deposit is located in the NW of the Iberian Variscan massif in the Galicia-Trás-os-Montes Zone, an amalgamation of several allochthonous terrains. The Órdenes complex is the most extensive of the allochthone complexes, and amphibolites and paragneisses host the deposit, characterized as being massive or semimassive (stringers) sulfides, mostly made up of pyrrhotite and chalcopyrite. The total resources are 103 Mt, containing 0.41% copper. A 3D model of the different orebodies and host rocks was generated using data from 1090 drill core logs. The model revealed that the structure of the area is a N–S-trending antiform. The orebodies crop out in the limbs and in the hinge zone. The mineralized structures are mostly tabular, up to 100 m in thickness and subhorizontal. Based on the petrography, geochemistry and the 3D model, the Touro deposit is classified as a VMS of the mafic-siliciclastic type formed in an Ordovician back-arc setting, which was buried and metamorphosed in Middle Devonian.
A Compact Answer Set Programming Encoding of Multi-Agent Pathfinding

Multi-agent pathfinding (MAPF) is the problem of finding k non-colliding paths connecting k given initial positions with k given goal positions on a given map. In its sum-of-costs variant, the total number of moves and wait actions performed by agents before they definitely reach the goal is minimized. Not surprisingly, since MAPF is combinatorial, a number of compilations to Boolean Satisfiability (SAT) and Answer Set Programming (ASP) exist. In this article, we describe in detail the first family of compilations to ASP that solve sum-of-costs MAPF over 4-connected grids. Compared to existing ASP compilations, a distinguishing feature of our compilation is that the number of total clauses (after grounding) grow linearly with the number of agents, while existing compilations grow quadratically. In addition, the optimization objective is such that its size after grounding does not depend on the size of the grid. In our experimental evaluation, we show that our approach outperforms search-based sum-of-costs MAPF solvers when grids are congested with agents. We also show that our approach is competitive with a SAT-based approach when follow conflicts are taken into account. We also explore the potential of our solver when finding makespanoptimal solutions, in which makespan is minimized first and then cost is minimized. Our results show that makespan-optimal solutions are slightly suboptimal in most benchmarks. Moreover, our MAPF solver, when run in that mode, is faster and scales better.
Negative Data Augmentation

Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations, we explore negative data augmentation strategies (NDA) that intentionally create out-of-distribution samples. We show that such negative out-of-distribution samples provide information on the support of the data distribution, and can be leveraged for generative modeling and representation learning. We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator. We prove that under suitable conditions, optimizing the resulting objective still recovers the true data distribution but can directly bias the generator towards avoiding samples that lack the desired structure. Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities. Further, we incorporate the same negative data augmentation strategy in a contrastive learning framework for self-supervised representation learning on images and videos, achieving improved performance on downstream image classification, object detection, and action recognition tasks. These results suggest that prior knowledge on what does not constitute valid data is an effective form of weak supervision across a range of unsupervised learning tasks.
Investigating design anti-pattern and design pattern mutations and their change- and fault-proneness

During software evolution, inexperienced developers may introduce design anti-patterns when they modify their software systems to fix bugs or to add new functionalities based on changes in requirements. Developers may also use design patterns to promote software quality or as a possible cure for some design anti-patterns. Thus, design patterns and design anti-patterns are introduced, removed, and mutated from one another by developers. Many studies investigated the evolution of design patterns and design anti-patterns and their impact on software development. However, they investigated design patterns or design anti-patterns in isolation and did not consider their mutations and the impact of these mutations on software quality. Therefore, we report our study of bidirectional mutations between design patterns and design anti-patterns and the impacts of these mutations on software change- and fault-proneness. We analyzed snapshots of seven Java software systems with diverse sizes, evolution histories, and application domains. We built Markov models to capture the probability of occurrences of the different design patterns and design anti-patterns mutations. Results from our study show that (1) design patterns and design anti-patterns mutate into other design patterns and–or design anti-patterns. They also show that (2) some change types primarily trigger mutations of design patterns and design anti-patterns (renaming and changes to comments, declarations, and operators), and (3) some mutations of design anti-patterns and design patterns are more faulty in specific contexts. These results provide important insights into the evolution of design patterns and design anti-patterns and its impact on the change- and fault-proneness of software systems.
Efficient Computation with Taste Shocks

Taste shocks result in nondegenerate choice probabilities, smooth policy functions, continuous demand correspondences, and reduced computational errors.They also cause significant computational cost when the number of choices is large.However, I show that, in many economic models, a numerically equivalent approximation may be obtained extremely efficiently.If the objective function has increasing differences (a condition closely tied to policy function monotonicity) or is concave in a discrete sense, the proposed algorithms are O(n log n) for n states and n choices-a drastic improvement over the naive algorithm's O(n 2 ) cost.If both hold, the cost can be further reduced to O(n).Additionally, with increasing differences in two state variables, I propose an algorithm that in some cases is O(n 2 ) even without concavity (in contrast to the O(n 3 ) naive algorithm).I illustrate the usefulness of the proposed approach in an incomplete markets economy and a long-term sovereign debt model, the latter requiring taste shocks for convergence.For grid sizes of 500 points, the algorithms are up to 200 times faster than the naive approach.
An Adaptive Learning Algorithm for Regularized Extreme Learning Machine

Extreme learning machine (ELM) has become popular in recent years, due to its robust approximation capacity and fast learning speed. It is common to add a ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> penalty term in basic ELM to avoid over-fitting. However, in ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -regularized extreme learning machine (ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -RELM), choosing a suitable regularization factor is random and time consuming. In order to select a satisfactory regularization factor automatically, we proposed an adaptive regularized extreme learning machine (A-RELM) by replacing the regularization factor with a function. The function is defined in terms of the output weights named regularization function. And an iterative algorithm is proposed for obtaining the output weights, therefore, allowing for deriving their values simultaneously. Besides, the constructed regularization function ensures the convexity of the model, which contributes to a globally optimal solution. The convergence analysis of the iterative algorithm guarantees the effectiveness of the model training. Experimental results on some UCI benchmarks and the Yale face database B indicate the superiority of our proposed algorithm.
Mobile emergency notification apps: current state, barriers and future potential

Abstract The main objective of this research is to show that mobile emergency alert apps have the potential to support the Emergency Management system all over the world. Domain experts conducted the decision-making process by evaluating three methods for communicating in an emergency situation: Voice Call, SMS and Mobile Applications. To establish the relative weights of evaluation criteria, the Multi-Criteria Analysis was used. The findings suggest that the ideal solution for communicating during an emergency is alternative no. 3, the Mobile Application. Following the result, we discussed the current state, barriers and future potential of mobile emergency alert applications used for emergency notifications.
TabCellNet: Deep learning-based tabular cell structure detection

There is an increasing demand for automated document processing techniques as the volume of electronic component documents increase. This is most prevalent in the supply chain optimization sector where vast amount of documents need to be processed and is time consuming and prone to error. Detection of tables and table structures serves as a crucial step to automate document processing. While table detection is a well investigated problem, tabular structure detection is more complex, and requires further improvements. To address this, this study proposes a deep learning model that focuses on high precision tabular cell structure detection. The proposed model creates a benchmark for the ICDAR2013 dataset cell structure with comparison to the previous state of the art table detection models as well as proposing alternative models. Our methodology approaches improving table structure detection through the detection of cells instead of row and columns for better generalization capabilities for heterogeneous table structures. Our proposed model advances prior models by improving major parts of the detection pipeline, mainly the two-stage detector, backbone, backbone architecture, and non-maximum-suppression (NMS). TabCellNet consists of Hybrid Task Cascade (HTC) with Combinational Backbone Network (CBNet), dual ResNeXt101 and Soft-NMS to achieve a precision of 89.2% and recall of 98.7% on the hand annotated ICDAR2013 cell structure dataset.
Agile Beeswax: Mobile App Development Process and Empirical Study in Real Environment

Mobile application development is a highly competitive environment; agile methodologies can enable teams to provide value faster, with higher quality and predictability, and a better attitude to deal with the continuous changes that will arise in the mobile context application (App), and the positive impact of that on sustainable development through continuous progress. App development is different from other types of software. For this reason, our objective is to present a new agile-based methodology for app development that we call Agile Beeswax. Agile Beeswax is conceived after identifying the mobile development process’s issues and challenges, and unique requirements. Agile Beeswax is an incremental, iterative development process composed of two main iterative loops (sprints), the incremental design loop and the incremental development loop, and one bridge connecting these two sprints. Agile Beeswax is structured in six phases, idea and strategy, user experience design, user interface design, design to development, handoff and technical decisions, development, and deployment and monitoring. One of its main strengths is that it has been created with academic and business perspectives to bring these two communities closer. To achieve this purpose, our research methodology comprises four main phases: Phase 1: Extensive literature review of mobile development methodologies, Phase 2: Interviews with mobile application developers working in small to medium software companies, Phase 3: Survey to extract valuable knowledge about mobile development (which was carefully designed based on the results of the first and the second phases), and Phase 4: Proposal of a new methodology for the agile development of mobile applications. With the aim of integrating both perspectives, the survey was answered by a sample of 35 experts, including academics and developers. Interesting results have been collected and discussed in this paper (on issues such as the development process, the tools used during this process, and the general issues and challenges they encountered), laying the foundations of the methodology Agile Beeswax proposed to develop mobile apps. Our results and the proposed methodology are intended to serve as support for mobile application developers.
Deep top similarity hashing with class-wise loss for multi-label image retrieval

One of the major challenges of learning to hash in large-scale image retrieval is the projective transformation from raw image to binary space with preserving semantic similarity. Recently, several deep hashing methods show many excellent properties compared with traditional hashing based on hand-designed representation. However, most of the existing hashing models only pay attention to the semantic similarity between image pairs, ignoring the ranking information of retrieval results, which limits its performance. In this paper, a novel deep hashing framework, named Deep Top Similarity Hashing with Class-wise loss (DTSH-CW), is proposed to preserve semantic similarity between top images of ranking list and query images. In this proposed framework, CNNs architecture with batch normalization module is adopted to extract deep semantic characteristics. With integrating the position information of images, a top similarity loss is carefully designed to ensure the similarities between top images of ranking list and query images. Unlike pair-wise or triplet-wise loss, by directly leveraging the class labels, a cubic constraint based on Gaussian distribution is introduced to optimize objective function so as to maintain semantic variations of different classes. Furthermore, in order to solve discrete optimization problem, Two-Stage strategy is developed to provide efficient model training. Quantities of comparison experiments on three multi-label benchmark datasets show that our proposed DTSH-CW achieves promising performance compared to several state-of-the-art hashing methods.