Reviewerly/exHarmony huggingface dataset:
Social Influence Attentive Neural Network for Friend-Enhanced Recommendation

With the thriving of online social networks, there emerges a new recommendation scenario in many social apps, called Friend-Enhanced Recommendation (FER) in this paper. In FER, a user is recommended with items liked/shared by his/her friends (called a friend referral circle). These friend referrals are explicitly shown to users. Different from conventional social recommendation, the unique friend referral circle in FER may significantly change the recommendation paradigm, making users to pay more attention to enhanced social factors. In this paper, we first formulate the FER problem, and propose a novel Social Influence Attentive Neural network (SIAN) solution. In order to fuse rich heterogeneous information, the attentive feature aggregator in SIAN is designed to learn user and item representations at both node- and type-levels. More importantly, a social influence coupler is put forward to capture the influence of the friend referral circle in an attentive manner. Experimental results demonstrate that SIAN outperforms several state-of-the-art baselines on three real-world datasets. (Code and dataset are available at https://github.com/rootlu/SIAN).
Assessment of Digital Competences in Communication Students across four Latin American Universities

Professional skills deemed necessary to work in the field of Communication in Latin America have shifted due to the ever more pressing necessities regarding Information and Communication Technologies. This study analyzes the cases of 4 different academic programs in Communications, belonging to 4 universities in the region (Argentina, Colombia, Peru, and Venezuela), comparing their students in 3 phases of their formation (first semester, fourth semester, eight or last semester). We employed questionnaires for new students, focus groups for fourth-semester students, and semi-structured interviews those who were close to graduation. The results reveal that while students do have high levels of internet connectivity, they lack digital competencies such as basic information literacy, which are crucial for their future as professionals.
Generalized Deduplication: Lossless Compression for Large Amounts of Small IoT Data

We show that a generalization of deduplication can enable compressed storage of sensor data. The method uses error-correcting codes in a non-traditional manner to identify similar elements, and then leverages this similarity for compression. Using Reed Solomon codes, our method has a theoretical potential to reduce the cost of storing chunks of 16 bytes to as much as 5 times less, and up to 65 times less for chunks of 255 bytes. We define a simple model for sensor data, and show how our approach is able to compress data from the model, realizing its compression potential with much smaller data sets than classic deduplication requires. This demonstrates that generalized deduplication can be a viable solution for practical lossless compression of small IoT data in scenarios where classic deduplication is ineffective.
Blockchain in Health Care Innovation: Literature Review and Case Study From a Business Ecosystem Perspective

Background Blockchain technology is leveraging its innovative potential in various sectors and its transformation of business-related processes has drawn much attention. Topics of research interest have focused on medical and health care applications, while research implications have generally concluded in system design, literature reviews, and case studies. However, a general overview and knowledge about the impact on the health care ecosystem is limited. Objective This paper explores a potential paradigm shift and ecosystem evolution in health care utilizing blockchain technology. Methods A literature review with a case study on a pioneering initiative was conducted. With a systematic life cycle analysis, this study sheds light on the evolutionary development of blockchain in health care scenarios and its interactive relationship among stakeholders. Results Four stages—birth, expansion, leadership, and self-renewal or death—in the life cycle of the business ecosystem were explored to elucidate the evolving trajectories of blockchain-based health care implementation. Focused impacts on the traditional health care industry are highlighted within each stage to further support the potential health care paradigm shift in the future. Conclusions This paper enriches the existing body of literature in this field by illustrating the potential of blockchain in fulfilling stakeholders’ needs and elucidating the phenomenon of coevolution within the health care ecosystem. Blockchain not only catalyzes the interactions among players but also facilitates the formation of the ecosystem life cycle. The collaborative network linked by blockchain may play a critical role on value creation, transfer, and sharing among the health care community. Future efforts may focus on empirical or case studies to validate the proposed evolution of the health care ecosystem.
On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators

The Tsetlin Machine (TM) is a recent machine learning algorithm with several distinct properties, such as interpretability, simplicity, and hardware-friendliness. Although numerous empirical evaluations report on its performance, the mathematical analysis of its convergence is still open. In this article, we analyze the convergence of the TM with only one clause involved for classification. More specifically, we examine two basic logical operators, namely, the "IDENTITY"- and "NOT" operators. Our analysis reveals that the TM, with just one clause, can converge correctly to the intended logical operator, learning from training data over an infinite time horizon. Besides, it can capture arbitrarily rare patterns and select the most accurate one when two candidate patterns are incompatible, by configuring a granularity parameter. The analysis of the convergence of the two basic operators lays the foundation for analyzing other logical operators. These analyses altogether, from a mathematical perspective, provide new insights on why TMs have obtained state-of-the-art performance on several pattern recognition problems.
Asymmetric CycleGAN for image-to-image translations with uneven complexities

CycleGAN is one of the famous and basic methods for unpaired image-to-image translation tasks. Inspired by the experiments of the NIR-RGB translation, which is a kind of translation where images are translated from simple to complex or vice versa, we concluded the definition of asymmetric translation task. Because of the complexity difference between two domains, the complexity inequality in bidirectional translations is significant. We analyzed and witnessed the limitation of the original CycleGAN in asymmetric translation tasks and proposed an Asymmetric CycleGAN model with generators of unequal sizes to adapt to the asymmetric need in asymmetric translations. An empirical metric was also given to determine the asymmetric task from the aspect of image entropy and could be treated as the auxiliary guidance to design the asymmetric generators. Besides, the edge-retain loss between the input and the generated images was introduced to enhance the structural visual quality. Residual-block-net based and U-net based generators were both applied here to verify the Asymmetric CycleGAN. The performance of different depth of generators for Asymmetric CycleGAN was also discussed on the basis of experiments. The qualitative visual evaluation demonstrated that our model had achieved great improvements compared to original CycleGAN.
Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection

Sarcasm is a type of figurative language broadly adopted in social media and daily conversations.The sarcasm can ultimately alter the meaning of the sentence, which makes the opinion analysis process error-prone.In this paper, we propose to employ bidirectional encoder representations transformers (BERT), and aspect-based sentiment analysis approaches in order to extract the relation between context dialogue sequence and response and determine whether or not the response is sarcastic.The best performing method of ours obtains an F1 score of 0.73 on the Twitter dataset and 0.734 over the Reddit dataset at the second workshop on figurative language processing Shared Task 2020.
Maneuver Planning for Automatic Parking with Safe Travel Corridors: A Numerical Optimal Control Approach

An optimal control problem is a straightforward description of a generic automatic parking maneuver planning scheme. However, the dimensionality of the optimal control problem is usually high, primarily because of the large-scale collision avoidance constraints. Among the complete collision avoidance constraints, some parts are redundant because the ego vehicle may not be subject to collisions with all of the obstacles at every moment. An idea to simplify the collision avoidance constraints is to plan a coarse path/trajectory, and then to construct a surrounding corridor which naturally separates the ego vehicle from the obstacles. This idea is well known as safe flight corridors (SFCs) in the research area of unmanned aerial vehicle (UAV) path planning. But SFCs are not directly applicable to automatic parking cases because the ego vehicle cannot be taken as a mass point or a circle in the tiny parking scenarios. To make the SFCs work for automatic parking schemes, we develop a concept called Safe Travel Corridors (STCs), which requires different parts of the ego vehicle to stay in different safe corridors. Through this, a reduced-scale optimal control problem is formulated, and the problem scale is completely irrelevant to the complexity of the environment. The efficiency and robustness of the proposed maneuver planner are evaluated through 96 benchmark cases.
An Exploration of the Potential Role of Digital Technologies for Promoting Learning in Foreign Language Classrooms: Lessons for a Pandemic

This research study focuses on long-term digital technologies assisted cross-cultural communication as an instructional model. Secondary school foreign language learners of German and English collaborated online with the same age-cohort peers to acquire new cultural knowledge, as well as develop digi-tal technologies skills. Findings revealed that digital technologies facilitated personalised student-led learning through an inquiry-based approach. Results further indicated that the learners had rich opportunities to share their stories and to think critically about themselves and others. Indeed, pupils enriched their understanding of their partner’s culture as well as developing an en-hanced understanding of the influence of culture on behaviour and thinking. The students learned to use digital technologies by creating digital profiles, posting their comments, asking questions, exchanging cultural information, and gaining linguistic and cultural experience of a native speaker in a virtual space. This paper shows that with the purposeful integration of digital tech-nologies in foreign language classrooms opportunities for richer student learning experiences can be promoted.
Learning to Purify Noisy Labels via Meta Soft Label Corrector

Recent deep neural networks (DNNs) can easily overfit to biased training data with noisy labels. Label correction strategy is commonly used to alleviate this issue by identifying suspected noisy labels and then correcting them. Current approaches to correcting corrupted labels usually need manually pre-defined label correction rules, which makes it hard to apply in practice due to the large variations of such manual strategies with respect to different problems. To address this issue, we propose a meta-learning model, aiming at attaining an automatic scheme which can estimate soft labels through meta-gradient descent step under the guidance of a small amount of noise-free meta data. By viewing the label correction procedure as a meta-process and using a meta-learner to automatically correct labels, our method can adaptively obtain rectified soft labels gradually in iteration according to current training problems. Besides, our method is model-agnostic and can be combined with any other existing classification models with ease to make it available to noisy label cases. Comprehensive experiments substantiate the superiority of our method in both synthetic and real-world problems with noisy labels compared with current state-of-the-art label correction strategies.
Organizational climate, organizational citizenship behaviour and turnover intention: Evidence from Jordan

The present paper attempts to analyse the effect of organizational climate on organizational citizenship behaviour (OCB), as well as the effect of OCB on turnover intention.Data on organizational climate, OCB and turnover intention were obtained from a sample of 78 internal auditors of Jordanian private universities.Hypotheses were proposed and were tested using PLS-SEM, and the outcomes demonstrate the positive linkage between organizational climate and OCB.Meanwhile, OCB and turnover intention were found to have inverse linkage.Strong organizational climate should be created in Jordanian private universities since it improves and predicts OCB.OCB will in turn cause turnover intention to decrease among internal auditors.With the use of PLS-SEM method, this study adds to the extant literature.
Predicting Cervical Cancer using Machine Learning Methods

In almost all countries, precautionary measures are less expensive than medical treatment.The early detection of any disease gives a patient better chances of successful treatment than disease discovery at an advanced stage of its development.If we do not know how to treat patients, any treatment we can provide would be useful and would provide a more comfortable life.Cervical cancer is one such disease, considered to be fourth among the most common types of cancer in women around the world.There are many factors that increase the risk of cervical cancer, such as age and use of hormonal contraceptives.Early detection of cervical cancer helps to raise recovery rates and reduce death rates.This paper aims to use machine learning algorithms to find a model capable of diagnosing cervical cancer with high accuracy and sensitivity.The cervical cancer risk factor dataset from the University of California at Irvine (UCI) was used to construct the classification model through a voting method that combines three classifiers: Decision tree, logistic regression and random forest.The synthetic minority oversampling technique (SMOTE) was used to solve the problem of imbalance dataset and, together with the principal component analysis (PCA) technique, to reduce dimensions that do not affect model accuracy.Then, stratified 10-fold cross-validation technique was used to prevent the overfitting problem.This dataset contains four target variables-Hinselmann, Schiller, Cytology, and Biopsy-with 32 risk factors.We found that using the voting classifier, SMOTE and PCA techniques helped raise the accuracy, sensitivity, and area under the Receiver Operating Characteristic curve (ROC_AUC) of the predictive models created for each of the four target variables to higher rates.In the SMOTE-voting model, accuracy, sensitivity and PPA ratios improved by 0.93 % to 5.13 %, 39.26 % to 46.97 % and 2 % to 29 %, respectively for all target variables.Moreover, using PCA technology reduced computational processing time and increasing model efficiency.Finally, after comparing our results with several previous studies, it was found that our models were able to diagnose cervical cancer more efficiently according to certain evaluation measures.
EXPLAIN IT TO ME – FACING REMOTE SENSING CHALLENGES IN THE BIO- AND GEOSCIENCES WITH EXPLAINABLE MACHINE LEARNING

Abstract. For some time now, machine learning methods have been indispensable in many application areas. Especially with the recent development of efficient neural networks, these methods are increasingly used in the sciences to obtain scientific outcomes from observational or simulated data. Besides a high accuracy, a desired goal is to learn explainable models. In order to reach this goal and obtain explanation, knowledge from the respective domain is necessary, which can be integrated into the model or applied post-hoc. We discuss explainable machine learning approaches which are used to tackle common challenges in the bio- and geosciences, such as limited amount of labeled data or the provision of reliable and scientific consistent results. We show that recent advances in machine learning to enhance transparency, interpretability, and explainability are helpful in overcoming these challenges.
Incremental Cost-Sensitive Support Vector Machine With Linear-Exponential Loss

Incremental learning or online learning as a branch of machine learning has attracted more attention recently.For large-scale problems and dynamic data problem, incremental learning overwhelms batch learning, because of its efficient treatment for new data.However, class imbalance problem, which always appears in online classification brings a considerable challenge for incremental learning.The serious class imbalance problem may directly lead to a useless learning system.Cost-sensitive learning is an important learning paradigm for class imbalance problems and widely used in many applications.In this article, we propose an incremental cost-sensitive learning method to tackle the class imbalance problems in the online situation.This proposed algorithm is based on a novel cost-sensitive support vector machine, which uses the Linear-exponential (LINEX) loss to implement high cost for minority class and low cost for majority class.Using the half-quadratic optimization, we first put forward the algorithm for the cost-sensitive support vector machine, called CSLINEX-SVM*.Then we propose the incremental cost-sensitive algorithm, ICSL-SVM.The results of numeric experiments demonstrate that the proposed incremental algorithm outperforms some conventional batch algorithms except the proposed CSLINEX-SVM*.
A Path Planning Method Based on Multi- Objective Cauchy Mutation Cat Swarm Optimization Algorithm for Navigation System of Intelligent Patrol Car

The intelligent patrol car with environmental sensing and autonomous navigation is a special robot, which is mostly used for equipment defect detection in industrial areas such as the power distribution room or data center room. A path planning algorithm for the navigation system of intelligent patrol car is proposed to ensure efficient and secure navigation in the complex indoor environment, and its effect is verified by simulation and experiment. First, a patrol car platform integrated with several intelligent devices is built to achieve global localization, mapping and path planning. Then a new co-optimization on multi-objective Cauchy mutation cat swarm optimization (MOCMCSO) and artificial potential field method (APFM) is proposed to solve the multi-objective optimization problem on shortest global path length and minimum total turning-angle variation. The optimal path is written into the navigation module to drive the patrol car to move and navigate. The simulations are carried out to confirm that the method can achieve a balance between the shortest path and good path smoothness, which has less optimization time and lower fitness value compared with multi-objective cat swarm optimization (MOCSO) and multi-objective particle swarm optimization (MOPSO), and is more suitable for global path planning in indoor environment. Finally, the experiments have been carried out in the data center equipment room to verify the effectiveness and superiority over the path planning algorithm on MOCMCSO.
Experimental realization of dual task processing with a photonic reservoir computer

We experimentally demonstrate the possibility to process two tasks in parallel with a photonic reservoir computer based on a vertical-cavity surface-emitting laser (VCSEL) as a physical node with time-delay optical feedback. The two tasks are injected optically by exploiting the polarization dynamics of the VCSEL. We test our reservoir with the very demanding task of nonlinear optical channel equalization as an illustration of the performance of the system and show the recover of two signals simultaneously with an error rate of 0.3% (3%) for a 25 km-fiber distortion (50 km-fiber distortion) at a processing speed of 51.3 Mb/s.
Nutrient Diagnosis of Eucalyptus at the Factor-Specific Level Using Machine Learning and Compositional Methods

Brazil is home to 30% of the world's Eucalyptus trees. The seedlings are fertilized at plantation to support biomass production until canopy closure. Thereafter, fertilization is guided by state standards that may not apply at the local scale where myriads of growth factors interact. Our objective was to customize the nutrient diagnosis of young Eucalyptus trees down to factor-specific levels. We collected 1861 observations across eight clones, 48 soil types, and 148 locations in southern Brazil. Cutoff diameter between low- and high-yielding specimens at breast height was set at 4.3 cm. The random forest classification model returned a relatively uninformative area under the curve (AUC) of 0.63 using tissue compositions only, and an informative AUC of 0.78 after adding local features. Compared to nutrient levels from quartile compatibility intervals of nutritionally balanced specimens at high-yield level, state guidelines appeared to be too high for Mg, B, Mn, and Fe and too low for Cu and Zn. Moreover, diagnosis using concentration ranges collapsed in the multivariate Euclidean hyper-space by denying nutrient interactions. Factor-specific diagnosis detected nutrient imbalance by computing the Euclidean distance between centered log-ratio transformed compositions of defective and successful neighbors at a local scale. Downscaling regional nutrient standards may thus fail to account for factor interactions at a local scale. Documenting factors at a local scale requires large datasets through close collaboration between stakeholders.
Convolutional Neural Networks: A Comprehensive Survey

Convolutional Neural Networks(CNNs) are drifting territory in Deep Learning.These days CNNs are utilized in the greater part of the Object Recognition errands.It is used in different application areas like Speech Recognition, Pattern Recognition, Computer Vision, Object Detection and other image processing applications.CNN orders the information based on a likelihood esteem.In this paper, a detailed analysis of CNN structure and applications are demonstrated.A comparative study of different types of CNN are also depicted in this work.
Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization

The particle swarm optimization algorithm (PSO) is not good at dealing with discrete optimization problems, and for the krill herd algorithm (KH), the ability of local search is relatively poor. In this paper, we optimized PSO by quantum behavior and optimized KH by simulated annealing, so a new hybrid algorithm, named the annealing krill quantum particle swarm optimization (AKQPSO) algorithm, is proposed, and is based on the annealing krill herd algorithm (AKH) and quantum particle swarm optimization algorithm (QPSO). QPSO has better performance in exploitation and AKH has better performance in exploration, so AKQPSO proposed on this basis increases the diversity of population individuals, and shows better performance in both exploitation and exploration. In addition, the quantum behavior increased the diversity of the population, and the simulated annealing strategy made the algorithm avoid falling into the local optimal value, which made the algorithm obtain better performance. The test set used in this paper is a classic 100-Digit Challenge problem, which was proposed at 2019 IEEE Congress on Evolutionary Computation (CEC 2019), and AKQPSO has achieved better performance on benchmark problems.