Reviewerly/exHarmony huggingface dataset:
Ship tracking method for resisting similar shape information under satellite videos

In maritime management, satellite videos with a wide imaging range can provide beneficial data supplements for ground monitoring systems and improve the comprehensive management level. But there are few ships detailed features and a large amount of similar shape information in ship tracking by satellite videos, which lead to tracking drift even false tracking. A ship tracking method is proposed to resisting the interference information of ship-similar shapes. First, the mutual convolution Siamese network is used to calculate the similarity between the object template and the search area to improve the significance of the ship in the feature map. Second, the region proposal network with hierarchical double regression is proposed. Double regression of candidate boxes according to intersection over union (IOU) thresholds can reduce the influence of the non-rigid motion of the water surface on tracking and improve the IOU between candidate boxes and object ships. Finally, the object discrimination model is designed to enhance the screening ability of candidate boxes using deep and shallow classification head fusion and online training. To verify the effectiveness of the proposed method, tests were conducted on Jilin-1 satellite videos and public remote sensing data sets. The results show that, compared with the existing methods, the proposed method can effectively reduce the interference of similar shape information to ship detail features, and the tracking accuracy and success rate are 83.4% and 58.9%, respectively.
Focusing on eigendynamic effects promotes students’ performance in complex problem solving: A log-file analysis of strategic behavior

Human beings are frequently confronted with complex systems that change dynamically over time. Correspondingly, so called eigendynamic effects are an important feature in complex problem solving (CPS) tasks. Eigendynamic effects describe increases or decreases in outcome variables over time without the individual's intervention. Identifying the eigendynamic effects from the start while exploring the system is reasonably an effective strategic behavior when systems are comprised of different effect types (i.e., eigendynamic effects alongside other effects). In this study, we hypothesized that the strategic behavior to identify eigendynamic effects early when exploring the system was related to CPS performance and mediated the relationship between intelligence and CPS performance. In a sample of N = 262 German high school students, the investigated strategic behavior predicted CPS performance when exploring the system (knowledge acquisition phase) and when controlling the system (knowledge application phase) in single CPS tasks and in a set of CPS tasks comprising of different effect types. In mediation models the strategic behavior to identify eigendynamic effects early mediated the intelligence-CPS performance relation in the knowledge acquisition phase, but not in the knowledge application phase. We discuss these findings in the light of how students interact with complex and dynamic systems, pointing to the importance of this strategic behavior for the relationship between intelligence and CPS. • We investigated students' behavior as they worked on complex problem solving tasks. • We focused on the strategic behavior to explore eigendynamics as an initial step. • The strategic behavior correlated with higher complex problem solving performance. • Intelligence manifested itself in the strategic behavior while exploring a problem. • The strategic behavior mediated the intelligence/complex problem solving relation.
Multi‐objective reliability‐based workflow scheduler: An elastic and persuasive task scheduler based upon <scp>modified‐flower</scp> pollination algorithm in cloud environment

SUMMARY This research article formulates contemporary approach named multi‐objective reliability‐based workflow scheduler. Numerous strategies have been proposed in the past to prioritize and map the tasks to cloud resources. Though the recent studies lead to efficient solutions however they are restrained in terms of performance due to lack of resource consideration based on utilization rate and reliability index. It is crucial to consider reliability parameter while mapping tasks onto the virtual machines and not just the reliability value, but the cost incurred must also be minimized. To this end, the proposed strategy has been categorized into four modules, (i) scrutiny of reliable VMs, (ii) task ranking, (iii) optimizing the task re‐ordering using flower pollination optimization, and (iv) task mapping onto the VM. It intends to map task onto the most suitable machine in terms of makespan, efficiency, and incurred cost. In the experimental setup, four scientific workflows have been considered namely, LIGO, Genome, Cybershake, and Montage, they have been tested on the proposed approach while making comparison with the existing approaches namely FPA, GWO, and GA. The simulation results justified the claims by allocating resources to the cloudlets efficiently and stabilizing all the aforementioned parameters by attaining performance measures adequately.
Shaping and Dilating the Fitness Landscape for Parameter Estimation in Stochastic Biochemical Models

The parameter estimation (PE) of biochemical reactions is one of the most challenging tasks in systems biology given the pivotal role of these kinetic constants in driving the behavior of biochemical systems. PE is a non-convex, multi-modal, and non-separable optimization problem with an unknown fitness landscape; moreover, the quantities of the biochemical species appearing in the system can be low, making biological noise a non-negligible phenomenon and mandating the use of stochastic simulation. Finally, the values of the kinetic parameters typically follow a log-uniform distribution; thus, the optimal solutions are situated in the lowest orders of magnitude of the search space. In this work, we further elaborate on a novel approach to address the PE problem based on a combination of adaptive swarm intelligence and dilation functions (DFs). DFs require prior knowledge of the characteristics of the fitness landscape; therefore, we leverage an alternative solution to evolve optimal DFs. On top of this approach, we introduce surrogate Fourier modeling to simplify the PE, by producing a smoother version of the fitness landscape that excludes the high frequency components of the fitness function. Our results show that the PE exploiting evolved DFs has a performance comparable with that of the PE run with a custom DF. Moreover, surrogate Fourier modeling allows for improving the convergence speed. Finally, we discuss some open problems related to the scalability of our methodology.
Identification, Location, and Remediation of Incipient Fault and Failure Conditions Using Waveform Monitoring and Automated Analysis

Faults and failures are inevitable on power systems. Many power system faults are singular, one-off events – for example a danger tree which falls and tears down a conductor, or a squirrel which spans the bushings of a single-phase transformer and falls to the ground. Over decades, protective engineers have developed strategies for dealing with these events and minimizing their effect on customers. Some faults, however, including some faults which are cleared by system protection and self-heal, are not one-off events. These faults are caused by certain environmental conditions which remain present even after the fault is cleared, and as a result have a chance to recur in the future, should similar conditions manifest. For example, wind might cause vegetation to push two conductors together, but only when it blows from a certain direction at a certain wind speed. In this scenario, protection might operate correctly for each fault event, but the series of faults is indicative of an incipient fault, because the latent condition persists on the system, and the possibility of future faults remains.Research at Texas A&M University has documented numerous cases of latent, incipient fault conditions. Drawing from a database of over 1,500 circuit-years of operational experience on real-world systems, researchers have developed a series of technologies which automatically identify faults with similar characteristics and present actionable information to utility personnel. In many cases, this enables utilities to find and remediate incipient fault conditions before they escalate to final failure. This paper presents illustrative case-studies from actual field examples.
Investigating the Value of Subtitles for Improved Movie Recommendations

Collaborative filtering (CF) is a highly effective recommendation approach based on preference patterns observed in user-item interaction data. Since pure collaborative methods can have certain limitations, e.g., when the data is sparse, hybrid approaches are a common solution, as they are able to combine collaborative information with side-information (SI) about the items. In this work, we explore the value of subtitle information for the problem of movie recommendation. Differently from previously explored types of movie SI, e.g., titles or synopsis, subtitles are not only longer, but also contain unique information that may help us to predict more accurately if a user will enjoy a movie. To assess the usefulness of subtitles, we propose a technical framework named SubtitleCF that combines user and item embeddings derived from interaction data and SI. The subtitles may be embedded in different ways, e.g., Latent Dirichlet Allocation (LDA) and neural techniques. Computational experiments with a framework instantiation that relies on Bayesian Personalized Ranking (BPR) as industry-strength method for item ranking and different text embedding methods demonstrate the value of subtitles in terms of prediction accuracy and coverage. Moreover, a user study (N=247) reveals that the information contained in subtitles can be leveraged to improve the decision-making processes of users.
Output-targeted baseline for neuron attribution calculation

Attribution methods explaining a particular decision for a given convolutional neural network (CNN) have gained a lot of attention over the last few years. Among them, approximation methods of Shapley values are considered to be better ways of assigning attribution scores such that several desirable axioms are satisfied. Nevertheless, these attribution scores may still be misleading or inaccurate due to the inappropriate selection of a baseline which is necessary to apply Shapley values to CNNs. Previous baseline studies have focused on developing a generic baseline selection method for all approximation methods; however, we find that designing a baseline under the essence of the selected approximation method itself produces better results than generic ones. With this observation, we propose two primal baseline properties for Aumann–Shapley-based attributions and design a general objective function of generating a baseline iteratively by gradient descent. To increase efficiency, we further reduce the objective function into a quadratic optimization problem where the gradients only need to be calculated once. We show that our method produces better attribution results than several state-of-the-art baseline selections and attribution methods on both qualitative and quantitative experiments.
Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation

A recommendation engine that relies solely on interactions between users and items will be limited in its ability to provide accurate, diverse and explanation-rich recommendations. Side information should be taken into account to improve performance. Methods like Factorisation Machines (FM) cast recommendation as a supervised learning problem, where each interaction is viewed as an independent instance with side information encapsulated. Previous studies in top-K recommendation have incorporated knowledge graphs (KG) into the recommender system to provide rich information about the relationships between users, items and entities. Nevertheless, these studies do not explicitly capture the preference of users for the side information. Furthermore, some studies explain the recommendation, but there is no unified method of measuring explanation quality.
Development and Teaching Application of Japanese Corpus Based on Big Data

The establishment of English and French corpora has provided great convenience for language application and research. However, in the field of Japanese language research, the construction of Japanese corpus has been slow due to the limitation of Japanese markup format. This paper discusses the scientificity, rationality, feasibility, and construction of “microcorpus” and “specialized corpus” for the application of Japanese corpus in Japanese language teaching. Textual corpus is a corpus that contains all text but no media such as audio and pictures, and it is mostly used in teaching. The Japanese language corpus is a corpus created by analyzing the existing Japanese classroom teaching situation in response to the current reform of Japanese language teaching mode. It is important for teaching Japanese in colleges and universities because it can help students to learn independently and improve their learning initiative and motivation. The teaching-based text corpus is a small corpus for Japanese language teaching and learning, which is derived from the teaching process. The corpus can be created independently on an ordinary PC machine through software, and the collected corpus can be imported into the corpus and used, which provides another way to improve students’ interest and learning ability on their own. Based on the search software, this paper selects representative novels from the Aozora Library and creates a simple corpus to serve the teaching of Japanese interpretation, which improves the teaching effect. At the same time, this paper discusses the boundaries and difficulties of individual self-built corpora with a view to making breakthroughs and progress in future research and development.
Apeak-CG: Automatically predicting emotion based dynamic multi-form knowledge fusion conversation generation

Multi-turn conversations commonly face the challenges of adequately expressing emotions and enriching or expanding the content of the dialogues. The existing knowledge-based dialogue model can not avoid the problem of too rational response or ignoring appropriate expression of emotion. However, the precise emotions of one conversational bot are crucial to improve user satisfaction. In our work, we design an auto-emotion prediction for a chatting machine that considers six common fine-grained emotions and automatically assigns corresponding emotion for responses according to historical emotion lines and contexts. Moreover, we creatively fuse the multi-forms of knowledge dynamically to improve the response information, which can take advantage of both abundant unstructured latent knowledge in the documents and the information expansion capabilities of the structured knowledge graph. Notably, in the aspect of unstructured knowledge, a novel virtual knowledge base is constructed and a new delay updating algorithm is proposed. We present a new dialogue generation model, Automatically predicting emotion based dynamic multi-form knowledge fusion Conversation Generation (Apeak-CG). Both automatic and human evaluation results indicate the effectiveness of our method in terms of emotional coherence and informativeness.
Helmet Detection using Machine Learning and Automatic License Plate Recognition

Bike mishaps have been quickly developing during that time in numerous nations. In India in excess of 37 million individuals utilize bikes. Subsequently, it is important to foster a framework for programmed location of protective cap wearing for street wellbeing. Subsequently, a custom item location model is made utilizing a Machine learning based calculation which can recognize Motorcycle riders. On the location of a Helmetless rider, the License Plate is separated and the License Plate number is perceived utilizing an Optical Character Recognizer. This Application can be executed continuously involving a Webcam or a CCTV as info.
Adaptive Fading-Memory Receding-Horizon Filters and Smoother for Linear Discrete Time-Varying Systems

In this paper, an adaptive fading-memory receding-horizon (AFMRH) filter is proposed by combining the receding-horizon structure and the adaptive fading-memory method. In the recent finite horizon, state error covariance is adapted with an adaptive fading factor; then the process noise covariance matrix adaption is realized by adjusting the properties of systems. An AFMRH fixed-lag smoother is also proposed by combining the proposed AFMRH filtering algorithm and a Rauch–Tung–Striebel smoothing algorithm to improve the estimation accuracy. Because the proposed AFMRH filter and smoother are reduced to the optimal receding-horizon (RH) filter and smoother when all measurements have the same weight, the proposed adaptive RH estimators could provide a more general solution than the optimal RH filter and smoother. To reduce the complexity and improve the estimation performance of the proposed RH estimators, an adaptive horizon adjustment method and a switching filtering algorithm based on an adaptive fading factor are also proposed. In particular, the proposed adaptive horizon adjustment method is designed to be computationally efficient, which makes it suitable for online and real-time applications. Through computer simulation, the performance and adaptiveness of the proposed approaches were verified by comparing them with existing fading-memory approaches.
Topological structural analysis based on self-adaptive growing neural network for shape feature extraction

Self-organizing neural network has been widely used to extract the topological structure of image shape because it features topological preservation, dynamic adaptation, clustering and dimensionality reduction. However, it is difficult to automatically extract the topology structure with an appropriate number of neurons from the complex and diverse data. In this paper, a novel self-organizing neural network called self-adaptive growing neural network (SAGNN) is proposed, which can generate an appropriate number of neurons autonomously according to the size of input data without setting the total number of neurons in advance. Firstly, Similarity Evaluation Index (SEI) is proposed to evaluate the similarity between the output network and the input space. Then, on the basis of growing neural gas (GNG) network, the SEI as a network growth control condition is introduced into the SAGNN, so that the SAGNN can grow neurons on demand until the expected quantization error is not significantly improved. Experiments involving both artificial and real data sets show that SAGNN can extract the topological structure from the unsupervised data without any prior conditions (including the appropriate number of neurons).
Revocable Policy-Based Chameleon Hash for Blockchain Rewriting

Abstract Policy-based chameleon hash is a useful primitive for blockchain rewriting systems. It allows a user to create a mutable transaction associated with an access policy, whereas a modifier who possesses sufficient rewriting privileges from a trusted authority satisfying the access policy can rewrite the mutable transaction. However, it lacks a revocation mechanism. The modifiers can always rewrite the mutable transactions even if their given rewriting privileges are compromised. In this work, we introduce revocable policy-based chameleon. The property of revocation allows some modifiers’ rewriting privileges to be revoked, regardless of whether their rewriting privileges are compromised or not.
Modified Multi-Key Fully Homomorphic Encryption Scheme in the Plain Model

Abstract Multi-key fully homomorphic encryption (MFHE) supports arbitrary meaningful computations on encrypted data under different public keys even without access to the secret key, which is well tailored for the secure multiparty computation scenarios. Based on the Gentry–Sahai–Waters scheme (a single-key FHE in Crypto 2013) with the underlying learning with errors problem, MW16 scheme (Eurocrypt 2016) utilizes the method of ‘linear combination procedure’ (LCP) as a subroutine to construct the auxiliary information for the expanded ciphertexts of MFHE scheme. However, every party shares a common random string (CRS) to be distributed by a trusted setup, which is unpractical. Meanwhile, the noise in the auxiliary information is too much compared with the one in fresh ciphertexts. In this paper, we propose a modified MFHE scheme in the plain model, i.e. without CRS, to enhance the practicability of MFHE. Specifically, every involved party generates his own public key independent on a CRS. Then a potential improvement on the LCP is developed to provide auxiliary information, which largely reduces the noise and leads to a smaller modulus for our MFHE. Furthermore, the feasibility of our proposal is also proved by theoretical performance comparisons.
Semantic-Based Hybrid Query Reformulation for Biomedical Information Retrieval

Abstract Query reformulation is a well-known technique intended to improve the performance of Information Retrieval Systems. Among the several available techniques, Query Expansion (QE) reformulates the initial query by adding similar terms, drawn from several sources (corpus, knowledge resources), to the query terms in order to retrieve more relevant documents. Most QE methods are based on the relationships between the original query term and candidate terms (new terms) in order to select the most similar expansion terms. In this paper, we suggested a new hybrid query reformulation through QE and term re-weighting techniques. The suggested approach aimed to demonstrate the effectiveness of QE with a semantic selection of candidate terms according to the specificity of original query terms in the improvement of retrieval performance. To this end, we exploited both relationships defined by knowledge resources and the distributed semantics, recently revealed by neural network analysis. For term re-weighting, we proposed a new semantic method based on semantic similarity measure that assigns a weight to each term of the expanded query. The conducted experiments on OHSUMED and TREC 2014 CDS test collections, including long and short queries, yielded significant results that outperformed the baseline and state-of-the-art approaches.
BERTOEIC: Solving TOEIC Problems Using Simple and Efficient Data Augmentation Techniques with Pretrained Transformer Encoders

Recent studies have attempted to understand natural language and infer answers. Machine reading comprehension is one of the representatives, and several related datasets have been opened. However, there are few official open datasets for the Test of English for International Communication (TOEIC), which is widely used for evaluating people’s English proficiency, and research for further advancement is not being actively conducted. We consider that the reason why deep learning research for TOEIC is difficult is due to the data scarcity problem, so we therefore propose two data augmentation methods to improve the model in a low resource environment. Considering the attributes of the semantic and grammar problem type in TOEIC, the proposed methods can augment the data similar to the real TOEIC problem by using POS-tagging and Lemmatizing. In addition, we confirmed the importance of understanding semantics and grammar in TOEIC through experiments on each proposed methodology and experiments according to the amount of data. The proposed methods address the data shortage problem of TOEIC and enable an acceptable human-level performance.