Reviewerly/exHarmony huggingface dataset:
Electric Bicycle Design with Power Charging by Using Wheel Rotation

The development of the world of transportation is growing rapidly at this time with all the conveniences offered and very helpful to humans. One of them is an electric bicycle that can be used for everyday vehicles. Electric bicycles are vehicles that do not use fuel and do not cause air pollution or noise. The main components of this electric bicycle are direct current electric motors without brush, controller and generator, and batteries as a substitute for fuel. Based on the above reasons, this study designed an electric bicycle that utilizes wheel rotation as charging so that the electric bicycle can run continuously without charging to the power source. Testing tool on this bicycle consists of two parts, namely: with a load of 55 kg and no load. The maximum speed of this electric bicycle is 40 km/h. From the test results will be obtained the magnitude of speed, voltage and current.
Research on Dynamic Detection of Java Dependency Conflict

Platform independence is one of the characteristics of the Java language. The Java program is compiled to generate bytecode files, then the Java runtime environment translates the bytecode into machine instructions for the corresponding platform and runs them. The JVM can load the corresponding classes when needed. Incorrect class loading is the root cause of dependency conflicts. Static code detection can be performed on this type of dependency conflict problem, and the function call graphs under different dependency paths can be analyzed and compared to find possible conflict problems. However, due to the dynamic nature of the Java language, based on static analysis tools for analysis, there will be some problems that cannot be handled. This paper proposes a set of processes and a solution to solve this kind of problem through bytecode analysis and enhanced methods.
The Application of Underground Power Cable Anti-breakage Device based on Biological Perception

Underground power cables are an important part of modern power systems, and underground power cables are often destroyed by external forces in daily life. This paper proposes a method for preventing external force damage to underground power cables based on biological perception. It aims to identify whether there are large construction machines or suspicious persons in key parts of underground cables. For large construction machines or suspicious persons that may damage underground cables, timely warning the relevant departments to minimize the loss as much as possible.
Fuzzy Control of Temperature on SACI Based on the Emotion Recognition

This article presents a new alternative for the design of the thermal comfort system for a classroom, based on an experimental and empirical fuzzy model, since it is currently important to provide solutions on topics such as health, well-being and learning performance in students within an academic institution, and because in the scientific literature there are no mathematical models of human behavior that help to design a control algorithm for the management of comfort applications, the present work proposes an alternative of modeling and control algorithms focused on studies that establish the relationship between emotions, temperature and academic performance of students, this being essential in the technological, educational and psychological fields, in such a way that simulations of the proposal were carried out using Matlab, which allowed determining relationships between the student’s emotion and the environmental factors, in addition to showing that a temperature control can influence the emotions of a group of students.
Application of spatial distribution patterns of multi-elements in geochemical maps for provenance and transfer process of marine sediments in Kyushu, western Japan

Abstract We investigated the origin of marine sediments and their transfer by water currents using the spatial distribution patterns of multi-elements in terrestrial and marine areas of Kyushu, western Japan. Quaternary volcanic material covers Cretaceous granitic rock and Jurassic–Paleogene sedimentary rock of an accretionary complex in this region. Cluster analysis based on chemical compositions identified the origin of marine sediments from stream sediments originating from the above lithologies. Pyroclastic-flow deposits associated with caldera formation, particularly that of the Kikai Caldera (7.3 ka), were characterized by low Cr/Ti and La/Yb ratios. In contrast, the La/Yb ratio was very high in sediments derived from granitic rock and sedimentary rock of the accretionary complex. The spatial distributions of low Cr/Ti and La/Yb ratios suggest that marine sediments containing pyroclastic materials, which are found within an 80 km radius of the Kikai Caldera, were distributed on the shelf and transported northeastwards by a branch of the Kuroshio Current. The continuous distribution of the medium Cr/Ti and high La/Yb ratios from the land to the coast, slope and deep basin on the Pacific Ocean side suggests that sediments supplied from the terrestrial area were transferred by gravitational transport from the shelf to the deep basin.
DFA: Improving Convolutional Networks with Dual Fusion Attention Module

The attention mechanism based on Convolutional Neural Networks (CNNs) adaptively recalibrates the feature distribution of processed objects by modeling the corresponding attention masks. These attention masks can usually be loaded into the feature vector of each layer in a certain multiplication manner, representing different feature responses. However, these attention masks are often developed independently on spatial module or channel module and less connected with each other, resulting in relatively single feature activation and localization. To this end, we present a Dual Fusion Attention (DFA) module that can tune the distribution of feature by producing an attention mask which relying on dual fusion of spatial location and channel information. So that every corresponding feature representation can adaptively enrich its discriminative regions and minimize the influence of background noise. The attention masks are only calculated by the compressed combination of the spatial and channel descriptors, thus the realization of DFA module is lightweight enough with tiny extra computational complexity and parameters. Integrated with modern CNN models, image classification experiments demonstrate the superiority of DFA module. Specifically, based on ResNet50, our method achieves 1.16% improvement on the CIFAR100 benchmark and 0.93% improvement on the ImageNet-200 benchmark.
Robust Microscope Image Stitching Using Multiple Zooming Levels

Microscopes usually have limited field of view so it is necessary to stitch the raw microscopy images into a virtual micorscopy image which combines the advantages of high details resolution brought by high magnification and a large field of view which gives the experience of the full image of the entire region of interests. Compared with regular panorama stitching or scanning stitching, two significant differences of microscope stitching is the amount of images need to be stitched is much larger and the features of microscopy images are quite different from landscape or texts so it is hard to make use of existing distortion correction methods. Advanced microscopes use motor platform to manipulate the subject to capture a grid structure layouts image set with their positions, and then use these data in the stitching progress to get a seamless stitched image of the region of interests. Compared with existing microscopy image stitching methods relying on motor platform, we introduce a multi-zooming-level microscope stitching method to stitch images captured through portable microscopes which does not require the position information of each images. Instead of using high zooming rate images only, we capture enough low zooming rate images of the same area and stitch them into a texture skeleton. For each high zooming rate image, we match it with all the low zooming rate image to find its rough position to use in the later stitching progress. In this way, the computational complexity is reduced significantly and the texture skeleton also helps deal with the distortion caused by accumulating errors.
Adversarial BiLSTM-CRF Architectures for Extra-Propositional Scope Resolution

Due to the ability of expressively representing narrative structures, proposition-aware learning models in text have been drawing more and more attentions in information extraction. Following this trend, recent studies go deeper into learning fine-grained extra-propositional structures, such as negation and speculation. However, most of elaborately-designed experiments reveal that existing extra-proposition models either fail to learn from the context or neglect to address cross-domain adaptation. In this paper, we attempt to systematically address the above challenges via an adversarial BiLSTM-CRF model, to jointly model the potential extra-propositions and their contexts. This is motivated by the superiority of sequential architecture in effectively encoding order information and long-range context dependency. On the basis, we come up with an adversarial neural architecture to learn the invariant and discriminative latent features across domains. Experimental results on the standard BioScope corpus show the superiority of the proposed neural architecture, which significantly outperforms the state-of-the-art on scope resolution in both in-domain and cross-domain scenarios.
It all Started with Compression: Another Look at Reconciliation Mechanism

In a (Ring-)LWE-based key exchange scheme, the error reconciliation technique is usually employed to help the two parties establish the exactly same shared key. In an error reconciliation mechanism, a hint should be produced and sent by one party, which makes the key exchange scheme sequential and the two parties asymmetrical. There is an alternative approach to realize key exchange, the encryption-based key encapsulation mechanism (KEM), where a message is encrypted by one party and the other party decrypts the corresponding ciphertext to derive the shared key from the recovered message. In this paper, we try to unify the two approaches and show that the two mainstream reconciliation instantiations, Ding et. al's branch and Peikert's branch, can both be derived by just choosing a certain message in some encryption-based KEMs we constructed, in which the compressed ciphertext is just the hint and the certain message is exactly the reconciled value. This explains why reconciliation-based key exchange scheme must be sequential and have two asymmetrical parties and will bring more generalization for reconciliation mechanism. As a byproduct, a new (Ring-)LWE-based encryption structure is proposed.
ACBI: An Alternating Clustering and Bayesian Inference approach for optimizing medical intervention budget under chance constraints

Unplanned readmissions to the emergency department (ED) have been identified as a key factor resulting in a negative effect on subjects' health and healthcare resource scheduling. Often, the readmission prediction is modeled as a binary classification problem whose objective is to predict if a subject will be readmitted or not. Nevertheless, it ignores the uncertainty nature of readmission and usually results in poor prediction quality. In this paper, the problem is defined as a chance-constrained medical intervention rationing problem: at-risk subjects are targeted and given supplemental medical interventions, while the remaining subjects are treated as outpatients. The objective is to profile subjects, identify at-risk subjects, and select specific groups of subjects to which additional medical interventions are recommended, while addressing the unknown number of at-risk subjects and the unknown subjects' readmission risks. We propose a white-box approach named Alternating Clustering and Bayesian Inference (ACBI) and investigate its efficiency on a real-life readmission data set. Results are promising and show the method could lead up to a 34.42% reduction in readmission rate.
Analysis and Design of Image Encryption Algorithms Based on Interlaced Chaos

For chaotic encryption algorithm, the algorithm of generating chaotic sequence is complex, and the data is float, which directly affects the speed of encryption. In this paper, an interlaced chaotic encryption algorithm is proposed, In this algorithm, the pixel interval is encrypted by chaotic sequence, and the chaotic sequences of other pixels are obtained by XOR the chaotic values of the front and back points, Experiments show that the algorithm can encrypt and decrypt images quickly and has good effect.
Knowledge-driven joint posterior revision of named entity classification and linking

In this work we address the problem of extracting quality entity knowledge from natural language text, an important task for the automatic construction of knowledge graphs from unstructured content. More in details, we investigate the benefit of performing a joint posterior revision, driven by ontological background knowledge, of the annotations resulting from natural language processing (NLP) entity analyses such as named entity recognition and classification (NERC) and entity linking (EL). The revision is performed via a probabilistic model, called jpark, that given the candidate annotations independently identified by NERC and EL tools on the same textual entity mention, reconsiders the best annotation choice performed by the tools in light of the coherence of the candidate annotations with the ontological knowledge. The model can be explicitly instructed to handle the information that an entity can potentially be NIL (i.e., lacking a corresponding referent in the target linking knowledge base), exploiting it for predicting the best NERC and EL annotation combination. We present a comprehensive evaluation of jpark along various dimensions, comparing its performances with and without exploiting NIL information, as well as the usage of three different background knowledge resources (YAGO, DBpedia, and Wikidata) to build the model. The evaluation, conducted using different tools (the popular Stanford NER and DBpedia Spotlight, as well as the more recent Flair NER and End-to-End Neural EL) with three reference datasets (AIDA, MEANTIME, and TAC-KBP), empirically confirms the capability of the model to improve the quality of the annotations of the given tools, and thus their performances on the tasks they are designed for.
Split and Merge: Component Based Segmentation Network for Text Detection

This paper presents a novel component-based detector to locate scene texts with arbitrary orientations, shapes and lengths. Our approach detects text by predicting four components like text region (TR), text skeleton (TS), text sub-region (TSR) and text connector (TC). TR and TS can well separate adjacent text instance. TSR are merged by TC to form a complete text instance. Experimental results show that the proposed approach outperforms state-of-the-art methods on two curved text datasets, i.e. 82.42% and 82.63% F-measures were achieved for the Total-Text and CTW1500, respectively. Our approach also achieves competitive performance on multi-oriented dataset, i.e. 85.86% f-measure for the ICDAR2015 was achieved.
Research on Tibetan Phrase Classification Method for Language Information Processing

Phrases as a level of linguistic analysis, occupying a very important position in this field. Effective phrase analysis is crucial to reduce difficulty of subsequent syntactic analysis, and reduce search space of syntactic analyzer, as well as improving accuracy of machine translation. At present, research in Tibetan phrases for information processing has just started; it needs to be further developed. Based on previous studies on boundary between Tibetan phrases and Tibetan sentences, with characteristics and requirements of Tibetan information processing, in accordance with grammatical function and the principle of automatic analysis to classification of phrases processing, and specify markup codes for Tibetan phrase units in information processing are discussed in this paper.
Information Technology Act 2000 and the Potential Use of Data Analytics in Reducing Cybercrime in India

Cybercrime is increasing rapidly in this digitized world. Be it business, education, shopping, or banking transactions, everything is on cyberspace. Cybercrime covers a wide range of different attacks such as financial cybercrime, spreading computer viruses or malware, internet fraud, pornography cybercrime, intellectual property rights violation, etc. Due to increased cyber-attacks these days, the online users must be aware of these kinds of attacks and need to be cautious with their data online. Each country has their own laws for dealing with cybercrime. The different measures taken by the government of India to combat cybercrime are explained in this chapter. How the potential use of data analytics can help in reducing cybercrime in India is also explained.
Discovery of Influence between Processes Represented by Hidden Markov Models

Learning the underlying structure between processes is a common problem found in the sciences, however not much work is dedicated towards this problem. In this paper, we attempt to use the language of structure learning to address learning the dynamic influence network between partially observable processes represented as hidden Markov models (HMMs). The importance of learning an influence network is for knowledge discovery and to improve density estimation in temporal distributions. We learn the dynamic influence network, defined by this paper, by first learning the optimal distribution for each process using hidden Markov models, and thereafter apply redefined structure learning algorithms for temporal models to reveal influence relationships. This paper provides the following contributions: we (a) provide a definition of influence between stochastic processes represented by HMMs; and (b) expand on the conventional structure learning literature by providing a structure score and learning procedure to learn influence relationships between HMMs. We provide empirical evidence of the effectiveness of our method over several baselines.
Fuzzy cognitive maps with linguistic concentric fuzzy hypergraphic approach to rank the benefits of outcome based education

Outcome Based Education (OBE) is becoming the academic ringtone of the higher educational institutions of Indian nation. The incorporation of this system of education from the developed countries is intended to enhance the standards of present teaching and learning process to create graduates with attributes pertaining to the three domains of cognitive, affective and psychomotor. Inspite of the implementation of OBE, the objection against this system of education is still climbing its peak, which puts the educational experts in the state of chaos.To uphold such circumstances of commotions, it is essential to determine and rank the impacts of OBE in a systematic and scientific manner, to substantiate the efficiency of this contemporary system of education over the conventional. To make the ranking of the benefits in a highly organized and logical way, the concept of Linguistic concentric Fuzzy hypergraph is introduced in this paper. A new ranking model adjoining Fuzzy Cognitive Maps (FCM) and Linguistic Concentric Fuzzy Hypergraphs (LCFHG) is proposed in this research work to unlock the pathways of determining optimal decisions by the decision makers in the field of education.
Use of Daubechies Wavelets in the Representation of Analytical Functions

This chapter aims to use Daubechies’ wavelets as basis functions to generate analytical functions, thus being able to rewrite the Taylor series using these wavelets. This makes it possible to analyze functions with a high degree of complexity, in problems that require a high degree of precision in their solution. Wavelet analysis can be applied to practical problems that require a high degree of precision, for example, in the study and analysis of electromagnetic propagation in optical fibers, solutions of differential equations involving engineering problems, in the transmission of WiFi signals, in the treatment and analysis of biomedical images, detection of oil sources through the study of seismic signals.
Flexible Parameter Sharing Networks

Deep learning models have flourished in recent years, but it still remains a complex optimization problem in that the parameters of each layer are independent. Although this problem can be alleviated by the coefficient vector based parameter sharing methods, it has brought up a new problem: different size of parameters cannot be generated from a fixed-size global parameter template, which may truncate latent connections among parameters. In order to generate different size of parameters from the same parameter template, a Flexible Parameter Sharing Scheme (FPSS) is proposed. We exploited the asymmetric characteristic of convolution operations to resize and transform the template to specific parameters. As a generalization of the coefficient vector based methods, FPSS incorporates 2-dimension convolution operations rather than linear combinations to make transformations on the global template. Since all parameters are generated from the same template, FPSS can be viewed as building latent connections among each parameter through the global template. Meanwhile, each layer needs much fewer parameters, which will reduce the search space and make it easier to train. Furthermore, we presented two deep models as applications of FPSS, Hybrid CNN and Adaptive DenseNet, which sharing the global template to different modules and blocks. One can easily find the similar parts of a deep network through our method. Experimental results on several text datasets show that the proposed models are comparable or better to state of the art model.
Descriptive Data Analytics on Dinesafe Data for Food Assessment and Evaluation Using R Programming Language

In the digital era of the 21st century, data analytics (DA) can be highlighted as 'finding conclusions based on observations' or unique knowledge discovery from data (KDD) in form of patterns and visualizations for ease of understanding. The city of Toronto consists of thousands of food chains, restaurants, bars based all over the streets of the city. Dinesafe is an agency-based inspection system monitored by the provincial and municipal regulations and ran by the Ministry of Health, Ontario. This chapter proposes an efficient descriptive data analytics on the Dinesafe data provided by the Health Ministry of Toronto, Ontario using an open-source data programming framework like R. The data is publicly available for all the researchers and motivates the practitioners for conveying the results to the ministry for betterment of the people of Toronto. The chapter will also shed light on the methodology, visualization, types and share the results from the work executed on R.