Reviewerly/exHarmony huggingface dataset:
Cooperative Behavior by Multi-agent Reinforcement Learning with Abstractive Communication

Reinforcement learning (RL) is a major area of machine learning that aims to develop intelligent agents that are able to adapt in random environments appropriately. In this regard, RL has shown good results when applied to complex tasks such as playing video games. In addition, in multi-agent environments, RL has shown strong potential especially with the recent developments. However, there exist few studies that focus on developing cooperation among learning agents. In general, cooperative behavior among learning agents shows higher performance than independent agent behavior. Therefore, in this research, we focus on the cooperative behavior on Predator-Prey game in a continuous space, which is widely used as one of the typical simulation of Multi-agent environment. Especially we focus on predators that their goal is to catch a prey. We propose Leader-Follower model as the organization of predators, and investigate how they cooperate with each other to achieve their goal considering the prey's policy using a model of RL. The results of our work indicate that a communication between Leader and Followers affects high performance. In addition, we acquire an interesting result as a process of achieving their goal. We investigate the movement locus of them in three cases which is different reward settings, and in each case, they take different policy depending on the reward. We visualize the movement of locus, and discuss about their cooperation and effectiveness.
Causality: An Overlooked Aspect in Anomaly Detection

This paper explains how the traditional anomaly detection techniques lack in exploring various aspects and perspectives of data analysis and lacks in understanding the nature of anomalies. This paper explains the importance and need of developing anomaly detection techniques considering the domain knowledge of the field where the anomaly detection technique is to be applied. Here we have considered the notion of causality(Granger's Causality[10]) in data to perform anomaly detection. This paper states some examples supporting the need of domain specific anomaly detection techniques. It also explains a few aspects which have not been considered in anomaly detection techniques till date.
Exploring the results of college English learning under the perspective of artificial intelligence

Abstract Based on the theory of output hypothesis and the perspective of Artificial Intelligence (AI), the purpose of present study is to verify the result of applying Virtual Reality (VR) experiment. A quantitative method has been used. 69 students as an experimental group and 68 as a control group, by random sampled, have participated this quasi-experiment. After half a year, experimental group has obtained better performance than control group in CET (College English Test). T-test shows significant differences in the result of CET total score (t=2.05, p&lt;0.05) and listening (t=2.70, p&lt;0.01) as well as writing (t=2.32, p&lt;0.05) section score. These results suggest that applying VR method brought positive result for college English learning.
Remote Data Authentication Scheme Based Balance Binary Sort Merkle Hash Tree

Now cloud storage has become the preferred way for users to store large amounts of data. In order to verify the integrity of remote data, Merkle hash tree is often used to generate data fingerprints. Aiming at the shortcomings of existing common schemes for remote data authentication based on Merkle hash tree, in this paper, based on data block index number constructs the balance binary sort Merkle hash tree, and using two-layer data nodes to shorten the authentication path. At the same time, by introducing “virtual nodes” to maintain binary sort Merkle tree balance and simplify insertion; In addition, considering the requirements of sensitive information confidentiality, by group hash for sensitive information and non-sensitive information to ensure that sensitive information is not leaked in the verification process. Theoretical analysis shows that the authentication structure can fulfill the function of data integrity audit well, and support the dynamic operation of data blocks while maintaining the balance of Merkle tree.
Low rank theory-based interframe forgery detection for blurry video

It is difficult to extract appropriate features of blurry digital video because of the low quality image for which traditional forgery detection methods are not effective. The video is often blurred due to various complicated conditions, such as limited bandwidth or low pixel complementary metal oxide semiconductor sensors. To address the feature extraction problem for blurry video, we explore one low rank theory to deblur video, which fuses multiple fuzzy kernels of key frame images by low rank decomposition. Further, we extract mean structural similarity features and information fidelity criterion features sequentially by the double detection mechanism to detect forgery points on blurred video frames. We can determine the forgery form of the video and locate the source replication area of the copy-move forgery. Experimental evaluation on two public video databases and videos that we recorded show that this method is robust in blurry video forgery detection, and compared with traditional video forgery detection methods, the efficiency is improved.
Plot Locator: An app for locating plots in the field

One of the challenges in field biology is locating previously sampled plots. The Plot Locator app was developed to assist field biologists with plot identification and location, with or without GPS or online connectivity.The Plot Locator Android app helps users locate field plots by creating a searchable database that stores study area information, such as site/plot names and numbers, distances from landmarks, optional cardinal directions and GPS coordinates, and field notes. A GPS assist and Google Maps can also be used with the app when connectivity is available. All study location data and field notes are stored in a downloadable CSV file on the user's device.The Plot Locator app provides a comprehensive searchable database of study area information, plot location information, and location aids, which are easily accessed in the field.
Single Image Haze Removal with Nonlinear Complex Diffusion Process

Hazy image enhancement is one of the challenging fields in the area of image processing because the presence of haze reduces the visibility of outdoor images. In recent years, various algorithms have been introduced to remove haze. Most of the algorithms remove haze but hardly enhance the image with better contrast. In this paper, we have proposed an effective method for removing haze from the image. The main benefit of our method is that it dehazes image as well as preserves ramp edges providing better contrast of the image. The qualitative and quantitative comparison among few states of the art methods have been performed which show that our method gives better output images compared to those methods.
Big Data in Cloud Computing and Internet of Things

The topic of big data has attracted outstanding and increasing attention in recent years. Cloud computing and Internet of Things (IoT) are two hot topics and have become parts of our daily lives. Besides, these technologies allow to collect huge amounts of data nowadays. In this study, the relationship between big data, cloud computing and IoT is investigated. The main aim of this study is to provide general information to the researchers who will work in this field.
A Novel Online Ensemble Convolutional Neural Networks for Streaming Data

In this study, we introduce an online ensemble method based on convolutional neural networks (CNNs) for streaming data. Recent work has shown that a convolution operation has been an effective way to extract features. In particular, we proposed a CNN working in an online manner as a base classifier. Then, an ensemble approach is devised to boost the performance of all base classifiers. We also propose two loss terms which can adapt to the imbalanced data stream as well as handling the forgetting issue of deep networks. The experiments conducted on a number of datasets chosen from different sources demonstrate that the proposed ensemble approach performs significantly better than a single network and some well-known online learning algorithms including additive models and Online Bagging.
Identifying Grammar Rules for Language Education with Dependency Parsing in German

We propose a method of determining the syntactic difficulty of a sentence, using syntactic patterns that identify grammatical rules on dependency parses.We have constructed a novel query language based on constraint-based dependency grammars and a grammar of German rules (relevant to primary school education) with patterns in our language.We annotated these rules with a difficulty score and grammatical prerequisites and built a matching algorithm that matches the dependency parse of a sentence in CoNLL-U format with its relevant syntactic patterns.We achieved 96% precision and 95% recall on a manually annotated set of sentences, and our best results on using parses from four parsers are 88% and 84% respectively.
Measurability of D-concurrence

Abstract An effective approach to quantify entanglement of any bipartite systems is D-concurrence, which is important in quantum information science. In this paper, we present a direct method for experimental determination of the D-concurrence of an arbitrary bipartite pure state. To do this, we show that measurement of the D-concurrence of bipartite pure state can be conversed into the measurement performed on some observables so called generalized Gell-Mann operators. We first introduce the concept of D-concurrence for a bipartite system. Then we explain the method of measuring this entanglement measure for the pure state. Finally, for clarify of the subject, we give an example consisting of two parties A and B with dimensions 3.
Image Analysis by Structural Dissimilarity Estimation

Image analysis using dissimilarity estimation methods is a crucial process in several image processing applications. For instance, it is used in frame reconstruction, pattern recognition and image inpainting. Dissimilarity estimation methods based on intensity information lead to impressive results with different image types, but failure cases still exist especially when dealing with local structural variations. In this paper, a metric for dissimilarity estimation, based on structural information, is proposed. Results show that using the proposed metric, local dissimilarities can be better estimated comparing to traditional intensity-based metrics, thus helping the analysis of images in remote sensing images.
Classification of stamps and handmade batik based on pattern recognition

Abstract Batik is a traditional cloth made by painting motifs with Indonesian culture nuance on plain cloth. It has always been used by the people, from their birth until marriage, even someone who died using batik cloth as a cover. Batik cloth made with philosophy and symbolic meaning. Every region in Indonesia has a characteristic according to the circumstances and character of the man himself. There are three ways to make batik, such as using stamps, handmade, printing. Every type of batik has different types of patterns, and there is a repetition of a pattern and a combination of several patterns. Based on the type of batik, handmade batik has the highest price, and printing batik has the lowest prices. For batik lovers, handmade batik would want to have, but not everyone knows how to characterize the stamps batik and often mistaken in the detection of the type of batik. Thus, only a few people can distinguish the differences between the three types of batik. It is difficult for ordinary people to recognize a batik pattern. The purpose of this research is to create a prototype system that can detect the patterns of batik motifs created by handmade or stamps. Cosine similarity is used in this research to recognize the pattern and classified handmade or stamps batik. The results of this research is 95% in detecting handmade and stamp batik, then 91% in detecting sum of pattern in stamps batik.
Semantic Based Question Answering System on Travel Ontology

Semantic web i.e. web with meanings attached to it is getting popular these days. One of the most common application of this technology is question answering system which takes natural language input from user in the form of questions and returns answer from open domain knowledge base such as DBpedia Freebase etc. This paper presents a regex-based approach for answering factoid questions from a Tourism QA system. The system first detects the type of question and converts it into a NL triple. Next, it generates SPARQL query based on the object and predicate identified in the first step and returns the answer to the user. The results show an overall accuracy of 80% from TREC 8 and 9 questions which are related to tourism domain.
The development 3-D augmented reality animation on radioactive concept

Abstract Augmented Reality is a technology that can provide positive perceptions of lessons, improve learning experiences, and improve learning achievement. It visualizes the phenomenon obtained by the results of laboratory research. This research was to design and develop the marker of radioactive concepts. The stages consist of determine the instructional needs and goals, develop the markers, and quality review. We generate two markers for this concept: radioactive light penetration and radioactive light beam by electric field. The markers were developed based upon the identification of instructional needs and expected to assist in the achievement of the instructional objectives. The results of the development show that AR animation markers on the radioactive concept can be read and have maximum readability of 25 cm.
Quantum-Secure (Non-)Sequential Aggregate Message Authentication Codes

Recently, the post-quantum cryptography becomes the object of attention, since quantum algorithms breaking the existing cryptosystems have been proposed and the development of quantum computers has been promoted. In fact, quantum-secure systems have been studied in both areas of public key cryptography and symmetric key cryptography. This paper studies quantum security of message authentication codes (MACs) with advanced functionality of compressing multiple tags, so-called aggregate message authentication codes (AMACs) and sequential aggregate message authentication codes (SAMACs). In this paper, we present AMAC/SAMAC schemes meeting quantum security in the model where adversaries can submit quantum queries. Specifically, we first formalize the quantum security for AMAC/SAMAC schemes. Second, we propose AMAC/SAMAC schemes satisfying the quantum security. Regarding AMACs, we show that Katz-Lindell scheme meets the quantum security. Regarding SAMACs, since the existing schemes are insecure, we newly present two generic constructions: One is constructed from quantum pseudorandom functions, and the other is constructed from randomized pseudorandom generators and (classical) pseudorandom functions.
A Tweet-Ranking System Using Sentiment Scores and Popularity Measures

Various fields look to Twitter as a marvelous repository of users’ opinions. Decision-makers rely on Twitter to examine tweets about various topics of interest. In doing so, they can determine the nature, context, and accompanying emotions of what people are discussing. The topics taken up in such discussions include current affairs, events, and products, and tweets can indicate both the intensity of discussions and how people react to them. In this paper, we propose a system that collects tweets and applies sentiment analysis to them. It allows users and decision-makers to browse an ordered and categorized list of tweets based on their sentiment and popularity measures, such as retweet and favorite. An essential advantage of our system is that it can be used more generally to explore any topic of interest with which the public is engaging on Twitter. Our findings show that using the number of retweets and the number of favorites enhances the sentiment scores and enables the highlighting of tweets’ most common opinion on a given topic.
Systematic Topology Design for Large-Scale Networks: A Unified Framework

For modern large-scale networked systems, ranging from cloud to edge computing systems, the topology design has a significant impact on the system performance in terms of scalability, cost, latency, throughput, and fault-tolerance. These performance metrics may conflict with each other and design criteria often vary across different networks. To date, there has been little theoretic foundation on topology designs from a prescriptive perspective, indicating that the current status quo of the design process is more of an art than a science. In this paper, we advocate a novel unified framework to describe, generate, and analyze topology design in a systematic fashion. By reverse-engineering existing topology designs and developing a fine-grained decomposition method for topology design, we propose a general procedure that serves as a common language to describe topology design. By proposing general criteria for the procedure, we devise a top-down approach to generate topology models, based on which we can systematically construct and analyze new topologies. To validate our approach, we leverage concrete tools based on combinatorial design theory and propose a novel layered topology model. With quantitative performance analysis, we reveal the trade-offs among performance metrics and generate new topologies with various advantages for different large-scale networks.
An Improvement Proposal of Genetic Algorithms Based on Information Entropy and Game Theory

In this paper, a hybrid genetic algorithm based on information entropy and game theory was proposed in order to cope with the defect that traditional genetic algorithm easily falls into local optimum. First of all, initial population was generated through the way that population diversity was calculated by information entropy. It, in combination with parallel genetic algorithm, performed evolutionary operation by adopting standard genetic algorithm (GA), partheno-genetic algorithm (PGA), and hybrid genetic algorithm integrating standard genetic algorithm and partheno-genetic algorithm (GA-PGA). Secondly, it operated game of complete information to population at parallel nodes in accordance with information entropy and fitness value of each sub-population, hoping to optimize the whole population. Finally, two program verification functions were introduced, i.e. Rosenbrock and Rastrigin, to analyze performance superiority of algorithm. It shows from the results that, when compared with the traditional genetic algorithm, this algorithm boasts of good optimization ability and solving precision and is characterized by high convergence rate and stability.
Efficient Compression Scheme for Large Natural Text Using Zipf Distribution

Data compression is the way toward modifying, encoding or changing over the bit structure of data in such a way that it expends less space. Character encoding is somewhat related to data compression which represents a character by some sort of encoding framework. Encoding is the way toward putting a succession of characters into a specific arrangement for effective transmission or capacity. Compression of data covers a giant domain of employments including information correspondence, information storing and database development. In this paper we propose an efficient and new compression algorithm for large natural datasets where any characters is encoded by 5 bits called 5-Bit Compression (5BC). The algorithm manages an encoding procedure by 5 bits for any characters in English and Bangla using table look up. The look up table is constructed by using Zipf distribution. The Zipf distribution is a discrete distribution of commonly used characters in different languages. 8 bit characters are converted to 5 bits by parting the characters into 7 sets and utilizing them in a solitary table. The character's location is then used uniquely encoding by 5 bits. The text can be compressed by 5BC is more than 60% of the actual text. The algorithm for decompression to recover the original data is depicted also. After the output string of 5BC is produced, LZW and Huffman techniques further compress the output string. Optimistic performance is demonstrated by our experimental result.
Secure and Robust Watermarking Scheme in Frequency Domain Using Chaotic Logistic Map Encoding

The paper proposes a secure and robust stereo image watermarking scheme based on chaotic logistic map encoding and frequency transformations. The chaotic logistic map encoding is employed to confuse both original stereo images and watermarked images. Since the high sensitivity to initial conditions introduced in the chaotic logistic map, a huge key space is provided for encoding images. Thus, the proposed system has a strong secure capability to resist brute-force and statistical attacks, in which the evaluation goes through various analysis methods such as sensitive key analysis, correlative adjacent pixels analysis. Moreover, to enhance the robustness of the watermarked image, the discrete cosine transform (DCT) and the singular value decomposition (SVD) are exploited for this purpose. Based upon the properties of the SVD and utilizing the advantages of the DCT, a watermarked image is embedded in the singular value. Performance evaluations show that the proposed watermarking scheme for stereo images is highly secure as well as strongly robust against different kinds of attacks.
From Global to Local State, Coalgebraically and Compositionally

We describe a type theory or metalanguage for constructing and reasoning about higher-order programs with global and local state, and its categorical model. This provides an encapsulation primitive for abstracting global state and making it local to an object, so that it is passed only between its invocations. Our calculus and its semantics extend the interpretation of lambda-terms in a Cartesian closed category with a monoidal action on a category of evaluation contexts — the sequoid — which is dual to the action of the function type. This gives an interpretation of a new type constructor which allows the representation of both global state — via “state-passing-style” interpretation which uses it to represent output states — and local state, via encapsulation, which corresponds to the unique map into a final coalgebra for the sequoid. This provides the equational theory of our calculus with a coinduction rule for proving equivalence between objects with local state. We show that this theory is sound and complete with respect to the categorical semantics by constructing a term model and we show that it is consistent by giving a concrete example based on a category of games and strategies previously used to interpret general references.
Research on Construction Practice of Assembled Substation——A Case in Anhui Xuancheng Xinhang 110kV Substation Project

The assembled substation is a substation construction that is "resource-saving, environment-friendly, and industrialized" through prefabrication and on-site installation. Taking the Xuancheng Xinhang 110kV substation project in Anhui as an example, this paper studies the standardization development of assembled industrial buildings and substations at home and abroad, points out the problems existing in the construction and buildingpractice of the assembled substation, and puts forward relevant suggestions for these problems.