Reviewerly/exHarmony huggingface dataset:
Feature Selection Meets Unsupervised Learning

FeatureUnsupervised learning selection is a fundamental problem in learning. We are immersed in a huge quantity of spatial and temporal data, and one of the crucial questions if we want to learn efficiently is to find the key cues that are correlated with our specific learning task. Often the task itself is not supervised, that is, we do not know exactly what we are looking for. In that case, we turn again our attention towards the natural clustering and correlations that take place in the spatiotemporal world. In this chapter, we present an efficient method that performs joint classifier learning and feature selection. The approach is able to discover sparse, compact representations of input features from a vast sea of candidates, with an almost unsupervised formulation. For the main algorithm to work, we require only the following knowledge, that is to know, for each cue, whether or not a particular feature has on average stronger values over positive samples than over negatives. We call this bit of knowledge the feature signFeature signs. What is interesting is that the mathematical formulation of the problem follows directly from the clustering approach from Chap. 3 , which is in turn related to the initial graph matchingGraph matching formulation from Chap. 2 . The main feature selection idea boils down to discovering the cluster of features that fire together and are strongly correlated. The spikes in strongly intercorrelated group firings are often robust indicators of the presence of the positive class. In experiments, we show that discovering the correct set of relevant features can be done using as few as a single-labeled training sample per class—used to estimate the feature signsFeature signs. Then, using these feature signsFeature signs, we extend an initial supervised learning problem into an unsupervised learningUnsupervised learning formulation that can incorporate new data without requiring ground truth labels. Thus our method works, simultaneously, both as a feature selection mechanism and as a fully competitive classifier. Our original algorithm has certain theoretical guarantees, low computational cost, and excellent accuracy, especially in difficult cases of very limited training data. Its practical value is demonstrated on large-scale recognition experiments in video: it outperforms in speed and accuracy established feature selection approaches such as AdaBoost, Lasso, greedy forward-backward selection, and powerful classifiers such as SVM, especially in the case of limited supervised training data.
Unsupervised Learning of Graph and Hypergraph Matching

GraphGraph matching and hypergraph matchingHypergraph matching are fundamental problems in computer vision, which have been receiving a steadily growing attention in the last two decades due to the recent modern algorithms and models that are both fast and accurate. Graph and hypergraph matchingHypergraph matching are successful in tackling many real-world tasks that require 2D and 3D feature matching and alignment. Matching is a general problem in vision and graphs have always been quintessential for modeling information that has both local and longer range connections. Therefore, graph matchingGraph matching will continue to influence computer vision and robotics research, crossing over many different mathematical and computational models, including Bayesian networks, conditional and Markov random fields, spectral graph models, and, more recently, deep neural networks. While graph matchingGraph matching is limited to using pairwise relationships, hypergraph matchingHypergraph matching permits the use of relationships between sets of features of any order. Consequently, it carries the promise to make matching more robust to changes in scale, deformations, and outliers. In this chapter, we present several key ideas, computational models, and optimization methods that introduced in the literature the possibility to learn graphGraph matching and hypergraph matchingHypergraph matching in a semi-supervised or even completely unsupervised fashion. We start by describing two methods for solving graphGraph matching and hypergraph matchingHypergraph matching based on spectral matching and integer projected fixed point methodsInteger Projected Fixed Point (IPFP) method, introduced in Chap. 1 . Then, based on the same intuition (also discussed in Chap. 1 ) that correct assignments form a strong cluster of agreements, we follow up by introducing the main approach to learning as it applies to graphGraph matching and hypergraph matchingHypergraph matching, as well as the more general Maximum A Posteriori (MAP) inference problem in probabilistic graphical models (MRFs and CRFs). In the last part of the chapter, we demonstrate experimentally the effectiveness of the algorithms presented and, whenever possible, provide extensive comparisons to other top published methods. Moreover, we show that our learning approach is general and can significantly boost the performance of different state-of-the-art matching algorithms in the literature.
Deep Reinforcement Learning for Adaptive Learning Systems

In this paper, we formulate the adaptive learning problem---the problem of how to find an individualized learning plan (called policy) that chooses the most appropriate learning materials based on learner's latent traits---faced in adaptive learning systems as a Markov decision process (MDP). We assume latent traits to be continuous with an unknown transition model. We apply a model-free deep reinforcement learning algorithm---the deep Q-learning algorithm---that can effectively find the optimal learning policy from data on learners' learning process without knowing the actual transition model of the learners' continuous latent traits. To efficiently utilize available data, we also develop a transition model estimator that emulates the learner's learning process using neural networks. The transition model estimator can be used in the deep Q-learning algorithm so that it can more efficiently discover the optimal learning policy for a learner. Numerical simulation studies verify that the proposed algorithm is very efficient in finding a good learning policy, especially with the aid of a transition model estimator, it can find the optimal learning policy after training using a small number of learners.
End-to-end speech-to-dialog-act recognition

Spoken language understanding, which extracts intents and/or semantic concepts in utterances, is conventionally formulated as a post-processing of automatic speech recognition. It is usually trained with oracle transcripts, but needs to deal with errors by ASR. Moreover, there are acoustic features which are related with intents but not represented with the transcripts. In this paper, we present an end-to-end model which directly converts speech into dialog acts without the deterministic transcription process. In the proposed model, the dialog act recognition network is conjunct with an acoustic-to-word ASR model at its latent layer before the softmax layer, which provides a distributed representation of word-level ASR decoding information. Then, the entire network is fine-tuned in an end-to-end manner. This allows for stable training as well as robustness against ASR errors. The model is further extended to conduct DA segmentation jointly. Evaluations with the Switchboard corpus demonstrate that the proposed method significantly improves dialog act recognition accuracy from the conventional pipeline framework.
Exploitation of HTTP/2 Proxies for Cryptojacking

In this paper we explore the feasibility of exploiting HTTP/2 proxies to facilitate cryptojacking, an adversary scenario in which compute resources are used for mining crypto currency without the consent of the owner of the resources. In particular, we intercept the encrypted traffic passing through the proxy and inject malicious cryptojacking script, used for mining Monero cryptocurrency, into the traffic. Here, we show the security implications that are possible even in an encrypted network, while using a middle-box like proxy. We conclude by proposing some radical and neoteric approach(s) that can be used to contain the effects of cryptomining in secure HTTP/2 networks. To the best of our knowledge, this paper is one of the initial studies that demonstrate the feasibility of intercepting HTTP/2 traffic at proxy level and injecting malicious code for performing cryptojacking. Our results are important as the Internet has several open Proxies, which could perform such activities.
An Efficient Surveillance System to Detect and Prevent Elephant Intrusion in Forest Borders

Now days the conflict takes place between human and elephant is one of the biggest issue throughout the world. In Tamilnadu the biggest elephant conflict arise in villages available near nilgris, muthumalai, etc. Most of the Asian and African countries has farm land, it gets affected by elephants. The crop land are affected by elephants it is one of the biggest issue throughout the world. There some traditional methods like use of fire crackers, drum beating, construction of elephant barriers, noise-making, fencing etc. are used so far. But these methods are not effective in all the situation to prevent the crop as well as human from elephants. The proposed system is more efficient and accurate than the other systems. The vibration sensor is used to detect the movement of elephant in forest border areas. The elephant is captured through camera. The animal is found out of camera zone it is detected through the sound produced by elephant. The captured image is send to database and the SMS is sent to nearby forest officers, crop field owner and also public people. The SURF algorithm is used to match the captured image with database. Likewise audio finger print algorithm is used to detect the algorithm through voice command. Here ZigBee is used to transmit the SMS to 25 meter around the forest areas.
Understanding the Message Passing in Graph Neural Networks via Power Iteration.

The mechanism of message passing in graph neural networks(GNNs) is still mysterious for the literature. No one, to our knowledge, has given another possible theoretical origin for GNNs apart from convolutional neural networks. Somewhat to our surprise, the message passing can be best understood in terms of the power iteration. By removing activation functions and layer weights of GNNs, we propose power iteration clustering (SPIC) models which are naturally interpretable and scalable. The experiment shows our models extend the existing GNNs and enhance its capability of processing random featured networks. Moreover, we demonstrate the redundancy of some state-of-the-art GNNs in designing and define a lower limit for model evaluation by randomly initializing the aggregator of message passing. All the findings in this paper push the boundaries of our understanding of neural networks.
Extraction of Relations Between Entities from Human-Generated Content on Social Networks

In this work, we present a method to extract new knowledge from content shared by users on social networks, with particular emphasis on extraction of evolving relations between entities. Our method combines natural language processing and machine learning for extracting relations in the form of triples (subject-relation-object). The method works on domain-specific content shared on social networks: users can define a domain through a set of criteria (social networks accounts, keywords or hashtags) and they can define a limited set of relations that are of interest for the given domain. Based on this input, our method extracts the relevant triples for the domain. The method is demonstrated on content retrieved from Twitter, belonging to different domain-specific scenarios, like fashion and chess. Results are promising, in terms of both precision and recall.
An Efficient Graph Based Trust-Aware Recommendation System

With the increase in amount of information, it becomes important to build recommendation systems which can map and provide the relevant information based on the preferences, tastes and trust of users. The data clustering is applied in recommendation system to reduce the computational overhead. It has been shown empirically that with the increase in number of clusters, the rating coverage decreases monotonically. To reduce the impact of clustering, the rating prediction is computed in terms of the user similarity, trust and Jaccard similarity with each term having some coefficient to give them weights. The optimal weights are decided for each clusters which are finally used to make the recommendation. Calculation of optimal parameters is one of the expensive steps and they are fixed for each users of the clusters. In this paper, we dynamically compute the optimal parameters for each pair of users instead of using static optimal parameters for each clusters. The optimal parameters in the proposed approach are individually calculated for two users according to the ratio of Pearson, trust and Jaccard similarity between them. It helps us to reduce the complexity of the system as well as it results into increasing the accuracy of overall recommendations. Experiment results on real datasets illustrate that the our improved 2D-Graph method defeats the competing approaches based on accuracy and rating coverage.
An Improved Method of Time-Frequency Joint Analysis of Mouse Behavior for Website User Trustworthy Authentication

With the increasingly prominent problem of information security, the research on user trustworthy authentication technology becomes more and more important. Identification and authentication methods based on user’s biological behavior characteristics have attracted widespread attention due to their low cost and difficulty in imitation, which represented by mouse dynamics. This study proposed an improved method for time-frequency joint analysis of mouse behaviors for trustworthy authentication of website users. We collected the behavior data of the user’s natural mouse operation under real website environment, and analyzed the timing and spatial characteristics of the user’s mouse movements. Based on extracting the time-frequency joint distribution characteristics and spatial distribution characteristics of the temporal signals of the user’s mouse movements, we used the random forest algorithm to establish a user’s trustworthy authentication model. Mouse behavior data of five users during twenty-eight months had been used as a case study to explore the effectiveness of this method in user trustworthy authentication. The results of case analysis showed that, comparing to the original research, the method proposed in this study significantly improved the accuracy of the website user trustworthy authentication.
Research on the Framework of Intellectual Capital Information Disclosure of Listed Companies in Coastal Areas

Yuan, B.; Xia, H.; Du, E., and Yang, D., 2020. Research on the framework of intellectual capital information disclosure of listed companies in coastal areas. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 242–246. Coconut Creek (Florida), ISSN 0749-0208.Considering that the role of intellectual capital information disclosure in listed companies in coastal areas is increasingly prominent, how to evaluate the quality of intellectual capital information disclosure has become a key issue. At present, most of the current data sources on intellectual capital information disclosure research are derived from annual reports and independent intellectual capital reports. However, with the development of self media, the data sources of information disclosure should not be limited to traditional information media, but should fully consider the information sources of microblogs, company websites, and other channels. This article takes the listed companies in the coastal areas as an example and applies the Delphi method to the construction of the intellectual capital information disclosure framework. Specifically, the author uses the Delphi method to find the expert questionnaire survey, selects the suitable factor index, constructs the multisource intellectual capital information disclosure framework, which integrates the information sources of the annual report, microblog, and official website. Then, the framework was verified in detail by the Krippendorff method. The framework can more scientifically reflect the quality of intellectual capital information disclosure of the listed companies in coastal areas. The research results provide new ideas for intellectual capital information disclosure.
Automatic Detection of Malaria Infected Erythrocytes Based on the Concavity Point Identification and Pseudo-Valley Based Thresholding

Automatic malaria diagnosis requires the segmentation of the erythrocytes from the microscopic blood smears images. In this paper, the system for the analysis of the erythrocyte to detect the malaria infection has been proposed. The challenging issues are: preprocessing, foreground extraction, clump erythrocyte segmentation and infected erythrocyte detection. The non-uniform illumination and other distortions of the acquired microscopic images are corrected by using median filtering technique. The foreground regions, i.e. erythrocyte regions are segmented from the other blood components in the smears by using Otsu’s thresholding along with morphological filtering. The clump erythrocytes are segmented based on the inherent geometrical property of the concavity point detection of the clump which is independent of any geometrical deformation or image acquisition errors. Further, the segmented erythrocytes are analysed to detect the infected erythrocytes using thresholding technique based on the “pseudo-valley” concept. The experimental analyses of the proposed method are carried out on three different sets of database. An accuracy of 97.95% was observed for the erythrocyte segmentation, with an improvement in accuracy of 8.76% and 0.87% as compared to classical watershed and marker controlled watershed segmentation technique respectively. Moreover, the infected erythrocyte was detected with an accuracy of 88.57%.
SPEECH SIGNAL CRYPTOGRAPHY USING WPT

Digital speech signal is one of the most important types of data used due to the large number of computerized applications that are needed for mankind. Some computerized applications that use digital signals need a high level of security to protect the signal, and to turn it into a vague and incomprehensible signal to any third party, and that is why we have to seek a secure method of cryptography to protect the speech signal. In this research paper we will introduce a method based wavelet packet tree decomposition and reconstruction to generate an encrypted speech, the method will reorder the original speech using a secret number of decomposition levels, the obtained wavelet decomposition sizes will be used to form a speech segments, which must be rearranged in a secret order to get the encrypted speech, the introduced method will be implemented and evaluated to prove the security issues.
Refined typed genetic programming as a user interface for genetic programming

Evolutionary algorithms often have to balance local search with the evolutionary search. Restricting the language of individuals acts as local search inside a Genetic Programming (GP) approach. We propose the use of Refined Typed Genetic Programming, an enhanced hybrid of Strongly Typed Genetic Programming (STGP) and Grammar-Guided Genetic Programming (GGGP) that features an advanced type system with polymorphism and dependent refinements. This approach allows the end-user to express the restrictions on the search problem in the same language as the solution.
A Neural Approach to Discourse Relation Signal Detection

Previous data-driven work investigating the types and distributions of discourse relation signals, including discourse markers such as 'however' or phrases such as 'as a result' has focused on the relative frequencies of signal words within and outside text from each discourse relation. Such approaches do not allow us to quantify the signaling strength of individual instances of a signal on a scale (e.g. more or less discourse-relevant instances of 'and'), to assess the distribution of ambiguity for signals, or to identify words that hinder discourse relation identification in context ('anti-signals' or 'distractors'). In this paper we present a data-driven approach to signal detection using a distantly supervised neural network and develop a metric, Δs (or 'delta-softmax'), to quantify signaling strength. Ranging between -1 and 1 and relying on recent advances in contextualized words embeddings, the metric represents each word's positive or negative contribution to the identifiability of a relation in specific instances in context. Based on an English corpus annotated for discourse relations using Rhetorical Structure Theory and signal type annotations anchored to specific tokens, our analysis examines the reliability of the metric, the places where it overlaps with and differs from human judgments, and the implications for identifying features that neural models may need in order to perform better on automatic discourse relation classification.
IOT based solar energy prophecy using RNN architecture

It is the 21 st century and scientists say that by the end of this century, resources will be replenished and the only way the future generations can access energy is through renewable resources— those which are inexhaustible. One such source is sunlight, which has a guaranteed stay in the long run. The energy thus given is termed as solar energy. In the present paper it is tried to solve the issue of limited resources and their adverse effects. Since the power generated from solar energy systems is highly variable, due to its dependence on meteorological conditions, an efficient method of usage of this fluctuating but precious energy source has to come in picture. This requires the scope of reliable forecast information as the development of predictive control algorithms for efficient energy management and monitoring for residential grid connected photovoltaic systems. The paper has given an overview of different applications and models for solar irradiance and photovoltaic power prediction, including time series models based on live measured data from rooftop solar power plant located at 17.5203° N, 78.3674° E. For experimentation, data collected over four years from the solar power plant was used in order to the train machine and understand the characteristics of the solar power plant and gives the predicted energy as the result. The use of Deep Learning is done where LSTM is used for the training and keras and tensorflow are used for obtaining the result. The mean square error thus obtained is 0.015.
Pemetaan Wilayah Potensial Terhadap Penjualan Sepeda Motor Honda Menggunakan K-Means Clustering

Indragiri Hilir regency consists of land and water which are divided into 20 districts, 39 sub-districts and 197 villages. Looking at the geographical condition of Indragiri Hilir Regency, motorcycle sales companies need to know the areas that have potential for motorcycle sales. Grouping potential areas is important in increasing sales profit for the company. This study aims to help PT. Capella Dinamik Nusantara in making the decision to increase sales to be more significant, promotion and marketing techniques were more targeted towards Honda motorcycle sales in the mapped areas. The data used in this study are Honda motorcycle sales data from 2017 to 2019. Data processing in this study uses the K-Means Clustering method with 3 clusters, namely Cluster 0 (C0) Less Potential, Cluster 1 (C1) Enough Potential, Cluster 2 (C2) Has the potential to sell Honda motorcycles. The result of the grouping process with 2 iterations states that for Cluster 0 there are 5 regions, for Cluster 1 there are 3 regions, and for Cluster 2 there are 2 regions.