Reviewerly/exHarmony huggingface dataset:
PARASOL: a hybrid approximation approach for scalable frequent itemset mining in streaming data

Abstract Here, we present a novel algorithm for frequent itemset mining in streaming data (FIM-SD). For the past decade, various FIM-SD methods in one-pass approximation settings that allow to approximate the support of each itemset have been proposed. They can be categorized into two approximation types: parameter-constrained (PC) mining and resource-constrained (RC) mining. PC methods control the maximum error that can be included in the approximate support based on a pre-defined parameter. In contrast, RC methods limit the maximum memory consumption based on resource constraints. However, the existing PC methods can exponentially increase the memory consumption, while the existing RC methods can rapidly increase the maximum error. In this study, we address this problem by introducing a hybrid approach of PC-RC approximations, called PARASOL . For any streaming data, PARASOL ensures to provide a condensed representation, called a Δ-covered set , which is regarded as an extension of the closedness compression; when Δ = 0, the solution corresponds to the ordinary closed itemsets. PARASOL searches for such approximate closed itemsets that can restore the frequent itemsets and their supports while the maximum error is bounded by an integer, Δ. Then, we empirically demonstrate that the proposed algorithm significantly outperforms the state-of-the-art PC and RC methods for FIM-SD.
Region Selection Model with Saliency Constraint for Fine-Grained Recognition

Learning discriminative local features is the key to improving the accuracy of fine-grained recognition. Methods based on local area labeling are very labor intensive and time costly. Existing weakly supervised methods decide the discriminative areas according to the response of the advanced feature maps. However, the details of the many small objects, which are vital for the region localization, were lost. We propose a region selection model with saliency constraint to capture the details, where the feature pyramid network is used to obtain higher resolution features with stronger semantics, and then the regions on different level feature maps that are consistently to be most informative are selected. This process enhances the model’s ability to represent details and capture small but discriminative local regions. Furthermore, a saliency extractor which shares convolutional layers with the backbone network is built to locate the object in an image, which will help to locate the discriminative regions and improve the training efficiency. We use a ranking loss function to optimize the multi-scale and multi-ratio regions which are selected by our model. Experimentally, the proposed method is implemented in datasets CUB-200-2011 and Stanford Cars and it is achieved the state-of-the-art results.
Modeling multi-objectivization mechanism in multi-agent domain

Many real-world tasks require making sequential decisions that involve multiple conflicting objectives. Furthermore, there exist multiple decision-makers, called multiagent, each of whom pursues its own profit. Thus, each agent should take into account the effect of other agents ` decisions to reach a point of compromise. For example, each agent decides with thought of other agents ` behavior in the decision of selecting the faster driving route to the destination, selecting a supermarket checkout line, and so on. For solving a sequential multi-objective decision problem, a multi-objective reinforcement learning (MORL) approach has been investigated.However, current research on MORL cannot deal with the multi-agent system where existing agents are influenced one another. Therefore, in this study, we expand the conventional multi-objective reinforcement learning by introducing the idea of multi-objectivization with dynamic weight setting of other decision-makers. In an experiment, our proposed model with dynamic weight can express the cooperative behaviors that seems to be considered other decision-makers in the multiagent environment.
Smart and fuzzy approach based on CSP for cloud resources allocation

In this study, we proposed a resource allocation approach that aims at fulfilling two main objectives. First, it equilibrates between the different cloud infrastructure particularities including load balancing, so it enhances the performance of infrastructure. Second, our approach provides a solution for the customer needs through shortening the execution time and reducing payments of the requested resources that have a dynamic nature. This paper suggests a new hybrid resources allocation approach based on three methods: multiagent system (MAS), distributed constraints satisfaction problems (DCSP), and the fuzzy logic (FL). The MAS represents the physical infrastructure. It provides an efficient management of the resources in the distribution and the heterogeneity of this infrastructure. DCSP, on the other hand, works side by side with MAS to maintain resources allocation policies in datacenters, while the FL is used to facilitate the representation of the dynamic resource values into linguistic terms (low, medium, high …) and helps the system to determine the best solution according to the criteria of customer requests. The experimental results show the efficiency of our approach in terms of load balancing, energy consumption cost, execution time, and the rate payment gain of customers.
HaGAN: Hierarchical Attentive Adversarial Learning for Task-Oriented Dialogue System

Task-oriented dialogue system is commonly formulated as a reinforcement learning problem. A reward served as a learning objective is offered at the end of the generated dialogue to help optimize the system. As fulfilling a specific task often takes many turns between the system and the user, a scalar reward signal after this long process can be delayed and sparse. To address the above problems in the reinforcement learning (RL) based task-completion system, we propose a novel hierarchical attentive adversarial network HaGAN which features a cascaded attentive generator CAG that explores a state-action space to generate a dialogue and global-local attentive discriminators GLAD to give a relevant reward at multi-scale dialogue states. Specifically, after every turn of the dialogue generation, the turn-based discriminator tests the current turn and give a local reward representing the generator’s current generating ability. When the dialogue finishes, the dialogue-based discriminator gives a global reward concerns the whole dialog. Finally, a synthesized reward computed by combining global and local reward is returned to the generator. By doing so, the generator is able to generate globally and locally fluent and informative dialogues. Through experiments on two public benchmark datasets demonstrate the superiority of our HaGAN over other representative state-of-the-art methods.
Analysis of learners' interests in a social learning environment

Social networks have arisen in the last years as powerful tools where people exchange knowledge and multimedia content. They help to share interests between groups of people with common features. Undoubtedly, there is an inherent social network in any e-learning system, where the main actors are teachers, learners and learning resources. New social environments for learning should appear to act as intelligent systems that better fit the needs of their users and especially students according to their interests, preferences, motivations, objectives and knowledge. Recently, there has been research work focused on Web Communities for learning and their formulation as Social Networks. Thus, social network analysis may be applied to infer group structures and to make intelligent recommendation systems and data mining.
Does Public Libraries’ Facebook Following Correlate with Library Usage?

Social media like Facebook are widely believed to be a useful marketing tool for organizations, likely to include public libraries. However, there has as of yet been little research to examine whether Facebook followership actually correlates to increased use of the library. This study utilizes library usage data from the Institute of Museum and Library Services (IMLS) and data describing the Facebook followings of 3,884 public libraries in the United States to statistically evaluate whether any significant relationships exist. Results indicate that Facebook followership per capita in a library’s service area is moderately-to-strongly correlated with both library visits per capita and event attendance per capita.
Time-optimal fold out of large-scale manipulators with obstacle avoidance

The autonomous operation of large-scale construction machines is of growing interest in the recent years. In this work, the optimal fold out and fold in maneuvers of the boom of a mobile concrete pump are considered. The five degrees of freedom large-scale manipulator (boom) of the mobile concrete pump is operated in an environment with several obstacles and its motion is restricted to meet a number of system and safety constraints. A trajectory planning strategy is presented in this work, which allows to obtain nearly time-optimal trajectories. The presented approach features a rather low computational complexity, which, in contrast to the state of the art, exhibits very fast calculation times. The feasibility of the proposed trajectory planning strategy is analyzed by simulation results for a realistic scenario of a real-world mobile concrete pump.
Content-based Filtering Model for Recommendation of Indonesian Legal Article Study Case of Klinik Hukumonline

Recommender system help users in getting the relevant information. For example, it may advise users on a related topic of article, or suggest complementary items to purchase. Application of recommender system is commonly found in commerce sites. Hukumonline is one of media companies and law services. The website consists of various legal content, such as news, consultation articles (they call it Klinik), data repository, event information, and journal (currently beta version). At present, Hukumonline site conduct a manual recommendation system, annotated by the content team as their daily routines. This paper describes our experiment of Content-based filtering (CBF) model for recommendation of Bahasa Indonesia to users of Hukumonline Klinik article on the site. So the manual and labor intensive process can be reduced. For this purpose, we use supervised learning method. Starting with data collection of 3,700 articles spread in 15 categories, followed by preprocessing, vector space representation, and the learning phase, which we experiment on K-Nearest Neighbor algorithm with cosine similarity as the distance metric. We use number hyperparameter K of 17. We separated ten percent of articles for our test data. For evaluation, we use K-Fold Cross Validation with K of 10. Our model generate accuracy rate of 0.75, precision rate of 0.76, recall value of 0.77, and F-Measure of 0.75.
The four factors influencing media architecture

Abstract Media architecture includes various objects (buildings, constructions) with any form of information, interactive and dynamic image technology, which are designed for information exchange of urban community. By means of digital media new architecture changes its external image and social role of public spaces of the city. The capabilities of a media object to influence the consciousness of a person and the urban environment are greatly enhanced by a wide range of visual and dynamic effects. The article deals with the aesthetic characteristics of media objects, on examples of a number of implemented projects. The aim of the work is to study factors influencing media architecture. The authors highlight the factors that drove the creation of media architecture: digital technology; media development; visualization of culture; Mass culture.
Practical Attribute-Based Conjunctive Keyword Search Scheme

Abstract To date cloud computing may provide considerable storage and computational power for cloud-based applications to support cryptographic operations. Due to this benefit, attribute-based keyword search (ABKS) is able to be implemented in cloud context in order to protect the search privacy of data owner/user. ABKS is a cryptographic primitive that can provide secure search services for users but also realize fine-grained access control over data. However, there have been two potential problems that prevent the scalability of ABKS applications. First of all, most of the existing ABKS schemes suffer from the outside keyword guessing attack (KGA). Second, match privacy should be considered while supporting multi-keyword search. In this paper, we design an efficient method to combine the keyword search process in ABKS with inner product encryption and deploy several proposed techniques to ensure the flexibility of retrieval mode, the security and efficiency of our scheme. We later put forward an attribute-based conjunctive keyword search scheme against outside KGA to solve the aforementioned problems. We provide security notions for two types of adversaries and our construction is proved secure against chosen keyword attack and outside KGA. Finally, all-side simulation with real-world data set is implemented for the proposed scheme, and the results of the simulation show that our scheme achieves stronger security without yielding significant cost of storage and computation.
Optimality of a Protocol by Feige-Kilian-Naor for Three-Party Secure Computation

In an influential work aimed at understanding the communication requirements of secure computation, Feige, Kilian and Naor introduced a minimal model of secure computation (STOC 1994). In that work, among other results, Feige et al. presented a simple protocol for the 2 input AND function. It has remained an intriguing question whether the communication and randomness used in this protocol are optimal. While previous work of Data et al. (CRYPTO 2014) showed that the communication from the two parties with inputs (Alice and Bob) to the third party who gets the output is optimal, the question of optimality for the third message in the protocol – a common reference string shared between Alice and Bob – remained open. In this note we show that in fact, this message (and hence all the randomness used in the protocol) is also optimal in the protocol of Feige et al. This improves on a previous result of Rajan et al. (ISIT 2016), which showed this optimality restricted to protocols where Alice and Bob are deterministic. Further, our result holds even if only a weak secrecy condition is required of the protocol.
Pedestrian motion recognition via Conv‐VLAD integrated spatial‐temporal‐relational network

Pedestrian motion recognition is one of the important components of an intelligent transportation system. Since commonly used spatial-temporal features are still not sufficient for mining deep information in frames, this study proposes a three-stream neural network called a spatial-temporal-relational network (STRN), where the static spatial information, dynamic motion and differences between adjunct keyframes are comprehensively considered as features of the video records. In addition, an optimised pooling layer called convolutional vector of locally aggregated descriptors layer (Conv-VLAD) is employed before the final classification step in each stream to better aggregate the extracted features and reduce the inter-class differences. To accomplish this, the original video records are required to be processed into RGB images, optical flow images and RGB difference images to deliver the respective information for each stream. After the classification result is obtained from each stream, a decision-level fusion mechanism is introduced to improve the network's overall accuracy via combining the partial understandings together. Experimental results on two public data sets UCF101 (94.7%) and HMDB51 (69.0%), show that the proposed method achieves significantly improved performance. The results of STRN have far-reaching significance for the application of deep learning in intelligent transportation systems to ensure pedestrian safety.
Feature Engineering based Approach for Prediction of Movie Ratings

The buying behavior of the consumer is grown nowadays through recommender systems.Though it recommends, still there are limitations to give a recommendation to the users.In order to address data sparsity and scalability, a hybrid approach is developed for the effective recommendation in this paper.It combines the feature engineering attributes and collaborative filtering for prediction.The proposed system implemented using supervised learning algorithms.The results empirically proved that the mean absolute error of prediction was reduced.This approach shows very promising results.
Holistic Approach for Studying Resource Failures at Scale

In large-scale distributed systems, such as data centers resource failures are the norm rather than an exception. In this paper, we propose a holistic approach to study resource failures from resource failure modelling to distributed system simulation to failure-aware scheduling algorithm design. In particular, we present (1) a simple and yet practical way to model resource failures using real-world failure traces, (2) a new distributed systems simulator and (3) two failure-aware scheduling algorithms. These scheduling algorithms are designed primarily to validate (1) and (2). Our evaluation results demonstrate the feasibility and effectiveness of our holistic approach.
Opposition based Gray Wolf Algorithm for Feature Selection in Classification Problems

With the rapid development of technology, the data is classified by distributing among various classes defined on a dataset for easier and faster access to increased data. By using classification algorithms developed for these classification processes, the data is clustered according to similar criteria. These classification algorithms are taught by training with the given training set, and then try to classify the data correctly when the test data that is not given the class is sent. Heuristic algorithms have become very popular in optimization problems in recent years. Gray Wolf Optimization (GWO) algorithm is a meta-heuristic optimization algorithm inspired by the life and hunting strategies of gray wolves in nature. In this study, Gray Wolf Algorithm was developed by using opposition based learning method for different feature selection of certain classifiers (such as KNN & SVM). The proposed improved GWO algorithm was compared with the original GWO algorithm for classification test data sets obtained from the literature.
Spreading-Based Voice Encryption by Means of OVSF Codes

This paper presents a new methodology to encrypt voice signals, in such a way that they simulate being a noise signal. The objective is to obtain a signal that does not generate suspicions about its content, while protecting the message. The process is based on the spread and scrambling of the signal through the use of OVSF (orthogonal variable spreading factor) codes. The security of the method is based mainly on the input value used for the randomization phase. From a computational cost point of view, the use of fixed-length codes allows for a shorter retrieval time compared to a similar method based on variable-length codes. Regarding the encrypted signal, its main characteristic is its high entropy (very close to the ideal value). Finally, in the recovery process, a signal identical to the original is obtained.
Ontology-based sentiment analysis and community detection on social media

Sentiment Analysis and Community Detection are two of the main methods used to analyze and comprehend human interactions on social media. These domains expanded immensely with the rise of social media, as it provided a free and ever-increasing quantity of data. Domain ontologies are of great assistance in collecting specific data, as it describes the domain's features and their existing relationships. Therefore, we utilize them in collecting subject-specific data on social media. This paper describes the framework we've designed in order to understand, in depth, the impact of a subject on social media users, and also to evaluate the difference between the Lexicon Approach and the Machine Learning Approach, by assessing the strengths and weaknesses of each. This framework also aims to deeply understand the connections that exist between users, depending on their point of view on a particular subject. The resulting framework not only analyzes textual data (by taking into account the negation and sentence POS tags), but also visual one, such as images. In order to test the framework, we chose to analyze the Brexit phenomenon by collecting ontology-based data from Twitter and Reddit, and it had some promising results.
Generalised Differential Cryptanalysis Check for Block Ciphers

This paper is based on cryptanalysis of block ciphers. With increase Differential attack [2] on encryption algorithms, it is necessary for the new upcoming algorithms to be resistant against differential cryptanalysis. The main objective the paper is, given any block cipher, we will be able to check the minimum number of Active S-Boxes for each round using the GLPK (GNU Linear Programming Kit) solver and hence find out how many minimum rounds are required by the block cipher to be resistant to Differential Cryptanalysis [2]. We will be doing Differential Cryptanalysis using mixed-integer linear programming (MILP) (suggested by Mouha N., Wang Q., Gu D., Preneel B in [1]). After that, we will be finding out the differential probability of the S-Box from the differential characteristic using the ddt table and hence find out if the given cipher is resistant against differential cryptanalysis. We will use the GLPK solver to solve the MILP equations. This paper will also contain the class diagrams required to design the system of [1] which will help us in finding out the minimum number of active s-boxes.
Quantitative ultrasound examination of peritumoral tissue improves classification of breast lesions

Quantitative Ultrasound (QUS) methods showed high suitability for classifying malignant and benign tumors based on ultrasound data from suspicious breast lesions. Apart from differences in internal structure, malignant and benign tumors have been also shown to have different effects on neighboring tissues. In our previous work we investigated the usefulness of QUS methods based on ultrasound data from surroundings of breast tumors. The present study is an attempt to answer the question of the optimal area of the surroundings to be used. The study included 116 tumors whose malignancy was determined by histopathological examination of biopsy samples. The parameters used in tumor classification were the shape parameter of the Nakagami distribution and ten texture parameters. The Linear Discriminant Analysis and the Leave-One-Out cross-validation were used to classify tumors. Classification results were assessed based on the area under the ROC curve (AUC). The best multi-parametric classifier for intra-tumor data has reached AUC = 0.82. In case of the data from the tumor surrounding area the best classification result was AUC = 0.89 and it was obtained for the surroundings range of 5 mm.