Reviewerly/exHarmony huggingface dataset:
Interoperating data-driven and model-driven techniques for the automated development of intelligent environmental decision support systems

This paper proposes an Intelligent Decision Support (IDS) methodology based on the integration of a data-driven technique —Case Based Reasoning (CBR)— and model-driven technique —Rule Based Reasoning (RBR)— for control, supervision and decision support on environmental systems. Design stage of control and decision support tools for environmental systems tend to be somehow ad-hoc regarding to the nature of the processes involved. Hence, an automated approach is proposed for the sake of scalability to different types and configurations of environmental systems. The proposed hybrid scheme provides complementarity in the set-point generation for the process controllers, increasing the reliability of the Intelligent Process Control System (IPCS), which is the core component of the IDS methodology. Furthermore, the IDS methodology is flexible and dynamic enough to be able to cope with the dynamic evolution of environmental systems, learning from its relevant experienced situations. The approach presented has been implemented in a real facility.
Anonymous Privacy Protection Algorithm Based on Sensitive Attribute Classification

At present, most personalized privacy protection algorithms can be divided into two methods for protecting sensitive attributes. One is to set different thresholds for different sensitive attributes; the other is to generalize sensitive attributes, and replace the original sensitive attribute values with lowprecision generalized values. The anonymized data of the two methods has the risk of sensitive information leakage or large information loss, as well as the problem of data availability. To this end, a personalized (α, p, k) anonymous privacy protection algorithm is proposed. According to the sensitive level of the sensitive attribute, different anonymous methods are adopted for the sensitive values of each level in the equivalence class, so as to realize personalized privacy protection of the sensitive attribute. Experiments show that this algorithm has an approximate time cost and lower information loss than other personalized privacy protection algorithms.
Phrases based Document Classification from Semi Supervised Hierarchical LDA

Different state-of-the-art document classification models are based on bag of words model such as Support Vector Machine, Naive Bayes and Neural Network. These models do not contain the word's semantic meaning. In any document, meaning of the words can be demonstrated by their presence and vicinity of particular words. Bag of Phrases is one technique by which author can preserve the vicinity of the words. This model is proficient to distinguish the capability of phrases in document classification. In this paper author proposes Semi-Supervised Hierarchical Latent Dirichlet Allocation (SSHLDA) model which uses the outstanding theme to isolate the phrases from the corpus. The proposed model incorporates the phrases in vector space model for document classification. Experiment performs on the organic document with Bag of Phrase technique and show the effective classification. When compare with state-of-the-models.
An Intent Taxonomy for Questions Asked in Web Search

We present a new, multi-faceted taxonomy to classify questions asked in web search engines based on the question intent, types of entities mentioned, types of question words, and granularity of the expected answer. Built based on the inspection of 1,000 real-life questions issued to a web search engine, the taxonomy reflects the recent search behavior of users and enables deep understanding of user intents, goals, and expected answers. This taxonomy is more fine-grained than previous query taxonomies, and is designed with the ultimate goal of reducing the inherent ambiguity in determining the intent of questions. In addition, we describe the formal procedure for conducting an editorial study of the taxonomy including its evaluation. The adopted procedure aims to increase assessor agreement without incurring too much overhead. Our results demonstrate that, despite being more fine-grained, the proposed intent categories result in higher agreement between assessors compared to an existing, commonly used taxonomy.
Adaptive classification using incremental learning for seismic-volcanic signals with concept drift

The accurate labeling of the volcanic earthquake signals is a crucial task in order to estimate the increase in volcanic activity (among other parameters), which contributes to determine a state of volcanic unrest (as a possible precursor of an eruption). Several automatic classification approaches have been proposed in different computer science areas in order to complement the exhaustive human task of manually labeling volcano-seismic events. However, most of these approaches have been designed under the assumption of stationarity; that is, by discarding the changing nature of the volcanic phenomenon that evolves over time. In this work, an adaptive classification strategy based on incremental learning is proposed, which identifies and learns concept drifts of data streams coming from seismic recordings. The proposed strategy uses a classifier ensemble in order to handle recurrent states and classifies data even while true labels are not yet available. Unlike usual experimental protocols in the state–of–the–art, we carried out experiments with seismic data from Villarrica volcano, considering the chronological order of the data during a long period (more than 4 years of registration) to achieve the detection of drifts. The results show that the proposed classification strategy satisfactorily counteracts the changes, in contrast to standard classifiers trained within a traditional learning scheme. The proposed strategy keeps a stable classification accuracy and achieves a reduction of up to 0.28 of the absolute error in episodes of abrupt changes.
A Multilevel Single Stage Network for Face Detection

Recently, tremendous strides have been made in generic object detection when used to detect faces, and there are still some remaining challenges. In this paper, a novel method is proposed named multilevel single stage network for face detection (MSNFD). Three breakthroughs are made in this research. Firstly, multilevel network is introduced into face detection to improve the efficiency of anchoring faces. Secondly, enhanced feature module is adopted to allow more feature information to be collected. Finally, two-stage weight loss function is employed to balance network of different levels. Experimental results on the WIDER FACE and FDDB datasets confirm that MSNFD has competitive accuracy to the mainstream methods, while keeping real-time performance.
Service-Aware Access Control Procedure for Blockchain Assisted Real-Time Applications

The design of distributed ledger, Asymmetric Key Algorithm (AKA) blockchain systems, is prominent in administering security and access control in various real-time services and applications.The assimilation of blockchain systems leverages the reliable access and secure service provisioning of the services.However, the distributed ledger technology's access control and chained decisions are defaced by pervasive and service unawareness.It results in degrading security through unattended access control for limited-service users.In this article, a service-aware access control procedure (SACP) is introduced to address the afore-mentioned issue.The proposed SACP de nes attended access control for all the service session by identifying the users and service provider availability.The distributed nature of the ledger systems and classi cation tree learning are combined to determine unattended access.The sole access is determined by summarizing the closed and open access requests and the service provider's availability and integrity checks.In this process, the learning process classi es the secured access request and completed the integrity checks of the current and previous service dissemination.This classi cation-based access administration reduces the service disconnections and false access rate of the applications.
2-designs and redundant syndrome extraction for quantum error correction

Imperfect measurement can degrade a quantum error correction scheme. A solution that restores fault tolerance is to add redundancy to the process of syndrome extraction. In this work, we show how to optimize this process for an arbitrary ratio of data qubit error probability to measurement error probability. The key is to design the measurements so that syndromes that correspond to different errors are separated by the maximum distance in the signal space, in close analogy to classical error correction codes. We find that the mathematical theory of 2-designs, appropriately modified, is the right tool for this. Analytical and simulation results for the bit-flip code, the 5-qubit code, and the Steane code are presented. The results show that design-based redundancy protocols show improvement in both cost and performance relative to conventional fault-tolerant error-correction schemes in situations, quite important in practice, where measurement errors are common. In the near term, the construction of a fault-tolerant logical qubit with a small number of noisy physical qubits will benefit from targeted redundancy in syndrome extraction.
Creating a Multifarious Cyber Science Major

Existing approaches to computing-based cyber undergraduate majors typically take one of two forms: a broad exploration of both technical and human aspects, or a deep technical exploration of a single discipline relevant to cybersecurity. This paper describes the creation of a third approach--a multifarious major, consistent with Cybersecurity Curricula 2017, the ABET Cybersecurity Program Criteria, and the National Security Agency Center for Academic Excellence--Cyber Operations criteria. Our novel curriculum relies on a 10-course common foundation extended by one of five possible concentrations, each of which is delivered through a disciplinary lens and specialized into a highly relevant computing interest area serving society's diverse cyber needs. The journey began years ago when we infused cybersecurity education throughout our programs, seeking to keep offerings and extracurricular activities relevant in society's increasingly complex relationship with cyberspace. This paper details the overarching design principles, decision-making process, benchmarking, and feedback elicitation activities. A surprising key step was merging several curricula proposals into a single hybrid option. The new major attracted a strong initial cohort, meeting our enrollment goals and exceeding our diversity goals. We provide several recommendations for any institution embarking on a process of designing a new cyber-named major.
Secure Transmission via Block Cipher Strategy using Chaotic Systems and High Gain Observers

This paper proposes an advanced secure transmission of information method founded on synchronizing chaotic systems with an optimal high gain observer. The challenging idea investigated in this work is to insulate the encryption process from the synchronization operation relying on two chaotic systems, where the two systems are part of the encryption process and generate pseudorandom sequence of signals, which will be treated with Exclusive-OR operations to form the cipher relying on a carefully chosen secret key. After that, the dynamic systems will acts as transmitters. Finally, the decryption operation is carried out by replacing the chaotic system by its estimation one using the same secret key. Numerical simulation based on a QR (“quick response”) code encryption/decryption is conducted as an illustration to demonstrate the outstanding performances of the designed transmission method.
WITHDRAWN: Bloom hash probabilistic data structure and Benaloh Cryptosystem for secured data storage and access control in cloud

Cloud service providers are responsible for these services to satisfy user requirements. The cloud users need SLAs to indicate their requirements related to the quality of service and security. A novel technique called Bloom Hashing Data Storage based Benaloh Cryptographic Data Accessing (BHDS-BCDA) is introduced for providing better service-level agreement with minimal space complexity and higher authentication accuracy. BHDS-BCDA technique contains three processes: registration and key generation, secure data storage, and access control to provide an efficient service level agreement between cloud user and server. In the registration, the cloud user details are registered to the cloud server. After the registration, the cloud server generates the public key and private key for every registered cloud user using Benaloh Cryptographic function. Whenever the cloud user needs to store the cloud server's data, they verify the user as an authorized or unauthorized user. The cloud server uses the Bloom hash probabilistic data structure to store the data in a cloud server with minimum space complexity for the authorized users. When a cloud user requests the data, the cloud service provider encrypts it and transmits it to the requested cloud user. From that point onward, the cloud user decrypts the data and obtains the original data. This, in turn, helps to improve the service level agreement during the data storage and access control. Experimental evaluation is carried out on space complexity, authentication accuracy, data confidentiality, and computation time with the number of cloud user requests. The outcomes confirmed that the presented BHDS-BCDA technique is better in higher authentication accuracy, data confidentiality rate with minimum space complexity, and computation time. Based on the observations, the BHDS-BCDA technique is more efficient than the other methods for handling secure data storage and access in the cloud.
Improved Lower Bounds for Permutation Arrays Using Permutation Rational Functions

We consider rational functions of the form V(x)/U(x), where both V(x) and U(x) are relatively prime polynomials over the finite field \(\mathbb {F}_q\). Polynomials that permute the elements of a field, called permutation polynomials (PPs), have been the subject of research for decades. Let \({\mathcal {P}}^1(\mathbb {F}_q)\) denote \(\mathbb {F}_q\cup \{\infty \}\). If the rational function, V(x)/U(x), permutes the elements of \({\mathcal {P}}^1(\mathbb {F}_q)\), it is called a permutation rational function (PRF). Let \(N_d(q)\) denote the number of PPs of degree d over \(\mathbb {F}_q\), and let \(N_{v,u}(q)\) denote the number of PRFs with a numerator of degree v and a denominator of degree u. It follows that \(N_{d,0}(q) = N_d(q)\), so PRFs are a generalization of PPs. The number of monic degree 3 PRFs is known []. We develop efficient computational techniques for \(N_{v,u}(q)\), and use them to show \(N_{4,3}(q) = (q+1)q^2(q-1)^2/3\), for all prime powers \(q \le 307\), \(N_{5,4}(q) > (q+1)q^3(q-1)^2/2\), for all prime powers \(q \le 97\), and give a formula for \(N_{4,4}(q)\). We conjecture that these are true for all prime powers q. Let M(n, D) denote the maximum number of permutations on n symbols with pairwise Hamming distance D. Computing improved lower bounds for M(n, D) is the subject of much current research with applications in error correcting codes. Using PRFs, we obtain significantly improved lower bounds on \(M(q,q-d)\) and \(M(q+1,q-d)\), for \(d \in \{5,7,9\}\).
A Content Analysis of the Websites of the World’s Top 50 Universities in Medicine

This study examined the quality of library websites of QS world top-ranked medical libraries’ services, resources, information dissemination tools, and training opportunities to library users. Moreover, this study also explores the medical resources provided by medical universities remotely. The study used web content analysis method. A checklist consisting on nine main categories and 87 indicators have been finalized. The website quality assessment score indicated that none of the university libraries in the discipline of “medicine” gained 100% score. However, two university libraries (University of Cambridge and University of Washington) scored more than 90%. The finding revealed that “patron-driven acquisition” was a least common service (n = 6) and significant number of university libraries (42%) were still not using mobile apps. The most famous five dissemination tools are Facebook, Twitter, Instagram, YouTube, and LinkedIn. While web of science, PubMed, PsycINFO are significantly available databases to most of the libraries. This is an in-depth and first study investigating the web content analysis of world top-ranked medical universities libraries.
Equi-Joins over Encrypted Data for Series of Queries

Encryption provides a method to protect data out-sourced to a DBMS provider, e.g., in the cloud. However, performing database operations over encrypted data requires specialized encryption schemes that carefully balance security and performance. In this paper, we present a new encryption scheme that can efficiently perform equi-joins over encrypted data using only software and a single server with better security than the state-of-the-art. In particular, our encryption scheme reduces the leakage to equality of rows that match a selection criterion and only reveals the transitive closure of the union of the leakages of each query in a series of queries. Our encryption scheme is provable secure. We implemented our encryption scheme and evaluated it over a dataset from the TPC- H benchmark.
A New Algorithm of Handwritten Numeral Recognition Based on GPU Multi-stream Concurrent and Parallel Model

Recognition of handwritten numeral using convolutional neural networks (CNN) is a hot research topic at present. However, CNN algorithm requires high hardware equipment and has slow prediction speed. Traditional machine learning classification algorithms usually rely on manual experience to extract features. This paper proposes a new algorithm of handwritten numeral recognition based on GPU multi-stream concurrent and parallel model. The new algorithm constructs a programming environment based on the CUDA architecture, uses CUDA/C++ programming to implement the convolutional neural network algorithm. Applying the convolutional neural network algorithm to the handwritten numeral recognition problem, and selecting the appropriate network model and related parameters, GPU has high concurrency performance, which can improve the speed of convolutional neural network training data. By comparing the GPU and CPU implementation process, it is verified that it is feasible and effective to perform CUDA parallelization training and recognition on the convolutional neural network algorithm. The experiment shows that the GPU-implemented convolutional neural network algorithm has the characteristics of fast convergence, high recognition rate and fast recognition speed. The features extracted by CNN have various characteristics, which could improve the efficiency of handwritten numeral feature recognition, and solve the process of traditional manual experience extracting features and dependence on prior knowledge.
Online Media as a Price Monitor: Text Analysis using Text Extraction Technique and Jaro-Winkler Similarity Algorithm

Online media has become an essential part of everyday life in modern society. Everyone or organization is free to share their opinions and feelings about any topic on it, including information or news about commodity price fluctuations. Commodity price data from the National Strategic Price Information Center (NSPIC) website is not real-time, so it is not sufficient as a basis for monitoring commodity price fluctuations. Meanwhile, the government needs to collect data and information quickly about these price fluctuations, hence immediately strategic decisions and policies can be made to stabilize the prices. This study explores the potential function of online media by extracting the text in it and analyzing text so that it can display the commodity price data sought. The commodities used as search keywords were commodities that had the highest consumption level in 2016 in Indonesia. The texts analyzed were taken from three online media, namely Twitter, Liputan6.com, and Detik.com. It was analyzed using text extraction techniques and the application of the Jaro-Winkler algorithm to find commodity prices in the text collection. Then compare the results of text analysis with commodity prices from the NSPIC website. The experimental data were 99,007 with a data collection time of three months. From only 122 data that match the keywords, it consists of 100 training data and 22 testing data. The results of the text analysis show that the text from the Detik.com website shows the commodity prices closest to the price data from the NSPIC, while Twitter shows the farthest results. The accuracy test with the confusion matrix is 75%. Based on this research, online media texts are a viable source for monitoring commodity price fluctuations.
A Correct Response Model in knowledge structure theory

In knowledge space theory, the (latent) knowledge state of a student consists of the subset of test items that he masters in principle. Even at a given stage of apprenticeship, the student’s knowledge state may vary in a given collection of subsets. The collection of all possible states of all potential students forms a knowledge structure. In the modeling of student answer production, the probabilities governing the knowledge state are parameters. Moreover, for each item, two additional parameters capture the probabilities of careless errors and lucky guesses in the answers to the item. From all the latter parameters, the Correct Response Model (CRM) predicts the probability of a correct answer to any isolated item. From the same parameters, the Basic Local Independence Model (BLIM) predicts the probability of any possible pattern of correct responses. Here, a particular pattern records at a given time all the items to which a given student would provide correct answers. While general properties of the BLIM (such as identifiability of parameters) have been investigated, the simpler Correct Response Model still requires scrutiny. The present paper investigates the CRM as regards testability, identifiability and characterizability. It either provides explicit answers or points out serious difficulties garnered from various mathematical disciplines.
Visualization and Interpretation of Gephi and Tableau: A Comparative Study

In graphs, data is organized in such a way so that the information becomes clearer to draw conclusions which help to make decisions. Graphical visualization of the data gives better insights for decision making, forecasting, and prediction. Graphs clustering tools helps and assist in completing the task in a faster way. It also helps in graphical visualization of data based on features. Identifying the best tool for visualization is a time-consuming task. In this study, graph clustering tools are reviewed. Experiments are performed on few graph clustering tools for further comparative study. Graph clustering tools mainly Gephi and Tableau are considered for experiment purpose. The comparison is based on visualization and clustering effect in both tools.