Reviewerly/exHarmony huggingface dataset:
UIP—A Smart Web Application to Manage Network Environments

UIP—Ultra-Info Portal is an advanced application for accessing the Web-related application that permits users to handle tasks using browser network environments. UIP application was developed by using ASP.NET and.NET framework. It gains attention through it several e-content packages which a great way is to arrived solution. Ultra-Info Portal is common in every big organization to provide services to customers. In today's world, consumers expect easy access to information about organizations, and meanwhile, Internet and Intranet are becoming common to all organizations. Organizations like banks and other service provider organizations; they compete for each other to provide services to the customers and to attract them. To provide easy services, they are searching for new and popular technology. To satisfy their wants Ultra-Info Portal will become a good tool to use. They can easily interact with organizations with this user-friendly tool.
Evaluating semantometrics from computer science publications

Abstract Identification of important works and assessment of importance of publications in vast scientific corpora are challenging yet common tasks subjected by many research projects. While the influence of citations in finding seminal papers has been analysed thoroughly, citation-based approaches come with several problems. Their impracticality when confronted with new publications which did not yet receive any citations, area-dependent citation practices and different reasons for citing are only a few drawbacks of them. Methods relying on more than citations, for example semantic features such as words or topics contained in publications of citation networks, are regarded with less vigour while providing promising preliminary results. In this work we tackle the issue of classifying publications with their respective referenced and citing papers as either seminal, survey or uninfluential by utilising semantometrics. We use distance measures over words, semantics, topics and publication years of papers in their citation network to engineer features on which we predict the class of a publication. We present the SUSdblp dataset consisting of 1980 labelled entries to provide a means of evaluating this approach. A classification accuracy of up to .9247 was achieved when combining multiple types of features using semantometrics. This is +.1232 compared to the current state of the art (SOTA) which uses binary classification to identify papers from classes seminal and survey. The utilisation of one-vector representations for the ternary classification task resulted in an accuracy of .949 which is +.1475 compared to the binary SOTA. Classification based on information available at publication time derived with semantometrics resulted in an accuracy of .8152 while an accuracy of .9323 could be achieved when using one-vector representations.
Assessing the effectiveness of approximate functional sizing approaches for effort estimation

Context: Functional Size Measurement (FSM) methods, like Function Points Analysis (FPA) or COSMIC, are well-established approaches to estimate software size. Several approximations of these methods have been recently proposed as they require less time/information to be applied, however their effectiveness for effort prediction is not known. Objective: The effectiveness of approximated functional size measures for estimating the development effort is a key open question, since an approximate sizing approach may miss to capture factors affecting the effort. Therefore, we empirically investigated the use of approximate FPA and COSMIC sizing approaches, also compared with their standard versions, for effort estimation. Method: We measured 25 industrial software projects realised by a single company by using FPA, COSMIC, two approximate sizing approaches proposed by IFPUG for FPA (i.e. High Level and Indicative FPA), and three approximate sizing approaches proposed by the COSMIC organisation for COSMIC (i.e. Average Functional Process, Fixed Size Classification, and Equal Size Band). Then we investigated the quality of the regression models built using the obtained measures to estimate the development effort. Results: Models based on High Level FPA are effective, providing a prediction accuracy comparable to the one of the original FPA, while those based on the Indicative FPA method show poor estimation accuracy. Models based on COSMIC approximate sizing methods are also quite effective, in particular those based on the Equal Size Band approximation provided an accuracy similar to the one of standard COSMIC. Conclusion: Project managers should be aware that predictions based on High Level FPA and standard FPA can be similar, making this approximation very interesting and effective, while Indicative FPA should be avoided. COSMIC approximations can also provide accurate effort estimates, nevertheless, the Fixed Size Classification and Equal Size Band approaches introduce subjectivity in the measurement.
Unsupervised Fuzzy Measure Learning for Classifier Ensembles From Coalitions Performance

In Machine Learning an ensemble refers to the combination of several classifiers with the objective of improving the performance of every one of its counterparts. To design an ensemble two main aspects must be considered: how to create a diverse set of classifiers and how to combine their outputs. This work focuses on the latter task. More specifically, we focus on the usage of aggregation functions based on fuzzy measures, such as the Sugeno and Choquet integrals, since they allow to model the coalitions and interactions among the members of the ensemble. In this scenario the challenge is how to construct a fuzzy measure that models the relations among the members of the ensemble. We focus on unsupervised methods for fuzzy measure construction, review existing alternatives and categorize them depending on their features. Furthermore, we intend to address the weaknesses of previous alternatives by proposing a new construction method that obtains the fuzzy measure directly evaluating the performance of each possible subset of classifiers, which can be efficiently computed. To test the usefulness of the proposed fuzzy measure, we focus on the application of ensembles for imbalanced datasets. We consider a set of 66 imbalanced datasets and develop a complete experimental study comparing the reviewed methods and our proposal.
Co-Clustering Ensemble Based on Bilateral K-Means Algorithm

Clustering ensemble technique has been shown to be effective in improving the accuracy and stability of single clustering algorithms. With the development of information technology, the amount of data, such as image, text and video, has increased rapidly. Efficiently clustering these large-scale datasets is a challenge. Clustering ensembles usually transform clustering results to a co-association matrix, and then to a graph-partition problem. These methods may suffer from information loss when computing the similarity among samples or base clusterings. Rich information between samples and base clusterings is ignored. Moreover, the results are not discrete. They need post-processing steps to obtain the final clustering result, which will deviate greatly from the real clustering result. To address this problem, we propose a co-clustering ensemble based on bilateral k-means (CEBKM) algorithm. Our algorithm can simultaneously cluster samples and base clusterings of a dataset, to fully exploit the potential information between the samples and the base clusterings. In addition, it can directly obtain the final clustering results without using other clustering algorithms. The proposed method, outperformed several state-of-the-art clustering ensemble methods in experiments conducted on real-world and toy datasets.
Playful Learning with a Location-Based Digital Card Environment: A Promising Tool for Informal, Non-Formal, and Formal Learning

Most people are using their smartphones daily and carry them all the time. Therefore, mobile learning applications can be integrated in daily routines to make learning a part of daily life. While numerous mobile learning applications exist, each with their own goal, our aim was to explore the possibility of creating an engaging mobile environment that could be useful for informal learning, as well as for other forms of learning, i.e., non-formal and formal learning. The result is TICKLE, a playful learning environment for youngsters. It is a mobile location-based smartphone application that offers youngsters an interactive environment for exploring their surroundings. It offers cards related to physical locations, which can be collected by performing small challenges (based on the principles of micro learning). A design science research approach has been used to develop this software environment. Persuasive techniques and gamification are used to stimulate usage. Furthermore, a personalized approach is applied. The environment was evaluated by means of formative evaluations in different contexts. We obtained positive results and received useful feedback to improve and extend the application. We can conclude that, in the context of these evaluations, the app was usable for youngsters and able to engage them, and we see indications that it may be able to increase the intrinsic motivation and learning capacity of youngsters. In addition, our demonstrations show that the app is usable in different contexts and for different purposes. In this way, the environment can be used to offer youngsters appealing learning related experiences.
Mediated Secure Multi-Party Protocols for Collaborative Filtering

Recommender systems have become extremely common in recent years and are utilized in a variety of domains such as movies, music, news, products, restaurants, and so on. While a typical recommender system bases its recommendations solely on users’ preference data collected by the system itself, the quality of recommendations can significantly be improved if several recommender systems (or vendors) share their data. However, such data sharing poses significant privacy and security challenges, both to the vendors and the users. In this article, we propose secure protocols for distributed item-based Collaborative Filtering. Our protocols allow to compute both the predicted ratings of items and their predicted rankings without compromising privacy nor predictions’ accuracy. Unlike previous solutions in which the secure protocols are executed solely by the vendors, our protocols assume the existence of a mediator that performs intermediate computations on encrypted data supplied by the vendors. Such a mediated setting is advantageous over the non-mediated one since it enables each vendor to communicate solely with the mediator. This yields reduced communication costs, and it allows each vendor to issue recommendations to its clients without being dependent on the availability and willingness of the other vendors to collaborate.
Few‐shot learning with deformable convolution for multiscale lesion detection in mammography

Purpose Image‐based breast lesion detection is a powerful clinical diagnosis technology. In recent years, deep learning architectures have achieved considerable success in medical image analysis however, they always require large‐scale samples. In mammography images, breast lesions are inconspicuous, multiscale, and have blurred edges. Moreover, few well‐labeled images exist. Because of these factors, the detection accuracy of conventional deep learning methods is low. Therefore, we attempted to improve the accuracy of mammary lesion detection by introducing transfer learning (TL) into a deep learning framework for the few‐shot learning task and thus provide a method that will further assist physicians in detecting breast lesions. Methods In this paper, we propose a method called “few‐shot learning with deformable convolution for multiscale lesion detection in mammography,” named FDMNet. Deformable convolution is introduced for enhancing the network’s ability to detect lesions, and the sensitivity of the multiscale feature space is reinforced by using a feature pyramid method. Furthermore, by introducing location information in the predictor, the sensitivity of the model to lesion location is also enhanced. The proposed method, through the TL technique that is applied mines the potentially common knowledge of features in the source domain and transfers it into the target domain to improve the accuracy of breast lesion detection in the few‐shot learning task. Results On the publicly available datasets for screening mammography CBIS‐DDSM and Mini‐MIAS, the proposed method performs better than five widely used detection methods. On the CBIS‐DDSM dataset, its comprehensive scores, sensitivity, precision, and the mean dice similarity coefficient are 0.911, 0.949, 0.873, and 0.913, respectively, and on the Mini‐MIAS dataset, these values are 0.931, 0.966, 0.882, and 0.941, respectively. Conclusions To achieve the few‐shot learning required for medical image analysis, the proposed method uses TL to execute feature knowledge transformation and includes deformable convolution to build a feature pyramid structure, which enhances the learning performance of the network for lesions. The results of comparative numerical experiments show that the proposed method outperforms some state‐of‐the‐art methods.
Understanding Smartphone Users From Installed App Lists Using Boolean Matrix Factorization

Smartphones are changing humans' lifestyles. Mobile applications (apps) on smartphones serve as entries for users to access a wide range of services in our daily lives. The apps installed on one's smartphone convey lots of personal information, such as demographics, interests, and needs. This provides a new lens to understand smartphone users. However, it is difficult to compactly characterize a user with his/her installed app list. In this article, a user representation framework is proposed, where we model the underlying relations between apps and users with Boolean matrix factorization (BMF). It builds a compact user subspace by discovering basic components from installed app lists. Each basic component encapsulates a semantic interpretation of a series of special-purpose apps, which is a reflection of user needs and interests. Each user is represented by a linear combination of the semantic basic components. With this user representation framework, we use supervised and unsupervised learning methods to understand users, including mining user attributes, discovering user groups, and labeling semantic tags to users. Extensive experiments were conducted on three data subsets from a large-scale real-world dataset for evaluation, each consisting of installed app lists from over 10 000 users. The results demonstrated the effectiveness of our user representation framework.
MyNLIDB: A Natural Language Interface to Database

Structured Query Language (SQL) is a powerful tool to retrieve or manage data held in a relational database management system. But only the person who has a good knowledge of the SQL language can interact with the database. So to allow the users who don't know SQL, Natural language Interface to Database (NLIDB) systems are being developed to perform querying in natural language (NL) with the databases. While constructing an NLIDB, it is very critical to bridge the semantic gap between a natural language query (NLQ) and the underlying data. Keyword mapping, which is the task of mapping individual keywords in the original NLQ to database elements such as relations, attributes or values of attributes is a method through which this bridging is exhibited. As it is very difficult to understand the true meaning of every word in NLQ because of the ambiguity involved in NL, to map individual keywords to the schema definition and contents of the underlying database is a challenging task. In this paper, we present a system MyNLIDB that has a good performance with respect to keyword mapping. It uses a Schema-Graph created from the underlying database, Stanford part-of-speech parser and dependency parser to convert NL Query to SQL using pipeline processing. MyNLIDB is domain independent and database independent. It works very well for simple queries.
Survey on renewable energy forecasting using different techniques

Wind and solar are the renewable technologies which are very popular and well known source of energies throughout the world. Fossil fuels are formed by natural processes which contain a high quantity of carbon include coal, natural gas and petroleum which comes under non-renewable energy sources. Wind and solar Energy Forecasting is done to estimate the output power and energy of renewable energy sources. Forecasting is done at regular intervals to balance the supply and demand of energy. Solar and wind power forecasting are completely depends on metrological parameters such as velocity and direction of the wind, temperature, and humidity As solar and wind variability is stochastic, many of statistical models along with linear and non-linear models such as ARIMA, kalman filters, ANN, and support vector machines respectively used to catch the randomness of solar and wind energy. Lot of disadvantages are there for various approaches along with its computation complexity and incapability to alter the time varying time-series systems. This paper provides a comprehensive review of the theoretical forecasting methodologies for both solar and wind energy and also merits and demerits of different methods. The study of time series prediction of solar and wind power generation mainly focus on reviewing the advantage of using Long-Short Term Memory (LSTM) and Recurrent Neural Network (RNN).
Covert Communication Over VoIP Streaming Media With Dynamic Key Distribution and Authentication

Voice over Internet Protocol (VoIP) is widely embedded into commercial and industrial applications. VoIP streams can be used as innocuous cover objects to hide the secret data in steganographic systems. The security offered by VoIP signaling protocols is likely to be compromised due to a sharp increase in computing power. This article describes a theoretical and experimental investigation of covert steganographic communications over VoIP streaming media. A new information-theoretical model of secure covert VoIP communications was constructed to depict the security scenarios in steganographic systems against the passive attacks. A one-way accumulation-based steganographic algorithm was devised to integrate dynamic key updating and exchange with data embedding and extraction, so as to protect steganographic systems from adversary attacks. The theoretical analysis of steganographic security using information theory proves that the proposed model for covert VoIP communications is secure against a passive adversary. The effectiveness of the steganographic algorithm for covert VoIP communications was examined by means of performance and robustness measurements. The results reveal that the algorithm has no or little impact on real-time VoIP communications in terms of imperceptibility, speech quality, and signal distortion, and is more secure and effective at improving the security of covert VoIP communications than the other related algorithms with the comparable data embedding rates.
A Bit-Level Approach to Side Channel Based Disassembling

Side-Channel Based Disassembling (SCBD) is a powerful application of side-channel analysis that allows recovering instructions executed by a processor from its physical leakages, such as the electromagnetic field (EM) emitted by the chip. These attacks directly compromise code confidentiality, but they can also reveal to an adversary many critical information on the system’s internals. In this work, we propose a new approach for SCBD that directly focuses the bit encoding of an instruction using local EM leakage. We exploit a very precise bit-level leakage model and derive from it new algorithms that aim at recovering the actual bit values. We also propose strategies to automate the complex tasks of finding the best EM probe positions and combining them to improve results. On a PIC16 target, our method succeed in recovering the bits of an instruction with an average rate of 99,41% per bit. Compared to the state of the art, our disassembler is easier to train, recovers more information about instructions than just opcode and requires almost no modifications to target other processor architectures. Thus, this kind of disassemblers might become a threat to more complex processors, where side-channel disassembling has not been proved to be feasible yet.
Quantum Pulse Position Modulation with Photon-Added Coherent States

Quantum communication systems exploit the quantum features of light to enhance the reliability of a classically described communication system. This paper proposes the use of photon-added coherent states (PACSs), instead of coherent states, for quantum pulse position modulation (PPM) systems. PACSs are the most simple example of non-classical non-Gaussian states of light, as they can not be described by the classical laws of physics. We show that employing PACS in a PPM communication system improves the reliability of the communication with respect to using a coherent state with the same energy.
Genetic algorithm based key sequence generation for cipher system

Stream cipher system seems to be one of the best alternatives in order to provide confidentiality and security in an on line and high-speed transmission. Cryptography is required to secure the secret information transmitting over the communication channel. Day by day the importance of security increases due to increase of online transaction processing and e-commerce. As well as encryption / decryption algorithm, randomness characteristics of key sequences prove the strength of the stream cipher. In this work the Linear Feedback Shift Register (LFSR) is used to produce non binary pseudo random key sequence. The length of the sequence has been enhanced by designing hybrid model using LFSR and Genetic Algorithm (GA). Achieving the length more than the maximum length of LFSR is the primary intention of this work. The statistical tests is conducted to assess the randomness of key sequence generated from hybrid model. Generated key sequences are used as key sequences in cryptographic applications and results are analyzed.
Toward Receiver-Agnostic RF Fingerprint Verification

A number of successful RF fingerprint classifiers have been demonstrated, but relatively few results evaluate the impact of changing the receiver used for training and inference. In this work, we record a set of 25 ZigBee transmitters with 10 independent, unsynchronized receivers and first show that similar performance may be achieved by a neural network-based RF fingerprint verification system on all receivers when training and inference are performed on the same receiver. Next, we show significant performance degradation when different receivers are used for training and inference. We propose two methods to address this shortcoming. The first method shows that the performance degradation is reduced when the training process uses several receivers. The second method proposes a calibration procedure whereby a single neural network-based transformation is learned per receiver and allows fingerprint verification models learned for a different receiver to be reused without retraining.