Reviewerly/exHarmony huggingface dataset:
The Empirical Duality Gap of Constrained Statistical Learning

This paper is concerned with the study of constrained statistical learning problems, the unconstrained version of which are at the core of virtually all of modern information processing. Accounting for constraints, however, is paramount to incorporate prior knowledge and impose desired structural and statistical properties on the solutions. Still, solving constrained statistical problems remains challenging and guarantees scarce, leaving them to be tackled using regularized formulations. Though practical and effective, selecting regularization parameters so as to satisfy requirements is challenging, if at all possible, due to the lack of a straightforward relation between parameters and constraints. In this work, we propose to directly tackle the constrained statistical problem overcoming its infinite dimensionality, unknown distributions, and constraints by leveraging finite dimensional parameterizations, sample averages, and duality theory. Aside from making the problem tractable, these tools allow us to bound the empirical duality gap, i.e., the difference between our approximate tractable solutions and the actual solutions of the original statistical problem. We demonstrate the effectiveness and usefulness of this constrained formulation in a fair learning application.
Semantic framework for data flow control in the network of information graphs

The mixed information stream of reliable, semi-reliable and obviously false data observed in the network is a data flow in the so-called facilitated format, the task of which is to somehow attract attention, influence the emotional sphere in the form of hype-information, which helps to make hasty decisions and harm-actions. A growing variety of forms of technological push-impact, which are successfully used for commercial purposes. Nevertheless, the study of various push-forms shows that they are based on a single semantic component that uses the offset of actual concepts to achieve the benefit of the information provider. The consumer of push services receives an apparent benefit that is far from his actual interests and intentions. A semantic computational model is proposed that captures the emerging bias of concepts. The concept of a semantic broadcasting channel is developing. The paper considers the task of constructing specialized semantic systems in the form of information graphs. The information graphs are considered as structured sets of vertices that define the method of processing information and are connected by arcs, describing the sequence of processing. In its turn, an information graph can be used as an arc, which gives hyper-graph nature to the network in the whole. The information graphs are compared with other data flow control tools. The tools for constructing transformational semantics for calculations above information graphs are offered, which assume the specialization for the class of computations associated with the vertices of the graph. A method for making an abstract computing machine with a given semantics is proposed. The paper considers an example of making an abstract machine for the case of deterministic semantics, which is illustrative for certain fragments of antimonopoly legislation.
A Comparative Performance of PV Power Simulation Software with an Installed PV Plant

Photovoltaic (PV) energy stands out due to the sustainability aspect, being the target of research investments. At University of Campinas, the sustainable campus project carried out the installation of PV plants at the university with the challenge of conducting research in the Campinas-Brazil region. In this way, the present paper simulated the largest PV plant of University of Campinas (336.96 kWp) in different PV power simulation software, aiming to have data for future comparisons with the installed PV system. For this, is it used PVsyst, PV*SOL, and HOMER for simulations, and then compared the result with the months containing the results of power generation for the installed PV plant. As a result, the three software performed well, with generation close to the installed PV plant. It was noticed HOMER optimism, PV*SOL conservatism and a more accurate result for PVsyst. Therefore, the PV plant worked as designed and the peculiarities of the three software were presented as a contribution of the paper.
Implementation of universal quantum gates by periodic two-step modulation in a weakly nonlinear qubit

Reducing the leakage from qubit levels to other levels is of importance for quantum computation. Here, we develop a feasible method to effectively suppress this leakage and perform universal quantum gates in a weakly nonlinear qubit. The method is to exploit periodical two-step modulation of single control parameters in a artificial large detuning regime. It works well, regardless of whether the strength of control parameter is known or not. In particular, qubit leakage can also be suppressed in a counterintuitive parameter regime, where the control field drives the transition of qubit levels with large detuning, but resonance transition with leakage level. The results demonstrate that this method is robust against parameter perturbations, and the deformation of square waves does not accumulate the error of qubit operations but affects the gate time. In addition, this method provides us with a viable approach to measure the control parameters.
G-TBSA: A Generalized Lightweight Security Algorithm for IoT

The Internet of Things (IoT) is a promising field that will connect billions of devices to communicate and share information in the future. The increased amount of information sharing causes a threat to data security and privacy. However, IoT devices are fundamentally resource-constrained, thus, traditional encryption algorithms are computationally expensive for this small size and low power devices. Therefore, a lightweight security algorithm is needed. In this paper, resource-friendly, energy-saving data encryption along with an efficient key generation mechanism is proposed namely Generalized Triangle Based Security Algorithm (G-TBSA). The proposed G-TBSA is applied in wireless sensor networks (WSNs) with low power Wi-Fi. The key generation process is the heart of the algorithm because of its usage of fewer resources to generate keys and thus, minimize the complexity of the algorithm and achieving its energy efficiency. It outperforms state-of-the-art methods in terms of power consumption while achieving all necessary security requirements.
Age and Gender prediction in Open Domain Text

The massive use of the social media and the huge number of messages that are shared on the internet, create a countless need to automatically detect the age and gender of the people who write these messages. Several sites and platforms attempt to mislead and cheat the people who are visiting them by providing deceptive information about the age and the gender of their customer. The traditional way to detect deceivers was by human judgment, but this way is no longer suitable since lots of interviews are not conducted face to face. This paper presents an automate tool with a unique set of features that used to analyze a given text. The features include the unigram, part of speech, and production rules. The accuracy results of the proposed method outperform the existing techniques. The best results achieved by using the production rules features.
Extracting Severe Negative Sentence Pattern from Bangla Data via Long Short-term Memory Neural Network

Social media has become important for sharing contents and people around the globe are expressing their feelings over social media. Analyzing these social interactions can be very much helpful to figure out the reasons behind people's feelings. In this work, we applied Long Short-term Memory (LSTM) Neural Networks for analyzing negative sentences in Bangla. We extracted negative Bangla tweets from twitter and also collected more negative Bangla sentences from people using google form. In our work, we have shown how hyper-parameters tuning can be useful for analyzing a small dataset and then applied to-fold cross-validation. Finally, we showed a relative comparison between our research and other previous works. This result will help psychologists to find out the reasons behind these social media interactions and prevent undesirable happenings resulted from negativity.
Exponentially Fitted Cubature Kalman Filter With Application to Oscillatory Dynamical Systems

This paper proposes a new nonlinear filtering technique under the Gaussian filtering approach, which is based on the numerical approximation of intractable integrals. The proposition is in the category of spherical-radial rule based Gaussian filtering (dominated by cubature Kalman filter), which is most commonly used in practical applications. The existing Gaussian filters with spherical-radial rule are accurate only for the systems modelled with polynomials of a certain order and suffer from poor estimation accuracy for the oscillatory systems. The proposed method, however, provides an efficient approach for estimation and filtering in oscillatory environment. It introduces an exponentially-fitted spherical-radial rule of numerical approximation, which is accurate for oscillatory functions. The exponentially-fitted spherical-radial rule is composed of a third-degree spherical-cubature rule and an exponentially-fitted Gauss-Laguerre quadrature rule. The proposed filter is named as exponentially-fitted cubature Kalman filter (ECKF). The estimation accuracy of the ECKF is analyzed for nonlinear filtering problems related to the Duffing and Coulomb oscillators in terms of root mean square error (RMSE). The RMSE analysis concludes an improved estimation accuracy for the proposed ECKF compared to the existing Gaussian filters.
Assessing Cross-Project Technique for Software Maintainability Prediction

Software Maintainability refers to the ease with which software maintenance activities like correction of faults, deletion of obsolete code, addition of new code etc. can be carried out to adapt to the modified environment. Predicting maintainability in early stages of development helps in reducing the cost of maintenance and ensures optimum utilization of resources. Sometimes, it becomes difficult to train prediction models using historical data of the same dataset for which the model is being developed because of the unavailability of sufficient amount of training data, in turn making a way for Cross-Project technique for Software Maintainability Prediction (CPSMP). In order to evaluate the proposed CPSMP technique, QUES dataset is used as training set and UIMS dataset is used as test set in this study with 19 different regression modelling methods. Performance of CPSMP model is evaluated using Root Mean Square Error (RMSE) as an accuracy measure. Results show that cross-project technique can successfully be applied for maintainability prediction. The average RMSE value calculated for all the modelling methods is found to be 82.310 without CPSMP whereas an average RMSE value of 71.532 is obtained with CPSMP resulting in an overall improvement in prediction performance by 13.09%. Also, 84.21% of the total techniques used in this study performed better with CPSMP.
Dynamic Image-Based Visual Servoing of Monocular Camera Mounted Omnidirectional Mobile Robots Considering Actuators and Target Motion via Fuzzy Integral Sliding Mode Control

In this article, an image-based visual servoing (IBVS) method for monocular camera mounted three-wheeled omnidirectional mobile robots (OMRs) is proposed using a novel fuzzy integral sliding mode control (FISMC) method. Most of the existing studies for IBVS of wheeled mobile robots (WMRs) have focused only on kinematics, even though no consideration for the OMR dynamics, actuators, and target motion can lead to much degraded performance. Accordingly, a camera mounted OMR is first modeled considering the OMR dynamics, actuator dynamics, and disturbance effects of the target object motion. On the basis of the OMR model, a dynamic IBVS method (i.e., IBVS based on kinematics, dynamics, and actuators) is then proposed to replace the kinematic IBVS method (i.e., IBVS based only on kinematics) such that the effects of the OMR dynamics, actuators, and target motion can be accommodated by using the FISMC method. In particular, the proposed FISMC method for the IBVS of OMRs can eliminate the reaching phase to make the states remain on the sliding surface all the time and improve the transient performance significantly when compared with the existing methods. The robust stability of the overall system using the proposed system is demonstrated by Lyapunov stability analysis. Simulation results are also provided to show that where the kinematic IBVS achieves much degraded performance owing to the OMR dynamics and target motion, the proposed FISMC-based dynamic IBVS method compensates for these effects and achieve the satisfactory feature point tracking performance, verifying the effectiveness of the proposed method.
Losing connection: the modal logic of definable link deletion

Abstract In this article, we start with a two-player game that models communication under adverse circumstances in everyday life and study it from the perspective of a modal logic of graphs, where links can be deleted locally according to definitions available to the adversarial player. We first introduce a new language, semantics and some typical validities. We then formulate a new type of first-order translation for this modal logic and prove its correctness. Then, a novel notion of bisimulation is proposed that leads to a characterization theorem for the logic as a fragment of first-order logic, and a further investigation is made of its expressive power against hybrid modal languages. Next, we discuss how to axiomatize this logic of link deletion, using dynamic-epistemic logics as a contrast. Finally, we show that our new modal logic lacks both the tree model property and the finite model property and that its satisfiability problem is undecidable.
The Study of Drunken Abnormal Human Gait Recognition using Accelerometer and Gyroscope Sensors in Mobile Application

International Classification of Diseases (ICD-10) defined that alcohol is a contributing factor that caused catastrophic health problems and the environment. Even 0.05% of Blood Alcohol Concentration (BAC) in the human body could affect the gait of a person. Thus, this research aims to develop a recognition technique to identify drunken humans by their gait using an accelerometer and the gyroscope sensors. The abnormal gait detection of a drunk person is important for the early recognition of a drunk person to avoid bad incidents to occur. For the collection of the data, the sensor was placed on the right leg of the volunteer. The gait data were recorded before and after the volunteer consumes alcohol. The Deep Neural Network algorithms were implemented in this model to predict either the person drunk or normal. The objectives of this research were successfully achieved by capturing the people's gait walking patterns. The collection of data and identification of the best algorithms used in detecting the behavior of the person also achieved. From this, we can conclude that this study using the Deep Neural Network algorithm fits well on the test data by achieving approximately 79% of the accuracy.
Blockchain Simulation and Development platforms: Survey, Issues and Challenges

Blockchain technology has been shining like a star after the introduction and widespread acceptance of the Bitcoin, the very first cryptocurrency in people's everyday lives. At the beginning, Blockchain was used only for monetary transactions but studies have proven that it has many more applications. Blockchain platforms introduce a decentralized mode of control are rapidly being developed in the world on a large scale and their survey along with their characteristics are being discussed in this paper. But since developing Blockchain applications on a Blockchain network are more costly, there is a need for a simulation platform to refine and test the applications before it can be committed to the network There are a huge number of Blockchain simulation platforms available. The real task is to choose one among them that is best suited for decentralised application (DApp) developers. In this paper, a study is made using an E-voting application [7] to compare different Ethereum simulation platforms.
Super-Resolution 3-D Laser Scanning Based on Interval Arithmetic

Most 3-D laser scanners are based on 3-D optical triangulation algorithms, where the location of each 3-D point is estimated as the intersection of a camera ray and a plane of light projected by a laser line generator. Since a physical laser-line generator projects a sheet of light of finite thickness, inaccurate measurements and errors result from assuming that the plane of light is infinitesimally thin. We propose a new mathematical formulation for 3-D optical triangulation based on interval arithmetic, where 3-D points are only determined within certain bounds along the camera rays, and multiple measurements are used to tighten these bounds. We propose the line segment cloud as an alternative surface representation to visualize the measurement errors within the proposed framework. We introduce the iterative line segment tightening algorithm to convert line segment clouds into point clouds, as a preprocessing step prior to surface reconstruction. We describe how to construct a low-cost laser-line 3-D scanner, where the camera is fixed with respect to the object and the laser-line generator is mounted on a high-resolution motion platform. We describe a GPU-based implementation where the large number of captured images is processed in real time. Finally, we present some experimental results.
Analysing Fuzzy Based Approach for Extractive Text Summarization

In today's era of information, there is gigantic amount of data available from various sources. Not only does the enormous volumes pose problems, searching of required information becomes a very difficult task. It is the need of the hour to have smaller but significant representation of large, bulky pieces of information in order to obtain the desired details. Text summarization is the process of condensing text documents into shorter and accurate representation conveying the meaning of the text precisely. It has found applications in numerous fields. In this paper, a process for extractive text summarization using fuzzy logic has been discussed meticulously. It takes various properties into account for identifying the most significant sentences for the formation of summary from a given text. The model proposed in this paper has been tested on BBC News Summary dataset and the results have been compared using the ROUGE measures. The obtained results indicate that the proposed model shows enhanced performance with improved f-measure values.
Enhanced Visual Cryptography: An Augmented Model for Image Security

Security of data has become a necessity in today's world. Data as all know is the raw form and information can be extracted from it. Thus, data needs to be secured. This concern becomes even more significant when share information and it mostly constitute of images. As already know that a picture speaks a thousand words, there is no reason not to believe that picture carry the most information. Furthermore, they can be easily manipulated. This paper presents two schemes to secure such information in the form of images. Under the concept of (k, n) - Visual Cryptography, authors present these two schemes to secretly share images, that can be practically used to secure the information sent. The (3, 3) - enhanced visual cryptographic scheme, enhances security of the currently existing (3, 3) - visual cryptographic scheme by utilizing the concept of keys to secure information. Authors also introduce a new scheme, (2, 3) - visual cryptographic scheme. The (2, 3) - visual cryptographic scheme also utilizes the concepts of keys but reduces the bandwidth requirement or increases reliability. This provides us two modes of operation that are more practical than existing scheme. The paper shall discuss the scheme with a schematic analysis and an analysis of its implementation both subjectively and objectively. Using several parameters of security, this paper presents our findings as our results. The schemes discussed are found to be more secure than existing visual cryptographic scheme and are more reliable and imperceptible.
On Box-Cox Transformation for Image Normality and Pattern Classification

A unique member of the power transformation family is known as the Box-Cox transformation. The latter can be seen as a mathematical operation that leads to finding the optimum lambda ({\lambda}) value that maximizes the log-likelihood function to transform a data to a normal distribution and to reduce heteroscedasticity. In data analytics, a normality assumption underlies a variety of statistical test models. This technique, however, is best known in statistical analysis to handle one-dimensional data. Herein, this paper revolves around the utility of such a tool as a pre-processing step to transform two-dimensional data, namely, digital images and to study its effect. Moreover, to reduce time complexity, it suffices to estimate the parameter lambda in real-time for large two-dimensional matrices by merely considering their probability density function as a statistical inference of the underlying data distribution. We compare the effect of this light-weight Box-Cox transformation with well-established state-of-the-art low light image enhancement techniques. We also demonstrate the effectiveness of our approach through several test-bed data sets for generic improvement of visual appearance of images and for ameliorating the performance of a colour pattern classification algorithm as an example application. Results with and without the proposed approach, are compared using the AlexNet (transfer deep learning) pretrained model. To the best of our knowledge, this is the first time that the Box-Cox transformation is extended to digital images by exploiting histogram transformation.
Translation of Sign Language Glosses to Text Using Sequence-to-Sequence Attention Models

This work deals with the problem of Sign Language Translation and more specifically with translating Glosses to text. We applied Sequence to Sequence models with attention mechanism to a parallel gloss to English corpus. This is the first work that used these models to translate American gloss sentences to English. We present our experiments on several network architectures with three different attention functions. The results are very promising and can be useful for the further implementation of a full sign language recognition system.
Scene Recognition from Image Using Convolutional Neural Network

This work is an attempt to solve one of the most common problems of scene recognition. The dataset called PLACES2 was used for the purpose of experimentation which contains nearly 7 million labelled pictures of various scenes which is a sufficiently exhaustive dataset to train and test a model to get the desired output. The Convolutional Neural Network was used to design a neural architecture for our machine to store and classify the images as desired for the recognition of scene in the dataset. Results strongly suggest that the trained model were able to perform successful scene recognition in the given data sample