Reviewerly/exHarmony huggingface dataset:
Competitive and Co-Operative Analysis of Genetic-Programming

Abstract Co-operative and competitive building blocks drive the evolution of GP. There exists useful schema which remains promising and persistent in terms of their occurrence over the generations. These schemas are majorly present at levels other than root level, and gradually this genetic material is pushed towards the root level by co-operation, where they compete with each other, few among those increase their occurrence at the root level and rest vanished. These schemas if extended to the deeper depths, still be useful and helps in achieving the goal of the problem. Lastly, some of these schemas are same even in different runs for the same problem. The experiment results show that the evolution is driven by co-operative rooted building blocks as well, along with the competitive building blocks. It shows the count of building blocks in different positions never reaches zero, which indicates the presence of the genetic material required for the construction of the building block at a particular position in the form of co-operation and competition with other schemas, which, at a later stage, may move to the correct position (root level). In our work, these behaviours are also demonstrated by injecting genetic material manually in the population and investigated their prominence throughout the evolution. This work contributes to many dimensions like; by combining these competitive building blocks appear at root level uncovers the problem solution over time. For that reason, by identifying, preserving and exchanging these building blocks explicitly, the system enactment can be improved severely. Additionally, the solutions of the developed versions of the same problem can also be handled through this.
Better Edges not Bigger Graphs: An Interaction-Driven Friendship Recommender Algorithm for Social Networks

Online social networks have been increasingly growing over the past few years. One of the critical factors that drive these social networks' success and growth is the friendship recommender algorithms, which are used to suggest relationships between users. Current friending algorithms are designed to recommend friendship connections that are easily accepted. Yet, most of these accepted relationships do not lead to any interactions. We refer to these relationships as weak connections. Facebook's Friends-of-Friends (FoF) algorithm is an example of a friending algorithm that generates friendship recommendations with a high acceptance rate. However, a considerably high percentage of Facebook algorithm's recommendations are of weak connections. The metric of measuring the accuracy of friendship recommender algorithms by acceptance rate does not correlate with the level of interactions, i.e., how much connected friends interact with one another. Consequently, new metrics and friendship recommenders are needed to form the next generation of social networks by generating better edges instead of merely growing the social graph with weak edges. This paper is a step towards this vision. We first introduce a new metric to measure the accuracy of friending recommendations by the probability that they lead to interactions. We then briefly investigate existing recommender systems and their limitations. We also highlight the consequences of recommending weak relationships within online social networks. To overcome the limitations of current friending algorithms, we present and evaluate a novel approach that generates friendship recommendations that have a higher probability of leading to interactions between users than existing friending algorithms.
A Dual Branch Attention Network for Multiple Degraded Image Restoration

Abstract In this paper, we propose a novel dual branch attention network (DBA-Net), which can efficiently restore multiple degradation images. Most methods of image restoration focus on a single degradation factor, such as blur, noise, raindrop, etc. However, these methods frequently fail to real degraded images, because the real images usually contain different types of degradation. Our network backbone consists of two branches, the residual branch and the information distillation branch, which have different receptive fields. We design a gate module to choose useful feature maps from the two branches. Then, an improved multi-channel attention selection module is proposed to allow the network to learn more features of real results. On DIV2K datasets, which contains different degradation types (noise, blur, and compression loss) and diverse degradation levels (mild, moderate and severe), our results on PSNR and SSIM outperform other advanced algorithms. In visual performance, the texture of objects in DBA-Net images is more similar to the original image.
Pentatope Based Elliptic Curve Encryption for Privacy and Protection of Multimedia Data

Now-a-days information security plays a vital role in this digitalized world. Data-breaching is the most serious threat in the current world due to the lack of proper security measures. In order to overcome such security issues, several cryptographic algorithms are developed. Most of these existing algorithms concentrated just on encrypting and decrypting process but not on the key size used and the computation time. To overwhelm this problem, a methodology was introduced by making use of the pentatope number in the elliptic curve cryptography, which tends to reduce the shared secrecy between sender and the receiver. It is used to provide security for multimedia data files such as text and image data present in various formats. In this ECC is used because it is the better alternative for the public key encryption compared to various encryption algorithms like RSA which offers a good level of security. The proposed methodology provides better security to the users in order to share their information in an ease manner.
Feature-Extracting Functions for Neural Logic Rule Learning

In this paper, we present a method aimed at integrating domain knowledge abstracted as logic rules into the predictive behaviour of a neural network using feature extracting functions. We combine the declarative first-order logic rules which represents the human knowledge in a logically-structured format akin to that introduced in [1] with feature-extracting functions which act as the decision rules presented in [2]. These functions are embodied as programming functions which can represent, in a straightforward manner, the applicable domain knowledge as a set of logical instructions and provide a cumulative set of probability distributions of the input data. These distributions can then be used during the training process in a mini-batch strategy. We also illustrate the utility of our method for sentiment analysis and compare our results to those obtained using a number of alternatives elsewhere in the literature.
MTDNNF: Building the Security Framework for Deep Neural Network by Moving Target Defense*

With the development of deep neural networks in pattern classification for recognizing handwritten digits on cheques, object classification for the automated surveillance, and autonomous vehicles, the problem of DNNs confront malicious inputs has been a hot topic. In this paper, we introduced a security-enhanced framework for DNNs to conduct classification based on moving target defense (MTDNNF). Also, we presented three pivotal characteristics to realize the framework, heterogeneity, selectivity, and adaptability, which enabled MTDNNF and guaranteed security and veracity. Also, we analyzed the security and performance of MTDNNF. Those analyses show that the MTDNNF can provide significant security improvements against malicious inputs, and extra cost in performance is inessential under both massive and minimum scenarios.
Data Consolidation in Global Software Development Projects: A Grounded Theory

Abstract Effective coordination is a crucial aspect of successful Global Software Development (GSD) projects. Limited studies have examined coordination strategies and their related indicators. Therefore, this study focuses on assessing the coordination processes that require specific strategies and related indicators that can contribute to effective coordination. Thus, the main aim of this research is to consolidate the coordination strategies and the related indicators that were extracted from two different sources namely, Systematic Review and Semi-structured interview. Grounded Theory was used for the consolidation of these two sources. The results of this study are a set of coordination strategies and related indicators that will be used to formulate the evaluation to assess the effectiveness of the coordination processes in GSD projects.
No-ReferenceColor Image Quality Assessment Using HOSVD Based Features and Neural Networks

Images and videos are seen as the most reliable source of visual information as they are a fundamental part of the multimedia world. For instance, face recognition technology utilizes images for security purposes. However, due to either the physical properties of the acquisition equipment (internal) or the nature of the environment (external), images can be affected by a wide range of distortions. Researchers have enumerated more than 24 distortions that can affect images. Among which four types are the most prominent ones. In this paper, a novel no-reference color image quality assessment technique is introduced. The technique is based on extracting a set of features from the High Order Singular Value Decomposition (HOSVD) of images. Such features are then used with a neural network regressor to predict the quality score. The results show excellent performance exceeding traditional techniques based only on gray-scale images.
Review on Anatomical Medical Images Classification Methods: Diagnostic Value

Abstract Deep learning (DL) based convolutional neural network (CNN) has grown rapidly and become a selected choice for medical imaging fields. The paper reviews into three categories; 1) exploring the supervised machine learning methods, 2) reviews the limitations in deep learning, 3) and finally reviews the majors deep learning techniques within a specific summarized on lesion classification-based DL in term of (application, method, type of lesion diseases classification, cons).
A simple and inexpensive irradiance monitoring system using photovoltaic panel

The sun has an important role in the lives of all beings on earth. Monitoring of solar irradiance is important because the data correlates with many disciplines. This research aimed to develop an irradiance monitoring instrument that has the ability to monitor the using a photovoltaic panel as the sensor. The principle of this instrument is that the photovoltaic panel produces electricity if it is exposed to direct sunlight and its output is proportional to the irradiance. The output is read by a microcontroller and it is calculated using an algorithm to estimate the irradiance value. This study is started by designing the hardware both the electronic parts including a microcontroller, radio transceiver, data logger, and the mechanical parts including the pole and the active solar tracker. The software is designed and developed using the Arduino IDE. Generally, the result shows that the photovoltaic panels can be used as a sensor to measure the irradiance. The maximum irradiance in Bengkulu during the test is 988.41 W/m2. The root mean square errors (RMSE) of the measurement compared to the standard instrument is about 14.06 which is acceptable. However, from this study, it is suggested to use good quality materials as it is an outdoor instrument that needs to be tough and durable.
Application Research of Face Recognition and Tracking Technology in the Tower Controller Visual Observation Action Recognition

In order to solve the problem that the visual observation actions of the tower controller during the take-off and ground-off phases of the aircraft can only be recognized manually by the management personnel, the visual observation action recognition software of the tower controller is established. In the PyCharm compilation environment, based on Python, call functions in the OpenCV library to obtain video frames, use functions in the Dlib library to implement face detection, implement face recognition based on the Caffe framework, and implement face rotation tracking recognition based on CamShift functions. Display the results of the visual observation action recognition of the tower controller in graphic form. The results show that the software has the advantage of extracting the deep features of the image, realizes the function of automatically identifying the visual observation actions of the tower controller, with high recognition accuracy, and provides a powerful automated tool for tower site management and incident investigation.
A Mahalanobis Distance-Based Integration of Suspicious Scores For Bug Localization

Once a software bug is reported, it is crucial to locate the source file causing the bug and fix it as soon as possible. To this end, there have been various studies for locating bugs with the version control system and the bug reports. AmaLgam+ is one of the most promising methods for localizing bugs. This method quantifies the degree to which a source file causes the bug from different five perspectives (metrics), and combines those values (suspicious scores) into a single integrated score of the source file. However, the method has a challenge regarding the computation time because it uses the genetic algorithm (GA) to combine the above five metrics. This paper proposes an application of the Mahalanobis distance to the suspicious score integration to overcome the above challenge. The proposed method considers the above five metrics to be a five-dimensional vector. It computes the Mahalanobis distance of the vector from the origin as an alternative integrated suspicious score. The empirical study using six open source software projects proves that the proposed method has almost the same bug localization accuracy as AmaLgam+ and can reduce the computation time by up to 98%: e.g., while AmaLgam+ took about 3.5 hours, the proposed method did it about 4 minutes.
Speech Parameters Extraction for Text-to-Speech Synthesis for Punjabi

Statistical parametric speech synthesis (SPSS) methods gain popularity over concatenative speech synthesis by generating the speech waveforms from the written text using acoustic model based on parametric representations of speech signals. Firstly, the speech signal is segmented into fixed size of frames with one frame overlaps with other frame then, speech parameters are extracted for each frame. These include the excitation parameters such as fundamental frequency and spectral coefficients such as Mel frequency cepstral coefficients (MFCCs) and their dynamic counterparts known as first- and second-order time derivatives. These two types of parameters are extracted from the frequency domain representation of speech signal. Fast Fourier transformation is used to convert signal from time domain to frequency domain. In addition to this, power of a speech signal is calculated from its time domain representation.
Reflections on hateful responses to research on hate in the United States

While researching hate and gaining media attention beginning in 2018, we incurred a good amount of hate toward us and our research in the form of online comments and direct correspondence. Rather than just pass the comments off as general hate, we collected the text and analyzed it in an attempt to better understand the beliefs, perceptions, and arguments of those that rejected our efforts toward gaining knowledge on geographical hate trends. We use content analysis to identify categories of anti-hate research argumentation based on the comments. A discussion is provided on the implications of our findings and insight from our experiences is given.
Robust and Novel Dynamic Password Using SVD and Linear Systems Involving Euclidean Algorithm

In this modern era, where most of the world is connected on the World Wide Web, privacy and security are major concerns for the people. Passwords are used by the public to maintain a level of security in their private lives. Passwords can be of two types—static and dynamic. Existing dynamic passwords like OTPs suffer a major flaw in a man-in-the-middle attack (also known as MITM attack). The following proposal consists of a way to avoid the MITM attack, especially on processes like bank transactions. The proposed method tries to tackle this problem. A base random matrix is generated on the server, and a singular value decomposition (SVD) of that matrix is calculated. A vector is created using that and is encrypted before being sent as a dynamic password. The dynamic password is decrypted on the users’ phone using data in an encrypted cookie, and the result is sent back to obtain the base random matrix. A new base random matrix is generated in every session, thus making it secure.
An Integrated Grey Wolf Optimizer with Nelder-Mead Method for Workflow Scheduling Problem

Cloud computing is one of the latest distributed system paradigms that comes with the opportunity of running workflows at reduced costs since it does not require owning any infrastructure Scientific workflows refer to a series of computations that facilitates data analysis in both structured & distributed manners. This paper formulated a new mathematical modeling for scientific workflow scheduling (SWS) problem. The formulated optimization problem is considered a multiobjective optimization task where MakeSpan, Cost, Energy, and FlowTime are handled as the objective functions. This study proposes a new hybrid optimization algorithm based on Grey Wolf Optimizer and Nealder Mead Method for solving multi-objective SWS problems. The obtained results based on several workflow templates showed that the proposed algorithm outperformed the well-known Heterogeneous Earliest First Time (HEFT) and Distributed HEFT (DHEFT). Moreover, its performance was better than that of the benchmarking algorithms.
Deniable search of encrypted cloud-storage data

Cloud storage services have grown extensively in recent years. For privacy and security purposes, outsourced data must be encrypted. Therefore, searching the encrypted cloud-stored data is a big challenge. Although there are plenty of searchable encryption schemes, most rely on keywords located in the encrypted data instead of more complex searching criteria. Moreover, they do not consider the malicious coercer issue. In this paper, we propose a searchable encryption scheme that supports expressive keyword-matching conditions and provides deniable searchability to the user.
A Generalized Notion of Non-interference for Flow Security of Sequential and Concurrent Programs

For the last two decades, a wide spectrum of interpretations of non-interference <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The notion of non-interference discussed in this paper enforces flow security in a program and is different from the concept of non-interference used for establishing functional correctness of parallel programs [1] have been used in the security analysis of programs, starting with the notion proposed by Goguen & Meseguer along with arguments of its impact on security practice. While the majority of works deal with sequential programs, several researchers have extended the notion of non-interference to enforce information flow-security in non-deterministic and concurrent programs. Major efforts of generalizations are based on (i) considering input sequences as a basic unit for input/output with semantic interpretation on a two-point information flow lattice, or (ii) typing of expressions as values for reading and writing, or (iii) typing of expressions along with its limited effects. Such approaches have limited compositionality and, thus, pose issues while extending these notions for concurrent programs. Further, in a general multi-point lattice, the notion of a public observer (or attacker) is not unique as it depends on the level of the attacker and the one attacked. In this paper, we first propose a compositional variant of non-interference for sequential systems that follow a general information flow lattice and place it in the context of earlier definitions of non-interference. We show that such an extension leads to the capturing of violations of information flow security in a concrete setting of a sequential language. Finally, we generalize non-interference for concurrent programs and illustrate its use for security analysis, particularly in the cases where information is transmitted through shared variables.
Transfer Learning for Unseen Slots in End-to-End Dialogue State Tracking

This paper proposes a transfer learning algorithm for end-to-end dialogue state tracking (DST) to handle new slots with a small set of training data, which has not yet been discussed in the literature on conventional approaches. The goal of transfer learning is to improve DST performance for new slots by leveraging slot-independent parameters extracted from DST models for existing slots. An end-to-end DST model is composed of a spoken language understanding module and an update module. We assume that parameters of the update module can be slot-independent. To make the parameters slot-independent, a DST model for each existing slot is trained by sharing the parameters of the update module across all existing slots. The slot-independent parameters are transferred to a DST model for the new slot. Experimental results show that the proposed algorithm achieves 82.5% accuracy on the DSTC2 dataset, outperforming a baseline algorithm by 1.8% when applied to a small set of training data. We also show its potential robustness for the network architecture of update modules.
Expert system for digital single lens reflex (DSLR) camera recommendation using forward chaining and certainty factor

The advance of technology in the digital era has enabled the availability of numerous Digital Single Lens Reflex (DSLR) camera products in the market. However, this condition makes choosing a DSLR camera that matches consumer’s preferences difficult. This paper proposes an expert system to help recommend suitable DSLR cameras using Forward Chaining and Certainty Factor. Forward Chaining was implemented to infer a list of recommended DSLR camera and then, Certainty Factor was calculated to rank the DSLR camera recommendation list according to consumer’s predefined specification priority. The evaluation was carried out using questionnaires to measure user’s satisfaction towards the recommendation result and then represented in Mean Opinion Score (MOS) value. The evaluation produced a MOS value of 3.5 out of 4, proving that the expert system could perform relevant recommendations.