Reviewerly/exHarmony huggingface dataset:
Subscriber Gender Prediction in Telecom Using Deep Learning

Organizations are changing their strategies to be called as data-driven. With this they get the benefit of estimating achievable targets, predicting budget allocations and goal-settings. But the true challenge lies in how accurate the considered data is. In this work, we propose various ways to derive one of the most sought after user information, the gender of a user from his/her presence in a telecommunications system. We considered the data from reality shows with prize money where gender is supposed to be mentioned correctly. We considered the user call pattern he/she does to find gender specific behaviour. We considered the app usage data like games and the type of Advertisements (Ads) a user reacts, to understand the uniqueness. We also considered browsing data of a user to highlight a significant difference in their behavior. Based on deep learning techniques, a model is built to apply the above sources as input to predict the gender with an accuracy of 81.2 percent. The major contribution in this work is towards predicting the gender from various telecom data sources of huge data sizes to a high accuracy.
An Improved Incremental Structure Learning Algorithm for Bayesian Networks

As traditional batch Bayesian structure algorithm may not meet the the new data stream. In such environment, it is important to learn Bayesian structure by modifying the current structure incrementally. In this paper, a new incremental learning algorithm for Bayesian network structures (IBN) is proposed. IBN learning is decomposed into three phases: improved batch Bayesian structure learning, structure matching judgment and structure revision. In the improved batch learning procedure, improved immune operator, improved self-adaptive crossover operator and mutation operator are embedded to simplified hill climbing algorithm, so that obtain a best structure which has better performance in accuracy and efficiency, at the same time a function is constructed to monitor new data stream. In the structure matching judgment procedure, a new evaluation criterion “IBIC” (implicit Bayesian information criterion) is proposed based on BIC and implicit method. In the structure refinement procedure, the mutation and climbing operators are used to correct existing structure automatically. Intensive experiments were conducted to evaluate our algorithm, compare with incremental Max-Min parents and Children (iMMPC) and improved hill climbing search(iHCS). Experiments results shown that accuracy and efficiency of our algorithm are better than iMMPC and iHCS. Besides, this paper provided a new way for incremental Bayesian structure learning in the future.
Face Detection Techniques Using Database Images

The paper presents a review of proposed face detection techniques.Face Detection using captured face pictures still a complicated task in the area of image processing, and presently it's an operative area of analysis.Automatic face detection is a critical process in face recognition, facial expression recognition.Using an ineffective algorithm, there will be a negative impact on the performance of face detection and face recognition.The face detection technique is not only a step in face recognition technique; however, it is also a self-sufficient widely-used technique.Developing a sustainable feature for face detection technique is necessary.Face detection has attracted massive attention as a result of its several demands in computer vision, pattern recognition, and Biometrics.Face detection may be a technique to analyze a face from several images that have many features in that image.The face detection process is necessary for facial emotion recognition, movement of the face, and posture assessment.Face detection may be a difficult task as a result of looks don't seem to be clear, and it may modify the length, height, width, appearance, tone, etc. Face detection becomes an additional difficult process whereas inputted image not visible and hidden by some various factors and not at all correct light conditions, etc.The suggested technique should be capable of analyzing the facial region including high accuracy, efficiency, and minimum errors.
Learning Question Similarity

Question-answer platforms are trending over the internet nowadays. Clustering questions which ask the same question is a challenging problem faced by such platforms. The authors will discuss and implement techniques that will tell them whether a given pair of questions are asking the same question. They will do this by finding the semantic relationship between the questions. Manhattan Long short-term memory model with Area Under ROC Curve 0.81 is the model which performs the best among all the trained and pre-trained models which they will be discussing in this paper.
A Novel Optimization AHBeeP Algorithm for Routing in MANET

The world around us is becoming increasingly complex every day and changes dynamically. The problems that we face require adaptive and scalable systems that can offer solutions with ever-rising level of autonomy. Traditional approaches are becoming obsolete because they were designed for a simpler world. Therefore, any advancement in understanding and solving complex problems can have an impact on the entire set of disciplines in engineering, biology, sociology, etc. In this paper the ant colony optimization (ACO), genetic algorithm is evaluated and compares their performance with the novel proposed adaptive honey bee protocol (AHBeeP). The algorithms, stimulated by the supportive behavior of nature in colonies of animals and social insects, were initially applied to solve the traditional optimization problems. In today’s scenario, the main challenge is to transfer the packets of data from source system to destination system. In the proposed approach, the optimization is used for transferring the data packets based on the honey bees intelligence to communicate each other in the form of dancing language that can be useful for finding the shortest route in the wireless networks and also in optimized way of pathfinding.
Vehicle Detection and Classification in Difficult Environmental Conditions Using Deep Learning

The time drivers spend stuck in traffic is increasing annually, on a global level. Time lost in traffic imposes costs both economically and socially. Tracking congestion throughout the road network is critical in an Intelligent Transportation System (ITS), of which vehicle detection is a core component. Great strides have been made in deep learning over the last few years particularly with the convolutional neural network (CNN), a deep learning architecture for image recognition and classification. One area in image recognition where the use of CNN has been studied is vehicle detection. This paper explores an area of vehicle detection where a little study has been made, that is the detection and classification of vehicles in difficult environmental conditions. The purpose of this paper is to build a CNN able to detect vehicles from low resolution, highly blurred images in low illumination and inclement weather and classify the vehicles in one of five classes. The final model built in this paper is able to achieve 92% classification accuracy on images in difficult environmental conditions. This model can be deployed to a smart traffic management system.
Region Pooling with Adaptive Feature Fusion for End-to-End Person Recognition

Current approaches for person recognition train an ensemble of region specific convolutional neural networks for representation learning, and then adopt naive fusion strategies to combine their features or predictions during testing. In this paper, we propose an unified end-to-end architecture that generates a complete person representation based on pooling and aggregation of features from multiple body regions. Our network takes a person image and the predetermined locations of body regions as input, and generates common feature maps that are shared across all the regions. Multiple features corresponding to different regions are then pooled and combined with an aggregation block, where the adaptive weights required for aggregation are obtained through an attention mechanism. Evaluations on three person recognition datasets - PIPA, Soccer and Hannah show that a single model trained end-to-end is computationally faster, requires fewer parameters and achieves improved performance over separately trained models.
Understanding the future of Deep Reinforcement Learning from the perspective of Game Theory

Abstract This paper is to discuss the development of Deep Reinforcement Learning and the future of it from the perspective of Game Theory. The relationship and potential interaction between these two areas are also introduced, especially the optimization method. This paper discusses about the situations both under non-cooperative and cooperative game. Recently, Artificial Intelligence (AI) and Machine Learning (ML) have grasp sufficient attention from various research areas. Deep Reinforcement Learning, as one of the most promising ML methods, enlighten more researchers to devote themselves in this area. However, even such accomplishment could not belie that, for most kinds of real-life problems, DRL is still unable to provide with an optimal strategy. Because unlike the well-adjusted environment in laboratory, real life problems are not always able to be converted into mathematical problems. Under such circumstances, most of real-life problems have no nominal “optimal solution”. Game Theory provides potential solutions to covert “real issues” into “mathematical problems”, then it is easier for researchers to handle.
An Efficient Adaptive Online Neural Spikes Detection and Classification Engine Based on Bayesian Inference

A new method, called Bayesian inference-based template matching (BIBTM) method, is proposed in this article, which is designed to detect and classify neural spikes from real neural signals. Through this spike detection and classification method, the templates do not need be given in advance, and they can be automatically generated. To evaluate the performance of our method, we built signals with diverse signal-to-noise ratios and firing rates, and also researched two spike template generation methods. Based on the experimental results and comparison, BIBTM method has excellent detection performance. The true positive rates (TPR) and false positive rates (FPR) of the spike detection can reach 0.92 and 0.05 respectively, and the average FPR and average TPR of the spike classification can reach 0.05 and 0.6 respectively. From the discussion and analysis, our proposed BIBTM method not only has high detection and classification accuracy, but also has a simple structure and low complexity.
Machine Learning Assisted PHYSEC Attacks and SLP Countermeasures for Multi-Antenna Downlink Systems

Most physical-layer security (PLS) works employ information theoretic metrics for performance analysis. In this paper, however, we investigate PLS from a signal processing point of view, where we rely on bit-error rate (BER) at the eavesdropper (Eve) as a metric for information leakage. Recently, symbol-level precoding (SLP) has been shown to enhance PLS in the presence of an Eve. In this work, nonetheless, we introduce a machine learning (ML) based attack to which even SLP schemes can be vulnerable. Namely, this attack manifests when an Eve utilizes ML in order to learn the precoding pattern when precoded pilots are sent. With this ability, an Eve can decode data with favorable accuracy. As a countermeasure to this attack, we propose a novel security enhanced precoding technique. The proposed countermeasure yields high BER at the Eve, which makes symbol detection practically infeasible for the latter, thus providing physical-layer security between the base station (BS) and the users. In the numerical results, we validate both the attack and the countermeasure, and show that this gain in security can be achieved at the expense of only a small additional power consumption at the transmitter.
An analysis of the calibration of angle difference in dual-illumination digital holographic interferometry for surface shape measurement

In dual-illumination digital holographic interferometry for surface shape measurement, the calibration of the angle difference is of significance to the measurement accuracy. For commonly used calibration method, the angle difference is calculated from the linear phase corresponding to the reference flat region that is placed behind the object or directly selected from the object, which requires the precise alignment to the system and easily cause the measurement error. In this paper, the effect of the calibration accuracy of the angle difference on measurement results is first studied, and then the problems existing in commonly used calibration method are analyzed. Finally, the solution based on precision hardware control is presented, and the experimental results demonstrate its effectiveness.
Accurate Image Recognition in Convolutional Neural Networks Based on Two-dimensional Discrete Fourier Transform

A two-dimensional discrete Fourier transform (DFT) can transform an image from a spatial domain to a frequency domain. This conversion can more intuitively observe and process the image from the perspective of the frequency domain, and is more advantageous for operations such as frequency domain filtering. In recent years, convolutional neural networks (CNNs) have become a hot topic in the field of image recognition. Relevant algorithms are generally preprocessed using two-dimensional DFT before inputting the network. However, in practice, most common CNNs are processed in the real number domain, and the frequency domain information and phase information cannot be fully utilized. This paper aims to exploring the application of two-dimensional DFT in CNNs for image recognition. Due to the globality of Fourier transform, we use the grayscale image of human face that has been aligned as the training data to design the experiment. The experiment includes the following steps: the feature is identified in the frequency domain after the image undergoes a two-dimensional DFT; the frequency domain image after the two-dimensional DFT is input together with the original image to expand the input layer of the CNNs and is then identified. The experiment shows that the image can also recognize the features in the frequency domain after the two-dimensional DFT when the frequency domain features are good and the recognition accuracy can be greatly improved.
Cybersecurity

The internet has revolutionized the way people communicate, how they manage their business, and even how they conduct their studies. Organizations can conduct meetings virtually and store all their data online. With this convenience, however, comes the risk of cybercrime (CC). Some of the world's most renowned organizations have found themselves having to incur huge recovery costs after falling prey to CC. Higher learning institutions' databases are increasingly falling victim to CCs, owing to the vast amounts of personal and research data they harbor. Despite this, the area of CCs in learning institutions remains understudied. This chapter seeks to identify how CC is manifested in such institutions and the specific cybersecurity measures that stakeholders could use to minimize their exposure to the same. The qualitative case study was designed to explore the research questions, and collected data through semistructured interviews. The findings showed hacking, phishing, and spoofing as the most common manifestations of cybercrime in higher learning institutions.
A Two-step Feature Extraction Algorithm for Face Recognition

Feature extraction is very important for face recognition. In the past, most of the methods directly transform samples from a high-dimensional space to a new low-dimensional subspace. In this paper, we propose a novel method for feature extraction. This method can get a new sample from the original sample, and the size of the new sample is much smaller than the original sample, but keep the core information of the original sample. Since this method can be combined with any feature extraction method after this step; we call it two-step feature extraction method. From experiments, we can find that our method is effective and efficient.
AI driven Surveillance in Cognitive Radio

Detecting the different types of wireless signals entering your system is very important. Including the detection whether the signal is jammed or not. This paper deals with heterogeneous signal detection based on modulation techniques, including the detection of a jammed signal. Convolutional Neural Network(CNN) is used to classify the type of incoming signal by using its amplitude and phase as input features and then compare which feature provides better accuracy. In order to detect jamming, we observe the effects of jamming on channel parameters such as channel power, channel bandwidth and also the amplitude changes for a BPSK modulated DSSS signal in the presence and absence of a jammer.
Fuzzyfication of Repeatable Trust Game

The purpose of the work is to check what consequences in the interpretation of the results of experiments under the so-called trust games has a fuzzy approach. In previous considerations it was assumed that players’ behaviors are implementations of specific probability distributions. For many reasons, the probabilistic approach can be considered inappropriate and too simplistic when explaining the choice of strategy. The study reinterpreted the obtained results of experiments assuming that the described behavior model is a model based on the fuzzy set theory.
Multi-task network based pedestrian re-identification

Person re-identification is widely regarded as an image retrieval problem. Given a pedestrian-of-interest (query) image in one camera, the person re-identification system aims to identify the pictures of the same person from an image pool (gallery). Due to the differences in camera pixels, pose, illumination, occlusion, and intra-class variations across different cameras, the task of person re-identification remained challenging to the community of computer vision scientists. In this paper, we propose a multi-task network, based on a uniform partition network, which computes the identification loss and verification loss of two input images simultaneously. Given a pair of images as input, the system predicts the identities of the two input images and outputs a similarity score at the same time, to indicate whether they belong to the same identity or not. To get more fine-grained part-level features, we adopted the part-based convolutional baseline network for feature extraction of each input image and output a convolutional descriptor consisting of six local features. Our model achieved 81.19% mAP and 93.34% rank-1 accuracy on Market-1501 datasets. It also achieved 72.12% mAP and 85.59% rank-1 accuracy on DukeMTMC-reID. Comparing them with those of state-of-the-art, our model outperformed the state-of-the-art by a margin of 3.79 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> mAP, 1.03% rank-1, and 6.02% mAP, 3.79% rank-1 on Market-1501 and DukeMTMC-reID, respectively.
A Novel Approach for Blog Feeds Recommendation Based on Meta-data

As the blogosphere continues to grow, finding good quality blog feeds has been very time consuming and requires much effort. So, recommending blog feeds, which handle topics close to user interests, can be useful. Recently, the number of bloggers who use the subscription services has been increasing. Subscription is a service using protocols like RSS and ATOM, which notify users when new entries are posted on the blogs that the users register for subscription. In this paper, we present an effective and efficient approach to recommending log feeds based on the subscription lists and meta-data of blogs. In order to find blogs that handle topics close to the blogs in subscription lists, we first model the topic of blogs by collecting and expanding tags in the blogs, and we then compare the topic models of blogs for recommendation. Also, the usefulness of blogs is an important factor. For choosing useful blogs, we adopt the update frequency and number of subscribers because useful blogs are the ones in which new entries will be frequently posted and to which many users will subscribe. In order to validate the proposed blog recommendation algorithm, experiments on real blog data have been conducted, and the results of experiments show that our blog feed recommendation can satisfy users who want to subscribe to blog feeds.
Designing an architecture description language for nontechnical actors and purposes (NOTE-ADL)

Purpose The paper proposes the development of a graphical architecture description language (ADL) that allows a better understanding of software architectures for nontechnical actors and purposes and, beyond, can serve as a communication tool between domain experts and IT experts, for instance, in a software development process. Design/methodology/approach The paper follows the methods and guidelines of design science research. By deriving characteristics and general requirements for ADLs from a research literature review and from industry standards, the paper provides a conceptual modeling approach for an ADL. The model design is based on typical requirements and suggestions derived from literature and related work. The application possibilities and advantages are then demonstrated with a usage scenario. Findings The paper elaborates a user-oriented ADL that makes software architecture comprehensible for stakeholders and end users. It provides a high level of abstraction and, thus, is not restricted to a particular domain. The paper also provides a corresponding modeling editor as well as an underlying catalogue with symbols and rules for the ADL. Research limitations/implications As this is a conceptual study, the ADL has not been practically evaluated yet. Thus, the usefulness of this academic approach for the industry remains to be validated. Originality/value The elaborated ADL can serve as a language to visualize software architectures, particularly in the business domain, in a comprehensible manner. Still, it retains the structured character of ADLs to facilitate communication on an IT-near level. In including nontechnical actors, the approach broadens the overall application capabilities of ADLs.
Design and Optimization of pn Junctions in Silicon Microdisk Modulators

An optoelectronic modulator is a key building block in silicon photonic telecommunication circuits. The dominant modulation mechanism uses free-carrier depletion in microring resonators, for its combined advantages of high speed, low power, low spatial footprint, and low fabrication cost and complexity. Recent advancements show that a microdisk modulator, with its pn junction oriented vertically, achieves a larger modulation efficiency due to the stronger interaction with the resonant optical mode. In this work, we verify and compare the simulated performance of metaheuristically optimized lateral and vertical pn junction microdisk modulators that are otherwise identical. We also present two new configurations: The double vertical and staggered double vertical pn junction microdisk modulators. Both of these exhibit stronger mode-junction interactions and greater modulation efficiencies than the state-of-the-art designs.