Reviewerly/exHarmony huggingface dataset:
Radar Waveform Recognition Using Fourier-Based Synchrosqueezing Transform and CNN

In this paper the problem of recognizing radar waveforms is addressed. Waveform classification is needed in spectrum sharing and radar-communications coexistence, cognitive radars and signal intelligence. Different radar waveforms exhibit different properties in time-frequency domain. We propose a deep learning method for waveform classification. The received signal is processed with Fourier synchrosqueezing transform that has excellent properties in revealing time-varying behavior, rate of, strength and number of oscillatory components in received signals. The resulting time-frequency description is represented as a bivariate image that is fed into a convolutional neural network. The proposed method has superior performance over the widely used the Choi-Williams distribution (CWD) method in distinguishing the polyphase waveforms even at low signal-to-noise ratio regime.
Emotion Analysis in Arabic Language Applying Transfer Learning

Applications of deep learning depend mainly on large training sets. In some cases a large data set is hardly found due to domain data rarity, data collection constraints and cost, or complicated data preparation and preprocessing. Deep transfer learning aims at exploiting the knowledge learned by a deep learning architecture to conduct another task in another domain. The problem of insufficient data for training in a target domain is manipulated by taking advantage of similar data, data distribution, network or task from a source domain. Both domains need to be related however they are different. Arabic emotion detection (ED) is a field characterized by lack of resources and available training sets are small for a deep learning implementation. On the other hand, applied deep Convolutional Neural Networks (CNNs) for sentiment analysis (SA) have reported enhanced results over the state-of-the-art. In this paper a deep CNN model is proposed for Arabic sentiment analysis based on character level representation. In addition, the model is used for applying Transfer Learning (TL) between sentiment analysis and emotion detection domains in Arabic language. The application intended to use Arabic character level features representation learned from a large sentiment data set to apply Arabic emotion detection. The implementation showed more enhanced performance in ED in terms of the accuracy measure.
Yemeni Paper Currency Detection System

With the great technological advances in colour printing, duplicating and scanning, counterfeit notes haves posed an increasing threat to currency markets all over the world. Over the past few years, the Yemeni currency market has suffered extensively from this problem. Although the Yemeni Paper Counterfeit- Currency Detection System (YPCDS) has made steps to counter this problem, it is limited to the 1000 denomination, leaving a considerable chance for counterfeiting other dominations. This paper offers a technical remedial solution to this problem for all denominations using image processing and machine learning techniques. The main aim of this system is to create an automatic system robot to recognize and detect Yemeni paper currency genuine or counterfeit. The system framework consists of five phases: acquisition of the currency image, preprocessing, feature extraction, classification, and verification. MATLAB is the main program for creating this system. The efficacy of the system was tested experimentally using four Yemeni currency categories and the system was found to be able to detect whether currency is genuine or counterfeit with high accuracy and fast processing time.
A Deep Learning Model for Source Code Generation

Natural Language Processing (NLP) models have been used extensively to study relationship among words in a corpus. Inspired by models such as n-gram we developed a model for analyzing source code via its Abstract Syntax Tree (AST). This study has applications in source code generation, code completion, and software forensics and investigation. The study also benefits software developers and programmers striving to improve code efficiency and speed up development process. With source code analysis, the Natural Language Tool Kit (NLTK) which is very useful in NLP, becomes severely limited by its inability to handle the semantic and syntactic properties of source codes. Therefore, we processed the source code datasets as Abstract Syntax Trees (ASTs) rather than the source code text itself to take advantage of the information-rich structure of the AST. The proposed model is built on the deep learning-based Long Short-Term Memory (LSTM) and Multiple Layer Perceptron (MLP) architectures. Results from our intrinsic evaluation on a corpus of python projects have demonstrated its ability of effectively predicting a sequence of source code tokens and show an improvement over previous work in this field.
Data Mining Algorithm for Cloud Network Information Based on Artificial Intelligence Decision Mechanism

Due to the rapid development of information technology and network technology, there is a lot of data, but the phenomenon of lack of knowledge is becoming more and more serious.Data mining technology has developed vigorously in this environment, and it has shown more and more vitality.Based on Spark programming model, this paper designs the parallel extension of fuzzy c-means.In order to enhance the performance of fuzzy c-means parallel expansion, the improvement strategy of k-means during the initialization phase is borrowed, and k-means// is extended to fuzzy c-means to obtain better clustering performance.Combined with Spark's programming model, this paper can obtain extended parallel fuzzy c-means algorithm.Several experiments on the data set of the algorithm proposed in this paper have shown good scalability and parallelism, effectively expanding fuzzy c-means clustering to distributed applications, greatly increasing the scale of the data processed by the algorithm.This improves the robustness of the algorithm and the adaptability of the algorithm to the shape and structure of the data, so that the parallel and scalable clustering algorithm can more effectively perform cluster analysis on big data.Three algorithms were simulated on MATLAB platform.We use simple data sets and complex two-dimensional data sets, and compare with the traditional fuzzy c-means algorithm and fuzzy c-means algorithm based on fuzzy entropy.Experiments show that the scalable parallel fuzzy c-means algorithm not only greatly improves the anti-noise performance, but also improves the convergence speed, and it can automatically determine the optimal number of clusters.
Ontology based Recommendation System for Domain Specific Seekers

Search engines are used to get the required information from the web. On the other hand, internet will not be available everywhere to search and even the search results on the web may not be the required information. Querying is one of the basic functionalities expected from database systems. Query efficiency will be adversely affected by increasing tables. Therefore, meta search engines combine the results of different search engines and improve the effectiveness of web search because of a wide coverage of web indexed data. Then the given query would be more specific to retrieve the more relevant information. By considering those problems, a system for recommendation is proposed using semantic similarity measure that refines the input query in a more specific manner for the generation of multiple queries. Initially, this method uses multiple queries instead of a single query with the help of wordnet ontology and it will result in a query-specific search. Semantics are applied on data and the database to make it meaningful and provide solution to those problems. Relationships among entities are not limited to syntactic constraints by applying semantic connections among it so that it identifies invisible, tacit and intangible among them. It extracts those hidden relationships between unrelated entity sets and stores them in a touchable form.
Gradients Cannot Be Tamed: Behind the Impossible Paradox of Blocking Targeted Adversarial Attacks

Despite their accuracy, neural network-based classifiers are still prone to manipulation through adversarial perturbations. These perturbations are designed to be misclassified by the neural network while being perceptually identical to some valid inputs. The vast majority of such attack methods rely on white-box conditions, where the attacker has full knowledge of the attacked network's parameters. This allows the attacker to calculate the network's loss gradient with respect to some valid inputs and use this gradient in order to create an adversarial example. The task of blocking white-box attacks has proved difficult to address. While many defense methods have been suggested, they have had limited success. In this article, we examine this difficulty and try to understand it. We systematically explore the capabilities and limitations of defensive distillation, one of the most promising defense mechanisms against adversarial perturbations suggested so far, in order to understand this defense challenge. We show that contrary to commonly held belief, the ability to bypass defensive distillation is not dependent on an attack's level of sophistication. In fact, simple approaches, such as the targeted gradient sign method, are capable of effectively bypassing defensive distillation. We prove that defensive distillation is highly effective against nontargeted attacks but is unsuitable for targeted attacks. This discovery led to our realization that targeted attacks leverage the same input gradient that allows a network to be trained. This implies that blocking them comes at the cost of losing the network's ability to learn, presenting an impossible tradeoff to the research community.
Remote surface motion measurements using defocused speckle imaging

When an object with a rough surface is illuminated using a coherent laser, the scattered light rays interfere to create a three-dimensional speckle field that fills the adjacent space. If the object is displaced or tilted, the entire speckle field moves in response. Therefore, the motion of an object can be measured remotely by tracking the movements of the objective speckle pattern recorded on a lensless imaging sensor placed within the speckle field. It is demonstrated that the same image acquisition can be made using a defocused camera, where the recorded speckle pattern corresponds to the image that would be recorded by a lensless sensor placed at the camera's focal plane. In this paper, this feature is experimentally demonstrated by showing the correspondence between the captured objective and defocused speckle patterns. The effective speckle field sampling position can then be freely chosen by controlling the camera focus distance, rather than needing to move the sensor physically. Furthermore, the defocused speckle patterns are scaled by the optical system in-focus magnification ratio, enabling further tuning of measurement sensitivity. This paper presents a series of displacement and tilt measurements designed to investigate the sensitivity characteristics of defocused speckle imaging. The measurements made at various object distances up to 16 m demonstrate the method's suitability for high-sensitivity remote measurement applications. A strategy to separate linear and rotational components under multiaxial object motion is also proposed.
ML-based Heterogeneous Container Orchestration Architecture

In recent years, the popularity of containerization technologies has been growing. When they are used, computational tasks are placed in lightweight containers that can be easily moved between different computing nodes. Containerization using Docker is especially popular at the moment. The use of these solutions opens up enormous opportunities for building distributed and cluster computing systems. To maintain the operability of such systems, special tools are used, and one of them is an orchestrator. However, existing orchestrators are focused on not-so-large computing systems in which performance can be maintained by simply moving computational tasks from non-working nodes to working ones. In large systems with many nodes and a huge number of computational tasks, it is also necessary to take into account the uneven consumption of resources by various tasks. This article proposes a system architecture that can solve the problem of container orchestration using machine learning methods and given the uneven consumption of resources by
Homomorphic Encryption Methods Review

Today, cloud technology continues to evolve. Using cloud services allows you to get financial benefitsio Still, many companies and users are in no hurry to transfer their infrastructure to the cloud due to incompletely resolved problems related to the security of data storage and processing. Since in the case of storing and processing open data, a cloud provider gets access to the data, and an attacker can also gain by hacking an account. In the case of encrypted data transmission to the cloud, confidentiality will be preserved only in data storage tasks, since data processing will require a decryption task. The use of homomorphic ciphers allows the processing of encrypted data without violating their privacy. Homomorphic encryption is actively beginning to be used in machine learning tasks to transfer and ensure the confidentiality of resource-intensive operations for training a neural network in the cloud. The article offers a review and comparison of existing methods of homomorphic encryption for machine learning tasks.
Beyond ‘mobility’: A new understanding of moving with technology

Abstract We report a surprising experience with mobile technology: the lead author found herself seeing and acting differently while running over part of her usual running track with the exercise‐tracking application ‘Strava’ on her phone, even without focal attention to the app. We apply the method of problematization to a detailed empirical account of this experience, in conjunction with a literature analysis of taken‐for‐granted assumptions underpinning research on ‘mobile technology use’. This reveals that, while the relationship of attention, perception, movement and technology was a key element of the surprise, these themes are not well accounted for in current IS literature. In response, we employ J. J. Gibson's ecological theory of visual perception to reinterpret the empirical account and thereby build a new understanding of the human plus mobile technology that we term moving‐with‐technology . We introduce to IS: moving‐with‐technology as a new analytical perspective; the new phenomena of digital sub‐species , digital‐niches and asynchronous co‐location ; and stimulus for new ecologically oriented ‘mobile methods’. Moving‐with‐technology also has practical implications for urban planners who are using data from digital trace‐making tools such as Strava in their decision‐making, thereby generating what we call ecological feedback loops .
A Multi-time-scale Time Series Analysis for Click Fraud Forecasting using Binary Labeled Imbalanced Dataset

Click fraud refers to the practice of generating random clicks on a link in order to extract illegitimate revenue from the advertisers. We present a generalized model for modeling temporal click fraud data in the form of probability or learning based anomaly detection and time series modeling with time scales like minutes and hours. The proposed approach consists of seven stages: Pre-processing, data smoothing, fraudulent pattern identification, homogenizing variance, normalizing auto-correlation, developing the AR and MA models and fine tuning along with evaluation of the models. The objective of the proposed work is to first, model multi-time-scale time series data on AR/MA by relying only on time and the label without the need of too many attributes and secondly, to model different time scales separately on Auto-regression (AR) and Moving Average (MA) models. Then, we evaluate the models by tuning forecasting errors and also by minimizing Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) to obtain a best fit model for all time scale data. Through our experiments we also demonstrated that the Probability based model approach is better as compared to the Learning based probabilistic estimator model.
Exploring the Channels of Multiple Color Spaces for Age and Gender Estimation from Face Images

Soft biometrics identify certain traits of individuals based on their sampled biometric characteristics. The automatic identification of traits like age and gender provides valuable information in applications ranging from forensics to service personalization. Color images are stored within a color space containing different channels. Each channel represents a different portion of the information contained in the image, including these of soft biometric patterns. The analysis of the age and gender information in the different channels and different color spaces was not previously studied. This work discusses the soft biometric performances using these channels and analyzes the sample error overlap between all possible channels to successfully prove that different information is considered in the decision making from each channel. We also present a multi-channel selection protocols and fusion solution of the selected channels. Beside the analyzes of color spaces and their channels, our proposed multi-channel fusion solution extends beyond state-of-the-art performance in age estimation on the widely used Adience dataset.
Is Cyber Security Enough- A study on Big Data Security Breaches in Financial Institutions

The evolution of technology has turned out to be both a boon and a bane for the financial industry- invaders are getting increasingly creative in their attacks. The financial sector across the globe is facing the heat of data breaches due to the strategic position it holds in the national and global economy. The study presents MECA (Model to Encounter Cyber Attacks) which could be adopted by financial institutions. MECA proposes the intelligent application of IDS, Security Patches and Big Data Analytics to mitigate cyber challenges in financial institutions. The focus of MECA is to technologically orient institutes to handle dynamic cyber breaches, to train employees to effectively apply this intelligent application and to develop them as the first line of defense to cyber-attacks. The paper also looks into the actors in a cyber-attack and the tools predominantly used to reach their objective. It casts light over the general cyber-attack framework, its stages and the effect it has on the financial institution system.
Multi-Feature Learning by Joint Training for Handwritten Formula Symbol Recognition

Given the similarity of handwritten formula symbols and various handwriting styles, this paper proposes a squeeze-extracted multi-feature convolution neural network (SE-MCNN) to improve the recognition rate of handwritten formula symbols.The system proposed in this paper integrates the eightdirectional feature of the original sequence in the convolutional layer, which significantly compensates for the lost dynamic trajectory information in the handwritten formula symbol.Meanwhile, the joint loss is constructed to improve the discriminability of features in the way of supervised learning, which enlarges the inter-class difference and decreases inner-class similarity.The standard mathematical formula symbol library provided by the Competition Organization on Recognition of Online Handwritten Mathematical Expression (CROHME) is used to verify the effectiveness of the proposed algorithm.Experiments show that the proposed SE-MCNN approach outperforms the state-of-the-art methods even at the condition of without using the data augmentation.
Drone Forensics: A Case Study on DJI Phantom 4

Unmanned Aerial Vehicles (UAVs) (a.k.a drones) have grown in popularity mainly due to its' ease of use, wide variety of uses, availability and inexpensiveness nature of the devices. This rapid proliferation of UAVs has also augmented with several security issues and societal crimes pertaining to the illicit activities, making them rich sources of evidence. Therefore, it is crucial for digital forensics examiners to have the capability to recover, analyze, and authenticate the source of content stored on these devices. In this research, we perform a forensic investigation on an Unmanned Aircraft System, specifically the DJI Phantom 4 Vision, using several smartphone devices such as iPhone 6, iPhone 7 Plus, iPhone 10, Samsung Note 3, Samsung S7, Microsoft Lumia, CKTEL G5 Plus and G-Tide_s4 with different operating systems (iOS, Windows Phone and Android). In addition, we investigate and examine the logical backup acquisition of the iPhone 6, iPhone 7 Plus and iPhone 10 mobile devices using Apple iTunes backup utility. It was found that the DJI Phantom 4 App contains a significant amount of forensics data. Moreover, we acquired useful data from the SD card of mobile devices including controller and the drone.
From playing games to committing crimes: A multi-technique approach to predicting key actors on an online gaming forum

We propose a systematic framework for analysing forum datasets, which contain minimal structure, and are nontrivial to analyse at scale, aiming to support future analysis of underground forum communities. We use a multi-technique approach which draws on a combination of features, including post classifications extracted using natural language processing tools, and apply clustering and predictive techniques to this dataset, to predict potential key actors-individuals who have a central role in overtly criminal activities, or activities which could lead to later offending, and hence might benefit most from interventions. We predict 49 key actors on an underground gaming-specific cheating and hacking forum, validated by observing only overlaps of techniques, combined with topic analysis, to build a classifier for key actor status. In addition, we also use these techniques to provide further insight of key actor activity. We found one cluster and two posting trajectories to contain a high proportion of key actors, logistic regression found an actor's h-index to have higher odds for prediction than other features, and partial dependence plots found reputation to have a significant change in prediction between values of 100 to 1000.
Shift invariant extrema based feature analysis scheme to discriminate the spiculation nature of mammograms

Since uncontrolled growth of malignant masses introduces uneven shape irregularities and spiculations in the boundary, shape representing shift invariant features are essential to resolve the problem of discrimination. However, ambiguous nature of shape, size, margin, orientation of masses produces imprecise feature values. In this view, a new concept of extrema based feature characterization scheme is proposed for capturing radiating nature of mass morphology. Computation of extrema patterns needs only few algorithmic steps. Beside this, present study employs an automated enhancement procedure to improve the classification accuracy. Experimental results show that extrema characterization reduces the feature redundancy to produce high efficiency in reasonably low time.