Reviewerly/exHarmony huggingface dataset:
Coarse-grained generalized zero-shot learning with efficient self-focus mechanism

For image classification in computer vision, the performance of conventional deep neural networks (DNN) may usually drop when labeled training samples are limited. In this case, few-shot learning (FSL) or particularly zero-shot learning (ZSL), i.e. classification of target classes with few or zero labeled training samples, was proposed to imitate the strong learning ability of human. However, recent investigations show that most existing ZSL models may easily overfit and they tend to misclassify the target instance as one class seen in the training set. To alleviate this problem, we proposed an embedding based ZSL method with a self-focus mechanism, i.e. a focus-ratio that introduces the importance of each dimension, into the model optimization process. The objective function will be reconstructed according to these focus-ratios encouraging that the embedding model focus exclusively on important dimensions in the target space. As the self-focus module only takes part in the training process, the over-fitting knowledge is apportioned, and hence the rest embedding model can become more generalized for the new classes during test. Experimental results on four benchmarks, including AwA1, AwA2, aPY and CUB, show that our method outperforms the state-of-the-art methods on coarse-grained ZSL tasks while not affecting the performance of fine-grained ZSL. Additionally, several comparisons demonstrate the superiority of the proposed mechanism.
A model for bimodal rates and proportions

The beta model is the most important distribution for fitting data with the unit interval. However, the beta distribution is not suitable to model bimodal unit interval data. In this paper, we propose a bimodal beta distribution constructed by using an approach based on the alpha-skew-normal model. We discuss several properties of this distribution, such as bimodality, real moments, entropies and identifiability. Furthermore, we propose a new regression model based on the proposed model and discuss residuals. Estimation is performed by maximum likelihood. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the results. An application is provided to show the modelling competence of the proposed distribution when the data sets show bimodality.
High-precision 3D shape measurement of rigid moving objects based on the Hilbert transform

Phase-shifting profilometry (PSP) is a three-dimensional (3D) measurement method of point-to-point calculation. The consistency of object position is the prerequisite to ensure the successful application of PSP in moving objects. The position information of an object can be well characterized by the modulation patterns, and hence a high-quality modulation pattern is the guarantee of pixel-matching accuracy. In this paper, a generic modulation pattern enhancement method for rigid moving objects based on the Hilbert transform is proposed. First, the Hilbert transform is employed to suppress the zero-frequency components of the fringe pattern, and a hybrid digital filter window is applied to filter out the positive fundamental frequency components for a higher signal-to-noise ratio. Then the grid-based motion statistics for fast, ultra-robust feature correspondence algorithm is used to match the high-quality modulation patterns between two adjacent frames, and the object positions in the three deformed patterns are made consistent by image clipping. Finally, the three-step PSP is used to reconstruct the 3D shape of the measured object. Experimental results demonstrate that the proposed method can substantially improve the quality of the modulation pattern, achieve high-precision pixel matching, and ultimately reduce the motion-introduced phase error.
Phishing Website Detection Using Fast.ai library

In recent years, we see an enormous amount of increase in online activities whether it be for using an internet banking services or for using social media or for education purpose. On other side, most of the peoples did not focus on the privacy because either they are not well aware about the harm of privacy breach or they simply neglect the security part since security comes at the cost of some constraints. Because of these reasons we are witnessing a rapid increase in cyber security attacks. Now-a-days one of the top ten cyber-attacks is phishing attack. Phishing attack is an attempt to mimic the legitimate websites to obtain or gain the confidential data such as credit or debit card details, username and password, etc. The problem of phishing attack detection is addressed so many times. Recent advancements in this domain is classification using the deep learning techniques. However the results of deep learning technique highly depends on the architecture of the system and parameter tuning of learnable parameters. Most of the previous techniques requires high computational power systems to train the deep learning or neural network models. Hence, such approaches requires more time to train the model. In this paper, we propose a convolutional neural network architecture using fast.ai library across GPU which solves the problem of traditional neural networks. By applying the proposed method, we were able to improve the phishing detection accuracy and training time when compared to other existing approaches. We use the ISCX-URL-2016 dataset to train our model for binary classification as phishing and legitimate. We got the accuracy of 99% for phishing URL's detection.
A machine learning-based forensic tool for image classification - A design science approach

Various fields have benefited from machine learning (ML) applications (e.g., health, finance, military), mainly for automating tasks or improving decision making capability. The area of digital forensics shows potential promise as categorizing content of seized devices for potential evidentiary value is a complicated task due to the increasing amount of data that needs to be processed. A rapid adaptation of ML-based products for such a real-life application requires the creation of engineering knowledge that would make ML models more accessible for developers while incorporating human-centric validation into user acceptance procedures. This study proposes a development process to incorporate pre-trained models to the development life-cycle of an ML-based digital forensic tool that classifies images. We applied the process and created a prototype tool that identifies handguns by rigorously following the design science methodology. We evaluated four ImageNet-trained models: InceptionV3, Xception, ResNet and VGG16. Using realistic datasets and various decision criteria, we selected the outperforming model and utilized it in the tool. The usability and learnability of the prototype were measured using the System Usability Scale (SUS) questionnaire as an early-phase usability test. Our tool was able to achieve an acceptable rating in the SUS results from the average of five respondents. This study successfully demonstrated that freely available pre-trained machine learning models without fine-tuning are feasible for use in practice.
Automated Collaborator Selection for Federated Learning with Multi-armed Bandit Agents

Rapid change in sensitive behaviour and profile of distributed mobile network elements necessitates privacy preserving distributed learning mechanism such as Federated Learning. Moreover, this mechanism needs to be robust that seamlessly sustains the jointly trained model accuracy. In order to provide a automated management of the learning process in FL on datasets that are not independently and identically distributed (non-iid), we propose a Multi-Arm Bandit (MAB) based method that helps the federation to select the nodes that benefits the overall model. This automated selection of the training nodes throughout each round yielded an improvement in accuracy, while decreasing network footprint.
Federated Multi-task Learning for Complaint Identification from Social Media Data

Complaining is a speech act that is often used by consumers to signify a breach of expectation, i.e., an expression of displeasure on a consumer's behalf towards an organization, product, or event. Complaint identification has been previously analyzed based on extensive feature engineering in centralized settings, disregarding the non-identically independently distributed (non-IID), security, and privacy-preserving characteristics of complaints that can hamper data accumulation, distribution, and learning. In this work, we propose a Bidirectional Encoder Representations from Transformers (BERT) based multi-task framework that aims to learn two closely related tasks,viz. complaint identification (primary task) and sentiment classification (auxiliary tasks) concurrently under federated-learning settings. Extensive evaluation on two real-world datasets shows that our proposed framework surpasses the baselines and state-of-the-art framework results by a significant margin.
Sensor positioning for a human activity recognition system using a double layer classifier

This paper describes the development of a human gait activity recognition system. A multi-sensor recognition system, which has been developed for this purpose, was reduced to a single sensor-based recognition system. A sensor election method was devised based on the maximum relevance minimum redundancy feature selector to determine the sensor's optimum position regarding activity recognition. The election method proved that the thigh has the highest contribution to recognize walking, stairs and ramp ascending, and descending activities. A recognition algorithm (which depends mainly on features that are classified by random forest, and selected by a combined feature selector using the maximum relevance minimum redundancy and genetic algorithm) has been modified to compensate the degradation that occurs in the prediction accuracy due to the reduction in the number of sensors. The first modification was implementing a double layer classifier in order to discriminate between the interfered activities. The second modification was adding physical features to the features dictionary used. These modifications succeeded to improve the prediction accuracy to allow a single sensor recognition system to behave in the same manner as a multi-sensor activity recognition system.
Normal inverse Gaussian autoregressive model using EM algorithm

In this article, normal inverse Gaussian (NIG) autoregressive model is introduced. The parameters of the model are estimated using expectation maximization (EM) algorithm. The efficacy of the EM algorithm is shown using simulated and real-world financial data. It is shown that NIG autoregressive model fit very well on the considered financial data and hence could be useful in modelling of various real-life time-series data.
Lessons Learned From Applying the NIST Privacy Framework

Contact tracing can be beneficial, such as helping to curb the spread of disease, but the handling of the resulting data poses privacy-related risks. This article provides some lessons learned and recommendations regarding the application of the NIST Privacy Framework.
Human Motion Enhancement via Tobit Particle Filtering and Differential Evolution

This paper proposes a novel approach to improve the quality of human motion data captured by a depth sensor. Depth-based motion capture (D-Mocap) data often suffer significant errors due to noise, self-occlusion, interference, and other algorithmic limitations. We aim to improve 3D joint trajectories to be more kinematically admissible and anthropometrically consistent. The Tobit model is incorporated with a particle filter (TPF) to handle censored measurements. We also embed the DE algorithm in the TPF, which allows particles to be re-distributed and re-weighted according to bone length consistency before re-sampling. This integration leads to a new TPF-DE algorithm that harmoniously takes advantage of kinematic and anthropometric constraints. We compare our methods with several nonlinear Kalman filters and deep learning-based methods to demonstrate the efficacy of TPF-DE on both simulated and real-world D-Mocap data.
Artificial Bee Colony Algorithm with an Adaptive Search Manner

Artificial bee colony (ABC) can effectively solve some complex optimization problems. However, its convergence speed is slow and its exploitation capacity is insufficient at the last search stage. In order to solve these problems, this paper proposes a modified ABC with an adaptive search manner (called ASMABC). There are two important search manners: exploration and exploitation. A suitable search manner is beneficial for the search. Then, an evaluating indicator is designed to relate the current search status. An explorative search strategy and another exploitative search strategy are selected to build a strategy pool. According to the evaluating indicator, an adaptive method is used to determine which kind of search manner is suitable for the current search. To verify the performance of ASMABC, 22 complex problems are tested. Experiment result shows that ASMABC achieves competitive performance when contrasted with four different ABC variants.
Forensicast: A Non-intrusive Approach &amp; Tool For Logical Forensic Acquisition &amp; Analysis of The Google Chromecast TV

The era of traditional cable Television (TV) is swiftly coming to an end. People today subscribe to a multitude of streaming services. Smart TVs have enabled a new generation of entertainment, not only limited to constant on-demand streaming as they now offer other features such as web browsing, communication, gaming etc. These functions have recently been embedded into a small IoT device that can connect to any TV with High Definition Multimedia Interface (HDMI) input known as Google Chromecast TV. Its wide adoption makes it a treasure trove for potential digital evidence. Our work is the primary source on forensically interrogating Chromecast TV devices. We found that the device is always unlocked, allowing extraction of application data through the backup feature of Android Debug Bridge (ADB) without device root access. We take advantage of this minimal access and demonstrate how a series of artifacts can stitch together a detailed timeline, and we automate the process by constructing Forensicast – a Chromecast TV forensic acquisition and timelining tool. Our work targeted (n=112) of the most popular Android TV applications including 69% (77/112) third party applications and 31% (35/112) system applications. 65% (50/77) third party applications allowed backup, and of those 90% (45/50) contained time-based identifiers, 40% (20/50) invoked some form of logs/activity monitoring, 50% (25/50) yielded some sort of token/cookie, 8% (4/50) resulted in a device ID, 26% (13/50) produced a user ID, and 24% (12/50) created other information. 26% (9/35) system applications provided meaningful artifacts, 78% (7/9) provided time based identifiers, 22% (2/9) involved some form of logs/activity monitoring, 22% (2/9) yielded some form of token/cookie data, 22% (2/9) resulted in a device ID, 44% (4/9) provided a user ID, and 33% (3/9) created other information. Our findings also illustrated common artifacts found in applications that are related to developer and advertising utilities, mainly WebView, Firebase, and Facebook Analytics. Future work and open research problems are shared.
Research on Machine Learning Optimization Algorithm of CNN for FPGA Architecture

Abstract In order to meet the application requirements of deep learning detection and recognition algorithm for Field Programmable Gate Array (FPGA) computing structure, this paper proposes a machine learning optimization algorithm of convolutional neural network (CNN) based on pruning and Int8 quantization. By analyzing the machine learning intelligent recognition network, the Yolo v3 target detection network is selected to verify the optimization algorithm. Aiming at the Yolo v3 network with balanced speed and precision, the optimization algorithm is studied in detail. Based on Amazon Cloud FPGA EC2 instance platform and ZCU104 FPGA hardware platform, machine learning optimization algorithms are used to accelerate the application of Yolo v3 network detection and recognition. Satisfactory results are obtained on both FPGA computing platforms. The experimental results show that the machine learning optimization technology can improve the inference speed of neural network based on both FPGA platforms. The network structure optimized by pruning and Int8 quantization algorithms can achieve high acceleration speed, with very limited accuracy loss.
Learning to Find Usages of Library Functions in Optimized Binaries

Much software, whether beneficent or malevolent, is distributed only as binaries, sans source code. Absent source code, understanding binaries' behavior can be quite challenging, especially when compiled under higher levels of compiler optimization. These optimizations can transform comprehensible, "natural" source constructions into something entirely unrecognizable. Reverse engineering binaries, especially those suspected of being malevolent or guilty of intellectual property theft, are important and time-consuming tasks. There is a great deal of interest in tools to "decompile" binaries back into more natural source code to aid reverse engineering. Decompilation involves several desirable steps, including recreating source-language constructions, variable names, and perhaps even comments. One central step in creating binaries is optimizing function calls, using steps such as inlining. Recovering these (possibly inlined) function calls from optimized binaries is an essential task that most state-of-the-art decompiler tools try to do but do not perform very well. In this paper, we evaluate a supervised learning approach to the problem of recovering optimized function calls. We leverage open-source software and develop an automated labeling scheme to generate a reasonably large dataset of binaries labeled with actual function usages. We augment this large but limited labeled dataset with a pre-training step, which learns the decompiled code statistics from a much larger unlabeled dataset. Thus augmented, our learned labeling model can be combined with an existing decompilation tool, Ghidra, to achieve substantially improved performance in function call recovery, especially at higher levels of optimization.
Sentiment Analysis of the CIA World Factbook

Three different sentiment analysis tools (NLTK, Textblob, Flair) are compared and implemented on the CIA World Factbook dataset. Findings are used for statistical analysis in order to whether the US portrays countries differently according to their relations with them. The quantitative study found that sentiment classification models didn’t perform optimal, statistical analysis of outcomes suggested that there is a weak correlation between a country’s affiliation with the US and the sentiment expressed regarding that country in the CIA World Factbook. This applies to countries labeled as Unfriendly, Friendly and Ally; this rule cannot be generalized to Enemy and Not sure states.
An Artificial Neural Network Based Forecasting Strategy for Estimating Weather parameters: Application for Sizing Stand-alone Renewable Power System

At present, fossil fuels (Non-renewable sources) are the main source for generation of Electricity in India. For nature friendly electric power generation, it is the need of time that we should develop highly efficient and Reliable power system based on these sustainable energy sources that can produce total energy demand. Therefore main focus of electric power generation is from photovoltaic(PV), Hydro and wind power systems. Renewable energy systems need prior weather information to plan their control activities and to manage power scheduling and dispatching problems efficiently. This paper proposes an Artificial Neural Network based weather prediction model that uses hourly weather data for training of neural networks and this model provides accurate short-term weather information using back-propagation algorithmic rule such as leven-berg Marquardt & Bayesian regularization, with standard NARX feed forward network built in MATLAB. This method provides very good results to predict up to one week ahead of weather information containing various parameters like solar irradiance, Temperature, wind speed and relative humidity.
College English Teaching Innovation under Artificial Intelligence

Artificial intelligence is changing people's traditional concepts, thinking and lifestyle, and improving human knowledge and education. It is not only the commanding height of information technology today, but also a research hotspot in the field of education technology. The purpose of applying artificial intelligence to the exploratory thinking in college English teaching is to optimize the college English teaching model and teaching effect. Using this system in teaching practice, students are placed in an intelligent, contextual and humanized language learning environment. After introducing the concept of artificial intelligence, it analyzes the main current situation of college English teaching, and then proposes innovations in college English teaching under artificial intelligence conditions.
The effect of non-symmetric fitness

Theory of evolutionary computation has brought plenty of useful recommendations to the practitioners on how to deal with local optima. Many of these results were obtained through the runtime analysis of evolutionary algorithms (EAs for brevity) on Jump benchmark function, which has a local optimum which is very hard to leave for most EAs. The performed analyses resulted into multiple new algorithms which showed a good performance on Jump function, including the (1 + (λ, λ)) GA (with dynamic parameters choices or with non-standard static parameters), the (μ + 1) GA with various diversity mechanisms, and the hybrid GA.
ForestDet: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation

Object detection and instance segmentation with a large number of object categories and long-tailed data distribution are challenging for most existing deep learning models. As the number of classes increases, the outputs of a classifier become sensitive to likely noisy logits, which can easily result in an incorrect recognition. To alleviate the large-vocabulary problem, we cluster fine-grained classes into coarser parent classes and then build a classification tree to classify an object into a fine-grained class via its parent class. Because the number of parent class is much fewer, their logits are more stable to suppress the wrong/noisy logits existed in the fine-grained class nodes. Due to a variety of ways for clustering fine-grained classes into parent classes, we can further construct multiple trees to build a classification forest where each single tree contributes its vote to the fine-grained classification. Moreover, a simple yet effective resampling method, termed as NMS Resampling, is proposed aiming at solving the long tail (data imbalance) problem. Our method, coined as ForestDet, serves as a plug-and-play module, which can be readily employed in both one-stage and two-stage object recognition models for recognizing more than 1000 categories. Extensive experiments are conducted on the large vocabulary dataset LVIS. Compared to the Mask R-CNN baseline, our two-stage counterpart Forest R-CNN significantly boosts the performance by 11.5&#x0025; and 3.9&#x0025; AP improvements on the rare categories and overall categories, respectively. Compared to the RetinaNet baseline, our one-stage counterpart Forest RetinaNet improves 2.1&#x0025; AP on overall categories. Moreover, we achieve state-of-the-art results on the LVIS dataset. <i>Code and models are available at <uri>https://github.com/JialianW/Forest_RCNN</uri>.</i>