Reviewerly/exHarmony huggingface dataset:
HW/SW Codesign and Implementation of an IMU Navigation Filter on Zynq SoC with Linux

In this paper, a complementary filter for pitch and roll angle estimation based on linear acceleration and angular rate is designed and implemented on a Xilinx Zynq System on Chip (SoC) device. The filter is partitioned into two parts as hardware (HW) and software (SW) as a result of the communication performance analysis of Advanced eXtensible Interface (AXI) bus for a parallel interface in the Programmable Logic (PL) part of the SoC. PetaLinux operates on the Processing System (PS) part of the SoC and runs a C application. PL performs the HW abstraction by transforming serial inputs/outputs with noise rejection filters into memory mapped variables. Real-time test results and SoC utilization results are reported.
Real-time Illumination and Visual Coherence for Photorealistic Augmented/Mixed Reality

A realistically inserted virtual object in the real-time physical environment is a desirable feature in augmented reality (AR) applications and mixed reality (MR) in general. This problem is considered a vital research area in computer graphics, a field that is experiencing ongoing discovery. The algorithms and methods used to obtain dynamic and real-time illumination measurement, estimating, and rendering of augmented reality scenes are utilized in many applications to achieve a realistic perception by humans. We cannot deny the powerful impact of the continuous development of computer vision and machine learning techniques accompanied by the original computer graphics and image processing methods to provide a significant range of novel AR/MR techniques. These techniques include methods for light source acquisition through image-based lighting or sampling, registering and estimating the lighting conditions, and composition of global illumination. In this review, we discussed the pipeline stages with the details elaborated about the methods and techniques that contributed to the development of providing a photo-realistic rendering, visual coherence, and interactive real-time illumination results in AR/MR.
Digital Image Denoising Method Based on Mean Filter

Digital image processing refers to the use of computers for the digital processing of visual information in scientific research and production, and the processing of image information to meet people’s visual psychology or application needs. As an important medium and means to transmit information, image information is especially important in the information accepted by human beings. As a non-linear filtering method, mean filtering can not only eliminate random noise and impulse interference, but also retain image edge information to a large extent. After the emergence of digital image processing technology, people can use it to modify and beautify the image according to their own needs, which makes the image a collection of pixels. The traditional mean filtering method can not meet the real-time requirements well, and it will lose some image details while removing impulse noise. Combined with the block mean filtering method in the multi window structure of automatic recognition, it can better preprocess the image when it is relatively complex.
Roman-Urdu News Headline Classification with IR Models using Machine Learning Algorithms

Objectives: Roman-Urdu consider as a non-standard language used frequently on the Internet. To classify text from article tagging on Roman-Urdu is such difficult task because of many irregularities in spellings, for example, the word khubsurat (beautiful) in Roman-Urdu has multiple spellings. It can also be written as khoobsurat, khubsoorat, and khobsorat. Methods/Statistical Analysis: In this study, we scrap Roman-Urdu language news headline from various online newspapers. Our corpus contains 12319 news headlines which contain seven categories i.e. Accident, Sports, Weather, Arrest, Conference, Operation and Violence. We also use different preprocessing approaches like Roman-Urdu Stop words and apply IR models i.e. TF-IDF and Count Vector for feature extraction before applying classifier algorithms. Findings: We also compare results between different Machine Learning algorithm such as RF, LSVC, MNB, LR, RC, PAC, Perceptron, NC, SGDC and NC. Our model predicts best result to identify desire class on SGD classifier which gives 93.50% accuracy. Application/ Improvements: It is recommended that SGD Classifiers should be used in roman-Urdu news headline text classification. Keywords: Linear SVC, Multinomial Naïve Bays (MNB), Ridge Classifier (RC), Random Forest, Roman-Urdu, Supervised Machine Learning, Stochastic Gradient Descent (SGD), Text Classification, Tf-Idf
Smart Wheelchair to Facilitate Disabled Individuals

This paper describes the design and implementation of a voice controlled smart wheelchair for disabled whom the manual operation is difficult due to lack of physical strength. The main objective of this research is to develop a smart wheelchair to facilitate disabled individuals which can be operated with lesser effort while operating the wheelchair. The proposed wheelchair can be controlled through voice commands which enables the user to control the wheelchair with less effort. This aids the disabled in carrying out daily activities independently within indoor environments. The proposed solution has five modules namely, speech recognition module, obstacle avoidance module, autonomous navigation module, health monitoring module, and central system controller. The wheelchair operates in two modes called manual mode and the autonomous mode. This paper presents a smart wheelchair that makes the disabled individuals' life easier with technology. Further this paper elaborates testing and evaluations carried out to prove the proposed title.
Arabic Text Processing Model: Verbs Roots and Conjugation Automation

The Natural Language Processing (NLP) is a process to automate the text or speech of Natural Languages. This automation is mainly conducted for Western languages. The Arabic Language got less focus in this area. This paper presents a Model to recognize an Arabic sentence. A new morphological model based on regular expressions is developed to recognize the Arabic verbs. A hash table containing all Arabic three-letters' root of verbs is implemented. The total number of Arabic verbs that are derived from three-letters' root size is 23090. The number of roots is 6104. A set of rules forming the Arabic grammar is used to derive and analyze the syntax of Arabic sentences. About 87% of the verbs represented in our regular expressions' engine are detected. Moreover, the sentences are also recognized. In several Surat of the Quran, only 9% of the detected verbs are false-positive (a non-verb declared as a verb), and 4% are considered false-negative (a verb is considered as a noun). This rate is mainly because we are not using vowels even that the Quran (our case study) is using them. The reason behind our decision is to be able to handle all Arabic texts, which mostly are not using vowels.
Sentimental Analysis on voice using AWS Comprehend

Sentimental analysis plays an important role in these days because many start-ups have started with user-driven content [1]. Sentiment analysis is an important research area in natural language processing. Natural language processing has a wide range of applications like voice recognition, machine translation, product review, aspect-oriented product analysis, sentiment analysis and text classification etc [2]. This process will improve the business by analyse the emotions of the conversation. In this project author going to perform sentimental analysis using Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract the content of the document. By using this service can extract the unstructured data like images, voice etc. Thus, will identify the emotions of the conversation and give the output whether the conversation is Positive, Negative, Neutral, or Mixed. To perform this author going to use some services from Aws like s3 which is used for the data store, Transcribe which is used for converting the audio to text, Aws Glue is used to generate the metadata from the comprehend file, Aws Comprehend is used to generate the sentiment file from the audio, Lambda is used to trigger from the data store s3, Aws Athena is used to convert text into structured data and finally there is quick sight where he can visualize the data from the given file.
A Visualization Tool for Eye Tracking Data Analysis in the Web

Usability analysis plays a significant role in optimizing Web interaction by understanding the behavior of end users. To support such analysis, we present a tool to visualize gaze and mouse data of Web site interactions. The proposed tool provides not only the traditional visualizations with fixations, scanpath, and heatmap, but allows for more detailed analysis with data clustering, demographic correlation, and advanced visualization like attention flow and 3D-scanpath. To demonstrate the usefulness of the proposed tool, we conducted a remote qualitative study with six analysts, using a dataset of 20 users browsing eleven real-world Web sites.
Methodology for modeling fuzzy Kalman filters of minimum realization from evolving clustering of experimental data

A methodology for evolving fuzzy Kalman filter identification, is proposed in this paper. The mathematical formulation contemplates the following aspects: for initial estimation, an offline GK clustering algorithm and an offline fuzzy version of OKID algorithm are used to estimate antecedent and consequent parameters, respectively. From each new sample of input–output experimental data from dynamical system, the evolving eTS algorithm and an evolving fuzzy version of OKID algorithm are used to estimate the antecedent and consequent parameters of the evolving fuzzy Kalman filter, respectively. Computational and experimental results considering the estimation of states and outputs of a nonlinear dynamic system and a 2DoF helicopter, respectively, show the efficiency and applicability of the proposed methodology.
LIT: Light-field Inference of Transparency for Refractive Object Localization

Translucency is prevalent in everyday scenes. As such, perception of transparent objects is essential for robots to perform manipulation. Compared with texture-rich or texture-less Lambertian objects, transparency induces significant uncertainty on object appearances. Ambiguity can be due to changes in lighting, viewpoint, and backgrounds, each of which brings challenges to existing object pose estimation algorithms. In this work, we propose LIT, a two-stage method for transparent object pose estimation using light-field sensing and photorealistic rendering. LIT employs multiple filters specific to light-field imagery in deep networks to capture transparent material properties, with robust depth and pose estimators based on generative sampling. Along with the LIT algorithm, we introduce the light-field transparent object dataset ProLIT for the tasks of recognition, localization and pose estimation. With respect to this ProLIT dataset, we demonstrate that LIT can outperform both state-of-the-art end-to-end pose estimation methods and a generative pose estimator on transparent objects. The link of supplementary material can be found at: https://sites.google.com/umich.edu/prolit.
Structural Autoencoders Improve Representations for Generation and Transfer.

We study the problem of structuring a learned representation to significantly improve performance without supervision. Unlike most methods which focus on using side information like weak supervision or defining new regularization objectives, we focus on improving the learned representation by structuring the architecture of the model. We propose a self-attention based architecture to make the encoder explicitly associate parts of the representation with parts of the input observation. Meanwhile, our structural decoder architecture encourages a hierarchical structure in the latent space, akin to structural causal models, and learns a natural ordering of the latent mechanisms. We demonstrate how these models learn a representation which improves results in a variety of downstream tasks including generation, disentanglement, and transfer using several challenging and natural image datasets.
Adversarial Robustness for Code

Machine learning and deep learning in particular has been recently used to successfully address many tasks in the domain of code such as finding and fixing bugs, code completion, decompilation, type inference and many others. However, the issue of adversarial robustness of models for code has gone largely unnoticed. In this work, we explore this issue by: (i) instantiating adversarial attacks for code (a domain with discrete and highly structured inputs), (ii) showing that, similar to other domains, neural models for code are vulnerable to adversarial attacks, and (iii) combining existing and novel techniques to improve robustness while preserving high accuracy.
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis

The notion of flat minima has played a key role in the generalization studies of deep learning models. However, existing definitions of the flatness are known to be sensitive to the rescaling of parameters. The issue suggests that the previous definitions of the flatness might not be a good measure of generalization, because generalization is invariant to such rescalings. In this paper, from the PAC-Bayesian perspective, we scrutinize the discussion concerning the flat minima and introduce the notion of normalized flat minima, which is free from the known scale dependence issues. Additionally, we highlight the scale dependence of existing matrix-norm based generalization error bounds similar to the existing flat minima definitions. Our modified notion of the flatness does not suffer from the insufficiency, either, suggesting it might provide better hierarchy in the hypothesis class.
Fault Diagnosis for Uncertain Cloud Environment through Fault Injection Mechanism

Cloud is the epitome of business computing as it is expeditiously taking over the long-established and conventional systems as a more efficient, reliable, elastic, cost-effective and scalable panacea. However, this introduces a continuous sense of uncertainty into the computational process. Cloud presents numerous challenges, with one of them being the Quality of Service management (QoS). Cloud is dynamic; therefore uncertainty can arise from various factors such as resource availability, unmatched predictions, unreliable data or unexpected faults in the system. The existing models fail to provide an efficient mechanism that detect, measure the uncertainty that has occurred and then rectify it. To determine the potency of the model proposed, parameters like Availability and Reliability are considered to render reliable and seamless cloud service.
Stable behaviour of infinitely wide deep neural networks.

We consider fully connected feed-forward deep neural networks (NNs) where weights and biases are independent and identically distributed as symmetric centered stable distributions. Then, we show that the infinite wide limit of the NN, under suitable scaling on the weights, is a stochastic process whose finite-dimensional distributions are multivariate stable distributions. The limiting process is referred to as the stable process, and it generalizes the class of Gaussian processes recently obtained as infinite wide limits of NNs (Matthews at al., 2018b). Parameters of the stable process can be computed via an explicit recursion over the layers of the network. Our result contributes to the theory of fully connected feed-forward deep NNs, and it paves the way to expand recent lines of research that rely on Gaussian infinite wide limits.
Handling Adaptive Compromise for Practical Encryption Schemes

We provide a new definitional framework capturing the multi-user security of encryption schemes and pseudorandom functions in the face of adversaries that can adaptively compromise users’ keys. We provide a sequence of results establishing the security of practical symmetric encryption schemes under adaptive compromise in the random oracle or ideal cipher model. The bulk of analysis complexity for adaptive compromise security is relegated to the analysis of lower-level primitives such as pseudorandom functions. We apply our framework to give proofs of security for the BurnBox system for privacy in the face of border searches and the in-use searchable symmetric encryption scheme due to Cash et al. In both cases, prior analyses had bugs that our framework helps avoid.
A Joint Markov Model for Communities, Connectivity and Signals Defined Over Graphs

Real-world networks are typically described in terms of nodes, links, and communities, having signal values often associated with them. The aim of this letter is to introduce a novel Compound Markov random field model (Compound MRF, or CMRF) for signals defined over graphs, encompassing jointly signal values at nodes, edge weights, and community labels. The proposed CMRF generalizes Markovian models previously proposed in the literature, since it accounts for different kinds of interactions between communities and signal smoothness constraints. Finally, the proposed approach is applied to (joint) graph learning and signal recovery. Numerical results on synthetic and real data illustrate the competitive performance of our method with respect to other state-of-the-art approaches.
Review Helpfulness Prediction Using Convolutional Neural Networks and Gated Recurrent Units

Product reviews are one of the most important types of user-generated contents that are becoming more and more available. These reviews are valuable sources of knowledge for users who want to make purchasing decisions and for producers who want to improve their products and services. However, not all product reviews are equally helpful and this makes the process of finding helpful reviews among the massive number of similar reviews very challenging. To address this problem, automatic review helpfulness prediction systems are designed to classify reviews according to their content. In this study, a deep model is proposed to utilize content-based, semantic, sentiment, and metadata features of reviews for predicting review helpfulness. In the proposed method, convolution layer is used for learning feature maps and gated recurrent units are employed for exploiting sequential context. The results of comparing the proposed method with five traditional learning methods and two deep models trained on the same types of features shows that the proposed method outperforms other methods by 4% and 2% in terms of F1-measure and accuracy. Moreover, results reveal that both textual and metadata features are important in detecting helpful reviews. The findings of this study may help online retailers to efficiently rank the product reviews.
Snooping Attacks on Deep Reinforcement Learning

Adversarial attacks have exposed a significant security vulnerability in state-of-the-art machine learning models. Among these models include deep reinforcement learning agents. The existing methods for attacking reinforcement learning agents assume the adversary either has access to the target agent's learned parameters or the environment that the agent interacts with. In this work, we propose a new class of threat models, called snooping threat models, that are unique to reinforcement learning. In these snooping threat models, the adversary does not have the ability to interact with the target agent's environment, and can only eavesdrop on the action and reward signals being exchanged between agent and environment. We show that adversaries operating in these highly constrained threat models can still launch devastating attacks against the target agent by training proxy models on related tasks and leveraging the transferability of adversarial examples.
Multi-sided Exposure Bias in Recommendation.

Academic research in recommender systems has been greatly focusing on the accuracy-related measures of recommendations. Even when non-accuracy measures such as popularity bias, diversity, and novelty are studied, it is often solely from the users' perspective. However, many real-world recommenders are often multi-stakeholder environments in which the needs and interests of several stakeholders should be addressed in the recommendation process. In this paper, we focus on the popularity bias problem which is a well-known property of many recommendation algorithms where few popular items are over-recommended while the majority of other items do not get proportional attention and address its impact on different stakeholders. Using several recommendation algorithms and two publicly available datasets in music and movie domains, we empirically show the inherent popularity bias of the algorithms and how this bias impacts different stakeholders such as users and suppliers of the items. We also propose metrics to measure the exposure bias of recommendation algorithms from the perspective of different stakeholders.
Development of Abnormal Red Blood Cells Classifier Using Image Processing Techniques with Support Vector Machine

In some studies conducted from the medical field, the abnormality in the morphological structure of red blood cells is used as an indicator of blood related diseases and conditions based on the classification of the abnormal red blood cells that have been found. The system provides an automated way of identification and classification of abnormal red blood cell based on morphology. This is done by capturing the microscopic image of a blood smear and applying image processing techniques. The captured image undergoes several processes such as image pre-processing, segmentation, and feature extraction. The system acquires important morphological properties of red blood cells through these processes. Properties such as diameter, shape geometric factor, central pallor and target flag are the main parameters used by the system. Support vector machine is applied as classifier to identify the type of the abnormal cell. The SVM will be able to do the classification by training it with dataset for each class of red blood cell included in the system. These datasets are images of the blood cells that have parameters within the range of a specific class. The system also provides a list of blood related diseases and condition associated with the abnormal red blood cells found to be present. Based on the result of the testing, the system was able to produce a high accuracy output of 96.67% for normal RBC, 100% for echinocytes, 97.5% for elliptocytes, 97.5% for dacrocytes, 98.33% for spherocytes, 97.5% for Target cells, 98.53% for stomatocytes and 86.67% for unknown class.
Development of a Hardware-Software System for the Assembled Helicopter-Type UAV Prototype by Applying Optimal Classification and Pattern Recognition Methods

The article is devoted to the development of a hardware-software system developed for the designed and assembled helicopter-type Unmanned Aerial Vehicle prototype. The system was developed for the classification and recognition of a person’s face. Various methods were used and applied to optimize the detection and pattern recognition. Proposed algorithm has been implemented for transmitting a video stream from an Unmanned Aerial Vehicle to a ground station.