Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Data Mining Process in Industry

Protecting the privacy of users in web-scale data mining applications and systems like online searching, recommender systems, crowd-sourced platforms, and analytics apps has developed into an incredibly essential component in recent years. This is especially true in light of recent data breaches and new legislation such as GDPR. In this tutorial, we'll go over a history of privacy violations from the last two decades, along with the lessons we gained from them, as well as important rules and legislation and how privacy tactics have evolved into different privacy definitions and techniques. Data mining raises unique privacy risks, despite being successful in many applications. The literature has looked at the trade-offs that the current system presents between utility and personal privacy. However, industrial deployment has its own unique challenges when it comes to the management of resource trade-offs between computation and communication. The industrial deployments that are taking place right now are rather scattered. Each device (such a mobile phone, for example) is responsible for storing a single data point, and it is the server's job to compute aggregates based on the data that is obtained from the distinct mix of public and private information that is associated with each device. In spite of the fact that computing on servers is quite inexpensive, computing on clients is somewhat pricey since clients are often comprised of low-power devices. The expectation maximization (EM) technique for distribution reconstruction is discussed in this study. It is superior to the existing approach regarding the degree of information loss. The percentage of information loss is only 4%. In particular, we show that the EM approach, when applied to data that has been subjected to a perturbation, eventually arrives at the maximum likelihood estimate of the distribution that was being modeled. We demonstrate that the EM algorithm produces reliable estimations of the original distribution when considerable data is available.
Implementation of Realtime design of crowd Enumeration via tracking using AI system

Crowd enumeration can help to evaluate and count the number of visitors to a place. There are many reasons that spana wide range of applications, from security considerations, optimization of operations to efficiency in profitability. In the paper, we propose to develop a prototype for implementing a high frame rate, low processing environment, high performance, and highly efficient real-time crowd enumeration system. The latest method for object detection is deep learning. When it comes to deep learning or machine learning, performance and computation are the key parameters. In our model, there is a provision to schedule the model for the required amount of time. In our work, we are using mobilenet SSD as an object detector to detect humans. It is a preprocessed, highly efficient, and light weight model which can run on low power device like jetson nano and is cost-efficient unlike others. The advantage of our model is if there is overcrowding in a specified location with known capacity, alarm is enabled.
Anomaly Detection for Video Surveillance using Deep Learning Techniques

Anonymous Detection is a device that identifies insignificant people's behavior. The most prominent issue in laptop thinking and science is finding the wrong human behavior is very important as finding leisure time can help a large number of apps. can find programs such as image monitoring, signal language dignity, object pursuit, and more. There are many other methods available that include low-cost intensity sensors; however, they have a few drawbacks as well as limited home use and low resolution and audio data from in-depth images, making it almost impossible to assess a person's posture. to solve the above problems, the proposed machine plans to use neural networks. The most important area of research is to capture suspicious human video tracking, within the context of portable computer perception. Several surveillance cameras are located in areas such as supermarkets, train stations, and schools, to detect suspicious activity at schools, universities, and other institutions acts including robbery, injury, etc. it is a tedious process to find and keep an eye on these deeds in congested environments, suggesting human behavior in real-time and dividing it into every day and abrupt events the machine wishes to have smart surveillance of video. Test outcomes show that the proposed method can detect unpredicted occasions in the video.
Cloud Oriented Multiple Owner Keyword Search for Shared Data

In primary, maximum of the present cloud based structures, the scenario is considered through only the solitary data proprietor. Subsequently, the need of protected conduits to assure the diffusion of secret keys after the data vendor to data operators. In the third stage, in some outlines, the data proprietor must be connected to assistance data operators when data operators mean to achieve the exploration, which is awkward. Searchable encryption permits cloud operators to subcontract the huge encoded data to the distant cloud and to pursuit in excess of the data deprived of illuminating the delicate data. Numerous structures have remained projected to provision the keyword exploration in a community cloud. Nevertheless, they have some possible restrictions. To support operators to rapidly class out the material of benefits since great encoded information, searchable encryption needs planned and augmented by several structures. In its place, decrypting the entire information, these arrangements let operators to exploration over the encoded information and solitary decrypt the conforming files. Searchable encryption arrangements must have remained planned to resolve the glitches triggered when the data proprietor segments the information through numerous operators.
A Knowledge based Approach for User Profiling from the Multilingual Texts in the Social Media Platforms

In the online platforms, people prefer to write their views in their own language in different creative ways which is a big challenge in the sentiment analysis task. In this report, an algorithm is proposed for sentiment analysis for the multilingual texts posted by the users in different online platforms. The goal of this work is to identify the sentiment behind the message of the user as well as the topic on which h(sh)e is commenting. The overall work has been carried out in three phases. In the first phase, the multilingual texts have been converted into a uniform language using the UNICODE range; second, the sense of the message has been derived based on a training data set; and third, the topic of the message is identified with the help of a newspaper archive and WordNet. The data sets are prepared from various social media and E-commerce platforms. The proposed model is tested on 10 different languages such as English, Bengali, Hindi, Tamil, Telugu, Gujrati, Punjabi, Malayalam, Urdu, and Kannada. The challenges and the pitfalls of this work are explained in detail at the end of this report.
Spoken Dialog Optimization

The basic idea is to make the system recognize human speech and in turn respond in the form of speech. The aim is to build an android application based ticket booking through which people can interact with the system directly through speech. Spoken Dialog Systems (SDS) are speech based interfaces for Human Computer Interaction (HCI). There are systems which responds in the form of text when the user talks and there are also some systems which responds in the form of voice when user gives the input in the form of text. There are systems which are called Interactive Voice Response (IVR) which operates following set of commands. Our project aims at developing a system that eliminates the drawback of existing systems. So, hereby we are going to develop a model which makes the user and system to get interacted without following a set of commands and thereby getting interacted in a natural language. The existing systems have a disadvantage where the user has to remember all the instructions, commands and keywords that are inbuilt in it to carry a specific task. These systems are not user friendly for illiterates and physically challenged. Therefore, to overcome all these hurdles we are going to propose a model to book a ticket.
Extending Action Recognition in the Compressed Domain

As the internet continues to extend its reach into every facet of society, video is becoming one of the most common mediums for the communication of ideas and information. Working with video in the compressed domain saves resources by avoiding decompression and allows for faster processing due to smaller input streams where redundancy is avoided. Most prior work on compressed video processing has focused on MPEG-4 part-2 codec which is dated in part due to its non-optimized compression ratio. For example, H.264 has a compression ratio <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\mathrm{x}$</tex> greater than MPEG-4 part-2 codec with improved quality. Due to the increasing prevalence of the more effective H.264 codec for video content, designing a network to infer directly on H.264 compressed video is essential. Hence, we propose a new video analytics architecture that uses only two streams of data from the compressed domain as compared to the three or more generally used for compressed recognition. The proposed architecture, coined Extended Codec Recognition Network (ECRN), is the first approach to our knowledge to support action recognition on both MPEG4 part-2 and H.264 compressed video. It is computationally efficient and achieves competitive accuracy to methods performing recognition solely on MPEG-4 part-2 streams. The ability to achieve competitive accuracy using a modern video codec creates the potential to extend compressed action recognition to a wide range of applications.
Lessons Learned from Utilizing Guided Policy Search for Human-Robot Handovers with a Collaborative Robot

We evaluate the performance of Guided Policy Search (GPS), a model-based reinforcement learning method, for generating the handover reaching motions of a collaborative robot arm. In a previous work, we evaluated GPS for the same task but only in a simulated environment. This paper provides a replication of the findings in simulation, along with new insights on GPS when used on a physical robot platform. First, we find that a policy learned in simulation does not transfer readily to the physical robot due to differences in model parameters and existing safety constraints on the real robot. Second, in order to successfully train a GPS model, the robot’s workspace needs to be severely reduced, owing to the joint-space limitations of the physical robot. Third, a policy trained with moving targets results in large worst-case errors even in regions spatially close to the training target locations. Our findings motivate further research towards utilizing GPS in humanrobot interaction settings, especially where safety constraints are imposed.
Wireless Sensor Networks Outlier Detection Framework with LOF

Wireless sensor networks (WSNs) are a class of technology in which low-cost, multi-functional nodes are used to send information over close range nodes using wireless connection. The technology is an open-media platform, it has also been built based on application-driven technology. Its use is not only limited to military applications but can also be applied to the monitoring of the environment, to the health sector, to emergency response, as well. In recent years, WSNs security had drawn quite a bit of interest, which is due to its characteristics and application scenario. A distributed network infrastructure that is built using nodes and other components is vulnerable to several types of attacks. It is vital to implement security mechanisms in WSNs so that their functionality can be ensured. There are several concerns when it comes to providing security in the WSN due to power constrained nodes and smaller network. This study uses the Local Outlier Factor (LOF) algorithm to detect the outlier nodes in WSN, for the purpose of securing the network. We conducted a research study by deploying 300 nodes of WSNs for the purpose of collecting temperature data from them. It was a promising result that could be implemented in a hardware structure in the future.
Reward Shaping Study for Sub-Goal Based Robotic Tasks

Robotics has been providing essential tools to improve the Industry’s flexibility and reconfiguration. Nowadays, the tasks are highly complex, making it mandatory to renew programming techniques and craft sophisticated solutions. This is where Reinforcement Learning comes in handy: it broadens the horizon for new and disruptive methodologies, incorporating tolerance to uncertainties using exploration. However, those solutions can be challenging when it comes to building the reward function. So, this work proposes a ‘‘divide and conquer’’ approach, which transforms a long and complex task into smaller and easier achieved goals that boosts training efficiency and performance. This study tested different combinations of parameters and proposes a set of guidelines that used a Pickand-Place environment. The observed performance shows higher success rates and faster convergence for two and three sub-goals when comparing the sub-goal approach versus a sequential and traditional reward.
LTE Network Planning Using AI and AR

In this paper, an optimal base station placement for a LTE cellular network is provided. The designed system relies on a web server that communicates with pre-trained machine learning models trained using measured data and deployed on MATLAB For training, a fingerprinting database has been created for a target RSSI versus the GPS coordinates as features in an outdoor and indoor environment on a multi floor buildings in the university campus. Gaussian Process Regression (GPR) has been used as a benchmark machine learning algorithm due to its outstanding performance. However, its performance was compared with different machine learning techniques in terms of computational complexity and accuracy. Validation of the system has shown that GPR outperformed the other techniques with mean square error of 4.3% at the expense of the time required for training. Moreover, the application has been provided with an augmented reality interface that shows the placed BS on the map along with the predicted RSSI value and the GPS coordinates. The whole system is designed on unity software and made available to mobile devices.
Human Visual System Algorithm for Medical Images Recovery

The human body is such a complicated structure full of fine details, small and big details, some diseases affect the small parts of the body, so a doctor has to use every tool to diagnose the disease like Lab testing and imaging (Imaging means sending the patient to do X-Ray, MRI, CT scan, etc.). So, receiving a clear image with no noise is important to reach a precise diagnosis rather than a different one. Thus, finding a program to find the lost data due to noise is the dream of every physician. A nonlinear two-dimensional image restoration filter structure is introduced in this work. A nonlinear prediction structure is proposed using nonlinear elements depending on the eye's visual phenomena of noise detection. Filter stability is demanded in this structure. Impulse noise recovery is guaranteed in this filter. An advantage of the filter is in preserving textures and keeping fine details. Median-based filters are proposed for noise recovery. The filter structure shows a superior method for noise detection and precise location determination.
Counterfeit Money Detection: A Hybrid Semi-Supervised GAN-based Approach

Despite recent advances in digital banking around the world, the use of banknotes is still predominant. In this context, the detection of fake money bills is important due to the severe impact of their circulation on the economic system. Automating and making detecting counterfeited bills accessible to anyone is still an under-investigated issue, which is mainly based on the visual inspection of bills. In this paper, we propose a novel semi-supervised generative adversarial network (GAN)-based detection approach that, by visually inspecting a bill, can determine if the latter is authentic or fake. Our method originally combines a parallelized GAN to a semi-supervised GAN. On a dataset of Tunisian bills, our method is able to achieve 100% accuracy, with a Fréchet Inception Distance (FID) below 1, i.e., generating diversified fake images with good quality. These performances are proven superior to those of several benchmark approaches.
An Efficient Optical Sparse Matrix Multiplication Accelerator for Graph Neural Networks

Graph Neural Network (GNN) is a variant of Deep Neural Networks on graphs and have been widely used in the real world. However, GNNs are more complex compared to traditional DNNs due to the unstructured and sparse graph data. Existing optical computing platforms do not support sparse operations, requiring a large number of invalid zero mapping to the computing unit to maintain the correct value of the final results. Therefore, in this paper, we propose an optical graph neural network (OGNN), a novel sparse matrix multiplication accelerator that can implement GNN to address this problem. OGNN features two key techniques: reconfigurable sparse matrix-vector multiplication (SpMV) operation and graph partitioning operation, which uses temporal, wavelength, and spatial dimensions to construct an optical parallel processing framework for graph neural network. We evaluate OGNN with two graphs datasets - Cora and PubMed. The results show that OGNN achieves competitive performance with the electronic counterpart and provides efficient computing unit utilization for sparse multiplication.
A Highly Adaptive Compilation Method to Break the Limitation of SNR on Calculation Precision in Photonic Neuromorphic System

We propose a compilation method in photonic neuromorphic system to improve calculation precision and provide high adaptability. The simulation results show that our method significantly reduces the SNR requirement for a precision and outperforms physics-aware training in model inference accuracy under the same SNR.
Transformer based Image Dehazing

This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challenging task, we propose an architecture based on transformers, which have been recently introduced and have shown great potential in different computer vision tasks. Our model is based on the SwinIR an image restoration architecture based on a transformer, but by modifying the deep feature extraction module, the depth level of the model, and by applying a combined loss function that improves styling and adapts the model for the non-homogeneous haze removal present in images. The obtained results prove to be superior to those obtained by state-of-the-art models.
An Intelligent Traffic Monitoring Embedded System using Video Data Mining

Traffic monitoring through video analysis facilitates understanding the daily driving profile, allowing action to be taken in operational deviations and mitigating the accidents risks or incidents resulting from human error, offering a safer operation for the population. This paper discusses the workflow to create a predictive traffic monitoring system using video data mining on embedded systems. Our proposal uses machine learning, classification models, and tracking techniques to efficiently and accurately extract information about vehicle type, direction, dimensions, and speed through image capture and analysis. The analysis will provide a means to forecast trends using current and historical data and is also intended to be used in conjunction with the NVIDIA JetPack SDK to lower the costs of installing and maintaining sensors. We show that it is possible to classify and track vehicles using artifacts at a lower cost than those already used and without having to interdict the road for sensor structuring.
Multi-Agent Path Optimization Based on STA* Algorithm

Multi-agent path planning (MAPF) plays an important role in logistics, military and other fields, and the research on its optimization algorithm has received extensive attention. In the field of agent path planning, traditional path planning methods can be divided into global path planning and local path planning according to the known environmental information. However, these methods have some shortcomings, such as poor robustness, slow convergence speed, long solving time, and easy to fall into local optimal problems. In this paper, based on the actual problem of MAPF, the superiority of STA* algorithm is verified by Hungarian algorithm, CL-CBS algorithm and so on in many steps. At the same time, the results of assignment model are verified by LINGO, and the results are analyzed in full detail, and an effective solution is put forward for this problem.
Fault Risk Prediction and Condition Maintenance of Distribution Network Based on Convolutional Neural Network

Traditional reliability prediction and reliability test evaluation can hardly give satisfactory prediction results. As an important branch of machine learning, deep learning has made great progress in the fields of distribution network fault line selection, voltage sag source identification and time series, and has the advantages of better classification performance and stronger adaptability. For distribution equipment such as switchgear, based on the CNN (Convolutional neural network), this paper proposes a fault risk prediction method and a condition based maintenance optimization model for distribution networks. Whether the equipment fails is considered as a secondary classification problem. If the equipment fails within 1 month after the specified date, the classification of the equipment is 1, otherwise it is 0. In the case of two classifications, the model can output the probability that the classification result is 1 through the softmax classifier, that is, the calculated equipment failure rate. The results show that the classification model based on multi-layer CNN can classify the types of defects well, and its accuracy is about 80%. It shows that the CNN-based fault diagnosis model designed in this paper matches the data set, which is practical and can better represent the fault information.
Watermarking for Data Provenance in Object Detection

Machine Learning (ML) has pervaded almost every industry including banking, autonomous vehicles, healthcare, retail, and defense. Effective use of ML can be a huge competitive advantage, and performance of an ML model is dependent on massive, well-constructed, annotated datasets used for training. These datasets are often sensitive and contain proprietary data. They can be costly to collect and require subject matter experts to annotate. Thus, annotated datasets are regarded as highly valuable intellectual property that must be protected. The owners of a proprietary training dataset need the ability to track its use in the wild to safeguard their intellectual property. We propose a novel methodology for object detection dataset traceability through ‘watermarking’ the training data. This watermark is embedded into object instances within a training dataset, which the model then learns. We design the watermark such that the model is sensitive to its presence within an image, even after the model is fine-tuned on another task. Additionally, the watermark is designed for negligible model performance fluctuations when compared to identical models trained on non-watermarked data. We demonstrate the effectiveness of our technique on multiple object detection model architectures, and conclude that this methodology can provide a means to effectively track dataset usage.