Reviewerly/exHarmony huggingface dataset:
A Novel Technique to Mathematically Represent the Human Knee Dynamics through the Application of the Integral Concept

The need and importance of developing comparatively affordable yet efficient assistive devices for physically disabled people is the inspiring factor for this research. This manuscript describes a novel method of mathematically formulating the dynamics of the knee joint during human locomotion through the application of the integral concept. The n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> order differential equation is modified suitably to accommodate the characteristics of human locomotion. The constant coefficients $(C_{0},\ C_{1},\ C_{2})$ of the modified infinite series represent the physical and psychological aspects of the person and are determined by processing the data obtained experimentally via optical technique. By the analysis of the data obtained through the conduction of experiments, the mathematical representation of knee dynamics was formulated and successfully validated. The accuracy of the representation was found to be within the acceptable tolerance limit of ± 10%. The resulting model can be further utilized for the design and development of assistive devices for physically disabled people thereby aiding them to reintegrate into society and lead a normal life.
Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images

Rendering high-resolution (HR) graphics brings substantial computational costs. Efficient graphics super-resolution (SR) methods may achieve HR rendering with small computing resources and have attracted extensive research interests in industry and research communities. We present a new method for real-time SR for computer graphics, namely Super-Resolution by Predicting Offsets (SRPO). Our algorithm divides the image into two parts for processing, i.e., sharp edges and flatter areas. For edges, different from the previous SR methods that take the anti-aliased images as inputs, our proposed SRPO takes advantage of the characteristics of rasterized images to conduct SR on the rasterized images. To complement the residual between HR and low-resolution (LR) rasterized images, we train an ultra-efficient network to predict the offset maps to move the appropriate surrounding pixels to the new positions. For flat areas, we found simple interpolation methods can already generate reasonable output. We finally use a guided fusion operation to integrate the sharp edges generated by the network and flat areas by the interpolation method to get the final SR image. The proposed network only contains 8,434 parameters and can be accelerated by network quantization. Extensive experiments show that the proposed SRPO can achieve superior visual effects at a smaller computational cost than the existing state-of-the-art methods.
DARwIn-OP: A Smart Elderly-Assistant and Companion

The alarming rate in increase of fall in elderly population has motivated the scientific community for the last two decades to develop autonomous assistance, while preserving their self-reliance, privacy and independent activity. The recent advancements in Internet of Things (IoT) and machine learning has improved the autonomous assistance features a lot but still there remains lacuna of research and efficiency, like accurate indoor localization, fall detection and secure human-robot interaction. This paper proposes a complete Robot Operating System (ROS) based architecture on a humanoid robot Dynamic Anthropomorphic Robot with Intelligence-Open Platform (DARwIn-OP) in collaboration with other intelligent entities like wearable tracker(UbiTrack) or Apple watch for localizing and a roomba to map environment for robust obstacle avoidance to respond immediately to a falling SOS by reaching the person autonomously, validating the SOS through robust fall recognition and interacting with the person if possible regarding the requirement of assistance and accordingly take action like calling emergency and next to kin for faster response.
Adaptive Robust Kalman filter for AUV Polar Integrated Navigation

AUV (Autonomous Underwater Vehicle) plays an growing essential role in the field of ocean exploration in polar regions. Due to the rapid convergence of meridians, traditional inertial navigation methods fail in polar regions. To tackle this problem, an INS/DVL integrated navigation system based on the transverse frame is designed in this article. Since the DVL noise is unknown and time-varying, the RKF (Robust Kalman filter) algorithm based on Mahalanobis distance is used to estimate the measurement noise adaptively. The method was proven effective in mid-latitude and polar regions through ship and semi-physical experiments. RKF can effectively avoid the influence of DVL outliers and improve positioning accuracy.
Does the COVID-19 Pandemic have Implications for Machine Ethics?

Algorithms have advanced in status from supporting human decision-making to making decisions for themselves. The fundamental issue here is the relationship between Big Data and algorithms, or how algorithms empower data with direction and purpose. In this paper, I provide a conceptual framework for analyzing and improving ethical decision-making in Human-AI interaction. On the one hand, I examine the challenges and the limitations facing the field of Machine Ethics and Explainability in its aim to provide and justify ethical decisions. On the other hand, I propose connecting counterfactual explanations with the emotion of regret, as requirements for improving ethical decision-making in novel situations and under uncertainty. To test whether this conceptual framework has empirical value, I analyze the COVID-19 epidemic in terms of “what might have been” to answer the following question: could some of the unintended consequences of this health crisis have been avoided if the available data had been used differently before the crisis happened and as it unfolded?
Study on Time-sharing Reservation Recommendation System of Scenic Spots Based on Relationship Graph

With the continuous development of the Internet, the content and information in the network are increasing explosively. Nowadays, with the rapid increase of data volume, many Internet companies need to consider how to promote their products to potential users and realize personalized recommendation. To solve this problem, people need to find relationships between data to build a relationship graph. Then, the potential relationship between each node and other nodes can be found according to the relationship graph, and personalized recommendation can be finally realized. Based on the reservation records of scenic spots in Gansu Province from 2020 to 2021, this paper builds a relationship graph between tourists and scenic spots. Neo4j graph database is used to store the relationship graph and display it intuitively. Then the relationship graph is used as the input of the recommendation system model in this paper to obtain the embedding representation of each type of tourists. Then, K-means is used to cluster tourists to obtain the reserved scenic spots of each category and calculate the popularity of scenic spots, so as to obtain TOP-K scenic spot recommendation and realize personalized recommendation.
JDE multi-object tracking algorithm integrating multi-level semantic enhancement

In order to solve the problem of target ID switching caused by target occlusion and insufficient ID information and location information extraction in JDE(joint detection and embedding) algorithm, an improved multi-target tracking algorithm based on JDE is proposed in this paper. Firstly, the SPA feature space pyramid attention module is used to expand the receptive field and obtain more abundant semantic information to improve the detection accuracy of the model for different scale targets. Secondly, the FCN network makes the header and ID Embedding task collaborative learning to alleviate the excessive competition and enhance the original semantic information, effectively reducing the number of ID switching. Finally, PCCs-Ma motion measurement can strengthen the connection between Kalman filtering prediction and observation, and improve the reliability of similarity discrimination of motion characteristics. In order to verify the effectiveness of the algorithm, the JDE algorithm and the proposed algorithm are compared in the same experimental environment. The experimental results show that the average accuracy of model detection is improved by 3.94 %. On the MOT16 dataset, the MOTA and IDF1 indexes are increased by 6.9 %, and the number of ID switching of the improved algorithm is significantly reduced, and good tracking results are achieved.
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling

Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations. On the other hand, a biased or inaccurate policy evaluation in a safety-critical system could potentially cause unexpected catastrophic failures during deployment. This paper proposes the Accelerated Policy Evaluation (APE) method, which simultaneously uncovers rare events and estimates the rare event probability in Markov decision processes. The APE method treats the environment nature as an adversarial agent and learns towards, through adaptive importance sampling, the zero-variance sampling distribution for the policy evaluation. Moreover, APE is scalable to large discrete or continuous spaces by incorporating function approximators. We investigate the convergence property of APE in the tabular setting. Our empirical studies show that APE can estimate the rare event probability with a smaller bias while only using orders of magnitude fewer samples than baselines in multi-agent and single-agent environments.
A Collaborative Filtering Algorithm Integrating Balance Factor and Time Weight

Traditional collaborative filtering algorithms still have data sparsity or cold start issues, and generally don't account for changes in user interest over time. This paper proposes two improved measures to address the above problems: a balance factor based on item co-rating users and a time-based weight function, which are combined and applied to the item-based collaborative filtering algorithm to improve the traditional similarity measure. Firstly, the balance factor is calculated using the ratio of the number of co-rated users between items to the total number of users who rated the items, and the influence of the balance factor on the similarity calculation is verified by dynamically changing the value of the parameter α. Then, we introduce a time weighting function to account for the effect of a large time span of user ratings on items on item similarity calculation. Finally, in item-based collaborative filtering, the traditional Pearson correlation coefficient and cosine similarity are improved, and the Movielens dataset is applied for experiments. The experimental results show that the proposed algorithm increases recommendation quality and reduces the average absolute error of the traditional collaborative filtering algorithm.
Cervical Cancer Image Processing with Convolutional Neural Network for Detection

The diagnostic method for detecting cervical cancer using Pap smear can be laborious and time-consuming. Therefore, research on computer-aided diagnosis is essential. The purpose of this study is to aid the distinguishing of Pap smear images from various categories of cervical cells by creating an alternative image processing and classification method. This is so that in the future, the burden on pathologists to manually analyze many Pap smear images can be reduced. The developed method will be able to help in the detection of abnormality or cancer. The processing methods include Gaussian filtering, Otsu thresholding, Canny edge detection, and Convolutional Neural Network. The analytical methods utilized were accuracy and loss curves, and the evaluation measures of accuracy, precision, recall, and F1 measure. The most optimal trained model had an accuracy, precision, recall, and F1 measure of 93.26%, 92.55%, 91.52%, and 91.84% respectively. It was concluded that the image processing and classification method could be used to distinguish multi-cell Pap smear images. Even with some limitations, it has the potential to improve single-cell analysis and also aid in classification. In the future, this method may be used in the medical field to help diagnose cervical cancer in Indonesia.
ASSISLT: Computer-aided speech therapy tool

This work proposes a new software system ASSISLT to support speech therapy for children and adults using deep learning approaches. The application offers an adjustable set of exercises recommended by a speech therapist and aims to motivate and help with regular home practice. Augmented reality is employed to lead the exercise moves and to appraise the effort. The core of the system is automatically evaluating exercises using a webcam and developing image processing and neural network methods for face, lips, teeth, and tongue detection. The pipeline is shown together with solutions of subtasks and with demonstrations of the functionality. The statistical validation of the ASSISLT is provided, comparing the performance of speech therapy specialists and the software.
Machine Learning Techniques-Based Banking Loan Eligibility Prediction

In our daily life, it is difficult to meet financial demand while in crisis. This financial crisis may be solved with financial assistance from the banks. The financial assistance is nothing but availing loan from the bank with proper agreement to repay the amount including calculated interest within the loan approved tenure. The customer can only avail loans against the submission of some valid and important supportive documents. However, although the customer is aware of the whole process of repayment and installment along with loan approval tenure, most of the time it is hard to get the approved loan within a shorter period. Therefore, the objective of this paper is to automate this manual and long process by predicting the chance of approval of the loan. The novelty of this research article is to apply machine learning techniques and classification algorithms to predict loan eligibility through an automatic online loan application process
End to End Framework for CNN Acceleration on FPGAs with Dynamic Algorithm Mapping

As CNNs are becoming more diverse with respect to per-layer computation characteristics, per-layer strategy selection and fine-grained tuning are required to achieve low end-to-end latency. DYNAMAP is a framework for efficiently selecting algorithms for different layers and re-using a unified accelerator. However, the end-to-end deployment of DYNAMAP faces some challenges in terms of productivity and portability. In this work, we develop an API for automatically extracting the ONNX graph from high-level programming libraries such as Pytorch and Tensorflow. We further integrate this with the front-end of DYNAMAP to increase its portability. We then define an HLS template of the accelerator supporting three parallel algorithm choices for convolution operations to increase the productivity of generating hardware. Using three state-of-the-art CNNs, we demonstrate that our framework is able to automatically deploy CNN models to achieve minimum latency which incurs very low overhead in the deployment process.
A QCNN Algorithm Deployed on A Quantum Computer

CNN (Convolutional neural network) is an excellent machine learning model, especially in image recognition, where they have excellent results. However, it is difficult to learn effectively if the given dimensionality of the data or model is too large. QCNN QCNN (Quantum Convolutional Neural Network) offer new solutions to the problems faced by CNN using a quantum computing environment and provide directions for improving the performance of existing learning models. However, most of the current QCNN models are only simulation programs running on a classical computer and cannot actually run on a quantum computer. In this paper, we propose a QCNN model with a quantum encoding circuit that can encode classical data into quantum data, allowing the QCNN to run on quantum computers. Firstly, we introduce the method of quantum state amplitude encoding to represent the high-dimensional classical data into the form of quantum states, constructing a bridge between traditional data and quantum state transformation, while significantly reducing the dimensionality of the processed data. Secondly, we construct the QCNN model and analyze how to implement the image recognition function by QCNN network and the optimization method of the model. Finally, we validate the learning effect of the model using MNIST handwritten digits and compare and discuss the learning effect of QCNN with classical models.
SECT: A Successively Conditional Transformer for Controllable Paraphrase Generation

Paraphrase generation has consistently been a challenging area in the field of NLP. Despite the considerable achievements made by previous work, existing methods lack a flexible way to include multiple controllable attributes to enhance the diversity of paraphrased sentences. To overcome this challenge, we propose a Successively Conditional Transformer (SECT) to tackle this task. SECT is based on a combination of Conditional Variational AutoEncoder (CVAE) and Transformer framework to generate diversified words. More specifically, our SECT deploys multi-head attention and memory gate mechanism to keep the interaction between each of the attributes and the corresponding encoder layer hidden state. To address the problem of absorbing flexible attributes, we apply a successive structure to our SECT, which enables the framework to couple the CVAE latent variables with the encoder layer hidden states progressively. In addition, our SECT is trained by minimizing a tailor-designed loss for producing paraphrased sentences as required. Finally, we conduct extensive experiments to substantiate the validity and effectiveness of our proposed model. The results show that SECT significantly outperforms the existing state-of-the-art approaches and generates more diverse paraphrased sentences.
Recognition of Hand Movements for Specially Abled

The ability to recognize gestures allows a user to employ fluid gestures to deliver or transmit communication, information, facts, emotions, and sentiments. For those who do not grasp the essence of it and hence fail to comprehend it, sign language remains the most fundamental way of communicating or exchanging information to this day. These people are classified as specially-abled. In this study, a model is proposed in which the user makes a gesture in front of the camera, which is then processed through a filter and then a classifier to help the algorithm determine which gesture is made. Also given is indeed a greater knowledge of this model and the procedure. The CNN model was employed to achieve the goal, which gathers up images in real time and translates them into textual form. The text was shown on a graphical user interface that was created with Python's ‘tkinter’ GUI package. As a consequence of the research, a CNN model was created that accurately predicted proper output for images with an accuracy of 95.8% with only one layer and 98.0 percent with both layers.
Face Mask Detection and Counting Using Deep Learning and Embedded Systems

The pandemic of the coronavirus COVID-19 is having an effect on public health around the globe. The WHO has advised a number of precautions to control the spread of the virus, one of which is the proper usage of a face mask. However, many people are violating this simple measure in public places, which could increase the risk of virus transmission. This inspires us to develop a face mask detection system using deep learning to identify whether individuals are wearing masks and how well they are wearing them in real-time. The system is proposed with two cutting-edge object detection models, YOLOv3 Tiny and YOLOv4 Tiny. This system is capable of detecting and classifying face masks as well as their wearing condition and provides class-wise counting in real-time. For real-time inference, these models were deployed on NVIDIA Jetson Nano hardware. The detection models obtained mAP of 84.2% and 88.4% and FPS of 23.94 and 23.58 for YOLOv3 Tiny and YOLOv4 Tiny respectively. In terms of mAP, YOLOv4 Tiny performed better than YOLOv3 Tiny while both have nearly the same FPS, thereby giving an efficient mask detection in real-time, making it a deployable system for the public or crowded places.
Data Reduction Based on Adaptive Stream Window Size for IoT Data

IoT produces data that is streamed to servers for processing. Such a stream is characterized by redundancy. Data streams are often processed in blocks referred to as windows with a given size known as the window size. The estimation of window size is affecting the accuracy and reduction rate. This paper uses a genetic algorithm to optimize window size for the reduction process. The genetic algorithm is combined with fuzzy subtractive clustering as a fitness function that is new method to choose window size. The experimental results show the impact of suitable window size on the accuracy and reduction rate for each type of sensor.
Low-light Image Enhancement Using Chain-consistent Adversarial Networks

The capability to generate clear and bright images in low light situations is crucial for photographers, engineers, and researchers. When it is not possible to modify the imaging conditions, an algorithm to enhance images is needed. Traditional methods require manually adjusting parameters to tune the image. Supervised learning methods need to collect a large amount of paired data for training. In this paper, we demonstrate an semi-supervised method for low light image enhancement, using a chain of cycle consistent generators. We show the effectiveness of our method by comparing it to existing image enhancement methods, both using standard image quality metrics and by using human perceptual judgements. We include an ablation study for features in our model. Our proposed method is computationally efficient and does not require paired training data.
Facial Attributes Recognition Combined with Feature Decoupling and Static-Dynamic Joint Graph Convolutional Network

The existing facial attributes recognition methods either use attributes grouping to extract features or calculate the attributes co-occurrence probability to construct static attributes relational graph. The former learns insufficient complementary information and it cannot determine the correlation of attributes between groups and the correlation degree between attributes. The latter may reduce the generality of the model due to the bias of occasional co-occurrence attributes pairs in face images. In order to solve the above problems, a facial attributes recognition method combined with feature decoupling and static-dynamic graph convolutional neural network is proposed. Firstly, deep convolutional neural network ResNet-50 is used to extract features containing attributes representation information. Then, a feature decoupling module is designed to learn the specific features corresponding to each attribute. Finally, the static graph and dynamic graph are combined to learn the correlation between attributes, and the one-dimensional convolution layer is used to recognize facial attributes. The average accuracy on CelebA and LFWA datasets reach 91.85% and 88.17%, respectively, which are superior to other state-of-the-art methods.