Reviewerly/exHarmony huggingface dataset:
Implementation of Artificial Neural Network on Regression Analysis

Artificial neural network (ANN) works as a very effective tool in both classification and regression problem. The main advantage lies in the fact that it can draw fine distinctions, patterns, or hidden information of data explicitly without complex mathematical considerations. This study aimed to use the neural network on regression problem to achieve a better performance with a lower computational cost. After constructing the initial architecture, we added randomized weights and biases to the model. The performance of the model is evaluated on a well-known benchmark regression dataset named Auto MPG. The datasets were split among train, validation, and testing in a ratio of 50%-30%-20%. After subsequent tuning of hyperparameters, the refined architecture was able to perform predicting regression variable with a great efficiency and less computational time. The metrics of accuracy were taken as mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage of error (MAPE). Comparative study with the others state-of-the-art metaheuristic algorithms reveals the effectiveness of the proposed ANN. Evaluating the experimental results, it was obvious that, most often ANN did lead the other approaches with all the metrics of accuracy with a least computational time. It is also seen that, in some cases, though ANN revealed a very competitive result with other approaches, however the reduction in computational cost overcame the drawbacks and put neural network in leading.
Efficient Noise Generation to Achieve Differential Privacy with Applications to Secure Multiparty Computation

This paper studies the problem of constructing secure multiparty computation protocols whose outputs satisfy differential privacy. We first provide a general framework for multiparty protocols generating shares of noise drawn from distributions capable of achieving differential privacy. Then, using this framework, we propose two kinds of protocols based on secret sharing. The first one is a constant-round protocol which enables parties to jointly generate shares of noise drawn from the discrete Laplace distribution. This protocol always outputs shares of noise while the previously known protocol fails with non-zero probability. The second protocol allows the parties to non-interactively obtain shares of noise following the binomial distribution by predistributing keys for pseudorandom functions in the setup phase. As a result, the parties can compute a share of noise enough to provide the computational analogue of $$\epsilon $$ -differential privacy with communication complexity independent of $$\epsilon $$ . It is much more efficient than the previous protocols which require communication complexity proportional to $$\epsilon ^{-2}$$ to achieve (information-theoretic) $$(\epsilon ,\delta )$$ -differential privacy for some $$\delta >0$$ .
SPIN: A Novel Hybrid Dimensionality Reduction Technique for Cervical Cancer Risk Classification

The number and scale of medical databases is increasingly growing, and sophisticated data mining models may be able to assist physicians and professionals in making more effective and applicable decisions. Cervical cancer is a major type of gynaecological cancer and is amongst the live major malignant cancers in women around the world. Cervical cancer signs are usually undetectable in the early stages. The risk factors are developed due to a number of causes, including the human papillomavirus, sexually transmitted diseases (STDs), and smoking. Dimensionality reduction aids in the removal of redundant or irrelevant features from high-dimensional datasets.This work brings forward a novel hybrid Dimensionality Reduction DR) technique to transform data from higher dimensions to lower-feature subspace. This method combines four major techniques of dimensionality reduction i. e. truncated Singular Value Decomposition (tSVD), Principal Component Analysis(PCA), Independent Component Analysis (ICA), and Non-negative Matrix Factorisation (NMF) and combines the components obtained from each technique into a newer reduced data. The title SPIN hence stands for the significant initials of the base techniques used as mentioned respectively. The proposed method is implemented on the Cervical Cancer Risk dataset. To evaluate performance, the classification for suspected Biopsy examination is done using Decision Tree and random Forest Classifiers, which report an accuracy of 95.283% and 99.057% respectively, which is significantly high as compared to the 98% to 98.67% range present in the recent literatue; even with reduced number of components in lower feature sub-space.
Speed Proportional Integrative Derivative Controller: Optimization Functions in Metaheuristic Algorithms

Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema.
MAAD: A Model and Dataset for "Attended Awareness" in Driving

We propose a computational model to estimate a person’s attended awareness of their environment. We define "attended awareness" to be those parts of a potentially dynamic scene which a person has attended to in recent history and which they are still likely to be physically aware of. Our model takes as input scene information in the form of a video and noisy gaze estimates, and outputs visual saliency, a refined gaze estimate and an estimate of the person’s attended awareness. In order to test our model, we capture a new dataset with a high-precision gaze tracker including 24.5 hours of gaze sequences from 23 subjects attending to videos of driving scenes. The dataset also contains third-party annotations of the subjects’ attended awareness based on observations of their scan path. Our results show that our model is able to reasonably estimate attended awareness in a controlled setting, and in the future could potentially be extended to real egocentric driving data to help enable more effective ahead-of-time warnings in safety systems and thereby augment driver performance. We also demonstrate our model’s effectiveness on the tasks of saliency, gaze calibration and denoising, using both our dataset and an existing saliency dataset. We make our model and dataset available at https://github.com/ToyotaResearchInstitute/att-aware/.
Transfer Learning Strategies for Anomaly Detection in IoT Vibration Data

An increasing number of industrial assets are equipped with IoT sensor platforms and the industry now expects data-driven maintenance strategies with minimal deployment costs. However, gathering labeled training data for supervised tasks such as anomaly detection is costly and often difficult to implement in operational environments. Therefore, this work aims to design and implement a solution that reduces the required amount of data for training anomaly classification models on time series sensor data and thereby brings down the overall deployment effort of IoT anomaly detection sensors. We set up several in-lab experiments using three peristaltic pumps and investigated approaches for transferring trained anomaly detection models across assets of the same type. Our experiments achieved promising effectiveness and provide initial evidence that transfer learning could be a suitable strategy for using pre-trained anomaly classification models across industrial assets of the same type with minimal prior labeling and training effort. This work could serve as a starting point for more general, pre-trained sensor data embeddings, applicable to a wide range of assets.
Federated learning-based aerial image segmentation for collision-free movement and landing

The utilization of drones has recently revolutionized remote sensing with their high spatial resolution and flexibility in capturing images. In the proposed work, we employ a swarm of drones that communicate in a wireless network. Each drone captures the image frames, and each frame is further used to locate and differentiate different objects in an image frame. The semantic segmentation of the captured images is done using deep learning algorithms. To identify the most suitable, cost-efficient, and accurate segmentation method, various state-of-the-art models, are appraised and compared based on different evaluation metrics. Resnet50 model with U-net segmentation model performs the best out of all used models by providing 91.51% pixel accuracy. Also, to give real-time predictions, we have used federated learning with the drone network. Each drone trains a local model using its accumulated data and then transfers the locally trained model to the central server that aggregates the received models, generates a global federated learning model, and transmits it in the swarm network.
Insight on Attention Modules for Skeleton-Based Action Recognition

Spatiotemporal modeling is crucial for capturing motion information in videos for action recognition task. Despite of the promising progress in skeleton-based action recognition by graph convolutional networks (GCNs), the relative improvement of applying the classical attention mechanism has been limited. In this paper, we underline the importance of spatio-temporal interactions by proposing different categories of attention modules. Initially, we focus on providing an insight into different attention modules, the Spatial-wise Attention Module (SAM) and the Temporal-wise Attention Module (TAM), which model the contexts interdependencies in spatial and temporal dimensions respectively. Then, the Spatiotemporal Attention Module (STAM) explicitly leverages comprehensive dependency information by the feature fusion structure embedded in the framework, which is different from other action recognition models with additional information flow or complicated superposition of multiple existing attention modules. Given intermediate feature maps, STAM simultaneously infers the feature descriptors along the spatial and temporal dimensions. The fusion of the feature descriptors filters the input feature maps for adaptive feature refinement. Experimental results on NTU RGB+D and Kinetics-Skeleton datasets show consistent improvements in classification performance, demonstrating the merit and a wide applicability of STAM.
Improved YOLOv4 Based on Dilated Convolution and Focal Loss

With the increasing application of object detection algorithms in practice, the single-stage detector YOLOv4 excellent in both detection precision and inference speed has been widely used in various scenarios. In order to further improve the small object detectability of YOLOv4, this paper proposes to combine the 8-times downsampling feature map of YOLOv4 with the feature map that is outputted by the second residual block of CSPDarknet-53 and processed via the Hybrid Dilated Convolution (HDC), to obtain new detection features. In addition, this paper comes up with an idea of using Focal Loss to improve the negative sample confidence formula for the loss function and alleviate the proportion imbalance between positive and negative samples of YOLOv4. The experimental results show that for the specific test set where small objects account for 47.7% of the total, the average precision and recall of the improved YOLOv4 increase by 8.8% and 16%, respectively, compared with those of the original YOLOv4. Similarly, for the PASCAL VOC test set, the average detection precision of the improved YOLOv4 is 3.4% higher than that of the original YOLOv4.
Binocular Vision Positioning Method for Safety Monitoring of Solitary Elderly

In nowadays society, the safety of the elderly population is becoming a pressing concern, especially for those who live alone. There might be daily risks such as accidental falling or treatment attack on them. Aiming at these problems, indoor positioning could be a critical way to monitor their states. With the rapidly development of the imaging techniques, wearable and portable cameras are very popular, which could be set on human individual. And in view of the advantages of the visual positioning, the authors propose a binocular visual positioning algorithm to real-timely locate the elderly indoor. In this paper, the imaging model has been established with the corrected image data from the binocular camera; then feature extraction has been completed to provide reference to adjacent image matching based on the binary robust independent elementary feature (BRIEF) descriptor, finally the camera movement and the states of the elderly have been estimated to distinguish their falling risk. In the experiments, the real-sense D435i sensors were adopted as the binocular cameras to obtain indoor images, and three experimental scenarios have been carried out to test the proposed method. The results show that the proposed algorithm can effectively locate the elderly indoor and improve the real-time monitoring capability.
AMEN: Adversarial Multi-space Embedding Network for Text-Based Person Re-identification

Many of the existing methods manage to extract modality-invariant features from both modalities by learning a joint latent space, in which the visual/textual feature vector can be better aligned. However, though misaligned information can be removed when mapping features from two high-dimensional spaces into a common space, discriminative clues as well may be lost. To this end, merely embedding features into a joint latent space may not be sufficient to give satisfactory performance, and the utilization of both visual and textual high-level spaces deserves more in-depth exploration. In this paper, we proposed a novel Adversarial Multi-space Embedding Network (AMEN) to learn and match embeddings in multiple spaces. Following an encoder-decoder manner, the inter-modal reconstruction paradigm works in concert with the intra-modal reconstruction paradigm to properly embedding a feature into the opposite modality space while learning a strong common space. A consistency constraint is adapted to ensure that the learned visual and textual spaces are trained jointly and work consistently. To enhance both the common space learning and feature reconstruction, the adversarial mechanism is utilized. Our proposed AMEN is evaluated on CUHK-PEDES, which is currently only accessible dataset for text-base person re-identification task. Extensive experimental results demonstrate that AMEN achieves the state-of-the-art performance.
Countering Adversarial Examples: Combining Input Transformation and Noisy Training

Recent studies have shown that neural network (NN) based image classifiers are highly vulnerable to adversarial examples, which poses a threat to security-sensitive image recognition task. Prior work has shown that JPEG compression can combat the drop in classification accuracy on adversarial examples to some extent. But, as the compression ratio increases, traditional JPEG compression is insufficient to defend those attacks but can cause an abrupt accuracy decline to the benign images. In this paper, with the aim of fully filtering the adversarial perturbations, we firstly make modifications to traditional JPEG compression algorithm which becomes more favorable for NN. Specifically, based on an analysis of the frequency coefficient, we design a NN-favored quantization table for compression. Considering compression as a data augmentation strategy, we then combine our model-agnostic preprocess with noisy training. We fine-tune the pre-trained model by training with images encoded at different compression levels, thus generating multiple classifiers. Finally, since lower (higher) compression ratio can remove both perturbations and original features slightly (aggressively), we use these trained multiple models for model ensemble. The majority vote of the ensemble of models is adopted as final predictions. Experiments results show our method can improve defense efficiency while maintaining original accuracy.
Sentiment Analysis for Informal Malay Text in Social Commerce

Sentiment Analysis (SA) is opinion mining which often defines as the study of emotions, opinions, or feedback that relates to the usage of computational linguistics, text analytics, and natural language processing. With the rise of social media posts, it is becoming more challenging to evaluate brief, casual, and non-structured texts to optimize consumer feedback and spot patterns. Meanwhile, social commerce involves social media for social interaction in assisting customers and merchants to do business transactions. From a social media perspective, the informal Malay Text is less explored by the researchers. Thus, it will directly yield difficulties in conducting and preparing the SA processes. Cross-Industry Standard Process for Data Mining (CRISP-DM) was adapted as a reference model for the methodology of this work with machine learning approaches in classifying the informal Malay textual data based on sentiment. The dataset was extracted from the Facebook platform of Pos Laju Malaysia pages. The comparison of the classification technique performances was analyzed in identifying the most accurate classifier for SA, within three different machine learning classifiers was experimented by using 1200 instances from an informal Malay textual dataset. The results of Decision Tree (J48), Support Vector Machine (SVM), and Naïve Bayes (NB) were analyzed and discussed. The result of the highest accuracy of Ten-Fold Cross-Validation is 69.7% and meanwhile, for the Percentage Split method, the highest accuracy result is 70.9%. It shows that Support Vector Machine (SVM) is the best classifier compared to other classifiers of text classification based on sentiment.
Interactive Inference Under Information Constraints

We study the role of interactivity in distributed statistical inference under information constraints, e.g., communication constraints and local differential privacy. We focus on the tasks of goodness-of-fit testing and estimation of discrete distributions. From prior work, these tasks are well understood under noninteractive protocols. Extending these approaches directly for interactive protocols is difficult due to correlations that can build due to interactivity; in fact, gaps can be found in prior claims of tight bounds of distribution estimation using interactive protocols. We propose a new approach to handle this correlation and establish a unified method to establish lower bounds for both tasks. As an application, we obtain optimal bounds for both estimation and testing under local differential privacy and communication constraints. We also provide an example of a natural testing problem where interactivity helps.
Performance Analysis of a Blockchain Based Peer-to-Peer Energy Trading Framework

Peer to Peer (P2P) energy trading were introduced to store, manage, share, track, and access the permanently stored energy data. In particular traditional energy trading systems provide a centralized approach and connectivity among the different participating trusted users. Recently Blockchain Technology shows a potential solution to address critical challenges such as gapped communications, security in an untrusted decentralized environment, inefficient transactions retrieval (low throughput, high latency, high CPU and memory utilization, etc.), and scattered energy records. In this paper a framework is proposed, to record the energy transactions, a JavaScript-based smart contract is deployed over the Hyperledger Sawtooth Proof of Elapsed Time (PoET). Additionally, the proposed framework is shown as a working prototype, and the performances are evaluated taking transaction per second, read and write latency, read write throughput, the number of successful and failed transactions, CPU utilization, network traffic input, and output. Also, it is shown that PoET's performance is better than PoW for multi-hosting environments. It has been observed that out of 1000 transactions sent for each round by clients through Hyperledger Caliper and REST API services, PoET could able to perform 946 transactions successfully, whereas PoW completed only 627 transactions. The results obtained in various rounds of evaluation affirm the superiority of the framework.
Scheme Invisible Internet Protocol (I2P) using Blockchain techniques for tracking Darkweb User Activities

The classic example of shared hidden data transformation is the Invisible Internet Protocol (I2P), which works with Tor web-net and make incognito relay confidential. The I2P offers an outstanding possibility for people seeking anonymity to circumvent authoritarian regimes. It can also be troublesome in corporate environments where I2P-routers over-use restricted web-net assets or provide a means for malware to exfiltrate information. In this research paper, the authors discussed the IDS possibility to track I2P web-net congestion and associate peers positively is done. Additionally, the authors given context details on the internal mechanisms of I2P, and showed the ways to recognize I2P routers throughout the bootstrapping process. The development and review of the new system is of the current state of the art. We intend to introduce Blockchain techniques for tracking Darkweb User Activities on a trust computational web-net to improve data protection in transparent blockchains (BC).