Reviewerly/exHarmony huggingface dataset:
Explaining How Deep Neural Networks Forget by Deep Visualization

Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life. Taking the advantages of interpretable machine learning (interpretable ML), this paper proposes a novel tool called Catastrophic Forgetting Dissector (or CFD) to explain catastrophic forgetting in continual learning settings. We also introduce a new method called Critical Freezing based on the observations of our tool. Experiments on ResNet-50 articulate how catastrophic forgetting happens, particularly showing which components of this famous network are forgetting. Our new continual learning algorithm defeats various recent techniques by a significant margin, proving the capability of the investigation. Critical freezing not only attacks catastrophic forgetting but also exposes explainability.
A curvature-segmentation-based minimum time algorithm for autonomous vehicle velocity planning

Velocity planning serves as an important issue in motion planning for autonomous vehicles. The presented paper proposes a novel velocity planning method with minimum moving time on the basis of path curvature which is accomplished in three steps. First, the assigned path is divided into some elementary parts based on the path curvature. Second, the velocity planning is transformed into an unconstrained optimization problem by assuming the velocity of vehicle to be a specific cubic polynomial on every elementary part to avoid a sudden acceleration in path switching. Finally, we use a modified projection particle swarm optimization (PPSO) algorithm to obtain the time-optimal velocity profile. The proposed method can generate a smooth time-optimal velocity profile while considering all possible relevant constraints. Three examples are provided on different types of path to demonstrate that the final velocity profile is efficient to avoid the sudden acceleration change. Furthermore, the modified PPSO algorithm in this paper is used to solve the optimization problem with high dimensional variables when its upper bound is known, which can not be achieved by the general PPSO algorithm.
An Inclusive Survey of Machine Learning based Hand Gestures Recognition Systems in Recent Applications

Abstract Hand gestures represent one of the most prevalent types of body language which can be utilized for interaction and communication. Although the other types of body language represent a more general state of emotional, hand gestures capable of possessing specified linguistic content inside it. Because of the expressiveness and speed in interaction, hand gestures are commonly utilized in human-computer interaction systems (HCI), sign languages, virtual reality, and gaming. In the process of recognizing hand gestures, the complexity and diversity of gestures will extremely impact on the recognition rate and reliability. The existence of machine learning techniques can be effectively exploited in the task of improving the rate of hand gesture recognition. This paper inspected the performance of machine learning techniques in recognizing vision and sensors based hand gestures in the recently existing applications. Additionally, the widely used architecture applied in various datasets has been considered which includes the acquisition of data, pre-processing, the extraction of features, and classification.
A Survey on Sentiment Analysis for Depression Detection

The advancement in technology and development of various communication platforms transforming the way human being communicate. WWW makes social networks (Facebook, Twitter, Instagram and Snapchat) as a popular platform for people to share their opinion, feelings, and emotions. Sentiment analysis is the process of studying text and extracting out the facts and relevant information from the users opinion. Depressed individuals have different language and they consider social media to share their emotions and views. Social network provides various clues about the person onset of depression like low social interaction and activity, taking medicinal concerns, mainly focus on self and high rate of activity at night. In this field, machine learning performs well but with changing scenario, deep learning is used and provides good results. This survey is a summary of work done in sentiment analysis for depression detection, challenges in sentiment analysis, and various techniques for depression detection.
Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme

Cybersecurity in Industrial Internet of Things (IIoT) has become critical as smart cities are becoming increasingly linked to industrial control systems (ICSs) used in critical infrastructure. Consequently, data-driven security systems for analyzing massive amounts of data generated by smart cities have become essential. A representative method for analyzing large-scale data is the game bot detection approach used in massively multiplayer online role-playing games. We reviewed the literature on bot detection methods to extend the anomaly detection approaches used in bot detection schemes to IIoT fields. Finally, we proposed a process wherein the data envelopment analysis (DEA) model was applied to identify features for efficiently detecting anomalous behavior in smart cities. Experimental results using random forest show that our extracted features based on a game bot can achieve an average F1-score of 0.99903 using 10-fold validation. We confirmed the applicability of the analyzed game-industry methodology to other fields and trained a random forest on the high-efficiency features identified by applying a DEA, obtaining an F1-score of 0.997 using the validation set approach. In this study, an anomaly detection method for analyzing massive smart city data based on a game industry methodology was presented and applied to the ICS dataset.
Document similarity for error prediction

In today's rushing world, there's an ever-increasing usage of networking equipment. These devices log their operations; however, there could be errors that result in the restart of the given device. There could be different patterns before different errors. Our main goal is to predict the upcoming error based on the log lines of the actual file. To achieve this, we use document similarity. One of the key concepts of information retrieval is document similarity which is an indicator of how analogous (or different) documents are. In this paper, we are studying the effectiveness of prediction based on cosine similarity, Jaccard similarity, and Euclidean distance of rows before restarts. We use different features like TFIDF, Doc2Vec, LSH, and others in conjunction with these distance measures. Since networking devices produce lots of log files, we use Spark for Big data computing.
Average behaviour in discrete-time imprecise Markov chains: A study of weak ergodicity

We study the limit behaviour of upper and lower bounds on expected time averages in imprecise Markov chains; a generalised type of Markov chain where the local dynamics, traditionally characterised by transition probabilities, are now represented by sets of ‘plausible’ transition probabilities. Our first main result is a necessary and sufficient condition under which these upper and lower bounds, called upper and lower expected time averages, will converge as time progresses towards infinity to limit values that do not depend on the process' initial state. Our condition is considerably weaker than that needed for ergodic behaviour; a similar notion which demands that marginal upper and lower expectations of functions at a single time instant converge to so-called limit—or steady state—upper and lower expectations. For this reason, we refer to our notion as ‘weak ergodicity’. Our second main result shows that, as far as this weakly ergodic behaviour is concerned, one should not worry about which type of independence assumption to adopt—epistemic irrelevance, complete independence or repetition independence. The characterisation of weak ergodicity as well as the limit values of upper and lower expected time averages do not depend on such a choice. Notably, this type of robustness is not exhibited by the notion of ergodicity and the related inferences of limit upper and lower expectations. Finally, though limit upper and lower expectations are often used to provide approximate information about the limit behaviour of time averages, we show that such an approximation is sub-optimal and that it can be significantly improved by directly using upper and lower expected time averages.
Detecting Dependency-Related Sentiment Features for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence toward a given aspect term or aspect category. For sentiment classification toward a given aspect term, some opinions may exist that are not the given aspect term's modifiers because a sentence may contain more than one aspect term. Hence, It is necessary to capture relevant opinion for a certain aspect term. To capture the nearest opinion of the aspect term, researchers have used the relative distance between an aspect term and all other words in a sentence. However, this can be infeasible when the sentence has a complex syntactic structure. In this paper, we introduce dependency relation to detect the dependency-related sentiment feature for the aspect term in the dependency parse tree, and integrate this relationship into the convolutional neural network and bidirectional long short-term memory. Experiments show that the related sentiment features for an aspect term help models discriminate its sentiment polarity. The proposed models achieve state-of-the-art results among neural networks. The codes and datasets are released on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LittleSummer114/DW-CNN</uri> .
GIS-Based Crisis Response System for Electric Distribution Utility: A Case Study Of Covid-19 Pandemic In Malaysia

Abstract COVID-19 pandemic has impacted 188 countries, with more than 6 million infected people, and Malaysia is not exceptional. With stringent control measures and rapid action by multi-sectoral agencies, Malaysia is only a few countries that collectively managed to reduce the number of human and economic losses, far better than the projection. Critical infrastructure remained intact and full of functionality during the unprecedented crisis. The supply and demand by the electric utility companies are of utmost critical to support the Government of Malaysia’s decision in infective prevention and control, rapid response, epidemiological forecasting, pandemic supply chain coordination, continuity of essential service, surveillance, and risk communication. To survive on this new normal, the business and operations of the utilities must rely on good data, advanced technology, and science-based decision support. This article presents local practices and experiences by GIS Distribution Network (GISDN), a project unit in the Distribution Network (DN) division of Tenaga Nasional Berhad (TNB) to co-support the decisions mentioned above. GISDN has supported the TNB Crisis Management Team (CMT) of the Kuala Lumpur and Selangor state, starting from the Movement Control Order (MCO) for COVID-19 pandemic response in Klang Valley. Maps and statistics of TNB assets in the areas of Enhanced Movement Control Order (EMCO) intelligently delivered by GISDN for use by the CMT groups. In the case of EMCO at Selangor Mansion and Malayan Mansion, geographical information provided to ensure the reliable electricity supply from 40 substations in the total lockdown area that involving around 6,000 residents in 365 residential and commercial units. The web-GIS application dashboard for CMT also developed by GISDN to monitor the status of more than 214 gazetted quarantine stations in Kuala Lumpur. Besides, the web-GIS dashboard application for the situational awareness of COVID-19 based on administrative boundaries in Klang Valley delivered to support the Standard of Operating Procedure (SOP) for Return to Work (RTW) program in GISDN, especially for the use of for site-work approval. These geospatial deliverables and web-GIS dashboard applications aid better visualization, risk-informed forecasting, and response planning for TNB to cope with COVID-19 clusters and zones updated by MOH on the daily basis. In conclusion, this study provides a new insight into the future advancement of spatiotemporal GIS capability, its intelligent architecture design, and an evidence-based decision support system to reduce pandemic risk in the future.
Health Causal Probability Knowledge Graph

Currently, most of the health-data research concentrates on applying Deep Learning technologies for prediction and reasoning. Deep Learning processes build the prediction model purely based on fitting weights on the raw data inside multiple neural layers, which is difficult to explain the prediction outputs. However, telling ‘WHY’ is crucial for healthcare research. The major difficulty to explain in Deep Learning models is a lack of knowledge-based analysis environment that not only can model the knowledge in a machine-understandable way but also can create causal probability relations inside the knowledge. In our research, we propose a Causal Probability Description Logic (CPDL) framework that extended the current Description Logic (DL). The key extension is to have a two-layer DL representation. One layer represents causality knowledge. The other layer takes observation inputs e.g. symptoms for generating a runtime probability knowledge graph based on the previous layer's knowledge. The CPDL framework can support probability-based causal reasoning tasks in a transparent and human-understandable way. CPDL can be easily implemented using existing programming standards such as OWL, RDF, SPARQL and probability network programming libraries. The experimental evaluations extract 383 common disease conditions from the UK NHS (National Healthcare Service) and enable automatically linked 418 condition terms from the DBpedia dataset. The CPDL-based knowledge graph can support disease prediction with traceable pieces of evidence behind the ranking results.
Recognition of Bengali Sign Language using Novel Deep Convolutional Neural Network

On our planet, speech and hearing-impaired people are a part of our society. When an interaction is needed between the impaired and the general people, communication becomes difficult. In several races, impaired people practice various sign languages for communication. For speech and hearing-impaired people, sign language is the fundamental communication method in their lifestyle. However, it is incredibly challenging to desegregate them into the mainstream because the majority part of our community is not aware of their practiced sign language. Nowadays, computer vision-based solutions remain fully appreciated to get their sign language comprehensible to general people. Many analysts are taking a shot at Recognition of Hand Gesture, one of the computer vision-based solutions to recognize sign language. It's been a popular area for research for an extended period now. Some recent studies have reached immense performance using models of deep learning in the region regarding Hand Gesture Recognition in Computer vision. Through this research work, our aim to reduce the communication difficulties among the speech and hearing-impaired people and the rest of Bangladesh by building an appropriate deep learning model that can recognize Bangla Sign Language alphabets precisely. In this work, a different CNN (Convolutional Neural Network) architecture is introduced to identify the alphabets of Bengali sign with the respective Ishara-Lipi database. This architecture accomplished a general precision of 99.86%, which surpassed all prior works regarding Bengali sign alphabet recognition.
Hybrid Convolution Architecture for Energy-Efficient Deep Neural Network Processing

This paper presents a convolution process and its hardware architecture for energy-efficient deep neural network (DNN) processing. A DNN in general consists of a number of convolutional layers, and the number of input features involved in the convolution of a shallow layer is larger than that of kernels. As the layer deepens, however, the number of input features decreases, while that of kernels increases. The previous convolution architectures developed for enhancing energy efficiency have tried to reduce the memory accesses by increasing the reuse of the data once accessed from the memory. However, redundant memory accesses are still required as the change in the numbers of data has not been considered. We propose a hybrid convolution process that selects either a kernel-stay or feature-stay process by taking into account the numbers of data, and a forwarding technique to further reduce the memory accesses needed to store and load partial sums. The proposed convolution process is effective in maximizing data reuse, leading to an energy-efficient hybrid convolution architecture. Compared to the state-of-the- art architectures, the proposed architecture enhances the energy efficiency by up to 2.38 times in a 65nm CMOS process.
Stationary Wavelet Based Energy Feature Extraction for Detection and Classification of Mammographic Microcalcifications

Since radiologists widely use mammographic microcalcifications as initial tool for breast cancer screening, accurate detection of microcalcifications is an inevitable stage to develop an effective diagnosis system. This paper proposes a Stationary Wavelet Transform (SWT) based novel technique for detection and classification of breast microcalcifications. To detect the suspected microcalcifications from mammograms, SWT was applied at multiple levels for decomposition purpose and Stationary Wavelet Energy (SWE) was then implemented for feature extraction from each obtained detailed SWT coefficient sub-bands. Four different Ensemble classifiers were employed for classification of microcalcifications as benign or malignant using these SWE features, conducting 10 fold cross validation. Mammographic Image Analysis Society (MIAS) mammographic database was used for experimental evaluation and at maximum a sensitivity of 94.12%, a specificity of 92.48%, a precision of 88.89% and an accuracy of 92.11% were obtained using Subspace Discriminant Ensemble classifier. Beside outcomes of comparative analysis prove the supremacy of our proposed approach over two state-of-the-art approaches.
Memory‐ and time‐efficient dense network for single‐image super‐resolution

Dense connections in convolutional neural networks (CNNs), which connect each layer to every other layer, can compensate for mid/high-frequency information loss and further enhance high-frequency signals. However, dense CNNs suffer from high memory usage due to the accumulation of concatenating feature-maps stored in memory. To overcome this problem, a two-step approach is proposed that learns the representative concatenating feature-maps. Specifically, a convolutional layer with many more filters is used before concatenating layers to learn richer feature-maps. Therefore, the irrelevant and redundant feature-maps are discarded in the concatenating layers. The proposed method results in 24% and 6% less memory usage and test time, respectively, in comparison to single-image super-resolution (SISR) with the basic dense block. It also improves the peak signal-to-noise ratio by 0.24 dB. Moreover, the proposed method, while producing competitive results, decreases the number of filters in concatenating layers by at least a factor of 2 and reduces the memory consumption and test time by 40% and 12%, respectively. These results suggest that the proposed approach is a more practical method for SISR.
Enhancing medical image registration via appearance adjustment networks

Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration lies in image appearance variations such as the variations in texture, intensities, and noise. These variations are readily apparent in medical images, especially in brain images where registration is frequently used. Recently, deep learning-based registration methods (DLRs), using deep neural networks, have shown computational efficiency that is several orders of magnitude faster than traditional optimization-based registration methods (ORs). DLRs rely on a globally optimized network that is trained with a set of training samples to achieve faster registration. DLRs tend, however, to disregard the target-pair-specific optimization inherent in ORs and thus have degraded adaptability to variations in testing samples. This limitation is severe for registering medical images with large appearance variations, especially since few existing DLRs explicitly take into account appearance variations. In this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations. Our AAN, when integrated into a DLR, provides appearance transformations to reduce the appearance variations during registration. In addition, we propose an anatomy-constrained loss function through which our AAN generates anatomy-preserving transformations. Our AAN has been purposely designed to be readily inserted into a wide range of DLRs and can be trained cooperatively in an unsupervised and end-to-end manner. We evaluated our AAN with three state-of-the-art DLRs - Voxelmorph (VM), Diffeomorphic Voxelmorph (DifVM), and Laplacian Pyramid Image Registration Network (LapIRN) - on three well-established public datasets of 3D brain magnetic resonance imaging (MRI) - IBSR18, Mindboggle101, and LPBA40. The results show that our AAN consistently improved existing DLRs and outperformed state-of-the-art ORs on registration accuracy, while adding a fractional computational load to existing DLRs.
Dynamic Rate Control in the Kafka System

In this paper we propose RACER (Rate Adjusting ConsumER), a novel framework which implements a smart filtering and queuing mechanism that is able to deal effectively with sudden bursts and overloads which are frequently experienced in Big Data messaging systems. RACER provides rate control capabilities to the Kafka's consumer API which allows us to effectively meet the requirements of the end-services without interference among them. Our experimental evaluation using smart city data, illustrates the benefits of our approach.
Hand Motion Recognition of Shipyard Welder Using 9-DOF Inertial Measurement Unit and Multi Layer Perceptron Approach

Abstract A viable system that can monitor the effective working time of welder in real-time is required to overcome the low use of effective welder time in the Shipbuilding Project in the Indonesian Shipyard. It is made possible by using a wearable sensor tri-axial accelerometer, gyroscope, and magnetometer. In this research, sensors are used to recognize typically hand motion of welder during welding activities: preparation, welding and cleaning slags, respectively in three welding positions 1G, 2G, and 3G. Initially, observations were made to recognize the relationship between welder activities and hand motion. Second, raw data containing hand movements from the welder is captured in the form of time-series signals using inertia sensors for various different activities. Third, the raw data of measurements for those activities is extracted and analyzed to identify significant features such as mean, root-mean-square, power spectral density using the welch method (autocorrelation, spectral peak, and spectral power). Finally, typical activities of welder are classified using the resulting feature data with Multi Layer Perceptron. The validation of results shows that the algorithm is capable to recognize the hand motion activities of the welder.