Reviewerly/exHarmony huggingface dataset:
Memory Recall: A Simple Neural Network Training Framework Against Catastrophic Forgetting

It is widely acknowledged that biological intelligence is capable of learning continually without forgetting previously learned skills. Unfortunately, it has been widely observed that many artificial intelligence techniques, especially (deep) neural network (NN)-based ones, suffer from catastrophic forgetting problem, which severely forgets previous tasks when learning a new one. How to train NNs without catastrophic forgetting, which is termed continual learning, is emerging as a frontier topic and attracting considerable research interest. Inspired by memory replay and synaptic consolidation mechanism in brain, in this article, we propose a novel and simple framework termed memory recall (MeRec) for continual learning with deep NNs. In particular, we first analyze the feature stability across tasks in NN and show that NN can yield task stable features in certain layers. Then, based on this observation, we use a memory module to keep the feature statistics (mean and std) for each learned task. Based on the memory and statistics, we show that a simple replay strategy with Gaussian distribution-based feature regeneration can recall and recover the knowledge from previous tasks. Together with the weight regularization, MeRec preserves weights learned from previous tasks. Based on this simple framework, MeRec achieved leading performance with extremely small memory budget (only two feature vectors for each class) for continual learning on CIFAR-10 and CIFAR-100 datasets, with at least 50% accuracy drop reduction after several tasks compared to previous state-of-the-art approaches.
Survey on Comparative Study of Pruning Mechanism on MobileNetV3 Model

Deep neural networks(DNN) are being extensively used in the field of image recognition, natural language processing(NLP), bioinformatics, computer vision domains and many other fields. DNN applications require huge models as they are computationally expensive, over-parameterized and have high memory footprints which becomes a bottleneck in deployment on embedded devices. Optimization of DNN models is crucial. In this paper, we have summarized the recent works done on optimization of DNNs using various pruning techniques and their comparisons. Pruning technique aims at eliminating less salient connections, neurons, channels, filters, thereby making DNNs model easier to deploy on embedded devices.
Reservoir Computing in Material Substrates

We overview Reservoir Computing (RC) with physical systems from an Unconventional Computing (UC) perspective. We discuss challenges present in both fields, including encoding and representation, or how to manipulate and read information; ways to search large and complex configuration spaces of physical systems; and what makes a “good” computing substrate.
Coding a Telegram Quiz Bot to Aid Learners in Environmental Chemistry

In light of the COVID-19 pandemic, the transition from traditional face-to-face lectures to online learning, as well as the adoption of technology-enabled active learning in the classroom, has been accelerated. Here, we introduce the use of Telegram as a technological tool to support remote learning by helping students identify gaps in their content knowledge with ease and convenience. This was done by designing a quiz based on relevant topics on the Telegram Quiz Bot, while adding an element of gamification, whereby students compete in group chats for correctness and speed. Overall, the majority of the students responded positively to the use of the Telegram Quiz Bot.
DDR-ID: dual deep reconstruction networks based image decomposition for anomaly detection

Image based anomaly detection (AD) refers to the task of predicting an unseen image as the normal class (inliers) or the anomalous classes (outliers) by learning only from normal class training images. Such task is of significant value in real-world applications like monitoring anomalous event from surveillance cameras since most captured data reflect normal conditions but identification of anomalous data is required. One pivot challenge for image anomaly (AD) detection is to learn discriminative information only from normal class training images. Most image reconstruction based AD methods rely on the discriminative capability of reconstruction error. This is heuristic as image reconstruction is unsupervised without incorporating normal-class-specific information. In this paper, we propose an AD method called dual deep reconstruction networks based image decomposition (DDR-ID). The networks are trained by jointly optimizing for three losses: the one-class loss, the latent space constrain loss and the reconstruction loss. After training, DDR-ID can decompose an unseen image into its normal class and the residual components, respectively. Two anomaly scores are calculated to quantify the anomalous degree of the image in either normal class latent space or reconstruction image space. Thereby, anomaly detection can be performed via thresholding the anomaly score. The experiments demonstrate that DDR-ID outperforms multiple related benchmarking methods in image anomaly detection using MNIST, CIFAR-10 and Endosome datasets and adversarial attack detection using GTSRB dataset.
ProcData: An R Package for Process Data Analysis

Process data refer to data recorded in log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents' response problem-solving behaviors. Process data analysis aims at enhancing educational assessment accuracy and serving other assessment purposes by utilizing the rich information contained in response processes. The R package ProcData presented in this article is designed to provide tools for inspecting, processing, and analyzing process data. We define an S3 class 'proc' for organizing process data and extend generic methods summary and print for 'proc'. Feature extraction methods for process data are implemented in the package for compressing information in the irregular response processes into regular numeric vectors. ProcData also provides functions for making predictions from neural-network-based sequence models. In addition, a real dataset of response processes from the climate control item in the 2012 Programme for International Student Assessment is included in the package.
High-speed serial deep learning through temporal optical neurons

Deep learning is able to functionally mimic the human brain and thus, it has attracted considerable recent interest. Optics-assisted deep learning is a promising approach to improve forward-propagation speed and reduce the power consumption of electronic-assisted techniques. However, present methods are based on a parallel processing approach that is inherently ineffective in dealing with the serial data signals at the core of information and communication technologies. Here, we propose and demonstrate a sequential optical deep learning concept that is specifically designed to directly process high-speed serial data. By utilizing ultra-short optical pulses as the information carriers, the neurons are distributed at different time slots in a serial pattern, and interconnected to each other through group delay dispersion. A 4-layer serial optical neural network (SONN) was constructed and trained for classification of both analog and digital signals with simulated accuracy rates of over 79.2% with proper individuality variance rates. Furthermore, we performed a proof-of-concept experiment of a pseudo-3-layer SONN to successfully recognize the ASCII codes of English letters at a data rate of 12 gigabits per second. This concept represents a novel one-dimensional realization of artificial neural networks, enabling a direct application of optical deep learning methods to the analysis and processing of serial data signals, while offering a new overall perspective for temporal signal processing.
ESPADA: Extended Synthetic and Photogrammetric Aerial-Image Dataset

We present a new aerial image dataset, named ESPADA, intended for the training of deep neural networks for depth image estimation from a single aerial image. Given the difficulty of creating aerial image datasets containing image pairs of chromatic images related to their depth images, simulators such as AirSim have been proposed to generate synthetic images from photorealistic scenes. The latter enables the generation of thousands of images that can be used to train and evaluate neural models. However, we argue that synthetic photorealistic aerial image datasets can be improved by adding images generated from photogrammetric models imported into the simulator, thus enabling a less artificial generation of both chromatic and depth images. To assess the quality of these images, we compare the performance of 4 deep neural networks whose pre-trained models and code for re-training are publicly available. We also use ORB-SLAM, in its RGB-D version, to indirectly assess the estimated depth image. To accomplish this, chromatic images from 3 aerial videos and their depth images, estimated with the networks trained with ESPADA, are fed into ORB-SLAM. The estimated camera pose is compared against the trajectory retrieved from the GPS flight trajectory. Our results indicate that images generated from photogrammetric models improve the performance of depth estimation from a single aerial image.
Detecting the Locations and Predicting the Maintenance Costs of Compound Architectural Debts

Architectural Technical Debt ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATD</i> ) refers to sub-optimal architectural design in a software system that incurs high maintenance “interest” over time. Previous research revealed that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATD</i> has significant negative impact on daily development. This paper contributes an approach to enable an architect to precisely locate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> , as well as capture the trajectory of maintenance cost on each debt, based on which, predict the cost of the debt in a future release. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> are expressed in four typical patterns, which entail the core of each debt. Furthermore, we aggregate compound <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> to capture the complicated relationship among multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATD</i> instances, which should be examined together for effective refactoring solutions. We evaluate our approach on 18 real-world projects. We identified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">persistently</i> incur significant (up to 95 percent of) maintenance costs in most projects. The maintenance costs on the majority of debts fit into a linear regression model—indicating stable “interest” rate. In five projects, 12.1 to 27.6 percent of debts fit into an exponential model, indicating increasing “interest” rate, which deserve higher priority from architects. The regression models can accurately predict the costs of the majority of (82 to 100 percent) debts in the next release of a system. By aggregating related <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> , architects can focus on a small number of cost-effective compound debts, which contain a relatively small number of source files, but account for a large portion of maintenance costs in their projects. With these capabilities, our approach can help architects make informed decisions regarding whether, where, and how to refactor for eliminating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> in their systems.
Enhanced Few-Shot Learning for Intrusion Detection in Railway Video Surveillance

Video surveillance is gaining increasing popularity to assist in railway intrusion detection in recent years. However, efficient and accurate intrusion detection remains a challenging issue due to: (a) limited sample number: only small sample size (or portion) of intrusive video frames is available; (b) high inter-scene dissimilarity: various railway track area scenes are captured by cameras installed in different landforms; (c) high intra-scene similarity: the video frames captured by an individual camera share a same background. In this paper, an efficient few-shot learning solution is developed to address the above issues. In particular, an enhanced model-agnostic meta-learner is trained using both the original video frames and segmented masks of track area extracted from the video. Moreover, theoretical analysis and engineering solutions are provided to cope with the highly similar video frames in the meta-model training phase. The proposed method is tested on realistic railway video dataset. Numerical results show that the enhanced meta-learner successfully adapts unseen scene with only few newly collected video frame samples, and its intrusion detection accuracy outperforms that of the standard randomly initialised supervised learning.
Factors Influencing Blockchain Adoption in Government Organization : A Proposed Framework

With the rapid digitalisation of the economy nowadays, it is becoming increasingly important for organisations to embrace digital innovation. The emergence of blockchain technology illustrates digital innovation's disruptive impact and, at the same time, poses challenges to organisations. This study has been assisting government organisations in blockchain adoption that is reflected in far-reaching measures in terms of technology, organisation, and environment. Based on this framework, this research builds on an empirical study to explore the technology, organisation, environment and trust adoption of blockchain technology. This proposed framework aims to assist the government in developing blockchain adoption based on the baseline theory models, which are Technology, Organization, Environment (TOE), emphasis on trust and other blockchain adoption theories. This research uses a conventional literature review to identify the factors influencing blockchain adoption. Preliminary investigation has been conducted prior to develop the proposed framework. The interview session was conducted to verify the availability of the blockchain technology in government organisations. The proposed conceptual model can be valuable to government organisations and may assist those government organisations which consider blockchain technology in the organisation to be more secure.
On the Deployment of Out-of-the-Box Embedded Devices for Self-Powered River Surface Flow Velocity Monitoring at the Edge

As reported in the recent image velocimetry literature, tracking the motion of sparse feature points floating on the river surface as done by the Optical Tracking Velocimetry (OTV) algorithm is a promising strategy to address surface flow monitoring. Moreover, the lightweight nature of OTV coupled with computational optimizations makes it suited even for its deployment in situ to perform measurements at the edge with cheap embedded devices without the need to perform offload processing. Despite these notable achievements, the actual practical deployment of OTV in remote environments would require cheap and self-powered systems enabling continuous measurements without the need for cumbersome and expensive infrastructures rarely found in situ. Purposely, in this paper, we propose an additional simplification to the OTV algorithm to reduce even further its computational requirements, and we analyze self-powered off-the-shelf setups for in situ deployment. We assess the performance of such set-ups from different perspectives to determine the optimal solution to design a cost-effective self-powered measurement node.
Graph-based broad learning system for classification

Broad learning system (BLS) is viewed as an alternative method of deep neural network (DNN). Comparing with DNN, BLS can reach a similar performance with much faster learning speed. BLS contains two kinds of features: the mapped features and the enhancement nodes. The mapped features are obtained by randomly generated connecting weights and biases which are then fine-tuned by sparse autoencoder. But sparse autoencoder, as an unsupervised leaning algorithm, will lose the information of ground truths. The enhancement nodes are formed based on the mapped features with randomly generated weights and biases. However, this random process cannot guarantee the quality of these formed nodes. It means BLS may be unable to learn useful enough representations of original data. Considering the effectiveness of extreme learning machine based autoencoder (ELM-AE), we first propose a novel graph-based ELM-AE (GBEAE) method. Then, a GBEAE-based broad learning system (GBEAE-BLS) is introduced to overcome the disadvantages discussed above. Finally, we use 23 datasets to test the performance of GBEAE-BLS. Experimental results demonstrate that GBEAE-BLS has evident superiorities on these datasets, comparing with related learning algorithms.
Filter pruning via separation of sparsity search and model training

Network pruning has been widely used in the field of model compression and inference acceleration for convolutional neural networks(CNN). Existing methods generally follow a “training-pruning-retraining” paradigm, known as a three-stage pipeline. However, it cannot play an effective role in a pre-trained model with a larger pruning rate. In addition, prevailing methods usually set pruning rates as super parameters, which fail to consider the sensitivity of different convolution layers. In this paper, a novel pruning approach, based on the separation of sparsity search and model training(SST), is proposed to solve the above problems. Specifically, an evolutionary algorithm is introduced into the process of searching for the most suitable number of pruned filters for every layer. After obtaining the best sparsity structure, a new pruning strategy, called the one-pruning pipeline, is utilized to prune the pre-trained model. Experiments on multiple advanced CNN architectures show that SST can greatly improve the pruning rate with a slight loss of accuracy, which is found to universally reduce more than 60% FLOPs on CIFAR-10. Notably, on ILSVRC-2012, pruning based on ResNet18 reduces FLOPs by 42.8%, while top-1 and top-5 accuracy only lose 1.19% and 0.62%, respectively.
Leveraging effectiveness and efficiency in Page Stream Deep Segmentation

The separation of documents contained in a page stream is a critical activity in some segments. That is the case of the Brazilian judiciary system since it is overwhelmed with files resulting from batch scanning of lawsuits, finishing in PDFs containing several types of mixed documents. To make such a file usable, we must divide it into cohesive sets of pages that result in a single piece. The typical approach to this task involves sorting the page into a stream that reveals the transition between documents. That is, it is about identifying the page that highlights a new piece in the stream. For this task, classification methods combining text and image got the best results, obtaining accuracy and kappa scores respectively of 91.9% and 83.1% in the Tobacoo800 dataset. This outcome, although remarkable, requires excessive computational demand. In this work, by changing the entry of image models and employing a novel labeling system, we achieved the same result, without the overhead that the modal text imposes on the solution. In addition, we built a new public dataset called AI.Lab.Splitter specifically aimed at page stream segmentation task with more than 30k labeled samples. Finally, in addition to VGG, we used EfficientNet, whose number of parameters is 1/6 of the former. We could observe an advantage close to 2.5% in f1 score, compared to the same proposal using VGG in our dataset.
IMPERSONAL: An IoT-Aided Computer Vision Framework for Social Distancing for Health Safety

Recently, pushed by the COVID-19 pandemic, the need of respecting social distancing has motivated several researchers to define novel technological solutions to monitor and track user movements. Information and Communications Technologies (ICT) world has addressed this challenge by means of the use of different technologies, such as Bluetooth, in order to track user interdistance and encounter time. Technology solutions should be able to not only track contacts, but also alert users to restore social distancing. In this article, we present IMPERSONAL framework, with the twofold aim of both: 1) tracking and monitoring social distancing and 2) alerting users in case of gatherings. The framework is based on a subnetwork of computer vision-based devices that are adopted to monitor and track users’ movements to estimate their interdistance and compute the encounter time. Such information is then the input to an Internet of Things subnetwork, aiming to retrieve the anonymous IDs of people belonging to a gathering, as well as to send alert messages to them. We assess IMPERSONAL framework by means of extensive Monte Carlo simulations and experimental results, showing its effectiveness in terms of accuracy in correctly identifying users and gatherings in videos taken from live cameras, both in case of indoor and outdoor real scenarios. The benefits of the IMPERSONAL framework are expressed in terms of the ability to track people, solve gatherings, and send warning messages.
A burning question – Using an intelligent grey literature search engine to change our views on early medieval burial practices in the Netherlands

This paper presents a case study on Early Medieval burial practices using AGNES, an intelligent search engine for Dutch archaeological grey literature. Traditionally, it is assumed that cremations phased out at the start of the Early Middle Ages, when the inhumation practice became more numerous. However, recent research (Lippok, 2020) shows that cremations might be more prevalent than previously assumed. Due to research efforts being concentrated on furnished inhumations, cremations and other types of burials have not received their share of research interest. It is suspected that unknown Early Medieval cremations may be found in grey literature research reports. The rapidly growing document collection requires more efficient methods to search through this big data, as manual searching is too time intensive. AGNES uses machine learning to allow searching on archaeological concepts (such as time periods and artefacts) in full texts, solving common problems with synonymy and polysemy. This paper describes a controlled search for Early Medieval cremations in the Netherlands, and a comparison of the new information uncovered from the reports to prior knowledge on the topic. The queries resulted in 2541 hits. Twenty-three Early Medieval cremations that were previously unknown to experts were uncovered, and 31 (of 77) known sites were identified. Forty-one possibly interesting documents were noted, where it was not clear from the report if Early Medieval cremations were present. 2446 documents were not relevant to the study for various reasons. The 23 new sites are an increase of 30% over the existing knowledge of experts. In the last 20 years only nine new sites featuring Early Medieval cremations were discovered, so being able to add another 23 is a major development. Adding previously unknown Early Medieval cremations to the dataset challenges the existing bias for inhumation graves, and supports striving towards viewing the Early Medieval burial repertoire as more heterogeneous and representative. This indicates that AGNES is useful for archaeological research, and the uncovered information can lead to a more cohesive view of the past.