Reviewerly/exHarmony huggingface dataset:
Incremental Attractor Neural Network Modelling of the Lifespan Retrieval Curve

The human lifespan retrieval curve describes the proportion of recalled memories from each year of life. It exhibits a reminiscence bump - a tendency for aged people to better recall memories formed during their young adulthood than from other periods of life. We have modelled this using an attractor Bayesian Confidence Propagation Neural Network (BCPNN) with incremental learning. We systematically studied the synaptic mechanisms underlying the reminiscence bump in this network model after introduction of an exponential decay of the synaptic learning rate and examined its sensitivity to network size and other relevant modelling mechanisms. The most influential parameters turned out to be the synaptic learning rate at birth and the time constant of its exponential decay with age, which set the bump position in the lifespan retrieval curve. The other parameters mainly influenced the general magnitude of this curve. Furthermore, we introduced the parametrization of the recency phenomenon - the tendency to better remember the most recent memories - reflected in the curve's upwards tail in the later years of the lifespan. Such recency was achieved by adding a constant baseline component to the exponentially decaying synaptic learning rate.
Fast Multi-label Tumor Classification Based on Homomorphic Encryption

The tumor classification based on the correlation between genomic information and tumors plays a vital role in early cancer diagnosis and treatment. Patients can outsource the genomic information to the cloud platform for early cancer diagnosis. However, anyone who has access to the cloud platform can obtain these genetic data stored in plaintext, and the patients’ privacy cannot be guaranteed. By employing homomorphic encryption technology, the cloud platform can directly perform operations on the gene ciphertext, ensuring patients’ privacy. This work proposes several fast, secure tumor classification models using a multi-layer perceptron and the homomorphic encryption scheme. In this paper, we first propose a preprocessing method to filter out the irrelevant genetic data to improve data quality and reduce the size of individual genetic data. We pack all genetic data of the test set into TLWE ciphertexts and then use TLWE ciphertexts and processed integer model weights to realize the parallel computing efficiently, and the model only takes about 0.67s to classify 258 testing samples. Finally, we propose the improved encryption of float numbers based on the LWE encryption scheme and then use the bootstrapping operation to efficiently realize the ciphertext comparisons for the sign function. After adding the sign activation function, the classification accuracy is improved by about 4%.
Hand-object Interaction Definition and Recognition for Analyzing Manual Assembly Behaviors

Recently, a worker’s subjective satisfaction, in other words Quality-of-Working Life (QWL), has attracted more attention than productivity or efficiency. To provide QWL-oriented working support in a factory manufacturing environment, this study proposes a framework for recognizing manual assembly behaviors that may reflect a worker’s inner state or physical condition. First, a new set of interactions is defined to describe the behavioral fluctuations and diversity that appear even in the same assembly task. We expand the conventional interaction definitions for manufacturing analysis in three ways: 1) we add primitive interactions that qualify the fundamental interactions, 2) we install a spatial attribute into the interaction definition, and 3) we allow the simultaneous occurrence of multiple interactions. Additionally, an image-based automatic recognition technique is designed to detect the newly defined interactions. Through experimental evaluations for a compressor attachment task, we found various differences in manual assembly behaviors and confirmed that they can be distinguished using the recognized interactions.
Modified Strong Tracking Slide Window Variational Adaptive Kalman Filter With Unknown Noise Statistics

The filter performance will be degraded in the measurements with time-varying and unknown noise statistics. To combat the above challenges, a modified strong tracking slide window variational adaptive Kalman filter algorithm is proposed in this article. First, the multiple fading factors are integrated into the proposed algorithm to adjust the error covariance. Next, an improved adaptive slide window method is designed for variational Bayesian (VB) Kalman filtering by adaptively adjusting the slide window size and correcting the previous state according to the later state, which improves the estimation accuracy and computational efficiency. Finally, the inverse Wishart distribution is considered for modeling process and measurement noise, and the state vector, as well as noise statistics, are inferred via the VB technique without prior noise covariance information. Simulation results demonstrate that the proposed filter algorithm is more robust than existing filters in counteracting measurement and process noise uncertainties.
AdaDeId: Adjust Your Identity Attribute Freely

Face de-identification has drawn increasing attention in recent years. It is important to protect people’s identity information meanwhile keeping the utility of the face data in many computer vision tasks. We propose a Adaptive De-identification (AdaDeId) method, a novel approach that can freely manipulate the identity attributes of given faces. We introduce an identity decoupling representation learning method, which is based on the autoencoder decoupling model as well as our proposed Identity Decoupling Representation (IDR) loss and Content Retention (CR) loss. Our method encodes the identity information of a face into a unit spherical space, where we can continuously manipulate the identity representation vector. Various de-identified faces derived from an original face can be generated through our method and maintain high similarity to the original image contents. Quantitative and qualitative experiments demonstrate our method achieves state-of-the-art on visual quality and de-identification validity.
Data Analysis in the Earth Sciences

Earth scientists make observations and gather data about the natural processes that operate on planet Earth. They formulate and test hypotheses on the forces that have acted on a particular region to create its structure, and they also make predictions about future changes to the planet. All of these steps in exploring the Earth involve acquiring and analyzing numerical data.
Mobile Deep Classification of UAE Banknotes for the Visually Challenged

This paper proposes an artificial intelligence-powered mobile application for currency recognition to assist sufferers of visual disabilities. The proposed application uses RCNN, a pre-trained MobileNet V2 convolutional neural network, transfer learning, hough transform, and text-to-speech reader service to detect and classify captured currency and generate an auditory signal. To train our AI model, we collect 700 ultra-high definition images from the United Arab Emirates banknotes. We include the front and back faces of each banknote from various distances, angles, and lighting conditions to avoid overfitting. When triggered, our mobile application initiates a capture of an image using the mobile camera. The image is then pre-processed and input to our on-device currency detector and classifier. We finally use text-to-speech to change the textual class into an audio signal played on the user’s Bluetooth earpiece. Our results show that our system can be an effective tool in helping the visually challenged identify and differentiate banknotes using increasingly available smartphones. Our banknote classification model was validated using test-set and 5-fold cross-validation methods and achieved an average accuracy of 70% and 88%, respectively.
PREDICTION APPROACH OF RISE PART OF REAL-TIME SEISMIC INTENSITY

In this study, we proposed a method to simply predict the rise part of real-time seismic intensity time-series. First, the duration between the P-wave arrival and achieving 95% of the maximum value of real-time seismic intensity was taken to be the characteristic time of the rise part. Then, its prediction equation using hypocentral distance, moment magnitude, source depth, average S-wave velocity up to 30 m depth, and top depth to the layer whose S-wave velocity is 1,400 m/s as explanatory variables was developed by regression analysis based on strong-motion records of 41 earthquakes. In addition, an approximate function based on a logarithmic function was proposed to reproduce the time-series shape of the rise part. Combining the prediction equation and function, the rise part of real-time seismic intensity at any point could be predicted and reproduced. We verified the effectiveness and limitations of this prediction approach with records of the 2016 Kumamoto earthquake and the 2011 Tohoku earthquake.
State-Space Systems

Linear state-space systems, like ARMA systems, are models for stationary processes, more precisely for the class of stationary processes with rational spectral density. ARMA models and state-space models (with white noise as input) represent the same class of stationary processes. State-space systems became particularly popular through the work of Rudolf Kálmán. They contain an, in general, unobserved variable, the state, which contains all information from the past of the process that is relevant for the future. State-space systems lead to the Kalman filter discussed in this chapter. They are applied much more frequently in control theory, than the equivalent ARMA systems. Two key results in this chapter are the equivalence of controllability and observability with minimality and the description of equivalence classes of observationally equivalent minimal systems. Further, we provide construction for obtaining a state-space system from the Wold decomposition. In Sect. 7.4, we discuss the Kalman filter, which is an algorithm for estimating the unobserved state from observations and for predicting these observations. The Kalman filter is of significant importance for prediction and maximum likelihood estimation.
IoT Platform for Distance Contest During COVID-19 Pandemic

Under the influence of the epidemic, many meetings or works have been changed to remote, and lots of competitions have been postponed or even suspended. Nowadays, few platforms replace physical competition in distance. Therefore, we provide and implement a platform for tug-of-war using strandbeest, enabling contestants from two places apart to compete at the same or different times. We create the strandbeest tug-of-war platform via the development board, database system, and backend logic. The uniqueness of the mac address of the development board and the current time of the round filter the strandbeests’ tension data and take the arithmetic average. After both contestants finish the round, the system compares the score of each contestant. The arithmetic average of the filtered tension data from the strandbeest is made to decide who the winner is. The maximum number of tension data is shown to be read and sent to the database at different times and the change before and after the round. Even if the pandemic slows down in the future, people can compete with this platform, especially contestants from different cities or countries, since it beats the constraint of distance.
Send to me first: Priority in synchronous message-passing

Abstract In this paper, we introduce a tiered-priority scheme for a synchronous message-passing language with support for selective communication and first-class communication protocols. Crucially, our scheme allows higher priority threads to communicate with lower priority threads, providing the ability to express programs that would be rejected by classic priority mechanisms that disallow any (potentially) blocking interactions between threads of differing priorities. We formalize our scheme in a novel semantic framework featuring a collection of actions to represent possible communications. Utilizing our formalism, we prove several important and desirable properties of our priority scheme. We also provide a prototype implementation of our tiered-priority scheme capable of expressing Concurrent ML and built in the MLton SML compiler and runtime. We evaluate the viability of our implementation through three case studies: a prioritized buyer-seller protocol and predictable shutdown mechanisms in the Swerve web server and eXene windowing toolkit. Our experiments show that priority can be easily added to existing CML programs without degrading performance. Our system exhibits negligible overheads on more modest workloads.
Development of Smart Chatbot Using Artificial Intelligence in the Fight Against the COVID-19 Pandemic

The invention of the Coronavirus (nCOV-19), which has turn out to be a worldwide pandemic has affected big range of humans of just about all age companies and socio-demographic popularity in the global. It was not only a pandemic with physical challenges, it brought emotional and mental challenges with itself (WHO Health Alert brings COVID-19 facts to billions via WhatsApp.). People were/are confused to what to do or how to react to the situation, which news to believe in, which not to ( www.messengerpeople.com |523: Origin is unreachable.). So, we came up with an idea of a voice automated chatbot, which would bring a better version of chatbot to the people. It is designed in such a way that people with disabilities can also use it because of the speech control. Our team also decided to bring the accurate information from the professionals to the very devices of the people by feeding in the updated information in the code on a regular basis. Unlike doctors our Botch would provide precise information 24/7 and would also prevent people from leaving their homes in this hour of tragedy. We have made sure that our chatbot is interactive enough so that the users will not find it uncomfortable to use a new app for their health assistance (Jadhav KP, Thorat SA, Toward designing conversational agent systems. In Advances in Intelligent Systems and Computing. Springer: Berlin, Germany, 2020). It is high time we have been confused by the vast information available, half of them are hardly true. So, we have tried to provide our users with the most precise information available.
A Method for Bilingual Tibetan-Chinese Scene Image Dataset Synthesis and Text Detection

In order to meet the requirement of bilingual Tibetan-Chinese text detection in a large number of scene images in reality, a dataset is synthesized and a deep-learning based model is trained, thus providing a solution to the issue of Tibetan-Chinese text detection in scene images. Firstly, to solve the problem of lacking large number of annotated samples, a synthesis method based on contour detection and Poisson image editing is proposed. A bilingual Tibetan-Chinese scene image dataset (BiTCSD) is synthesized, including 87 680 synthetic images and 5 550 manually annotated images. Secondly, the effectiveness of training model using synthetic dataset is verified. Thirdly, the connectionist text proposal network (CTPN) based on deep learning is trained on different datasets and the text detection performance of the model is evaluated for different languages on the test set. The experimental results demonstrate that the detection performance of CTPN can be greatly improved by training the model via synthetic samples, the trained CTPN can detect the bilingual Tibetan-Chinese text lines in scene images with high accuracy and recall, with the the precision of 0.91, the recall of 0.85 and the <italic>F</italic> value of 0.88 for Tibetan text detection, and the detection precision, recall and <italic>F</italic> value for Chinese text detection is 0.89, 0.83 and 0.86, respectively.
Crucible-Blowpipe Symbol for Gold and Ratti-Seed Symbol Signifying 'Indian Karat' Engraved on Mohenjo-Daro’s Gold-Assaying Needles: Rosetta-Stones for Decoding Indus Script?

The functionalities of three tiny inscribed ‘needle-shaped’ gold objects found from a jewelry-hoard of Mohenjo-daro, and a tiny gold-tipped-copper-rod excavated from the Indus settlement Kotada Bhadli, are still debated. Considering archaeological evidence that Indus goldsmiths used touchstone method of gold-assaying (e.g., Banawali’s gold-streaked touchstone), and comparing modern gold-tipped-copper-bodied gold-testing-needles with Kotada-Bhadli’s gold-tipped-copper-rod, this article identifies it as an ancient gold-testing-needle. Mohenjo-Daro’s “gold-needles” are also identified as gold-assaying-needles, as they closely resemble Kotada-Bhadli’s gold-assaying-needle; they were found in a jeweler’s hoard which included unfinished ornaments and gold-objects of different purities; and they were dented by use, indicating that they were goldsmith’s tools, not articles preserved for selling.
Local Taxonomy Construction: An Information Retrieval Approach Using Representation Learning

In specific domains, such as e-commerce, inducing a taxonomy over a given set of ‘target’ concepts is an important problem with applications ranging from good website design to knowledge organization and recommender systems. Automatically inducing a full or ‘global’ taxonomy over a large set of concepts is a difficult problem in the AI literature, typically requiring human intervention. A more tractable version of the problem is called Local Taxonomy Construction (LTC). Rather than induce a global taxonomy, LTC attempts to induce the local neighborhood of each concept in the target concept-set. Despite having much practical importance, LTC has not been properly formalized and explored in the applied AI community. In this paper, we present such a formalism on LTC, including a set of viable, minimally supervised solutions based on pre-existing representation learning algorithms in the natural language processing community. We also conduct a detailed experimental study using three widely used, real-world e-commerce datasets. We also present qualitative assessments, and discuss potential applications of LTC in industry.
Knowledge Structuring and Acquisition for Data Exchange

Unlike other well-known business ecosystems, such as the financial markets or service ecosystems, the data marketplace and its ecosystem are immature and the observable interactions are quite limited. To understand the aspects and interactions of data, stakeholders, and other entities in the marketplace, not only is the analysis of the individual pieces of data important but the investigation of the structural characteristics of the marketplace is also important. In this chapter, we attempt to elucidate the value chain of data businesses and stakeholder interactions in the ecosystem, using the structured knowledge for data utilization generated in the IMDJ workshops, the variable-based data networks, and stakeholder-centric data business networks.
Deep Learning based Multistep Registration Focusing on Regions of Change

During the last decades, the remote sensing community has gained access to a wide satellite imagery material, resulting in great progress on various applications. Most of these applications firstly require that the employed images are in the same coordinate system, without any registration errors that will deteriorate their performance. In this work we employ a multistep fully convolutional network in order to improve the image registration task. Moreover, we propose the relaxation of the registration constraints on the regions where changes have occurred. In this way, the model learns to keep the proper structures of objects that change in the multitemporal pair while at the same time provides dense deformations for the rest of the region. Our method is very efficient, fast and requires annotations for the regions of change only in training time. We conduct experiments on the very high resolution Attica VHR dataset comparing it with other deformable registration approaches from the literature. Our method outperforms all compared methods both quantitatively and qualitatively, setting up a foundation for further future research.
Virtual Reality Simulation for Emergency Management teams – A Literature Review

The objective of this review was to provide a summary of virtual reality (VR) simulation for emergency management (EM) teams and suggest future research agenda items. A systematic literature review was conducted using three databases: PsycINFO, Compendex (Engineering Village), and Web of Science. A total of five articles met inclusion criteria and thus were reviewed here. Our review identified four common aspects of VR-based EM team simulation: coordination, communication, decision-making, and situation awareness. Future research is recommended to investigate other aspects of team cognition and collective characteristics as to how these are related to team performance. The level of fidelity of the VR simulation was identified as an important factor to consider when designing VR simulations for different populations with varying experience level. The adoption of more immersive VR systems would be beneficial to improving ecological validity. Moreover, a deep analysis of fidelity and simulation effectiveness is recommended.
Practical Applications of Set Shaping Theory in Huffman Coding

One of the biggest criticisms of the Set Shaping Theory is the lack of a practical application. This is due to the difficulty of its application. In fact, to apply this technique from an experimental point of view we must use a table that defines the correspondences between two sets. However, this approach is not usable in practice, because the table has |𝐴|^𝑁 elements, with |𝐴| number of symbols and N length of the message to be encoded. Consequently, these tables can be implemented in a program only when |𝐴| and N have a low value. Unfortunately, in these cases, there are no compression algorithms with such efficiency as to detect the improvement introduced by this method. In this article, we use a function capable of performing the transform without using the correspondence table; this allows us to apply this theory to a wide range of values of |𝐴|and N. The results obtained confirm the theoretical predictions.
GaSaver: A Static Analysis Tool for Saving Gas

Smart contracts are programs running on Ethereum, whose deployment and use require gas. Gas measures the cost of performing specific operations as an index designed to quantify the computing power consumption. Existing unoptimized smart contracts make contract developers and users spend extra gas. To save gas and optimize smart contracts, this paper proposes a new tool named GaSaver for automatically detecting gas-expensive patterns based on Solidity source code. Specifically, we first identify 12 gas-expensive patterns in smart contracts and classify them into three categories: storage-related, judgment-related, and loop-related. Then, we deploy gas-expensive patterns and group them into three levels according to gas waste degree. By conducting extensive experiments on real data sets, we find that 89.68 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of the 1172 smart contracts suffer from gas-expensive patterns, 94.27 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of 1100 new smart contracts are gas-expensive, and 80.56 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of 72 widely used smart contracts are affected. Finally, the experiment results show that the proposed GaSaver can effectively optimize smart contracts. Besides, the proportion of gas-expensive cases in widely used smart contracts is lower than that in the newly released smart contracts.