Reviewerly/exHarmony huggingface dataset:
Biased resampling strategies for imbalanced spatio-temporal forecasting

Extreme and rare events, such as spikes in air pollution or abnormal weather conditions, can have serious repercussions. Many of these sorts of events develop through spatio-temporal processes. Timely and accurate predictions are a most valuable tool in addressing their impact. We propose a new set of resampling strategies for imbalanced spatio-temporal forecasting tasks, which introduce bias into formerly random processes. This bias is a combination of a spatial and a temporal weight, which can be either static or relevance-aware, and includes a hyper-parameter that regulates the relative importance of the temporal and spatial dimensions in the selection of observations during under- or over-sampling. We test and compare our proposals against standard versions of the strategies on 10 different geo-referenced numeric time series, using 3 distinct off-the-shelf learning algorithms. Experimental results show that our proposals provide an advantage over random resampling strategies in imbalanced numerical spatio-temporal forecasting tasks.
Appropriating Biosensors as Embodied Control Structures in Interactive Music Systems

We present a scoping review of biosensors appropriation as control structures in interactive music systems (IMSs). Technical and artistic dimensions promoted by transdisciplinary approaches, ranging from biomedicine to musical performance and interaction design fields, support a taxonomy for biosensor-driven IMSs. A broad catalog of 70 biosensor-driven IMSs, ranging in publication dates from 1965 to 2019, was compiled and categorized according to the proposed taxonomy. From the catalog data, we extrapolated representative historical trends, notably to critically verify our working hypothesis that biosensing technologies are expanding the array of control structures within IMSs. Observed data show that our hypothesis is consistent with the historical evolution of the biosensor-driven IMSs. From our findings, we advance future challenges for novel means of control across humans and machines that should ultimately transform the agents involved in interactive music creation to form new corporalities in extended performative settings.
Increasing Side-Channel Resistance by Netlist Randomization and FPGA-Based Reconfiguration

Modern FPGAs are equipped with the possibility of Partial Reconfiguration (PR) which along with other benefits can be used to enhance the security of cryptographic implementations. This feature requires development of alternative designs to be exchanged during run-time. In this work, we propose dynamically alterable circuits by exploring netlist randomization which can be utilized with PR as a countermeasure against physical attacks, in particular side-channel attacks. The proposed approach involves modification of an AES implementation at the netlist level in order to create circuit variants which are functionally identical but structurally different. In preliminary experiments, power traces of these variants have been shuffled to replicate the effect of partial reconfiguration. With these dynamic circuits, our experimental results show an increase in the resistance against power side-channel attacks by a factor of $${\sim }12.6$$ on a Xilinx ZYNQ UltraScale+ device.
Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

It aims to improve the degree of visualization of building data, ensure the ability of intelligent detection, and effectively solve the problems encountered in building data processing. Convolutional neural network and augmented reality technology are adopted, and a building visualization model based on convolutional neural network and augmented reality is proposed. The performance of the proposed algorithm is further confirmed by performance verification on public datasets. It is found that the building target detection model based on convolutional neural network and augmented reality has obvious advantages in algorithm complexity and recognition accuracy. It is 25 percent more accurate than the latest model. The model can make full use of mobile computing resources, avoid network delay and dependence, and guarantee the real-time requirement of data processing. Moreover, the model can also well realize the augmented reality navigation and interaction effect of buildings in outdoor scenes. To sum up, this study provides a research idea for the identification, data processing, and intelligent detection of urban buildings.
Designing With Pictographs: Envision Topics Without Sacrificing Understanding

Past studies have shown that when a visualization uses pictographs to encode data, they have a positive effect on memory, engagement, and assessment of risk. However, little is known about how pictographs affect one’s ability to understand a visualization, beyond memory for values and trends. We conducted two crowdsourced experiments to compare the effectiveness of using pictographs when showing part-to-whole relationships. In Experiment 1, we compared pictograph arrays to more traditional bar and pie charts. We tested participants’ ability to generate high-level insights following Bloom’s taxonomy of educational objectives via 6 free-response questions. We found that accuracy for extracting information and generating insights did not differ overall between the two versions. To explore the motivating differences between the designs, we conducted a second experiment where participants compared charts containing pictograph arrays to more traditional charts on 5 metrics and explained their reasoning. We found that some participants preferred the way that pictographs allowed them to envision the topic more easily, while others preferred traditional bar and pie charts because they seem less cluttered and faster to read. These results suggest that, at least in simple visualizations depicting part-to-whole relationships, the choice of using pictographs has little influence on sensemaking and insight extraction. When deciding whether to use pictograph arrays, designers should consider visual appeal, perceived comprehension time, ease of envisioning the topic, and clutteredness.
Blockchain-based Big Data Integrity Service Framework for IoT Devices Data Processing in Smart Cities

Nowadays, smart cities are using advanced technologies to control and coordinate physical, social, and commercial enterprise systems to deliver high-quality services to their residents while guaranteeing the effective usage of available resources. Numerous major corporations, as well as government bodies, are involved in the construction of smart cities because smart city activities also need information that is obtained from organizations. Blockchain-based technology allows for peer-to-peer exchanges as a public ledger that maintains records of transactions, smart contracts, agreements, and shipments without third-party mediators. In the future, smart cities will be equipped with several innovative technologies, a huge amount of data, a lot of Internet of Things (IoT) devices, and a heterogeneous environment. However, data processing among IoT devices in futuristic smart cities is a challenging task. In this paper, the author proposed a blockchain-based big data integrity service framework for IoT devices data processing in smart cities. The author collected the necessary data from various resources and applied three experiments to evaluate the key performance indicators. K-mean algorithm was used to classify the data, and blockchain strategy was applied to ensure secure communication among IoT devices. The framework was simulated and tested using 100 devices. The proposed framework allowed IoT devices to efficiently use information collected from different resources to engage in operational processes and exchange information.
Distributed nonparametric function estimation: Optimal rate of convergence and cost of adaptation

Distributed minimax estimation and distributed adaptive estimation under communication constraints for Gaussian sequence model and white noise model are studied. The minimax rate of convergence for distributed estimation over a given Besov class, which serves as a benchmark for the cost of adaptation, is established. We then quantify the exact communication cost for adaptation and construct an optimally adaptive procedure for distributed estimation over a range of Besov classes. The results demonstrate significant differences between nonparametric function estimation in the distributed setting and the conventional centralized setting. For global estimation, adaptation in general cannot be achieved for free in the distributed setting. The new technical tools to obtain the exact characterization for the cost of adaptation can be of independent interest.
Testing a Quantum Annealer as a Quantum Thermal Sampler

Motivated by recent experiments in which specific thermal properties of complex many-body systems were successfully reproduced on a commercially available quantum annealer, we examine the extent to which quantum annealing hardware can reliably sample from the thermal state in a specific basis associated with a target quantum Hamiltonian. We address this question by studying the diagonal thermal properties of the canonical one-dimensional transverse-field Ising model on a D-Wave 2000Q quantum annealing processor. We find that the quantum processor fails to produce the correct expectation values predicted by Quantum Monte Carlo. Comparing to master equation simulations, we find that this discrepancy is best explained by how the measurements at finite transverse fields are enacted on the device. Specifically, measurements at finite transverse field require the system to be quenched from the target Hamiltonian to a Hamiltonian with negligible transverse field, and this quench is too slow. The limitations imposed by such hardware make it an unlikely candidate for thermal sampling, and it remains an open question what thermal expectation values can be robustly estimated in general for arbitrary quantum many-body systems.
GLOVE: Towards Privacy-Preserving Publishing of Record-Level-Truthful Mobile Phone Trajectories

Datasets of mobile phone trajectories collected by network operators offer an unprecedented opportunity to discover new knowledge from the activity of large populations of millions. However, publishing such trajectories also raises significant privacy concerns, as they contain personal data in the form of individual movement patterns. Privacy risks induce network operators to enforce restrictive confidential agreements in the rare occasions when they grant access to collected trajectories, whereas a less involved circulation of these data would fuel research and enable reproducibility in many disciplines. In this work, we contribute a building block toward the design of privacy-preserving datasets of mobile phone trajectories that are truthful at the record level. We present GLOVE, an algorithm that implements k -anonymity, hence solving the crucial unicity problem that affects this type of data while ensuring that the anonymized trajectories correspond to real-life users. GLOVE builds on original insights about the root causes behind the undesirable unicity of mobile phone trajectories, and leverages generalization and suppression to remove them. Proof-of-concept validations with large-scale real-world datasets demonstrate that the approach adopted by GLOVE allows preserving a substantial level of accuracy in the data, higher than that granted by previous methodologies.
Application Research of Personalized Recommendation Technology in College English Teaching Reform under The Background of Big Data

To study the impact of big data on education activities and English teaching activities, this paper proposes an improvement scheme of online teaching by personalized recommendation technology, integrated with big data cloud storage platform. First, through reading and sorting a large number of literature such as education big data and personalized teaching, this paper analyzes the current situation and existing problems. Then Hadoop is adopted as the key analysis and processing technology, and K-means clustering and association rules algorithm are used to establish the user learning preference model. Finally, according to the model, a big data recommendation live platform is implemented and tested. The results show that our scheme not only effectively solves the problem of massive resource storage, but also meets the personalized needs of learners in the learning process, which has a wide range of practical value in the field of teaching research.
Examining LDA2Vec and Tweet Pooling for Topic Modeling on Twitter Data

The short lengths of tweets present a challenge for topic modeling to extend beyond what is provided explicitly from hashtag information. This is particularly true for LDAbased methods because the amount of information available from pertweet statistical analysis is severely limited. In this paper we present LDA2Vec paired with temporal tweet pooling (LDA2VecTTP) and assess its performance on this problem relative to traditional LDA and to Biterm Topic Model (Biterm), which was developed specifically for topic modeling on short text documents. We paired each of the three topic modeling algorithms with three tweet pooling schemes: no pooling, authorbased pooling, and temporal pooling. We then conducted topic modeling on two Twitter datasets using each of the algorithms and the tweet pooling schemes. Our results on the largest dataset suggest that LDA2VecTTP can produce higher coherence scores and more logically coherent and interpretable topics.
A Framework for Dynamic Composition and Management of Emergency Response Processes

An emergency response process outlines the workflow of different activities that need to be performed in response to an emergency. Effective emergency response requires communication and coordination with the operational systems belonging to different collaborating organizations. Therefore, it is necessary to establish information sharing and system-level interoperability among the diverse operational systems. Unlike typical e-government processes that are well structured and have a well-defined outcome, emergency response processes are knowledge-centric and their workflow structure and execution may evolve as the incident unfolds. It is impractical to define static plans and response process workflows for every possible situation. Instead, a dynamic response should be adaptable to the changing situation. We present an integrated approach that facilitates the dynamic composition of an executable response process. The proposed approach employs ontology-based reasoning to determine the default actions and resource requirements for the given incident and to identify relevant response organizations based on their jurisdictional and mutual aid agreement rules. The Web service APIs of the identified response organizations are then used to generate an executable response process that evolves dynamically. The proposed approach is implemented and experimentally validated using an example scenario derived from the FEMA Hazardous Materials Tabletop Exercises Manual.
PROTEST-ER: Retraining BERT for Protest Event Extraction

We analyze the effect of further pre-training BERT with different domain specific data as an unsupervised domain adaptation strategy for event extraction.Portability of event extraction models is particularly challenging, with large performance drops affecting data on the same text genres (e.g., news).We present PROTEST-ER, a retrained BERT model for protest event extraction.PROTEST-ER outperforms a corresponding generic BERT on outof-domain data of 8.1 points.Our best performing models reach 51.91-46.39F1 across both domains.
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines

Tsetlin Machines (TMs) capture patterns using conjunctive clauses in propositional logic, thus facilitating interpretation. However, recent TM-based approaches mainly rely on inspecting the full range of clauses individually. Such inspection does not necessarily scale to complex prediction problems that require a large number of clauses. In this paper, we propose closed-form expressions for understanding why a TM model makes a specific prediction (local interpretability). Additionally, the expressions capture the most important features of the model overall (global interpretability). We further introduce expressions for measuring the importance of feature value ranges for continuous features making it possible to capture the role of features in real-time as well as during the learning process as the model evolves. We compare our proposed approach against SHAP and state-of-the-art interpretable machine learning techniques.
Real-time Face Recognition System for Remote Employee Tracking

During the COVID-19 pandemic, most of the human-to-human interactions have been stopped. To mitigate the spread of deadly coronavirus, many offices took the initiative so that the employees can work from home. But, tracking the employees and finding out if they are really performing what they were supposed to turn out to be a serious challenge for all the companies and organizations who are facilitating “work from home.” To deal with the challenge effectively, we came up with a solution to track the employees with face recognition. We have been testing this system experimentally for our office. To train the face recognition module, we used FaceNet with KNN using the Labeled Faces in the Wild (LFW) dataset and achieved 97.8% accuracy. We integrated the trained model into our central system, where the employees log their time. In this paper, we discuss in brief the system we have been experimenting with and the pros and cons of the system.
Data mining and machine learning for porosity, saturation, and shear velocity prediction: recent experience and results

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations

The growing popularity of Virtual Assistants poses new challenges for Entity Resolution, the task of linking mentions in text to their referent entities in a knowledge base. Specifically, in the shopping domain, customers tend to mention the entities implicitly (e.g., “organic milk”) rather than use the entity names explicitly, leading to a large number of candidate products. Meanwhile, for the same query, different customers may expect different results. For example, with “add milk to my cart”, a customer may refer to a certain product from his/her favorite brand, while some customers may want to re-order products they regularly purchase. Moreover, new customers may lack persistent shopping history, which requires us to enrich the connections between customers through products and their attributes. To address these issues, we propose a new framework that leverages personalized features to improve the accuracy of product ranking. We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings. After that, we incorporate product, customer, and history representations into a neural reranking model to predict which candidate is most likely to be purchased by a specific customer. Experiment results show that our model substantially improves the accuracy of the top ranked candidates by 24.6% compared to the state-of-the-art product search model.
A Digital Signature Model Using XAdES Standard as a Rest Service

The digital signature of documents and degrees is a topic widely discussed in the Federal Public Administration. Several laws and ordinances were created to standardize the issuance, validation and legal validity of digitally signed documents in national territory, such as the ordinances created by the Ministry of Education (MEC) to regulate the issuance of degrees in digital format. These ordinances created guidelines and standards that must be adopted by Federal Universities for the signing of in digital format. The main objective of this work is to study these ordinances, the main technologies and digital signature standards used in the literature to create a digital signature system model for University of Brasília-UnB, which complies with the MEC and ICP-Brazil standards. Moreover, the model must be developed with the main standards and technologies in the market, cohesive to the current UnB architecture, easy to maintain and update to new standards that may emerge, and also be a fully open source project. An architectural model and a prototype in Java language were developed using XAdES4j library as a microservice intermediated by the bus used in UnB. The prototype developed was compared with the current digital signature system named C3Web. The comparative tests and results between the two solutions showed that the current system used in UnB does not perform the signature in accordance with the standard proposed by the MEC, in addition to being a private system using proprietary technologies for the execution of digital signatures. The tests performed with the proposed model demonstrated that it performs the digital signature in accordance with the XAdES-T standard, regulations of the MEC and ICP-Brazil. In addition, the solution presented a performance comparable to the current system used by UnB with a little more effective security than the current system used. The current model developed in this work can be a basis for the creation of future subscription systems for Brazilian Universities.
Pharmaceutical Blister Package Identification Based on Induced Deep Learning

Prescription dispensing accuracy is of paramount importance for all hospitals.However, human errors are inevitable due to multiple reasons, such as fatigue, stress, heavy workload, lack of effective verification measures, mismanagement.Such human errors pose serious safety and health concerns on the part of patients and may as well lead to a series of medical disputes.Based on induced deep learning, this paper proposes a real-time Blister Package Identification System (BPIS) to assist pharmacists' drug verification and dispensing.Under the guidance of the induction strategy, image preprocessing is introduced to form a standardized image containing the front and back side of the blister package, which is subsequently sent to CNN-based object identification network for feature extraction and identification.This preprocessing method allows the identification system to promote the deep learning system to focus on feature learning to obtain more information about the appearance of the package ruling out confounding factors such as background noise, size, shape or positioning.In addition, this article collects and establishes an image dataset of adult lozenges.Under this dataset, this paper verifies the enhancement of Induced Deep Learning (IDL) on YOLO v2, ResNet, and SENet.By optimizing the deep learning identification network with the help of the embedded technology and a two-side extraction mechanism, a real-time BPIS is built.Long-term tests in hospitals prove the effectiveness of the proposed system.