Reviewerly/exHarmony huggingface dataset:
Adaptive and Efficient Streaming Time Series Forecasting with Lambda Architecture and Spark

The rise of the Internet of Things (IoT) devices and the streaming platform has tremendously increased the data in motion or streaming data. It incorporates a wide variety of data, for example, social media posts, online gamers in-game activities, mobile or web application logs, online e-commerce transactions, financial trading, or geospatial services. Accurate and efficient forecasting based on real-time data is a critical part of the operation in areas like energy & utility consumption, healthcare, industrial production, supply chain, weather forecasting, financial trading, agriculture, etc. Statistical time series forecasting methods like Autoregression (AR), Autoregressive integrated moving average (ARIMA), and Vector Autoregression (VAR), face the challenge of concept drift in the streaming data, i.e., the properties of the stream may change over time. Another challenge is the efficiency of the system to update the Machine Learning (ML) models which are based on these algorithms to tackle the concept drift. In this paper, we propose a novel framework to tackle both of these challenges. The challenge of adaptability is addressed by applying the Lambda architecture to forecast future state based on three approaches simultaneously: batch (historic) data-based prediction, streaming (real-time) data-based prediction, and hybrid prediction by combining the first two. To address the challenge of efficiency, we implement a distributed VAR algorithm on top of the Apache Spark big data platform. To evaluate our framework, we conducted experiments on streaming time series forecasting with four types of data sets of experiments: data without drift (no drift), data with gradual drift, data with abrupt drift and data with mixed drift. The experiments show the differences of our three forecasting approaches in terms of accuracy and adaptability.
Cybersecurity advocates: discovering the characteristics and skills of an emergent role

Cybersecurity advocates safeguard their organizations by promoting security best practices. However, little is known about what constitutes successful advocacy.We conducted 28 in-depth interviews of cybersecurity advocates.Effective advocates not only possess technical acumen, but also interpersonal skills, communication acumen, context awareness, and a customer service orientation.We are the first to define and enumerate competencies for the role of cybersecurity advocate.Non-technical skills are deemphasized in cybersecurity training, limiting career progression into the cybersecurity advocate role for existing security professionals and those from other disciplines. We suggest improvements for professional development that encourage greater security workforce diversity.
Using text mining to glean insights from COVID-19 literature

The purpose of this study is to develop a text clustering-based analysis of COVID-19 research articles. Owing to the proliferation of published COVID-19 research articles, researchers need a method for reducing the number of articles they have to search through to find material relevant to their expertise. The study analyzes 83,264 abstracts from research articles related to COVID-19. The textual data are analysed using singular value decomposition (SVD) and the expectation-maximisation (EM) algorithm. Results suggest that text clustering can both reveal hidden research themes in the published literature related to COVID-19, and reduce the number of articles that researchers need to search through to find material relevant to their field of interest.
Accuracy and Uncertainty Analysis of Selected Methodological Approaches to Earthquake Early Warning in Europe

Abstract Earthquake early warning (EEW) is becoming an increasingly attractive real-time strategy for mitigating the threats posed by potentially devastating incoming seismic events. As efforts accelerate to develop practical EEW-based solutions for earthquake-prone countries in Europe, it is important to understand and quantify the level of performance that can be achieved by the underlying seismological algorithms. We conduct a conceptual study on EEW performance in Europe, which explicitly focuses on the accuracy and associated uncertainties of selected methodological approaches. Twenty-three events from four diverse European testbeds are used to compare the quality of EEW predictions produced by the Virtual Seismologist and PRobabilistic and Evolutionary early warning SysTem algorithms. We first examine the location and magnitude estimates of the algorithms, accounting for both bias and uncertainty in the resulting predictions. We then investigate the ground-shaking prediction capabilities of the source-parameter estimates, using an error metric that can explicitly capture the propagation of uncertainties in these estimates. Our work highlights the importance of accounting for EEW parameter uncertainties, which are often neglected in studies of EEW performance. Our findings can be used to inform current and future implementations of EEW systems in Europe. In addition, the evaluation metrics presented in this work can be used to determine EEW accuracy in any worldwide setting.
A Literature Review on Text Classification and Sentiment Analysis Approaches

Sentiment analysis is an important branch task of text classification and the related system usually is applied to in perception of user emotion and public opinion monitoring. By comparison, the text classification can be applied to more fields than sentiment analysis. In the system architecture, same as text classification, the complete classification system mainly contains data acquisition, data pre-process, feature extraction, classification algorithm and result output. The Web crawler usually be used in first step, the URL Link, hashtags, Non-Chinese text should be removed in second step. In feature extraction, the IG, TF-IDF, Word2vec usually be used. Then, the SVM, Naive Bayes, KNN or Neural network algorithm usually be used in classifier. Furthermore, as a system that can run automatically, the sentiment analysis system should be able to extract significant feature from corpus and make accurately analysis about emotional polarity of text corpus. At present, the system improvement direction of related system focuses on 3 aspects: data acquisition, feature extraction and classifier algorithm.
Bal-R$^2$CNN: High Quality Recurrent Object Detection With Balance Optimization

It is a common practice to refine object detection results using recurrent detection paradigm. We evaluate the recurrent detection on Faster R-CNN, but the improvement is far away from expected. We consider that the performance bottleneck is from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">imbalance optimization</i> caused by the biased distribution of training data. Low-IoU-skewed RPN proposals could suppress the contribution of High-IoU examples at the training stage. Besides, data imbalance and statistical discrepancy on regression targets between low-IoU and high-IoU examples are not considered in the regression task; this design could impede localization quality. In this work, we propose Bal-R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CNN for high-quality recurrent object detection. There are two new components in Bal-R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CNN. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Self-iteration box sampling</i> collects object boxes from recurrent steps and increases the number of high-IoU training examples. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoU-sensitive bounding-box regression</i> sends proposal boxes with different IoUs to specified regression branches for more accurate bounding-box prediction. Both two new components could induce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">balanced optimization</i> and be helpful. With the resulting Bal-R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CNN detector, evaluation on PASCAL VOC and MSCOCO reveal that our method has a significant improvement on the existing solution and could reach a better performance than several state-of-the-art methods.
Research of professional responsibility of students of technical specialities by means of information and communication technologies

Abstract The article is devoted to the use of information technologies for pedagogical research aimed at studying the formation of key competencies and learning outcomes in higher education institutions, in particular, the professional responsibility of students of technical specialties. The diagnostic capabilities of the ATutor system, which operates at the Ternopil National Technical University named after Ivan Pului (Ukraine), are presented. The process of data collection involving distance learning technologies and the results of the analysis of the study are described. The state of formation of professional responsibility in students of technical specialties is determined, the main problems and difficulties in the formation of professional responsibility of future specialists in the technical field are clarified, and the possibilities and role of social disciplines are revealed. The structural and logical scheme of integrating disciplines of a humanitarian cycle in a distance learning course “Professional Responsibility of a Technical Specialist in Modern Conditions” is presented.
Creative AI Through Evolutionary Computation: Principles and Examples

The main power of artificial intelligence is not in modeling what we already know, but in creating solutions that are new. Such solutions exist in extremely large, high-dimensional, and complex search spaces. Population-based search techniques, i.e. variants of evolutionary computation, are well suited to finding them. These techniques make it possible to find creative solutions to practical problems in the real world, making creative AI through evolutionary computation the likely "next deep learning."
Developing children’s speech recognition system for low resource Punjabi language

Building an automatic speech recognition (ASR) system for children is a very challenging problem especially when the domain-specific data for training is absent or insufficient. In this paper, we present our efforts towards developing a children's ASR system in Punjabi which a low-resourced language. To begin with, since speech data from children in the case of the Punjabi language is unavailable, we first created a small speech corpus consisting of data from both adult and child speakers. Next, an ASR system was developed on a mix of adults' and children's speech and tested on children's speech. Due to the differences in acoustic attributes such as formant frequency, pitch, and speaking-rate differences between adults' and children's speech, the developed ASR system is observed to result in a highly degraded recognition rate. To reduce the acoustic mismatch, we have explored vocal-tract length normalization (VTLN), explicit pitch, and duration modification. All the three explored approaches are observed to be highly effective. To deal with training data scarcity, the role of prosody-modification-based out-of-domain data augmentation is studied. For that purpose, the pitch and speaking-rate of adults' speech training set are explicitly changed to render it similar to children's speech. The original and prosody modified data are then pooled together before learning the acoustic models. Significantly reduced error rates are observed by prosody-modification-based out-of-domain data augmentation. In addition to these, we have also studied the effect of varying the number of senones, the number of hidden nodes, and hidden layers as well as early stopping resulting in 32.1% of Relative Improvement (RI) in comparison to the baseline system with varied senones.
Broadband flux-pumped Josephson parametric amplifier with an on-chip coplanar waveguide impedance transformer

Abstract The rapid progress towards scalable quantum processors demands amplifiers with large bandwidths and high saturation powers. For this purpose, we present a broadband flux-pumped Josephson parametric amplifier integrated with an on-chip coplanar waveguide impedance transformer. Our device can be fabricated with simple and straightforward photo-lithography. This device experimentally achieves an operational bandwidth over 600 MHz with a gain above 15 dB, and a high saturation power with quantum-limited noise performance. In addition, the center frequency of this device can be tuned over several hundred megahertz, which in turn broadens the effective operational bandwidth to around 1 GHz.
Adapting Behavior Driven Development (BDD) for large-scale software systems

Large-scale software projects require interaction between many stakeholders. Behavior-driven development (BDD) facilitates collaboration between stakeholders, and an adapted BDD process can help improve cooperation in a large-scale project. The objective of this study is to propose and empirically evaluate a BDD based process adapted for large-scale projects. A technology transfer model was used to propose a BDD based process for large-scale projects. We conducted six workshop sessions to understand the challenges and benefits of BDD. Later, an industrial evaluation was performed for the process with the help of practitioners. From our investigations, understanding of a business aspect of requirements, their improved quality, a guide to system-level use-cases, reuse of artifacts, and help for test organization are found as benefits of BDD. Practitioners identified the following challenges: specification and ownership of behaviors, adoption of new tools, the software projects' scale, and versioning of behaviors. We proposed a process to address these challenges and evaluated the process with the help of practitioners. The evaluation proved that BDD could be adapted and used to facilitate interaction in large-scale software projects in the software industry. The feedback from the practitioners helped in improving the proposed process.
Eye-tracking support for architects, conservators, and museologists. Anastylosis as pretext for research and discussion

Abstract Conservators, museologists, and architects make extremely complex decisions capable of affecting the way people perceive monuments. One might give this idea deeper consideration while pondering anastylosis. One of the things a designer should do when selecting a method of merging together parts of a damaged monument is answer the question whether the chosen method will facilitate the interest of onlookers in the presented object. In which case will the observers spend most of their time looking at the authentic relic fragments and distinguishing between the old and the new parts? The definitions in force do not explain how to approach this topic. By using eye-tracking research, we can learn how observers look at historical objects that have been reassembled again. By combining the observation of visual behaviours with a survey of people looking at such objects, it is possible to see how the process of classifying what is new and old actually works. This knowledge allows for more conscious approach to heritage management processes. In future, results of eye-tracking experiments should help experts plan sustainable conservation projects. Thanks to knowing the reactions of regular people, one will be able to establish conservation programmes in which the material preservation of a monument will reflect the way in which this object affects contemporary onlookers. Such an approach ought to result in real social and economic benefits.
Performance analysis of Word Embeddings for Cyberbullying Detection

Abstract Cyber bullying activities are increasing day by day with the increase of Social Media Platforms such as Face book, Twitter, Instagram etc. Bullies take the advantage of these large online connected platforms due to which it became as a big challenging task in Natural Language Processing (NLP). In this paper, we compare the performance of various word embedding methods from basic word embedding methods to recent advanced language models such as RoBERTa, XLNET, ALBERT, etc. for cyberbullying detection. We used LightGBM and Logistic regression classifiers for the classification of bullying and non-bullying tweets. Among all the models, RoBERTa is outperformed as compared to state-of-the-art models.
iGOS++

The black-box nature of the deep networks makes the explanation for "why" they make certain predictions extremely challenging. Saliency maps are one of the most widely-used local explanation tools to alleviate this problem. One of the primary approaches for generating saliency maps is by optimizing for a mask over the input dimensions so that the output of the network for a given class is influenced the most. However, prior work only studies such influence by removing evidence from the input. In this paper, we present iGOS++, a framework to generate saliency maps for blackbox networks by considering both removal and preservation of evidence. Additionally, we introduce the bilateral total variation term to the optimization that improves the continuity of the saliency map especially under high resolution and with thin object parts. We validate the capabilities of iGOS++ by extensive experiments and comparison against state-of-the-art saliency map methods. Our results show significant improvement in locating salient regions that are directly interpretable by humans. Besides, we showcased the capabilities of our method, iGOS++, in a real-world application of AI on medical data: the task of classifying COVID-19 cases from x-ray images. To our surprise, we discovered that sometimes the classifier is overfitted to the text characters printed on the x-ray images when performing classification rather than focusing on the evidence in the lungs. Fixing this overfitting issue by data cleansing significantly improved the precision and recall of the classifier.
L4L: Experience-Driven Computational Resource Control in Federated Learning

As the large-scale deployment of machine learning applications, there is much research attention on exploiting a vast amount of data stored on mobile clients. To preserve data privacy, federated learning has been proposed to enable large-scale machine learning by massive clients without exposing raw data. Existing works of federated learning struggle for accelerating the learning process, but ignore the energy efficiency that is critical for resource-constrained clients. In this article, we propose to improve the energy efficiency of federated learning by lowering CPU cycle frequencies of clients who are faster in the training group. Based on this idea, we formulate an optimization problem aiming to minimize the total system cost defined as a weighted sum of learning time and energy consumption. Due to the hardness of the formulated optimization problem and unpredictability of network quality, we propose L4L (Learning for Learning), an experience-driven computational resource control approach based on the deep reinforcement learning, which can derive the near-optimal solution with only the clients’ bandwidth information in the previous training rounds. We conduct the experiments using both real-world traces and synthetic traces to evaluate the proposed L4L approach. The results demonstrate the superiority of L4L as compared with the state-of-the-art solutions.
Certificateless threshold signcryption scheme with secret sharing mechanism

A (t,n) threshold signcryption scheme makes the distributor authorize a group with n members, any t or more signcrypters can cooperatively use the signcryption keys to sign the message; however, t−1 or fewer signcrypters cannot. Threshold signcryption has broad applications in the electronic auction, electronic election, distributed environment, cloud computing and block chain. In this paper, we combine the threshold signcryption with certificateless cryptosystem to construct a secure and efficient certificateless threshold signcrypton scheme with secret sharing mechanism (SSM-CLTSCS), which is provably indistinguishable and unforgeable under the hardness of the DBDH (decisional bilinear Diffie–Hellman) and CDH (computational Diffie–Hellman) problems. Analysis shows SSM-CLTSCS outperforms the existing schemes in computational efficiency; and so it will obtain much more attention in the application fields of information security.
Emotion Detection for Conversations Based on Reinforcement Learning Framework

In this article, we propose a novel reinforcement learning network that keeps track of the gradual emotional changes from every utterance throughout the conversation and uses this information for each utterance's emotion detection. Concretely, we first establish an agent and, then, utilize sliding windows to extract the accumulated emotional information before the current utterance. We define the concatenation of accumulated emotional information and the contextual information as the state of the reinforcement learning framework. The action of the established agent is formulated as the emotional label of the current utterance. On this basis, we formulate the progressive emotional interaction process throughout the conversation as a sequential decision problem and solve it with the reinforcement learning framework. Detailed evaluations on the published multimodal MELD dataset demonstrate the effectiveness of our approach.
Challenges for the educational system during lockdowns: A possible new framework for teaching and learning for the near future

The COVID-19 outbreak has heightened several challenges in higher education. In this paper, we focus on the use of technology, teaching methodologies and literacy skills. The challenges relating to the use of technology in higher education include the digital divide created by unequal access to the internet in many countries. In terms of teaching methodologies, distance learning may be faced with additional challenges in student evaluation or in the learning process itself, as in some educational disciplines face-to-face teaching is essential. Literacy skills with an emphasis on digital skills will be a part of the solution going forward if lock-downs persist. Finally, librarians are in an excellent position to engage with their communities by teaching literacy skills and digital skills in effectively using technology, which will be a much-needed part of the educational system as new teaching methodologies emerge.