Reviewerly/exHarmony huggingface dataset:
Designing for Green and Grey: Insights from Single-Use Plastic Water Bottles

Recognizing modern society's multiple risks, this study examines single-use plastic water bottles at the intersection of environmental degradation and societal carelessness for the elderly. While prioritising economic profits and plastic waste, we have neglected bottles' typical poor openability for older people. Thus, we evaluated the openability of bottles with environmentally friendly and socially inclusive designs in South Korea by comparing older and younger adults' experiences. Integrating different attributes than existing studies that analyse opening torque or one-handed opening, the test results show that older adults experience the poorest two-handed openability when bottles have both a weight thickness lower than 14.42 g and an easily squeezable bottle structure. In South Korea, companies advocate eco-friendliness by valuing lighter weight with less plastic and support user-friendliness by adopting easily squeezed sidewall patterns, while the industry maintains broad opening torque regulations; however, we show this combination exceeds older users' capabilities. That is, for openability, bottles need to keep a weight thickness greater than 12 g, abandon easily squeezed sidewall patterns or reduce the opening torque regulation range to 100 N-cm. These socially favourable but ecologically adverse measures will be sustainable when the efficient linear economy transitions to an effective circular economy.
Geographic Disaggregation of Textual Social Media Data: A Machine Learning-based Approach

This research aims to identify the geographic origin of Arabic-speaking social media users by analyzing textual data they produce and share. The paper presents an approach to infer users’ region (i.e country) of origin through identification of the dialect they use in their written interactions. An Integrated Dataset for Arabic Dialect Detection (IADD) is proposed and used to train multiple classifiers which succeed in identifying the users’ region and country of origin with an accuracy of 0.89 and 0.93, respectively.
Deep Learning Methods for the Prediction of Information Display Type Using Eye Tracking Sequences

Eye tracking data can help design effective user interfaces by showing how users visually process information. In this study, three neural network models were developed and employed to classify three types of information display methods by using eye gaze data that was collected in visual information processing behavior studies. Eye gaze data was first converted into a sequence and was fed into neural networks to predict the information display type. The results of the study show a comparison between three methods for the creation of eye tracking sequences and how they perform using three neural network models including CNN-LSTM, CNN-GRU, and 3D CNN. The results were positive with all models having an accuracy of higher than 88 percent.
Restore Buffer Overflow Attacks: Breaking Undo-Based Defense Schemes

Transient execution attacks have been severe security threats since such attacks exploit architectural vulnerabilities in out-of-order processors. Researchers proposed several architectural solutions to defend against transient execution attacks. By restoring the victim blocks stored temporarily in a restore buffer, the undo-based approaches can revoke the cache state changed by speculative loads. Thus it is known that the undo-based defense mechanisms can protect processors from transient execution attacks.In this paper, we reveal the undo-based protection scheme is still vulnerable to the elaborated Prime+Probe type attacks. Under the undo-based protection, the victim blocks by the speculative loads are stored in the restore buffer that has limited resources. Thus if the restore buffer is full, part of the victim blocks in the restore buffer can be evicted from the restore buffer. Then the cache state cannot be restored since the processor cannot find the victim blocks required for restoring the cache state. We design a restore buffer overflow attack that can leak secret data even if the processor is protected under the undo-based scheme. We evaluate the attack mechanism using the architectural simulator. Our evaluation exhibits that the attack can leak part of secret data successfully.
Performance Evaluation of Corrupted QR Code Scanners

Quick Response (QR) Code is touted for its ease and convenience. It has therefore seen tremendous applications today. The application of the codes for education, healthcare and marketing cannot be overemphasized. However, like other symbology technologies, the use of QR Codes may pose a challenge for users when scanners cannot scan codes at crucial moments. Unsuccessful or invalid scanning may be due to poor QR Code, the camera quality of the smartphone, corrupted QR Code image and poor implementation of scanning algorithm. In this paper, an evaluation of 10 highly rated QR code scanners is performed. Specifically, scanners resilience to corrupted images is evaluated. NeoReader QR outperformed the other scanners, while Kaspersky QR Scanner performed least. Generally, it was observed that scanners perform well when the number of characters encoded in the QR code is few, whether codes are corrupted or not. QR Codes with a high number of characters encoded in them are sensitive to noise. Again, scanners can handle Gaussian noise better than salt-and-pepper. Integration of median filter in the scanning pipeline to help suppress salt-and-pepper noise can make QR Code scanners robust.
Multi-Obstacle Avoidance Algorithm for Autonomous Vehicles

This paper presents a local path planning approach that avoids both static and dynamic multi-obstacles for autonomous car. The proposed algorithm is able to alter the predefined waypoints from global path planning when obstacles are detected. A set of paths are generated based on the sigmoid function for overtaking obstacles. Every single path is assessed according to a cost function which includes path safety, comfortability, and length. Genetic algorithm is used to optimize parameters of the selected sigmoid function for smoothness and minimum length. Non-linear Model Predictive Controller is designed for trajectory tracking considering vehicle's dynamics. Simulation results reveal that the algorithm carries out obstacle avoidance and can handle multi-obstacles furthermore, the lateral acceleration is reduced by 44.27% and 50% in both static and dynamic scenarios effectively.
Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter (KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements. The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation. The proposed algorithm exhibits good robustness, adaptability, and value on applications.
Solar Cycle Signal in Climate and Artificial Neural Networks Forecasting

Natural climate variability is partially attributed to solar radiative forcing. The purpose of this study is to contribute to a better understanding of the influence of solar variability on the Earth’s climate system. The object of this work is the estimation of the variation of multiple climatic parameters (temperature, zonal wind, relative and specific humidity, sensible and latent surface heat flux, cloud cover and precipitable water) in response to solar cycle forcing. An additional goal is to estimate the response of the climate system’s parameters to short-term solar variability in multiple forecasting horizons and to evaluate the behavior of the climate system in shorter time scales. The solar cycle is represented by the 10.7 cm solar flux, a measurement collected by terrestrial radio telescopes, and is provided by NOAA/NCEI/STP, whereas the climatic data are provided by the NCEP/NCAR reanalysis 1 project. The adopted methodology includes the development of a linear regression statistical model in order to calculate the climatic parameters’ feedback to the 11-year solar cycle on a monthly scale. Artificial Neural Networks (ANNs) have been employed to forecast the solar indicator time series for up to 6 months in advance. The climate system’s response is further forecasted using the ANN’s estimated values and the regression equations. The results show that the variation of the climatic parameters can be partially attributed to solar variability. The solar-induced variation of each of the selected parameters, averaged globally, was of an order of magnitude of 10−1–10−3, and the corresponding correlation coefficients (Pearson’s r) were relatively low (−0.5–0.5). Statistically significant areas with relatively high solar cycle signals were found at multiple pressure levels and geographical areas, which can be attributed to various mechanisms.
Active Learning and Machine Teaching for Online Learning: A Study of Attention and Labelling Cost

Interactive Machine Learning (ML) has the potential to lower the manual labelling effort needed, as well as increase classification performance by incorporating a human-in-the loop component. However, the assumptions made regarding the interactive behaviour of the human in experiments are often not realistic. Active learning typically treats the human as a passive, but always correct, participant. Machine teaching provides a more proactive role for the human, but generally assumes that the human is constantly monitoring the learning process. In this paper, we present an interactive online framework and perform experiments to compare active learning, machine teaching and combined approaches. We study not only the classification performance, but also the effort (to label samples) and attention (to monitor the ML system) required of the human. Results from experiments show that a combined approach generally performs better with less effort compared to active learning and machine teaching. With regards to attention, the best performing strategy varied depending on the problem setup.
PFCC: Predictive Fast Consensus Convergence for Mobile Blockchain over 5G Slicing-enabled IoT

As the security requirements increases, 5G slicing-enabled Internet of things needs to adopt end-to-end standalone networking, which limits the consensus convergence of mobile blockchain. Although the rise of FIBRE (Fast Internet Bitcoin Relay Engine) gives a huge promotion to block propagation, existing approaches cannot consider the influence of link outages among 5G slices on mobile blockchain. In this paper, we focus on decreasing the block propagation time among blockchain peers under the scenario where link outages among 5G slices exist. A predictive fast consensus convergence (PFCC) scheme is proposed for mobile blockchain over 5G slicing-enabled internet of things. In PFCC, federated semi-supervised learning is used to learn the features of withdraw packets, reroutes the packets of blockchain peers, and ultimately reduces the scale of link outages quickly. With PFCC, different blockchain peers located in standalone 5G slices of IoT can transact local sensing data more efficiently. To the best of our knowledge, this is the first work to improve the consensus convergence speed of mobile blockchain by optimizing communications between 5G slices. Experiments shows the feasibility of proposed scheme.
Design of Networked Intelligent Translation System Based on Machine Learning Algorithm

Abstract China’s social and economic development is relatively rapid, and China’s opening to the outside world is further expanding. Under this background, the demand for foreign language professionals is also expanding. This paper designs a networked intelligent translation system based on machine learning algorithm, and proposes a cross-language information extraction method based on bilingual word representation. The learning of bilingual word representation can be divided into two stages: unsupervised and supervised. Capturing Chinese and English bilingual semantic information. The cross-language information extraction method based on machine learning algorithm reduces the impact of translation errors and language gaps on cross-language classification performance, obtains effective bilingual semantic information and annotation information, and improves the cross-language information extraction performance. The test results show that the designed automatic English translation system can effectively and quickly realize intelligent English translation of memory-assisted long characters, with high data recall rate, good accuracy and reliability, and good system compatibility, which is worthy of popularization and practical application.
Music and the Cybernetic Mundane

Few intellectual movements have been as influential as cybernetics was in the 1950s and ’60s. Fewer still have seen their stock fall so precipitously in the years since. Despite the growing body of literature that has reassessed this postwar “cybernetics moment” (Hayles, Kline, Pickering, Medina, et al.), its far-reaching impact remains curiously underappreciated, especially as regards music. This article seeks to redress this neglect, by focusing not on works and practices that spectacularize cybernetics (the “cybernetic sublime”), but instead on those activities, discourses, and projects that so thoroughly internalized and normalized the cybernetic ethos that it eludes notice (the “cybernetic mundane”). A first case study considers the little-known role played by information theory and cybernetics in the design of the RCA Synthesizer, one of the first instruments of its ilk to be developed. Among other things, I contend that cybernetic thinking pervaded the instrument’s conception to such an extent that it paradoxically contributed to the subsequent erasure of its influence from accounts of the instrument’s development and subsequent implementation as part of the Columbia-Princeton Electronic Music Center. The second case concerns more recent applications of cybernetic ideals to digital music distribution, exemplified by the platform Spotify, whose routinization of these ideals has ensured not just their persistence, but their persistent misrecognition.
Sequences of selective rotation operators for three group clustering on qutrits by means of quantum annealing

Recently, it was demonstrated<sup>1</sup> that the clustering considered in this articlec, which consists in partitioning a set of data points into subsets depending on the proximity of some properties, can be performed using quantum annealing on a system of two-level quantum elements - qubits. In previous work<sup>2</sup>, we proposed to pass from qubits to qutrits - three-level quantum elements showed the advantages of such a replacement and obtained a time-dependent effective Hamiltonian. In the present paper, we have found a sequence of selective rotation operators that allows one to realize adiabatic evolution with this effective Hamiltonian in the discrete-time approximation. On five qutrits, represented by spins <i>S</i> = 1, we performed a simulation of clustering a set of six points on a plane into three groups by means of quantum annealing.
Framing karstology

Abstract We describe the creation of a knowledge base in the field of karstology using the frame-based approach. Apart from providing a new multilingual resource using manually annotated definitions as the source of structured information, the main focus is on exploring text mining methods to identify targeted knowledge structures in specialised corpora. The first stage of this process is the design of a domain model and its implementation in a definition annotation task. Once annotation is completed, an analysis of typical co-occurrence patterns between semantic categories and the relations describing them allows us to discern ideal definition templates. We demonstrate that such templates contribute to a more comprehensive and structured representations of concepts, but also help us design targeted text mining experiments to retrieve new semantic relations from text. Two such experiments are presented, the first using intersections of word embeddings to identify words expressing a specific semantic relation, and the second using the embedding of the semantic relation to extract multiword units which contain the target relation. Results suggest that the proposed methods are promising for capturing the semantic properties of relations in frame-based knowledge modelling.
Topical language generation using transformers

Abstract Large-scale transformer-based language models (LMs) demonstrate impressive capabilities in open-text generation. However, controlling the generated text’s properties such as the topic, style, and sentiment is challenging and often requires significant changes to the model architecture or retraining and fine-tuning the model on new supervised data. This paper presents a novel approach for topical language generation (TLG) by combining a pre-trained LM with topic modeling information. We cast the problem using Bayesian probability formulation with topic probabilities as a prior, LM probabilities as the likelihood, and TLG probability as the posterior. In learning the model, we derive the topic probability distribution from the user-provided document’s natural structure. Furthermore, we extend our model by introducing new parameters and functions to influence the quantity of the topical features presented in the generated text. This feature would allow us to easily control the topical properties of the generated text. Our experimental results demonstrate that our model outperforms the state-of-the-art results on coherency, diversity, and fluency while being faster in decoding.
Performance Evaluation Method of Enterprise Human Resource Management Based on Machine Learning

To improve the reference value of enterprise human resource management performance evaluation data, an enterprise human resource management performance evaluation method based on machine learning is proposed. From the three aspects of performance evaluation objectives, subjects and objects, this paper constructs the performance evaluation system of enterprise human resource management. Human resource management performance indicators are selected and standardized. Machine learning algorithms are used to collect management performance data and determine the specific value of performance indicators. Using the analytic hierarchy process to calculate the weight of performance evaluation indicators, through the calculation of comprehensive performance evaluation indicators and the comparison with the set standards, the performance evaluation results of enterprise human resource management performance are obtained. The experimental results show that the average scoring error of the designed enterprise human resource management performance evaluation method is less than 0.5, which meets the application requirements of human resource management performance.
Identification of Stock Market Manipulation with Deep Learning

Anomaly detection is a common and critical data mining task, it seeks to identify observations that differ significantly from others. Anomalies may indicate rare but significant events that require action. Market manipulation is an activity that undermines stock markets worldwide. This paper shares five large real-world, labelled data sets of anomalous stock market data where market manipulation is alleged to have occurred. Cutting edge deep learning techniques are then shown to successfully detect the anomalous periods. An LSTM based method with dynamic thresholding is particularly promising in this domain as it was able to identify contextual local anomalies in the data quickly, taking seconds to score two years of trading data for each stock, which can often be a challenge for deep learning approaches.
Prediction New Cases of COVID-19 in Indonesia Using Vector Autoregression (VAR) and Long-Short Term Memory (LSTM) Methods

The addition of Covid-19 cases is still uncontrolled, especially in Indonesia. Often the addition of Covid-19 cases in Indonesia always experiences a significant upward trend after a slightly loose government policy. This is because the government does not think there will be a spike in cases after cases go down. This is where the importance of predicting new cases of Covid-19 in Indonesia to be a reference for the government in taking policy. With deep learning, the prediction results will be more accurate. The implementation of vector autoregression (VAR) and long-short term memory (LSTM) methods can reach an accretion rate of up to 98%. With this method, the prediction results can be used for the government in anticipating if there is a surge in new cases per day because it has been predicted from the beginning. In fact, this method can predict new cases for up to a year.
Hate Speech Detection using Text and Image Tweets Based On Bi-directional Long Short-Term Memory

Due to the obvious exponential growth in the usage of the internet by individuals of all ethnicities and educational backgrounds, dangerous internet media has become a serious concern in today's society. In the automated identification of hazardous text material, distinguishing between offensive speech and offensive language is a major problem. Most of the current approaches revolve around TF-IDF feature extraction, followed by the traditional classification techniques like Support Vector Machines (SVM), Decision Trees etc., As a result, there is a scope of improvement in the Accuracy of Emotion Detection and long training times. Most of the works considered only tweet data only. But in this work, we would like to include image characters and image components also. We propose a technique in this study for automatically classifying tweets on Twitter into two categories: Hate speech, Offensive speech and non-hate speech. A training and testing step are included in the suggested technique. Traditional Tweet preparation procedures such as removing Twitter handles, URLs, punctuation, stop words, and stemming were used. In both testing and training, we pad each tweet to its maximum length based on the vocabulary. This padding can have an impact on how the network works and can have a significant impact on performance and accuracy. The normalized characteristics are supplied into Bi-directional Long Short-Term Memory, which learns bidirectional long-term relationships between time steps in a time series or sequential twitter data. In comparison research, we compare the models utilizing each of these approaches. We used the Kaggle data set to predict Hate, offensive and Neutral Messages. After conducting many tests, we discovered that the suggested technique outperforms state-of-the-art algorithms by more than 90 percent.