Reviewerly/exHarmony huggingface dataset:
Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification

Recently, language representation techniques have achieved great performances in text classification. However, most existing representation models are specifically designed for English materials, which may fail in Chinese because of the huge difference between these two languages. Actually, few existing methods for Chinese text classification process texts at a single level. However, as a special kind of hieroglyphics, radicals of Chinese characters are good semantic carriers. In addition, Pinyin codes carry the semantic of tones, and Wubi reflects the stroke structure information, etc. Unfortunately, previous researches neglected to find an effective way to distill the useful parts of these four factors and to fuse them. In our works, we propose a novel model called Moto: Enhancing Embedding with Multiple Joint Factors. Specifically, we design an attention mechanism to distill the useful parts by fusing the four-level information above more effectively. We conduct extensive experiments on four popular tasks. The empirical results show that our Moto achieves SOTA 0.8316 (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score, 2.11% improvement) on Chinese news titles, 96.38 (1.24% improvement) on Fudan Corpus and 0.9633 (3.26% improvement) on THUCNews.
Quantitative comparison of supervised algorithms and feature sets for traffic sign recognition

Nowadays a timely detection of relevant events and an efficient recognition of objects in an environment is a critical activity for many Cyber-Physical Systems (CPS), which may have severe impact on citizens, infrastructures or the environment when incurring malfunctions. In the automotive domain, the detection and recognition of traffic signs (TSDR) from images was and is currently being investigated as it heavily impacts the behavior of (semi-)autonomous vehicles. Despite many classifiers and feature extraction strategies applied to images sampled by webcams installed on cars have been developed throughout the years, those efforts did not escalate into a clear benchmark nor comparison of the most common techniques on multiple datasets and feature sets. This study tackles this problem by providing a comprehensive quantitative comparison of traditional supervised Machine Learning algorithms with different feature sets and Deep Learning models for the recognition of traffic signs from three publicly available datasets. A perfect classification was achieved by many ML algorithms on the German GTRSB dataset, while with the BelgiumTSC and DITS datasets no algorithms provided perfect classification.
Super Resolution Image Restoration Algorithm : Based on Wavelet and Interpolation

Aiming at the problem of face image restoration, a super resolution image restoration algorithm based on wavelet and bicubic interpolation is proposed. Low-resolution image is decomposed by wavelet transform. Then, the high frequency sub images of the original low-resolution image are interpolated in the wavelet transform domain, and the high-resolution image is obtained by the bicubic interpolation of the low-resolution image. The experimental results show that the image restoration algorithm based on wavelet and bicubic interpolation is better than the traditional interpolation algorithms (nearest neighbor interpolation, bilinear interpolation, bicubic interpolation) in face image restoration.
A Multi-party Secure SaaS Cloud Accounting Platform Based on Lattice-based Homomorphic Encryption System

Under the background of big financial data, the cloud accounting system based on SaaS model plays an important role in the process of Enterprise Financial Information. This paper designs a more efficient and safer SaaS Cloud Accounting Platform based on the Multi-party Secure Computing technology and Lattice-based Homomorphic Encryption system and also looks forward to its future development prospect, which provides a useful reference for advancing SaaS service field.
Distributional formal semantics

Natural language semantics has recently sought to combine the complementary strengths of formal and distributional approaches to meaning. However, given the fundamentally different ‘representational currency’ underlying these approaches—models of the world versus linguistic co-occurrence—their unification has proven extremely difficult. Here, we define Distributional Formal Semantics, which integrates distributionality into a formal semantic system on the level of formal models. This approach offers probabilistic, distributed meaning representations that are inherently compositional, and that naturally capture fundamental semantic notions such as quantification and entailment. Furthermore, we show how the probabilistic nature of these representations allows for probabilistic inference, and how the information-theoretic notion of “information” (measured in Entropy and Surprisal) naturally follows from it. Finally, we illustrate how meaning representations can be derived incrementally from linguistic input using a recurrent neural network model, and how the resultant incremental semantic construction procedure intuitively captures key semantic phenomena, including negation, presupposition, and anaphoricity.
Bidirectional Language Modeling: A Systematic Literature Review

In transfer learning, two major activities, i.e., pretraining and fine-tuning, are carried out to perform downstream tasks. The advent of transformer architecture and bidirectional language models, e.g., bidirectional encoder representation from transformer (BERT), enables the functionality of transfer learning. Besides, BERT bridges the limitations of unidirectional language models by removing the dependency on the recurrent neural network (RNN). BERT also supports the attention mechanism to read input from any side and understand sentence context better. It is analyzed that the performance of downstream tasks in transfer learning depends upon the various factors such as dataset size, step size, and the number of selected parameters. In state-of-the-art, various research studies produced efficient results by contributing to the pretraining phase. However, a comprehensive investigation and analysis of these research studies is not available yet. Therefore, in this article, a systematic literature review (SLR) is presented investigating thirty-one (31) influential research studies published during 2018–2020. Following contributions are made in this paper: (1) thirty-one (31) models inspired by BERT are extracted. (2) Every model in this paper is compared with RoBERTa (replicated BERT model) having large dataset and batch size but with a small step size. It is concluded that seven (7) out of thirty-one (31) models in this SLR outperforms RoBERTa in which three were trained on a larger dataset while the other four models are trained on a smaller dataset. Besides, among these seven models, six models shared both feedforward network (FFN) and attention across the layers. Rest of the twenty-four (24) models are also studied in this SLR with different parameter settings. Furthermore, it has been concluded that a pretrained model with a large dataset, hidden layers, attention heads, and small step size with parameter sharing produces better results. This SLR will help researchers to pick a suitable model based on their requirements.
A comparative study of forest methods for time-to-event data: variable selection and predictive performance

Abstract Background As a hot method in machine learning field, the forests approach is an attractive alternative approach to Cox model. Random survival forests (RSF) methodology is the most popular survival forests method, whereas its drawbacks exist such as a selection bias towards covariates with many possible split points. Conditional inference forests (CIF) methodology is known to reduce the selection bias via a two-step split procedure implementing hypothesis tests as it separates the variable selection and splitting, but its computation costs too much time. Random forests with maximally selected rank statistics (MSR-RF) methodology proposed recently seems to be a great improvement on RSF and CIF. Methods In this paper we used simulation study and real data application to compare prediction performances and variable selection performances among three survival forests methods, including RSF, CIF and MSR-RF. To evaluate the performance of variable selection, we combined all simulations to calculate the frequency of ranking top of the variable importance measures of the correct variables, where higher frequency means better selection ability. We used Integrated Brier Score ( IBS ) and c-index to measure the prediction accuracy of all three methods. The smaller IBS value, the greater the prediction. Results Simulations show that three forests methods differ slightly in prediction performance. MSR-RF and RSF might perform better than CIF when there are only continuous or binary variables in the datasets. For variable selection performance, When there are multiple categorical variables in the datasets, the selection frequency of RSF seems to be lowest in most cases. MSR-RF and CIF have higher selection rates, and CIF perform well especially with the interaction term. The fact that correlation degree of the variables has little effect on the selection frequency indicates that three forest methods can handle data with correlation. When there are only continuous variables in the datasets, MSR-RF perform better. When there are only binary variables in the datasets, RSF and MSR-RF have more advantages than CIF. When the variable dimension increases, MSR-RF and RSF seem to be more robustthan CIF Conclusions All three methods show advantages in prediction performances and variable selection performances under different situations. The recent proposed methodology MSR-RF possess practical value and is well worth popularizing. It is important to identify the appropriate method in real use according to the research aim and the nature of covariates.
Local Gradient Difference Features for Classification of 2D-3D Natural Scene Text Images

Methods developed for normal 2D text detection do not work well for text that is rendered using decorative, 3D effects, etc. This paper proposes a new method for classification of 2D and 3D natural scene text images so that an appropriate recognition method can be chosen accordingly based on the classification results for better performance. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure, called COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed into a Neural Network (NN) for classification. The proposed method is tested on (i) a new dataset introduced in this work (ii) a second dataset assembled from standard natural scene datasets (iii) Non-Text Image datasets which does not contain text, rather it contains objects. Experimental results of the proposed method on images with text and non-text show that the proposed method is independent of text. The proposed approach improves text detection and recognition performance significantly after classification.
A bargaining game based energy-aware load balancing in cloud data centers

This paper addresses the energy-aware load balancing problem in the cloud computing environment. The problem considers two performance criteria- response time and energy. First, it is modeled as a cooperative game, and then an efficient algorithm is proposed to compute the Nash bargaining solution (NBS). The proposed algorithm is compared with an existing approach called NCG. The experimental results show that the proposed algorithm provides less response time while consuming less energy.
Bayesian Optimization and Gradient Boosting to Detect Phishing Websites

We propose an Extreme Gradient Boosting framework for classification and regression problems emerging in machine learning for small-sized data sources sampled from a discrete distribution, i.e. data containing discrete or quantized attributes. The model parameters are iteratively refined from a prior belief for specific use cases using Bayesian optimization. We focus the application area of this framework on detecting fraudulent websites. With properly stated reasoning, we empirically test our methodology on a publicly available and bench-marked UCI Phishing dataset to demonstrate the superior performance of this approach as compared to existing methods in the literature.
Sentiment analysis using an ensemble approach of BiGRU model: A case study of AMIS tweets

This paper presents a comparably simpler yet effective deep learning approach for sentiment analysis of Twitter topics. We automatically collected positive and negative tweets and labeled them manually, and thus created a new dataset. We then leveraged BiGRU model with an ensemble approach for the binary classification of tweets. Our finalized BiGRU model offered an accuracy of 84.8% as well as an averaged F1-measure of 84.8%(±0.3). Moreover, the ensemble approach, using an averaged prediction of 5-fold strategy, provided the accuracy of 86.3% along with the averaged F1-measure of 86.3%(±0.05). Consequently, the ensemble approach offered better performance even on a smaller dataset used in this study.
Privacy-Preservation in Distributed Deep Neural Networks via Encryption of Selected Gradients

In this paper, privacy preservation in Distributed Deep Neural Networks (DDNN) is re-visited. We focus on the use of Homomorphic Encryption (HE) in the DDNN. Although HE provides some level of effective way to bring privacy to the DDNN, its use is usually associated with high overheads, especially communication cost between the Honest-but-Curious (HbC) cloud server and participants. To this end, we propose an alternative approach for privacy preservation in the DDNN. Ability to select and encrypt selected gradients homomorphically, is the main appeal of this paper and has proven to provide good privacy, reduce communication cost to a considerable level (ie. 0.04MB and 0.13MB when 1% and 10% of total gradients are selected, encrypted and shared respectively) whilst maintaining acceptable level of accuracy. A learning with error (LWE) based additive Homomorphic Encryption is used and comparison is made with closely related works to show how efficient our approach is. Furthermore, we propose the use of non-malleable code to protect the integrity of ciphertexts that are transmitted between participants and the HbC cloud server. This paper is the first to combine partial sharing and Homomorphic Encryption in the DDNN.
Sinking or Swimming?

This chapter presents the findings of a recent study conducted on the adult learners of the University of Eswatini (UNESWA) that explored how Moodle enhanced self-directed learning among the adult learners and how these adult learners mastered the content using the Moodle platform. It was found that while some learners excelled in using the platform, others lacked the technological skills to manoeuvre the platform. Also, participants lamented that the lecturers did not post material Moodle. Notably, Moodle enabled independent learning for those learners who were employed. The following conclusions were made: 1) lecturers need to post more material on Moodle, 2) refresher videos to keep learners abreast with Moodle should be made, and 3) periodic institutional assessment on the uptake of Moodle should be done.
Distributed Kalman filter for linear system with complex multi‐channel stochastic uncertain parameter and decoupled local filters

Summary This article considers a distributed Kalman filtering problem for linear system contaminated by complex multi‐channel random uncertain parameter in which a number of nodes cooperative without central coordination to estimate a common state based on local measurements and data received from neighbors. We propose an approach to eliminate this error propagation. The proposed local filters are guaranteed to be stable under some mild conditions on certain global structural data, and their fusion yields the centralized Kalman estimate. Then, we extend this method to smoothing and deconvolution algorithm. Finally, simulation experiments demonstrate the validity of the proposed approach.
Bangla Aspect-Based Sentiment Analysis Based on Corresponding Term Extraction

Aspect-based sentiment analysis is a text analysis technique that extracts and separates each aspect term and identifies the sentiment polarity associated with each aspect term. Bangla is the seventh most spoken language in the world. Sentiment analysis in the Bangla language is considered a crucial and well-timed research topic. Aspect-based sentiment analysis of the Bangla language is treated as a complicated task because of the scarcity of resources like annotated datasets, corpora, etc. In this research, we have proposed a new technique named PSPWA (Priority Sentence Part Weight Assignment) to perform aspect category or term extraction on publicly available datasets named Cricket and Restaurant. We have used conventional supervised learning algorithms and Convolutional Neural Network (CNN) to demonstrate results. Dataset preparation, feature engineering, description of PSPWA, CNN architecture, experimental results along with a state-of-art comparison has been shown in this paper. The public dataset was imbalanced. CNN has performed better among other learning algorithms. CNN has achieved an f1-score of 0.59 and 0.67 for the cricket and the restaurant dataset respectively.
Finding the Needle in the Haystack: Metrics for Best Trace Selection in Unsupervised Side-Channel Attacks on Blinded RSA

For asymmetric ciphers, such as RSA and ECC, side-channel attacks on the underlying exponentiation are mitigated by countermeasures like constant-time implementation and blinding. This restricts an attacker to a single side-channel trace for an attack as a different representation of the private key is used for each exponentiation. In this work, we propose an unsupervised machine learning framework for side-channel attacks on asymmetric cryptography that analyzes leakage in multiple side-channel traces, identifying the best trace for key retrieval. We apply Principal Component Analysis (PCA) preprocessing followed by a classification step that assigns segments of traces to elementary operations of the Square and Multiply exponentiation of RSA. In order to estimate the attack complexity for each trace in terms of key enumeration effort, we introduce two new metrics: The Entropy-based Cost Function (EBCF) is used to select a trace for the attack as well as bits which have to be brute-forced if not all bits can be determined correctly from this single trace. To reduce brute-force complexity further, we introduce Illegal Sequence Detection (ISD) to remove brute-force candidates which do not fit to the Square-and-Multiply scheme. We first provide a proof of concept for 320-bit key length traces and, moving towards a more realistic scenario, retrieve the key from a 1024-bit RSA implementation protected by message and exponent blinding. We are able to select the trace with the least remaining brute-force complexity from 1000 power measurements of the signature generation with randomized inputs and blinding values on a 32-bit ARM Cortex-M4 microcontroller.
Is Vulnerability Report Confidence Redundant? Pitfalls Using Temporal Risk Scores

The Common Vulnerability Scoring System score is the de facto standard to assess risk of software vulnerabilities, with three temporal components: exploitability, remediation level, and report confidence. We discuss how the latter may be inferred from the first two, pointing practical and conceptual issues in the usage of temporal risk scores.
A hybridization approach with predicted solution candidates for improving population-based optimization algorithms

The recent proliferation of population-based meta-heuristics designed for solving optimization problems and their successes confirm that more promising techniques inspired by physical phenomena or biological systems are desired. Therefore, in this paper, a novel hybridization approach is proposed to improve the performance of optimization algorithms. The approach replaces a small number of worst solutions obtained by a meta-heuristic with predicted candidates without altering its search operators. Specifically, a target fitness value of the predicted candidate is determined based on the fitness of the population and a search strategy. Then, a calibration problem is solved to infer its decision variables. In this study, the proposed hybridization technique is applied to ten state-of-the-art population-based algorithms. The meta-heuristics and hybrids are evaluated on 82 functions, four engineering problems, and a new challenging problem of estimating a constant in Markov’s inequality using minimal polynomials of different degrees. The experimental results reveal the superiority of the hybrids over their counterparts and confirm the suitability of the proposed approach for improving the efficiency of meta-heuristics.
Probabilistic causes in Markov chains

Abstract By combining two of the central paradigms of causality, namely counterfactual reasoning and probability-raising , we introduce a probabilistic notion of cause in Markov chains. Such a cause consists of finite executions of the probabilistic system after which the probability of an $$\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ω</mml:mi> </mml:math> -regular effect exceeds a given threshold. The cause, as a set of executions, then has to cover all behaviors exhibiting the effect. With these properties, such causes can be used for monitoring purposes where the aim is to detect faulty behavior before it actually occurs. In order to choose which cause should be computed, we introduce multiple types of costs to capture the consumption of resources by the system or monitor from different perspectives, and study the complexity of computing cost-minimal causes.
Discovering predictive trend-event patterns in temporal clinical data

Overwhelming amounts of clinical data are retrieved daily, and healthcare stakeholders may want to derive new knowledge from them. One of the methodological tools proposed to analyze clinical data is pattern mining, even with its temporal extensions.
Debates About EdTech in a Time of Pandemics Should Include Youth’s Voices

The EdTech debate continues to gather attention among academia, digital rights and privacy advocates, and the general public. While much ink is spilled by EdTech critics, with best interest for students facing a datafied and platformised education at heart, youth’s voice is rarely present. Much is written about what EdTech means for education processes and what they might do to students or for them than is said in their own words. With the pandemic having kept children and youth at a computer distance from each other and society for over a year, what has been happening in their lives during this time should be heard – in their own words. This article follows on two emergencies gripping education globally as highlighted during the 2020 United Nations Internet Governance Forum (IGF), emphasised by two IGF youth representatives. On one hand, the pandemic crisis has exacerbated digital inequalities worldwide; on the other, the ‘overdigitisation’ of education has increased the risks of dataveillance and privacy loss. These two extremes intersect in their techno-deterministic drive, which risks side-lining students’ perspectives. To counter this risk, this paper brings these youths’ perspectives and reflections over the dichotomy of ‘none’ versus ‘too much’ EdTech and what ‘works’ and does not in their learning in a time of pandemic.