Reviewerly/exHarmony huggingface dataset:
Recursive Input and State Estimation: a General Framework for Learning from Time Series With Missing Data

Time series with missing data are signals encountered in important settings for machine learning. Some of the most successful prior approaches for modeling such time series are based on recurrent neural networks that transform the input and previous state to account for the missing observations, and then treat the transformed signal in a standard manner. In this paper, we introduce a single unifying framework, Recursive Input and State Estimation (RISE), for this general approach and reformulate existing models as specific instances of this framework. We then explore additional novel variations within the RISE framework to improve the performance of any instance. We exploit representation learning techniques to learn latent representations of the signals used by RISE instances. We discuss and develop various encoding techniques to learn latent signal representations. We benchmark instances of the framework with various encoding functions on three data imputation datasets, observing that RISE in-stances always benefit from encoders that learn representations for numerical values from the digits into which they can be decomposed.
A new model for small target adult image recognition

With the advent of the era of 5G high speed and massive information, the audience of netizens is developing at a younger age, and the proliferation of adult images on the Internet is becoming more and more serious. The recognition and filtering of adult pictures has become very important to prevent young people from contacting harmful information. But, because of the wide variety of adult images, automatic recognition in these pictures is a very difficult task. This paper proposes a new model based on Residual neutral network. Our method consists of two parts. The first part is to divide images into "large target skin color image" and "small target skin color image" according to the proportion of skin color pixels of the image; The second part is to input the two types of images into two different models for recognition. According to the results of comparative experiments, the proposed network can further improve the classification accuracy of adult images of small targets with excessive background noise, which is better than other single model.
Superways: A Datacenter Topology for Incast-heavy workloads

Several important datacenter applications cause incast congestion, which severely degrades flow completion times of short flows and throughput of long flows. Further, because most flows are short and the incast duration is shorter than typical round-trip times, reactive mechanisms that rely on congestion control are not effective. While modern datacenter topologies provide high bisection bandwidth to support all-to-all traffic, incast is fundamentally a many-to-one traffic pattern, and therefore, requires deep buffers or high bandwidth at the network edge.
An Incremental Fuzzy Learning Approach for Online Classification of Data Streams

Online learning concerns analyzing a continuous stream of transient data and progressively updating the knowledge model without revisiting previously encountered examples. This paper proposes a new incremental fuzzy learning approach for online classification of data streams. It enables an existing fuzzy rule set to be efficiently updated based on a new training example without learning from scratch. The proposed algorithm can not only incrementally construct new fuzzy classification rules but also update the content with old rules to assimilate information from new data. The efficacy of the proposed incremental fuzzy learning method has been demonstrated in a set of simulation tests where the benchmark data sets were treated as data streams for learning.
NAUI : Neural Attentive User Interest Model for Cross-Domain CTR Prediction

Abstract In the existing works for recommender systems, the design of Cross-Domain CTR prediction is less mentioned. In order to solve the data sparsity problem in CTR prediction, Cross-Domain Recommendation (CDR) leverages a wealth of information from a source domain to improve the CTR prediction performance on a target domain with sparse information, which is better than Single-Domain CTR prediction. Ads are usually displayed with natural content, which gives Cross-Domain CTR prediction the opportunity for problemsolving. In this paper, we propose a CDR novel named NAUI which can leverage auxiliary data information to improve the CTR prediction performance. NAUI utilizes an extracting user interest method which reduces excessive punishment for active users, and jointly training Cross Net and MLP components together on CDR. NAUI can capture more information about nonlinear features and combined features efficiently capture implicit and explicit high-order features interactions, and greatly improve the expression ability of the model on Cross-Domain CTR recommendation. Furthermore, we add an auxiliary classifier to the deep neural network to improve recommendation performance. Our experiment results has demonstrated that NAUI outperforms several frequently state-of-the-art methods of CTR prediction.
Distance learning of ELT for Chinese students in ICUTK: digital literacy practices

Abstract English Language Teaching (ELT) is part of a teaching and learning activity focused on English development. The ELT’s distance learning enactment is used for English communication. These activities may be linked to digital literacy practices, particularly for Chinese students as non-native speakers, in stimulating English as regular communication in spoken and written centered on communication. From distance learning, some Chinese ICUTK students have contributed a lot to using English to teach and practice contact at any time. Running digital literacy practices as an ELT approach and strategy will help improve English skills in the era of Industrial Revolution 4.0, especially by enacting distance learning from China Mainland to Thailand. The purpose of this research is to know how well students can learn ELT. It describes how essential social media platforms are in delivering ELT lessons for distance learning countries between Thailand and the People’s Republic of China and how Chinese students practice digital literacy through media apps synchronously and asynchronously. This study uses a qualitative approach focused on classroom learning. Simultaneously, researchers learn how useful ELT activity for Chinese students through asynchronous and synchronous methods such as WeChat apps and Ding Talks apps. These online platforms demonstrate how effective distance learning for Chinese students in the ELT is and how useful a forum is to help teach and learn in English.
Cloud Performance Variability Prediction

Cloud computing plays an essential role in our society nowadays. Many important services are highly dependant on the stable performance of the cloud. However, as prior work has shown, clouds exhibit large degrees of performance variability. Next to the stochastic variation induced by noisy neighbors, an important facet of cloud performance variability is given by changepoints---the instances where the non-stationary performance metrics exhibit persisting changes, which often last until subsequent changepoints occur. Such undesirable artifacts of the unstable application performance lead to problems with application performance evaluation and prediction efforts. Thus, characterization and understanding of performance changepoints become important elements of studying application performance in the cloud. In this paper, we showcase and tune two different changepoint detection methods, as well as demonstrate how the timing of the changepoints they identify can be predicted. We present a gradient-boosting-based prediction method, show that it can achieve good prediction accuracy, and give advice to practitioners on how to use our results.
Application of Neurological Networks in an AI for Chess Game

Neurological networks in an AI is the term used for mapped networks generated by an artificial intelligence, they are inspired by the biological neural networks and are a form of connectionism a term used for model behavior and mental phenomena. These systems learn and grow by seeing or processing various examples for doing a task without actually doing specific programming for the same the network will learn and grow according to the set of instructions provided to it by the programmer and modify itself to recognize and create patterns based on the requirements. The aim is to create an ultimate chess game that can learn using image modulation techniques and play against a real human. The initial data set for learning was provided using the ultimate chess guide as well as number of games against human and itself.
ATJ-Net: Auto-Table-Join Network for Automatic Learning on Relational Databases

A relational database, consisting of multiple tables, provides heterogeneous information across various entities, widely used in real-world services. This paper studies the supervised learning task on multiple tables, aiming to predict one label column with the help of multiple-tabular data. However, classical ML techniques mainly focus on single-tabular data. Multiple-tabular data refers to many-to-many mapping among joinable attributes and n-ary relations, which cannot be utilized directly by classical ML techniques. Besides, current graph techniques, like heterogeneous information network (HIN) and graph neural networks (GNN), are infeasible to be deployed directly and automatically in a multi-table environment, which limits the learning on databases.
Research on Watershed Algorithm Based on Image Marking Method Optimization

In image processing, due to the presence of noise or other unpredictable interference factors, the use of traditional watershed algorithms often results in excessive segmentation. In order to solve this problem, the watershed algorithm optimized based on the image tagging method can be used to guide the computer to complete the watershed algorithm segmentation by artificially setting the prior knowledge in order to obtain a better image segmentation effect. Finally, it is verified by experiments that the watershed algorithm based on the image labeling method is better than the traditional watershed algorithm in segmenting some complex images.
Harmless label noise and informative soft-labels in supervised classification

Manual labelling of training examples is common practice in supervised learning. When the labelling task is of non-trivial difficulty, the supplied labels may not be equal to the ground-truth labels, and label noise is introduced into the training dataset. If the manual annotation is carried out by multiple experts, the same training example can be given different class assignments by different experts, which is indicative of label noise. In the framework of model-based classification, a simple, but key observation is that when the manual labels are sampled using the posterior probabilities of class membership, the noisy labels are as valuable as the ground-truth labels in terms of statistical information. A relaxation of this process is a random effects model for imperfect labelling by a group that uses approximate posterior probabilities of class membership. The relative efficiency of logistic regression using the noisy labels compared to logistic regression using the ground-truth labels can then be derived. The main finding is that logistic regression can be robust to label noise when label noise and classification difficulty are positively correlated. In particular, when classification difficulty is the only source of label errors, multiple sets of noisy labels can supply more information for the estimation of a classification rule compared to the single set of ground-truth labels.
Fourier Transformation Autoencoders for Anomaly Detection

Anomaly detection is a challenging problem, mainly due to the lack of a sufficient set of abnormal samples that represents every possible anomaly. Therefore unsupervised methods are employed to model normality and anomaly is detected as an outlier to such models. This paper introduces Fourier Trans-forms into AutoEncoders to demonstrate how the inclusion of a frequency domain presents less noisy features for a deep learning network to detect anomalies. Comparing our results to the state of the art on a variety of datasets, we show how the proposed method can provide competitive results.
Comparing logic programming and formal argumentation; the case of ideal and eager semantics

The connection between logic programming and formal argumentation has been studied starting from the landmark 1995 paper of Dung. Subsequent work has identified a standard translation from logic programs to (instantiated) argumentation frameworks, under which pairwise correspondences hold between various logic programming semantics and various formal argumentation semantics. This includes the correspondence between 3-valued stable and complete semantics, between well-founded and grounded semantics and between 2-valued stable (LP) and stable (argumentation) semantics. In the current paper, we show that the existing translation is able to yield the additional correspondence between ideal semantics for logic programming and ideal semantics for formal argumentation. We also show that correspondence does not hold between eager semantics for logic programming and eager semantics for formal argumentation, at least when translating from logic programming to formal argumentation. Overall, the current work should be seen as completing the analysis of correspondences between mainstream admissibility-based argumentation semantics and their logic programming counterparts.
Historical Kannada Handwritten Character Recognition Using Machine Learning Algorithm

A character is the smallest unit in every line of a script, the development of the script stretches over millions of inscriptions in Kannada, and these inscriptions play a vital role in the reconstruction of Karnataka’s history and its culture. The Kannada script has evolved for more than 1500 years and many inventions, and variations have taken place in the scripts used by various dynasties. Due to these variations occurred in the script, it has been improved in text and formed in a new different style. The objective of the present paper is to digitize and restore the historical Kannada handwritten scripts by applying image enhancement techniques and recognize the individual characters by extracting the HOG features. The LDA, K-nearest neighbour (K-NN), and SVM classifiers are used to identify the dynasties of the characters, whether it belongs to the dynasty of the Hoysala or Vijayanagara or the Mysore Wodeyar. The average classification accuracy of the historical Kannada handwritten characters from the different regimes is; the LDA classifier has yielded 68.4%, the K-NN classifier achieved 85.7%, and the SVM classifier yielded 87.5%. Based on the experimentation, it is noted that the overall classification accuracy is relatively improved with the SVM classifier compared to LDA and K-NN classifiers. Further, the results are also verified manually obtained by epigraphists and linguistic experts, which proves the efficacy of the proposed approach.
A Countermeasure of Power Attack for Lightweight Cryptographic Algorithm Using Bit Permutation Operation

Abstract The implantation security of cryptographic algorithm is very important. This paper proposes a method to resist power consumption attack for lightweight cryptographic algorithms. Using bit permutation operation as a countermeasure, we ensure that the position of each data of the original intermediate value data has changed after the bit is permutated, which greatly increases the difficulty of the attack. SKINNY is taken as an example for evaluate the effect of the proposed countermeasure. The experiment results show that the key will not reveal over 100,000 energy traces by using the countermeasure.
Revisiting Multivariate Ring Learning with Errors and Its Applications on Lattice-Based Cryptography

The “Multivariate Ring Learning with Errors” problem was presented as a generalization of Ring Learning with Errors (RLWE), introducing efficiency improvements with respect to the RLWE counterpart thanks to its multivariate structure. Nevertheless, the recent attack presented by Bootland, Castryck and Vercauteren has some important consequences on the security of the multivariate RLWE problem with “non-coprime” cyclotomics; this attack transforms instances of m-RLWE with power-of-two cyclotomic polynomials of degree n=∏ini into a set of RLWE samples with dimension maxi{ni}. This is especially devastating for low-degree cyclotomics (e.g., Φ4(x)=1+x2). In this work, we revisit the security of multivariate RLWE and propose new alternative instantiations of the problem that avoid the attack while still preserving the advantages of the multivariate structure, especially when using low-degree polynomials. Additionally, we show how to parameterize these instances in a secure and practical way, therefore enabling constructions and strategies based on m-RLWE that bring notable space and time efficiency improvements over current RLWE-based constructions.
Sparse intrinsic decomposition and applications

This paper proposes an intrinsic decomposition method from a single RGB-D image. To remedy the highly ill-conditioned problem, the reflectance component is regularized by a sparsity term, which is weighted by a bilateral kernel to exploit non-local structural correlation. As shading images are piece-wise smooth and have sparse gradient fields, the sparse-induced ℓ1-norm is used to regularize the finite difference of the direct irradiance component, which is the most dominant sub-component of shading and describes the light directly received by the surfaces of the objects from the light source. To derive an efficient algorithm, the proposed model is transformed into an unconstrained minimization of the augmented Lagrangian function, which is then optimized via the alternating direction method. The stability of the proposed method with respect to parameter perturbation and its robustness to noise are investigated by experiments. Quantitative and qualitative evaluation demonstrates that our method has better performance than state-of-the-art methods. Our method can also achieve intrinsic decomposition from a single color image by integrating existed depth estimation methods. We also present a depth refinement method based on our intrinsic decomposition method, which obtains more geometry details without texture artifacts. Other application, e.g., texture editing, also demonstrates the effectiveness of our method.
DCSpell: A Detector-Corrector Framework for Chinese Spelling Error Correction

Spelling Error Correction (SEC) that detects and corrects spelling errors in a text has a wide range of applications in human language understanding. Earlier solutions, including statistic-based methods, one-stage, and two-stage machine learning-based methods, cannot build deeply bidirectional models and significantly confine the learning ability. With the recently emerging masked language models, transformer-based networks have achieved remarkable success in SEC. However, current transformer-based Chinese SEC algorithms are all end-to-end methods, which suffer from high false alarm rates because they correct each character of the sentence regardless of its correctness. This issue becomes even more severe when there exist only a small fraction of incorrect characters in the whole sentence. To solve this problem, we propose a cloze-style detector-corrector framework (DCSpell) that firstly detects whether a character is erroneous before correcting it. Specifically, DCSpell employs the discriminator of ELECTRA as the Detector to detect the positions of incorrect characters. The Detector is trained by a sample-efficient replaced token detection pre-training task, and thus allows domain adaption with a small amount of data. After that, a transformer-based Corrector is used to find the correct character for each detected position. It employs sentence pairs as the input, which potentially incorporates the knowledge of phonological and visual similarity. A confusion-set-based post-processing is used to further improve the performance. Experiments show that DCSpell achieves 15.7% improvement on the SIGHAN dataset and 6.6% improvement on a dataset transcribed from a real-world acoustic speech corpus compared to the state-of-the-art methods in terms of the F1 score.
Scheduling Cloudlets to Improve Response Time Using CloudSim Simulator

The sustainability of any system is based on the performance and the service it gives to the users. The main objective of this work is to assess the performance of the cloud system in terms of its response time using CloudSim simulator. The performance is evaluated on a set of heterogenous cloudlets and the way the processing cores of the host are provision to the cloudlets and virtual machines (VMs) in the execution engine. The default scheduling algorithm used by the CloudSim simulator to allocate these cloudlets from the datacenter broker to the VM is based on the first come first serve (FCFS) scheduling algorithm. This paper proposed the shortest job first (SJF) algorithm to allocate the incoming cloudlets to VM in the ascending order of its length whose response time is evaluated when space-shared and time-shared provisioning policy is implemented. The experimental evaluation of the proposed algorithm shows better improvement when the order of length is considered.
Factors Affecting Task Allocation and Coordination in Distributed Agile Software Development

The advantages in terms of low-cost resources are driving software industry to shift towards global markets in recent years. The distributed agile software development (DASD) companies aim at achieving high productivity by using resources around the world. Together with benefits, DASD presents some issues and challenges, which require further studies and research. Adopting an appropriate task allocation process is important for the smooth running of the DASD projects. Assigning tasks to remote teams requires a number of factors to be taken into consideration. Therefore, there is the need to identify factors that influence task allocation and coordination in agile distributed environment. In this study, a survey is conducted with agile professionals to assess the importance of the identified factors in the literature. Snowball sampling technique has been chosen due to small population size in Mauritius. The target population of the survey consists basically of agile project managers, agile team leaders, agile team members, agile researcher and other decision-makers involved in the process. The results show that some factors are valued differently from the literature.
Optimization of regularization coefficient and kernel parameters of KELM in face recognition using genetic algorithm

Extreme Learning machine (ELM), a linear system model introduced originally for feedforward neural networks having single-hidden layer, which is non-iteratively tuned. In ELM feature mapping is achieved explicitly with the help of activation functions. ELM was further prospected with kernel functions for performance improvement by exploiting implicit feature mapping. The classification accuracy of kernel ELM relies on the kernel and structural parameters, which are experimentally tuned. It is very challenging and computationally hard to assess the optimal choice of these parameters from a specific domain of values using Brute force method. Therefore an optimal algorithm is required to find the finest combination of these parameters for enhanced performance. In this paper optimized kernel ELM is unveiled, in which genetic algorithm is utilized to evaluate the optimal solution of regularization coefficient and kernel parameters. Experiments on face image databases denote that the developed algorithm is more accurate and efficacious than state of art existing algorithms in literature.