Reviewerly/exHarmony huggingface dataset:
hKVS

In-memory key-value store (KVS) is a crucial component of data center applications. Since DRAM provides high bandwidth and low latency, the major performance bottleneck of common in-memory KVS lies in the network stack. Prior works have attempted to replace the traditional network stack with remote direct memory access (RDMA), which achieve orders of magnitude higher throughput and reduce the response latency. To further increase the throughput of an in-memory KVS, we propose a framework called hKVS, which enables the developers to design high-throughput heterogeneous KVS systems by adding the latest generations of smart network interface cards (SmartNIC), such as the NVIDIA BlueField DPU, to the host machines. The hKVS enables a host server to efficiently exploit the computational resources and utilize the RDMA capability of the SmartNICs to offload the workload for the CPU and increase the network bandwidth. The hKVS allows popular key-value objects to be replicated from the host to SmartNIC to form a high-throughput RDMA KVS jointly. We design the architecture of the hKVS, optimize its software implementation, and conduct a series of experiments to evaluate the resulted performance in realistic applications. By adding a SmartNIC to the host, hKVS achieves up to 1.86X and 1.48X higher throughput in 100% and 95% read workloads, which is cost-effective and scalable compared to building a KVS with multiple hosts, considering the SmartNIC costs much less than a high-performance server and multiple SmartNICs can be added to scale the throughput if needed.
TensorHE

In recent years, deep learning (DL) techniques have grown rapidly and facilitated advancements in different applications. With the concerns about information security increasing, the privacy sensitivity of machine learning algorithms needs to be taken into consideration. Homomorphic encryption (HE) based solutions have been proposed to preserve data privacy while enjoying the benefits of deep learning technologies. However, the existing approaches require extensive expert knowledge and human attention, which hampers the opportunities for promoting technologies in different application domains. In this work, we developed a transformer, called TensorHE, to automatically convert a pre-trained convolutional neural network (CNN) into the HE-based version. A systematical method is proposed to facilitate automatic transformation, enabling to build and deploy domain-specific applications. Our experimental results demonstrate the applicability and scalability of TensorHE by converting the CNN models for handling the MNIST and CIFAR-10 datasets. The delivered accuracies of the transformed models are as close as (less than 3%) those of the input CNN models.
Sequential Banking Products Recommendation and User Profiling in One Go

How can banks recommend relevant banking products such as debit, credit cards or term deposits, as well as learn a rich user representation for segmentation and user profiling, all via a single model? We present a sequence-to-item recommendation framework that uses a novel input data representation, accounting for the sequential and temporal context of both item ownership and user metadata, fed to a multi-head self-attentive encoder. We assess the performance of our model on the largest publicly available banking product recommendation dataset. Our model achieves 98.9% Precision@1 and 40.2% Precision@5, outperforming a state-of-the-art model as well as a common XGBoost-based baseline model tailored for this dataset and a system reportedly employed in industry for this task. Next, using the encoder embedding we obtain a continuous representation of users and their past product behavior. We demonstrate, in a case study, that this representation can be used for user segmentation and profiling, both critical to decision-making in organizations; for example, in designing and differentiating value propositions. The proposed approach is more inclusive and objective than the traditional ones employed by banks. With this work, we expose the benefits of employing a recommendation model based on self-attention in a real-world setting. The continuous user representation learned can yield far more impact than individual user-level recommendations. Both the proposed model and approach to segmentation and profiling are also applicable in other industries, beyond banking.
An Interpretable Deep Classifier for Counterfactual Generation

Counterfactual explanation has been the core of interpretable machine learning, which requires a trained model to be able to not only infer but also justify its inference. This problem is crucial in many fields, such as fintech and the healthcare industry, where accurate decisions and their justifications are equally important. Many studies have leveraged the power of deep generative models for counterfactual generation. However, most focus on vision data and leave the latent space unsupervised. In this paper, we propose a new and general framework that uses a supervised extension to the Variational Auto-Encoder (VAE) with Normalizing Flow (NF) for simultaneous classification and counterfactual generation. We show experiments on two tabular financial data-sets, Lending Club (LCD) and Give Me Some Credit (GMC), which show that the model can achieve a state-of-art level prediction accuracy while also producing meaningful counterfactual examples to interpret and justify the classifier’s decision.
CaPE: Category Preserving Embeddings for Similarity-Search in Financial Graphs

Similarity-search is an important problem to solve for the payment industry having user-merchant interaction data. It finds out merchants similar to a given merchant and solves various tasks like peer-set generation, recommendation, community detection, and anomaly detection. Recent works have shown that by leveraging interaction data, Graph Neural Networks (GNNs) can be used to generate node embeddings for entities like a merchant, which can be further used for such similarity-search tasks. However, most of the real-world financial data come with high cardinality categorical features such as city, industry, super-industries, etc. which are fed to the GNNs in a one-hot encoded manner. Current GNN algorithms are not designed to work for such sparse features which makes it difficult for them to learn these sparse features preserving embeddings. In this work, we propose CaPE, a Category Preserving Embedding generation method which preserves the high cardinality feature information in the embeddings. We have designed CaPE to preserve other important numerical feature information as well. We compare CaPE with the latest GNN algorithms for embedding generation methods to showcase its superiority in peer set generation tasks on real-world datasets, both external as well as internal (synthetically generated). We also compared our method for a downstream task like link prediction.
Interactions between voice-activated AI assistants and human speakers and their implications for second-language acquisition

Voice-activated artificially intelligent (voice-AI) assistants, such as Alexa, are remarkably effective at processing spoken commands by native speakers. What happens when the command is produced by an L2 speaker? In the current study, we focused on Korean-speaking L2 learners of English, and we asked (a) whether Alexa could recognize intended productions of two vowel contrasts, /i/ vs. /ɪ/ and /æ/ vs. / ε /, that occur in English but not in Korean, and (b) whether L2 talkers would make clear-speech adjustments when Alexa misrecognized their intended productions. L2 talkers ( n = 10) and native English ( n = 10) controls asked Alexa to spell out words. Targets were words that formed minimal vowel pairs, e.g., beat - bit, pet - pat . Results showed that Alexa achieved a 55% accuracy rate with L2 productions, compared to 98% for native productions. When Alexa misrecognized an intended production (e.g., spelling P-E-T when the speaker intended pat ), L2 talkers adjusted their subsequent production attempts by altering the duration, F1 and F2 of individual vowels (except for / ε /), as well as increasing vowel duration difference between contrasting vowels. These results have implications for theories of speech adaptation, and specifically for our understanding of L2 speech modifications oriented to voice-AI devices.
An extension of holonomic sequences: C2-finite sequences

Holonomic sequences are widely studied as many objects interesting to mathematicians and computer scientists are in this class. In the univariate case, these are the sequences satisfying linear recurrences with polynomial coefficients and also referred to as D-finite sequences. A subclass are C-finite sequences satisfying a linear recurrence with constant coefficients. We investigate the set of sequences which satisfy linear recurrence equations with coefficients that are C-finite sequences and call them C2-finite sequences. These sequences are a natural generalization of holonomic sequences. In this paper, we show that C2-finite sequences form a difference ring and provide methods to compute in this ring. Furthermore, we provide an analogous construction for D2-finite sequences, i.e., sequences satisfying a linear recurrence with holonomic coefficients. We show that these constructions can be iterated and obtain an increasing chain of difference rings.
Fast visual tracking with lightweight Siamese network and template-guided learning

Siamese-based trackers suffer from over-parameterization and online learning limitations, making it difficult to balance high performance and real-time execution when deployed on resource-constrained devices. In this paper, we propose a unified tracking framework that integrates lightweight Siamese network and template-guided learning. Specifically, we propose a two-step pruning method for compressing the Siamese network, which examines both the statistical distribution and correlation patterns of filters. By removing the filters with less importance and replaceable contribution, as well as their connected feature maps, network optimization is realized without custom architectures. Furthermore, we construct a template-guided learning model to capture target appearance information and suppress distracters. This can effectively ensure tracking performance in specific scenarios. Extensive experiments on OTB50, OTB-2013, OTB-2015, DTB70, UAV123, UAV20L, VOT2019, GOT-10K, LaSOT and TrackingNet indicate that the proposed method outperforms several state-of-the-art trackers and is faster. In particular, our lightweight Siamese network reduces the model size by 3.4 × and FLOPs by 3.7 × without significantly sacrificing performance while running at 116 fps.
Resampling-based inferences for compositional regression with application to beef cattle microbiomes

Gut microbiomes are increasingly found to be associated with many health-related characteristics of humans as well as animals. Regression with compositional microbiomes covariates is commonly used to identify important bacterial taxa that are related to various phenotype responses. Often the dimension of microbiome taxa easily exceeds the number of available samples, which creates a serious challenge in the estimation and inference of the model. The sparse log-contrast regression method is useful for such cases as it can yield a model estimate that depends on only a small number of taxa. However, a formal statistical inference procedure for individual regression coefficients has not been properly established yet. We propose a new estimation and inference procedure for linear regression models with extremely low-sample-sized compositional predictors. Under the compositional log-contrast regression framework, the proposed approach consists of two steps. The first step is to screen relevant predictors by fitting a log-contrast model with a sparse penalty. The screened-in variables are used as predictors in the non-sparse log-contrast model in the second step, where each of the regression coefficients is tested using nonparametric, resampling-based methods such as permutation and bootstrap. The performances of the proposed methods are evaluated by a simulation study, which shows they outperform traditional approaches based on normal assumptions or large sample asymptotics. Application to steer microbiome data successfully identifies key bacterial taxa that are related to important cattle quality measures.
Perspectives of teachers and students towards COVID-19-forced online examinations in higher education institutions

Purpose With the current COVID-19 outbreak, the majority of the higher educational institutes (HEI) are conducting online examinations to assess their students, where both teachers and students are facing various constraints. Under these circumstances, it is very important to understand the perspectives of teachers and students towards online examinations in higher education institutions and the factors affecting their perspectives. Design/methodology/approach This study has been conducted with the mentioned objective through online survey responses and rigorous statistical analysis of the data. The data analysis has been performed and presented using self-explanatory bar graphs, partial least squares structural equation modeling (PLS-SEM) and sentiment analysis. Findings While the students responded diversely for items in one of the constructs: scope for malpractice, the teachers agreed with the items, indicating that the online exams are not sufficiently effective in dealing with cheating and malpractice. Few more issues related to Internet connections, short time duration for uploading answer scripts are identified as problems and need attention while framing the rules to conduct the online examinations in future. Originality/value A new questionnaire was drafted to measure the effect of different constructs. An attempt is made to understand the perspectives of both the teachers and the students towards online examinations with respect to each of these considered constructs with a rigorous statistical analysis.
Multiview nonlinear discriminant structure learning for emotion recognition

Multiview subspace learning (MSL) has been widely used in various practical applications including emotion recognition. Despite the recent progress in MSL, two challenges remain to address. First, most existing MSL methods indiscriminately utilize both helpful and defective information contained in different views. Second, the most recent methods are linear approaches that do not perform well on emotion datasets with weak linear separability. Therefore, in this study, we introduce a framework for emotion recognition: multiview nonlinear discriminant structure learning (MNDSL). MNDSL fully exploits useful information in each input through local information preservation and discriminant reconstruction (LPDR) and obtains latent subspaces using multiview discriminant latent subspace learning (MDLSL). In addition, an out-of-sample extension was introduced to satisfy the requirements of large-scale applications and obtain the projections of new samples. The proposed framework constructs interviews and intra-view-weighted connections to explore discriminant structures and preserve locality under complementarity and correlation principles. The results demonstrate the superiority of the proposed framework compared with state-of-the-art methods.
Concurrent kernel execution and interference analysis on GPUs using deep learning approaches

Graphic processing units (GPUs) are becoming gradually popular in large-scale data centers and cloud computing. Sharing a GPU across different applications is an important feature in these platforms to improve resource utilization and system productivity. However, in situations where GPUs are competitively shared, some challenges arise. In this paper, concurrent kernel execution and interference detection are investigated using Extreme Gradient Boosting, Convolutional Neural Network, Gated Recurrent Unit, and Conditional Generative Adversarial Network learning techniques on Tesla P100 and RTX-2080 GPU architectures. The experimental results obtained by applying the deep learning models on the dataset, which consists of four GPU resources: blocks per grid, threads per block, number of registers, and shared memory, show that the Extreme Gradient Boosting and Multi-Channel Convolutional Neural Network models have the promising capability of concurrent kernel execution classification and interference detection. In comparison to the existing work, the proposed models investigated using real and synthesized datasets show clear outperformance and generalization capability in terms of recall and precision.
A Joint Detection Method of Groundwater-Level Change Based on Radial Basis Function Method with Multi-Social Networks

Groundwater is one of the important water sources for production and life, and the detection of groundwater level change is important to avoid over-exploitation of resources and ground collapse. Groundwater is the main source of water for industrial and agricultural development, and groundwater has many characteristics, such as excellent water quality, strong sustainability, uniform distribution, stable water supply, etc. Groundwater is extremely widely used in some areas of China and has become the only water source. On the other hand, the means of monitoring groundwater resources should be constantly increased, and the data should be analyzed in a timely manner to minimize the losses caused by excessive groundwater mining and to ensure the reasonable use of groundwater resources.
Characterizing "permanently dead" links on Wikipedia

It is common for a web page to include links which help visitors discover related pages on other sites. When a link ceases to work (e.g., because the page that it is pointing to either no longer exists or has been moved), users could rely on an archived copy of the linked page. However, due to the incompleteness of web archives, a sizeable fraction of dead links have no archived copies.
Maximum Decentral Projection Margin Classifier for High Dimension and Low Sample Size problems

Compared with relatively easy feature creation or generation in data analysis, manual data labeling needs a lot of time and effort in most cases. Even if automated data labeling​ seems to make it better in some cases, the labeling results still need to be checked and verified by manual. The High Dimension and Low Sample Size (HDLSS) data are therefore very common in data mining and machine learning. For classification problems with the HDLSS data, due to data piling and approximate equidistance between any two input points in high-dimension space, some traditional classifiers often give poor predictive performance. In this paper, we propose a Maximum Decentral Projection Margin Classifier (MDPMC) in the framework of a Support Vector Classifier (SVC). In the MDPMC model, the constraints of maximizing the projection distance between decentralized input points and their supporting hyperplane are integrated into the SVC model in addition to maximizing the margin of two supporting hyperplanes. On ten real HDLSS datasets, the experiment results show that the proposed MDPMC approach can deal well with data piling and approximate equidistance problems. Compared with SVC with Linear Kernel (SVC-LK) and Radial Basis Function Kernel (SVC-RBFK), Distance Weighted Discrimination (DWD), weighted DWD (wDWD), Distance-Weighted Support Vector Machine (DWSVM), Population-Guided Large Margin Classifier (PGLMC), and Data Maximum Dispersion Classifier (DMDC), MDPMC obtains better predictive accuracy and lower classification errors than the other seven classifiers on the HDLSS data.
Overcoming illiteracy through game-based learning in refugee camps and urban slums

Today 3.7 million refugee children are out of school. The ones forcibly displaced across borders are likely to remain there much of their childhoods and go through an entire school cycle in exile. Without access to quality education, these children have diminished likelihood of breaking free from circular dependency, scarcity, and marginalization. At the same time education sectors globally are adapting to the inevitable increase of digital learning. This study was motivated by the potential availability of digital education, and it argues for non-formal digital game-based learning in refugee and low-resource environments, with a special focus on early literacy. The participants (N = 359) consisted of marginalized, most vulnerable, and out-of-school children aged between 5 and 8. They participated in interventions in Pakistan and Bangladesh for 90 days, two hours a day. The children played digital learning games at their own pace following the learning goals of their national curriculums. The learning outcomes were measured using the EGRA framework. The study found that the intervention children achieved or surpassed the learning gains of a control group studying through formal education. These results suggest that digital learning games show promise for improving early grade literacy, even in low-resource contexts.
Deep E-Learning RecommendNet: An Acute E-Learning Recommendation System with Meta-Heuristic-Based Hybrid Deep Learning Architecture

Information and communication technology requires an adaptive learning concept for improving the outcomes of students based on their preferences. E-learning recommendation system with hybrid deep learning is developed for providing best recommending reviews for right users. Here, the different reviews from various E-learning platforms were taken as the input text data. These data are given into the preprocessing by performing tokenization, stemming and stop words removal to obtain the preprocessed text data. Then, the term frequency-inverse document frequency (TF-IDF) and glove embedding techniques are utilized for extracting text features. Further, the weighted feature selection is performed, in which the weight optimization and the selection of optimal features are done using the adaptive cat and mouse based optimizer (ACMO) strategy. The weighted features are further involved for the recommendation stage by developing a hybrid deep learning network named DELRNet that includes the combination of recurrent neural network and autoencoder for recommending the best E-learning platform for the users. The parameters in deep architectures are tuned with help of ACMO strategy to improve the effectiveness of the recommendation model. The experiments are carried out on the developed E-learning recommendation system to elevate the efficiency by comparing with other comparative approaches using different measures.