Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of NoSQL Document Databases: Couchbase, CouchDB, and MongoDB

NoSQL document databases emerged as an alternative to relational databases for managing large volumes of data. NoSQL document databases ensure big data storage and good query performance and are essential when the data scheme does not fit into the scheme of relational databases. They store their data in the form of documents and can handle unstructured, semi-structured, and structured data. This work evaluates the top three open-source NoSQL document databases: Couchbase, CouchDB, and MongoDB with Yahoo! Cloud Serving Benchmark (YCSB), which has become a standard for NoSQL database evaluation. The performance and scale-up of document databases are assessed using YCSB workloads with a different number of records and threads, where the runtime is measured for each database. In the experimental evaluation, we concluded that MongoDB is the database with the best runtime, except for the workload composed by scan operations. In addition, we identified CouchDB as the database with the best scale-up when varying the number of threads.
From Bitcoin to Farm Bank: An idiotic inquiry into blockchain speculation

Bitcoin’s uptake in Turkey has ranked among the highest in the world. Meanwhile, moral judgment about cryptocurrency has remained unsure about its comparison to other types of investment: what is a scam and what is a legitimate investment? Who is ‘daringly visionary’ and who is merely ‘gullible’ or ‘blinded’ by illusory promise? Displacing decidedly North American stories of Bitcoin’s origins and ‘Web3’ futures, this article considers the local appeal and sociotechnical potential of cryptocurrencies and blockchain-centric innovation. I analyze the open-endedness of such potential as well as its foreclosures. The case of Turkey shows that speculation regarding cryptocurrency not only implies wagering on price developments, but it also entails speculating about the possible, plausible or expectable futures of technological innovation. Blockchain-based innovation has elicited myths of the technological sublime and utopian sentiment, as it promises to usher in disruptive futures hardly imaginable from the vantage point of the present. Referencing Simondon’s philosophy of technology, my main question is: how do speculative technologies and discourses shape the present, along with the futures that emerge from it? Drawing on interviews with speculators in combination with a digital methods analysis of Turkish ‘crypto Twitter’, my answer highlights the asymmetries and inequalities of media ecologies as well as discursive contestations over the boundaries between the possible/impossible, realism/utopianism and common sense/idiocy. Moreover, engaging theories of utopia by Srnicek and Williams, Stengers, and Jameson, I ponder, what would a speculative engagement with seemingly ‘implausible’ or ‘impossible’ futures comprise that deserves not to be dismissed as simply gullible or as blinded by the technological sublime?
Photo forgery detection using RGB color model permutations

A detection of fake photos is a serious concern since general users can easily create with mobile apps and computer software. In this paper, a novel method that can detect fake photos accurately is proposed. RGB color model permutations are considered and non-decimated shift-invariant wavelet transform is applied. The proposed method extracts features using the Markov process and texture operator based on co-occurrence in both the spatial and frequency domains. The feature vector dimension is reduced by using an infinite feature selection algorithm and feature selection provides quality features to improve a detection accuracy and reduce a classification model training time. The experimental analysis is performed on four photo forgery datasets and demonstrated the accuracy of the proposed scheme is outstanding for both types of forgery, splicing and copy-move when compared with previous forgery detection schemes.
The Influence of Online Communication on the English Language in Real Life

Online communication is how people communicate, connect, transact to send, retrieve or receive information of any kind via the internet using digital media. The past two decades have seen a massive development in the methods and means of online communication. From texting a friend on social media to attending an online video conference for work to shopping online from the comfort of your home, online communication has revolutionized our lives in innumerable ways. The coronavirus pandemic of the year 2020 has brought online communication to the foreground. Online communication has undoubtedly become an indispensable part of our lifestyle today. The knowledge of the English language being a prerequisite for online communication, has inevitably led to the increase in outreach of the English language as a by- product of the progress of online communication. Although several modes of online communication are available in multiple languages today, this is not true for all and despite that, a basic knowledge of the English language remains compulsory. The pandemic transformed online communication from a choice to an unignorable necessity since it was the only means of communication in the time of crisis, hence forcing increasing number of individuals to learn the English language to successfully communicate online. Even though online communication has been a boosting factor for the English language, one can also not ignore its ill effects on the language. Social media has changed the way we speak and write English including the advent of an alphabet soup of acronyms, abbreviations and neologisms which has grown up around technologically mediated communication. This paper explains how online communication has aided the spread of the English language besides exploring the many ways in which the English language has been adversely affected by online communication.
On the cubic Pell equation over finite fields

AbstractThe classical Pell equation can be extended to the cubic case considering the elements of norm one in , which satisfyThe solution of the cubic Pell equation is harder than the classical case, indeed a method for solving it as Diophantine equation is still missing [3]. In this paper, we study the cubic Pell equation over finite fields, extending the results that hold for the classical one. In particular, we provide a novel method for counting the number of solutions in all possible cases depending on the value of r. Moreover, we are also able to provide a method for generating all the solutions.Mathematics Subject Classification (2010): 11D2511A07Key words: Cubic Pell equationPell equationfinite fields
Roman Research Progress and Prospects of “Artificial Intelligence + Education” Area Based on CITESPACE Knowledge Spectrum

Currently, artificial intelligence has a very important position in the field of natural sciences, and applications in the field of education are increasingly wide. With the rapid development of educational technology in all countries, artificial intelligence has increasing organic integration in education, and has obtained depth applications. In order to better promote artificial intelligent service education, the artificial intelligence is educated. This study focused on 562 in the core data of Web Of Science Database as a study sample, using Cite Space knowledge The map software analyzes the literature and knowledge maps, and found that the research hotspots of “artificial intelligence + education” at home and abroad are mainly concentrated in concepts, technical support, application areas and development direction. Through the comprehensive analysis of the four levels of concept, technology, application, development, analyze the hotspots and focus of “artificial intelligence + education” research, to provide innovative development ideas to promote the development of current “artificial intelligence + education”.
A Framework for Learning Dynamic Movement Primitives with Deep Reinforcement Learning

Dynamic movement primitives (DMPs) are a learning from demonstration (LfD) method that imitates movement primitives. Recently, reinforcement learning (RL) has been used to modify DMPs. In this regard, most methods have concentrated on improving DMPs with RL after DMPs are learned with supervised learning. In this way, there is no guarantee that the DMPs stay close to demonstrations while modified. To address this problem, in this study, a framework is proposed for learning DMPs with deep RL, which can be further used to modify DMPs. To do so, first, DMPs are modeled in the framework of deep RL. Then, after the reward function for learning is defined, they are learned with deep RL. The deep RL algorithm used in the present study is the twin-delayed deep deterministic policy gradient (TD3) algorithm. Our proposed algorithm is evaluated with various demonstrations, and a case study of a pick-and-place task is done. The results show that our method can learn all demonstrations with high accuracy.
Story point level classification by text level graph neural network

Estimating the software projects' efforts developed by agile methods is important for project managers or technical leads. It provides a summary as a first view of how many hours and developers are required to complete the tasks. There are research works on automatic predicting the software efforts, including Term Frequency - Inverse Document Frequency (TFIDF) as the traditional approach for this problem. Graph Neural Network is a new approach that has been applied in Natural Language Processing for text classification. The advantages of Graph Neural Network are based on the ability to learn information via graph data structure, which has more representations such as the relationships between words compared to approaches of vectorizing sequence of words. In this paper, we show the potential and possible challenges of Graph Neural Network text classification in story point level estimation. By the experiments, we show that the GNN Text Level Classification can achieve as high accuracy as about 80% for story points level classification, which is comparable to the traditional approach. We also analyze the GNN approach and point out several current disadvantages that the GNN approach can improve for this problem or other problems in software engineering.
Boosting Object Detection Ensembles with Error Diversity

Object detection has played a pivotal role in numerous mission-critical applications. This paper presents a focal error diversity framework, called EDI, for strengthening the robustness of object detection ensembles under benign and adversarial scenarios. We introduce an ensemble pruning method for object detection using a novel focal error diversity measure as the robustness synergy indicator. Given a base model pool, it recommends top sub-ensembles with a smaller ensemble size yet achieving equivalent or even better mAP performance than using all available object detection models as a large ensemble. This is made possible by our negative sampling methods for object detection to capture the degree of negative correlations and the focal error diversity to measure the failure independence of component detection models in an ensemble. Extensive experiments on three object detection benchmark datasets validate that EDI effectively selects space-time efficient object detection ensembles with high mAP performance.
FineFormer: Fine-Grained Adaptive Object Transformer for Image Captioning

Image captioning is still a challenging task aiming at describing the contents of image by words. Current image caption methods usually assume the object relation to be important if the semantic and spatial geometric relationships between objects are close and large, but the relations meeting this assumption are not necessarily important to describe the contents of image in a fine-grained way. That is, the importance of fine-grained object relations is not properly taken into account. Besides, current Transformer based image caption models also fail to consider the importance of fine-grained objects, since they generate all the words of a sentence at one time, which cannot Figure out which objects are more important and vice versa. In this paper, we propose a novel Fine-grained Adaptive Object Transformer (FineFormer) network, which can jointly discover the importance of fine-grained objects and object relations for image captioning. Specifically, a new concept of adaptive soft-foreground attention is proposed to highlight the fine-grained objects dominating the descriptive contents. To characterize and calculate the important relations between fine-grained objects, we also propose an adaptive object relation attention to refine the object relation from the generation process of relation. As such, FineFormer can describe the contents of image more accurately, by reducing the interference of unimportant objects in the background. Extensive experiments on the highly-competitive MS-COCO dataset demonstrated the superiority of our FineFormer.
PropG—An Online Application for Learning Plant Propagation Glossary Terms

A mobile web application called PropG was developed for students to quickly access more than 270 glossary terms defined in a plant propagation textbook. The functionality and usefulness of the app was evaluated by 53 students enrolled in a semester-long online course in plant propagation. Means of pre- and post-test responses to 17 knowledge items showed students perceived a significant knowledge gain in the course for each of the subject categories evaluated. Most students agreed or strongly agreed this learning tool was organized and easy to navigate and would use it in the future. Since 2021, PropG received 153,645 total page views, 90,818 unique visits, and 17,216 returning visits, showing its widespread use in plant propagation.
Empowerment-driven Policy Gradient Learning with Counterfactual Augmentation in Recommender Systems

Deep reinforcement learning (DRL) has been proven its efficiency in capturing users’ dynamic interests in recent literature. However, training a DRL agent is challenging, because of the sparse environment in recommender systems (RS), DRL agents could spend times either exploring informative user-item interaction trajectories or using existing trajectories for policy learning. It is also known as the exploration and exploitation trade-off which affects the recommendation performance significantly when the environment is sparse. It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems. As a step to address this issue, We design a novel empowerment-driven exploration method to increase the capability of exploring informative interaction trajectories in the sparse environment, which are further enriched via a counterfactual augmentation strategy for more efficient exploitation. The extensive experiments on four offline datasets and an online simulation platform demonstrate the superiority of our model to a set of existing state-of-the-art methods.
A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation

Historical Quality-of-Service (QoS) data regarding past user-service invocations are vital to understand the user behaviors and cloud service conditions. A Matrix Factorization (MF)-based Collaborative Filtering (CF) model has proven to be highly effective in performing representation learning to such QoS data. However, its performance is hindered by its linear interaction and implicit encoding of collaborative QoS signal. To address this critical issue, this paper presents a Two-stream Light Graph Convolution Network-based latent factor (TLGCN) model with the three-fold ideas: 1) constructing a multilayered and fully-connected network to represent services’ nonlinear latent features; 2) integrating the user-service interactions, i.e., the bipartite graph structure into the representation learning process with a light graph convolution network for illustrating the high-order connectivity information in QoS data; and 3) incorporating the data density-oriented modeling mechanism into the input and output of TLGCN for high computational efficiency. Experimental results on two real QoS datasets demonstrate that the proposed TLGCN model significantly outperforms its state-of-the-art peers in both estimation accuracy for missing QoS data and computational efficiency.
Feature-Oriented Sampling for Fast and Scalable GNN Training

Recently Graph Neural Networks (GNNs) have achieved great success in many applications. To apply GNNs to large graphs, mini-batch training and sampling are widely adopted by recent works. However, existing works generate mini-batches following a topology-oriented sampling style, which first samples a subgraph and then fetches the corresponding node features to construct a mini-batch. This inevitably incurs intensive random access of graph data, the exponential growth of the batch size, and constrained candidates during sampling. In this work, we advocate adopting a feature-oriented sampling style which can overcome these drawbacks. We first sample the node features and then induce the corresponding subgraph to form a mini-batch. We apply the feature-oriented sampling method to three mainstream GNN models to demonstrate the effectiveness and efficiency of this sampling style. Experiments on four large-scale datasets show that feature-oriented sampling can achieve comparable accuracy as topology-oriented sampling while speeding up the training procedure by 2.2 ~ 7.9 times.
Personalized Federated Learning via Heterogeneous Modular Networks

Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention. Despite its popularity, it has been observed that existing PFL approaches result in sub-optimal solutions when the joint distribution among local clients diverges. To address this issue, we present Federated Modular Network (FedMN), a novel PFL approach that adaptively selects sub-modules from a module pool to assemble heterogeneous neural architectures for different clients. FedMN adopts a light-weighted routing hypernetwork to model the joint distribution on each client and produce the personalized selection of the module blocks for each client. To reduce the communication burden in existing FL, we develop an efficient way to interact between the clients and the server. We conduct extensive experiments on the real-world test beds and the results show both effectiveness and efficiency of the proposed FedMN over the baselines.
Deep Stable Representation Learning on Electronic Health Records

Deep learning models have achieved promising disease prediction performance of the Electronic Health Records (EHR) of patients. However, most models developed under the I.I.D. hypothesis fail to consider the agnostic distribution shifts, diminishing the generalization ability of deep learning models to Out-Of-Distribution (OOD) data. In this setting, spurious statistical correlations that may change in different environments will be exploited, which can cause sub-optimal performances of deep learning models. The unstable correlation between procedures and diagnoses existed in the training distribution can cause spurious correlation between historical EHR and future diagnosis. To address this problem, we propose to use a causal representation learning method called Causal Healthcare Embedding (CHE). CHE aims at eliminating the spurious statistical relationship by removing the dependencies between diagnoses and procedures. We introduce the Hilbert-Schmidt Independence Criterion (HSIC) to measure the degree of independence between the embedded diagnosis and procedure features. Based on causal view analyses, we perform the sample weighting technique to get rid of such spurious relationship for the stable learning of EHR across different environments. Moreover, our proposed CHE method can be used as a flexible plug-and-play module that can enhance existing deep learning models on EHR. Extensive experiments on two public datasets and five state-of-the-art baselines unequivocally show that CHE can improve the prediction accuracy of deep learning models on out-of-distribution data by a large margin. In addition, the interpretability study shows that CHE could successfully leverage causal structures to reflect a more reasonable contribution of historical records for predictions.
Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations

Kubernetes (K8s) defines standardized APIs for container-based cluster orchestration such that it becomes possible for application managers to deploy their applications in a portable and interopable manner. However, a practical problem arises when the same application must be replicated in a distributed fashion across different edge, fog and cloud sites; namely, there will not exist a single K8s vendor that is able to provision and manage K8s clusters across all these sites. Hence, the problem of feature incompatibility between different K8s vendors arises. A large number of documented features in the open-source distribution of K8s are optional features that are turned off by default but can be activated by setting specific combinations of parameters and plug-in components in configuration manifests for the K8s control plane and worker node agents. However, none of these configuration manifests are standardized, giving K8s vendors the freedom to hide the manifests behind a single, more restricted, and proprietary customization interface. Therefore, some optional K8s features cannot be activated consistently across K8s vendors and applications that require these features cannot be run on those vendors. In this paper, we present a unified, vendor-agnostic feature management approach for consistently configuring optional K8s features across a federation of clusters hosted by different Kubernetes vendors. We describe vendor-agnostic reconfiguration tactics that are already applied in industry and that cover a wide range of optional K8s features. Based on these tactics, we design and implement an autonomic controller for declarative feature compatibility management across a cluster federation. We found that the features configured through our vendor-agnostic approach have no impact on application performance when compared with a cluster where the features are configured using the configuration manifests of the open-source K8s distribution. Moreover, the maximum time to complete reconfiguration of a single feature is within 100 seconds, which is 6 times faster than using proprietary customization interfaces of mainstream K8s vendors such as Google Kubernetes Engine. However, there is a non-negligible disruption to running applications when performing the reconfiguration to an existing cluster; this disruption impact does not appear using the proprietary customization methods of the K8s vendors due to the use of rolling upgrade of cluster nodes. Therefore, our approach is best applied in the following three use cases: (i) when starting up new K8s clusters, (ii) when optional K8s features of existing clusters must be activated as quickly as possibly and temporary disruption to running applications can be tolerated or (iii) when proprietary customization interfaces do not allow to activate the desired optional feature.