Reviewerly/exHarmony huggingface dataset:
JoinSketch: A Sketch Algorithm for Accurate and Unbiased Inner-Product Estimation

Inner-product estimation is the base of many important tasks in a variety of big data scenarios, including measuring similarity of streams in data stream processing, estimating join size in database, and analyzing cosine similarity in various applications. Sketch, as a class of probability algorithms, is promising in inner-product estimation. However, existing sketch solutions suffer from low accuracy due to their neglect of the high skewness of real data. In this paper, we design a new sketch algorithm for accurate and unbiased inner-product estimation, namely JoinSketch. To improve accuracy, JoinSketch consists of multiple components, and records items with different frequency in different components. We theoretically prove that JoinSketch is unbiased, and has lower variance compared with the well-known AGMS and Fast-AGMS sketch. The experimental results show that JoinSketch improves the accuracy by 10 times in average while maintaining a comparable speed. All code is open-sourced at Github.
DBPA: A Benchmark for Transactional Database Performance Anomalies

Anomaly diagnosis is vital to the performance of online transaction processing (OLTP) systems. In the meanwhile, machine learning techniques can reason complex relationships beyond human abilities and perform well on such problems. However, they rely on a large number of training samples for anomalies, which are in serious shortage in both industry and academia due to the difficulty of collection. The problem raises the demand of a benchmark for anomaly reproduction and data collection. In this paper, we propose DBPA, a benchmark for transactional database performance anomalies. Specifically, we identify nine common anomalies rooted in the diverse influence factors. For each anomaly, we carefully design a reproduction procedure, which consists with its root cause in real-world databases. With the reproduction procedures, users can easily generate a dataset in a new environment and extend new anomaly types. For compound anomalies, we provide a generation algorithm that allows users to generate compound anomalies data of any possible combinations with existing collected data. We also provide a large dataset of both normal and anomalous monitoring data collected from various environments, facilitating the training of machine learning models and the evaluation of new algorithms for anomaly diagnosis.
Pontus: Finding Waves in Data Streams

The bumps and dips in data streams are valuable patterns for data mining and networking scenarios such as online advertising and botnet detection. In this paper, we define the wave, a data stream pattern with a serious deviation from the stable arrival rate for a period of time. We then propose Pontus, an efficient framework for wave detection and estimation. In Pontus, a lightweight data structure is utilized for the preliminary processing of incoming packets in the data plane to take advantage of its high processing speed; then, the powerful control plane carries out computationally intensive wave detection and estimation. In particular, we propose the Multi-Stage Progressive Tracking strategy which detects waves in stages and removes any disqualified items promptly to save memory. Hash collisions are addressed by a Stage Variance Maximization technique to reduce estimation error. Moreover, we prove the theoretical error bound and establish upper bounds of false positive and false negative. Experiment results show that the software version of Pontus can achieve around 97% F1-Score even under scarce memory when baselines fail. Furthermore, the implemented prototype of Pontus based on P4 achieves 842x higher throughput than the baseline strawman solution.
How fraudsters are using deepfakes for ‘crime-as-a-service’

Joe Palmer, iProov's chief product &amp; innovation officer, explains how deepfakes are being used to defraud organisations at scale, and what companies can do to defeat these attacks.
Efficient Resistance Distance Computation: The Power of Landmark-based Approaches

Resistance distance is a fundamental metric to measure the similarity between two nodes in graphs which has been widely used in many real-world applications. In this paper, we study two problems on approximately computing resistance distance: (i) single-pair query which aims at calculating the resistance distance r(s, t) for a given pair of nodes (s, t); and (ii) single-source query which is to compute all the resistance distances r(s, u) for all nodes u in the graph with a given source node s. Existing algorithms for these two resistance distance query problems are often costly on large graphs. To efficiently solve these problems, we first establish several interesting connections among resistance distance, a new concept called v-absorbed random walk, random spanning forests, and a newly-developed v-absorbed push procedure. Based on such new connections, we propose three novel and efficient sampling-based algorithms as well as a deterministic algorithm for single-pair query; and we develop an online and two index-based approximation algorithms for single-source query. We show that the two index-based algorithms for single-source query take almost the same running time as the algorithms for single-pair query with the aid of a linear-size index. The striking feature of all our algorithms is that they are allowed to select an easy-to-hit node by random walks on the graph. Such an easy-to-hit landmark node v can make the v-absorbed random walk sampling, spanning tree sampling, as well as the v-absorbed push more efficient, thus significantly improving the performance of our algorithms. Extensive experiments on 5 real-life datasets show that our algorithms substantially outperform the state-of-the-art algorithms for two resistance distance query problems in terms of both running time and estimation errors.
Comparative network analysis as a new approach to the editorship profiling task: A case study of the Mishnah and Tosefta from Rabbinic literature

Abstract Social network analysis of characters in historical works is a popular research methodology in the study of historical literature. This article proposes using this methodology to characterize and comparatively analyze editing styles of similar historical literary works to determine whether they were edited by the same hand. To that end, the study proposes constructing a network of characters for each of the works being studied and to compute standard statistical measures for these networks, thus producing a network-based profile for the editing style of each work, which can be compared to the profiles of various other works. To determine the effectiveness of this new approach, it was tested on two similar works from the realm of Rabbinic literature—the Mishnah and the Tosefta. Our findings show that despite the abundant structural, thematic, and linguistic similarities of the works, their network-based profiles demonstrated clear differences between them with respect to various parameters, like the degree of connectivity, density, and centrality of the networks and their communities, and also with respect to the usage of different types of relationships in each network. These differences are reflected in the network features of the works, rather than in their texts, and so it would be difficult to identify them using direct stylometric analysis on the texts of the works, especially given the stylistic and thematic similarity between them. The approach presented in this article forms a basis for developing automatic classifiers to identify different editors and editing styles based on works’ network-based profiles.
Development of Artificial Intelligence Ethics Awareness Scale for Elementary School Students

인공지능 윤리교육을 효과적으로 설계하기 위해서는 학생들의 인공지능 윤리 인식 수준을 객관적으로 판단하는 것이 필요하다. 이를 위해 본 연구에서는 타당하고 신뢰할 수 있는 초등학생 인공지능 윤리 인식 척도를 개발하고자 하였다. 이를 위해 인공지능 윤리 인식을 Rest의 4구성요소를 참고해 민감성, 판단력, 동기화의 세 구성요소로 이루어진 개념이라 정의하였다. 그리고 과학기술정보통신부와 정보통신정책연구원이 제시한 인공지능 윤리원칙의 10대 핵심요건(인권보장, 프라이버시 보호, 다양성 존중, 침해금지, 공공성, 연대성, 데이터 관리, 책임성, 안전성, 투명성)을 측정 요인으로 설정하였으며, 인공지능 윤리 문제 상황을 웹툰으로 제시하고 초등학생들이 관련된 문항에 응답하는 형식의 척도를 개발하고자 하였다. 개발한 예비 검사지의 내용 타당도 검증을 위해 두 차례에 걸쳐 델파이 조사를 실시하였다. 이후 서울특별시 및 경기도 소재 초등학교 5, 6학년 학생을 대상으로 12차 검사를 실시하였다. 각 검사결과에 대하여 요인 분석 및 신뢰도 검증을 실시하여 최종 30문항을 추려 초등학생 인공지능 윤리 인식 척도를 개발하였다. 본 연구에서 개발한 척도가 초등학생의 인공지능 윤리 인식 발달 수준을 진단하는 객관적인 자료로, 또 인공지능 윤리 수업을 위한 교육 자료로 역할을 하여 교육 현장에서 인공지능 윤리 인식 함양을 위한 체계적인 교육이 이루어지도록 일조하기를 기대한다.The need for education to solve ethical problems that may arise due to AI has been recognized. In this study, we set out to develop an AI ethics awareness scale that can assess the level of AI ethics awareness of elementary school students. AI ethical awareness was defined as a concept consisting of three components: AI ethical sensitivity, judgment, and motivation by referring to Rest's four components. Furthermore the Ministry of Science and ICT proposed 10 requirements. These 10 requirements are as follows. Human rights protection, Privacy protection, Respect for diversity, Prohibition of human rights violation, Publicity, Solidarity, Data management, Accountability, Safety, and Transparency. In order to measure these requirements, a scale was created by using these requirements as measurement factors. This was done by showing elementary school students AI ethics problem scenarios through webtoons and having them respond to related questions. In order to verify the validity of the contents for the developed preliminary questions, a Delphi survey was conducted twice. The 1st and 2nd tests were conducted on 5th and 6th grade elementary school students. Factor analysis was also conducted on the test results to verify validity. Finally, 30 items were selected through reliability verification. The scale developed in this study serves as objective data to diagnose the level of AI ethics awareness, and is expected to contribute to more systematic education for the cultivation of AI ethics awareness in the field of elementary education in the future.
An adaptive asymmetric loss function for positive unlabeled learning

We introduce a new and efficient solution to the Positive and Unlabeled (PU) problem which is tailored specifically for a deep learning framework. We demonstrate the merit of this method using image classification. When only positive and unlabeled images are available for training, our custom loss function, paired with a simple linear transform of the output, results in an inductive classifier where no estimate of the class prior is required. This algorithm, known as the aaPU (Adaptive Asymmetric Positive Unlabeled) algorithm, provides near supervised classification accuracy with very low levels of labeled data on several image benchmark sets. aaPU demonstrates significant performance improvements over current state-of-the-art positive unlabeled learning algorithms.
Master Surgical Scheduling via Answer Set Programming

Abstract The problem of finding a Master Surgical Schedule (MSS) consists of scheduling different specialties to the operating rooms (ORs) of a hospital clinic. To produce a proper MSS, each specialty must be assigned to some ORs, where the number of assignments is different for each specialty and can also vary during the considered planning horizon. The problem is enriched by considering resource availability such as beds, surgical teams and nurses. Realizing a satisfying schedule is of upmost importance for a hospital clinic, since a poorly scheduled MSS may lead to unbalanced specialties availability and increase patients’ waiting list, thus negatively affecting both the administrative costs of the hospital and the patient satisfaction. In this paper, we present compact solutions based on Answer Set Programming (ASP) to the MSS problem. We tested our solutions on different scenarios: experiments show that our ASP solutions provide satisfying results in short time, also when compared to other logic-based formalisms. Finally, we describe a web application we have developed for easy usage of our solution.
Research on Multirelational Entity Modeling based on Knowledge Graph Representation Learning

Background: A research concern revolves around as to what can make the representation of entities and relationships fully integrate the structural information of the knowledge atlas to solve the entity modeling capability in complex relationships. World knowledge can be organized into a structured knowledge network by mining entity and relationship information in real texts. In order to apply the rich structured information in the knowledge map to downstream applications, it is particularly important to express and learn the knowledge map. In the knowledge atlas with expanding scale and more diversified knowledge sources, there are many types of relationships with complex types. The frequency of a single relationship in all triples is further reduced, which increases the difficulty of relational reasoning. Thus, this study aimed to improve the accuracy of relational reasoning and entity reasoning in complex relational models. Methods: For the multi-relational knowledge map, CTransR based on the TransE model and TransR model adopts the idea of piecewise linear regression to cluster the potential relationships between head and tail entities, and establishes a vector representation for each cluster separately, so that the same relationship represented by different clusters still has a certain degree of similarity. Results: The CTransR model carried out knowledge reasoning experiments in the open dataset, and achieved good performance. Conclusion: The CTransR model is highly effective and progressive for complex relationships. In this experiment, we have evaluated the model, including link prediction, triad classification, and text relationship extraction. The results show that the CTransR model has achieved significant improvement.
Designing Adaptive, Mixed-Mode HCI Research for Resilience

Abstract We describe the design of a mixed-mode study that illustrates an adaptive approach to conducting HCI research. This mixed-mode approach is resilient to circumstances created by public health crises such as the COVID-19 pandemic. We based our study around a web-based survey instrument that was used both online (N = 205) and in an HCI lab (N = 29). Both modalities offer their respective advantages and limitations but complement each other to paint a picture that could not be obtained with either modality by itself. We believe that this approach may be timely and helpful for HCI researchers who moved to online data collection during the pandemic and are looking for ways to augment that data with lab data. This study design provides a potential template for mixed-mode research that can be resilient to future societal crises.
Evaluating the Effect of Letter Case on Named Entity Recognition Performance

Letter case information can impact named entity recognition (NER) by affecting the way entities are represented in the text. For instance, if proper nouns are capitalized, NER models can utilize this information to identify named entities. Although studies have analyzed the performance drop of NER models without capitalization information, it is not clear how different letter-case scenarios affect the NER performance for different types of data and domains. In this study, we examine the impact of different letter-case features on NER and investigate their effectiveness in improving NER system performance and robustness. The analysis of the effect of different letter-case scenarios on NER performance is performed for one domain and across multiple domains. The experimental results demonstrate that capitalization errors significantly affect the NER performance in both in- and cross-domain evaluation. The case-insensitive (BERT-base-uncased) model is more robust to inconsistencies in capitalization that may occur in noisy text data, whereas the case-sensitive (BERT-base-cased) model performs better on well-written text that provides clear case information. However, when the case-sensitive model is required in the application, we propose a simple data augmentation heuristic based on a letter case that improves the model’s robustness against capitalization errors commonly observed in user-generated text. Overall, our findings suggest that changing the style of the source domain to match that of the target domain can lead to better domain adaptation for NER and that the choice of the BERT model should consider the nature of the text data being analyzed. Our code and data for reproducing this work are available at https://github.com/daotuanan/Letter-case-NER .
Less is More: A Prototypical Framework for Efficient Few-Shot Named Entity Recognition

Few-shot named entity recognition (NER) aims to leverage a small number of labeled examples to extract novel-class named entities from unstructured text. Although existing few-shot NER methods, such as ESD and DecomposedMetaNER, are effective, they are quite complex and not efficient, which makes them unsuitable for real-world applications when the prediction time is a critical factor. In this paper, we propose a simple span-based prototypical framework that follows the metric-based meta-learning paradigm and does not require time-consuming fine-tuning. In addition, the BERT encoding process in our model can be pre-computed and cached, making the final inference process even faster. Experiment results show that, compared with the state-of-the-art models, the proposed framework can achieve comparable effectiveness with much better efficiency.
Detecting Artificially Generated Academic Text: The Importance of Mimicking Human Utilization of Large Language Models

The advent of Large Language Models (LLMs) has led to a surge in Natural Language Generation (NLG), aiding humans in composing text for various tasks. However, there is a risk of these models being misused. For instance, detecting artificially generated text from original text is a concern in academia. Current research works on detection do not attempt to replicate how humans would use these models. In our work, we address this issue by leveraging data generated by mimicking how humans would use LLMs in composing academic works. Our study examines the detectability of the generated text using DetectGPT and GLTR, and we utilize state-of-the-art classification models like SciBERT, RoBERTa, DEBERTa, XLNet, and ELECTRA. Our experiments show that the generated text is difficult to detect using existing models when created using a LLM fine-tuned on the remainder of a paper. This highlights the importance of using realistic and challenging datasets in future research aimed at detecting artificially generated text.
A Hybrid Algorithm by Incorporating Neural Network and Metaheuristic Algorithms for Function Approximation and Demand Prediction Estimation

This study intends to heighten the training expression of radial basis function neural network (RbfN) via artificial neural network (ANN) and metaheuristic (MH) algorithms. Further, the self-organizing map neural network (SOMN), artificial immune system (AIS) and ant colony optimization (ACO)-based algorithms are employed to train RbfN for function approximation and demand prediction estimation. The proposed hybrid of SOMN with AIS-based and ACO-based (HSIA) algorithm incorporates the complementarity of exploitation and exploration potentialities to attain problem settle. The experimental consequences have evidenced that AIS-based and ACO-based algorithms can be integrated cleverly and propose a hybrid algorithm which attempts for receiving the optimal training expression among corresponding algorithms in this study. Additionally, method appraisal consequences for two benchmark nonlinear test functions illustrate that the proposed HSIA algorithm surpasses corresponding algorithms in accuracy of function approximation issue and the industrial computer (IC) demand prediction exercise.
A temporal–spatiotemporal domain transformation-based modeling method for nonlinear distributed parameter systems

Abstract Complex nonlinear distributed parameter systems (DPSs) exist widely in advanced industrial thermal processes. The modeling of such highly nonlinear systems is a challenge for traditional time/space-separation-based methods since they employ linear methods for the model reduction and spatiotemporal reconstruction, which may lead to an inefficient application of the nonlinear spatial structure features represented by the spatial basis functions. To overcome this problem, a novel spatiotemporal modeling framework composed of nonlinear temporal domain transformation and nonlinear spatiotemporal domain reconstruction is proposed in this paper. Firstly, local nonlinear dimension reduction based on the locally linear embedding technique is utilized to perform nonlinear temporal domain transformation of the spatiotemporal output of nonlinear DPSs. In this step, the original spatiotemporal data can be directly transformed into low-order time coefficients. Then, the extreme learning machine (ELM) method is utilized to establish a temporal model. Finally, through the spatiotemporal domain reconstruction based on the kernel-based ELM method, the prediction of the temporal dynamics obtained from the temporal model can be reconstructed back to the spatiotemporal output. The effectiveness and performance of the proposed method are demonstrated in experiments on the thermal processes of a snap curing oven and a lithium-ion battery.
In-class Collaborative Learning Environment for Middle School Children: A Usability Study

Creating effective middle school STEM curricula requires a combination of individual and collaborative learning. Prior studies showed that finding a proper balance and providing uninterrupted knowledge transmission between different learning modes can be challenging in such mixed pedagogical approaches. In this paper, we present a multi-device interactive educational platform named SimSnap to teach biology curriculum to middle school children. SimSnap facilitates interactions among touchscreen Chromebooks to perform in-class individual and group activities. We present a usability analysis study with eight middle school children where they learn about the influence of temperature on tomato plant growth. Our study demonstrated that SimSnap facilitates group discussions to complete collaborative tasks. It also creates seamless knowledge propagation between prior to current tasks to learn about more complex concepts from previous simpler activities. Middle school children gave overall high usability ratings and positive feedback on SimSnap. This study also helped to outline some design recommendations for future improvements of SimSnap.
<scp>QoS‐</scp>aware web service recommendation via exploring the users' personalized diversity preferences

Abstract With the popularity and wide adoption of SOA (service‐oriented architecture), a massive amount of Web services emerge on the Internet. It is difficult for users to find the desired services from a large number of services. Thus, service recommendation becomes an effective means to improve the efficiency of using service. Considering that the users' QoS (quality of service) preferences are often unknown or uncertain, the recent QoS‐aware service recommendation methods recommend QoS‐diversified services for users to increase the probability of fulfillment of the service list with a limited number of services on users' potential QoS preferences. However, the existing QoS‐diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the diversified preference requirements are not considered. To this end, this article proposes a service diversity adjustment algorithm, which selects more diversified services outside of the original service recommendation list to replace the services in the present recommendation list to approximate the QoS diversity preference of the active user. In this way, the probability of meeting the user's potential QoS preference requirements is improved. Comprehensive experimental results show that the proposed approach can not only provide personalized and diversified services but also ensure the overall accuracy of the recommendation results.