Reviewerly/exHarmony huggingface dataset:
Answer Quality Evaluation of Social Q&amp;A Platforms Based on Feature Filtering

At present, social Q&A platforms mainly evaluate and rank the quality of answers based on the user voting mechanism, and lack an objective answer quality evaluation mechanism. This leads to the behavior of some users can let some low-quality answers be ranked higher, which affects users' access to relevant knowledge. Based on this phenomenon, our study proposes an answer quality evaluation model based on a text feature filtering mechanism. It distinguishes high-quality answers from general answers by quantifying various text features, ranking the importance of those text features, and assigning weights to them. The experimental results from 49758 answers to the Zhihu website demonstrate that the proposed model can be more accurately filter and predict high-quality answers to users than the state-of-the-art model.
Time-Frequency Phase Retrieval for Audio—The Effect of Transform Parameters

In audio processing applications, phase retrieval (PR) is often performed from the magnitude of short-time Fourier transform (STFT) coefficients. Although PR performance has been observed to depend on the considered STFT parameters and audio data, the extent of this dependence has not been systematically evaluated yet. To address this, we studied the performance of three PR algorithms for various types of audio content and various STFT parameters such as redundancy, time-frequency ratio, and the type of window. The quality of PR was studied in terms of objective difference grade and signal-to-noise ratio of the STFT magnitude, to provide auditory- and signal-based quality assessments. Our results show that PR quality improved with increasing redundancy, with a strong relevance of the time-frequency ratio. The effect of the audio content was smaller but still observable. The effect of the window was only significant for one of the PR algorithms. Interestingly, for a good PR quality, each of the three algorithms required a different set of parameters, demonstrating the relevance of individual parameter sets for a fair comparison across PR algorithms. Based on these results, we developed guidelines for optimizing STFT parameters for a given application.
Multiple clusterings of heterogeneous information networks

Traditional clustering algorithms focus on a single clustering result; as such, they cannot explore potential diverse patterns of complex real world data. To deal with this problem, approaches that exploit meaningful alternative clusterings in data have been developed in recent years. Existing algorithms, including single view/multi-view multiple clustering methods, are designed for applications with i.i.d. data samples, and cannot handle the data samples with dependency presented in networks, especially in heterogeneous information networks (HIN). In this paper, we propose a framework (NetMCs) that can explore multiple clusterings in HIN. Specifically, NetMCs adopts a set of meta-path schemes with different semantics on HIN, and considers each meta-path scheme as a base clustering aspect. Guided by the meta-path schemes, NetMCs then introduces a variation of the skip-gram framework that can jointly optimize multiple clustering aspects, and simultaneously obtain the respective embedding representations and individual clusterings therein. To reduce redundancy between alternative clusterings, NetMCs utilizes an explicit regularization term to control the embedding diversity of the same nodes among different clustering aspects. Experiments on benchmark HIN datasets confirm the performance of NetMCs in generating multiple clusterings with high quality and diversity.
Existence of 4-GDDs with at most 50 points and 4-GDDs of types 6 3 and 9 3

In this paper we give two new refinements to the known necessary conditions for the existence of a 4-GDD and list all the feasible group types for 4-GDDs where the number of points v satisfies 31≤v≤50. For v in this range, we obtain solutions for all feasible group types when v≡0 (mod3), and solutions for all feasible types of the form 4s1tw1 except 4811101. We also obtain various other 4-GDDs. These include solutions for 4-GDDs of types 6s3t and 9s3t for all but a finite number of feasible values of s and t.
Dynamic partitioning of search patterns for approximate pattern matching using search schemes

Search schemes constitute a flexible and generic framework to describe how all approximate occurrences of a search pattern in a text can be found efficiently. We propose an algorithm for the dynamic partitioning of search patterns which can be universally applied to any kind of search scheme and demonstrate that this technique significantly reduces the search space. We present Columba, a software tool written in C++, in which a multitude of search schemes are implemented. We discuss implementation aspects such as memory interleaving of Burrows-Wheeler transform representations and the reduction of redundancy that is inherently associated with the edit distance metric. Ultimately, we demonstrate that Columba has superior performance to the state of the art. Using a single CPU core, Columba is able to retrieve all occurrences of 100,000 Illumina reads and their reverse complements within a maximum edit distance of four in the human genome in less than 3 min.
Permutation test applied to lexical reconstructions partially supports the Altaic linguistic macrofamily

In this paper, we present the results of our analysis of the 110-item basic wordlists for four reconstructed and one ancient languages, the linguistic ancestors of five language families which are hypothesized to constitute the Altaic (a.k.a. Transeurasian) macrofamily: Proto-Turkic, Proto-Mongolic, Proto-Tungusic, Middle Korean and Proto-Japonic wordlists. Protolanguage wordlists were reconstructed according to strict criteria of semantic reconstruction, based on accurate semantic glossing of forms in daughter languages. Each involved form was encoded into a bi-consonantal CC-shaped sequence using the consonant class method, after which a recently developed weighted permutation test was applied. In a typical situation, our algorithm makes a small number of type 1 errors (false positive), but the number of type 2 errors (false negative) can be substantial. Our main finding is that pairs between the Nuclear Altaic taxa - Turkic, Mongolic and Tungusic - as well as the Turkic-Japonic and Tungusic-Japonic pairs demonstrate significant p-values. In some cases, this can be attributed to either ancient contacts or genealogical relationships, but at least for the Turkic-Japonic pair, a contact scenario is unlikely owing to geographical remoteness.
Evaluation of E-Learning in Higher Education during COVID-19 Pandemic: A Case Study in University of Sulaimani

This research investigates e-learning in higher education during the global COVID-19 epidemic. This airborne virus has affected the daily lives, social interactions and education of highly populated cities. Universities have faced many challenges in this pandemic to reduce likelihood of infection in the highly dense student population; by transitioning from traditional teaching to e-learning. The University of Sulaimani, based in Kurdistan Region of Iraq suspended the majority of classes on campus to reduce rates of infection. The adaptation of using e-learning system was novel for the University of Sulaimani, due to socio-economic factors, and lack of experience. This study employed a quantitative approach, by using a self-report questionnaire for collecting primary data. The data has been collected via an online survey in the University of Sulaimani to analyse the e-learning experience, from students and teaching staff perspective during COVID-19 pandemic. The results revealed a variety of factors impacting on the e-learning experience. This included a lack of facilities ICT infrastructures, e-resources and access to the internet. Thus, the ICT infrastructure needs to be updated with e-resources and appropriate IT facilities, with support for students who do not have access to electronic equipment. In this way e-learning would be successfully deployed in higher education during any crises, such as COVID-19 pandemic.
Course Reserves during the COVID 19 Pandemic: Pivoting Print to Electronic Reserves through a Data Driven Process

Libraries worldwide were physically shut down, although remotely quite active, for some or much of 2020 in reaction to the COVID-19 virus sweeping the globe. This happened mid-semester for many academic libraries, as the pandemic hit the U.S. and transmission rates started to rise in March 2020. Yale University needed to quickly pivot a heavily physical course reserves service - print books and physical video material held at library service points, - to all-electronic formats. Using data driven methods, Yale was able to match print holdings on physical reserve with digital items held in or able to be licensed through various fulfillment methods. This article outlines that process and provides suggestions for further development of Course Reserves services in remote learning environments.
Optimal unambiguous discrimination of pure quantum states using SDP method

In the present paper, an exact analytic solution for the optimal unambiguous state discrimination(OPUSD) problem involving an arbitrary number of pure linearly independent quantum states with real and complex inner product is presented. Using semidefinite programming and Karush-Kuhn-Tucker convex optimization method, we derive an analytical formula which shows the relation between optimal solution of unambiguous state discrimination problem and an arbitrary number of pure linearly independent quantum states.
TwiCS: Lightweight Entity Mention Detection in Targeted Twitter Streams

Microblogging sites, like Twitter, continuously generate a large volume of streaming data. This streaming environment creates new challenges for two concomitant Information Extraction tasks: Entity Mention Detection (EMD) and Entity Detection (ED). The new challenges include (1) continuously evolving topics, which may deprecate model-based approaches quickly; (2) non-literary nature of posts, which makes traditional NLP techniques less effective; and (3) huge volume of streaming data, which makes computationally expensive approaches less suitable. In this paper, we propose an approach for EMD/ED whose creation is guided by the constraints specific to streaming environments from the ground up. Our system TwiCS implements this approach. TwiCS employs a computationally light two-phase process. In the first phase, it exploits simple (low computation) syntactic cues to suggest Entity Mention (EM) candidates. In the second phase, it uses occurrence mining to classify candidates according to their likelihood of being true EMs. Our experiments show that TwiCS achieves an average effectiveness improvement of 14.6%, while maintaining at least 2.64 times higher throughput, when compared to several state-of-the-art systems.
Cybersecurity in Healthcare: Comparing Cybersecurity Maturity and Experiences Across Global Healthcare Organizations

Health systems around the world are increasingly reliant on digital technology. Such reliance requires that healthcare organizations consider effective cybersecurity and digital resilience as a fundamental component of patient safety, with recent cyberattacks highlighting the risks to patients and targeted organizations. To better understand how well-prepared organizations are to meet this challenge we developed and administered a survey to examine the current cybersecurity landscape and preparedness level across global healthcare organizations. Cyber threats were a common concern for the 17 healthcare organizations who participated. The principal concerns highlighted were data security, including the manipulation or loss of electronic health records; loss of trust in the organization; and risks of service disruption. Cybersecurity maturity scoring showed that despite the majority of organizations having established cybersecurity practices, levels of awareness and education were universally poor. Policymakers should consider raising awareness and improving education/training on cybersecurity as a fundamental tenet of patient safety.Funding Statement: This work was supported by the World Innovation Summit for Health (WISH), Qatar Foundation. Infrastructure support for this research was provided by the NIHR Imperial Biomedical Research Centre (BRC).Declaration of Interests: The authors declare no conflicts of interest associated with this research.Ethics Approval Statement: The research protocol was reviewed and institutional; ethical approval was granted by the Imperial College London Joint Research Compliance Office (JRCO).
Discovering Undisclosed Paid Partnership on Social Media via Aspect-Attentive Sponsored Post Learning

The transparency issue of sponsorship disclosure in advertising posts has become a significant problem in influencer marketing. Although influencers are urged to comply with the regulations governing sponsorship disclosure, a considerable number of influencers fail to disclose sponsorship properly in paid advertisements. In this paper, we propose a learning-to-rank based model, Sponsored Post Detector (SPoD), to detect undisclosed sponsorship of social media posts by learning various aspects of the posts such as text, image, and the social relationship among influencers and brands. More precisely, we exploit image objects and contextualized information to obtain the representations of the posts and also utilize Graph Convolutional Networks (GCNs) on a network which consists of influencers, brands, and posts with embed social media attributes. We further optimize the model by conducting manifold regularization based on temporal information and mentioned brands in posts. The extensive studies and experiments are conducted on sampled real-world Instagram datasets containing 1,601,074 posts, which mention 26,910 brands, published over 6 years by 38,113 influencers. Our experimental results demonstrate that SPoD significantly outperforms the existing baseline methods in discovering sponsored posts on social media.
Multi-view content-context information bottleneck for image clustering

Image clustering is one of the most significant problems in computer vision and data mining. To mitigate the influence brought by appearance variation, many scholars attempt to cluster images with multiple features, a.k.a, multi-view image clustering. However, the majority of existing methods simply consider context information across images or content information within images, failing to combine both sides. We in this paper propose a novel auto-weighted multi-view content-context information bottleneck (AMC2IB) method for image clustering. The AMC2IB method can simultaneously utilize the content and context information for partitioning images. The “content” characterizes the intrinsic information within each image, e.g., appearance feature of color or shape; The “context” describes the close relationships among images of each view, e.g., inter-image similarity. The mechanism of maximum entropy is also introduced to automatically learn the view weight, and thus the importance of different views can be integrated for effective clustering. Additionally, to remove the extra weight regularization parameter in AMC2IB, we further propose an auto-weighted multi-view content-context information bottleneck without weight regularization (AMC2IBW) method. Afterwards, the above problems can be formulated as information loss functions by maintaining the context and content information maximally when the input images are compressed. Eventually, a new alternating iterative method is designed for the optimization of both proposed objective functions. Experimental results on five real-world multi-view image datasets show the superiority of the proposed method.
Integrating Performance and Side Factors into Embeddings for Deep Learning-Based Knowledge Tracing

In computer-aided education systems, Deep Learning-based Knowledge Tracing (DLKT) models outperform traditional models on tracing learners’ knowledge in recent years. First, we propose a new Performance Factors-based Embedding (PFE) model for DLKT by extending learner’s historical performances on exercises into the existing Rasch Model-based Embedding (RME) model. Second, we find that side factors (e.g., template and hint) are helpful to reflect the individualized difficulties of different exercises covering the same skill by analysing data. Therefore, we introduce an extensible embedding framework to synthesize skill, exercise, performance factors and side factors, dubbed BPS. BPS consists of three components: base embedding, performance embedding and side embedding which allows one or more side factors related to the difficulty of exercise to be extended in BPS. Finally, experiments on three real-world benchmark datasets show that PFE and BPS significantly improve the state-of-the-art DLKT model on predicting future learner responses.
Deep Adaptive Blending Network for 3D Magnetic Resonance Image Denoising

The visual quality of magnetic resonance images (MRIs) is crucial for clinical diagnosis and scientific research. The main source of quality degradation is the noise generated during MRI acquisition. Although denoising MRI by deep learning methods shows great superiority compared with traditional methods, the deep learning methods reported to date in the literature cannot simultaneously leverage long-range and hierarchical information, and cannot adequately utilize the similarity in 3D MRI. In this paper, we address the two issues by proposing a deep adaptive blending network (DABN) characterized by a large receptive field residual dense block and an adaptive blending method. We first propose the large receptive field residual dense block that can capture long-range information and fuse hierarchical features simultaneously. Then we propose the adaptive blending method that produces denoised pixels by adaptively filtering 3D MRI, which explicitly utilizes the similarity in 3D MRI. Residual is also considered as a compensating item after adaptive filtering. The blending adaptive filter and residual are predicted by a network consisting of several large receptive field residual dense blocks. Experimental results show that the proposed DABN outperforms state-of-the-art denoising methods in both clinical and simulated MRI data.
Single-Server Private Linear Transformation: The Joint Privacy Case

This paper introduces the problem of Private Linear Transformation (PLT) which generalizes the problems of private information retrieval and private linear computation. The PLT problem includes one or more remote server(s) storing (identical copies of) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> messages and a user who wants to compute <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> independent linear combinations of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> -subset of messages. The objective of the user is to perform the computation by downloading minimum possible amount of information from the server(s), while protecting the identities of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> messages required for the computation. In this work, we focus on the single-server setting of the PLT problem when the identities of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> messages required for the computation must be protected jointly. We consider two different models, depending on whether the coefficient matrix of the required <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> linear combinations generates a Maximum Distance Separable (MDS) code. We prove that the capacity for both models is given by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L/(K-D+L)$ </tex-math></inline-formula> , where the capacity is defined as the supremum of all achievable download rates. Our converse proofs are based on linear-algebraic and information-theoretic arguments. For each model, we also present an achievability scheme that relies on MDS codes.
A Similarity Measurement with Entropy-Based Weighting for Clustering Mixed Numerical and Categorical Datasets

Many mixed datasets with both numerical and categorical attributes have been collected in various fields, including medicine, biology, etc. Designing appropriate similarity measurements plays an important role in clustering these datasets. Many traditional measurements treat various attributes equally when measuring the similarity. However, different attributes may contribute differently as the amount of information they contained could vary a lot. In this paper, we propose a similarity measurement with entropy-based weighting for clustering mixed datasets. The numerical data are first transformed into categorical data by an automatic categorization technique. Then, an entropy-based weighting strategy is applied to denote the different importances of various attributes. We incorporate the proposed measurement into an iterative clustering algorithm, and extensive experiments show that this algorithm outperforms OCIL and K-Prototype methods with 2.13% and 4.28% improvements, respectively, in terms of accuracy on six mixed datasets from UCI.
Lessons Learned from Remote User-Centered Design with People with Dementia

Modified approaches to user-centered design have been developed to better accommodate the unique needs of people with dementia within the design process. These approaches were developed to address challenges with abstract thought and verbal communication of people with dementia, as well as bridge the gap in the everyday life experiences of designers to those of people with dementia. With these new methods to better facilitate design work with people with dementia, we are missing an understand of how to conduct such design work remotely. In this paper we present a case-study of the use of remote methods for a user-centered design process which included interviews, low-fidelity prototyping sessions, and asynchronous evaluation of prototypes with tech-savvy people with mild to moderate dementia. The main contribution includes a discussion of the benefits and challenges of using this remote method with tech-savvy people with mild to moderate dementia.