Reviewerly/exHarmony huggingface dataset:
Preserving Data Journalism: A Systematic Literature Review

News organisations have longstanding practices for archiving and preserving their content. The emerging practice of data journalism has led to the creation of complex new outputs, including dynamic data visualisations that rely on distributed digital infrastructures. Traditional news archiving does not yet have systems in place for preserving these outputs, which means that we risk losing this crucial part of reporting and news history. Following a systematic approach to studying the literature in this area, this paper provides a set of recommendations to address lacunae in the literature. This paper contributes to the field by (1) providing a systematic study of the literature in the fields, (2) providing a set of recommendations for the adoption of long-term preservation of dynamic data visualisations as part of the news publication workflow, and (3) identifying concrete actions that data journalists can take immediately to ensure that these visualisations are not lost.
Densities of Almost Surely Terminating Probabilistic Programs are Differentiable Almost Everywhere

We study the differential properties of higher-order statistical probabilistic programs with recursion and conditioning. Our starting point is an open problem posed by Hongseok Yang: what class of statistical probabilistic programs have densities that are differentiable almost everywhere? To formalise the problem, we consider Statistical PCF (SPCF), an extension of call-by-value PCF with real numbers, and constructs for sampling and conditioning. We give SPCF a sampling-style operational semantics a la Borgstrom et al., and study the associated weight (commonly referred to as the density) function and value function on the set of possible execution traces. Our main result is that almost-surely terminating SPCF programs, generated from a set of primitive functions (e.g. the set of analytic functions) satisfying mild closure properties, have weight and value functions that are almost-everywhere differentiable. We use a stochastic form of symbolic execution to reason about almost-everywhere differentiability. A by-product of this work is that almost-surely terminating deterministic (S)PCF programs with real parameters denote functions that are almost-everywhere differentiable. Our result is of practical interest, as almost-everywhere differentiability of the density function is required to hold for the correctness of major gradient-based inference algorithms.
Adaptive and hybrid context-aware fine-grained word sense disambiguation in topic modeling based document representation

Abstract We propose a hybrid context based topic model with an adaptive context window length for word sense disambiguation in document representation. Document representation is an essential part of various document based tasks, and word sense disambiguation is to capture the distinctions of word senses in the representation. Traditional methods mainly rely on knowledge libraries for data enrichment; however, semantics division for a word may vary in different domain-specific datasets. We aim to discover finer-grained word semantic differences, such as different entities or standpoints, and handle the disambiguation problem without data enrichment. There are two challenges for this disambiguation task: (1) dividing various senses for each polysemous word, and (2) preserving the differences between synonyms. Most of the existing models are either based on separate context clusters or integrating an auxiliary module to specify word senses. They can hardly achieve both (1) and (2) since different senses of a word are assumed to be independent and their intrinsic relationships are ignored. To solve this problem, we introduce the “Bag-of-Senses” (BoS) assumption: a document is a multiset of word senses, and the senses are generated instead of the words. The word senses are estimated by both the context in which it occurs and the contexts of its other occurrences. Besides, to handle the different scopes of the sense related context to each word occurrence, we introduce a variable to adjust the context window length adaptively. Our experiments on three standard datasets show that our proposal outperforms other state-of-the-art methods in terms of word sense estimation, topic modeling, and document classification.
RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions

In recent years, all-neural end-to-end approaches have obtained state-of-the-art results on several challenging automatic speech recognition (ASR) tasks. However, most existing works focus on building ASR models where train and test data are drawn from the same domain. This results in poor generalization characteristics on mismatched-domains: e.g., end-to-end models trained on short segments perform poorly when evaluated on longer utterances. In this work, we analyze the generalization properties of streaming and non-streaming recurrent neural network transducer (RNN-T) based end-to-end models in order to identify model components that negatively affect generalization performance. We propose two solutions: combining multiple regularization techniques during training, and using dynamic overlapping inference. On a long-form YouTube test set, when the non-streaming RNN-T model is trained with shorter segments of data, the proposed combination improves word error rate (WER) from 22.3% to 14.8%; when the streaming RNN-T model trained on short Search queries, the proposed techniques improve WER on the YouTube set from 67.0% to 25.3%. Finally, when trained on Librispeech, we find that dynamic overlapping inference improves WER on YouTube from 99.8% to 33.0%.
Multimodal Data Collection Made Easy: The EZ-MMLA Toolkit

While Multimodal Learning Analytics (MMLA) is becoming a popular methodology in the LAK community, most educational researchers still rely on traditional instruments for capturing learning processes (e.g., click-stream, log data, self-reports, qualitative observations). MMLA has the potential to complement and enrich traditional measures of learning by providing high frequency data on learners’ behavior, cognition and affects. However, there is currently no easy-to-use toolkit for recording multimodal data streams. Existing methodologies rely on the use of physical sensors and custom-written code for accessing sensor data. In this paper, we present the EZ-MMLA toolkit. This toolkit was implemented as a website that provides easy access to the latest machine learning algorithms for collecting a variety of data streams from webcams: attention (eye-tracking), physiological states (heart rate), body posture (skeletal data), hand gestures, emotions (from facial expressions and speech), and lower-level computer vision algorithms (e.g., fiducial / color tracking). This toolkit can run from any browser and does not require special hardware or programming experience. We compare this toolkit with traditional methods and describe a case study where the EZ-MMLA toolkit was used in a classroom context. We conclude by discussing other applications of this toolkit, potential limitations, and future steps.
Self-supervised graph representation learning via bootstrapping

Graph neural networks (GNNs) apply deep learning techniques to graph-structured data and have achieved promising performance in graph representation learning. However, existing GNNs rely heavily on labeled data or well-designed negative samples. To address these issues, we propose a new self-supervised graph representation method: deep graph bootstrapping (DGB). DGB consists of two neural networks: online and target networks, and the input of them are different augmented views of the initial graph. The online network is trained to predict the target network while the target network is updated with a slow-moving average of the online network, which means the online and target networks can learn from each other. As a result, the proposed DGB can learn graph representation without negative examples in an unsupervised manner. In addition, we summarize three kinds of augmentation methods for graph-structured data and apply them to the DGB. Experiments on the benchmark datasets show the DGB performs better than the current state-of-the-art methods and how the augmentation methods affect the performances.
Experimental demonstration of pyramidal neuron-like dynamics dominated by dendritic action potentials based on a VCSEL for all-optical XOR classification task

We experimentally and numerically demonstrate an approach to optically reproduce a pyramidal neuron-like dynamics dominated by dendritic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>Ca</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>+</mml:mo> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> action potentials (dCaAPs) based on a vertical-cavity surface-emitting laser (VCSEL) for the first time. The biological pyramidal neural dynamics dominated by dCaAPs indicates that the dendritic electrode evoked somatic spikes with current near threshold but failed to evoke (or evoked less) somatic spikes for higher current intensity. The emulating neuron-like dynamics is performed optically based on the injection locking, spiking dynamics, and damped oscillations in the optically injected VCSEL. In addition, the exclusive OR (XOR) classification task is examined in the VCSEL neuron equipped with the pyramidal neuron-like dynamics dominated by dCaAPs. Furthermore, a single spike or multiple periodic spikes are suggested to express the result of the XOR classification task for enhancing the processing rate or accuracy. The experimental and numerical results show that the XOR classification task is achieved successfully in the VCSEL neuron enabled to mimic the pyramidal neuron-like dynamics dominated by dCaAPs. This work reveals valuable pyramidal neuron-like dynamics in a VCSEL and offers a novel approach to solve XOR classification task with a fast and simple all-optical spiking neural network, and hence shows great potentials for future photonic spiking neural networks and photonic neuromorphic computing.
An Authenticatable (2, 3) Secret Sharing Scheme Using Meaningful Share Images Based on Hybrid Fractal Matrix

Secret image sharing (SIS) scheme is an important technology to protect secret information.It distributes the secret data into multiple shares so that the participants can obtain the embedded secret by sharing their authenticated shares.Through a simple process of these shares, the secret data can be extracted.In this paper, we propose a (2, 3) SIS scheme based on a fractal matrix.Through guidance of the proposed fractal matrix, the secret data can be distributed into three shares which are indistinguishable from their corresponding cover images.Any two of the three distinct shares can cooperate to extract the exact secret data.Moreover, we devise two authentication mechanisms to prevent from tampering.Experimental results show that our proposed scheme can provide efficient payloads with shares of good visual quality.The authentications are also effective and easy to implement.
User Engagement Prediction for Clarification in Search

Clarification is increasingly becoming a vital factor in various topics of information retrieval, such as conversational search and modern Web search engines. Prompting the user for clarification in a search session can be very beneficial to the system as the user's explicit feedback helps the system improve retrieval massively. However, it comes with a very high risk of frustrating the user in case the system fails in asking decent clarifying questions. Therefore, it is of great importance to determine when and how to ask for clarification. To this aim, in this work, we model search clarification prediction as user engagement problem. We assume that the better a clarification is, the higher user engagement with it would be. We propose a Transformer-based model to tackle the task. The comparison with competitive baselines on large-scale real-life clarification engagement data proves the effectiveness of our model. Also, we analyse the effect of all result page elements on the performance and find that, among others, the ranked list of the search engine leads to considerable improvements. Our extensive analysis of task-specific features guides future research.
Convergence of Blockchain, Autonomous Agents, and Knowledge Graph to Share Electronic Health Records

In this article, we discuss a data sharing and knowledge integration framework through autonomous agents with blockchain for implementing Electronic Health Records (EHR). This will enable us to augment existing blockchain-based EHR Systems. We discuss how major concerns in the health industry, i.e., trust, security and scalability, can be addressed by transitioning from existing models to convergence of the three technologies – blockchain, agent-based modeling, and knowledge graph in a decentralized ecosystem. Each autonomous agent is responsible for instantiating key processes, such as user authentication and authorization, smart contracts, and knowledge graph generation through data integration among the participating stakeholders in the network. We discuss a layered approach for the design of the proposed system leading to an enhanced, safer clinical decision-making system. This can pave the way toward more informed and engaged patients and citizens by delivering personalized healthcare.
Pathidea: Improving Information Retrieval-Based Bug Localization by Re-Constructing Execution Paths Using Logs

To assist developers with debugging and analyzing bug reports, researchers have proposed information retrieval-based bug localization (IRBL) approaches. IRBL approaches leverage the textual information in bug reports as queries to generate a ranked list of potential buggy files that may need further investigation. Although IRBL approaches have shown promising results, most prior research only leverages the textual information that is “visible” in bug reports, such as bug description or title. However, in addition to the textual description of the bug, developers also often attach logs in bug reports. Logs provide important information that can be used to re-construct the system execution paths when an issue happens and assist developers with debugging. In this paper, we propose an IRBL approach, Pathidea, which leverages logs in bug reports to re-construct execution paths and helps improve the results of bug localization. Pathidea uses static analysis to create a file-level call graph, and re-constructs the call paths from the reported logs. We evaluate Pathidea on eight open source systems, with a total of 1,273 bug reports that contain logs. We find that Pathidea achieves a high recall (up to 51.9 percent for Top@5). On average, Pathidea achieves an improvement that varies from 8 to 21 and 5 to 21 percent over BRTracer in terms of Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) across studied systems, respectively. Moreover, we find that the re-constructed execution paths can also complement other IRBL approaches by providing a 10 and 8 percent improvement in terms of MAP and MRR, respectively. Finally, we conduct a parameter sensitivity analysis and provide recommendations on setting the parameter values when applying Pathidea.
Leaning compact and representative features for cross-modality person re-identification

This paper pays close attention to the cross-modality visible-infrared person re-identification (VI Re-ID) task, which aims to match pedestrian samples between visible and infrared modes. In order to reduce the modality-discrepancy between samples from different cameras, most existing works usually use constraints based on Euclidean metric. Because of the Euclidean based distance metric strategy cannot effectively measure the internal angles between the embedded vectors, the existing solutions cannot learn the angularly discriminative feature embedding. Since the most important factor affecting the classification task based on embedding vector is whether there is an angularly discriminative feature space, in this paper, we present a new loss function called Enumerate Angular Triplet (EAT) loss. Also, motivated by the knowledge distillation, to narrow down the features between different modalities before feature embedding, we further present a novel Cross-Modality Knowledge Distillation (CMKD) loss. Benefit from the above two considerations, the embedded features are discriminative enough in a way to tackle modality-discrepancy problem. The experimental results on RegDB and SYSU-MM01 datasets have demonstrated that the proposed method is superior to the other most advanced methods in terms of impressive performance. Code is available at https://github.com/IVIPLab/LCCRF.
Secure Efficient Revocable Large Universe Multi-Authority Attribute-Based Encryption for Cloud-Aided IoT

With the help of cloud computing, the ubiquitous and diversified Internet of things (IoT) has greatly improved human society.Revocable multi-authority attribute-based encryption (MA-ABE) is considered a promising technique to solve the security challenges on data access control in the dynamic IoT since it can achieve dynamic access control over the encrypted data.However, on the one hand, the existing revocable large universe MA-ABE suffers the collusion attack launched by revoked users and non-revoked users.On the other hand, the user collusion avoidance revocable MA-ABE schemes do not support large attributes (or users) universe, i.e. the flexible number of attributes (or users).In this article, the author proposes an efficient revocable large universe MA-ABE based on prime order bilinear groups.The proposed scheme supports user-attribute revocation, i.e., the revoked user only loses one or more attributes, and she/he can access the data so long as her/his remaining attributes satisfy the access policy.It is static security in the random oracle model under the q-DPBDHE2 assumption.Moreover, it is secure against the collusion attack launched by revoked users and non-revoked users.Meanwhile, it meets the requirements of forward and backward security.The limited-resource users can choose outsourcing decryption to save resources.The performance analysis results indicate that it is suitable for large-scale cross-domain collaboration in the dynamic cloud-aided IoT.
Gaussian Mixture Model Clustering with Incomplete Data

Gaussian mixture model (GMM) clustering has been extensively studied due to its effectiveness and efficiency. Though demonstrating promising performance in various applications, it cannot effectively address the absent features among data, which is not uncommon in practical applications. In this article, different from existing approaches that first impute the absence and then perform GMM clustering tasks on the imputed data, we propose to integrate the imputation and GMM clustering into a unified learning procedure. Specifically, the missing data is filled by the result of GMM clustering, and the imputed data is then taken for GMM clustering. These two steps alternatively negotiate with each other to achieve optimum. By this way, the imputed data can best serve for GMM clustering. A two-step alternative algorithm with proved convergence is carefully designed to solve the resultant optimization problem. Extensive experiments have been conducted on eight UCI benchmark datasets, and the results have validated the effectiveness of the proposed algorithm.
Criteria for Determining the Genesis of Placers and Their Different Sources Based on the Morphological Features of Placer Gold

Based on the identified typomorphic features of placer gold, a set of determined morphogenetic criteria is proposed to identify the genesis of placer gold content and different sources in the platform areas, which allow more correctly selecting search methods and improving the efficiency of forecasting ore and placer gold deposits. Gold particles larger than 0.25 mm with signs of wind-worn processing indicate the formation of autochthonous aeolian placers. Gold particles with signs of wind-worn processing with a size of 0.1–0.25 mm, forming an extensive halo of dispersion, indicate the formation of allochthonous placers in Quaternary deposits. Deflationary (autochthonous) placers of native gold can be found by the halo of its distribution of toroidal and spherical hollow forms, which, of course, are the search morphogenetic criterion of aeolian placers. The presence of disc-shaped and lamellar gold particles with ridgelike edges in alluvial placers is typical for placers of heterogeneous origin, formed due to deflation of proluvial placers. The discovery of pseudo-ore gold in alluvial placers indicates the arrival of gold from intermediate gold-bearing sources of different ages and not from primary sources, which is a morphogenetic criterion for determining different sources of the placer. In modern gold placers, the presence of gold of a pseudo-ore appearance can serve as a search criterion for the discovery of gold-bearing conglomerates with high gold content. The developed method for diagnosing the genotype of placer gold by its morphological characteristics (alluvial, aeolian, pseudo-ore) can be successfully used by industrial geological organizations to search and explore ore and placer gold deposits.
RMBERT: News Recommendation via Recurrent Reasoning Memory Network over BERT

Personalized news recommendation aims to alleviate information overload and help users find news of their interests. Accurately matching candidate news and users' interests is the key to news recommendation. Most existing methods separately encode each user and news into vectors by news contents and then match the two vectors. However, a user's interest may differ in each news or each topic of one news. It's necessary to dynamically learn user and news vector and model their interaction. In this work, we present Recurrent Reasoning Memory Network over BERT (RMBERT) for news recommendation. Compared with other methods, our approach can leverage the ability of content modeling from BERT. Moreover, the recurrent reasoning memory network which performs a series of attention based reasoning steps can dynamically learn user and news vector and model their interaction in each step. As a result, our approach can better model user's interests. We conduct extensive experiments on a real-world news recommendation dataset and the results show that our approach significantly outperforms existing state-of-the-art methods.
A New Medical Image Enhancement Algorithm Based on Fractional Calculus

The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images. The captured images may present with low contrast and low visibility, which might infl... | Find, read and cite all the research you need on Tech Science Press