Reviewerly/exHarmony huggingface dataset:
Causality in the Social Sciences: a structural modelling framework

There is no unified theory of causality in the sciences and in philosophy. In this paper, we focus on a particular framework, called structural causal modelling (SCM), as one possible perspective in quantitative social science research. We explain how this methodology provides a fruitful basis for causal analysis in social research, for hypothesising, modelling, and testing explanatory mechanisms. This framework is not based on a system of equations, but on an analysis of multivariate distributions. In particular, the modelling stage is essentially distribution-free. Adopting an SCM approach means endorsing a particular view on modelling in general (the hypothetico-deductive methodology), and a specific stance on exogeneity (namely as a condition of separability of inference), on the one hand, and in interpreting marginal–conditional decompositions (namely as mechanisms), on the other hand.
Perceptual Image Enhancement by Relativistic Discriminant Learning With Cross-Scale Aggregated Representation

In this paper, we introduce an automatic method for image quality enhancement. Our method relies on a multi-level connected generator and a pair-wise relativistic discriminator. Moreover, we also incorporate auxiliary loss functions to exhibit a high-quality image enhancement. Different from other image style transfer approaches, our method has two appealing properties: 1) different scale information not only pass on the same scale but also flow from encoder to decoder freely and 2) more contextual information is provided to help the discriminator to perform pair-wise discriminative learning. The learned generative model can then automatically enhance the low-quality image into a digital single lens reflex (DSLR) quality. The extensive experiments demonstrate that the proposed framework surpasses the state-of-the-art methods with a clear margin.
Communication Cost of Single-user Gesturing Tool in Laparoscopic Surgical Training

Multi-user input over a shared display has been shown to support group process and improve performance. However, current gesturing systems for instructional collaborative tasks limit the input to experts and overlook the needs of novices in making references on a shared display. In this paper, we investigate the effects of a single-user gesturing tool on the communication between trainer and trainees in a laparoscopic surgical training. By comparing the communication structure and content between the trainings with and without the gesturing tool, we show that the communication becomes more imbalanced and the trainees become less active when using the single-user gesturing tool. Our findings highlight the needs to grant all parties the same level of access to a shared display and suggest further directions in designing a shared display for instructional collaborative tasks.
A Framework to Explain the Relation Between ICT and Development: Combining Affordances and the Capability Approach

In this paper, we suggest a framework to better explain the relation between ICT and development. The framework combines two theories: The Capability Approach and Affordances. The capability approach defines development as freedom of choice; and the affordances explains the relational aspects of people and technology. These two theories complement each other by connecting the means (technology) to the ends (development). A case study of ICT and study-circle education in rural areas of Kenya is used to illustrate the framework. Using the framework, the study revealed how actualization of affordances of internet-cafe enhanced income capabilities of marginalized people; however, at the same time, shows perception and actualization of affordances were contingent on the availability of resources, skills, socio-cultural norms, and infrastructure.
TDN: An Integrated Representation Learning Model of Knowledge Graphs

Knowledge graph (KG) is playing an important role in many artificial intelligence applications.Representation learning of KGs aims to project both entities and relations into a continuous low-dimensional space.The representation learning technique based on embedding has been used to implement the KG completion, which aims to predict potential triples (head, relation, and tail) in KG.Most current methods concentrate on learning representations based on triple information while ignoring integrating the textual knowledge and network topology of KG.This leads to ambiguous completions.To address this problem and implement more accurate KG completion, we propose a new representation learning model, TDN model, which integratedly embeds the information of triples, text descriptions, and network structure of KG in a low-dimensional vector space.The framework of TDN is defined and the methodology of implementing TDN embedding is explored.To verify the effectiveness of the proposed model, we evaluate TDN via the experiments of link prediction on the real-world datasets.The experimental results confirm the above claims and show that TDN-based embedding significantly outperforms other baselines.
Student Performance Prediction

Predicting Students performance beforehand can be very beneficial for educational institutions to improve their teaching quality.This paper proposes to predict students performance by considering their academic details.For classification, decision tree, Random forest, Support vector machine, Linear regression, Naive Bayes are used.This paper also focuses on the use of Artificial Neural network algorithms such as MLP and Convolutional Neural network for predicting the performance.
Spatiotemporal-Aware Region Recommendation with Deep Metric Learning

Personalized points of interests (POI) recommendation is an important basis for location-based services. A typical application scenario is to recommend a region with reliable POIs to a user when he/she travels to an unfamiliar area without any background knowledge. In this study, we explore spatiotemporal-aware region recommendation to manage this learning task. We propose a unified deep learning model that comprehensively incorporates dynamic personal and global user preferences across regions, along with spatiotemporal dependencies, into check-in region history. We model and fuse user preferences through a pyramidal ConvLSTM component, and capture the dynamic region attributes through a recurrent component. Two components are seamlessly assembled in a unified framework to yield next time region recommendation. Extensive experiments on real-word datasets demonstrate the effectiveness of the proposed model.
Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition

Connectionist Temporal Classification (CTC) based end-to-end speech recognition system usually need to incorporate an external language model by using WFST-based decoding in order to achieve promising results. This is more essential to Mandarin speech recognition since it owns a special phenomenon, namely homophone, which causes a lot of substitution errors. The linguistic information introduced by language model will help to distinguish these substitution errors. In this work, we propose a transformer based spelling correction model to automatically correct errors especially the substitution errors made by CTC-based Mandarin speech recognition system. Specifically, we investigate using the recognition results generated by CTC-based systems as input and the ground-truth transcriptions as output to train a transformer with encoder-decoder architecture, which is much similar to machine translation. Results in a 20,000 hours Mandarin speech recognition task show that the proposed spelling correction model can achieve a CER of 3.41%, which results in 22.9% and 53.2% relative improvement compared to the baseline CTC-based systems decoded with and without language model respectively.
An efficient estimation of distribution algorithm with rank-one modification and population reduction

As a type of model-based metaheuristic, estimation of distribution algorithms (EDAs) show certain advantages over other metaheuristics by using statistical learning method to estimate the distribution of promising solutions. However, the commonly-used Gaussian EDAs (GEDAs) usually suffer from premature convergence that severely limits their efficiency. In this paper, we first attempt to enhance the performance of GEDA by improving its model estimation method. The new estimation method shifts the weighted mean of high-quality solutions towards the fitness improvement direction and estimates the covariance matrix by taking the shifted mean as the center. Theoretical analyses show that the new covariance matrix is essentially a rank-one modification (R1M) of the original one. It could effectively adjust both the search scope and the search direction of GEDA, and thus improving the search efficiency. Furthermore, considering the importance of the population size tuning in GEDA, we develop a population reduction (PR) strategy which linearly reduces the population size throughout the evolution. By this means, the exploration and exploitation ability of GEDA could be balanced better in different search stages and a more proper utilization of limited computation resource can be achieved. Combining GEDA with the R1M and PR strategies, a novel EDA variant named EDA-R1M-PR is developed. The performance of EDA-R1M-PR was comprehensively evaluated and compared with that of several state-of-the-art evolutionary algorithms. Experimental results indicate that the R1M and PR strategies effectively enhance the global optimization ability of GEDA and the resultant EDA-R1M-PR significantly outperforms its competitors on a set of benchmark functions.
Distorted Image Reconstruction Method with Trimmed Median

The distorted image reconstruction is an interesting problem that has many applications in practice. The distorted images are usually corrupted by scratches, dust, noise, human actions and/or environment factors. In this paper, we propose a distorted image reconstruction method based on the trimmed median. This method is effective for images corrupted by scratches caused by human, environment etc. In the experiment, we generate masks to simulate the scratches and apply them over the reference original images to make the distorted images. This way is necessary to assess the image quality after recovering by the peak signal-to-noise ratio and the structure similarity metrics. We also compare the proposed method with the harmonic method.
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization

Pairwise similarities and dissimilarities between data points might be easier to obtain than fully labeled data in real-world classification problems, e.g., in privacy-aware situations. To handle such pairwise information, an empirical risk minimization approach has been proposed, giving an unbiased estimator of the classification risk that can be computed only from pairwise similarities and unlabeled data. However, this direction cannot handle pairwise dissimilarities so far. On the other hand, semi-supervised clustering is one of the methods which can use both similarities and dissimilarities. Nevertheless, they typically require strong geometrical assumptions on the data distribution such as the manifold assumption, which may deteriorate the performance. In this paper, we derive an unbiased risk estimator which can handle all of similarities/dissimilarities and unlabeled data. We theoretically establish estimation error bounds and experimentally demonstrate the practical usefulness of our empirical risk minimization method.
Detection of Breast Cancer Using Digital Breast Tomosynthesis

The tumor is an unwanted tissue that develops in breast. Breast cancer may include a swelling, change in shape of the breast, rashes on the skin, and the watery-like substance coming from the nipple—American Cancer Society (Breast cancer, 2011) [1]. As the cancer spreads, there may be different symptoms like pain in breast and swelling. In conventional two-dimensional mammography, overlapping of the tissues is very considerable problem. The new methodology digital breast tomosynthesis is used for the identification of the breast masses. The input images are preprocessed using adaptive median filtering technique which reduces the impulse noise. In the next stage, image is segmented using Gaussian mixture model (GMM) where GMM is a category of the clustering algorithm. Next, the images are subjected to feature extraction. And finally, it is considered for feature classification using probabilistic neural network (PNN) classifier.
Key-and-Argument-Updatable QA-NIZKs

There are several new efficient approaches to decreasing trust in the CRS creators for NIZK proofs in the CRS model. Recently, Groth et al. (CRYPTO 2018) defined the notion of NIZK with updatable CRS (updatable NIZK) and described an updatable SNARK. We consider the same problem in the case of QA-NIZKs. We also define an important new property: we require that after updating the CRS, one should be able to update a previously generated argument to a new argument that is valid with the new CRS. We propose a general definitional framework for key-and-argument-updatable QA-NIZKs. After that, we describe a key-and-argument-updatable version of the most efficient known QA-NIZK for linear subspaces by Kiltz and Wee. Importantly, for obtaining soundness, it suffices to update a universal public key that just consists of a matrix drawn from a $$\mathrm {KerMDH}$$ -hard distribution and thus can be shared by any pairing-based application that relies on the same hardness assumption. After specializing the universal public key to the concrete language parameter, one can use the proposed key-and-argument updating algorithms to continue updating to strengthen the soundness guarantee.
Improved Continuous Authentication System with Counterfeit Protection

The article examines the possibility of improving the security of user workstations equipped with web cameras.This is achieved by implementation of continuous user authentication through face recognition.This method significantly increases a security level of the system from the threat of gaining access while the user is not at the workplace.In addition, various methods to protect the system from imitating the images of a genuine user were developed.Testing various approaches to solving the problem was performed and the reliability of the system was estimated.
Re-composing Sounds … and Other Things

In this article, based on four decades of experience of using samples in diverse ways in experimental, particularly electroacoustic compositions, the author investigates the world of what he calls ‘sample-based sound-based music’ and suggests that there is a relative lack of scholarship in this important area. The article’s contextual sections focus on briefly delineating this world of sonic creativity and placing it within today’s sampling culture as well as dealing with two political aspects of sampling, a musician’s attitude towards the reuse of sonic materials and the legality of sampled sounds, including musical passages, in the discussion of which current legislation related to sampling is challenged. Following this, a number of categories are presented in terms of the types of sampling material that is being used as well as how sample-based works are presented. The subsequent section is perhaps the most poignant in the article, namely the opening up of this form of innovative composition from a more traditional ‘artist creates work’ mode of operation to a more collaborative one which is essentially already part of most other forms of sampling culture. The objective here is to suggest that such collaborative approaches will enable sample-based sound-based music to become part of the lives of a much broader group than those currently involved with it.
Image colourisation using deep feature‐guided image retrieval

In this study, the authors aim to colourise a greyscale image using a fully automated framework which retrieves similar images from a reference database and then transfers the colour from the most similar retrieved images to perform colourisation. Inspired by the recent success of deep learning techniques in extracting semantic information from images, they first use fc7 features from AlexNet to retrieve similar images from the reference database. Top-k retrieved images are considered for colour transfer to the target greyscale image, using various pixel level features. The images which result from the previous step are given a colour enhancement with Reinhard stain normalisation. They follow a pixel-wise colour saturation based averaging technique to impart colour at pixel level. The final image is rectified using joint bilateral filtering. The resulting coloured images have a realistic appearance, similar in quality to the original coloured images. The proposed method outperforms several previous colourisation techniques, yielding superior performance both quantitatively and qualitatively. The method also enhances low-contrast images.
A hybrid deep learning network for modelling opinionated content

The ability to accurately understand opinionated content is critical for a large set of applications. Models targeting at learning from such content should overcome the inherent difficulties of the data. We propose a novel hybrid neural network embedded in a deep learning framework that can be used for sentiment classification. Our method consists of an independent set of feed forward learning models that are able to identify rich linguistic patterns through recurrent semantic trees. We evaluate our method in four sentiment classification problems that include both binary and multi-class classification tasks. Moreover, we compare our model's prediction accuracy with state-of-the-art methods. We observe that our method outperforms the alternative approaches. The strengths of the proposed approach are due to i) a novel Convolutional Neural Network which can be employed autonomously or as part of a greater framework, ii) a hybrid framework which consists of a set of independent blocks that propagates information and improve the classification task.
Continuous Architecture: Towards the Goldilocks Zone and Away from Vicious Circles

This paper identifies three improvement areas related to system design and architecture, where an organization can change to better support continuous integration and continuous delivery: “The product's architecture”, “Ways to work with system design and architecture”, and “The role of the architect”. The three improvement areas are based on a literature review, two series of interviews and a cross-company workshop with three case-study companies. Furthermore, the paper proposes three actionable strategies corresponding to the three identified improvement areas: “Systems with a modular and loosely coupled architecture”, “A balanced approach where system design and architecture is focused on the system's most important characteristics”, and “Architects shifting perspective from control to facilitation”.
The Future of Cataloging in a FOLIO Environment

“The Future of Libraries Is Open” (FOLIO) is a community-driven effort to develop an open source library services platform. This article introduces the FOLIO community, provides background on the University of Chicago’s involvement with the project, and discusses application development with an emphasis on key infrastructure features that will make FOLIO flexible enough to accommodate the uncertain future of cataloging. The article also provides insight into how the Metadata Management (MM) Special Interest Group (SIG), the FOLIO special interest group charged with defining essential bibliographic and holdings management functions, addresses current and emerging requirements in developing the infrastructure and data model to support MARC, BIBFRAME, and linked data.
Setting the boundaries: Partitive verbs in Estonian verb classifications

The paper examines a class of Estonian transitive verbs referred to as ‘partitive verbs’: verbs that appear with partitive-marked objects. This class is more heterogeneous than previously assumed. Firstly, there are verbs that cannot have total (accusative) objects. Secondly, there are verbs that can have total objects in lexically restricted combinations. Thirdly, some verbs combine freely with a boundarymarking element. Finally, there is a group of less studied verbs that normally represent unbounded situations but nevertheless allow total object marking if the context highlights the boundary of a situation or result. We clarify the conditions of partitive verbs appearing with total objects, hoping to enhance Estonian L2 instruction and lexicography. *** Piiride seadmisest: partitiivverbid eesti verbiklassifikatsioonides Artiklis vaatleme eesti keele transitiivverbide klassifikatsioonides partitiivverbideks liigitatud verbe, keskendudes nn pehmetele partitiivverbidele. Kasutusnäidetest ilmneb, et pehmete partitiivverbide näol on tegemist heterogeensema verbiklassiga, kui on eelnevate uurimuste alusel järeldatud. Täiustame seniseid klassifikatsioone eesmärgiga täpsustada verbide esitust eesti keele õppesõnaraamatuis. Eristame partitiivverbide klassi, mis tüüpjuhtudel väljendavad tulemuseta ja piiritlemata situatsioone, ent esinevad siiski ka totaalobjektiga, kui diskursuse kontekst tõstab esile situatsiooni piirid ja tulemuse.
Graph-Based Variability Modelling: Towards a Classification of Existing Formalisms

Software product line engineering is a reuse-driven paradigm for developing families of similar products from a generic product backbone with identified options. A customised product is then derived by combining the artefacts implementing the backbone with the ones implementing the chosen options. Variability analysis and representation is a central task of this paradigm: it consists in suitably defining and structuring the scope, the commonalities, and the differences between the derivable products. Several formalisms have been proposed: some are textual, such as propositional logic or constraint programming, while others are based on annotated graph representations. In this paper, we aim to survey and compare existing graph-based variability representations. Among them, conceptual structures have been used rather early and occasionally employed: this survey highlights their original position which is due to some of their properties, including canonicity and the dual view on product configurations versus their features.
Theoretical Limits of One-Shot Distributed Learning.

We consider a distributed system of $m$ machines and a server. Each machine draws $n$ i.i.d samples from an unknown distribution and sends a message of bounded length $b$ to the server. The server then collects messages from all machines and estimates a parameter that minimizes an expected loss. We investigate the impact of communication constraint, $b$, on the expected error; and derive lower bounds on the best error achievable by any algorithm. As our main result, for general values of $b$, we establish a $\tilde{\Omega}\big( (mb)^{-{1}/{\max(d,2)}} n^{-1/2} \big)$ lower bounded on the expected error, where $d$ is the dimension of the parameter space. Moreover, for constant values of $b$ and under the extra assumption $n=1$, we show that expected error remains lower bounded by a constant, even when $m$ tends to infinity.