Reviewerly/exHarmony huggingface dataset:
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency

The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders, including the system engineers, the system’s operators and the individuals whose case is being decided. While a variety of interpretability and explainability methods is available, none of them is a panacea that can satisfy all diverse expectations and competing objectives that might be required by the parties involved. We address this challenge in this paper by discussing the promises of Interactive Machine Learning for improved transparency of black-box systems using the example of contrastive explanations—a state-of-the-art approach to Interpretable Machine Learning. Specifically, we show how to personalise counterfactual explanations by interactively adjusting their conditional statements and extract additional explanations by asking follow-up “What if?” questions. Our experience in building, deploying and presenting this type of system allowed us to list desired properties as well as potential limitations, which can be used to guide the development of interactive explainers. While customising the medium of interaction, i.e., the user interface comprising of various communication channels, may give an impression of personalisation, we argue that adjusting the explanation itself and its content is more important. To this end, properties such as breadth, scope, context, purpose and target of the explanation have to be considered, in addition to explicitly informing the explainee about its limitations and caveats. Furthermore, we discuss the challenges of mirroring the explainee’s mental model, which is the main building block of intelligible human–machine interactions. We also deliberate on the risks of allowing the explainee to freely manipulate the explanations and thereby extracting information about the underlying predictive model, which might be leveraged by malicious actors to steal or game the model. Finally, building an end-to-end interactive explainability system is a challenging engineering task; unless the main goal is its deployment, we recommend “Wizard of Oz” studies as a proxy for testing and evaluating standalone interactive explainability algorithms.
Collaborative weighted multi-view feature extraction

Most of the current multi-view feature extraction methods mainly consider the consistency and complementary information between multi-view samples, therefore have some drawbacks. They ignore the manifold structure of the single-view itself, and also ignore the differences among the similarities between any two views when the number of views is greater than two, because of assigning the same weight to them. In this paper, we propose a novel multi-view feature extraction method termed as collaborative weighted multi-view feature extraction or CWMvFE. Here the local collaborative representative (LCR) method is utilized to preserve the local correlation in between-view and within-view respectively. Furthermore, it realizes that less similar view pairs should share more consistency and complementary information, where Jensen Shannon divergence is used to reflect the similarity between different view pairs. Therefore, the proposed CWMvFE not only preserves the local correlation in multi-view, including local correlation in both between-view and within-view, but also explores the differences in similarities between different view pairs. Experiments on four image datasets demonstrate that CWMvFE has better performance than other related methods.
Directional dense‐trajectory‐based patterns for dynamic texture recognition

Representation of dynamic textures (DTs), well‐known as a sequence of moving textures, is a challenging problem in video analysis due to the disorientation of motion features. Analysing DTs to make them ‘understandable’ plays an important role in different applications of computer vision. In this study, an efficient approach for DT description is proposed by addressing the following novel concepts. First, the beneficial properties of dense trajectories are exploited for the first time to efficiently describe DTs instead of the whole video. Second, two substantial extensions of local vector pattern operator are introduced to form a completed model which is based on complemented components to enhance its performance in encoding directional features of motion points in a trajectory. Finally, the authors present a new framework, called directional dense trajectory patterns, which takes advantage of directional beams of dense trajectories along with spatio‐temporal features of their motion points in order to construct dense‐trajectory‐based descriptors with more robustness. Evaluations of DT recognition on different benchmark datasets (i.e. UCLA, DynTex, and DynTex++) have verified the interest of the authors’ proposal.
Local Culture-based Storybook and Its Effect on Reading Competence

Reading is the basic literacy as the foundation for intellectual development.Researchers found that Indonesian students have low reading competence.This is due to the fact that they do not possess a good reading habit.Hence, the study aimed at investigating the effect of local culture-based storybooks, one of which is a fable on students' reading competence and analyzing the effect size of the treatment.It was an experimental research administering post-test only control group design.The population was sixth grade students of elementary schools in Buleleng Regency, Bali.The samples were determined using intact group cluster random sampling technique of 34 students from two elementary schools.Experimental group was treated by using storybook, whereas control group was taught by using text-based story.The data were collected through post-test.The obtained data were analyzed descriptively and inferentially using t-test.Cohen's d was administered to check the effect size.The result proves that there was a significant effect of local culture-based storybook on students' reading competence, while Cohen's d shows that the effect size was medium.Thus, teachers are recommended to regularly use stories for improving reading competence.
<i>StyloThai:</i>

Authorship identification helps to identify the true author of a given anonymous document from a set of candidate authors. The applications of this task can be found in several domains, such as law enforcement agencies and information retrieval. These application domains are not limited to a specific language, community, or ethnicity. However, most of the existing solutions are designed for English, and a little attention has been paid to Thai. These existing solutions are not directly applicable to Thai due to the linguistic differences between these two languages. Moreover, the existing solution designed for Thai is unable to (i) handle outliers in the dataset, (ii) scale when the size of the candidate authors set increases, and (iii) perform well when the number of writing samples for each candidate author is low. We identify a stylometric feature space for the Thai authorship identification task. Based on our feature space, we present an authorship identification solution that uses the probabilistic k nearest neighbors classifier by transforming each document into a collection of point sets. Specifically, this document transformation allows us to (i) use set distance measures associated with an outlier handling mechanism, (ii) capture stylistic variations within a document, and (iii) produce multiple predictions for a query document. We create a new Thai authorship identification corpus containing 547 documents from 200 authors, which is significantly larger than the corpus used by the existing study (an increase of 32 folds in terms of the number of candidate authors). The experimental results show that our solution can overcome the limitations of the existing solution and outperforms all competitors with an accuracy level of 91.02%. Moreover, we investigate the effectiveness of each stylometric features category with the help of an ablation study. We found that combining all categories of the stylometric features outperforms the other combinations. Finally, we cross compare the feature spaces and classification methods of all solutions. We found that (i) our solution can scale as the number of candidate authors increases, (ii) our method outperforms all the competitors, and (iii) our feature space provides better performance than the feature space used by the existing study.
A Novel Approach for Pose Invariant Face Recognition in Surveillance Videos

The face detection and face recognition methods are introduced to confirm the abnormal human activity in the video surveillance system. Face detection is carried out by Viola-Jones face detector. It is composed of three concepts namely integral image, AdaBoost and the cascade structure. After face detection, Histogram of Oriented Gradient(HOG) and Weighted Local Binary Patterns(WLBP) features are extracted and those are used in Orthogonal Locality Preserving Projection(OLPP) for face recognition. The detected faces may contain pose variation which dramatically degrades the OLPP based face recognition. So, pose-invariant OLLP-based face recognition is proposed where Histogram of Face Orientation(HFO) and Histogram of Face Direction(HFD), HOG and WLBP features are used in OLPP for efficient face recognition.
goDASH — GO Accelerated HAS Framework for Rapid Prototyping

In this short paper, we present goDASH, an infrastructure for headless streaming of HTTP adaptive streaming (HAS) video content, implemented in the language golang, an open-source programming language supported by Google. goDASH's main functionality is the ability to stream HAS content without decoding actual video (headless player). This results in low memory requirements and the ability to run multiple players in a large-scale-based evaluation setup. goDASH comes complete with numerous state-of-the-art HAS algorithms, and is fully written in the Google golang language, which simplifies the implementation of new adaptation algorithms and functions. goDASH supports two transportation protocols Transmission Control Protocol (TCP) and Quick UDP Internet Connections (QUIC). The QUIC protocol is a relatively new protocol with the promise of performance improvement over the widely used TCP. We believe that goDASH is the first emulation-based HAS player that supports QUIC. The main limitation in using QUIC protocol is the need for a security certificate setup on both ends (client and server) as QUIC demands an encrypted connection. This limitation is eased by providing our own testbed framework, known as goDASHbed. This framework uses a virtual environment to serve video content locally (which allows setting security certificates) through the Mininet virtual emulation tool. As part of Mininet, goDASH can be used in conjunction with other traffic generators.
Generating Spatial Referring Expressions in a Social Robot: Dynamic vs. Non-ambiguous

Generating spatial referring expressions is key to allowing robots to communicate with people in an environment. The focus of most algorithms for generation is to create a non-ambiguous description, and how best to deal with the combination explosion this can create in a complex environment. However this is not how people naturally communicate. Humans tend to give an under-specified description and then rely on a strategy of repair to reduce the number of possible locations or objects until the correct one is identified, what we refer to here as a dynamic description. We present here a method for generating these dynamic descriptions for Human Robot Interaction, using machine learning to generate repair statements. We also present a study with 61 participants in a task on object placement. This task was presented in a 2D environment that favoured a non-ambiguous description. In this study we demonstrate that our dynamic method of communication can be more efficient for people to identify a location compared to one that is non-ambiguous.
Implementing AR/MR – Learning factories as protected learning space to rise the acceptance for Mixed and Augmented Reality devices in production

When talking about digitization, changes in the way of working are inevitable: The implementation of intelligent machines or dealing with real-time data lead to new tasks supported by new technology. Also digital technologies such as Augmented and Mixed Reality (AR/MR) are pushing the market and setting new standards in collaboration, prototyping or maintenance. The correct handling of AR/MR devices requires a change in the employees’ behavior; changing working routines are followed by a new skill set and a change in the culture. The acceptance of employees can therefore be regarded as a critical success factor for the implementation of such technologies. Thus, the present paper answers the research question ‘what factors influence the employee’s acceptance of AR and MR data glasses in industry’. On the basis of a comprehensive literature analysis, an implementation workshop was developed and validated in cooperation with an industrial partner. The results were transformed into a workshop within the learning and research factory ‘Smart Production Lab’ to give employees and students the opportunity to train the handling of data glasses in a protected learning space in order to increase the acceptance for the technology.
Optimization of special cryptocurrency portfolios

Purpose This paper aims to elaborate on the optimization of two particular cryptocurrency portfolios in a mean-variance framework. In general, cryptocurrencies can be classified to as coins and tokens where the first can be thought of as a medium of exchange and the latter accounts for security or utility tokens depending upon its design. Design/methodology/approach Against this backdrop, this empirical study distinguishes, in particular, between pure coin and token portfolios. Both portfolios are optimized by maximizing the Sharpe ratio and, subsequently, compared with alternative portfolio strategies. Findings The empirical findings demonstrate that the maximum utility portfolio of coins, with a risk aversion of λ = 10, outweighs alternative frameworks. The portfolios optimized by maximizing the Sharpe ratio for both coins and tokens indicate a rather poor performance. Testing the maximized utility for different levels of risk aversion confirms the findings of this empirical study and confers them more robustness. Research limitations/implications Further investigation is strongly recommended as tokens represent a new phenomenon in the cryptocurrency universe, for which only a limited amount of data are available, which restricts the sampling. Furthermore, future study is to include more sophisticated optimization models using different constraints in portfolio creation. Practical implications In light of the persistently substantial volatility in cryptocurrency markets, the empirical findings assert that portfolio managers are advised to construct a global minimum variance portfolio. In the absence of sophisticated optimization models, private investors can invest according to the market values of cryptocurrencies. Despite minor differences in the risk and reward ratios of the portfolios tested, tokens tend to be more speculative, especially, if the Tether token is excluded, which may require enhanced supervision and investor protection by regulating authorities. Originality/value As the current literature investigates on diversification effects of blended cryptocurrency portfolios rather than making an explicit distinction, this paper reflects one of the first to explore the investability and role of diversifying coins and tokens using a classic Markowitz approach.
An Adaptive Dual Prediction Scheme Based on Edge Intelligence

Content-based sensor search is a core application in the Internet of Things (IoT), where target sensors can be quickly found by predicting the current output of the sensors. Due to the lack of update mechanism, the established prediction model in existing search architectures will gradually become unavailable in the highly dynamic IoT environment. Hence, a dual prediction structure based on edge intelligence is proposed in this article, to maintain the performance of the prediction model with minimum communication cost in long-term prediction. To implement our architecture, a more effective online learning algorithm is proposed to update prediction models online combined with our proposed adaptive window pattern clustering (AWPC) algorithm. Meanwhile, based on the dual prediction scheme (DPS), a mechanism is deployed on edge sensors to achieve transfer decision making in our architecture, where edge computing is performed to achieve selectively reporting data. With the designed architecture, about 76.56% of the communication energy consumption could be saved while achieving a 95.47% average prediction accuracy in continuous long-term prediction.
COVID-19 and Contact Tracing Apps: Technological Fix or Social Experiment?

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Multi-Oriented Text Detection in Natural Scene Images Based on the Intersection of MSER With the Locally Binarized Image

The problem of text extraction is an interesting area of research in computer vision domain. In the recent years, emergence of various applications on smart hand-held devices such as translation of text from one language to another in real time, computerized aid for visually impaired, user navigation & traffic monitoring and driving assistance systems, has stimulated the renewed research interest in this domain. Retrieving text directly from natural scene images or videos is a challenging task due to variant patterns and orientations of scene text. Although various research investigations are available on horizontal oriented text detection in natural scene images, a little number of studies exist on text detection of multiple orientations. In this article, a robust method has been proposed for scene text detection having multiple orientations. The text in the images are horizontally, non-horizontally and curve oriented. The proposed method contains two main stages—maximally stable extremal region (MSER) computation and stroke width transformation (SWT). MSERs have been used to detect maximally stable extremal regions in the images. It is followed by implementing Canny edge detector for enhancement of edges in the images. To remove the non-text regions in the images, the combination of SWT image and geometric information has been used. The performance of the proposed method has been assessed on the IITR text datasets and it produces very encouraging results.
A Topic Information Fusion and Semantic Relevance for Text Summarization

With the continuous development of deep learning, pre-trained models have achieved sound effects in the field of natural language processing. However, text summarization research is far from what people want, especially in abstractive summarization. A high-quality summarization system needs to focus on the topic content of the document and the similarity between the summary and the source document. In this paper, we propose a topic information fusion and semantic relevance for text summarization based on Fine-tuning BERT(TIF-SR). Primarily, considering the critical role of topic information in summary generation, we extract topic keywords and fusion them with source documents as part of the input. Secondly, make the summary closer to the source document by calculating the semantic similarity between the generated summary and the source document, the quality of the abstract is improved. The experimental data indicate that the ROUGE index and readability have improved in this model, so these shreds of evidence suggest that the method proposed by our model is sufficient.
Comparative Analysis of Methodologies for Domain Ontology Development: A Systematic Review

Interlocking Institutional Worlds (IWs) is a concept explaining the need to interoperate between institutions (or players), to solve problems of common interest in a given domain.Managing knowledge in the IWs domain is complex, but promoting knowledge sharing based on standards and common terms agreeable to all players is essential and is something that must be established.In this sense, ontologies, as a conceptual tool and a key component of knowledge-based systems, have been used by organizations for effective knowledge management of the domain of discourse.There are many methodologies that have been proposed by several researchers during the last decade.However, designing a domain ontology for IWs needs a welldefined ontology development methodology.Therefore, in this article, a survey has been conducted to compare ontology development methodologies between 2015 and 2020.The purpose of this survey is to identify limitations and benefits of previously developed ontology development methodologies.The criteria for the comparison of methodologies has been derived from evolving trends in literature.Our findings give some guidelines that help to define a suitable methodology for designing any domain ontology under the domain of interlocking institutional worlds.
Fluent Response Generation for Conversational Question Answering

Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA (ConvQA) subtask.One notable limitation of recent ConvQA efforts is the response being answer span extraction from the target corpus, thus ignoring the natural language generation (NLG) aspect of high-quality conversational agents.In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness.From a technical perspective, we use data augmentation to generate training data for an end-to-end system.Specifically, we develop Syntactic Transformations (STs) to produce question-specific candidate answer responses and rank them using a BERT-based classifier (Devlin et al., 2019).Human evaluation on SQuAD 2.0 data (Rajpurkar et al., 2018) demonstrate that the proposed model outperforms baseline CoQA and QuAC models in generating conversational responses.We further show our model's scalability by conducting tests on the CoQA dataset. 1