Reviewerly/exHarmony huggingface dataset:
Integration of geophysics and remote sensing techniques in mapping zones mineralised with disseminated gold and sulphide minerals in Lolgorien, Narok County, Kenya

Even though ground geophysical surveys (especially Induced polarization and resistivity) are applied in mineral exploration, their effectiveness in identification of mineralised zones is often enhanced by integrating other mineral exploration techniques such as remote sensing and geological investigations. Integrating different techniques helps in reducing uncertainty that is often associated with mineral exploration. The methods being integrated also depend on characteristics of mineralisation and those of host rock. In this study, geophysical survey methods (induced polarization and resistivity) were integrated with remote sensing and geological methods to delineate mineralised zones in Lolgorien beyond reasonable doubt. By integrating these methods, it was found that Lolgorien’s gold and sulphide minerals (disseminated minerals) are hosted in massive quartz veins and auriferous quartz veins hosted in Banded Iron Formations. It was also found that this mineralisation was controlled by faults which mainly trend in two directions (NW-SE) and (NE-SW).&#x0D; Keywords: hydrothermal alteration, chargeability, resistivity, band ratio, lineament density
Breast Tumor Detection Via Active Contour Technique

The task of segmenting breast tumours in mammograms is very difficult, as its difficulty lies in the lack of contrast between the tumour and the surrounding breast tissue, especially when dealing with small tumours that are not clear boundaries and hidden under the tissues.Segmentation algorithms often lose the path of tumor boundaries in an attempt to determine the position of them.Active contours are used widely for segmentation as a high-level technology for boundary recognition.The main aim to create a clear contrast between the tumour and the normal breast region.In this study, two approaches to active contour are applied: snakes and level sets.The proposed methods were applied to all abnormal mammogram images taken from mini-MIAS database.The first approach showed a weakness in the segmentation of this type of image, while the other approach was able to segment all the mammogram's tumours.The Chan-Vese method was the most superior of all the active contour segmentation methods.The proposed models were tested in two ways, the first is statistical the best result was for the Chan-Vese method and it came as follows (90%,95% 98%, 97%, 97%) for Jaccard, Dice, PF-Score, Precision, and Sensitivity respectively.And the other is based on the segmented region's characteristics, Chan-Vese was able to accurately determine the location and shape of the tumor.The proposed Chan-Vese approach is appropriate in adopting computer assisted detection systems to predict tumor boundaries and locations in mammography for its reliability and superior performance over other algorithms.
Deep Style Transfer for Line Drawings

Line drawings are frequently used to illustrate ideas and concepts in digital documents and presentations. To compose a line drawing, it is common for users to retrieve multiple line drawings from the Internet and combine them as one image. However, different line drawings may have different line styles and are visually inconsistent when put together. In order that the line drawings can have consistent looks, in this paper, we make the first attempt to perform style transfer for line drawings. The key of our design lies in the fact that centerline plays a very important role in preserving line topology and extracting style features. With this finding, we propose to formulate the style transfer problem as a centerline stylization problem and solve it via a novel style-guided image-to-image translation network. Results and statistics show that our method significantly outperforms the existing methods both visually and quantitatively.
Error Detection in Large-Scale Natural Language Understanding Systems Using Transformer Models

Large-scale conversational assistants like Alexa, Siri, Cortana and Google Assistant process every utterance using multiple models for domain, intent and named entity recognition. Given the decoupled nature of model development and large traffic volumes, it is extremely difficult to identify utterances processed erroneously by such systems. We address this challenge to detect domain classification errors using offline Transformer models. We combine utterance encodings from a RoBERTa model with the Nbest hypothesis produced by the production system. We then fine-tune end-to-end in a multitask setting using a small dataset of humanannotated utterances with domain classification errors. We tested our approach for detecting misclassifications from one domain that accounts for <0.5% of the traffic in a large-scale conversational AI system. Our approach achieves an F1 score of 30% outperforming a bi- LSTM baseline by 16.9% and a standalone RoBERTa model by 4.8%. We improve this further by 2.2% to 32.2% by ensembling multiple models.
Are Rotten Apples Edible? Challenging Commonsense Inference Ability with Exceptions

Previous studies have argued that pre-trained language models encode commonsense relational knowledge (e.g. that apples are edible).However, simultaneous work has revealed that such models are often insensitive to context, even ignoring overt contextual cues such as negations.In this paper, we investigate whether masked language models (the BERT family) can move beyond naive associative biases (e.g., apple → edible) when the context warrants (e.g.ranking inedible higher when presented with the information that the apple is rotten).We introduce the WINOVENTI procedure, which adversarially exploits generic associations in masked language models to create model-specific Winograd-style entailment schemas.Using our constructed WINOVENTI challenges set of over 2, 000 schemas, we show that language models in the BERT family experience a steep drop in performance on prompts that require them to pick answers which require reasoning about context (e.g., from 89.8% to 18.4% for BERT LARGE ).We present evidence that language models exhibit different associative biases, suggesting a need for future work in developing and analyzing frameworks similar to WINOVENTI that are tuned to model-specific weaknesses.
Efficient Feature Set Developed for Acoustic Gunshot Detection in Open Space

This paper presents an efficient approach to automatic gunshot detection based on a combination of two feature sets: adapted standard sound features and hand-crafted novel features. The standard features are mel-frequency cepstral coefficients adapted for gunshot recognition in terms of uniform gamma-tone filters linearly spaced over the whole frequency range from 0 kHz to 16 kHz. The first 18 coefficients calculated from the 41 filters represent the best set of the optimized cepstral coefficients. The novel features were derived in the time domain from individual significant points of the raw waveform after amplitude normalization. Experiments were performed using single and ensemble neural networks to verify the effectiveness of the novel features for supplementing the standard features. The novelty of the work is the proposed feature combination, which allows to achieve very effective detection of gunshots from hunting weapons using 23 features and a simple neural network. In binary classification, the developed approach achieved an accuracy of 95.02 % in gunshot detection and 98.16 % in disregarding other sounds (i.e., non-gunshot).
Self-Perceived Digital Competencies in Educational Online Migration Due to COVID-19 Confinement

Objectives: The purpose of the study was to validate a measurement scale to assess self-perceived digital competencies of Mexican university students who have migrated from a mixed school-digital system to a fully digitalized educational environment because of COVID-19 confinement. The instrument was based on the European Union Digital Competence Framework. Method: 1,118 participants aged between 18 and 47 years completed the assessment. This included 677 females (60.6%) and 429 males (38.4%). Confirmatory factor analysis (CFA) was used to assess scale structure. Results: Results of the CFA showed an excellent fit to the data, C&lt;sup&gt;2&lt;/sup&gt;/&lt;em&gt;df &lt;/em&gt;= 3.27, &lt;em&gt;p&lt;/em&gt; = .01, comparative fit index (CFI) = 0.97, root-mean-square error of approximation (RMSEA) = 0.05. Cronbach’s a values for the scale and subscales ranged from .78 to .83. Three of the four subscales predicted number of assignments completed and submitted, a measure of predictive validity. Conclusions: The Digital Competence Scale for Online Migration is a valid scale for Mexican students and demonstrates predictive validity. Implication for Theory and/or Practice: The scale may be useful in planning activities for the reinforcement of digital competencies and to identify difficulties and support specific pedagogy for online teaching/learning processes.
The technology of using telegram messenger as an e-learning tool

This article objective is providing methodological guidelines on Telegram messenger using by educational process participants in the context of e-learning. The problem analysis is focused on examination of opportunities for messenger tools and technologies in interacting with users. The procedures of messenger using by teachers as part of e-learning are systematized. The trend for messenger using expansion as a tool for fast communication and monitoring task execution in e-learning is described. Methodological guidelines for teachers on using Telegram messenger for improving the educational organization efficiency in e-learning have been developed. The theoretical significance of the results lies in summarizing Russian and foreign experience in messenger гising for e-learning purposes. The necessity of using messengers in educational activity arrangement is justified. The effectiveness of their use is well-grounded.
An Efficient User-Centric Consent Management Design for Multiservices Platforms

This paper presents an efficient user-centric consent management system to access online services of the Territorial Collectivities and Public Administration (TCPA) as well as user-authorized third parties. It defines a novel PII manager that supports a set of sources obeying to different authorization and PII retrieval protocols. This contribution is motivated by the necessity to interface TCPA services with remote sources that provide Personally Identifiable Information (PII). Hence, the originality of our solution is multifold. First, the burden for enforcing the interoperability between the sources and the TCPA services collecting the PII is reduced from the point of view of the user, the administrator of the User-Relationship Management (URM) platform, and the territorial agent responsible for processing the user’s queries. Second, it defines a unified consent model supporting four types of sources. Third, it goes into details of practical implementations. Fourth, the relevance of the proposed PII manager for a relevant TCPA use case is demonstrated through a functional analysis.
A Mixture-of-Experts Model for Antonym-Synonym Discrimination

Discrimination between antonyms and synonyms is an important and challenging NLP task.Antonyms and synonyms often share the same or similar contexts and thus are hard to make a distinction.This paper proposes two underlying hypotheses and employs the mixture-of-experts framework as a solution.It works on the basis of a divide-and-conquer strategy, where a number of localized experts focus on their own domains (or subspaces) to learn their specialties, and a gating mechanism determines the space partitioning and the expert mixture.Experimental results have shown that our method achieves the state-of-the-art performance on the task.
A Research Agenda for Financial Opinion Mining

Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent financial technology (FinTech) development, some interdisciplinary researchers start to involve in the in-depth analysis of investors' opinions. In this position paper, we first define the financial opinions from both coarse-grained and fine-grained points of views, and then provide an overview of the issues already tackled. In addition to listing research issues of the existing topics, we further propose a road map of fine-grained financial opinion mining for future researches, and point out several challenges yet to explore. Moreover, we provide possible directions to deal with the proposed research issues.
Social Construction of Blockchain on Social Media: Framing Public Discourses on Twitter

Blockchain has become a hot topic in technology, finance, regulation, and the wider society in recent years. Along the way, various users and interests have shaped the technology materially and discursively. This paper investigates the debate taking place on Twitter surrounding blockchain technology to understand the nature and development of its online public discourses. We collected and analyzed a Twitter dataset containing a total of 267,512 tweets that reference blockchain by 105,734 unique users. We conducted a mixed method research study involving qualitative and quantitative approaches. The results indicate that the majority of the retweeted posts are educational and promotional in nature, while the lowest numbers of frames are critical or skeptical of the new technology. The most active users seem to be largely involved in promoting the technology including some that are human created bots. The paper employs the theory of Social Construction of Technology (SCOT) that emphasizes the way our actions and discourses shape technology. We argue that a number of active Twitter users, for a variety of motives including financial ones, are shaping the discourse about blockchain by mostly framing it as a positive development in the global market, allegedly creating a revolution in the financial sector. More importantly, the social construction of technology on Twitter does not seem to be exclusively organic, for it includes bots and online spammers who mostly tweet promotional blockchain hashtags.
Exploiting Unlabeled Data via Partial Label Assignment for Multi-Class Semi-Supervised Learning

In semi-supervised learning, one key strategy in exploiting unlabeled data is trying to estimate its pseudo-label based on current predictive model, where the unlabeled data assigned with pseudo-label is further utilized to enlarge labeled data set for model update. Nonetheless, the supervision information conveyed by pseudo-label is prone to error especially when the performance of initial predictive model is mediocre due to limited amount of labeled data. In this paper, an intermediate unlabeled data exploitation strategy is investigated via partial label assignment, i.e. a set of candidate labels other than a single pseudo-label are assigned to the unlabeled data. We only assume that the ground-truth label of unlabeled data resides in the assigned candidate label set, which is less error-prone than trying to identify the single ground-truth label via pseudo-labeling. Specifically, a multi-class classifier is induced from the partial label examples with candidate labels to facilitate model induction with labeled examples. An iterative procedure is designed to enable labeling information communication between the classifiers induced from partial label examples and labeled examples, whose classification outputs are integrated to yield the final prediction. Comparative studies against state-of-the-art approaches clearly show the effectiveness of the proposed unlabeled data exploitation strategy for multi-class semi-supervised learning.
Enhancing Chinese Word Segmentation via Pseudo Labels for Practicability

Pre-trained language models (e.g., BERT) significantly alleviate two traditional challenging problems for Chinese word segmentation (CWS): segmentation ambiguity and out-ofvocabulary (OOV) words.However, such improvements are usually achieved on traditional benchmark datasets and not close to an important goal of CWS: practicability (i.e., low complexity as a standalone task and high beneficiality to downstream tasks).To make a trade-off between traditional evaluation and practicability for CWS, we propose a semisupervised neural method via pseudo labels.The neural method consists of a teacher model and a student model, which distills knowledge from unlabeled data to the student model so as to improve both in-domain and out-ofdomain CWS.Experiments show that our proposed method can not only keep the practicability of the lightweight student model but also improve the performance of segmentation effectively.We also evaluate a range of heterogeneous neural architectures of CWS on downstream Chinese NLP tasks.Results of further experiments demonstrate that our proposed segmenter is reliable and practical as a pre-processing step of the downstream NLP tasks at the minimum cost.
John praised Mary because _he_? Implicit Causality Bias and Its Interaction with Explicit Cues in LMs

Some interpersonal verbs can implicitly attribute causality to either their subject or their object and are therefore said to carry an implicit causality (IC) bias.Through this bias, causal links can be inferred from a narrative, aiding language comprehension.We investigate whether pre-trained language models (PLMs) encode IC bias and use it at inference time.We find that to be the case, albeit to different degrees, for three distinct PLM architectures.However, causes do not always need to be implicit-when a cause is explicitly stated in a subordinate clause, an incongruent IC bias associated with the verb in the main clause leads to a delay in human processing.We hypothesize that the temporary challenge humans face in integrating the two contradicting signals, one from the lexical semantics of the verb, one from the sentence-level semantics, would be reflected in higher error rates for models on tasks dependent on causal links.The results of our study lend support to this hypothesis, suggesting that PLMs tend to prioritize lexical patterns over higher-order signals.
Alih Kode dan Campur Kode dalam Pembelajaran Bahasa Indonesia

This study aims to describe the form of code switching and code mixing used in Indonesian language learning at SMP N 01 Limpung, Batang Regency. This research is included in qualitative descriptive. Data processing that examines code switching and code mixing is thus the data in this study in the form of oral data, namely, speech code switching and code mixing that exist in Indonesian language learning. The data sources came from Indonesian language students and teachers at SMP N 01 Limpung. Data collection techniques in this study used the observation and note technique. The first step, the researcher listens to the use of language in Indonesian language learning. Furthermore, the researcher recorded the speech data including code switching and code mixing. The results showed that the speech used when learning Indonesian at SMP N 01 Limpung included: (1) code switching from Indonesian to Javanese and (2) code switching from Javanese to Indonesian. The form of code mixing that occurs in Indonesian language learning is in the form of: (1) the form of code mixing in the form of word insertion, (2) code mixing in the form of phrases
Identifying High-accuracy Regions in Traffic Camera Images to Enhance the Estimation of Road Traffic Metrics: A Quadtree-based Method

The growing number of real-time camera feeds in urban areas has made it possible to provide high-quality traffic data for effective transportation planning, operations, and management. However, deriving reliable traffic metrics from these camera feeds has been a challenge because of the limitations of current vehicle detection techniques, as well as the various camera conditions, such as height and resolution. In this work, a quadtree-based algorithm is developed to continuously partition the image extent until only regions with high detection accuracy remain. These regions are referred to as high-accuracy identification regions (HAIRs) in this paper. We demonstrate how the use of HAIRs can improve the accuracy of traffic density estimates using images from traffic cameras at different heights and resolutions in Central Ohio. Our experiments show that the proposed algorithm can be used to derive robust HAIRs in which vehicle detection accuracy is 41% higher than that in the original image extent. The use of HAIRs also significantly improves the traffic density estimation with an overall decrease of 49% in root mean squared error.
Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications

Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced dense object descriptors as a novel visual object representation for the robotics community. It is suitable for many applications including object grasping, policy learning, etc. DONs map an RGB image depicting an object into a descriptor space image, which implicitly encodes key features of an object invariant to the relative camera pose. Impressively, the self-supervised training of DONs can be applied to arbitrary objects and can be evaluated and deployed within hours. However, the training approach relies on accurate depth images and faces challenges with small, reflective objects, typical for industrial settings, when using consumer grade depth cameras. In this paper we show that given a 3D model of an object, we can generate its descriptor space image, which allows for supervised training of DONs. We rely on Laplacian Eigenmaps (LE) to embed the 3D model of an object into an optimally generated space. While our approach uses more domain knowledge, it can be efficiently applied even for smaller and reflective objects, as it does not rely on depth information. We compare the training methods on generating 6D grasps for industrial objects and show that our novel supervised training approach improves the pick-and-place performance in industry-relevant tasks.
Modeling the Impact of Corruption, Degree of Freedom to Invest and Democracy on Domestic Investment: Evidence from MENA Countries

This study examines the impact of corruption, investment freedom, and democracy on domestic investment in countries in the Middle East and North Africa. In order to achieve this, the GMM model was used to test the annual data from 2011 to 2017. The empirical results show that corruption has a negative impact on domestic investment, but the degree of investment freedom and democracy have a positive impact on domestic investment. One of the major contributions of this work is to assert the requirement to wheel more awareness to the direct link between corruption, governance, investment freedom, and domestic investment.