Reviewerly/exHarmony huggingface dataset:
Camera-Radar Fusion for 3-D Depth Reconstruction

We introduce and study the problem of camera-radar fusion for 3-D depth reconstruction. This problem is motivated by autonomous driving applications, in which we can expect to have access to both front-facing camera and radar sensors. These two sensors are complementary in several respects: the camera is a passive sensor measuring azimuth and elevation; the radar is an active sensor measuring azimuth and range. Fusing their measurements is therefore beneficial. Our fusion solution uses a modified encoder-decoder deep convolutional neural network. We train and evaluate this network on over 100 000 samples collected in highway environments. Our results demonstrate an improvement in reconstruction accuracy and robustness from fusing the two sensors.
Fair Training of Decision Tree Classifiers.

We study the problem of formally verifying individual fairness of decision tree ensembles, as well as training tree models which maximize both accuracy and individual fairness. In our approach, fairness verification and fairness-aware training both rely on a notion of stability of a classification model, which is a variant of standard robustness under input perturbations used in adversarial machine learning. Our verification and training methods leverage abstract interpretation, a well established technique for static program analysis which is able to automatically infer assertions about stability properties of decision trees. By relying on a tool for adversarial training of decision trees, our fairness-aware learning method has been implemented and experimentally evaluated on the reference datasets used to assess fairness properties. The experimental results show that our approach is able to train tree models exhibiting a high degree of individual fairness w.r.t. the natural state-of-the-art CART trees and random forests. Moreover, as a by-product, these fair decision trees turn out to be significantly compact, thus enhancing the interpretability of their fairness properties.
Reducing the Uncertainty of Agile Software Development Using a Random Forest Classification Algorithm

Background: Companies operating in the software industry or those which rely on new technologies are facing a rising level of complexity in building products. To address these new circumstances, enterprises are investing more resources in modern approaches to software delivery, such as agile methodologies. Amongst these methodologies, relative effort estimation is widely adopted. Outcomes of the estimation process are often not predictable or reliable. Aims: The objective of this paper is to research the random forest classification algorithm’s effectiveness for high-level effort estimating. Method: Authors are focusing on defining complexity factors that are treated as model features. In addition, the authors have empirically tested the proposed solution in a commercial environment. Besides these, authors have analyzed the effective impact of each complexity factor. The analysis was done on the set of seventy thousands of Jira work items. Observation has been made empirically across four major releases. Results: The results indicate that the empirical way of defining model features has a significant impact on effort estimation accuracy. During research, the authors have found several key factors that have a significant impact on model accuracy. Teams that are using agile techniques or methods for effort estimation can enhance planning outcomes with tools supporting high-level estimation. Finding out and fine-tuning such tools needs a structured process for finding the most significant key complexity factors. Conclusion: Usage of metrics such as effort estimations and their accuracy in the software development process in agile organizations could lead to more accurate planning and forecasting of project outcomes. Problems with planning on program level could also be actioned with a structured estimation framework, enhanced by modern tools such as classification models. We should remember that complexity is growing with scaling delivery structures within companies.
Detecting Organic Audience Involvement on Social Media Platforms for Better Influencer Marketing and Trust-Based E-Commerce Experience

This study addresses a major problem of online advertisement. Usage of Internet and specifically social media platforms for promoting businesses and services has seen a drastic increase in the recent times. Influencer-based marketing is a major area for promotion. With its major dominance, comes its downsides. This influencer-based model alternatively being referred to as influencer marketing has a major drawback which concerns one of the foremost aspects of digital media, organic and inorganic results. Many of the social media platforms involve inorganic audience which includes fake popularity for the concerned account. This heavily impacts the advertisements business being run on social media and other platforms. This could lead to wrong audience impact involving losses for the business as well as the customers being targeted. In this study, a trust-based model is designed in which the social media platform, Instagram, is checked for fake audience involvement and their consecutive detection for better future impressions. For the detection of inorganic audience involvement, firstly, a study is done regarding the parameters that could assist the detection process. These parameters are further used to generate the nodes of a decision tree. Second of all, the dataset is prepared for the detection process. Machine learning is used to make the predictions on the dataset as with minimum human intervention, it can help automate the entire process and make it much faster than manual detection. Decision tree is then applied to the dataset for building a prediction based model, which is used to predict the occurrence of fake accounts in test dataset. This is followed by the concluding of the result in which prediction has been done. Also, the accuracy of the same has been checked in the conclusion with the help of confusion matrix which turns out to have a good accuracy, thus, successfully concluding the study.
METRIC APPROACH TO SEMANTIC-MORPHOLOGICAL IMAGE COMPARISON

In this paper we propose and consider different metric approaches to image comparison based on Morpho-Semantic (MS) and Semantic-Morphological (SM) models. The first proposed class-based approach presumes the embedding of MS and SM models to the metric space with weighted Lp metrics. This approach is based on representation of SM models as mosaic vector functions composed of semantic-morphological class expression maps. The feature description of these maps provides a global feature description of SM models by SM vectors. The second proposed class-based approach is based on resource models, which include semantic-morphological class expression maps with area recourse values. This approach implements the embedding of these mosaic class expression maps with area recourse values to the metric space with Earth Mover’s Distance (EMD) based on resource transportation between these maps. Finally, we propose the object-based approach to metric embedding of SM models inspired by Geometrical Difference Distance (GDD), which performs the comparison of mosaic image shapes via weighted pairwise comparison of their region shapes. In this way we obtain the SM Difference Distance (SMDD) and its EMD-version (SMDD). The practical applicability of proposed SM-metrics is largely determined by the strategy of feature set forming and parameter estimation scheme. The SM-metrics parameter tuning for comparison of some visual scenes/objects could be performed both as MS-modeling (interpretation) of human subjective reasoning and as MS-modeling (interpretation) of deep learning results. In both cases, SM models and SM metrics fitting could allow: making partially transparent the human or DNN reasoning in scene comparison tasks; Comparing (grouping, clustering) different experts (algorithms) in terms of different parameters settings for SM-models; performing the personalized post-training of neural network models with taking into account the individual SM-settings of concrete users, operators or experts. This will combine the effectiveness of deep learning on huge training bases with partial transparency of reasoning and the possibility of directly taking into account the wishes of users in terms of SM-models, rather than by creating the artificial training bases via artificial augmentation.
Social Media Medical Concept Normalization using RoBERTa in Ontology Enriched Text Similarity Framework

Pattisapu et al. (2020) formulate medical concept normalization (MCN) as text similarity problem and propose a model based on RoBERTa and graph embedding based target concept vectors. However, graph embedding techniques ignore valuable information available in the clinical ontology like concept description and synonyms. In this work, we enhance the model of Pattisapu et al. (2020) with two novel changes. First, we use retrofitted target concept vectors instead of graph embedding based vectors. It is the first work to leverage both concept description and synonyms to represent concepts in the form of retrofitted target concept vectors in text similarity framework based social media MCN. Second, we generate both concept and concept mention vectors with same size which eliminates the need of dense layers to project concept mention vectors into the target concept embedding space. Our model outperforms existing methods with improvements up to 3.75% on two standard datasets. Further when trained only on mapping lexicon synonyms, our model outperforms existing methods with significant improvements up to 14.61%. We attribute these significant improvements to the two novel changes introduced.
Detection of Sybil Attacks in Social Networks

The advent of the Internet revolutionized communication between people in different geographical locations. It has brought about a reduction in the turnaround time, for exchange of information between people, from days to seconds. Social networks have become an important part of life. They provide a platform for people to connect and communicate with one’s kith. As they have an important role in communication, social networks are under the threat of cyber-attacks. Sybil attacks are a form of security breach where the network is infiltrated with forged identities. Multiple duplicate identities are created by malicious users to flood the system with fake information, negatively influencing the performance of the system. These attacks make the system look unreliable. This paper builds on Asadian and Javadi’s [1] work of detecting sybil nodes in a network. They proposed using Jaccard index as a similarity measure which is then used by Louvain algorithm to divide the dataset into communities. Apart from the two usual communities in which nodes are allocated, Honest and Sybil, this paper introduces a third community, the Questionable community, where suspicious nodes are assigned. This allows the nodes to be quarantined and keep tabs on their activities without removing them from the network and risking loss of goodwill. We then explore the Honest community using L2 Norm and those found below a threshold are assigned to the Questionable community. This refines the results found in the first part of the system. The results obtained are promising and should provide favorable results in real time systems. The system presented in this paper returns a precision value of 0.98, equal to SICTF and Improved KD-Tree method [2] and returns a recall value of 0.94, which is better than other methods available.
A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysis

We present a new dataset with annotated eye movements. The dataset consists of over 800,000 gaze points recorded during a car ride in the real world and in the simulator. In total, the eye movements of 19 subjects were annotated. In this dataset there are several data sources such as the eyelid closure, the pupil center, the optical vector, and a vector into the pupil center starting from the center of the eye corners. These different data sources are analyzed and evaluated individually as well as in combination with respect to their goodness of fit for eye movement classification. These results will help developers of real-time systems and algorithms to find the best data sources for their application. Also, new algorithms can be trained and evaluated on this data set. The data and the Matlab code can be downloaded here this https URL
Learning Structural Edits via Incremental Tree Transformations

While most neural generative models generate outputs in a single pass, the human creative process is usually one of iterative building and refinement. Recent work has proposed models of editing processes, but these mostly focus on editing sequential data and/or only model a single editing pass. In this paper, we present a generic model for incremental editing of structured data (i.e. ''structural edits''). Particularly, we focus on tree-structured data, taking abstract syntax trees of computer programs as our canonical example. Our editor learns to iteratively generate tree edits (e.g. deleting or adding a subtree) and applies them to the partially edited data, thereby the entire editing process can be formulated as consecutive, incremental tree transformations. To show the unique benefits of modeling tree edits directly, we further propose a novel edit encoder for learning to represent edits, as well as an imitation learning method that allows the editor to be more robust. We evaluate our proposed editor on two source code edit datasets, where results show that, with the proposed edit encoder, our editor significantly improves accuracy over previous approaches that generate the edited program directly in one pass. Finally, we demonstrate that training our editor to imitate experts and correct its mistakes dynamically can further improve its performance.
MTNER: A Corpus for Mongolian Tourism Named Entity Recognition

Name Entity Recognition is the essential tool for machine translation. Traditional Named Entity Recognition focuses on the person, location and organization names. However, there is still a lack of data to identify travel-related named entities, especially in Mongolian. In this paper, we introduce a newly corpus for Mongolian Tourism Named Entity Recognition (MTNER), consisting of 16,000 sentences annotated with 18 entity types. We trained in-domain BERT representations with the 10 GB of unannotated Mongolian corpus, and trained a NER model based on the BERT tagging model with the newly corpus. Which achieves an overall 82.09 F1 score on Mongolian Tourism Named Entity Recognition and lead to an absolute increase of +3.54 F1 score over the traditional CRF Named Entity Recognition method.
A Decentralized Blockchain-based Architecture for a Secure Cloud-Enabled IoT

The integration of cloud computing and Internet of Things (IoT) offers a promising, rich platform for data collection and analysis in smart healthcare. In such a model, IoT devices collect data about patient health status through multiple intelligent sensors, whereas cloud offers scalable resources to quickly meet workload demands. Despite these remarkable improvements, the current architectures do not sufficiently address the security needs for patient medical records. In this perspective, and bearing in mind the specific characteristics of each technology, we propose a distributed security mechanism in a way that fits with IoT and cloud constraints. Our contribution to secure cloud-enabled IoT is twofold. First, we rely on OM-AM (Objective, Model, Architecture and Mechanism) for modeling and analysing the security and privacy requirements of smart healthcare. Second, we use blockchain architecture along with Attribute-Based Access Control (ABAC) model as a decentralized flexible system to support access control decisions. In particular, we rely on XACML (eXtensible Access Control Markup Language) to easily build and implement robust policies required for maintaining a secure IoT-based environment. The novelty of the proposed framework lies at smartly leveraging the recent technologies to keep health information confidential. In fact, putting blockchain and IoT together would undoubtedly create a totally new solution for remote patient monitoring. The simulation results show that the proposal is an efficient way of implementing ubiquitous and cognitive tools for smart healthcare systems.
Human-Paraphrased References Improve Neural Machine Translation

Automatic evaluation comparing candidate translations to human-generated paraphrases of reference translations has recently been proposed by freitag2020bleu. When used in place of original references, the paraphrased versions produce metric scores that correlate better with human judgment. This effect holds for a variety of different automatic metrics, and tends to favor natural formulations over more literal (translationese) ones. In this paper we compare the results of performing end-to-end system development using standard and paraphrased references. With state-of-the-art English-German NMT components, we show that tuning to paraphrased references produces a system that is ignificantly better according to human judgment, but 5 BLEU points worse when tested on standard references. Our work confirms the finding that paraphrased references yield metric scores that correlate better with human judgment, and demonstrates for the first time that using these scores for system development can lead to significant improvements.
AED: a novel visual representation based on AR and empathy computing in manual assembly

Adding personal perceptions to manufacturing engineering can be very challenging, especially when engineering-based methods are used to make artisans understand the designer's ideas. Unfortunately, the two-dimensional engineering definition can be extremely time-consuming for individuals who lack creativity or imagination, and model-based definition would be incapable of breaking the separation between the virtual space and the real world, which makes the interaction between natural persons exist spatial perception error. The emergence of Augmented Reality (AR), which allows individuals to perceive the intentions and strategies of the designer with visual cues that are attached to actual objects, fills this gap. In this paper, augmented engineering definition (AED) is proposed to enhance the information exchange between natural persons in a succinct, accurate and acceptable form of visual impression. Motivated by visual representation in remote collaboration, the specific empathy scenario bases on the AED design, which leads to the establishment of a mapping relationship between the visual cues and the augmented information. An inquiry had been conducted by involving participants who were paired up for the parts’ inspection, interacting via 2D visualization data only, interaction with 3D projection data, interaction with 3D visualization data, AED-based communication. The experimental results showed that participants with AED exhibited higher situational appeal and information understanding than using three other interactions. Besides, we discussed the feasibility of using AED in a collaborative manufacturing environment and the impact on AED users.
Sentiments analysis : Recitation of the concealed emotions of professionals during COVID-19

AbstractNovel Corona Virus embarked from China and proliferated all over the world. The Government has bestowed various restrictions to curb the spread of the virus like lock down and restriction on moving out of their home along with follow up of social distancing. The pandemic has resulted in drastic change in people emotions and reactions to deal with sudden epidemic. Sudden manifest of perilous corona virus has disrupted the habitual regime of people along with emotions undermined. During lockdown and following the path of social distancing has made the maximum usage of online mode of communication. The online forum makes the people connected by comprehending each other’s emotions and rich involvement of empathy. People are using online mode like Twitter, Instagram, Blogs etc to share their feelings and emotions. The sentiments are palpable through various tweets across the nation. The study aims to understand the emotions of working professionals during COVID-19. In order to understand the emotional uproar, sentiment analysis is done. Sentiment analysis is vital tool for fetching up useful information through text documents. The data for the research was chosen from Twitter. The sentiments and emotions displayed by professional during COVID-19 will help to understand the frame of mind of people in such epidemic scenario and thereby craft suitable strategy to help them for coping the same. The research aims to explore the emotions and sentiments of working professionals through the post and text in twitter.Subject Classification: 68T35Keywords: EmotionsSentiments analysisLockdownData analyticsText miningEmployeesNetwork cluster analysis
Effects of Exchange Rate Volatility on Economic Growth: Evidence from West African Monetary Zone

Abstract: This study examined the effects of exchange rate volatility on economic growth in four WAMZ countries. The study uses the pooled ordinary least squares, fixed effects and random effects models, and obtains a robust standard error estimate of the model by applying xtreg, cluster()fe. The empirical analysis shows that the effects of exchange rate volatility on economic growth is insignificant. The results also show a positive correlation between exports and economic growth. This implies that policies aimed at increasing exports through an appropriate exchange rate may be beneficial countries. In addition, the analysis also shows a positive and significant link between imports and economic growth rates. Therefore, this confirms that the countries actually benefit from imports resulting from the competitive pressure generated by the import of consumer goods and professional knowledge, and also from the transfer of technology embodied in the import of goods by producers. Hence, the policy of removing import barriers will benefit the countries. In addition, the results show that there is a positive correlation between the nominal exchange rate and economic growth rate. Therefore, it shows that the nominal exchange rate depreciation policy can play an important role in improving the economic growth of the countries. However, the research results show that there is an inverse relationship between the real exchange rate and economic growth. Considering the importance of the real exchange rate, this study suggests the introduction of a common currency in the WAMZ to reduce the negative effects of the real exchange rate on economic growth.
Linking unstructured evidence to structured observations

Many professionals, like journalists, writers, or consultants, need to acquire information from various sources, make sense of this unstructured evidence, structure their observations, and finally create and deliver their product, such as a report or a presentation. In formative interviews, we found that tools allowing structuring of observations are often disconnected from the corresponding evidence. Therefore, we designed a sensemaking environment with a flexible observation graph that visually ties together evidence in unstructured documents with the user’s structured knowledge. This is achieved through bi-directional deep links between highlighted document portions and nodes in the observation graph. In a controlled study, we compared users’ sensemaking strategies using either the observation graph or a simple text editor on a large display. Results show that the observation graph represents a holistic, compact representation of users’ observations, which can be linked to unstructured evidence on demand. In contrast, users taking textual notes required much more display space to spatially organize source documents containing unstructured evidence. This implies that spatial organization is a powerful strategy to structure observations even if the available space is limited.