Reviewerly/exHarmony huggingface dataset:
Geostatistical modeling and conservation implications for an endemic Ipomoea species in the Eastern Brazilian Amazon

Abstract The Canga ecosystem, in the Eastern Brazilian Amazon, is a biodiversity and endemism hotspot as well a potential resource for iron mining, raising conflicts between conservation and economic development. Here, we collected data from 60 randomly selected points in the Canga ecosystem in the Carajas National Forest, Brazil, to analyze the spatial patterns of Ipomoea cavalcantei occurrence in different phytophysiognomies. Through ordinary kriging and a GLM model, we identified that this endemic species occupies a very restricted range within the Canga ecosystem, specifically in areas of Open Rupestrian Fields and Shrub Rupestrian Fields where the iron crust restricts the soil depth to a few centimeters and the vegetation is mainly herbaceous or savannah shrub vegetation, with low sparse tree canopies. These conditions increased the passage of sunlight, revealing the importance of light conditions for the species. However, because the species occurs in suitable mining sites, there is continuing conflict between environmental and economic decisions. Spatial data showed that mining has increased in Carajas National Forest over the last 32 years, reducing the potential locations for I. cavalcantei to less than 50 % of its original range. Mining will potentially occupy the entire species range over the next three decades. Restoration outside its original habitat is difficult in the short term, because there is no knowledge of the species’ capacity for growth, survival, and replication. Conservation initiatives for the Canga ecosystem should prioritize strategies that reduce regional dependence on mining and provide alternative economic activities, such as sustainable exploitation of bio-products and ecological tourism.
Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge

Intelligent video analytics systems have come to play an essential role in many fields, including public safety, transportation safety, and many other industrial areas, such as automated tools for data extraction, and analyzing huge datasets, such as multiple live video streams transmitted from a large number of cameras. A key characteristic of such systems is that it is critical to perform real-time analytics so as to provide timely actionable alerts on various tasks, activities, and conditions. Due to the computation-intensive and bandwidth-intensive nature of these operations, however, video analytics servers may not fulfill the requirements when serving a large number of cameras simultaneously. To handle these challenges, we present an edge computing-based system that minimizes the transfer of video data from the surveillance camera feeds on a cloud video analytics server. Based on a novel approach of utilizing the information from the encoded bitstream, the edge can achieve low processing complexity of object tracking in surveillance videos and filter non-motion frames from the list of data that will be forwarded to the cloud server. To demonstrate the effectiveness of our approach, we implemented a video surveillance prototype consisting of edge devices with low computational capacity and a GPU-enabled server. The evaluation results show that our method can efficiently catch the characteristics of the frame and is compatible with the edge-to-cloud platform in terms of accuracy and delay sensitivity. The average processing time of this method is approximately 39 ms/frame with high definition resolution video, which outperforms most of the state-of-the-art methods. In addition to the scenario implementation of the proposed system, the method helps the cloud server reduce 49% of the load of the GPU, 49% that of the CPU, and 55% of the network traffic while maintaining the accuracy of video analytics event detection.
Quick Evaluation of a Software Architecture Using the Decision-Centric Architecture Review Method: An Experience Report

Software architecture evaluations allow systematic checking of software architecture fitness regarding the context and business. However, selecting and using an evaluation method always have some challenges and issues. This article reports an architecture review while developing an innovation projects support platform for a Chilean R&D and engineering institution. We chose DCAR (Decision-Centric Architecture Review) because it has lightweight requirements on documentation and resources, it can evaluate a project already running, and it did not impact a schedule where architecture reviews had not been considered from the start. We describe the review of three accepted and one rejected decisions. Lessons learned and benefits observed include recording decisions' rationale, visibilization of some technological issues, and rethinking of some previously made architectural decisions. Finally, we recommend making frequent mini-reviews of architecture decisions, to understand the architecture, formalize it with its resulting reports, and raise its visibility in the team itself.
Introduction to Random Forests with R

The two algorithms discussed in this book were proposed by Leo Breiman: CART trees, which were introduced in the mid-1980s, and random forests, which emerged just under 20 years later in the early 2000s. This chapter offers an introduction to the subject matter, beginning with a historical overview. Some notations, used to define the various statistical objectives addressed in the book, are also introduced: classification, regression, prediction, and variable selection. In turn, the three R packages used in the book are listed, and some competitors are mentioned. Lastly, the four datasets used to illustrate the methods’ application are presented: the running example (spam), a genomic dataset, and two pollution datasets (ozone and dust).
Path Following Control for Unmanned Aerial Vehicle Based on Carrot Chasing Algorithm and PLOS

One of the reasons that UAVs play an important role in many fields is that they can follow paths accurately. The information on the comparison of the path following algorithms is far from enough. In order to make an intensive study of path following problem, we use a kinematic model to compare and analyze Carrot chasing algorithm and PLOS algorithm. The comparison of the path following algorithms is carried out through simulations. The results show that the UAV can track the target paths accurately with each of the algorithms. We can figure out that it is important to select the proper gains in the guidance laws for great path following performances.
Multi-Label Classification of Text Documents Using Deep Learning

Recently, studies in the field of Natural Language Processing and its related applications continue to mount up. Machine learning is proven to be predominantly data-driven in the sense that generic model building methods are used and then tailored to specific application domains. Needless to say, this has proven to be a very effective approach in modeling the complicated data dependencies we frequently experience in practice, making very few assumptions, and allowing the information to talk for themselves. Examples of these applications can be found in chemical process engineering, climate science, healthcare, and linguistic processing systems for natural languages, to name a few. Text classification is one of the important machine learning tasks that is used in many digital applications today; such as in document filtering, search engines, document management systems, and many more. Text classification is the process of categorizing of text documents into a given set of labels. Furthermore, multi-label text classification is the task of categorization of text documents into one or more labels simultaneously. Over the years, many methods for classifying text documents have been proposed, including the popularly known bag of words (BoW) method, support vector machine (SVM), tree induction, and label-vector embedding, to mention a few. These kinds of tools can be used in many digital applications, such as document filtering, search engines, document management systems, etc. Lately, deep learning-based approaches are getting more attention, especially in extreme multi-label text classification case. Deep learning has proven to be one of the major solutions to many machine learning applications, especially those involving high-dimensional and unstructured data. However, it is of paramount importance in many applications to be able to reason accurately about the uncertainties associated with the predictions of the models. In this paper, we explore and compare the recent deep learning-based methods for multi-label text classification. We investigate two scenarios. First, multi-label classification model with ordinary embedding layer, and second with Glove, word2vec, and FastText as pre-trained embedding corpus for the given models. We evaluated these different neural network model performances in terms of multi-label evaluation metrics for the two approaches, and compare the results with the previous studies.
E-Voting Application Using RSA Algorithm Method Based Prototype Android

Voting is a method of decision making. An example of the voting activity was the election of the Chair of the Communication and Information Technology Faculty (FTKI) Association at the National University which was still conducted conventionally. However, the obstacle that Occurs is that most students from each study program can not use Reviews their voting rights due to other activities. So we need a system that can Facilitate voting activities. The purpose of this research is to build an Android mobile application-based voting system to Facilitate students to be Able to vote only by using an Android smartphone connected to a particular Internet. For the data security, the RSA algorithm is used. From the system testing Carried out using the Android system version 9.0 (Pie) and the prototype method aims to produce a perfect prototype for use to the public.
Comparative significance of human resource management practices on banking financial performance with analytic hierarchy process

Banking financial performance (BFP) has been recognized as a causation of human resource management (HRM).The causal linkages from HRM practices to BFP are different.Nevertheless, almost none of the studies has ranked and compared this difference among the practices of HRM in enhancing BFP.The current study applied an analytic hierarchy process to rank the relative importance of HRM practices.For the robustness of the results from the process of analytic hierarchy, the current work employed an analytic hierarchy process to reassess the relatively important levels of HRM practices on BFP as well.The findings are robust across both of the techniques.The practice of training and development (TT) plays the most critical part in BFP, followed by the practice of performance evaluation (PN) and the practice of reward system (RM) as the third most important.In contrast, the practice of recruitment and selection (RN) takes the least important position in BFP.Moreover, the findings also provide statistical evidence on the causal links from the practices of HRM to BFP.This work makes some contribution to how managers should decide on HRM practices in order to obtain the best possible BFP.
CHaPR: Efficient Inference of CNNs via Channel Pruning

To deploy a CNN on resource-constrained edge platforms, channel pruning techniques promise a significant reduction of implementation costs including memory, computation, and energy consumption without special hardware or software libraries. This paper proposes CHaPR, a novel pruning technique to structurally prune the redundant channels in a trained deep Convolutional Neural Network. CHaPR utilizes a proposed subset selection problem formulation for pruning which it solves using pivoted QR factorization. CHaPR also includes an additional pruning technique for ResNet-like architectures which resolves the issue encountered by some existing channel pruning methods that not all the layers can be pruned. Experimental results on VGG-16 and ResNet-50 models show 4.29X and 2.84X reduction, respectively in computation cost while incurring 2.50% top-1 and 1.40% top-5 accuracy losses. Compared to many existing works, CHaPR performs better when considering an Overall Score metric which accounts for both computation and accuracy.
Virtual environment for smart wheelchair simulation

The difficulty of designing equipment for individuals who have some degree of special needs is that each individual has a unique clinical picture, hampering the development of new technologies. In this sense, this article explores the ways in which the design and evaluation of virtual devices can be combined with new prototyping techniques to create and develop equipment that will allow wheelchair accessibility to be improved. To this end, a computer vision control structure and a virtual software were created to assist in the development of a wheelchair coupling project, in order to study and evaluate the functional characteristics of the device. In this way, the present work incorporates a virtual system that represents the automation and mechanical design of new wheelchairs. Thus defining a prototyping methodology with the aid of virtual environments, based on movement controls adaptive to the parameters stipulated in real environments. Therefore, the present work results in a simulator for an electric wheelchair docking platform for multiple disabilities, which allows the validation of the project, the acquisition of development needs, the proposition of improvements in the preliminary acquisition platform and a system of computer vision control.
Be Good Neighbors: A Novel Application Isolation Metric Used to Optimize the Initial Container Placement in CaaS

Many cloud providers benefit from fine-grained resource management in Container as a Service (CaaS), e.g., energy savings. However, while optimizing power consumption, cloud providers also need to consider service performance. Few studies in the literature performed a balanced optimization of the two objectives in the initial container placement (ICP). As such, the obtained solutions are often locally optimal. In this work, we model the ICP as a bi-objective optimization problem. Unfortunately, directly applying a conventional performance modeling method in solving the problem is complex and costly, especially for a large-scale application scenario. It also fails to capture the impacts of some non-resource constraints such as high availability constraints. To tackle these issues, we propose a novel application isolation metric to quantify the overall service performance. With regard to the power consumption objective, we opt for a non-linear model since a linear one makes power consumption indistinguishable in a homogeneous setting. With the two building blocks, we introduce an optimization algorithm named First Fit based improving Genetic Algorithm (denoted by FF-based-IGA) to find the best ICP solution. Our experimental results are as follows: 1) the proposed application isolation metric is effective at improving service performance, and 2) FF-based-IGA achieves a desirable balance between power consumption and service performance.
Multi-sector Assessment Framework – a New Approach to Analyse Cybersecurity Challenges and Opportunities

This paper presents a new approach to analyse cybersecurity challenges and opportunities, focused on the development of a new risk assessment and management framework. Such a multi-sector assessment framework should be able to evaluate and prioritize cybersecurity risks in trans-sectoral and inter-sectoral contexts. It leads toward proper resource allocations and mitigation actions. To achieve this goal, the analysis of existing risk assessment and management frameworks was performed. Also, an overview on common multi-sectoral technological challenges and opportunities were provided being derived from sector-specific use cases as well as transversal and inter-sectoral challenges and opportunities. As a result of this analysis the architecture of the ECHO Multi-sector Assessment Framework was proposed. The identified technological challenges and opportunities, multi-sector dependencies, and transversal aspects determine the input data for the new framework. The architecture is applicable in healthcare, energy, maritime transportation, and defence sectors, being extensible to others. The framework enables the definition of governance models or the design of cybersecurity technology roadmaps.
Data Integrity, Control and Tokenization

Data is the lifeblood within the digital ecosystem and more broadly the economies of the future. Whilst there is increasing focus on data protection (e.g. the EU General Data Protection Regulation ‘GDPR’), the broader role of data in enabling the development and growth of a convergent ecosystem has only recently been brought into sharp focus. Artificial intelligence, virtual reality, augmented reality, as well as many applications used in the digital environment require vast amounts of data inputs (sometime diverse sets of data that may be geographically dispersed) and vast computing resources to process these.
Voting for POS tagging of Latin texts: Using the flair of FLAIR to better Ensemble Classifiers by Example of Latin

Despite the great importance of the Latin language in the past, there are relatively few resources available today to develop modern NLP tools for this language. Therefore, the EvaLatin Shared Task for Lemmatization and Part-of-Speech (POS) tagging was published in the LT4HALA workshop. In our work, we dealt with the second EvaLatin task, that is, POS tagging. Since most of the available Latin word embeddings were trained on either few or inaccurate data, we trained several embeddings on better data in the first step. Based on these embeddings, we trained several state-of-the-art taggers and used them as input for an ensemble classifier called LSTMVoter. We were able to achieve the best results for both the cross-genre and the cross-time task (90.64% and 87.00%) without using additional annotated data (closed modality). In the meantime, we further improved the system and achieved even better results (96.91% on classical, 90.87% on cross-genre and 87.35% on cross-time).
Detecting Direct Speech in Multilingual Collection of 19th-century Novels

Fictional prose can be broadly divided into narrative and discursive forms with direct speech being central to any discourse representation (alongside indirect reported speech and free indirect discourse). This distinction is crucial in digital literary studies and enables interesting forms of narratological or stylistic analysis. The difficulty of automatically detecting direct speech, however, is currently under-estimated. Rule-based systems that work reasonably well for modern languages struggle with (the lack of) typographical conventions in 19th-century literature. While machine learning approaches to sequence modeling can be applied to solve the task, they typically face a severed skewness in the availability of training material, especially for lesser resourced languages. In this paper, we report the result of a multilingual approach to direct speech detection in a diverse corpus of 19th-century fiction in 9 European languages. The proposed method finetunes a transformer architecture with multilingual sentence embedder on a minimal amount of annotated training in each language, and improves performance across languages with ambiguous direct speech marking, in comparison to a carefully constructed regular expression baseline.
AI and Blockchain

Most people believe that the paper from 2008 by Satoshi Nakamoto, a pseudonym used by a yet unknown author, introduced the concept of blockchain. However, the key idea is actually 17 years older. The first mentioning of key blockchain concepts goes back to 1991 when Stuart Haber and Scott Stornetta described the concept of a cryptographically secured chain of blocks for the first time.
Reflections on technology, change and academic libraries

The book includes an overview and literature review of technology and change management in academic libraries, a consideration of the application of change management principles in academic libraries, and a number of case studies looking at the adoption of different technologies. This concluding chapter presents an analysis of the literature and the content of the various chapters and case studies which make up the book, identifying the key themes, observations and lessons learned and presenting them under the headings: benefits; constraints and key issues. This provides a useful checklist for those planning or implementing technology and change projects.
On the probabilistic analysis of neural networks

Neural networks are powerful tools for automated decision-making, seeing increased application in safety-critical domains, such as autonomous driving. Due to their black-box nature and large scale, reasoning about their behavior is challenging. Statistical analysis is often used to infer probabilistic properties of a network, such as its robustness to noise and inaccurate inputs. While scalable, statistical methods can only provide probabilistic guarantees on the quality of their results and may underestimate the impact of low probability inputs leading to undesired behavior of the network.
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs.

Over the past decade, knowledge graphs became popular for capturing structured domain knowledge. Relational learning models enable the prediction of missing links inside knowledge graphs. More specifically, latent distance approaches model the relationships among entities via a distance between latent representations. Translating embedding models (e.g., TransE) are among the most popular latent distance approaches which use one distance function to learn multiple relation patterns. However, they are mostly inefficient in capturing symmetric relations since the representation vector norm for all the symmetric relations becomes equal to zero. They also lose information when learning relations with reflexive patterns since they become symmetric and transitive. We propose the Multiple Distance Embedding model (MDE) that addresses these limitations and a framework to collaboratively combine variant latent distance-based terms. Our solution is based on two principles: 1) we use a limit-based loss instead of a margin ranking loss and, 2) by learning independent embedding vectors for each of the terms we can collectively train and predict using contradicting distance terms. We further demonstrate that MDE allows modeling relations with (anti)symmetry, inversion, and composition patterns. We propose MDE as a neural network model that allows us to map non-linear relations between the embedding vectors and the expected output of the score function. Our empirical results show that MDE performs competitively to state-of-the-art embedding models on several benchmark datasets.
Smart Doctor: Performance of Supervised ART-I Artificial Neural Network for Breast Cancer Diagnoses

Wisconsin Breast Cancer Dataset (WBCD) was employed to show the performance of the Adaptive Resonance Theory (ART), specifically the supervised ART-I Artificial Neural Network (ANN), to build a breast cancer diagnosis smart system. It was fed with different learning parameters and sets. The best result was achieved when the model was trained with 50% of the data and tested with the remaining 50%. Classification accuracy was compared to other artificial intelligence algorithms, which included fuzzy classifier, MLP-ANN, and SVM. We achieved the highest accuracy with such low learning/testing ratio.