Reviewerly/exHarmony huggingface dataset:
A comparative study of hard clustering algorithms for vegetation data

Abstract Questions Which clustering algorithms are most effective according to different cluster validity evaluators? Which distance or dissimilarity measure is most suitable for clustering algorithms? Location Hyrcanian forest, Iran (Asia), Virginia region forest, United States (North America), beech forests, Ukraine (Europe). Methods We tested 25 clustering algorithms with nine vegetation data sets comprised of three real data sets and six simulated data sets exhibiting different cluster separation values. The clustering algorithms included both hierarchical and non‐hierarchical partitioning. Five evaluators were employed on each cluster solution to evaluate different clustering algorithms. Algorithms were ranked from best to worst on each clustering evaluator for each data set. Results The comparison revealed that the OPTSIL initiated from a Flexible‐ β (−0.25) solution achieved particularly good performance. We also found that Ward's method and Flexible‐ β (−0.1) implementations were accurate. K ‐means with Hellinger distance was superior to Partitioning Around Medoids (PAM) algorithms. Accordance between distance measures and clustering algorithms was also observed. Bray–Curtis dissimilarity combined with a range of clustering algorithms was successful in most cases. Bray–Curtis dissimilarity proved superior to other distance measures for heterogeneous data sets. Conclusions All in all, the results demonstrate that choosing the most suitable method before clustering is critical for achieving maximally interpretable clusters. The complexity of vegetation data sets makes this issue even more important. The choice of distance measure had more effect than the choice of clustering method on the quality of results. Our results illustrate that OPTSIL Flexible‐ β (−0.1) and OPTPART could prove superior to alternative conventional clustering algorithms when internal evaluation criteria are used to optimize clustering.
Non-rigid registration of biomedical image for radiotherapy based on adaptive feature density flow

This paper presents a non-rigid biomedical image registration method based on Adaptive feature density flow (AFDF). Considering the muhiplicative characteristics of metadata signal for instance Magnetic Resonance Imaging (MRI) with high-resolution and high-sensitivity, the Non-Local Means Filtering Adapted to Rician Noise (NLMr) is employed to remove Rician noise rather than Gaussian noise with higher Peak Signal to Noise Ratio (PSNR). The deformation information and local shape information are homogeneous, so a flexible dimension of Fast Local Self-Similarity (FLSS) feature descriptor can be constructed to obtain adaptive FLSS (AFLSS) feature descriptor with adaptive Angle and Radial intervals. On the basis of AFLSS, the Adaptive feature density flow (AFDF) is proposed by calculating the dense feature flow field via Belief Propagation algorithm and thereby further optimized by the Pyramid iterative method. Finally, the image registration is completed by using the bicubic interpolation algorithm according to the dense feature flow field. Aiming at radiotherapy positioning control through precise robust registration of biomedical images, experiments of human brain MR images are carried out. The comparative results with PSNR, Root Mean Square Error (RMSE) and normalize mutual information (NMI) show that AFDF performs better than the optical flow method and Scale Invariant Feature Transform (SIFT) flow method, especially for the biomedical scenario with unconscious creep deformation.
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition

Confidence estimation, in which we estimate the reliability of each recognized token (e.g., word, sub-word, and character) in automatic speech recognition (ASR) hypotheses and detect incorrectly recognized tokens, is an important function for developing ASR applications. In this study, we perform confidence estimation for end-to-end (E2E) ASR hypotheses. Recent E2E ASR systems show high performance (e.g., around 5% token error rates) for various ASR tasks. In such situations, confidence estimation becomes difficult since we need to detect infrequent incorrect tokens from mostly correct token sequences. To tackle this imbalanced dataset problem, we employ a bidirectional long short-term memory (BLSTM)-based model as a strong binary-class (correct/incorrect) sequence labeler that is trained with a class balancing objective. We experimentally confirmed that, by utilizing several types of ASR decoding scores as its auxiliary features, the model steadily shows high confidence estimation performance under highly imbalanced settings. We also confirmed that the BLSTM-based model outperforms Transformer-based confidence estimation models, which greatly underestimate incorrect tokens.
IPT: A Dataset for Identity Preserved Tracking in Closed Domains

We present a public dataset for Identity Preserved Tracking (IPT) consisting of sequences of depth data recorded using an Orbbec Astra depth sensor. The dataset features sequences in ten different locations with a high amount of background variation and is designed to be applicable to a wide range of tasks. Its labeling is versatile, allowing for tracking in either 3d space or image coordinates. Next to frame-by-frame 3d and inferred bounding box labeling we provide supplementary annotation of camera poses and room layouts, split in multiple semantically distinct categories. Intended use-cases are applications where both a high level understanding of scene understanding and privacy are central points of consideration, such as active and assisted living (AAL), security and industrial safety. Compared to similar public datasets IPT distinguishes itself with its sequential data format, 3d instance labeling and room layout annotation. We present baseline object detection results in image coordinates using a YOLOv3 network architecture and implement a background model suitable for online tracking applications to increase detection accuracy. Additionally we propose a novel volumetric non-maximum suppression (V-NMS) approach, taking advantage of known room geometry. Last we provide baseline person tracking results utilizing Multiple Object Tracking Challenge (MOTChallenge) evaluation metrics of the CVPR19 benchmark.
Predicting Mouse Click Position Using Long Short-Term Memory Model Trained by Joint Loss Function

Knowing where users might click in advance can potentially improve the efficiency of user interaction in desktop user interfaces. In this paper, we propose a machine learning approach to predict mouse click location. Our model, which is LSTM (long short-term memory)-based and trained by joint supervision, can predict the rectangular region of mouse click with feeding mouse trajectories on the fly. Experiment results show that our model can achieve a result of a predicted rectangle area of 58 × 79 pixels with 92% accuracy, and reduce prediction error when compared with other state-of-the-art prediction methods using a multi-user dataset.
3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties

Face recognition is a widely accepted biometric verification tool, as the face contains a lot of information about the identity of a person. In this study, a 2-step neural-based pipeline is presented for matching 3D facial shape to multiple DNA-related properties (sex, age, BMI and genomic background). The first step consists of a triplet loss-based metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. Most studies in the field of metric learning have only focused on 2D Euclidean data. In this work, geometric deep learning is employed to learn directly from 3D facial meshes. To this end, spiral convolutions are used along with a novel mesh-sampling scheme that retains uniformly sampled 3D points at different levels of resolution. The second step is a multi-biometric fusion by a fully connected neural network. The network takes an ensemble of embeddings and property labels as input and returns genuine and imposter scores. Since embeddings are accepted as an input, there is no need to train classifiers for the different properties and available data can be used more efficiently. Results obtained by a to-fold cross-validation for biometric verification show that combining multiple properties leads to stronger biometric systems. Furthermore, the proposed neural-based pipeline outperforms a linear baseline, which consists of principal component analysis, followed by classification with linear support vector machines and a Naïve Bayes-based score-fuser.
Towards the Identification of Security Tokens on Ethereum

Crypto tokens are digital assets, similar to the coins of a cryptocurrency, except that they do not have their own blockchain or distributed ledger. Rather, they are built on top of an existing one. Areas of application include their use as means of investment, as a local currency in a decentralized application, as well as means for building an ecosystem or a community. Depending on the purpose, it is common to categorize tokens into payment tokens, security tokens and utility tokens. The distinction is of interest since in most jurisdictions, security tokens are more heavily regulated than other tokens. In this paper, we present a heuristic approach towards automatic detection of security tokens from blockchain data. To this end, we first discuss several methods for the (semi-) automatic identification of token contracts. Then we attempt to identify the token type. For our analysis, we examine both the deployed bytecode and the calls to token contracts that we extract from transaction data of the Ethereum main chain up to block 9500000, mined on Feb 17, 2020.
ADD-RRV for motion planning in complex environments

Abstract In this paper, we present a novel sampling-based motion planning method in various complex environments, especially with narrow passages. We use online the results of the planner in the ADD-RRT framework to identify the types of the local configuration space based on the principal component analysis (PCA). The identification result is then used to accelerate the expansion similar to RRV around obstacles and through narrow passages. We also propose a modified bridge test to identify the entrance of a narrow passage and boost samples inside it. We have compared our method with known motion planners in several scenarios through simulations. Our method shows the best performance across all the tested planners in the tested scenarios.
Blockchain for Cybersecurity in IoT

In the space of a few years, the concept of the Internet of Things (IoT) has become increasingly large and diversified. In addition, the use of new technologies is paramount and especially for businesses and institutions, also IoT considered as the engine of smart cities because it can be applied in different smart city services to facilitate the human daily lifestyle. However, this evolution brings cybersecurity challenges. Thus, security and privacy must be the main subject in this scenery. Also making the protection and sustainability of The Internet of Things issue essential by putting in place effective security, so that it can guarantee the availability of the services offered by a smart city ecosystem. In this chapter, we present a review of Blockchain technology and how it can help in shaping a safer IoT system, and we explore the major challenges and security issues stunting the growth of this concept.
Research on Named Entity Recognition for Information Extraction

In natural language processing, named entity recognition, as a basic task used in natural language processing, aims at identifying entities with specific meaning in the text, mainly including personal names, place names, institutional names, proper nouns, etc. Named entity recognition technology is an indispensable part of many natural language processing methods, such as information extraction, information retrieval, machine translation and question answering systems. This paper first briefly introduces the developing process of named entity recognition, and describes the basic concept, objectives and difficulties of named entity recognition. Then, it summarizes the methods of named entity recognition from based on rules and dictionary method, based on statistical method, to the deep learning method and migration of learning, and looks forward to the future development of named entity recognition.
RGB-Infrared Person Re-identification via Image Modality Conversion

As a cross modality retrieval task, RGB-infrared person re-identification(Re-ID) is an important and challenging task, because of its important role in video surveillance applications and large cross-modality variations between visible and infrared images. Most previous works addressed the problem of cross-modality gap with feature alignment by original feature representation learning straightly. In this paper, different from existing works, we propose a novel network(CE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L) to tackle the cross-modality gap with feature alignment. CE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L mainly focuses on adding discriminative information and learning robust features by converting modality between visible and infrared images. Its merits are highlighted in two aspects: 1)Using CycleGAN to convert infrared images into color images can not only increase the recognition characteristics of images, but also allow the network to better learn the two modal image features; 2)Our novel method can serve as data augmentation. Specifically, it can increase data diversity and total data against over-fitting by converting labeled training images to another modal images. Extensive experimental results on two datasets demonstrate superior performance compared to the baseline and the state-of-the-art methods.
Distribution Awareness for AI System Testing

As Deep Learning (DL) is continuously adopted in many safety critical applications, its quality and reliability start to raise concerns. Similar to the traditional software development process, testing the DL software to uncover its defects at an early stage is an effective way to reduce risks after deployment. Although recent progress has been made in designing novel testing techniques for DL software, the distribution of generated test data is not taken into consideration. It is therefore hard to judge whether the identified errors are indeed meaningful errors to the DL application. Therefore, we propose a new distribution aware testing technique which aims to generate new unseen test cases relevant to the underlying DL system task. Our results show that this technique is able to filter up to 55.44% of error test case on CIFAR-10 and is 10.05% more effective in enhancing robustness.
A Quantitative Evaluation Framework of Video De-Identification Methods

We live in an era of privacy concerns, motivating a large research effort in face de-identification. As in other fields, we are observing a general movement from hand-crafted to deep learning methods, mainly involving generative models. Although these methods produce more natural de-identified images or videos, we claim that the mere evaluation of the de-identification is not sufficient, especially when it comes to processing the images/videos further. In this note, we take into account the issue of preserving privacy, facial expressions, and photo-reality simultaneously, proposing a general testing framework. The quantitative evaluation is applied to four open-source tools, producing a baseline for future de-identification methods.
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages

With recent advancements in language technologies, humans are now speaking to devices. Increasing the reach of spoken language technologies requires building systems in local languages. A major bottleneck here are the underlying data-intensive parts that make up such systems, including automatic speech recognition (ASR) systems that require large amounts of labelled data. With the aim of aiding development of spoken dialog systems in low resourced languages, we propose a novel acoustics based intent recognition system that uses discovered phonetic units for intent classification. The system is made up of two blocks - the first block is a universal phone recognition system that generates a transcript of discovered phonetic units for the input audio, and the second block performs intent classification from the generated phonetic transcripts. We propose a CNN+LSTM based architecture and present results for two languages families - Indic languages and Romance languages, for two different intent recognition tasks. We also perform multilingual training of our intent classifier and show improved cross-lingual transfer and zero-shot performance on an unknown language within the same language family.
The effect of image enhancement algorithms on convolutional neural networks

Convolutional Neural Networks (CNNs) are widely used due to their high performance in many tasks related to computer vision. In particular, image classification is one of the fields where CNNs are employed with success. However, images can be heavily affected by several inconveniences such as noise or illumination. Therefore, image enhancement algorithms have been developed to improve the quality of the images. In this work, the impact that brightness and image contrast enhancement techniques have on the performance achieved by CNNs in classification tasks is analyzed. More specifically, several well known CNNs architectures such as Alexnet or Googlenet, and image contrast enhancement techniques such as Gamma Correction or Logarithm Transformation are studied. Different experiments have been carried out, and the obtained qualitative and quantitative results are reported.
Investigating the Homogenization of Web Design: A Mixed-Methods Approach

Visual design provides the backdrop to most of our interactions over the Internet, but has not received as much analytical attention as textual content. Combining computational with qualitative approaches, we investigate the growing concern that visual design of the World Wide Web has homogenized over the past decade. By applying computer vision techniques to a large dataset of representative websites images from 2003–2019, we show that designs have become significantly more similar since 2007, especially for page layouts where the average distance between sites decreased by over 30%. Synthesizing interviews from 11 experienced web design professionals with our computational analyses, we discuss causes of this homogenization including overlap in source code and libraries, color scheme standardization, and support for mobile devices. Our results seek to motivate future discussion of the factors that influence designers and their implications on the future trajectory of web design.
Rethinking Technology Design and Deployment in Africa: Lessons from an African Standpoint

Research in HCI4D has emphasized the need for a critical analysis of how conventional design paradigms and analytical orientations work in non-western contexts. This necessitates an examination of how indigenous modes of knowing could inform the framing and making of technological innovation in Africa. This paper draws on four empirical cases to show how stereotypical (often colonial and neo-colonial) design paradigms might have hastily misrepresented the situated practices of designing and deploying educational technologies in Nigeria. The paper argues that a situated standpoint orientation provides a way of approaching and analysing the plurality of the African context – which in essence relies on indigenous practices and knowledge in designing operational interventions that can be adopted and used to support teaching and learning. Thus, the temporal analysis of the four cases points to the material implications of the interactivity between culture and locale in extending indigenous practices of design.
Mining Software Repositories with a Collaborative Heuristic Repository

Many software engineering studies or tasks rely on categorizing software engineering artifacts. In practice, this is done either by defining simple but often imprecise heuristics, or by manual labelling of the artifacts. Unfortunately, errors in these categorizations impact the tasks that rely on them. To improve the precision of these categorizations, we propose to gather heuristics in a collaborative heuristic repository, to which researchers can contribute a large amount of diverse heuristics for a variety of tasks on a variety of SE artifacts. These heuristics are then leveraged by state-of-the-art weak supervision techniques to train high-quality classifiers, thus improving the categorizations. We present an initial version of the heuristic repository, which we applied to the concrete task of commit classification.
Real-time Semantic Segmentation via Region and Pixel Context Network

Real-time semantic segmentation is a challenging task as both segmentation accuracy and inference speed need to be considered at the same time. In this paper, we present a Dual Context Network (DCN et) to address this challenge. It contains two independent sub-networks: Region Context Network and Pixel Context Network. Region Context Network is main network with low-resolution input and feature re-weighting module to achieve sufficient receptive field. Meanwhile, utilizing Pixel Context Network with location attention module to capture the location dependencies of each pixel for assisting the main network to recover spatial detail. A contextual feature fusion is introduced to combine output features of these two subnetworks. The experiments show that DCNet can achieve high-quality segmentation while keeping a high speed. Specifically, for Cityscapes test dataset, we achieve 76.1 % Mean IOU with the speed of 82 FPS on a single GTX 2080Ti GPU when using ResNet50 as backbone, and 71.2% Mean IOU with the speed of 142 FPS when using ResNet18 as backbone.
Study of RTPPS algorithm in UWB communication medium for a surveillance system to protect agricultural crops from wild animals

Ultra-wide band communication medium is for fast speed and high date rate communication [1] In this paper, a unique algorithm named “Real Time Pixel by Pixel Subtraction (RTPPS)” has been presented in ultra-wide band communication medium to protect agriculture crops from different wild animals in agriculture field. The algorithm is developed with less complexity such that it can consume less time to execute the algorithm. This algorithm is capable of taking preventive measures instantly based on the presence of wild animals in agriculture field. To design the system, Raspberry Pi 3 B+is used as a main controller unit that supports 2.4 to 5GHz IEEE 802.11.b/g/n/ac wireless local area network (WLAN). The algorithm is developed using image processing techniques in MATLAB 2018 version. The hardware prototype model was tested in different regions of Nagaland, India during the month of May-June 2020. During the testing of this device it was found that at a threshold value of 7710, this device can detect animals like wild buffalo, unfastened cow, wild pigs and macaques.