Reviewerly/exHarmony huggingface dataset:
AutoML: From Methodology to Application

Machine Learning methods have been adopted for a wide range of real-world applications, ranging from social networks, online image/video-sharing platforms, and e-commerce to education, healthcare, etc. However, in practice, a large amount of effort is required to tune several components of machine learning methods, including data representation, hyperparameter, and model architecture, in order to achieve a good performance. To alleviate the required tunning efforts, Automated Machine Learning (AutoML), which can automate the process of applying machine learning methods, has been studied in both academy and industry recently. In this tutorial, we will introduce the main research topics of AutoML, including Hyperparameter Optimization, Neural Architecture Search, and Meta-Learning. Two emerging topics of AutoML, Automatic Feature Generation and Machine Learning Guided Database, will also be discussed since they are important components for real-world applications. For each topic, we will motivate it with application examples from industry, illustrate the state-of-the-art methodologies, and discuss some future research directions based on our experience from industry and the trends in academy.
Comprehensive and Comparative Analysis of GAM-Based PV Power Forecasting Models Using Multidimensional Tensor Product Splines against Machine Learning Techniques

In recent years, as photovoltaic (PV) power generation has rapidly increased on a global scale, there is a growing need for a highly accurate power generation forecasting model that is easy to implement for a wide range of electric utilities. Against this background, this study proposes a PV power forecasting model based on the generalized additive model (GAM) and compares its forecasting accuracy with four popular machine learning methods: k-nearest neighbor, artificial neural networks, support vector regression, and random forest. The empirical analysis provides an intuitive interpretation of the multidimensional smooth trends estimated by the GAM as tensor product splines and confirms the validity of the proposed modeling structure. The effectiveness of GAM is particularly evident in trend completion for missing data, where it is able to flexibly express the tangled trend structure inherent in time series data, and thus has an advantage not only in interpretability but also in improving forecast accuracy.
Automatic Methods and Neural Networks in Arabic Texts Diacritization: A Comprehensive Survey

Arabic diacritics are signs used in Arabic orthography to represent essential morphophonological and syntactic information.It is a common practice to leave out those diacritics in written Arabic.Most Arabic electronic texts lack such diacritics.The processing of those texts for various puposes of Natural Language Processing is a complicated task.Diacritized words are necessary for applications such as machine translation, sentiment analysis, and speech synthesis.To address this problem, several studies proposed automatic systems to restore diacritics in Arabic texts.The present paper presents an in-depth survey of 56 most recent Arabic diacritization studies.The studies encompassed in this survey have been selected from the following databases: IEEE Xplore, Clarivate, Analytics, Google Scholar, and Science Direct.Based on the diacritization approach, the studies are grouped into four sections in terms of method; rule-based, simple statistical, hybrid, and Neural Networks.While rule-based methods such as morphological analyzers and lexicon retrievals were the earliest approaches, results indicated that they are still valuable tools that can aid in the process of diaciritization.Effective statistical methods that produced diacritics with acceptable accuracy include Hidden Markov Model, n-grams, and Support Vector Machines.They are often accompanied by either rule-based or neural networks in hybrid systems.Neural networks, specifically Bidirectional Long Short Term Memory, reached very high diacritization accuracy levels.Studies employing neural networks focused on evaluating and comparing the efficacy of several types of neural networks or a hybrid of them, testing alternatives of input units or suggested schemes for partial daicritization.The study synthesizes the results of the studies, identifies research gaps, and offers recommendations for future research.
A Graph Neural Network-Based Algorithm for Point-of-Interest Recommendation Using Social Relation and Time Series

POI recommendation has gradually become an important topic in the field of service recommendation, which is always achieved by mining user behavior patterns. However, the context information of the collaborative signal is not encoded in the embedding process of traditional POI recommendation methods, which is not enough to capture the collaborative signal among different users. Therefore, a POI recommendation algorithm is presented by using social-time context graph neural network model (GNN) in location-based social networks. First, it finds similarities between different social relationships based on the users' social and temporal behavior. Then, the similarity among different users is calculated by an improved Euclidean distance. Finally, it integrates the graph neural network, the interaction bipartite graph of users and social-time information into the algorithm to generate the final recommendation list in this paper. Experiments on real datasets show that the proposed method is superior to the state-of-the-art POI recommendation methods.
NLP4IP: Natural Language Processing-based Recommendation Approach for Issues Prioritization

This paper proposes a recommendation approach for issues (e.g., a story, a bug, or a task) prioritization based on natural language processing, called NLP4IP. The proposed semi-automatic approach takes into account the priority and story points attributes of existing issues defined by the project stakeholders and devises a recommendation model capable of dynamically predicting the rank of newly added or modified issues. NLP4IP was evaluated on 19 projects from 6 repositories employing the JIRA issue tracking software with a total of 29,698 issues. A comprehensive benchmark study was also conducted to compare the performance of various machine learning models. The results of the study showed an average top@3 accuracy of 81% and a mean squared error of 2.2 when evaluated on the validation set. The applicability of the proposed approach is demonstrated in the form of a JIRA plug-in illustrating predictions made by the newly developed machine learning model. The dataset has also been made publicly available in order to support other researchers working in this domain.
Semantic Concept Testing in Autonomous Driving by Extraction of Object-Level Annotations from CARLA

With the growing use of Deep Neural Networks (DNNs) in various safety-critical applications comes an increasing need for Verification and Validation (V&V) of these DNNs. Unlike testing in software engineering, where several established methods exist for V&V, DNN testing is still at an early stage. The data-driven nature of DNNs adds to the complexity of testing them. In the scope of autonomous driving, we showcase our validation method by leveraging object-level annotations (object metadata) to test DNNs on a more granular level using human-understandable semantic concepts like gender, shirt colour, age, and illumination. Such an enhanced granularity, as we detail, can prove useful in the construction of closed-loop testing or the investigation of dataset coverage/completeness. Our add-on sensor to the CARLA simulator enables us to generate datasets with this granular metadata. For the task of semantic segmentation for pedestrian detection using DeepLabv3+, we highlight potential insights and challenges that become apparent on this level of granularity. For instance, imbalances within a CARLA generated dataset w.r.t. the pedestrian distribution do not directly carry over into weak spots of the DNN performances and vice versa.
Certification and Trade-off of Multiple Fairness Criteria in Graph-based Spam Detection

Spamming reviews are prevalent in review systems to manipulate seller reputation and mislead customers. patterns to achieve state-of-the-art detection accuracy. The detection can influence a large number of real-world entities and it is ethical to treat different groups of entities as equally as possible. However, due to skewed distributions of the graphs, GNN can fail to meet diverse fairness criteria designed for different parties. We formulate linear systems of the input features and the adjacency matrix of the review graphs for the certification of multiple fairness criteria. When the criteria are competing, we relax the certification and design a multi-objective optimization (MOO) algorithm to explore multiple efficient trade-offs, so that no objective can be improved without harming another objective. We prove that the algorithm converges to a Pareto efficient solution using duality and the implicit function theorem. Since there can be exponentially many trade-offs of the criteria, we propose a data-driven stochastic search algorithm to approximate Pareto fronts consisting of multiple efficient trade-offs. Experimentally, we show that the algorithms converge to solutions that dominate baselines based on fairness regularization and adversarial training.
DeLiEve-Net: Deblurring Low-light Images with Light Streaks and Local Events

Modern blind deblurring methods usually show degenerate performance when handling images captured in low-light conditions because these images often contain saturated regions of light sources, and the image contents and details in dark regions are poorly visible. In contrast, event cameras can faithfully record the positions and polarities of intensity changes with a very high dynamic range and low latency, which suffer less in the dark than conventional cameras. However, existing event-based deblurring methods require guidance from global events with the same spatial resolution as the blurry image (typically 346 × 260 pixels), which significantly limits the spatial resolution of images they can process. In this paper, we address this problem in a two-stage way by proposing a neural network named DeLiEve-Net, which learns to Deblur low-Light images with light streaks and local Events. An RGB-DAVIS hybrid camera system is built to validate that our method can deblur high-resolution RGB images with events in low-light conditions.
Text Summarization of an Article Extracted from Wikipedia Using NLTK Library

Internet is an excellent source of information, where you can get information on all the topics. But due to the large availability of content, it becomes a challenging job to extract exact information. A text summarization system's main objective is defining and presentingthe most relevant information from the given text to the end-users. Nowadays, the data is available in a considerable quantity. It becomes difficult for the user to deal with exact information. It's not possible to read all the information and make a conclusion for specific data. With text summarization, a significantcontent of data is converted into a short set of information. If we talk specifically about Wikipedia's information, almost all the areas areopen on this website. If we search for a specific keyword, it will provide huge details. Text summarization will convey the same extensive information by converting it into small pieces without losing its message. We have taken data from Wikipedia and applied summarization techniques to reduce the content without changing its meaning for demonstrating the concept during this research. Abstractive methods generate an internal semantic representation of the original content. For several years, text summarization has been an active area of study. By summarizing parts of the source document, abstraction can transform the removed content to condense at more strongly than extraction.
What is the expected return on Bitcoin? Extracting the term structure of returns from options prices

We infer the forward-looking Bitcoin risk premium from options contracts. Using data from 2018 to 2020, we show that the expected excess returns for Bitcoin are time-varying and significantly higher than in equities or gold, averaging almost 80% per annum. A temporal analysis of the term structure of Bitcoin risk premia reveals an upward sloping term structure during Bitcoin bull markets and downward sloping during Bitcoin bear markets.
A Critical Review of how Public Display Interfaces Facilitate Placemaking

Although public interfaces are promised to facilitate placemaking by offering a technological platform between citizens and decision makers, little is known about whether they actually bring these stakeholders closer together towards local transformative change. By systematically analysing the infrastructural concepts, methods and tools of 40 interface deployments, this review presents a relational model that describes how a public interface can afford the communication, reflection or inquiry of civic feedback. Our analysis also reveals how most public interfaces: are based on utilitarian motives rather than facilitating placemaking; provoke dialogues among citizens instead of between stakeholders; fail to upend the hierachical dependencies between stakeholders; make use of certain technological means that limit citizen agency; and are controlled by gatekeepers who operate covertly and without accountability. Based on these findings, we propose five ”middle-out” considerations that inform how the next generation of placemaking interfaces can facilitate more meaningful and democratic bilateral dialogues between citizens and decision makers.
Violence Recognition Based on Auditory-Visual Fusion of Autoencoder Mapping

In the process of violence recognition, accuracy is reduced due to problems related to time axis misalignment and the semantic deviation of multimedia visual auditory information. Therefore, this paper proposes a method for auditory-visual information fusion based on autoencoder mapping. First, a feature extraction model based on the CNN-LSTM framework is established, and multimedia segments are used as whole input to solve the problem of time axis misalignment of visual and auditory information. Then, a shared semantic subspace is constructed based on an autoencoder mapping model and is optimized by semantic correspondence, which solves the problem of audiovisual semantic deviation and realizes the fusion of visual and auditory information on segment level features. Finally, the whole network is used to identify violence. The experimental results show that the method can make good use of the complementarity between modes. Compared with single-mode information, the multimodal method can achieve better results.
Feature-based Egocentric Grasp Pose Classification for Expanding Human-Object Interactions

This paper presents a framework for classifying human hand pose, especially in grasping object intuitively. First, we propose a system based on the stereo infra-red image as a sensor that can produce hand coordinates in 3-dimensional space. We use egocentric vision because it can get uniform and natural data with only a single sensor module. Second, we transformed the position to get the angle information for each joint on the finger. Third, we designed an intelligent system based on Multi-Layer Perceptron (MLP) to process angular data to obtain classification results according to the Cutkosky grasp taxonomy. Finally, we compared the results on several similar objects and evaluated their classification accuracy. In the validation phase, the results yielded an accuracy of 16 grasp pose classification is 89,60%. In real-time testing, the results yielded an accuracy of 81.93%. This result shows feature-based learning can reduce the complexity and training time of the MLP. Furthermore, a small amount of training data is sufficient for the training and implementation.
Interpretable Feedback for AutoML and a Proposal for Domain-customized AutoML for Networking

The barrier to entry for network operators to use machine learning (ML) is high for operators who are not ML experts. Automated machine learning (AutoML) promises operators the ability to train ML models without requiring the expertise of data scientists or the need to learn ML. However, AutoML today: (a) is black-box; and (b) does not allow operators to leverage domain expertise. We start this paper by describing our broader vision for a domain-customized AutoML platform for networking and propose a set of potential solutions to realize that vision. As the first step, we introduce our feedback solution for AutoML that allows domain experts (who are not experts in ML) to better understand how to improve the input data to AutoML in order to achieve better accuracy.
Dynamic mode decomposition for aero-optic wavefront characterization

Aero-optical beam control relies on the development of low-latency forecasting techniques to quickly predict wavefronts aberrated by the turbulent boundary layer around an airborne optical system, and its study applies to a multidomain need from astronomy to microscopy for high-fidelity laser propagation. We leverage the forecasting capabilities of the dynamic mode decomposition (DMD) — an equation-free, data-driven method for identifying coherent flow structures and their associated spatiotemporal dynamics — to estimate future state wavefront phase aberrations to feed into an adaptive optic control loop. We specifically leverage the optimized DMD (opt-DMD) algorithm on a subset of the Airborne Aero-Optics Laboratory-Transonic experimental dataset, characterizing aberrated wavefront dynamics for 23 beam propagation directions via the spatiotemporal decomposition underlying DMD. Critically, we show that opt-DMD produces an optimally debiased eigenvalue spectrum with imaginary eigenvalues, allowing for arbitrarily long forecasting to produce a robust future state prediction, while exact DMD loses structural information due to modal decay rates.
Self-Supervised Pretraining and Controlled Augmentation Improve Rare Wildlife Recognition in UAV Images

Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation.Recent models are generally based on deep learning and thus require vast amounts of training data.Due to their scarcity and minuscule size, annotating animals in aerial imagery is a highly tedious process.In this project, we present a methodology to reduce the amount of required training data by resorting to self-supervised pretraining.In detail, we examine a combination of recent contrastive learning methodologies like Momentum Contrast (MoCo) and Cross-Level Instance-Group Discrimination (CLD) to condition our model on the aerial images without the requirement for labels.We show that a combination of MoCo, CLD, and geometric augmentations outperforms conventional models pretrained on Im-ageNet by a large margin.Crucially, our method still yields favorable results even if we reduce the number of training animals to just 10%, at which point our best model scores double the recall of the baseline at similar precision.This effectively allows reducing the number of required annotations to a fraction while still being able to train highaccuracy models in such highly challenging settings.
A Brief History of Artificial Intelligence Research

November 02 2021 A Brief History of Artificial Intelligence Research Christoph Adami Christoph Adami Michigan State University, Department of Microbiology and Molecular Genetics, BEACON Center for the Study of Evolution in Action, Program in Evolution, Ecology, and Behavior. adami@msu.edu * Contact author. Search for other works by this author on: This Site Google Scholar Author and Article Information Christoph Adami Michigan State University, Department of Microbiology and Molecular Genetics, BEACON Center for the Study of Evolution in Action, Program in Evolution, Ecology, and Behavior. adami@msu.edu * Contact author. Online Issn: 1530-9185 Print Issn: 1064-5462 © 2021 Massachusetts Institute of Technology2021Massachusetts Institute of Technology Artificial Life (2021) 27 (2): 131–137. https://doi.org/10.1162/artl_a_00349 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Search Site Citation Christoph Adami; A Brief History of Artificial Intelligence Research. Artif Life 2021; 27 (2): 131–137. doi: https://doi.org/10.1162/artl_a_00349 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentAll JournalsArtificial Life Search Advanced Search Artificial Intelligence, neuroevolution, deep learning Day by day, however, the machines are gaining ground upon us; day by day we are becoming more subservient to them; more men are daily bound down as slaves to tend them, more men are daily devoting the energies of their whole lives to the development of mechanical life. —Samuel Butler, "Darwin Among the Machines," 1863 Can machines ever be sentient? Could they perceive and feel things; be conscious of their surroundings? From a materialistic point of view the answer must surely be "Yes," in particular if we accept that we ourselves are mere machines, albeit made from flesh and bones and neurons. But a point of view is not a proof: Only if we succeed in creating sentient machines can this question be answered definitively. But what are the prospects of... © 2021 Massachusetts Institute of Technology2021Massachusetts Institute of Technology You do not currently have access to this content.
Semantics Processing of Conditional Connectives: German wenn ‘if’ Versus nur wenn ‘only if’

In this paper, the meaning and processing of the German conditional connectives (CCs) such as wenn 'if' and nur wenn 'only if' are investigated. In Experiment 1, participants read short scenarios containing a conditional sentence (i.e., If P, Q.) with wenn/nur wenn 'if/only if' and a confirmed or negated antecedent (i.e., P/not-P), and subsequently completed the final sentence about Q (with or without negation). In Experiment 2, participants rated the truth or falsity of the consequent Q after reading a conditional sentence with wenn or nur wenn and a confirmed or negated antecedent (i.e., If P, Q. P/not-P. // Therefore, Q?). Both experiments showed that neither wenn nor nur wenn were interpreted as biconditional CCs. Modus Ponens (If P, Q. P. // Therefore, Q) was validated for wenn, whereas it was not validated in the case of nur wenn. While Denial of the Antecedent (If P, Q. not-P. // Therefore, not-Q.) was validated in the case of nur wenn, it was not validated for wenn. The same method was used to test wenn vs. unter der Bedingung, dass 'on condition that' in Experiment 3, and wenn vs. vorausgesetzt, dass 'provided that' in Experiment 4. Experiment 5, using Affirmation of the Consequent (If P, Q. Q. // Therefore, P.) to test wenn vs. nur wenn replicated the results of Experiment 2. Taken together, the results show that in German, unter der Bedingung, dass is the most likely candidate of biconditional CCs whereas all others are not biconditional. The findings, in particular of nur wenn not being semantically biconditional, are discussed based on available formal analyses of conditionals.