Reviewerly/exHarmony huggingface dataset:
Outlier Detection using AI: A Survey

An outlier is an event or observation that is defined as an unusual activity, intrusion, or a suspicious data point that lies at an irregular distance from a population. The definition of an outlier event, however, is subjective and depends on the application and the domain (Energy, Health, Wireless Network, etc.). It is important to detect outlier events as carefully as possible to avoid infrastructure failures because anomalous events can cause minor to severe damage to infrastructure. For instance, an attack on a cyber-physical system such as a microgrid may initiate voltage or frequency instability, thereby damaging a smart inverter which involves very expensive repairing. Unusual activities in microgrids can be mechanical faults, behavior changes in the system, human or instrument errors or a malicious attack. Accordingly, and due to its variability, Outlier Detection (OD) is an ever-growing research field. In this chapter, we discuss the progress of OD methods using AI techniques. For that, the fundamental concepts of each OD model are introduced via multiple categories. Broad range of OD methods are categorized into six major categories: Statistical-based, Distance-based, Density-based, Clustering-based, Learning-based, and Ensemble methods. For every category, we discuss recent state-of-the-art approaches, their application areas, and performances. After that, a brief discussion regarding the advantages, disadvantages, and challenges of each technique is provided with recommendations on future research directions. This survey aims to guide the reader to better understand recent progress of OD methods for the assurance of AI.
Develop English Electronic Module for Tourism Through Analysis of Learner’s and Context

Tourism development in Lampung province has been developing rapidly and the need for English communication skills at the tourism workplace is highly required due to a lot of visitors are foreigners. The state Polytechnic of Lampung has opened a new program study of tour and travel to support the government in the tourism sector and train students to have better English skills therefore need to develop good materials for teaching which meet with tourism industry. Developing good English materials for tourism based on an Android smartphone is needed since students lack academic hours for learning English. The objective of this research is to develop an electronic module (e-module) of English for tourism based on analysis learners and context that become important step in Dick and Carey Model. The development of e-module of English for tourism applied collaboration of research and development (R&amp;amp;D) that focus on the third of ten steps of Dick and Carey Model. The result of this research is a prototype of e-module of English for tourism that has been validated and published on google play store.
Fixed points, descents, and inversions in parabolic double cosets of the  symmetric group

We consider statistics on permutations chosen uniformly at random from fixed parabolic double cosets of the symmetric group. We show that the distribution of fixed points is asymptotically Poisson and establish central limit theorems for the distribution of descents and inversions. Our proofs use Stein's method with size-bias coupling and dependency graphs, which also gives convergence rates for our distributional approximations. As applications of our size-bias coupling and dependency graph constructions, we obtain concentration of measure results on the number of fixed points, descents, and inversions.
ADEPT: Automatic Differentiable DEsign of Photonic Tensor Cores

Photonic tensor cores (PTCs) are essential building blocks for optical artificial intelligence (AI) accelerators based on programmable photonic integrated circuits. PTCs can achieve ultra-fast and efficient tensor operations for neural network (NN) acceleration. Current PTC designs are either manually constructed or based on matrix decomposition theory, which lacks the adaptability to meet various hardware constraints and device specifications. To our best knowledge, automatic PTC design methodology is still unexplored. It will be promising to move beyond the manual design paradigm and "nurture" photonic neurocomputing with AI and design automation. Therefore, in this work, for the first time, we propose a fully differentiable framework, dubbed ADEPT, that can efficiently search PTC designs adaptive to various circuit footprint constraints and foundry PDKs. Extensive experiments show superior flexibility and effectiveness of the proposed ADEPT framework to explore a large PTC design space. On various NN models and benchmarks, our searched PTC topology outperforms prior manually-designed structures with competitive matrix representability, 2x-30x higher footprint compactness, and better noise robustness, demonstrating a new paradigm in photonic neural chip design.
From Assistants to Friends: Investigating Emotional Intelligence of IPAs  in Hindi and English

Intelligent Personal Assistants (IPAs) like Amazon Alexa, Apple Siri, and Google Assistant are increasingly becoming a part of our everyday. As IPAs become ubiquitous and their applications expand, users turn to them for not just routine tasks, but also intelligent conversations. In this study, we measure the emotional intelligence (EI) displayed by IPAs in the English and Hindi languages; to our knowledge, this is a pioneering effort in probing the emotional intelligence of IPAs in Indian languages. We pose utterances that convey the Sadness or Humor emotion and evaluate IPA responses. We build on previous research to propose a quantitative and qualitative evaluation scheme encompassing new criteria from social science perspectives (display of empathy, wit, understanding) and IPA-specific features (voice modulation, search redirects). We find EI displayed by Google Assistant in Hindi is comparable to EI displayed in English, with the assistant employing both voice modulation and emojis in text. However, we do find that IPAs are unable to understand and respond intelligently to all queries, sometimes even offering counter-productive and problematic responses. Our experiment offers evidence and directions to augment the potential for EI in IPAs.
Producing augmentation-invariant embeddings from real-life imagery

This article presents an efficient way to produce feature-rich, high-dimensionality embedding spaces from real-life images. The features produced are designed to be independent from augmentations used in real-life cases which appear on social media. Our approach uses convolutional neural networks (CNN) to produce an embedding space. An ArcFace head was used to train the model by employing automatically produced augmentations. Additionally, we present a way to make an ensemble out of different embeddings containing the same semantic information, a way to normalize the resulting embedding using an external dataset, and a novel way to perform quick training of these models with a high number of classes in the ArcFace head. Using this approach we achieved the 2nd place in the 2021 Facebook AI Image Similarity Challenge: Descriptor Track.
Reactive Message Passing for Scalable Bayesian Inference

We introduce Reactive Message Passing (RMP) as a framework for executing schedule-free, robust and scalable message passing-based inference in a factor graph representation of a probabilistic model. RMP is based on the reactive programming style that only describes how nodes in a factor graph react to changes in connected nodes. The absence of a fixed message passing schedule improves robustness, scalability and execution time of the inference procedure. We also present ReactiveMP.jl, which is a Julia package for realizing RMP through minimization of a constrained Bethe free energy. By user-defined specification of local form and factorization constraints on the variational posterior distribution, ReactiveMP.jl executes hybrid message passing algorithms including belief propagation, variational message passing, expectation propagation, and expectation maximisation update rules. Experimental results demonstrate the improved performance of ReactiveMP-based RMP in comparison to other Julia packages for Bayesian inference across a range of probabilistic models. In particular, we show that the RMP framework is able to run Bayesian inference for large-scale probabilistic state space models with hundreds of thousands of random variables on a standard laptop computer.
Ontology-Based Skill Description Learning for Flexible Production  Systems

The increasing importance of resource-efficient production entails that manufacturing companies have to create a more dynamic production environment, with flexible manufacturing machines and processes. To fully utilize this potential of dynamic manufacturing through automatic production planning, formal skill descriptions of the machines are essential. However, generating those skill descriptions in a manual fashion is labor-intensive and requires extensive domain-knowledge. In this contribution an ontology-based semi-automatic skill description system that utilizes production logs and industrial ontologies through inductive logic programming is introduced and benefits and drawbacks of the proposed solution are evaluated.
Assessment of Artificial Neural Network to Identify Compositional Differences in Ultrahigh-Resolution Mass Spectra Acquired from Coal Mine Affected Soils

This study assessed the applicability of artificial neural networks (ANNs) as a tool to identify compounds contributing to compositional differences in coal-contaminated soils. An artificial neural network model was constructed from laser desorption ionization ultrahigh-resolution mass spectra obtained from coal contaminated soils. A good correlation (R2 = 1.00 for model and R 2 = 0.99 for test) was observed between the measured and predicted values, thus validating the constructed model. To identify chemicals contributing to the coal contents of the soils, the weight values of the constructed model were evaluated. Condensed hydrocarbon and low oxygen containing compounds were found to have larger weight values and hence they were the main contributors to the coal contents of soils. In contrast, compounds identified as lignin did not contribute to the coal contents of soils. These findings were consistent with the conventional knowledge on coal and results from the conventional partial least square method. Therefore, we concluded that the weight interpretation following ANN analysis presented herein can be used to identify compounds that contribute to the compositional differences of natural organic matter (NOM) samples.
The first-order logic of CZF is intuitionistic

We prove that the first-order logic of CZF is intuitionistic first-order logic. To do so, we introduce a new model of transfinite computation (Set Register Machines) and combine the resulting notion of realisability with Beth semantics. On the way, we also show that the propositional admissible rules of CZF are exactly those of intuitionistic propositional logic.
Makeup216: Logo Recognition with Adversarial Attention Representations

One of the challenges of logo recognition lies in the diversity of forms, such as symbols, texts or a combination of both; further, logos tend to be extremely concise in design while similar in appearance, suggesting the difficulty of learning discriminative representations. To investigate the variety and representation of logo, we introduced Makeup216, the largest and most complex logo dataset in the field of makeup, captured from the real world. It comprises of 216 logos and 157 brands, including 10,019 images and 37,018 annotated logo objects. In addition, we found that the marginal background around the pure logo can provide a important context information and proposed an adversarial attention representation framework (AAR) to attend on the logo subject and auxiliary marginal background separately, which can be combined for better representation. Our proposed framework achieved competitive results on Makeup216 and another large-scale open logo dataset, which could provide fresh thinking for logo recognition. The dataset of Makeup216 and the code of the proposed framework will be released soon.
Trivial bundle embeddings for learning graph representations

Embedding real-world networks presents challenges because it is not clear how to identify their latent geometries. Embedding some disassortative networks, such as scale-free networks, to the Euclidean space has been shown to incur distortions. Embedding scale-free networks to hyperbolic spaces offer an exciting alternative but incurs distortions when embedding assortative networks with latent geometries not hyperbolic. We propose an inductive model that leverages both the expressiveness of GCNs and trivial bundle to learn inductive node representations for networks with or without node features. A trivial bundle is a simple case of fiber bundles,a space that is globally a product space of its base space and fiber. The coordinates of base space and those of fiber can be used to express the assortative and disassortative factors in generating edges. Therefore, the model has the ability to learn embeddings that can express those factors. In practice, it reduces errors for link prediction and node classification when compared to the Euclidean and hyperbolic GCNs.
Improving Traffic Sign Recognition by Active Search

We describe an iterative active-learning algorithm to recognise rare traffic signs. A standard ResNet is trained on a training set containing only a single sample of the rare class. We demonstrate that by sorting the samples of a large, unlabeled set by the estimated probability of belonging to the rare class, we can efficiently identify samples from the rare class. This works despite the fact that this estimated probability is usually quite low. A reliable active-learning loop is obtained by labeling these candidate samples, including them in the training set, and iterating the procedure. Further, we show that we get similar results starting from a single synthetic sample. Our results are important as they indicate a straightforward way of improving traffic-sign recognition for automated driving systems. In addition, they show that we can make use of the information hidden in low confidence outputs, which is usually ignored.
An Interface between Legacy and Modern Mobile Devices for Digital  Identity

In developing regions a substantial number of users rely on legacy and ultra-low-cost mobile devices. Unfortunately, many of these devices are not equipped to run the standard authentication or identity apps that are available for smartphones. Increasingly, apps that display Quick Response (QR) codes are being used to communicate personal credentials (e.g., Covid-19 vaccination certificates). This paper describes a novel interface for QR code credentials that is compatible with legacy mobile devices. Our solution, which we have released under open source licensing, allows Web Application Enabled legacy mobile devices to load and display standard QR codes. This technique makes modern identity platforms available to previously excluded and economically disadvantaged populations.
Vibrating tilt platform enhancing immersive experience in VR

Virtual reality systems, since their initial development, suffered some disadvantages. One of them was the fact that they had only visual interfaces. This limitation, however, has been successfully overcome with the development of haptic technology. Peripheral solutions reinforcing and enriching VR experience are now commonplace, and many haptic systems are being developed for deepening VR immersion. This paper discusses a new peripheral solution &#x2013; a vibrating tilt platform with three angles of inclination to be used for enhancing VR experience.
Research on the application method of NLP technology based on AutoPrompt

Sentiment analysis methods usually rely on a large amount of labeled data, but in practice, due to the limitation of corpus resources, most of the existing sentiment analysis methods are based on English texts, and there are few studies on languages with scarce corpus resources. On the other hand, for the same task in different languages, each task has to do feature engineering again. The research on language-specific feature and representation extraction is continuously refined, and re-extracting features is very time-consuming and labor-intensive. The purpose of this paper is to find a way to learn common expressions in different languages, so that sentiment analysis of unlabeled text in target language can be done using only labeled data in the source language. The paper is based on the task of sentiment analysis, through adversarial training on the basis of the pre-training model, and at the same time in order to alleviate overfitting and catastrophic forgetting caused by the lack of target language labels, a knowledge distillation method is used for joint training. In order to further strengthen the “sentiment” factor of the model, “emoji” is used as an auxiliary label to learn the unique expression patterns of different languages, and further improve the effect of cross-language sentiment analysis.
Blockchain-based carbon allowance trading market construction

In order to successfully achieve the goal of “carbon peak and carbon neutrality” and to more effectively promote carbon emission reduction, this article starts from the power industry and proposes a carbon allowance trading scheme based on blockchain technology, To restrict the amount of carbon dioxide produced by thermal power plants, and contribute to the “dual carbon” goal. Users can buy and sell carbon quotas on the platform, and use blockchain technology to protect the security of user transactions, making market transactions more transparent. All transaction information is backed up and stored on all nodes of the blockchain, which is truly Centralized storage.
Information Disclosure and Privacy Protection System in Epidemic Prevention and Control under the Background of Big Data

In order to effectively solve the conflict and contradiction between citizens&#x0027; personal information disclosure and citizens&#x0027; right to privacy protection in epidemic prevention and control, this paper proposes a novel information disclosure and privacy protection system in epidemic prevention and control under the background of big data. The system fully combines the advantages of big data technology and the characteristics of epidemic prevention and control work, and starts from the dimensions of informed consent rules, personal information right, right to know, public interest and personal privacy. Based on this, the system also emphasizes the legitimacy and transparency of the epidemic prevention and control work, as well as the importance of building a sound and efficient accountability mechanism. The results show that the system can effectively solve the contradiction and conflict between personal information disclosure and personal privacy to a certain extent.
Tensor network to learn the wavefunction of data

How many different ways are there to handwrite digit 3? To quantify this question imagine extending a dataset of handwritten digits MNIST by sampling additional images until they start repeating. We call the collection of all resulting images of digit 3 the "full set." To study the properties of the full set we introduce a tensor network architecture which simultaneously accomplishes both classification (discrimination) and sampling tasks. Qualitatively, our trained network represents the indicator function of the full set. It therefore can be used to characterize the data itself. We illustrate that by studying the full sets associated with the digits of MNIST. Using quantum mechanical interpretation of our network we characterize the full set by calculating its entanglement entropy. We also study its geometric properties such as mean Hamming distance, effective dimension, and size. The latter answers the question above -- the total number of black and white threes written MNIST style is $2^{72}$.
A Targeted Assessment of the Syntactic Abilities of Transformer Models for Galician-Portuguese

This paper presents a targeted syntactic evaluation of Transformer models for Galician-Portuguese. We defined three experiments that allow to explore how these models, trained with a masked language modeling objective, encode syntactic knowledge. To do so, we created a new dataset including test instances of number (subject-verb), gender (subject-predicative adjective), and person (subject-inflected infinitive) agreement. This dataset was used to evaluate monolingual and multilingual BERT models, controlling for various aspects such as the presence of attractors or the distance between the dependent elements. The results show that Transformer models perform competently in many cases, but they are generally confounded by the presence of attractors in long-distance dependencies. Moreover, the different behavior of monolingual models trained with the same corpora reinforces the need for a deep exploration of the network architectures and their learning process.
Learning Query Expansion over the Nearest Neighbor Graph

Query Expansion (QE) is a well established method for improving retrieval metrics in image search applications. When using QE, the search is conducted on a new query vector, constructed using an aggregation function over the query and images from the database. Recent works gave rise to QE techniques in which the aggregation function is learned, whereas previous techniques were based on hand-crafted aggregation functions, e.g., taking the mean of the query's nearest neighbors. However, most QE methods have focused on aggregation functions that work directly over the query and its immediate nearest neighbors. In this work, a hierarchical model, Graph Query Expansion (GQE), is presented, which is learned in a supervised manner and performs aggregation over an extended neighborhood of the query, thus increasing the information used from the database when computing the query expansion, and using the structure of the nearest neighbors graph. The technique achieves state-of-the-art results over known benchmarks.
Intelligent Systems of Telemedicine Monitoring for Countryside and Agriculture

The software platform for telemedicine monitoring with Artificial Intelligence (AI) elements is presented. The principles and methods of development of the platform are described and discussed. The systems created on a base of the platform would be useful for countryside inhabitants’ life quality. Now the platform includes specific Medical Messenger, Subsystem of Intelligent Agents (IA), Subsystem of storage of the personal medical data. IA unload a doctor from routine work. The IA inform the doctor when his direct urgent attention and actions are needed. They also provide a patient and a physician with general information relevant to the patient statement picking out and consolidating it from the Patient and the Doctor Digital Libraries. Good results of the test exploitation of the systems created on a base of the platform are presented. It is displayed that objective medical indicators as well as subjective perception by the doctors and patients of RHM were positive. Already created RHM systems can be used now (and were used in the pilot running) for countryside inhabitants relating to corresponding patient category and diseases class. It is also expedient to develop specific RHM system for health safety of people living in countryside. So, there are perspective opportunities to improve social conditions of countryside and promote agriculture.The similar approach can be used also directly for agricultural stock-raising. The “televeterinary” platform for home animals had been already developed. Systems implementing remote monitoring with sensors placed on the animals and with participation of veterinary surgeons should be created. This article invites to multidisciplinary co-operation to develop and implement RHM systems for countryside and agriculture.