Reviewerly/exHarmony huggingface dataset:
Low Complexity Image Inpainting Using AutoEncoder

Image inpainting is filling the missing or corrupted pixels in an image in a realistic way that cannot be differentiated by human eye. Deep learning is widely used in image inpainting and it exhibits better performance than classical inpainting methods, but it requires high processing resources and longer time to train the model. In this paper, we propose an autoencoder architecture that outperforms other deep learning techniques in literature methods with lower processing and time complexity.
Constellate: A Pedagogy-Focused Text Analytics Platform

Constellate is a text analytics platform offered by ITHAKA, a nonprofit organization devoted to providing platforms for educational and research purposes. It uses data sources such as JSTOR, Portico, Chronicling America, DocSouth, South Asian Open Archive, and Reveal Digital. It is a great tool for users of these data sources who would like to engage in data analytics projects without any exposure to programming languages. Because Constellate was designed primarily for pedagogical purposes, ITHAKA offers a growing number of training workshops and documentations in text analysis methods, Python programming, and data visualization. The integrated design of document search, dataset builder, and visualization reduces the learning curve for beginners.
Research and Application of intelligent purchasing and transportation model for coal-fired power plant

Abstract At present, the decision-making method of coal purchase in most coal-fired power stations is based on the experience of blending coal and burning in thermal power units, but this method does not consider the factors of economy and transportation. In order to solve this problem, this paper proposes a method to optimize the coal purchasing scheme of coal-fired power plants based on the joint optimization decision model of coal purchasing and dispatching. First fuel characteristics of mixed coal and purchasing cost model was constructed to determine the fountainhead procurement constraints, and mixing coal prices, then dispatching model was constructed to determine the scheduling constraints and transport price, the final will buy coal cost and transportation cost summation minimum and the nature of the mixed coal constraints as a joint decision model, and USES the particle swarm optimization algorithm to solve it. The decision model is applied to a power plant for verification, and the calculation results show that the total purchasing cost of power plant is greatly reduced.
Privacy-Preserving Speaker Verification and Speech Recognition

This paper proposes an approach to speaker verification and speech recognition in environments that require authentication and privacy protection, while accuracy and data utility must remain high. Our methodology aims at protecting audio files and users’ identities through the use of encryption and hashing algorithms, while at the same time providing accurate speaker’s identity prediction. In addition, for speech recognition, we introduce a mechanism to anonymize the resulting transcript of the recognized spoken language using the Named Entity Recognition method by removing sensitive entities from the text according to the user’s preferences. Furthermore, a privacy-preserving version of the original audio is obtained by performing a text-to-speech translation of the anonymized transcript, which together, the anonymous audio and transcript can be transmitted to third parties or service providers without violating privacy restrictions. The proposed methodology has been validated with a set of experiments on a well-known audio dataset, the Librispeech dataset. A type of Time Delay Neural Networks, ECAPA-TDNN was used for speaker verification, Deep Speech as a type of Recurrent Neural Networks was used for speech recognition, NER for entity recognition, cryptography and hashing for privacy protection. The results demonstrate the validity of our approach to protecting the privacy of user data and biometric information while simultaneously performing data analysis with a high degree of accuracy and similarity with the results obtained with no privacy mechanisms in place, also considering the use of several privacy mechanisms.
Neural machine translation and the indivisibility of culture and language

Abstract Neural machine translation (NMT) is becoming a common resource both for professional translators and for people who need small, occasional translations. The wide use of NMT for professional purposes reshapes the conditions of translation assignments. Moreover, the effects on the target text and language are not well known, although some studies already suggest a stronger influence of the source language on them. On the other hand, since many NMT tools are easily and freely available and even accessible via mobile devices, they are being increasingly used by non-professionals to carry out short translations mostly. These occasional translators use these tools both to understand a text originally written in a language they do not fluently speak (translation into their first language) and to publish a text in the language they do not master (translation into their second language or even into a language in which the user has poor or no knowledge at all). Finally, NMT is also present in everyday digital products even without users being aware of its intervention, for example in specific apps on smart devices. This article proposes a reflection on the effects of NMT in all these scenarios, with a special focus on the effects on the reception of the target text and on the target language standard.
Does Bitcoin Contribute to Portfolio Performance?

This study analyzes the role of Bitcoin as an investment asset in a global multi-asset portfolio. For portfolio construction, we use a risk-based portfolio strategy that excludes estimates of future expected returns. We derive the optimal asset allocation ratio of Bitcoin included in the global multi-asset portfolio and compare the performance with the general portfolio. We find that the average investment weight of Bitcoin in the portfolio is 1.8%, and the portfolio containing Bitcoin shows superior investment performance compared to the portfolio without Bitcoin.
Distributed sequential estimation procedures

Abstract Data collected from distributed sources or sites commonly have different distributions or contaminated observations. Active learning procedures allow us to assess data when recruiting new data into model building. Thus, combining several active learning procedures together is a promising idea, even when the collected data set is contaminated. Here, we study how to conduct and integrate several adaptive sequential procedures at a time to produce a valid result via several machines or a parallel‐computing framework. To avoid distraction by complicated modelling processes, we use confidence set estimation for linear models to illustrate the proposed method and discuss the approach's statistical properties. We then evaluate its performance using both synthetic and real data. We have implemented our method using Python and made it available through Github at https://github.com/zhuojianc/dsep .
Monocular Multiple Image Recovery

Obtaining multiple images with redundant information can also be accomplished by collecting changing images of scene at the same location. These images can be obtained with only one (fixed) camera, so it is also called the monocular method. This chapter will analyze the imaging process from the light source to the scene and then to the lens, showing that the image gray level depends on the intensity of the light source and the reflection characteristics of the scenery, as well as the geometric relationship between them. This chapter will discuss the establishment of the relationship between the image gray level and the orientation of the scenery, and the orientation of the scenery is determined by the change of the image gray level. This chapter will introduce how to detect the motion of the scenery and uses the optical flow equation to describe and solve the principle and several special cases of the optical flow equation. This chapter will further introduce the realization of the restoration of the shape and structure of the scenery by the solution of the optical flow equation. Here, the analytical optical flow equation is solved by the transformation of the coordinate system.
Video Image and Motion Information

Video represents a special kind of sequence image, which describes the radiation intensity of the scene obtained by three separate sensors when a 3-D scene is projected onto a 2-D image plane over time. Digital video can be acquired by means of a digital camera using a CCD sensor or the like. From the perspective of learning image technology, video can be seen as an extension of (still) images. In view of the characteristics of videos containing motion information, the original image processing technology also needs to be extended accordingly. This chapter will introduce the basic content of the video, including the representation, modeling, display and format of the video, and the color model in the color TV system. This chapter will discuss the classification of the motion information in the video compared to the still image, as well as the characteristics and representation methods of foreground motion and background motion. This chapter will discuss the motion detection methods using image difference, the principle of motion detection in the frequency domain, and the detection of the direction of motion. This chapter will discuss the filtering methods that combine the characteristics of the video and consider the motion information.
Collaborative and adversarial network for text‐independent speaker verification in domain adaptation

Speaker verification models have achieved good results on the single genre data. But the performance degrades when model training and testing are not in the same domain. The adversarial training method is proposed to solve this problem by minimizing domain distribution differences. However, the adversarial training ignores domain-specific information for the domain-invariant speaker representations. In this paper, an improved collaborative adversarial network for domain adaptation in speaker verification is performed. Compared to the adversarial training, a collaborative discriminator is newly incorporated that learns domain-specific information at the lower layers. Further, the projection block is added to the collaborative discriminator. It reduces the noise introduced by the collaborative discriminator. Experiments are conducted in different mismatch scenarios and using different speaker encoders. All the experimental results show that the performance of this method is better than the baseline and previous work using adversarial training.
Effects of Solar Resource Sampling Rate and Averaging Interval on Hourly Modeling Errors

Solar energy modeling errors due to time-averaged hourly inputs are significant where solar resource variability and inverter loading ratio are both high. However, predictions of photovoltaic (PV) system performance are most frequently made with hourly solar resource inputs, typically computed from satellite data obtained every 15 or 30 min. Therefore, we studied the effects of solar resource sampling rate and time-averaging interval on hourly modeling errors by using high-frequency measurements from eight different locations across the United States. When we selected minute-average measurements at various sampling rates and averaged them to hourly data, we observed increasing modeling errors for sampling rates 30 min or shorter. At a 30-min sampling rate averaged hourly, we observed an error that was 50% of 1-min samples averaged hourly. As sampling rate approached 60 min, modeling errors decreased, partially canceling out due to the randomness of the low-frequency sampled data. We examined PV systems with dc–ac ratios <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 1.3 and observed that clipping errors dominated modeling errors from other sources like transposition to plane-of-array irradiance at sites with greater solar variability. Based on our analysis, we recommend that an hourly modeling correction be applied whenever hourly inputs are used, especially at sites with high solar variability and dc–ac ratios greater than one.
Kinematic primitives in action similarity judgments: A human-centered computational model

This paper investigates the role that kinematic features play in human action similarity judgments. The results of three experiments with human participants are compared with the computational model that solves the same task. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The comparative experimental results show that both model and human participants can reliably identify whether two actions are the same or not. Specifically, most of the given actions could be similarity judged based on very limited information from a single feature domain (velocity or spatial). Both velocity and spatial features were however necessary to reach a level of human performance on evaluated actions. The experimental results also show that human performance on an action identification task indicated that they clearly relied on kinematic information rather than on action semantics. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features, which can provide an essential basis for classifying human actions.
Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning

Proactive edge association is capable of improving wireless connectivity at the cost of increased handover (HO) frequency and energy consumption, while relying on a large amount of private information sharing required for decision making. In order to improve the connectivity-cost trade-off without privacy leakage, we investigate the privacy-preserving joint edge association and power allocation (JEAPA) problem in the face of the environmental uncertainty and the infeasibility of individual learning. Upon modelling the problem by a decentralized partially observable Markov Decision Process (Dec-POMDP), it is solved by federated multi-agent reinforcement learning (FMARL) through only sharing encrypted training data for federatively learning the policy sought. Our simulation results show that the proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.
A Generative Approach to Condition-Aware Score Calibration for Speaker Verification

In speaker verification, score calibration is employed to transform verification scores to log-likelihood ratios (LLRs) which are statistically interpretable. Conventional calibration techniques apply a global score transform. However, in condition-aware (CA) calibration, information conveying signal conditions is provided as input, allowing calibration to be adaptive. This paper explores a generative approach to condition-aware score calibration. It proposes a novel generative model for speaker verification trials, each which includes a trial score, a trial label, and the associated pair of speaker embeddings. Trials are assumed to be drawn from a discrete set of underlying signal conditions which are modeled as latent Categorical random variables, so that trial scores and speaker embeddings are drawn from condition-dependent distributions. An Expectation-Maximization (EM) Algorithm for parameter estimation of the proposed model is presented, which does not require condition labels and instead discovers relevant conditions in an unsupervised manner. The generative condition-aware (GCA) calibration transform is then derived as the log-likelihood ratio of a verification score given the observed pair of embeddings. Experimental results show the proposed approach to provide performance improvements on a variety of speaker verification tasks, outperforming static and condition-aware baseline calibration methods. GCA calibration is observed to improve the discriminative ability of the speaker verification system, as well as provide good calibration performance across a range of operating points. The benefits of the proposed method are observed for task-dependent models where signal conditions are known, for universal models which are robust across a range of conditions, and when facing unseen signal conditions.
An Extraction and Validity Evaluation Method Proposal for Monitoring Points for In-Vehicle Systems: Deriving Cybersecurity Requirements

To counter increasing threats of cyberattacks on automobiles, it is important to practice “secure by design.” For this reason, the regulations and standards that require a risk-based security design are being formulated in the automotive industry. In the concept phase described in ISO/SAE21434:2021, it is necessary to determine cybersecurity controls based on results of the threat analysis (TA) and the risk assessment (RA). On the other hand, the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) defines four categories of cybersecurity controls: protection, detection, response, and recovery demand to select cybersecurity controls from these categories. To be able to formulate appropriate cybersecurity control for detection, this article proposes an extension of the threat analysis and risk assessment (TARA) based on Japanese Automotive Standards Organization (JASO) TP15002:2016 to derive candidate monitoring points (MPs) to implement detection functions. We also present evaluation metrics for the MPs derived by our proposed method and discuss future research directions.
Self-Supervised Adversarial Video Summarizer With Context Latent Sequence Learning

Video summarization attempts to create concise and complete synopsis of a video through identifying the most informative and explanatory parts while removing redundant video frames, which facilitates retrieving, managing and browsing video efficiently. Most existing video summarization approaches heavily rely on enormous high-quality human-annotated labels or fail to produce semantically meaningful video summaries with the guidance of prior information. Without any supervised labels, we propose Self-supervised Adversarial Video Summarizer (2SAVS) that exploits context latent sequence learning to generate satisfying video summary. To implement it, our model elaborates a novel pretext task of identifying latent sequences and normal frames by training self-supervised generative adversarial network (GAN) with several well-designed losses. As the core components of 2SAVS, Clip Consistency Representation (CCR) and Hybrid Feature Refinement (HFR) are developed to ensure semantic consistency and continuity of clips. Furthermore, a novel separation loss is designed to explicitly enlarge the distance between prediction frame scores to effectively enhance the model’s discriminative ability. Differently, latent sequences, additional finetune operations and generators are not required when inferring video summary. Experiments on two challenging and diverse datasets demonstrate that our approach outperforms other state-of-the-art unsupervised and weakly-supervised methods, and even produces comparable results with several excellent supervised methods.
Noble Approach on Sensor Fused Bio Intelligent Path Optimisation and Single Stage Obstacle Recognition in Customized Mobile Agent

Optimized PTP (Point to Point) Navigation by customized mobile agent constitutes extensive research domain in Machine Intelligence. Distinct Literature survey confirms the proposal of SLAM (Simultaneous Localization And Mapping) mediated sensor usage for visual data collection in a particular GPS (Global Positioning System) - denied indoor environment. This has been further followed by fused data collection using multi sensor for obtaining long time and high precision localization and obstacle detection in MRN (Mobile Robot Navigation). This paper proposes an idea to incorporate machine learning module for data modeling so that the obstacle detection and identification results in relatively more precise, accurate and efficient. The comparative numerical analysis has been presented to establish the proposed methodology. Along with this, Fusion SLAM algorithm has been created and proposed which combines features of both 2D LiDAR (Light Detection And Ranging) and RGB-D (Red, Green, Blue Depth) SLAM, resulting in visual reference of combined detected periphery and reconstructed 3D depth of on-path obstacles. The aforesaid procedure is further extended with integration of Bio Intelligent path planning approach. Experimental analysis carried out with sample candidate functions with respect to concerned mobile robot and considered indoor environment has been presented which would prove to be important citation for further research in the domain of collision-free optimum MRN.
A Hybrid Classifier-based Ontology driven image Tag Recommendation framework for social image tagging

With the burgeoning of social network sites, social image tagging, where images are annotated with tags, is gaining traction. But, in the domain of social media, this task becomes complex as images are associated with plenitude of object information. In this paper, we have put forth a Hybrid Classifier-based Ontology driven image Tag Recommendation framework for social image tagging which is based on a knowledge-centric, metadata generating, semantically-inclined and ontology-driven strategy. The COIL-100 dataset is used for the implementation. The proposed HCOntoTR approach is evaluated using sundry performance measures by comparison with baseline models like UIT, HRSDL and RSSVM. The proposed HCOntoTR model gives finer results set against the baseline models, with a precision of 94.49 % which surpasses the others, owing to the amalgamation of a profusion of knowledge from heterogeneous knowledge sources namely the Wikidata and Google's Knowledge API.
Emotion Cause Pair Extraction By Multi Task Learning on Enhanced English Dataset

Emotion analysis and detection is a step further in sentiment analysis that aims to detect the emotion portrayed. A deeper task in mining opinions and emotions is to know the trigger behind the emotion or its stimuli and that is achieved through Emotion Cause Extraction. However, since it requires annotating sentences, limited research has been carried out in this area. Hence, there is a need to explore emotion cause extraction and expand its dataset in the most used language worldwide - English. This paper aims to extract causes without needing annotation of emotion by the proposed approach of Emotion Cause Pair Extraction on English language. For this, we have constructed a dataset of about 6000 English emotionally annotated sentences spanning across six primary emotions - happy, sad, fear, guilt, shame, disgust. Added to this, we have proposed algorithms to extract the causes of emotions from these sentences. The experimental models of our proposed approach gives encouraging results (a ∼ 9% increase from the state of the art methods) and also uses a larger dataset as compared to existing research on English language. We conclude by sharing the results, and discussing future advancements in this field.