Reviewerly/exHarmony huggingface dataset:
An Unsupervised Video Stabilization Algorithm Based on Key Point Detection

In recent years, video stabilization has improved significantly in simple scenes, but is not as effective as it could be in complex scenes. In this study, we built an unsupervised video stabilization model. In order to improve the accurate distribution of key points in the full frame, a DNN-based key-point detector was introduced to generate rich key points and optimize the key points and the optical flow in the largest area of the untextured region. Furthermore, for complex scenes with moving foreground targets, we used a foreground and background separation-based approach to obtain unstable motion trajectories, which were then smoothed. For the generated frames, adaptive cropping was conducted to completely remove the black edges while maintaining the maximum detail of the original frame. The results of public benchmark tests showed that this method resulted in less visual distortion than current state-of-the-art video stabilization methods, while retaining greater detail in the original stable frames and completely removing black edges. It also outperformed current stabilization models in terms of both quantitative and operational speed.
Grasshopper optimization algorithm based path planning for autonomous mobile robot

Autonomous mobile robots have become very popular and essential in our life, especially in industry. One of the crucial activities of the robot is planning the path from a start point to a target point, avoiding obstacles in the environment. Recently, path planning received more attention, and many methodologies have been proposed. Path planning studies have shown the effectiveness of swarm intelligence in complex and known or unknown environments. This paper presents a global path planning method based on grasshopper optimization algorithm (GOA) in a known static environment. This algorithm is improved using the bias factor to increase the efficiency and improve the resulting path. The resulting path from this algorithm is further enhanced using an improved version multinomial logistic regression algorithm (MLR). The algorithms were evaluated using three different large environments of varying complexities. The GOA algorithm has been compared with the ant colony optimization algorithm (ACO) using the same environments. The experiments have shown the superiority of our algorithm in terms of time convergence and cost.
Spectral control of quantum emitters in quantum information processing

We demonstrate the use of external field protocols to control optical properties of quantum spins for optimized photon-mediated operations in quantum information processing. Specifically, we study two-photon interference operations between spectrally different quantum emitters with realistic control protocols. We show that, well beyond their idealized versions, appropriate external field protocols can suppress spectral diffusion, mitigate inhomogeneous broadening and restore photon indistinguishability between spectrally different quantum emitters. These protocols can play an important role in enabling more efficient light-matter interfaces that are essential for scalable quantum information processing platforms.
Sketch+ for Visual and Correlation-Based Exploratory Data Analysis: A Case Study with COVID-19 Databases

The amount of data daily generated by different sources grows exponentially and brings new challenges to the information technology experts. The recorded data usually include heterogeneous attribute types, such as the traditional date, numerical, textual, and categorical information, as well as complex ones, such as images, videos, and multidimensional data. Simply posing similarity queries over such records can underestimate the semantics and potential usefulness of particular attributes. In this context, the Exploratory Data Analysis (EDA) technology is well-suited to understand data and perform knowledge extraction and visualization of existing patterns. In this paper, we propose Sketch+ , a technique and a corresponding supporting tool to compare electronic health records (provided by hospitals) by similarity, supporting correlation-based exploratory analysis over attributes of different types and allowing data preprocessing tasks for visualization and knowledge extraction. Sketch+ computes partial and overall data correlation considering distance spaces induced by the attributes. It employs both ANOVA and association rules with lift correlations to study relationships between variables, allowing extensive data analysis. Among the tools provided, a pixel-oriented one drives the analysts to observe visual correlations among dates, categorical and numerical attributes. As a running case study, we employed three open databases of COVID-19 cases, showing that specialists can benefit from the inference modules of Sketch+ to analyze electronic records. The study highlights how Sketch+ can be employed to spot strong correlations among tuples and attributes, with statistically significant results. The exploratory analysis has been shown to be an essential complement for similarity search tasks, identifying and evaluating patterns from heterogeneous attributes.
Creating an Enhanced Infrastructure of Parliamentary Archives for Better Democratic Transparency and Legislative Research

Abstract In this paper, we summarise the key findings of the forum “Data4Parliaments” Parliamentary Data for a Better Democracy, organised by the OPTED network at the European Parliament on 15 June 2022. The aim of the conference was to provide a forum for the exchange of ideas related to sharing and analysing parliamentary text data between official archives and different user groups from the social sciences and the civil society.
Probabilistic learning for pulsar classification

Abstract In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the effect of class imbalance, the performance of the models, achieving relatively high probability of differentiating the positive class from the negative one ( roc-auc ∼ 0.98), is very promising overall. We estimate the predictive entropy of each model predictions and find that DKL is more confident than DGP in its predictions and provides better uncertainty calibration. Upon investigating the effect of training with imbalanced dataset on the models, results show that each model performance decreases with an increasing number of the majority class in the training set. Interestingly, with a number of negative class 10× that of positive class, the models still provide reasonably well calibrated uncertainty, i.e. an expected Uncertainty Calibration Error (UCE) less than 6%. We also show in this study how, in the case of relatively small amount of training dataset, a convolutional neural network based classifier trained via Bayesian Active Learning by Disagreement (BALD) performs. We find that, with an optimized number of training examples, the model — being the most confident in its predictions — generalizes relatively well and produces the best uncertainty calibration which corresponds to UCE = 3.118%.
Design and Flight Testing of a Rocket-Launched Folding UAV for Earth and Planetary Exploration Applications

With the advances in Uncrewed Aerial Vehicles&#x0027; (UAVs) control capabilities, advances in additive manufacturing technology, and miniaturization of components new opportunities have arisen for the application of UAVs to applications such as search and rescue, high-altitude surveying, and planetary exploration. UAVs can now be rapidly prototyped and manufactured to suit many applications. They have been proven to have the capability to survey large areas and perform complex scientific missions during flight. Recently, the Ingenuity Mars Helicopter performed a series of powered, controlled flights on another planet. Planetary exploration applications of UAVs pose challenges that are unique in comparison to terrestrial ones. Among these challenges are the deployment of UAVs, as well as the significant area required to be covered by a UAV conducting aerial surveying. Current applications rely on surface deployment of the UAV after successful landing of a primary payload, such as the Mars Perseverance rover. Deploying a UAV at altitude comes with the benefits of increased flight time and range associated with the mission profile being activated at an elevation above the planetary surface. Utilizing a morphological airframe results in a smaller pre-deployment footprint, which would allow for multiple UAVs to be packed into a single payload cannister and deployed progressively during descent. This paper presents the design and development of a low-cost, 3D printed, folding UAV for terrestrial and extra-planetary exploration applications using Commercial Off-The-Shelf (COTS) components. The flight systems are designed in such a way that the UAV can self-arm, re-orient if required, and acquire a stable hover pose before landing at a pre-determined GPS location. Other than employing GPS navigation for landing, the UAV will require no external input. The paper will also cover the design and development of a deployment system using a small high-power rocket to simulate atmospheric deployment of the UAV. Testing will aim to demonstrate the viability of deploying UAVs from a payload cannister during atmospheric injection. The project is unique in that it utilizes a novel method for deploying a UAV from a carrier vehicle while in ballistic descent, allowing for atmospheric insertion of multiple small-form-factor UAVs for planetary exploration purposes.
Mix-DANN and Dynamic-Modal-Distillation for Video Domain Adaptation

Video domain adaptation is non-trivial due to video is inherently involved with multi-dimensional and multi-modal information. Existing works mainly adopt adversarial learning and self-supervised tasks to align features. Nevertheless, the explicit interaction between source and target in the temporal dimension, as well as the adaptation between modalities, are unexploited. In this paper, we propose Mix-Domain-Adversarial Neural Network and Dynamic-Modal-Distillation (MD-DMD), a novel multi-modal adversarial learning framework for unsupervised video domain adaptation. Our approach incorporates the temporal information between source and target domains, as well as the diversity of adaptability between modalities. On the one hand, for every single modality, we mix the frames from source and target domains to form mix-samples, then let the adversarial-discriminator predict the mix ratio of a mix-sample to further enhance the ability of the model to capture domain-invariant feature representations. On the other hand, we dynamically estimate the adaptability for different modalities during training, then pick the most adaptable modality as a teacher to guide other modalities by knowledge distillation. As a result, modalities are capable of learning transferable knowledge from each other, which leads to more effective adaptation. Experiments on two video domain adaptation benchmarks demonstrate the superiority of our proposed MD-DMD over state-of-the-art methods.
Towards Open-Ended Text-to-Face Generation, Combination and Manipulation

Text-to-face (T2F) generation is an emerging research hot spot in multimedia, and its main challenge lies in the high fidelity requirement of generated portraits. Many existing works resort to exploring the latent space in a pre-trained generator, e.g., StyleGAN, which has obvious shortcomings in efficiency and generalization ability. In this paper, we propose a generative network for open-ended text-to-face generation, which is termed OpenFaceGAN. Differing from existing StyleGAN-based methods, OpenFaceGAN constructs an effective multi-modal latent space that directly converts the natural language description into a face. This mapping paradigm can fit the real data distribution well and make the model capable of open-ended and even zero-shot T2F generation. Our method improves the inference speed by an order of magnitude, e.g., 294 times than TediGAN. Based on OpenFaceGAN, we further explore text-guided face manipulation (editing). In particular, we propose a parameterized module, OpenEditor, to automatically disentangle the target latent code and update the original style information. OpenEditor also makes OpenFaceGAN directly applicable for most manipulation instructions without example-dependent searches or optimizations, greatly improving the efficiency of face manipulation. We conduct extensive experiments on two benchmark datasets namely Multi-Modal CelebA-HQ and Face2Text-v1.0. The experimental results not only show the superior performance of OpenFaceGAN to the existing T2F methods in both image quality and image-text matching degree but also greatly confirm its outstanding ability in the zero-shot generation. Codes will be released at: \textcolormagenta \urlhttps://github.com/pengjunn/OpenFace
Robust visual odometry using sparse optical flow network

Deep learning-based visual odometry (VO) has recently attracted much attention. In performing computer vision tasks, deep models are often more robust than manually extracted features. Deep models, however, have limited reliability and generalization ability which constrain their application in the VO systems. The existing approaches to address these issues are often based on creating more realistic datasets or employing strategies such as unsupervised learning and online fine-tuning. In contrast to the previous research, here we tackle these issues from the model structure standpoint. We present a simple yet robust VO (namely SF-VO) based on an especially designed sparse optical flow network. We then show that this network becomes suitable for VO applications by decomposing the global optimization problem into a single-point optimization. Combining the network with a consistency verification module and a Perspective-n-Point (PnP) solver, we then form a frame-to-frame VO system using the traditional pose estimation pipeline. Extensive experiments show that the proposed VO system effectively generalizes to real scenes while only synthetic datasets are used in the training process. It is also shown that the proposed model also outperforms other deep learning-based methods with a model size of only 1.69 M. Comparisons with the state-of-art optical flow models and performing expansion experiments further confirm that the designed network demonstrates a higher level of generalization ability and is capable of being trained based on limited datasets.
Meta-Virtuality: Strategies of Disembeddedness in Virtual Interiorities

To reclaim their seat in the rapidly growing market of virtual space, designers of the built environment can benefit from reevaluating theories that see the virtual as a mere extension/reflection of the physical. By claiming ontological autonomy from external worlds, the virtual is liberated from the hegemonic control of the physical. To explore the opportunities that such disruption in the physical/virtual continuum offers, I reflect on a series of pedagogical experiments that challenge the “myth of total virtuality”—the idea that the ultimate virtual experience is total immersion. The persistent obsession to fully immerse the user in a supposedly unmediated interiority of the virtual is evident in the minimization of the virtual reality apparatus to a state of almost nothingness. In this paper, I introduce a series of alienation/defamiliarization strategies, through which designers can invoke awareness about the mediation involved in a virtual experience—a condition that I refer to as “metavirtual.” One strategy emphasizes the pixelated ontology of the virtual space by techniques of glitching, low-resolution, low-fidelity, and low-color bitmap renders. Another involves manipulating the phenomenological expectations that our perception often experiences in non-virtual environments. This includes, not only a reconceptualization of the spatial object but also revisiting the agency of the subject in the virtual world. Different modes of spatial experience through portals, flying, and teleportation, affect the subject’s perception of space, and thereby alter their measurement of time.
Research on Decentralized Storage Based on a Blockchain

The current distributed storage solutions are still concentrated in third-party storage service providers, and the stored data are concentrated in a few cloud servers, which inevitably brings the risk of data loss, leakage, and tampering, so it is imperative to study a distributed storage and decentralized storage system. How to maintain the consistency of data in a distributed environment has become a problem in building decentralized applications, until the emergence of blockchain technology, whose decentralized, non-tamperable, and traceable features can solve this problem well. In this paper, we design a decentralized storage system combining Hyperledger Fabric and Inter Planetary File System (IPFS). In addition, from the perspective of security and availability of the decentralized storage system, we study the partitioning and the k-r allocation scheme of the stored data, propose the allocation function about the stored files, derive the mathematical formula of file security and availability based on the allocation function, and discuss the optimal parameter setting of the allocation function based on the formula to guarantee the high security and availability of the stored files. The experimental results show that the performance of the k-r allocation policy based on the minimum number nodes (MNN) is better than that of the k-r allocation policy based on the minimum slices number (MSN); however, with the same security and availability guarantees, the MNN policy will have more copies relative to the MSN policy, which is relatively wasteful of space.
ALS-MRS: Incorporating aspect-level sentiment for abstractive multi-review summarization

Multi-review summarization, the process of automatically generating a summary for a set of reviews, is of value to customers when they are making informed decisions. Incorporating sentiments can improve the performance of abstractive review summarization, as shown in some prior studies. However, these studies assume that each review contains a single sentiment, which does not reflect reality in many real conditions. In practice, a review always contains many aspects with conflicting sentiments. In this paper, we present ALS-MRS, a novel abstractive multi-review summarization model that combines multi-review representations and aspect-level sentiment. We propose an aspect-level sentiment consistency function to keep the sentiments of the various aspects in the generated summaries the same as those in the reference summaries. Specifically, ALS-MRS constructs aspect-sentiment tuples via an aspect extractor and a sentiment analysis model. In the aspect extractor, the aspects are identified according to the aspect terms obtained by an unsupervised neural attention model, and the sentiment polarity of a sentence about the aspect is detected in the sentiment analysis model. The experimental results, evaluated by automatic and human metrics on two public datasets, show that ALS-MRS performs favorably when compared against many state-of-the-art approaches.
Image Caption Generator Using Deep Learning

Abstract: Nowadays, an image caption generator has become the need of the hour, be it for social media enthusiasts or visually impaired people. It can be used as a plugin in currently trending social media platforms to recommend suitable captions for people to attach to their post or can be used by visually impaired people to understand the image content on the web thus eradicating any ambiguity in image meaning in turn also free of any discrepancy in knowledge acquisition. The proposed paper aims to generate a description of an image also called as image captioning, using CNN-LSTM architecture such that CNN layers will help in extraction of the input data and LSTM will extract relevant information throughout the processing of input such that the current word acts as an input for the prediction of the next word. The programming language used will be Python 3 and machine learning techniques. This paper will also elaborate on the functions and structure of the various Neural networks involved.
RESOLVING PERCEPTUAL CHALLENGES OF VISUALIZING UNDERGROUND UTILITIES IN MIXED REALITY

Abstract. Augmented reality (AR) is intensely explored due to its wide range of potential applications, from education and engineering to medicine and defense. Nevertheless, utilizing AR for visualizing underground utilities still faces a number of challenges, including the unavailability of reliable, readily accessible digital information about underground utilities, localization of AR devices in a GNSS-deprived environment, and visual perceptual challenges. This paper discusses the utilization of Mixed reality (MR) to address the visual perceptual challenges, which can provide a realistic visualization and convenient user experience compared to AR. Six MR visualization prototypes have been developed, namely, "General view", "General + Range view", "General + Elevator view", "X-Ray box view", "X-Ray box + Depthslider view", and "X-Ray + box + Clipping view", and they are deployed to Microsoft HoloLens 2 for testing purposes. These new MR visualization methods can potentially resolve AR's visual perceptual challenges for visualizing underground utilities.
Construction and Coordination Mechanism of College Students’ Employment and Labor Relations in the Internet+ Environment

Revealing the law of two-way selection between college students and employers is of great value to the proper construction of college students’ employment and labor relations and the coordination of employment issues. So far, there has been little research on the feasibility testing of building a harmonious employment and labor relations network for college students and on the coordination countermeasures for employment issues. To this end, this paper studies the construction and coordination mechanism of college students’ employment and labor relations in the Internet+ environment. Firstly, the spatial autocorrelation of the distribution of college students’ employment and labor relations was checked based on Moran’s I index, and a random effect spatial Durbin model was constructed based on the employment data of college students in the study region within the study period to study and analyze the spatial effects of colleges and majors on the construction of college students’ employment and labor relations in the region. Then, based on the simple graph neural network theory, a representation learning method was put forward for college students’ employment and labor relations network, and the employment and labor relations of college students was built through full utilization of the implicit structural similarity existing in the known employment and labor relations network of college students. The experimental results verified the effectiveness of the proposed construction method.