Reviewerly/exHarmony huggingface dataset:
IdentityMask: Deep Motion Flow Guided Reversible Face Video De-Identification

Unprecedented video collection and sharing have exacerbated privacy concerns and led to increasing interest in privacy-preserving tools. A satisfactory video de-identification tool should be able to remove sensitive identity information from face videos while maintaining useful information for other identity-agnostic tasks. Meanwhile, it is necessary to allow the authority to inspect real identity when abnormal events are detected. Existing methods only focus on the study of de-identification, and lack the desired recovery ability when granting permissions. Furthermore, they all process the videos frame by frame, which hardly benefit from motion and inter-frame information. In this paper, we propose a modular architecture for reversible face video de-identification, called IdentityMask, which leverages deep motion flow to avoid per-frame evaluation. Our framework consists of two processes: the de-identification process provides a protective mask for identity information, while the recovery process can remove the protective mask if and only if the right key is provided. To this end, a Protection Module and a Recovery Module are built as two major functional modules, both based on an identity disentanglement network and guided by a crucial Motion Flow Module. An Affine Transformation Module provides simple but reliable assistance. Extensive experiments on a diverse natural video dataset (gender, ethnicity, age, etc.) demonstrate the effectiveness of the proposed framework for reversible face video de-identification.
Concept Commons Enhanced Knowledge Graph Representation

Knowledge graphs (KGs) are regarded as important resources for a variety of artificial intelligence (AI) and auxiliary decision tasks but suffer from incompleteness. To address this challenge, a number of knowledge graph representation (KGR) and knowledge graph completion (KGC) methods have been developed using graph embeddings manners. Most existing methods focus on the structured information of native triples in encyclopaedia KG and maximize the likelihood of them. However, they neglect semantic commons contained in lexical KG. Recent researches aims at investigating the general framework for enhancing KGR or KGC methods with semantic signals (e.g., entity types, entity descriptions etc.,). However, their work almost modeled the semantic resources by an implicit way, leading their results lacked interpretability. To overcome this drawback, we propose a novel Concept Commons enhanced Knowledge Graph Representation model (named as C $$^2$$ KGR), that integrates the structured information in encyclopaedia KG and the entity’s concepts in lexical KG, via both implicit manner and explicit manner. Experimental results demonstrate the efficiency and interpretability of the proposed model on the real-world datasets.
Path Planning and Energy Management of Hybrid Air Vehicles for Urban Air Mobility

A novel coupled path planning and energy management problem for a hybrid unmanned air vehicle is considered, where the hybrid vehicle is powered by a dual gas/electric system. Such an aerial robot is envisioned for use in an urban setting where noise restrictions are in place in certain zones necessitating battery only operation. We consider the discrete version of this problem, where a graph is constructed by sampling the boundaries of the restricted zones, and develop a path planning algorithm. The planner simultaneously solves the path planing along with the energy mode switching control, under battery constraints and noise restrictions. This is a coupled problem involving discrete decision making to find the path to travel, and determining the state of charge of the battery along the path, which is a continuous variable. A sampling based algorithm to find near optimal solution to this problem is presented. To quantify the efficacy of the solution, an algorithm that computes tight lower bounds is also presented. The algorithms presented are verified using numerical simulations, and the average gap between the feasible solutions (upper bounds) and the lower bounds are, empirically, shown to be within 15% of each other.
Transformer-Based 3D Face Reconstruction With End-to-End Shape-Preserved Domain Transfer

Learning-based face reconstruction methods have recently shown promising performance in recovering face geometry from a single image. However, the lack of training data with 3D annotations severely limits the performance. To tackle this problem, we proposed a novel end-to-end 3D face reconstruction network consisting of a conditional GAN (cGAN) for cross-domain face synthesis and a novel mesh transformer for face reconstruction. Our method first uses cGAN to translate the realistic face images to the specific rendered style, with a 2D facial edge consistency loss function. The domain-transferred images are then fed into face reconstruction network which uses a novel mesh transformer to output 3D mesh vertices. To exploit the domain-transferred in-the-wild images, we further propose a reprojection consistency loss to restrict face reconstruction network in a self-supervised way. Our approach can be trained with annotated dataset, synthetic dataset and in-the-wild images to learn a unified face model. Extensive experiments have demonstrated the effectiveness of our method.
Generative GaitNet

Understanding the relation between anatomy and gait is key to successful predictive gait simulation. In this paper, we present Generative GaitNet, which is a novel network architecture based on deep reinforcement learning for controlling a comprehensive, full-body, musculoskeletal model with 304 Hill-type musculotendons. The Generative GaitNet is a pre-trained, integrated system of artificial neural networks learned in a 618-dimensional continuous domain of anatomy conditions (e.g., mass distribution, body proportion, bone deformity, and muscle deficits) and gait conditions (e.g., stride and cadence). The pre-trained GaitNet takes anatomy and gait conditions as input and generates a series of gait cycles appropriate to the conditions through physics-based simulation. We will demonstrate the efficacy and expressive power of Generative GaitNet to generate a variety of healthy and pathological human gaits in real-time physics-based simulation.
The relationship between artificial intelligence (AI) and its aspects in higher education

Purpose This paper aims to provide better understanding on this phenomenon, as well as some recommendations to assist in preparing Al technology and its related aspects during crisis like Covid period and beyond. Design/methodology/approach The study consisted of two phases. Phase one focused on identifying top higher education institutions according to the QS world university ranking. As a result, 180 higher education institutions in the Arab region were found, with the 2022 QS Arab region university ranking. The study selected top 20 performing institutions. Each institution has been assessed according to six metrics including: international student ratio, international research network, international ration, Web impact, citation per page and paper per faculty. Phase two explored the adapted Al applications and their related aspects that are used in each institution using a learning online questionnaire. Pilot tests were used to determine the validity and reliability of the measures. Findings Numerous factors that lead to enhancing high education using Artificial Intelligence in higher education institutions have emerged from this study. These factors include; knowledge management, trust, learning, technical resources and complexity. First, effective knowledge management assists in maintaining information in a place where it is easy to share and access. Second, trust is considered a critical factor in dealing with uncertainties like covid-19. It needs to be effectively addressed by students and staff members, especially in higher education. Without trust, decisions cannot be successfully made and actions may be delayed or stalled. Third, continuous learning is a key factor that tends to enhance the capability of dealing with new routines or potential challenges. Fourth, technical resources refer to electronic devices and tools that are used by students and academics to interact and share knowledge. Originality/value Students and faculty academics perceive things and use Al technology differently, which can lead to misunderstanding and create tension between them. Many higher education institutions have adapted great Al technology applications, but they are significantly less proficient than previously thought at understanding how to leverage this technology to produce helpful solutions to their business difficulties.
“So-called influencers”: Stancetaking and (de)legitimation in mediatized discourse about social media influencers

As contemporary wordsmiths and new-generation copywriters, Social Media Influencers (henceforth SMIs) are inherently germane to critical sociolinguistics. Interested in wider cultural discourses about contemporary forms of 'independent' language work, this paper examines English-language news media representations of SMIs. The empirical focus of my analysis is a dataset of 143 news stories collected from major 'broadsheet' newspapers and LexisNexis. Specifically, I identify two contradictory stances – celebration and derision – by which SMIs are popularly framed. It is in this way, and following van Leeuwen (2007), that their cultural status and work is (de)legitimized. Using the legitimation tactics of theoretical rationalisation and mythopoesis, celebratory stances in my data construct SMIs as a perfect fit for today's ideal of entrepreneurial success – as ambitious, self-optimizing and risk-taking individuals – ultimately contributing to the recasting of independent and sometimes precarious employment as aspirational 'entrepreneurship'. Conversely, derisory stances built on the legitimation tactics of moral evaluation and authorisation lament their lack of work ethic as well as their interloping into industries that do not want them. Thus, the news media appear to both applaud SMIs for their entrepreneurial careers and be vested in sanctioning them for foregoing gatekeepers by not following traditional career paths to stable employment. Ironically, and perhaps even hypocritically, the latter are precisely the kind of employment that are increasingly difficult for many young people to access while the former still prerequisite considerable privilege to be able to pursue.
Field performance of the GaugeCam image-based water level measurement system

Image-based stage and discharge measuring systems are among the most promising new non-contact technologies available for long-term hydrological monitoring. This article evaluates and reports the long-term performance of the GaugeCam ( www.gaugecam.org ) image-based stage measuring system in situ . For this we installed and evaluated the system over several months in a tidal marsh to obtain a good stratification of the measured stages. Our evaluation shows that the GaugeCam system was able to measure within about ±5 mm for a 90% confidence interval over a range of about 1 m in a tidal creek in a remote location of North Carolina, USA. Our results show that the GaugeCam system nearly performed to the desired design of ±3 mm accuracy around 70% of the time. The system uses a dedicated target background for calibration and geometrical perspective correction of images, as well as auto-correction to compensate for camera movement. The correction systems performed well overall, although our results show a ‘croissant-shaped’ mean error (-1 to +4 mm,) varying with water stage. We attribute this to the small, yet present, ‘fish-eye’ effect embedded in images, for which our system did not entirely correct in the tested version, and which might affect all image-based water level measurement systems.
American Sign Language Translation Using Wearable Inertial and Electromyography Sensors for Tracking Hand Movements and Facial Expressions

A sign language translation system can break the communication barrier between hearing-impaired people and others. In this paper, a novel American sign language (ASL) translation method based on wearable sensors was proposed. We leveraged inertial sensors to capture signs and surface electromyography (EMG) sensors to detect facial expressions. We applied a convolutional neural network (CNN) to extract features from input signals. Then, long short-term memory (LSTM) and transformer models were exploited to achieve end-to-end translation from input signals to text sentences. We evaluated two models on 40 ASL sentences strictly following the rules of grammar. Word error rate (WER) and sentence error rate (SER) are utilized as the evaluation standard. The LSTM model can translate sentences in the testing dataset with a 7.74% WER and 9.17% SER. The transformer model performs much better by achieving a 4.22% WER and 4.72% SER. The encouraging results indicate that both models are suitable for sign language translation with high accuracy. With complete motion capture sensors and facial expression recognition methods, the sign language translation system has the potential to recognize more sentences.
Construction of Binary Quantum Error-Correcting Codes from Orthogonal Array

By using difference schemes, orthogonal partitions and a replacement method, some new methods to construct pure quantum error-correcting codes are provided from orthogonal arrays. As an application of these methods, we construct several infinite series of quantum error-correcting codes including some optimal ones. Compared with the existing binary quantum codes, more new codes can be constructed, which have a lower number of terms (i.e., the number of computational basis states) for each of their basis states.
Characterizing X86 and ARM Serverless Performance Variation

In this paper, we leverage a Natural Language Processing (NLP) pipeline for topic modeling consisting of three functions for data preprocessing, model training, and inferencing to analyze serverless platform performance variation. Specifically, we investigated performance using x86_64 and ARM64 processors over a 24-hour day starting at midnight local time on four cloud regions across three continents on AWS Lambda. We identified public cloud resource contention by leveraging the CPU steal metric, and examined relationships to NLP pipeline runtime. Intel x86_64 Xeon processors at the same clock rate as ARM64 processors (Graviton 2) were more than 23% faster for model training, but ARM64 processors were faster for data preprocessing and inferencing. Use of the Intel x86_64 architecture for the NLP pipeline was up to 33.4% more expensive than ARM64 as a result of incentivized pricing from the cloud provider and slower pipeline runtime due to greater resource contention for Intel processors.
GCKG: Novel Gated Convolutional embedding model for Knowledge Graphs

With the aim of constructing a low-dimensionsal representation space, knowledge graph embedding has gradually become a hot spot in various information retrieval and machine learning tasks. However, most existing knowledge graphs suffer from incompleteness due to the fact that it is unable to collect a complete world knowledge. For knowledge graph completion (KGC), this paper introduces a novel embedding method, named gated Convolution for Knowledge Graph (GCKG), to automatically predict missing links for large-scale Knowledge Graphs. The model GCKG advances the existing models by employing a gated mechanism on convolutional neural network, so that it can capture global relationships and attribute characteristics between entities and relations in knowledge graphs. First, three embedding vectors of each triple are combined into three different matrices. Then, the matrices are fed to three convolutional layers. Finally, the network generates three different maps which are controlled to transmit the information by a gated mechanism. By performing extensive and comprehensive experiments, we evaluate GCKG with two benchmark datasets, FB15k-237 and WN18RR. Experiments show that GCKG achieves substantial improvements against state-of-the-art baselines, and it is respectively improved about 37% in MRR and 4% in Hits@10 on FB15k-237 database compared with CapsE. • Gated Mechanism for KGs is proposed considering characteristic of a triple. • Gated Mechanism is inspired by the chain rule and the Markov assumption. • A Gated convolution Embedding for KGs . • The model achieved a good performance on benchmark datasets.
Comparative evaluations of visualization onboarding methods

Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To support novices in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other fields and first approaches also exist in the visualization domain. However, empirical evidence on the effectiveness of such approaches is scarce. Therefore, we conducted three studies with Amazon Mechanical Turk (MTurk) workers and students investigating visualization onboarding at different levels: (1) Firstly, we explored the effect of visualization onboarding, using an interactive step-by-step guide, on user performance for four increasingly complex visualization techniques with time-oriented data: a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. We performed a between-subject experiment with 596 participants in total. The results showed that there are no significant differences between the answer correctness of the questions with and without onboarding. Particularly, participants commented that for highly familiar visualization types no onboarding is needed. However, for the most unfamiliar visualization type — the parallel coordinates plot — performance improvement can be observed with onboarding. (2) Thus, we performed a second study with MTurk workers and the parallel coordinates plot to assess if there is a difference in user performances on different visualization onboarding types: step-by-step, scrollytelling tutorial, and video tutorial. The study revealed that the video tutorial was ranked as the most positive on average, based on a sentiment analysis, followed by the scrollytelling tutorial and the interactive step-by-step guide. (3) As videos are a traditional method to support users, we decided to use the scrollytelling approach as a less prevalent way and explore it in more detail. Therefore, for our third study, we gathered data towards users’ experience in using the in-situ scrollytelling for the VA tool Netflower. The results of the evaluation with students showed that they preferred scrollytelling over the tutorial integrated in the Netflower landing page. Moreover, for all three studies we explored the effect of task difficulty. In summary, the in-situ scrollytelling approach works well for integrating onboarding in a visualization tool. Additionally, a video tutorial can help to introduce interaction techniques of visualization.
A multi-class workflow ensemble management system using on-demand and spot instances in cloud

Nowadays, cloud computing is an attractive and competitive market, and many computational jobs have migrated to cloud resources. Scheduling a workflow is a common issue in cloud computing. In some applications, a group of interrelated workflows with the same structure but different sizes and inputs is considered as a single job called an ensemble. Executing such applications in the cloud is expensive, and designing a cost-efficient approach is still challenging. The current paper proposes a new algorithm for scheduling workflow ensembles that are subject aware to of budget and deadline constraints. To achieve this aim, the present study takes advantage of Amazon spot instances. These instances are offered by bid at a lower price but with lower reliability. In the proposed algorithm, workflow tasks are classified according to deadlines and priorities, and the requested spot instances are classified based on bid prices. More reliable instances with higher bid prices are assigned to more important classes of tasks. Furthermore, an efficient workflow acceptance and instance provisioning procedure are proposed to launch and terminate instances based on the number of ready tasks and their deadlines. The present research's simulation results show that the proposed system increases the number of completed workflows and reduces the number of partially executed ones.
Low resource machine translation of english–manipuri: A semi-supervised approach

The language barrier is one of the practical challenges human being face during communication. To overcome this, researchers are focusing on using machines to translate a source language to a target language using the textual representations of the languages. Thus, machine translation (MT) could achieve a near human-level performance in terms of translation quality for several resource-rich languages. However, machine translation performance is still far from a production-level quality for the low resource languages. This work reports a semi-supervised neural machine translation system to boost the translation quality for an extremely resource constraint language pair, i.e. English–Manipuri. Our proposed approach exploits self-training and back-translation in a combined technique. The quantitative evaluation shows that the system performance improves by +0.9 BLEU score after introducing external noise to the input data. Additionally, a multi-reference test dataset developed in-house is used to evaluate the linguistic diversity of the highly agglutinative and morphologically rich Manipuri language. Experimental result attests that the proposed semi-supervised system outperforms the supervised, the pretrained mBART and existing semi-supervised baselines in terms of automatic score and subjective evaluation parameters by a significant margin up to +4.5 and +1.2 BLEU improvements against the supervised and mBART baselines respectively.
Variational learning of deep fuzzy theoretic nonparametric model

Nonparametric approaches are a good choice for function approximation as they are flexible, robust to overfitting, and provide well calibrated predictive uncertainty. The nonparametric form of fuzzy modeling can be promising, however, not analytically studied to compete with the Gaussian processes-based Bayesian framework in statistics and machine learning. In this work, a fuzzy modeling framework is built for data analysis of both supervised and unsupervised learning task, as a deterministic fuzzy analogous against the probabilistic Gaussian process, which defines a fuzzy membership over possible membership functions and is updated in light of data via the rules of variational inference. A fast variational inference framework has been suggested to study the propagation of the uncertainty through the layers of the deep model which can jointly infers the inducing inputs and the hyperparameters. The maximization problem is analytically solved using variational optimization with derived lower bound. We provide the sufficient number of experiments to support our argument that the proposed approach works well in practice for the problem which has the requirements to update the model dynamically. In addition, the application potential of the proposed methodology in data representation learning is also investigated, the “auxiliary inducing points” are used to express general features of tasks with smaller datasets. Nevertheless, this study offers new contents to the theory of nonparametric model and presents the potential for the design of practical fuzzy machine learning method.