Reviewerly/exHarmony huggingface dataset:
Machine Learning for Per-Title Encoding

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Video streaming content varies in terms of complexity and requires title-specific encoding settings to achieve a certain visual quality. Classic “one-size-fits-all” encoding ladders ignore video-specific characteristics and apply the same encoding settings across all video files. In the worst-case scenario, this approach can lead to quality impairments, encoding artifacts, or unnecessarily large media files. A per-title encoding solution has the potential to significantly decrease the storage and delivery costs of video streams while improving the perceptual quality of the video. Conventional per-title encoding solutions typically require a large number of test encodes, resulting in high computational times and costs. In this article, we describe a solution that implements the conventional per-title encoding approach and uses its resulting data for machine learning-based improvements. By applying supervised, multivariate regression algorithms like random forest regression, multilayer perceptron (MLP), and support vector regression, we can predict video quality metric (VMAF) values. These video quality metric values are the foundation for deriving the optimal encoding ladder. As a result, the test encodes are eliminated while preserving the benefits of conventional per-title encoding</i> .
Movement Augmentation in Virtual Reality: Impact on Sense of Agency Measured by Subjective Responses and Electroencephalography

Virtual movement augmentation, which refers to the visual amplification of remapped movement, shows potential to be applied in motion-related virtual reality programs. Sense of agency (SoA), which measures the user&#x0027;s feeling of control in their action, has not been fully investigated for augmented movement. This study investigated the effect of augmented movement at three different levels (baseline, medium, and high) on users&#x0027; SoA using both subjective responses and electroencephalography (EEG). Results show that SoA can be boosted slightly at medium augmentation level but drops at high level. The augmented virtual movement only helps to enhance SoA to a certain extent.
On Explaining Multimodal Hateful Meme Detection Models

Hateful meme detection is a new multimodal task that has gained significant traction in academic and industry research communities. Recently, researchers have applied pre-trained visual-linguistic models to perform the multimodal classification task, and some of these solutions have yielded promising results. However, what these visual-linguistic models learn for the hateful meme classification task remains unclear. For instance, it is unclear if these models are able to capture the derogatory or slurs references in multimodality (i.e., image and text) of the hateful memes. To fill this research gap, this paper propose three research questions to improve our understanding of these visual-linguistic models performing the hateful meme classification task. We found that the image modality contributes more to the hateful meme classification task, and the visual-linguistic models are able to perform visual-text slurs grounding to a certain extent. Our error analysis also shows that the visual-linguistic models have acquired biases, which resulted in false-positive predictions.
Knowledge Enhanced GAN for IoT Traffic Generation

Network traffic data facilitates understanding the Internet of Things (IoT) behaviors and improving IoT service quality in the real world. However, large-scale IoT traffic data is rarely accessible, and privacy issues also impede realistic data sharing even with anonymous personal identifiable information. Researchers propose to generate synthetic IoT traffic but fail to cover the multiple services provided by widespread real-world IoT devices. In this work, we take the first step to generate large-scale IoT traffic via a knowledge-enhanced generative adversarial network (GAN) framework, which introduces both the semantic knowledge (e.g., location and environment information) and the network structure knowledge for various IoT devices via a knowledge graph. We use a condition mechanism to incorporate the knowledge and device category for IoT traffic generation. Then, we adopt LSTM and a self-attention mechanism to capture the temporal correlation in the traffic series. Extensive experiment results show that the synthetic IoT traffic datasets generated by our proposed model outperform state-of-art baselines in terms of data fidelity and applications. Moreover, our proposed model is able to generate realistic data by only training on small real datasets with knowledge enhanced.
Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems

Recently, user-oriented auto-encoders (UAEs) have been widely used in recommender systems to learn semantic representations of users based on their historical ratings. However, since latent item variables are not modeled in UAE, it is difficult to utilize the widely available item content information when ratings are sparse. In addition, whenever new items arrive, we need to wait for collecting rating data for these items and retrain the UAE from scratch, which is inefficient in practice. Aiming to address the above two problems simultaneously, we propose a mutually-regularized dual collaborative variational auto-encoder (MD-CVAE) for recommendation. First, by replacing randomly initialized last layer weights of the vanilla UAE with stacked latent item embeddings, MD-CVAE integrates two heterogeneous information sources, i.e., item content and user ratings, into the same principled variational framework where the weights of UAE are regularized by item content such that convergence to a non-optima due to data sparsity can be avoided. In addition, the regularization is mutual in that user ratings can also help the dual item content module learn more recommendation-oriented item content embeddings. Finally, we propose a symmetric inference strategy for MD-CVAE where the first layer weights of the UAE encoder are tied to the latent item embeddings of the UAE decoder. Through this strategy, no retraining is required to recommend newly introduced items. Empirical studies show the effectiveness of MD-CVAE in both normal and cold-start scenarios. Codes are available at https://github.com/yaochenzhu/MD-CVAE.
Evaluating the Randomness of Chaotic Binary Sequences Via a Novel Period Detection Algorithm

When chaotic systems are applied to stream ciphers, chaotic real-valued sequences generally need to be converted into binary sequences with the purpose of encrypting data. However, the performance of binary sequences will be degraded under the joint influence of round-off and quantization errors. In this case, the randomness of some chaotic binary sequences may be weakened in a local range. Taking advantage of parallel computing, a fast period detection algorithm is designed to locate all local “periodicities” contained in chaotic binary sequences quickly and accurately. This algorithm evaluates the randomness of a chaotic binary sequence from a new perspective of periodicity which enriches the randomness test methods for binary sequences. Different logistic binary sequences are analyzed to demonstrate the effectiveness and practicability of the proposed algorithm.
NNetEn2D: Two-Dimensional Neural Network Entropy in Remote Sensing Imagery and Geophysical Mapping

Measuring the predictability and complexity of 2D data (image) series using entropy is an essential tool for evaluation of systems’ irregularity and complexity in remote sensing and geophysical mapping. However, the existing methods have some drawbacks related to their strong dependence on method parameters and image rotation. To overcome these difficulties, this study proposes a new method for estimating two-dimensional neural network entropy (NNetEn2D) for evaluating the regularity or predictability of images using the LogNNet neural network model. The method is based on an algorithm for converting a 2D kernel into a 1D data series followed by NNetEn2D calculation. An artificial test image was created for the study. We demonstrate the advantage of using circular instead of square kernels through comparison of the invariance of the NNetEn2D distribution after image rotation. Highest robustness was observed for circular kernels with a radius of R = 5 and R = 6 pixels, with a NNetEn2D calculation error of no more than 10%, comparable to the distortion of the initial 2D data. The NNetEn2D entropy calculation method has two main geometric parameters (kernel radius and its displacement step), as well as two neural network hyperparameters (number of training epochs and one of six reservoir filling techniques). We evaluated our method on both remote sensing and geophysical mapping images. Remote sensing imagery (Sentinel-2) shows that brightness of the image does not affect results, which helps keep a rather consistent appearance of entropy maps over time without saturation effects being observed. Surfaces with little texture, such as water bodies, have low NNetEn2D values, while urban areas have consistently high values. Application to geophysical mapping of rocks to the northwest of southwest Australia is characterized by low to medium entropy and highlights aspects of the geology. These results indicate the success of NNetEn2D in providing meaningful entropy information for 2D in remote sensing and geophysical applications.
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning

Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in clas-sical machine learning. The FL capability is especially important to domains such as biomedicine and smart grid, where data may not be shared freely or stored at a central location because of policy regulations. Thanks to the capability of learning from decentralized datasets, FL is now a rapidly growing research field, and numerous FL frameworks have been developed. In this work we introduce APPFL, the Argonne Privacy-Preserving Federated Learning framework. APPFL allows users to leverage implemented privacy-preserving algorithms, implement new al-gorithms, and simulate and deploy various FL algorithms with privacy-preserving techniques. The modular framework enables users to customize the components for algorithms, privacy, communication protocols, neural network models, and user data. We also present a new communication-efficient algorithm based on an inexact alternating direction method of multipliers. The algorithm requires significantly less communication between the server and the clients than does the current state of the art. We demonstrate the computational capabilities of APPFL, including differentially private FL on various test datasets and its scalability, by using multiple algorithms and datasets on different computing environments.
RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for Low Latency IoT Systems

Although Deep Neural Networks (DNN) have become the backbone technology of several ubiquitous applications, their deployment in resource-constrained machines, e.g., Internet of Things (IoT) devices, is still challenging. To satisfy the resource requirements of such a paradigm, collaborative deep inference with IoT synergy was introduced. However, the distribution of DNN networks suffers from severe data leakage. Various threats have been presented, including black-box attacks, where malicious participants can recover arbitrary inputs fed into their devices. Although many countermeasures were designed to achieve privacy-preserving DNN, most of them result in additional computation and lower accuracy. In this paper, we present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy, without sacrificing the model performance. Particularly, we examine different DNN partitions that make the model susceptible to black-box threats and we derive the amount of data that should be allocated per device to hide proprieties of the original input. We formulate this methodology, as an optimization, where we establish a trade-off between the latency of co-inference and the privacy-level of data. Next, to relax the optimal solution, we shape our approach as a Reinforcement Learning (RL) design that supports heterogeneous devices as well as multiple DNNs/datasets.
Artificial Intelligence Based Approach for Fault and Anomaly Detection Within UAVs

A non-predicted defect in Unmanned Aerial Vehicles (UAVs) could occur provisionally during their deployment process. Consequently, it is critical to optimize the detection of these instances. More specifically, the deviations in normal behavior indicate the possibility of triggered attacks, failures, and flaws. Therefore, intrusion detection (ID) is mandatory for UAVs security. Meanwhile, ID performance remains an arguable problem. Most of IDSs are applied for one predefined application. There is no general model for accurately detecting both anomalies and faults. To investigate this issue, this paper presents a new dynamic approach for UAVs fault and anomaly detection to investigate this issue. To resolve the drawbacks mentioned above, we propose an attack and fault detection approach. Our method shows better performance using a large dataset, trained on only a small fraction corresponding to normal flight strategy.
Face recognition algorithm based on stack denoising and self-encoding LBP

Abstract To optimize the weak robustness of traditional face recognition algorithms, the classification accuracy rate is not high, the operation speed is slower, so a face recognition algorithm based on local binary pattern (LBP) and stacked autoencoder (AE) is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder (SAE) learning, use the unified mode LBP operator to extract the histogram of the blocked face image, connect to form the LBP features of the entire image. It is used as input of the stacked AE, feature extraction is done, realize the recognition and classification of face images. Experimental results show that the recognition rate of the algorithm LBP-SAE on the Yale database has achieved 99.05%, and it further shows that the algorithm has a higher recognition rate than the classic face recognition algorithm; it has strong robustness to light changes. Experimental results on the Olivetti Research Laboratory library shows that the developed method is more robust to light changes and has better recognition effects compared to traditional face recognition algorithms and standard stack AEs.
Leaning-Based Interfaces Improve Ground-Based VR Locomotion in Reach-the-Target, Follow-the-Path, and Racing Tasks

Using standard handheld interfaces for VR locomotion may not provide a believable self-motion experience and can contribute to unwanted side effects such as motion sickness, disorientation, or increased cognitive load. This paper demonstrates how using a seated leaning-based locomotion interface –HeadJoystick– in VR ground-based navigation affects user experience, usability, and performance. In three within-subject studies, we compared controller (touchpad/thumbstick) with a more embodied interface (”HeadJoystick”) where users moved their head and/or leaned in the direction of desired locomotion. In both conditions, users sat on a regular office chair and used it to control virtual rotations. In the first study, 24 participants used HeadJoystick versus Controller in three complementary tasks including reach-the-target, follow-the-path, and racing (dynamic obstacle avoidance). In the second study, 18 participants repeatedly used HeadJoystick versus Controller (8 one-minute trials each) in a reach-the-target task. To evaluate potential benefits of different brake mechanisms, in the third study 18 participants were asked to stop within each target area for one second. All three studies consistently showed advantages of HeadJoystick over Controller: we observed improved performance in all tasks, as well as higher user ratings for enjoyment, spatial presence, immersion, vection intensity, usability, ease of learning, ease of use, and rated potential for daily and long-term use, while reducing motion sickness and task load. Overall, our results suggest that leaning-based interfaces such as HeadJoystick provide an interesting and more embodied alternative to handheld interfaces in driving, reach-the-target, and follow-the-path tasks, and potentially a wider range of scenarios.
Text Similarity Measurement Method and Application of Online Medical Community Based on Density Peak Clustering

Text similarity measurement is a link between basic research such as text modeling and upper-level application research of text potential information. In order to improve the accuracy of text similarity measurement, this paper proposes a semantic similarity calculation method integrating word2vec model and TF-IDF, and applies it to the density peak clustering of Chinese text data consulted by patients in online medical community. Experimental results show that the proposed similarity measurement method is superior to the traditional method. Furthermore, the study is among the first to apply the density peak clustering algorithm to online medical community, which offers a reference for how to find out user demands from medical text data in the big data environment.
Online course learning outcome evaluation method based on big data analysis

In order to solve the low credibility and poor timeliness of the online course learning effect evaluation, a method based on big data for online course learning results evaluation is proposed, which can better solve the problems existing in traditional methods. A statistical average analysis model for big data of online course learning outcomes is constructed for learning outcome big data analysis based on the sample regression analysis method; a decision objective function of online course learning outcome evaluation is established. The experimental results show that when the method proposed in this paper is adopted to evaluate online course learning outcomes, the method has relatively high confidence and overall timeliness, and the time taken to evaluate the effectiveness of online courses is 28 s and 29 s less than that of the other two methods, so it can provide accurate and reliable evaluation results.
Video watermarking scheme based on IDR frames using MPEG-2 structure

A robust, imperceptible and blind video watermarking scheme based on MPEG-2 is presented. The proposed algorithm using the DC coefficients from 8 × 8 block of discrete coefficient transform generated by picking candidate IDR frames based on column transposition method in order to embed the scrambled binary watermark. The watermark can only be extracted by using the secret keys, enhances the security of watermark. The robustness is evaluated by testing against spatial synchronisation attacks, temporal synchronisation attacks and re-encoding attacks by evaluating two parameters as normalised correlation and bit error rate in order to find the degree of similarity and degree of dissimilarity respectively between the original and extracted watermark. The superiority of the proposed algorithm is that the excellent robustness achieved against temporal synchronisation attacks and compression attack by comparing it with previous work and also good perceptibility obtained without changing the motion vectors during the DPCM process of MPEG-2 encoding scheme.
TRAINABILITY OF ReLU NETWORKS AND DATA-DEPENDENT INITIALIZATION

In this paper we study the trainability of rectified linear unit (ReLU) networks at initialization. A ReLU neuron is said to be dead if it only outputs a constant for any input. Two death states of neurons are introduced−tentative and permanent death. A network is then said to be trainable if the number of permanently dead neurons is sufficiently small for a learning task. We refer to the probability of a randomly initialized network being trainable as trainability. We show that a network being trainable is a necessary condition for successful training, and the trainability serves as an upper bound of training success rates. In order to quantify the trainability, we study the probability distribution of the number of active neurons at initialization. In many applications, overspecified or overparameterized neural networks are successfully employed and shown to be trained effectively. With the notion of trainability, we show that overparameterization is both a necessary and a sufficient condition for achieving a zero training loss. Furthermore, we propose a data-dependent initialization method in an overparameterized setting. Numerical examples are provided to demonstrate the effectiveness of the method and our theoretical findings.
The Improved Equilibrium Optimization Algorithm with Averaged Candidates

Abstract In this paper, we proposed the improvement of the newly raised equilibrium optimization (EO) algorithm by hybridizing the grey wolf optimization (GWO) algorithm. Simulation experiments were carried out and results showed that the hybrid EO algorithm with averaged candidates would perform better than the original one. Considering the better results and the smooth convergence curve on different types of benchmark functions, we recommend to withdraw the concept of equilibrium pool in the construction of the EO algorithm henceforth, and keep the guiding equation relevant to the averaged concentration of four best candidates instead.
An experiential learning program for entrepreneurship education

Despite extensive research into entrepreneurship education (e.g., Collins et al., 2004; Pittaway and Cope, 2007; Solomon, 2007; Blenker et al., 2014), many studies focus upon pedagogical 'good practice', rather than establishing its effectiveness. As well as achieving the social goals of any curriculum in terms of enabling students to fulfil their potential and ultimately to obtain gainful employment, entrepreneurship can also potentially be evaluated to establish whether it meets its desired objectives or learning outcomes. Our aim, therefore, is to conduct a critical appraisal of how experiential approaches can enhance the achievement of learning outcomes in entrepreneurship education. A partial least square path modelling (PLS-PM) was used to test the relationships involved in the research model.
Information Placement in Virtual Reality

In this poster, we develop a technique for placing informational labels in complex Virtual Environments (VEs). The ability to effectively and efficiently present labels in VEs is valuable in Virtual Reality (VR) for many reasons, but the motivation is to bring us closer to a system that delivers information in VR in an optimal way without causing information overload. The novelty of this technique lies in the use of eye tracking as an accurate indicator of attention to identifying objects of interest. Labels associated with such objects of interest are revealed when the user attends to them. We conduct a series of experiments to evaluate label placement based on user attention. This attention is measured using eye tracking. Results show that one method in particular results in 100% accuracy in some scenarios in a search task with little difference in time taken to complete the task.