Reviewerly/exHarmony huggingface dataset:
An ILM-cosine transform-based improved approach to image encryption

Abstract The chaos-based cryptography techniques are used widely to protect digital information from intruders. The chaotic systems have some of special features that make them suitable for the purpose of encryption. These systems are highly unpredictable and are highly sensitive or responsive to the initial conditions, also known as butterfly effect. This sensitive dependence on initial conditions make these systems to exhibit an intricate dynamical behaviour. However, this dynamical behaviour is not much complex in simple one-dimensional chaotic maps. Hence, it becomes easy for an intruder to predict the contents of the message being sent. The proposed work in this paper introduces an improved method for encrypting images, which uses cosine transformation of 3-D Intertwining Logistic Map (ILM). The proposed approach has been split into three major parts. In the first part, Secure Hash Function-256 (SHA-256) is used with cosine transformed ILM (CT-ILM) to generate the chaotic sequence. This chaotic sequence is used by high-efficiency scrambling to reduce the correlations between the adjacent pixels of the image. In the second part, the image is rotated to move all the pixels away from their original position. In the third part, random order substitution is applied to change the value of image pixels. The effectiveness of the proposed method has been tested on a number of standard parameters such as correlation coefficient, Entropy and Unified average change in intensity. The proposed approach has also been tested for decryption parameters like mean square error and peak signal to noise ratio. It can easily be observed from the obtained results that the proposed method of image encryption is more secure and time efficient than some earlier proposed techniques. The approach works for both color and grey scale images.
An adaptive variational Bayesian filter for nonlinear multi-sensor systems with unknown noise statistics

An improved adaptive variational Bayesian cubature information fusion algorithm for nonlinear multi-sensor systems with uncertain noise statistics is proposed in this paper. Aiming to estimate uncertain process and measurement noise covariances in nonlinear systems, the variational Bayesian theory is combined with the inverse Wishart distribution. System states and uncertain noise covariances are jointly estimated for nonlinear systems by means of cubature sampling, deriving the variational Bayesian cubature Kalman filter (VBCKF-QR). In addition, a variational Bayesian Cubature Information filter (VBCIF-QR) is proposed, and a distributed information feedback fusion algorithm is also derived for multi-sensor systems with unknown noise statistics. Simulation results demonstrate that the proposed VBCKF-QR/VBCIF-QR outperform conventional cubature Kalman/information filters.
Data-driven inference of unknown tilt and azimuth of distributed PV systems

Information about the orientation (i.e. tilt and azimuth angles) of PV modules is a fundamental input for PV performance studies. However, this type of metadata is difficult to obtain for distributed in-use PV systems, which considerably impedes monitoring and diagnostics of PV systems and power grid management. Recently proposed parameterization methods to derive PV tilt and azimuth have limited practical applicability because they rely on data that is often not accessible. Hence, the aim of this research is to develop a novel method to infer tilt and azimuth angles of distributed PV systems, utilizing widely available data. The proposed method, which is based on a curve-matching procedure, is designed to be scalable because it only requires PV generation data and off-site irradiance data at a 1-hour time interval. The accuracy of this method has been tested using notional PV systems with a wide variety of orientations, as well as with data from real PV roofs distributed across the Netherlands. These tests show that the proposed method can parameterize azimuth and tilt angles of PV panels as far as 195 km away from the irradiance measurement site with mean absolute errors of 4.5° and 4.3° respectively. A demonstration case of the proposed approach written in Python is uploaded online for other researchers to use.
PV Generation Forecasting With Missing Input Data: A Super-Resolution Perception Approach

For practical PV generation forecasting, it is sometimes the case that the input data is missing or incomplete due to measurement and recording errors, which makes the application of a machine learning-based PV forecasting model difficult or impossible. This letter presents a new forecasting framework to address this practical and important issue. The super-resolution perception convolutional neural network (SRPCNN) is used to recover the missing data and the stochastic configuration network (SCN) is used to forecast PV generation with the recovered data. The proposed method is tested on an open dataset, and two existing methods are compared as benchmarks. The testing results verify its excellent missing-data recovery ability for accurately forecasting PV generation even under severe missing-data conditions.
Exploring Inherent Sensor Redundancy for Automotive Anomaly Detection

The increasing autonomy and connectivity have been transitioning automobiles to complex and open architectures that are vulnerable to malicious attacks beyond conventional cyber attacks. Attackers may non-invasively compromise sensors and spoof the controller to perform unsafe actions. This concern emphasizes the need to validate sensor data before acting on them. Unlike existing works, this paper exploits inherent redundancy among heterogeneous sensors for detecting anomalous sensor measurements. The redundancy is that multiple sensors simultaneously respond to the same physical phenomenon in a related fashion. Embedding the redundancy into a deep autoencoder, we propose an anomaly detector that learns a consistent pattern from vehicle sensor data in normal states and utilizes it as the nominal behavior for the detection. The proposed method is independent of the scarcity of anomalous data for training and the intensive calculation of pairwise correlation among senors as in existing works. Using a real-world data set collected from tens of vehicle sensors, we demonstrate the feasibility and efficacy of the proposed method.
Continuous variable B92 quantum key distribution protocol using single photon added and subtracted coherent states

In this paper, a continuous variable B92 quantum key distribution (QKD) protocol is proposed using single photon added and subtracted coherent states, which are prepared by adding and subsequently subtracting a single photon on a coherent state. It is established that in contrast to the traditional discrete variable B92 protocol, this protocol for QKD is intrinsically robust against the unambiguous state discrimination attack, which circumvents the requirement for any uninformative states or entanglement used in corresponding discrete variable case as a remedy for this attack. Further, it is shown that the proposed protocol is intrinsically robust against the eavesdropping strategies exploiting classical communication during basis reconciliation, such as beam splitter attack. Security against some individual attacks, key rate, and bit-error rate estimation for the proposed scheme are also provided. Specifically, the proposed scheme ensures very small bit-error rate due to properties of the states used. Thus, the proposed scheme is shown to be preferable over the corresponding discrete variable B92 protocol as well as some similar continuous variable quantum key distribution schemes.
Hyperparameter Setting of LSTM-based Language Model using Grey Wolf Optimizer

Hyperparameters is one of the most essential part of deep learning, because they can give big impact to the performance of the model. Recent works show that if the hyperparameters of a Long Short-Term Memory (LSTM) are carefully adjusted or optimized, its performance can achieve the same performance as the more complex LSTM model. Previously, several methods such as grid search and random search is introduced to solve this hyperparameters optimization problem, but it is still not effective enough. Hence, it opens opportunities for meta-heuristic nature-inspired approach like Swarm Intelligence (SI) method to solve this hyperparameter optimization problem. The main advantage of this method is the behaviour of the algorithm that has exploring-exploiting process in order to find the global optima solution in the search space. Algorithm such as Grey Wolf Optimizer (GWO) are one of the SI algorithms that have a promising performance in optimization problem in various field. The algorithm has balanced exploring-exploiting process, that can make the optimization are more effective. Therefore, in this paper the GWO is exploited to optimize the LSTM hyperparameters for a language modeling task. Evaluation for the Penn Tree Bank dataset shows that GWO is capable of giving an optimum hyperparameters of the LSTM.
Investigating the effects of gender, dialect, and training size on the performance of Arabic speech recognition

Abstract Research in Arabic automatic speech recognition (ASR) is constrained by datasets of limited size, and of highly variable content and quality. Arabic-language resources vary in the attributes that affect language resources in other languages (noise, channel, speaker, genre), but also vary significantly in the dialect and level of formality of the spoken Arabic they capture. Many languages suffer similar levels of cross-dialect and cross-register acoustic variability, but these effects have been under-studied. This paper is an experimental analysis of the interaction between classical ASR corpus-compensation methods (feature selection, data selection, gender-dependent acoustic models) and the dialect-dependent/register-dependent variation among Arabic ASR corpora. The first interaction studied in this paper is that between acoustic recording quality and discrete pronunciation variation. Discrete pronunciation variation can be compensated by using grapheme-based instead of phone-based acoustic models, and by filtering out speakers with insufficient training data; the latter technique also helps to compensate for poor recording quality, which is further compensated by eliminating delta-delta acoustic features. All three techniques, together, reduce Word Error Rate (WER) by between 3.24% and 5.35%. The second aspect of dialect and register variation to be considered is variation in the fine-grained acoustic pronunciations of each phoneme in the language. Experimental results prove that gender and dialect are the principal components of variation in speech, therefore, building gender and dialect-specific models leads to substantial decreases in WER. In order to further explore the degree of acoustic differences between phone models required for each of the dialects of Arabic, cross-dialect experiments are conducted to measure how far apart Arabic dialects are acoustically in order to make a better decision about the minimal number of recognition systems needed to cover all dialectal Arabic. Finally, the research addresses an important question: how much training data is needed for building efficient speaker-independent ASR systems? This includes developing some learning curves to find out how large must the training set be to achieve acceptable performance.
Data Comics for Reporting Controlled User Studies in Human-Computer Interaction

Inspired by data comics, this paper introduces a novel format for reporting controlled studies in the domain of human-computer interaction (HCI). While many studies in HCI follow similar steps in explaining hypotheses, laying out a study design, and reporting results, many of these decisions are buried in blocks of dense scientific text. We propose leveraging data comics as study reports to provide an open and glanceable view of studies by tightly integrating text and images, illustrating design decisions and key insights visually, resulting in visual narratives that can be compelling to non-scientists and researchers alike. Use cases of data comics study reports range from illustrations for non-scientific audiences to graphical abstracts, study summaries, technical talks, textbooks, teaching, blogs, supplementary submission material, and inclusion in scientific articles. This paper provides examples of data comics study reports alongside a graphical repertoire of examples, embedded in a framework of guidelines for creating comics reports which was iterated upon and evaluated through a series of collaborative design sessions.
AdaHGNN

Multi-label image classification is an important and challenging task in computer vision and multimedia fields. Most of the recent works only capture the pair-wise dependencies among multiple labels through statistical co-occurrence information, which cannot model the high-order semantic relations automatically. In this paper, we propose a high-order semantic learning model based on adaptive hypergraph neural networks (AdaHGNN) to boost multi-label classification performance. Firstly, an adaptive hypergraph is constructed by using label embeddings automatically. Secondly, image features are decoupled into feature vectors corresponding to each label, and hypergraph neural networks (HGNN) are employed to correlate these vectors and explore the high-order semantic interactions. In addition, multi-scale learning is used to reduce sensitivity to object size inconsistencies. Experiments are conducted on four benchmarks: MS-COCO, NUS-WIDE, Visual Genome, and Pascal VOC 2007, which cover large, medium, and small-scale categories. State-of-the-art performances are achieved on three of them. Results and analysis demonstrate that the proposed method has the ability to capture high-order semantic dependencies.
Evaluation of Sampling Methods for Scatterplots

Given a scatterplot with tens of thousands of points or even more, a natural question is which sampling method should be used to create a small but “good” scatterplot for a better abstraction. We present the results of a user study that investigates the influence of different sampling strategies on multi-class scatterplots. The main goal of this study is to understand the capability of sampling methods in preserving the density, outliers, and overall shape of a scatterplot. To this end, we comprehensively review the literature and select seven typical sampling strategies as well as eight representative datasets. We then design four experiments to understand the performance of different strategies in maintaining: 1) region density; 2) class density; 3) outliers; and 4) overall shape in the sampling results. The results show that: 1) random sampling is preferred for preserving region density; 2) blue noise sampling and random sampling have comparable performance with the three multi-class sampling strategies in preserving class density; 3) outlier biased density based sampling, recursive subdivision based sampling, and blue noise sampling perform the best in keeping outliers; and 4) blue noise sampling outperforms the others in maintaining the overall shape of a scatterplot.
Capacitivo

We present Capacitivo, a contact-based object recognition technique developed for interactive fabrics, using capacitive sensing. Unlike prior work that has focused on metallic objects, our technique recognizes non-metallic objects such as food, different types of fruits, liquids, and other types of objects that are often found around a home or in a workplace. To demonstrate our technique, we created a prototype composed of a 12 x 12 grid of electrodes, made from conductive fabric attached to a textile substrate. We designed the size and separation between the electrodes to maximize the sensing area and sensitivity. We then used a 10-person study to evaluate the performance of our sensing technique using 20 different objects, which yielded a 94.5% accuracy rate. We conclude this work by presenting several different application scenarios to demonstrate unique interactions that are enabled by our technique on fabrics.
Mutual teaching for graph convolutional networks

Graph convolutional networks generate reasonable predictions of unlabeled samples because of transductive label propagation. Samples can have different predicted confidences, and therefore, we consider high-confidence predictions as pseudo labels to select more samples for updating models. We propose a new training strategy called mutual teaching, wherein dual models are first trained and they then teach each other during each batch process. Each network feeds forward all samples, and the samples with high-confidence predictions are used to expand the label set; then, each model is updated by the selected samples of its peer network. We consider the high-confidence predictions as useful knowledge, and each network teaches its peer network using this knowledge. In the proposed strategy, the pseudo-label set of a network is derived from its peer network, and this strategy helps improve the performance significantly. Experiments are conducted on the three citation network datasets and experimental results demonstrate that our method achieves superior performance over state-of-the-art methods under the condition of very low label rates.
Efficacy of augmented visual environments for reducing sickness in autonomous vehicles

The risk of motion sickness is considerably higher in autonomous vehicles than it is in human-operated vehicles. Their introduction will therefore require systems that mitigate motion sickness. We investigated whether this can be achieved by augmenting the vehicle interior with additional visualizations. Participants were immersed in motion simulations on a moving-base driving simulator, where they were backward-facing passengers of an autonomous vehicle. Using a Head-Mounted Display, they were presented either with a regular view from inside the vehicle, or with augmented views that offered additional cues on the vehicle's present motion or motion 500ms into the future, displayed on the vehicle's interior panels. In contrast to the hypotheses and other recent studies, no difference was found between conditions. The absence of differences between conditions suggests a ceiling effect: providing a regular view may limit motion sickness, but presentation of additional visual information beyond this does not further reduce sickness.
Cartoon Face Recognition

Recent years have witnessed increasing attention in cartoon media, powered by the strong demands of industrial applications. As the first step to understand this media, cartoon face recognition is a crucial but less-explored task with few datasets proposed. In this work, we first present a new challenging benchmark dataset, consisting of 389,678 images of 5,013 cartoon characters annotated with identity, bounding box, pose, and other auxiliary attributes. The dataset, named iCartoonFace, is currently the largest-scale, high-quality, rich-annotated, and spanning multiple occurrences in the field of image recognition, including near-duplications, occlusions, and appearance changes. In addition, we provide two types of annotations for cartoon media, i.e., face recognition, and face detection, with the help of a semi-automatic labeling algorithm. To further investigate this challenging dataset, we propose a multi-task domain adaptation approach that jointly utilizes the human and cartoon domain knowledge with three discriminative regularizations. We hence perform a benchmark analysis of the proposed dataset and verify the superiority of the proposed approach in the cartoon face recognition task. The dataset is available at https://iqiyi.cn/icartoonface.
Language-Aware Fine-Grained Object Representation for Referring Expression Comprehension

Referring expression comprehension expects to accurately locate an object described by a language expression, which requires precise language-aware visual object representations. However, existing methods usually use rectangular object representations, such as object proposal regions and grid regions. They ignore some fine-grained object information like shapes and poses, which are often described in language expressions and important to localize objects. Additionally, rectangular object regions usually contain background contents and irrelevant foreground features, which also decrease the localization performance. To address these problems, we propose a language-aware deformable convolution model (LDC) to learn language-aware fine-grained object representations. Rather than extracting rectangular object representations, LDC adaptively samples a set of key points based on the image and language to represent objects. This type of object representations can capture more fine-grained object information (e.g., shapes and poses) and suppress noises in accordance with language and thus, boosts the object localization performance. Based on the language-aware fine-grained object representation, we next design a bidirectional interaction model (BIM) that leverages a modified co-attention mechanism to build cross-modal bidirectional interactions to further improve the language and object representations. Furthermore, we propose a hierarchical fine-grained representation network (HFRN) to learn language-aware fine-grained object representations and cross-modal bidirectional interactions at local word level and global sentence level, respectively. Our proposed method outperforms the state-of-the-art methods on the RefCOCO, RefCOCO+ and RefCOCOg datasets.
Can Avatar Appearance Influence Physical Activity? User-Avatar Similarity and Proteus Effects on Cardiac Frequency and Step Counts

This study combined user-avatar similarity and Proteus effect predictions to incentivize physical activity. 305 participants ran while wearing accelerometers and a heart rate monitor. They were randomly assigned to onscreen motion-capturing avatars displaying either participant or stranger faces dressed in sports or formal clothes. Participants assigned to avatars displaying their own face showed increased cardiac frequency compared with those exposed to avatars with a stranger's face. Relative to the remaining conditions, participants assigned to avatars with their own face also wearing sports clothes showed increased cardiac frequency but participants assigned to avatars with a stranger's face wearing formal clothes showed decreased cardiac frequency. The results imply that user-avatar similarity and the Proteus effect can be harnessed to influence physical activity.
Memory Capacity of Neural Networks with Threshold and Rectified Linear Unit Activations

Overwhelming theoretical and empirical evidence shows that mildly overparametrized neural networks---those with more connections than the size of the training data---are often able to memorize the training data with 100% accuracy. This was rigorously proved for networks with sigmoid activation functions [M. Yamasaki, Proceedings of the International Conference on Artificial Neural Networks, 1993, pp. 546--549; G.-B. Huang, IEEE Trans. Neural Netw., 14 (2003), pp. 274--281] and, very recently, for rectified linear unit (ReLU) activations [C. Yun, S. Sra, and A. Jadbabaie, Proceedings of the Conference on Neural Information Processing Systems, 2019, pp. 15532--15543]. Addressing a open question of Baum [J. Complexity, 4 (1988), pp. 193--215], we prove that this phenomenon holds for general multilayered perceptrons, i.e., neural networks with threshold activation functions, or with any mix of threshold and ReLU activations. Our construction is probabilistic and exploits sparsity.