Reviewerly/exHarmony huggingface dataset:
Cache: Modeling Contribution-Aware Context Hierarchically for Long-Range Dialogue State Tracking

Recently, many studies on dialogue state tracking (DST) based on the copy-augmented encoder-decoder framework have been proposed and have achieved encouraging performance. However, these studies commonly lose earlier information during encoding the long dialogues with RNNs, and have difficulty for the decoder to focus on specific dialogue turns from lengthy context, which causes decreased performance as the dialogue gets longer. In this work, we propose a novel method to model Contribution-Aware Context HiErarchically (CACHE) with a hierarchical encoder and a slot-turn attention module. The hierarchical encoder is designed to prevent information loss by reducing the length of the sequence sent to each encoder. The slot-turn attention module is explored to help the decoder focus on the slotrelated dialogue turn information. To evaluate models more appropriately, we introduce a new metric continued joint accuracy considering the prediction accuracy of both current and historical dialogue turns. Experiments on MultiWOZ 2.0 show that CACHE is an effective model for tracking states especially in long context.
Encryption technology of OFDM satellite system based on five-dimensional hyperchaotic synchronization

This paper proposes a novel 5-dimensional hyperchaotic coupling synchronization system for physical layer security in OFDM satellite systems. The proposed approach is designed based on the Lyapunov stability theory. The proposed chaotic-based satellite data encryption/decryption system is validated using a numerical simulation study. Additionally, to demonstrate the efficiency of the proposed chaotic encryption structure, we analyzed its key space. The proposed chaotic encryption structure is very sensitive to the initial key, and a tiny discrepancy as small as 10−19 would lead to a completely different sequence. The key space of the proposed scheme is up to 10<sup>340</sup>.
Total Quality Assurance Networking Model for New Defect Prevention Techniques

In order to survive globalization and worldwide quality competition, Japanese manufacturing must work towards the simultaneous achievement of quality, cost, and delivery (QCD) in order to shorten development times, ensure high quality, and lower production costs. Therefore, in this chapter, the author creates the Total Quality Assurance Networking Model (TQA-NM) for the new defect prevention that uses the product quality assurance tools for the new manufacturing management. This model focuses on integrated quality assurance spanning different departments from design to production in automobile manufacturing companies and extending further to part manufacturers. TQA-NM is developed, and its effectiveness is verified at Toyota and its suppliers.
A Semi-Handcrafted Keypoint Detector with Discriminative Feature Encoding

Most previous handcrafted keypoint methods focus on designing specific structural patterns using human-defined knowledge. These methods, however, ignore the fact that whether they have enough flexibility to harvest diverse local structures. Recently, the semi-handcrafted approaches based on sparse coding have emerged as a new trend of alleviating the above issue. And yet, the intrinsic relationships of key-points have not been explored actively, which may lead to the ambiguity of feature codes for further analysis. To tackle this problem, in this paper, we introduce a novel semi-handcrafted keypoint detector through a scheme of discriminative feature representations (SDFR). Specifically, we cast keypoint detection as an optimization problem on a visual dictionary that explicitly models the visual relationships of feature points to preserve the consistency of similar features and distance dissimilar ones. Further, we propose an iterative solver for the SDFR model. Experimental results on challenge benchmarks demonstrate that the proposed method performs favorably against state-of-the-art in literature.
Implementation Details of Neural Networks Using Dataflow

This chapter presents dataflow paradigm in general and loop unrolling and data pipelines as key points for acceleration and discusses implementation details of multilayer perceptron neural networks. The iterative nature of the algorithm makes it suitable for dataflow implementation using matrix multiplication as a basic operation. Also, it presents major differences in code execution between conventional controlflow paradigm and dataflow paradigm. It is shown how part of an algorithm (feed-forward phase) can be migrated to the accelerator while the rest remains the same.
Digital watermarking and tamper detection in speech signal using blind detection

Advancement of digital signal processing and networking has raised many security and copyright concerns, thus it is very important to protect the authentication of digital data. In this work, an audio watermarking algorithm has been proposed which can be efficiently used for tamper detection and is also robust against reasonable attacks. Also, the watermarks are inaudible. The proposed algorithm can easily detect tampering as the watermarks are embedded at each frame without causing any audio degradation. In the proposed technique, first the audio signal is compressed using Graph Based Transform (GBT), for which watermarks are embedded into Line Spectral coefficients (LSFs) that are derived from linear prediction (LP) analysis with dither modulation-quantization index modulation (DM-QIM). Watermarks thus embedded in all frames are not only inaudible to the Human auditory system but also potentially provide robustness against meaningful attacks. This work also focuses on Blind tamper detection which is made effortless due to the proposed embedding algorithm. To measure the robustness of the algorithm, general processing of watermarked signals was done along with fragility testing. Quality of the audio was measured using Perceptual Evaluation of Speech Quality (PESQ) and Short-time objective intelligibility (STOI). The maximum PESQ score and STOI score of 2.8781 and 0.8150 respectively was observed without any attack on the audio signal. Tamper detection and quality measurement are the major contributions of this work. Detailed metric evaluation for attacks such as Scaling, Resampling, Filtering, Compression and Addition of White Gaussian noise (AWGN) has been computed and compared. The proposed technique makes tamper identification easier and gives framewise security.
Privacy Analysis of Query-Set-Size Control

The publication of user data for statistical analysis and research can be extremely beneficial for both academic and commercial uses, such as statistical research and recommendation systems. To maintain user privacy when such a publication occurs many databases employ anonymization techniques, either on the query results or the data itself. In this article, we examine and analyze the privacy offered when using the query-set-size control method for aggregate queries over a data structures representing various topologies. We focus on the mathematical queries of minimum, maximum, median, and average and show some query types that may be used to extract hidden information. We prove some combinations of these queries will maintain a measurable level of privacy even when using multiple queries. We offer a privacy probability measure, indicating the probability of an attacker to obtain information defined as sensitive by utilizing legitimate queries over such a system. Our results are mathematically proven and backed by simulations using vehicular network data based on the TAPASCologne project.
Elderly Behavior Prediction Using a Deep Learning Model in Smart Homes

The goal of a smart home is to keep track of the behaviors of the older adults with disabilities within the home, and then anticipate their activity to help with other actions. Elderly and disabled people have problems with their daily lives, while most other people are unaware of their difficulties. Helping the elderly to live independently allows them to lead their daily lives in a better manner. The implementation of analytics and machine learning algorithms leads to a predictive approach to health care services. In this chapter, a learning model in a smart home concept focuses on making it possible for the elderly to remain safe and comfortable at home. The transformative home security device learning architecture of the smart home platform is a valuable solution to studying mobility patterns at home, with the ability to identify behavioral changes related to issues of wellbeing. A predictive learning system can effectively recognize and identify the behavior of the elderly. A learning model, a recurrent neural network (RNN) is proposed to evaluate the people's activity. The focus of the present study is to forecast the deterioration in mental function and give warnings for the benefit of seniors.
Optimizing Train-Test Data for Person Re-Identification in Real-World Applications

Person re-identification (re-ID) aims to recognize an identity in non-overlapping camera views. Recently, re-ID received increased attention due to the growth of deep learning and its prominent applications in the field of automated video surveillance. The performance of deep learning-based methods relies heavily on the quality of training datasets and protocols. Particularly, parameters associated to the train and test set construction affect the overall performance. However, public re-ID datasets usually come with a fixed set of parameters, which are partly suitable for optimizing re-ID applications. In this paper, we study dataset construction parameters to improve re-ID performance. To this end, we first experiment on the temporal subsampling rate of the sequence of bounding boxes. Second, an experiment is performed on the effects of bounding-box enlargement under various temporal sampling rates. Thirdly, we analyze how the optimal choice of such dataset design parameters change with the dataset size. The experiments reveal that a performance increase of 2.1% Rank-1 is possible over a state-of-the-art re-ID model when optimizing the dataset construction parameters, thereby increasing the state-of-the-art performance from 91.9% to 94.0% Rank-1 on the popular DukeMTMC-reID dataset. The obtained results are not specific for the applied model and likely generalize to others.
Data Gathering, Processing, and Visualization for COVID-19

The novel coronavirus has been impacting human society since 2019. The global death toll has exceeded 400 million as of October 2021. To better understand the dynamic of COVID-19, big data technologies can in principle be applied to provide the overview and the preview for how it spread spatially and temporally. This chapter introduces how big data helps in terms of tracking, processing, visualizing, and analyzing COVID-19 information by illustrating the major components of data visualization designs. Topics cover a broad range of cutting-edge techniques that deal with a variety of big data problems, such as data aggregation, data preprocessing, big data pipeline construction, data workflow architecture design, visual mining, and web-based dashboard development.
Non-Rigid Transformation Based Adversarial Attack Against 3d Object Tracking

It is well-recognized that 3D visual tasks based on deep neural networks are vulnerable to adversarial attacks. Existing methods to generate adversarial examples are mainly developed from injecting imperceptible perturbations into the inputs. However, aggressive characteristic of geometric transformations, which are common in 3D objects, are rarely investigated. In this paper, we propose the non-rigid transformation based adversarial attack method against 3D object tracking. The adversarial example is generated by deforming parts of the tracking template, leading to deviation of the tracking predictions from the ground truth. Specifically, a clustering-based region segmentation module is designed to divide the tracking template into local regions. Furthermore, an objective function, which combines IoU loss, confidence loss and distance loss, is leveraged to update the poses of local regions. Experiments conducted on an efficient 3D tracker demonstrate that 3D trackers are extremely vulnerable to non-rigid deformation.
SP Attack: Single-Perspective Attack for Generating Adversarial Omnidirectional Images

The safety of Deep Neural Networks (DNNs) processing omnidirectional images (ODIs) is an under-researched topic. In this paper, we propose a novel sparse attack, named Single-Perspective (SP) Attack, towards fooling these models by perturbing only one perspective image (PI) rendered from the target ODI. The attack is launched from the perspective domain, and finally the perturbation is transferred to the original ODI. To this end, we propose an effective PI position searching algorithm based on Bayesian Optimization, and then corrupt the PI centered on the desirable position with unconstrained/constrained perturbations. Extensive experiments on synthetic and real-world omnidirectional datasets demonstrate that SP Attack can overcome the projection deformation of ODIs, and mislead the neural networks by limiting the perturbations in a single patch on the target ODI.
Mannet: A Large-Scale Manipulated Image Detection Dataset And Baseline Evaluations

The sharing of fake content on social media platforms has become a major concern. In many cases, the same content with small variations is shared multiple times on different social media platforms. This leads to the circulation of manipulated content on the web. With the rapid advancement in deep learning algorithms, the generation of manipulated images with small variations in original images has become an easy task. These contents raise serious concerns when used for malicious activities. Therefore, detection of manipulated contents is of paramount importance. However, no large-scale dataset having manipulated images generated using both handcrafted and deep learning algorithms is available. Therefore, in this research, we have proposed a large dataset with more than 5.5 million images, termed as ManNet dataset. Additionally, we have benchmarked the performance of existing algorithms for manipulated image detection. The experimental results highlight that inter-set (disjoint training testing) evaluations are the major challenge of manipulated image detection.
Fighting Anti-Asian Hate in and After the COVID-19 Crisis With Big Data Analytics

Racism and physical attacks on Asian communities have spread in the U.S. and around the world. Xenophobia is a virus that may lead to an ongoing social problem in the post-pandemic era. Although existing studies have been done to classify anti-Asian haters, little is known on monitoring, tracking, and characterizing anti-Asian haters on social media platforms. In this chapter, a systematic examination of anti-Asian haters tracking and profiling methods is designed by using big data analytics with deep learning algorithms. Target haters are investigated and tracked by analyzing public opinions towards key topics in 2020, including the U.S. elections, stimulus checks, and economy opening strategies throughout data collection and preprocessing, text classification, sentiment analysis, data visualization, and association rule. Such a comprehensive study provides a variety of research opportunities in dealing with anti-Asian racism and xenophobia in and after the COVID-19 pandemic.
Arbitrary conditional inference in variational autoencoders via fast prior network training

Variational Autoencoders (VAEs) are a popular generative model, but one in which conditional inference can be challenging. If the decomposition into query and evidence variables is fixed, conditionally trained VAEs provide an attractive solution. However, to efficiently support arbitrary queries over pre-trained VAEs when the query and evidence are not known in advance, one is generally reduced to MCMC sampling methods that can suffer from long mixing times. In this paper, we propose an idea of efficiently training small conditional prior networks to approximate the latent distribution of the VAE after conditioning on an evidence assignment; this permits generating query samples without retraining the full VAE. We experimentally evaluate three variations of conditional prior networks showing that (i) they can be quickly optimized for different decompositions of evidence and query and (ii) they quantitatively and qualitatively outperform existing state-of-the-art methods for conditional inference in pre-trained VAEs.
Free Lunch for Cross-Domain Occluded Face Recognition without Source Data

Most recognizing occluded faces methods focus on synthetic-occluded faces for training due to the lack of real-occluded data. However, the performance may suffer from degradation since the synthetic-occluded and real-occluded face images are under different distributions. Hence, it draws our eyes to transfer the model from the synthetic to the real-world domain. In this paper, we propose a source data-free domain adaptive occluded face recognition framework to optimize the network in the target domain via redefining it as a pseudo labels denoising problem. To obtain reliable pseudo labels, we train synthetic-occluded and non-occluded images via distribution alignment to extract occlusion-robust features. Nonetheless, completely correct labels are still unattainable. Then, a denoising strategy is proposed to optimize pseudo labels by centroid-based feature clustering. Experiments show that the proposed approach can effectively recognize the real-occluded face; it also reminds the occluded faces recognition community about the feasibility of domain adaptation in existing tasks.
GLNet: low-light image enhancement via grayscale priors

Low-light images are generally produced by shooting in a low light environment or a tricky shooting angle, which not only affect people's perception, but also leads to the bad performance of some artificial intelligence algorithms, such as object detection, super-resolution, and so on. There are two difficulties in the low-light enhancement algorithm: in the first place, applying image processing algorithms independently to each low-light image often leads to the color distortion; the second is the need to restore the texture of the extremely low-light area. To address these issues, we present two novel and general approaches: firstly, we propose a new loss function to constrain the ratio between the corresponding RGB pixel values on the low-light image and the high-light image; secondly, we propose a new framework named GLNet, which uses the dense residual connection block to obtain the deep features of the low-light images, and design a gray scale channel network branch to guide the texture restoration on the RGB channels by enhancing the grayscale image. The ablation experiments have demonstrated the effectiveness of the proposed module in this paper. Extensive quantitative and perceptual experiments show that our approach obtains state-of-the-art performance on the public dataset.
Method of Controlling a Tankendo Robot Using a Mecanum Wheel Trolley Robot

Tankendo which is a form of martial arts that originated in Japan is suitable for even aged persons or women and is good for promotion of health since a short and easy to maneuver shinai (bamboo sword) is used during matches as well as during practice. However, since the population of persons active in Tankendo is small, it is not easy to gain experience of actual play against other human players. In this research, the authors propose a method of control of a Tankendo robot that employs a trolley robot that uses a mecanum wheel as the movement mechanism. Although this robot cannot start suddenly or stop suddenly like humans, since it has seven pivots, it is possible to finely control the direction or posture of the vehicle as well as its speed. The control commands are of the three types of “speed”, “direction of travel”, and “rotation ratio (curvature of the curve)”, and, in addition, trapezoidal speed control is being carried out in order to prevent loss of synchronization. In other words, there is a time delay until the target speed of the control command is reached. Using a trolley robot with these characteristics, the inputs are the "robot state" (position, posture, etc.) and the "opponent state" (direction of the shinai, movement speed, distance from the opponent, etc.) at the current instant of time, and the "robot control commands" from several instants of time earlier. For the output, an optimal reference table using "speed," "direction of travel," and " rotation ratio" is machine-learned using reinforcement learning. The authors propose a robot control method that can defend against a human attack by moving to an appropriate position at an appropriate time, just before the attack becomes effective strike.
Classification Algorithms and Control-Flow Implementation

Supervised classification algorithms exploit many features that are tightly related to control-flow architecture. This reduces the possibility of applying these algorithms to dataflow architecture. This chapter makes an overview of some features characteristic to various classification algorithms that cannot be implemented on dataflow architecture. The chapter provides examples of applying various classification algorithms to three datasets with different types of material.
Reversible Data Hiding in Encrypted Images Based on Multi-Predictor and Quad-tree Block Encoding

With the rapid development of the Internet, people's awareness of privacy protection is also gradually improved. Reversible data hiding of encrypted image is a technique that encrypts the original image and allows the embedding of secret data without knowing the original image information. This method can accurately extract the secret data and reconstruct the original image completely. In this paper, a reversible data hiding algorithm of encrypted image based on multi-predictor and quad-tree block encoding is proposed. First, the original image is divided into 8×8 non-overlapping blocks. Then, multi-predictor is used to predict each block to get the prediction error image, then block scrambling is used to encrypt it. Since the pixel values of the encrypted image are small integer closes to zero, the quad-tree block encoding method can be used to embed a large amount of data. Experimental results show that the embedding rate of this method is better than that of some new algorithms.
Towards Joint Frame-Level and MOS Quality Predictions with Low-Complexity Objective Models

The evaluation of the quality of gaming content, with low-complexity and low-delay approaches is a major challenge raised by the emerging gaming video streaming and cloud-gaming services. Considering two existing and a newly created gaming databases this paper confirms that some low-complexity metrics match well with subjective scores when considering usual correlation indicators. It is however argued such a result is insufficient: gaming content suffers from sudden large quality drops that these indicators do not capture. In addition to proposing three new low-complexity models based on various machine learning techniques, this paper introduces a new indicator to capture sudden quality variations and reports poor results for most of the models when applying this indicator. Consequently, an original way to train the models, using jointly the subjective scores and the frame level scores of a full-reference metric, is proposed. The high correlation through traditional indicators is preserved, while the efficiency on the new indicator is drastically improved.