Reviewerly/exHarmony huggingface dataset:
Wearable Sensors Based Automatic Box and Block Test System

Objective manual dexterity assessment is useful for early detection of age-related functional decline and for prediction of cognitive decline. Such assessments are carried out by clinicians using traditional tests and scales. The Box and Blocks Test (BBT) is one of such scale, focusing on the measurement of unilateral gross manual dexterity. Subjects are scored based on the number of blocks transferred from one compartment to the other compartment in a time window. Currently, most works on automatic BBT take advantage of the data captured by deep camera. However, these kind of solutions are limited by computational complexity and the accuracy of color segmentation. Based on recent success of wearable equipments, in this paper, we research multimodal wearable sensors based Box and Block Test system (wBBT). wBBT takes advantage of 5 kinds of time series signal, including Electromyographic, Accelerometer, Gyroscope, Orientation and Orientation Euler for automatic manual dexterity assessment. In addition, we research 9 deep neural network structures (3 multimodal signal fusion methods × 3 network cells) for multimodal signal based blocks moving motion recognition. To evaluate the feasibility of wBBT, we collect the data of 37 subjects, including 25 subjects with Mild Cognitive Impairment (MCI) and 12 normal subjects. Experimental results show that wBBT is a useful tool for automatic assessment of unilateral gross manual dexterity.
An Improved Container Cloud Resource Scheduling Strategy

Docker as the representative container technology promotes the rapid development of container cloud with its flexible, efficient and fast characteristics. As a better resource virtualization technology, container is widely recognized and favored by the industry. The resource scheduling strategy in the container cloud has an important impact on the cluster performance. The default priorities strategy of Kubernetes container orchestration tool only considers CPU and memory. With the complexity of the production environment, only considering the above two factors in the priorities stage can easily lead to some node load imbalance, which is difficult to guarantee the cluster service performance. In order to balance the resources of container clusters, this paper proposes a new priorities stage strategy based on Kubernetes' default scheduling strategy, which takes the storage and network bandwidth of task nodes into account, then calculates the weight of each resource, and finally puts it into the scoring formula as the basis of resource scheduling. By comparing the load balancing rate of the default priorities scheduling algorithm and the improved priorities scheduling algorithm, it is verified that the improved container cloud resource scheduling strategy can make the load of the task nodes in the cluster more balanced.
Numerical Inverse Transformation Methods for Z-Transform

Numerical inverse Z-transformation (NIZT) methods have been efficiently used in engineering practice for a long time. In this paper, we compare the abilities of the most widely used NIZT methods, and propose a new variant of a classic NIZT method based on contour integral approximation, which is efficient when the point of interest (at which the value of the function is needed) is smaller than the order of the NIZT method. We also introduce a vastly different NIZT method based on concentrated matrix geometric (CMG) distributions that tackles the limitations of many of the classic methods when the point of interest is larger than the order of the NIZT method.
Low Complexity Single Image Super-Resolution with Channel Splitting and Fusion Network

Recently, deep convolutional neural networks (CNNs) have made remarkable progress on single image super-resolution (SISR). However, many of these methods use very deep or wide convolutional layers to achieve good performance, which treat all feature channels indiscriminately and neglect the difference among the contribution of each channel to the output results. In this paper, we propose a low complexity solution based on channel splitting and fusion network (CSFN) to address this problem. Our method uses channel splitting and channel fusion to enhance feature maps and make full use of valuable information, and then multiple residual channel splitting and fusion blocks (CSFB) are cascaded to continuously extract more important information for reconstruction. To further minimize redundant parameters and improve efficiency, we adopt group and recursive con-volutional layer strategy in CSFB. Experiments demonstrate that our proposed CSFN could achieve higher performance with low computational complexity than most state-of-the-art methods.
The Importance of Loss Functions for Increasing the Generalization Abilities of a Deep Learning-Based Next Frame Prediction Model for Traffic Scenes

This paper analyzes in detail how different loss functions influence the generalization abilities of a deep learning-based next frame prediction model for traffic scenes. Our prediction model is a convolutional long-short term memory (ConvLSTM) network that generates the pixel values of the next frame after having observed the raw pixel values of a sequence of four past frames. We trained the model with 21 combinations of seven loss terms using the Cityscapes Sequences dataset and an identical hyper-parameter setting. The loss terms range from pixel-error based terms to adversarial terms. To assess the generalization abilities of the resulting models, we generated predictions up to 20 time-steps into the future for four datasets of increasing visual distance to the training dataset—KITTI Tracking, BDD100K, UA-DETRAC, and KIT AIS Vehicles. All predicted frames were evaluated quantitatively with both traditional pixel-based evaluation metrics, that is, mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), and recent, more advanced, feature-based evaluation metrics, that is, Fréchet inception distance (FID), and learned perceptual image patch similarity (LPIPS). The results show that solely by choosing a different combination of losses, we can boost the prediction performance on new datasets by up to 55%, and by up to 50% for long-term predictions.
Exploring A Zero-Order Direct Hmm Based on Latent Attention for Automatic Speech Recognition

In this paper, we study a simple yet elegant latent variable attention model for automatic speech recognition (ASR) which enables an integration of attention sequence modeling into the direct hidden Markov model (HMM) concept. We use a sequence of hidden variables that establishes a mapping from output labels to input frames. Inspired by the direct HMM model, we assume a decomposition of the label sequence posterior into emission and transition probabilities using zero-order assumption and incorporate both Transformer and LSTM attention models into it. The method keeps the explicit alignment as part of the stochastic model and combines the ease of the end-to-end training of the attention model as well as an efficient and simple beam search. To study the effect of the latent model, we qualitatively analyze the alignment behavior of the different approaches. Our experiments on three ASR tasks show promising results in WER with more focused alignments in comparison to the attention models.
Learning Plug-And-Play Proximal Quasi-Newton Denoisers

Plug-and-play (PnP) denoising for solving inverse problems has received significant attention recently thanks to its state of the art signal reconstruction performance. However, the performance improvement hinges on carefully choosing the noise level of the Gaus-sian denoiser and the descent step size in every iteration. We propose a strategy for training a Gaussian denoiser inspired by an unfolded proximal quasi-Newton algorithm, where the noise level of the input signal to the denoiser is estimated in each iteration and at every entry in the signal. Our scheme deploys a small convolutional neural network (mini-CNN) to estimate an element-wise noise level, mimicking a diagonal approximation of the Hessian matrix in quasi-Newton methods. Empirical simulation results on image deblurring demonstrate that our proposed approach achieves approximately 1dB improvement over state of the art methods, such as, BM3D-PnP and proximal gradient descent-PnP that are supplied with the true noise level, as well as over an end-to-end retrained FFDNet architecture that was trained to estimate the noise level and recover the deblurred images.
Anti-Predatory NIA Based Approach for Optimizing Basic COCOMO Model

Software Effort Estimation (SEE) is an important activity during development and production of software projects. The estimated effort is directly associated with the various planning and financial activities. It is also directly associated with business success. Constructive Cost Model (COCOMO) is a widely accepted SEE model. But in the current development scenario, existing parameters of COCOMO don't give realistic results. In the recent past, many researchers improved the performance of COCOMO by optimizing the parameters with the help of various Nature-Inspired Algorithms (NIAs). In this paper, a recently proposed NIA which is based on the frog's anti-predator behavior is used for the optimizing the parameters of basic COCOMO for SEE of 18 software projects listed in NASA data set. The performance of the Anti-Predatory NIA (APNIA) based proposed approach is also evaluated on NASA18 software data set in terms of the Mean Absolute Error (MAE). The result obtained shows 93.41% improvement in terms of MAE as compared to the basic COCOMO, 40.69% improvement as compared to Genetic Algorithm (GA) and 0.93% improvement as compared to Particle Swarm optimization (PSO) with inertia weight in effort estimation by proposed approach.
Identification and Classification of Expertise Using Eye Gaze—Industrial Use Case Study with Software Engineers

Identifying and classifying based on expertise in an objective manner is a challenge as it is difficult to distinguish between ability to solve with and without stress. Ability to complete the task in a comfortable manner results in a more productive and healthier workforce by eliminating competency-related stress at work. We use cognitive load as an indicator of stress while understanding the skill and comfort level of software testers in an industrial setting as a case study. We conducted our study using eye tracking techniques. Our findings are reported, and they were corroborated by interacting the participants of the study pre- and post-eye-tracking experiments. The results are encouraging to extend such exercises for additional use cases, e.g., trainer effectiveness evaluation.
A Privacy Aware Architecture for IoT Enabled Systems

The Internet of Things has been used widely for the development of many different types of smart systems. But the constant monitoring of activities and behaviours and the collection along with the use of confidential data have led to increasing concerns about privacy which have indeed become one of the main barriers to technology acceptance and adoption. The development of new personalised technologies increases the use of sensitive data which should be kept hidden from third parties. The current methods are not sufficient and effective to assess and mitigate the significant privacy risks exposed by IoT and to support the design and adoption of privacy-aware smart systems. In this context, this paper proposes an architecture for privacy preservation which enables fine-grained control over data, anonymization and authentication. In particular, we introduce a privacy aware IoT architecture that utilises physical unclonable functions and deep learning to ensure the privacy of the collected IoT data.
LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification

In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification, where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.
The Power of Few Qubits and Collisions – Subset Sum Below Grover’s Bound

Let $$a_1, \ldots a_n, t$$ be a solvable subset sum instance, i.e. there exists a subset of the $$a_i$$ that sums to t. Such a subset can be found with Grover search in time $$2^{\frac{n}{2}}$$ , the square root of the search space, using only $$\mathcal {O}(n)$$ qubits. The only quantum algorithms that beat Grover’s square root bound – such as the Left-Right-Split algorithm of Brassard, Hoyer, Tapp – either use an exponential amount of qubits or an exponential amount of expensive classical memory with quantum random access (QRAM). We propose the first subset sum quantum algorithms that breaks the square root Grover bound with linear many qubits and without QRAM. Building on the representation technique and the quantum collision finding algorithm from Chailloux, Naya-Plasencia and Schrottenloher (CNS), we obtain a quantum algorithm with time $$2^{0.48n}$$ . Using the Schroeppel-Shamir list construction technique, we further improve downto run time $$2^{0.43n}$$ . The price that we have to pay for beating the square root bound is that as opposed to Grover search our algorithms require classical memory, but no QRAM, i.e. we get a time/memory/qubit tradeoff. Thus, our algorithms have to be compared to purely classical time/memory subset sum trade-offs such as those of Howgrave-Graham and Joux. Our quantum algorithms improve on these purely classical algorithms for all memory complexities $$M < 2^{0.2n}$$ . As an example, for memory $$2^{0.1n}$$ we obtain run time $$2^{0.47n}$$ as opposed to $$2^{0.63n}$$ for the best classical algorithm.
Robust face tracking using multiple appearance models and graph relational learning

This paper addresses the problem of appearance matching across different challenges while doing visual face tracking in real-world scenarios. In this paper, FaceTrack is proposed that utilizes multiple appearance models with its long-term and short-term appearance memory for efficient face tracking. It demonstrates robustness to deformation, in-plane and out-of-plane rotation, scale, distractors and background clutter. It integrates on the advantages of the tracking-by-detection by using a face detector that tackles the drastic scale appearance change of a face. A weighted score-level fusion strategy is proposed to obtain the face tracking output having the highest fusion score by generating candidates around possible face locations. FaceTrack showcases impressive performance when initiated automatically by outperforming several state-of-the-art trackers, except Struck by a very minute margin: 0.001 in precision and 0.017 in success, respectively.
Anti-Forensics of Double Compressed MP3 Audio

The widespread availability of audio editing software has made it easy to create acoustically convincing digital audio forgeries. To address this problem, more and more attention has been paid to the field of digital audio forensics. There has been little work, however, in the field of anti-forensics, which seeks to develop a set of techniques designed to fool current forensic methodologies. The compression history of an audio sample can be used to provide evidence of audio forgeries. In this work, we present a simple method for distinguishing the MP3 compression history of an audio sample. We show the proposed anti-forensics method to remove the artifacts of MP3 double compression by destroying the audio frame structure. In addition, effectiveness of the proposed method is verified by three double compression detection methods. The experimental results show that the proposed method can effectively resist detection from three methods.
Deep Product Quantization Module for Efficient Image Retrieval

Product Quantization (PQ) is one of the most popular Approximate Nearest Neighbor (ANN) methods for large-scale image retrieval, bringing better performance than hashing based methods. In recent years, several works extend the hard quantization to soft quantization with specially designed deep neural architectures. We propose a simple but effective deep Product Quantization Module (PQM) to jointly learn discriminative codebook and precise hard assignment in an end-to-end manner. In this work, we use the straight-through estimator to make it feasible to directly optimize the discrete binary representations in deep neural networks with stochastic gradient descent. Different from previous deep vector quantization methods, PQM is a plug-and-play module which can be adaptive to various base networks in the scenarios of image search or compression. Besides, we propose a reconstruction loss to minimize the domain gap between the original embedding features and codebook. Experimental results show that PQM outperforms state-of-the-art deep supervised hashing and quantization methods on several image retrieval benchmarks.
A multi-view approach for Mandarin non-native mispronunciation verification

Traditionally, the performance of non-native mispronunciation verification systems relied on effective phone-level labelling of non-native corpora. In this study, a multi-view approach is proposed to incorporate discriminative feature representations which requires less annotation for non-native mispronunciation verification of Mandarin. Here, models are jointly learned to embed acoustic sequence and multi-source information for speech attributes and bottleneck features. Bidirectional LSTM embedding models with contrastive losses are used to map acoustic sequences and multi-source information into fixed-dimensional embeddings. The distance between acoustic embeddings is taken as the similarity between phones. Accordingly, examples of mispronounced phones are expected to have a small similarity score with their canonical pronunciations. The approach shows improvement over GOP-based approach by +11.23% and single-view approach by +1.47% in diagnostic accuracy for a mispronunciation verification task.
S-DOD-CNN: Doubly Injecting Spatially-Preserved Object Information for Event Recognition

We present a novel event recognition approach called Spatially-preserved Doubly-injected Object Detection CNN (S-DOD-CNN), which incorporates the spatially preserved object detection information in both a direct and an indirect way. Indirect injection is carried out by simply sharing the weights between the object detection modules and the event recognition module. Meanwhile, our novelty lies in the fact that we have preserved the spatial information for the direct injection. Once multiple regions-of-intereset (RoIs) are acquired, their feature maps are computed and then projected onto a spatially-preserving combined feature map using one of the four Rol Projection approaches we present. In our architecture, combined feature maps are generated for object detection which are directly injected to the event recognition module. Our method provides the state-of-the-art accuracy for malicious event recognition.
Improved Feature Learning: A Maximum-Average-Out Deep Neural Network for the Game Go

Computer game-playing programs based on deep reinforcement learning have surpassed the performance of even the best human players. However, the huge analysis space of such neural networks and their numerous parameters require extensive computing power. Hence, in this study, we aimed to increase the network learning efficiency by modifying the neural network structure, which should reduce the number of learning iterations and the required computing power. A convolutional neural network with a maximum-average-out (MAO) unit structure based on piecewise function thinking is proposed, through which features can be effectively learned and the expression ability of hidden layer features can be enhanced. To verify the performance of the MAO structure, we compared it with the ResNet18 network by applying them both to the framework of AlphaGo Zero, which was developed for playing the game Go. The two network structures were trained from scratch using a low-cost server environment. MAO unit won eight out of ten games against the ResNet18 network. The superior performance of the MAO unit compared with the ResNet18 network is significant for the further development of game algorithms that require less computing power than those currently in use.
Ernet Family: Hardware-Oriented Cnn Models For Computational Imaging Using Block-Based Inference

Convolutional neural networks (CNNs) demand huge DRAM bandwidth for computational imaging tasks, and block-based processing has recently been applied to greatly reduce the bandwidth. However, the induced additional computation for feature recomputing or the large SRAM for feature reusing will degrade the performance or even forbid the usage of state-of-the-art models. In this paper, we address these issues by considering the overheads and hardware constraints in advance when constructing CNNs. We investigate a novel model family-ERNet-which includes temporary layer expansion as another means for increasing model capacity. We analyze three ERNet variants in terms of hardware requirement and introduce a hardware-aware model optimization procedure. Evaluations on Full HD and 4K UHD applications will be given to show the effectiveness in terms of image quality, pixel throughput, and SRAM usage. The results also show that, for block-based inference, ERNet can outperform the state-of-the-art FFDNet and EDSR-baseline models for image denoising and super-resolution respectively.
A tight upper bound on the generalization error of feedforward neural networks

We give a tight upper bound on the generalization error of 2-times continuously differentiable feedforward neural networks if the loss function is 2-times continuously differentiable as well. The upper bound consists of two terms, the first term indicates how well the empirical error estimates the error at the mean of the sample space. The second term indicates the expected sensitivity of the error to the changes of the input. Furthermore, we provide explicit formulas for the calculation of the second term.
Bayesian Estimation of Plda with Noisy Training Labels, with Applications to Speaker Verification

This paper proposes a method for Bayesian estimation of probabilistic linear discriminant analysis (PLDA) when training labels are noisy. Label errors can be expected during e.g. large or distributed data collections, or for crowd-sourced data labeling. By interpreting true labels as latent random variables, the observed labels are modeled as outputs of a discrete memoryless channel, and the maximum a posteriori (MAP) estimate of the PLDA model is derived via Variational Bayes. The proposed framework can be used for PLDA estimation, PLDA domain adaptation, or to infer the reliability of a PLDA training list. Although presented as a general method, the paper discusses specific applications for speaker verification. When applied to the Speakers in the Wild (SITW) Task, the proposed method achieves graceful performance degradation when label errors are introduced into the training or domain adaptation lists. When applied to the NIST 2018 Speaker Recognition Evaluation (SRE18) Task, which includes adaptation data with noisy speaker labels, the proposed technique provides performance improvements relative to unsupervised domain adaptation.