Reviewerly/exHarmony huggingface dataset:
Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer

Data augmentation have been intensively used in training deep neural network to improve the generalization, whether in original space (e.g., image space) or representation space. Although being successful, the connection between the synthesized data and the original data is largely ignored in training, without considering the distribution information that the synthesized samples are surrounding the original sample in training. Hence, the behavior of the network is not optimized for this. However, that behavior is crucially important for generalization, even in the adversarial setting, for the safety of the deep learning system. In this work, we propose a framework called Stochastic Batch Augmentation (SBA) to address these problems. SBA stochastically decides whether to augment at iterations controlled by the batch scheduler and in which a ''distilled'' dynamic soft label regularization is introduced by incorporating the similarity in the vicinity distribution respect to raw samples. The proposed regularization provides direct supervision by the KL-Divergence between the output soft-max distributions of original and virtual data. Our experiments on CIFAR-10, CIFAR-100, and ImageNet show that SBA can improve the generalization of the neural networks and speed up the convergence of network training.
Extrapolation for Large-batch Training in Deep Learning

Deep learning networks are typically trained by Stochastic Gradient Descent (SGD) methods that iteratively improve the model parameters by estimating a gradient on a very small fraction of the training data. A major roadblock faced when increasing the batch size to a substantial fraction of the training data for improving training time is the persistent degradation in performance (generalization gap). To address this issue, recent work propose to add small perturbations to the model parameters when computing the stochastic gradients and report improved generalization performance due to smoothing effects. However, this approach is poorly understood; it requires often model-specific noise and fine-tuning. To alleviate these drawbacks, we propose to use instead computationally efficient extrapolation (extragradient) to stabilize the optimization trajectory while still benefiting from smoothing to avoid sharp minima. This principled approach is well grounded from an optimization perspective and we show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer. We demonstrate that in a variety of experiments the scheme allows scaling to much larger batch sizes than before whilst reaching or surpassing SOTA accuracy.
Offline Handwritten Mathematical Expression Evaluator Using Convolutional Neural Network

Recognition of Offline Handwritten Mathematical Expression (HME) is a complicated task in the field of computer vision. The proposed method in this paper follows three steps: segmentation, recognition and evaluation of the HME image (which may include multiple mathematical expressions and linear equations). The segmentation of symbols from image incorporates a novel pre-contour filtration technique to remove distortions from segmented symbols. Then, recognition of segmented symbols is done using Convolutional Neural Network which is trained on an augmented dataset prepared from EMNIST and custom-built dataset giving an accuracy of 97% in recognizing the symbols correctly. Finally, the expressions/equations are evaluated by tokenizing, converting into postfix expressions and then solving using a custom-built parser.
Learning in-place residual homogeneity for single image detail enhancement

An image detail enhancement algorithm is proposed based on in-place residual homogeneity (IP). Residual homogeneity is a physical law, which mainly explains the texture similarity between the same image residual at slightly different resolutions. As we all know, a single image can be divided into a base layer and a detail layer, and the effective estimation of the detail layer is the key in a detail enhancement algorithm. In the experiment, we find that the residual layer of an image obtained by bilinear interpolation is closely related to its detail layer, hence it can be used as the initial estimation of the detail layer, then residual homogeneity is applied to update the residual layer until the accurate detail layer is acquired. In the process of updating residuals, a searching method called fast in-place searching (FIPS) is used. FIPS only takes advantage of the residual homogeneity within the in-place region, which accelerates the project about 93%. Different from the local-based and global-based methods, our IP gets the detail layer directly and amplifies it. It has many good properties, such as being fast, edge-aware, robust, and parameter-free. Good performance has been demonstrated on several widely used datasets by both subjective and objective evaluations.
Vid2Player: Controllable Video Sprites that Behave and Appear like Professional Tennis Players

We present a system that converts annotated broadcast video of tennis matches into interactively controllable video sprites that behave and appear like professional tennis players. Our approach is based on controllable video textures, and utilizes domain knowledge of the cyclic structure of tennis rallies to place clip transitions and accept control inputs at key decision-making moments of point play. Most importantly, we use points from the video collection to model a player's court positioning and shot selection decisions during points. We use these behavioral models to select video clips that reflect actions the real-life player is likely to take in a given match play situation, yielding sprites that behave realistically at the macro level of full points, not just individual tennis motions. Our system can generate novel points between professional tennis players that resemble Wimbledon broadcasts, enabling new experiences such as the creation of matchups between players that have not competed in real life, or interactive control of players in the Wimbledon final. According to expert tennis players, the rallies generated using our approach are significantly more realistic in terms of player behavior than video sprite methods that only consider the quality of motion transitions during video synthesis.
A Deep Structured Model for Video Captioning

Video captions help people to understand in a noisy environment or when the sound is muted. It helps people having impaired hearing to understand much better. Captions not only support the content creators and translators but also boost the search engine optimization. Many advanced areas like computer vision and human-computer interaction play a vital role as there is a successful growth of deep learning techniques. Numerous surveys on deep learning models are evolved with different methods, architecture, and metrics. Working with video subtitles is still challenging in terms of activity recognition in video. This paper proposes a deep structured model that is effective towards activity recognition, automatically classifies and caption it in a single architecture. The first process includes subtracting the foreground from the background; this is done by building a 3D convolutional neural network (CNN) model. A Gaussian mixture model is used to remove the backdrop. The classification is done using long short-term memory networks (LSTM). A hidden Markov model (HMM) is used to generate the high quality data. Next, it uses the nonlinear activation function to perform the normalization process. Finally, the video captioning is achieved by using natural language.
Discovering Potential Partners via Projection-Based Link Prediction in the Supply Chain Network

As reserving a certain number of potential partners plays a significant role in alleviating existing partners' collaborative interruption risks, we investigate the process of discovering potential partners to improve the supply chain network's resilience.Most of the existing research confines its focus on discovering potential partners in the supply chain on the basic of sufficient partners' information, but very few works consider discovering potential partners in the supply chain network according to the structure of the supply chain network when the partner information is insufficient.In this situation, a novel model which applies projection-based link prediction method to discover potential partners in the supply chain network is proposed.The proposed model is composed of three stages.The first stage is predicting the candidate partnerships links based on the projection onemodel graph which is transformed from the supply chain network according to its structure.The second stage is discovering potential partners by comparing the acquired connectivity of candidate partnership links with the maximal connectivity of existent partnerships.In the third stage, a resilience evaluation framework considering the both connectivity and flexibility indexes is presented to determine whether the supply chain network's agility is improved.In the experimental design, a supply chain network which is formed from a real dataset containing mobile phone suppliers, manufacturers and packers is used to evaluate the proposed algorithm's prediction accuracy.The results reveal that the algorithm achieves highest area under curve (AUC) scores and the supply chain network's resilience is improved by discovering potential partners.
Sentiment Analysis on Movie Review Using Deep Learning RNN Method

The usage of social media grows rapidly because of the functionality like easy to use and it will also allow user to connect with all around the globe to share the ideas. It is desired to automatically use the information which is user’s interest. One of the meaningful information that is derived from the social media sites are sentiments. Sentiment analysis is used for finding relevant documents, overall sentiment, and relevant sections; quantifying the sentiment; and aggregating all sentiments to form an overview. Sentiment analysis for movie review classification is useful to analyze the information in the form of number of reviews where opinions are either positive or negative. In this paper we had applied the deep learning-based classification algorithm RNN, measured the performance of the classifier based on the pre-process of data, and obtained 94.61% accuracy. Here we had used RNN algorithm instead of machine learning algorithm because machine learning algorithm works only in single layer while RNN algorithm works on multilayer that gives you better output as compared to machine learning.
A Lattice-Based Key-Insulated and Privacy-Preserving Signature Scheme with Publicly Derived Public Key

As a widely used privacy-preserving technique for cryptocurrencies, Stealth Address constitutes a key component of Ring Confidential Transaction (RingCT) protocol and it was adopted by Monero, one of the most popular privacy-centric cryptocurrencies. Recently, Liu et al. [EuroS&P 2019] pointed out a flaw in the current widely used stealth address algorithm that once a derived secret key is compromised, the damage will spread to the corresponding master secret key, and all the derived secret keys thereof. To address this issue, Liu et al. introduced Key-Insulated and Privacy-Preserving Signature Scheme with Publicly Derived Public Key (PDPKS scheme), which captures the functionality, security, and privacy requirements of stealth address in cryptocurrencies. They further proposed a paring-based PDPKS construction and thus provided a provably secure stealth address algorithm. However, while other privacy-preserving cryptographic tools for RingCT, such as ring signature, commitment, and range proof, have successfully found counterparts on lattices, the development of lattice-based stealth address scheme lags behind and hinders the development of quantum-resistant privacy-centric cryptocurrencies following the RingCT approach. In this paper, we propose the first lattice-based PDPKS scheme and prove its security in the random oracle model. The scheme provides (potentially) quantum security not only for the stealth address algorithm but also for the deterministic wallet. Prior to this, the existing deterministic wallet algorithms, which have been widely adopted by most Bitcoin-like cryptocurrencies due to its easy backup/recovery and trustless audits, are not quantum resistant.
A Biological Retina Inspired Tone Mapping Processor for High-Speed and Energy-Efficient Image Enhancement

In this work, a biological retina inspired tone mapping processor for high-speed and energy-efficient image enhancement has been proposed. To achieve high throughput and high energy efficiency, several hardware design techniques have been proposed, including data partition based parallel processing with S-shape sliding, adjacent frame feature sharing, multi-layer convolution pipelining, and convolution filter compression with zero skipping convolution. Implemented on a Xilinx’s Virtex7 FPGA, the proposed design achieves a high throughput of 189 frames per second for 1024 × 768 RGB images while consuming 819 mW. Compared with several state-of-the-art tone mapping processors, the proposed design shows higher throughput and energy efficiency. It is suitable for high-speed and energy-constrained image enhancement applications.
A New Qubits Mapping Mechanism for Multi-programming Quantum Computing

For a specific quantum chip, multi-programming helps to improve the overall throughput and resource utilization. However, previous solutions for mapping multiple programs often lead to resource under-utilization, high error rate, and low fidelity. In this paper, we propose a new approach to map concurrent quantum programs. Our approach has three critical components. The first one is the Community Detection Assisted Partition (CDAP) algorithm, which partitions physical qubits for concurrent quantum programs by considering physical typology and the error rate, avoiding the waste of robust resources. The second one is the X-SWAP scheme that enables inter-program SWAPs and prioritizes SWAPs associated with critical gates to reduce the SWAP overheads. Finally, we propose a compilation task scheduler, which dynamically selects concurrent quantum programs to be compiled and executed together based on estimated fidelity for the best practice. We evaluate our work on publicly available quantum computer IBMQ16 and a simulated quantum chip IBMQ50. Our work outperforms the state-of-the-art work for multi-programming on fidelity and compilation overheads by 12.0% and 11.6%, respectively.
Adaptive Layout Decomposition with Graph Embedding Neural Networks

Multiple patterning lithography decomposition (MPLD) has been widely investigated, but so far there is no decomposer that dominates others in terms of both the optimality and the efficiency. This observation motivates us exploring how to adaptively select the most suitable MPLD strategy for a given layout graph, which is non-trivial and still an open problem. In this paper, we propose a layout decomposition framework based on graph convolutional networks to obtain the graph embeddings of the layout. The graph embeddings are used for graph library construction, decomposer selection and graph matching. Experimental results show that our graph embedding based framework can achieve optimal decompositions under negligible runtime overhead even comparing with fast but non-optimal heuristics.
The Impact of Flyer with Infographics on Public Awareness and Interest to Transportation Project

Informing citizens at an early stage of the planning process is very important so as to increase awareness and interest to the transportation project. It helps us to increase meeting participants and activate discussion. This study investigates the effectiveness of infographics in public notice documents through randomized controlled trial. Two groups of people received flyer with different design, which is ordinary design and infographic design. The answers to the questionnaire of the second survey were compared with the first survey. Result showed that infographics flyer can increase awareness more than ordinary flyer. Statistical t-tests confirmed the difference between two designs. However, neither infographics flyer nor ordinary flyer can increase interest to the project. Because there are many people who have already interested in the project, the difference is not significant.
Dissipative Rabi model in the dispersive regime

This paper presents analytic results on the dispersive regime of the dissipative Rabi model, without taking the rotating wave approximation of the underlying Hamiltonian. The analytic expressions for all dynamical parameters show qualitative differences at intermediate and large detuning, which allows the author to update the model of the interaction between qubits and resonators.
Analysis of Modern Methods to Ensure Data Integrity in Cyber-Physical System Management Protocols

At present, the problem of creating methodological security of cyberphysical systems, in particular, the design and implementation of information security subsystems is acute. At the same time, the landscape of threats and vulnerabilities typical for a wide range of hardware and software technologies used in cyberphysical systems is extremely wide and complex. In this context, the security of application layer protocols is of paramount importance, as these protocols are the basis for interaction between applications and services running on different devices, as well as in cloud infrastructures. With the constant interaction of the systems under study with the real physical infrastructure, the challenge is to determine effective measures to ensure the integrity of the transferred control commands, as disruption of the performed critical processes can affect human life and health. The paper provides an analytical review of the main methods of data integrity assurance in management protocol of cyberphysical systems, as well as an overview of application layer protocols vulnerabilities widely used in cyberphysical systems of different types. Classical methods of data integrity assurance, new methods, in particular, blockchain, as well as the main directions of increasing the efficiency of data integrity protocols in cyberphysical systems are considered. Analysis of application layer vulnerabilities is carried out on the example of the most popular MQTT, CoAP, AMQP, DDS, XMPP specifications and their implementations. It is established that despite the presence of basic security mechanisms in all these protocols, researchers continue to regularly identify vulnerabilities in popular implementations, that often endangers critical infrastructure services. In the course of preparing the review of the existing methods of data integrity assurance for the examined class of systems, the key problems of these methods integration and ways of their solution were defined.
Machine Learning-Based Pipeline for High Accuracy Bioparticle Sizing

High accuracy measurement of size is essential in physical and biomedical sciences. Various sizing techniques have been widely used in sorting colloidal materials, analyzing bioparticles and monitoring the qualities of food and atmosphere. Most imaging-free methods such as light scattering measure the averaged size of particles and have difficulties in determining non-spherical particles. Imaging acquisition using camera is capable of observing individual nanoparticles in real time, but the accuracy is compromised by the image defocusing and instrumental calibration. In this work, a machine learning-based pipeline is developed to facilitate a high accuracy imaging-based particle sizing. The pipeline consists of an image segmentation module for cell identification and a machine learning model for accurate pixel-to-size conversion. The results manifest a significantly improved accuracy, showing great potential for a wide range of applications in environmental sensing, biomedical diagnostical, and material characterization.
Emotion Classification by Jointly Learning to Lexiconize and Classify

Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018).Previous studies handle emotion lexicon construction and emotion classification separately.In this paper, we propose an emotional network (EmNet) to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context.The dynamic emotion lexicons are useful for handling words with multiple emotions based on different context, which can effectively improve the classification accuracy.We validate the approach on two representative architectures -LSTM and BERT, demonstrating its superiority on identifying emotions in English tweets.Our model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.
Neural networks with late-phase weights

The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD). Here, we show that the solutions found by SGD can be further improved by ensembling a subset of the weights in late stages of learning. At the end of learning, we obtain back a single model by taking a spatial average in weight space. To avoid incurring increased computational costs, we investigate a family of low-dimensional late-phase weight models which interact multiplicatively with the remaining parameters. Our results show that augmenting standard models with late-phase weights improves generalization in established benchmarks such as CIFAR-10/100, ImageNet and enwik8. These findings are complemented with a theoretical analysis of a noisy quadratic problem which provides a simplified picture of the late phases of neural network learning.
Heuristic Approach to Distributed Server Allocation with Preventive Start-Time Optimization against Server Failure

This paper presents a distributed server allocation model with preventive start-time optimization against a single server failure. The presented model preventively determines the assignment of servers to users under each failure pattern to minimize the largest maximum delay among all failure patterns. We formulate the proposed model as an integer linear programming (ILP) problem. We prove the NP-completeness of the considered problem. As the number of users and that of servers increase, the size of ILP problem increases; the computation time to solve the ILP problem becomes excessively large. We develop a heuristic approach that applies simulated annealing and the ILP approach in a hybrid manner to obtain the solution. Numerical results reveal that the developed heuristic approach reduces the computation time by 26% compared to the ILP approach while increasing the largest maximum delay by just 3.4% in average. It reduces the largest maximum delay compared with the start-time optimization model; it avoids the instability caused by the unnecessary disconnection permitted by the run-time optimization model.
The Optimization of Object Detection and Localization in Complex Background for Vision-based Robot

This paper studies the object detection and localization for vision-based robots. The vision-based robot collects the stream of images by using the wide-angle monocular camera to get more-detailed information of objects around the robot. Based on the captured camera image, we first preprocess the collected image information to extract useful points by distortion correction, color space conversion and noise reduction. After getting color and shape information of object, we propose several algorithms to cancel the interfere from complex background with a close operation, to use a multi-threshold limitation for object detection and localization, and to find minimum circumscribed rectangle of target in order to recognize obstacles and adjust robot's direction and position. Finally, the robot performs well with the presented methods.