Reviewerly/exHarmony huggingface dataset:
A Real-Time Forward Collision Warning Technique Incorporating Detection and Depth Estimation Networks

The visual perception is of great significance for advanced driving assistance systems or autonomous driving vehicles to recognize the surrounding scenes. In the adaptation to the real environments for collision warnings, a sensor system should be efficient and has the strong ability to detect small objects. This paper presents a forward collision warning technique which incorporates the object detection and depth estimation networks. A deep convolutional neural network is constructed with transfer connection blocks for object detection and classification. It is capable of small object detection under the real-time processing requirement. For depth estimation, a monocular based disparity estimation network is adopted to the stereo vision framework. The epipolar constraint is applied to increase the prediction accuracy. In the experiments, the performance evaluation is carried out on public driving datasets. The comparison with the state-of-the-art networks has demonstrated the feasibility of the proposed technique.
A Novel Restricted Boltzmann Machine Training Algorithm With Dynamic Tempering Chains

Restricted Boltzmann machines (RBMs) are commonly used as pre-training methods for deep learning models. Contrastive divergence (CD) and parallel tempering (PT) are traditional training algorithms of RBMs. However, these two algorithms have shortcomings in processing high-dimensional and complex data. In particular, the number of temperature chains in PT has a significant impact on the training effect, and the PT algorithm cannot fully utilize parallel sampling from multiple temperature chains for the divergence of the algorithm. The training can quickly converge with fewer temperature chains, but this impacts the accuracy. More temperature chains can help PT achieve higher accuracy in theory, but severe divergence at the beginning of the training may ruin the training result. To exploit fully the advantages of PT and improve the ability of RBMs to process high-dimensional and complex models, this article proposes dynamic tempering chains (DTC). By dynamically changing the number of temperature chains during the training process, DTC starts training with fewer temperature chains and gradually increase the number of temperature chains with training going on, and finally get an accurate RBM. And one-step reconstruction error is proposed to measure the convergence, which can decrease the influence of the dynamic training strategy on reconstruction error. Experiments on MNIST, MNORB, Cifar 10, and Cifar 100 indicate that, compared with PT, the classification accuracy of DTC algorithm improved by up to 8&#x0025;. DTC quickly converges in the early stage of training because of few exchanges among temperature chains and produces higher accuracy at the end for the global optimum model learned by more temperature chains, especially when learning high-dimensional and complex data. This proves that the DTC algorithm effectively utilizes parallel sampling of multiple temperature chains, overcomes divergence challenges, and further improves the training effect of the RBM.
sKAdam: An improved scalar extension of KAdam for function optimization

This paper presents an improved extension of the previous algorithm of the authors called KAdam that was proposed as a combination of a first-order gradient-based optimizer of stochastic functions, known as the Adam algorithm and the Kalman filter. In the extension presented here, it is proposed to filter each parameter of the objective function using a 1-D Kalman filter; this allows us to switch from matrix and vector calculations to scalar operations. Moreover, it is reduced the impact of the measurement noise factor from the Kalman filter by using an exponential decay in function of the number of epochs for the training. Therefore in this paper, is introduced our proposed method sKAdam, a straightforward improvement over the original algorithm. This extension of KAdam presents a reduced execution time, a reduced computational complexity, and better accuracy as well as keep the properties from Adam of being well suited for problems with large datasets and/or parameters, non-stationary objectives, noisy and/or sparse gradients.
A Scalable Bit-Parallel Word-Serial Multiplier with Fault Detection on GF(2^m)

In this paper, a scalable bit-parallel word-serial (BPWS) finite field multiplier with fault detection capability on binary extension fields is proposed for elliptic curve cryptography (ECC). The proposed multiplier aims at low-complexity implementation meanwhile obtaining high fault detection performance. Firstly, to reduce area cost, we use a 9-bit register for the configuration of modular reduction so the multiplier can accommodate all five finite field recommended by National Institute of Standard and Technology (NIST). Then, a parity-based concurrent error detection (CED) approach is employed to increase the reliability. Finally, we analyze the capability and overheads of the CED scheme based on ASIC simulation. We show that the employed 1-bit parity detection approach results in 100% stuck-at fault detection c with small time and area overheads, while the best existing one use 4-bit parity detection to achieve 100% fault detection.
A Modeling Method for Model-Driven API Management

This article reports on the Design Science engineering cycle for implementing a modeling method to support model-driven, process-centric API management. The BPMN standard was hereby enriched on semantic, syntactic and tool levels in order to provide a viable solution for integrating API requests with diagrammatic business process models in order to facilitate the documentation or testing of REST API calls directly in a modeling environment. The method can be implemented by stakeholders that need to map and manage their API ecosystem, thus gaining more API management agility and improving their software engineering productivity. By assimilating API ecosystem conceptualization in the modeling environment, the proposal differs from both RPA (which typically employs non-BPMN process diagramming e.g., in UIPath) and BPM Systems (which typically isolate all API-related semantics outside the process modeling language to keep the diagrammatic representation standard-compliant).
Design of collaborative multimodal strategies for disruption management in urban railway systems

In order to increase the attractiveness and the resilience of multimodal transport systems, each transport mode should be exploited according to its peculiar characteristics in an integrated way with the other modes. In this regard, it is possible to imagine a “synchromodal framework” relying on a “rail backbone”, where the synchronization of the different modes is driven by rail transport. On the other hand, the limited number of path alternatives that characterizes the rail network makes it vulnerable to service disruptions and may result in a significant Level of Service loss. In this context, this paper proposes a methodology for urban public service providers that allows to design a multimodal service able to react, in a quasi-real-time framework, to unexpected events by offering multimodal transport solution to users.
Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks

Stochastic neural networks with discrete random variables are an important class of models for their expressiveness and interpretability. Since direct differentiation and backpropagation is not possible, Monte Carlo gradient estimation techniques are a popular alternative. Efficient stochastic gradient estimators, such Straight-Through and Gumbel-Softmax, work well for shallow stochastic models. Their performance, however, suffers with hierarchical, more complex models. We focus on stochastic networks with Boolean latent variables. To analyze such networks, we introduce the framework of harmonic analysis for Boolean functions to derive an analytic formulation for the bias and variance in the Straight-Through estimator. Exploiting these formulations, we propose \emph{FouST}, a low-bias and low-variance gradient estimation algorithm that is just as efficient. Extensive experiments show that FouST performs favorably compared to state-of-the-art biased estimators and is much faster than unbiased ones.
Dissipative preparation of multipartite Greenberger-Horne-Zeilinger states of Rydberg atoms*

The multipartite Greenberger–Horne–Zeilinger (GHZ) states play an important role in large-scale quantum information processing. We utilize the polychromatic driving fields and the engineered spontaneous emissions of Rydberg states to dissipatively drive three- and four-partite neutral atom systems into the steady GHZ states, at the presence of the next-nearest neighbor interaction of excited Rydberg states. Furthermore, the introduction of quantum Lyapunov control can help us optimize the dissipative dynamics of the system so as to shorten the convergence time of the target state, improve the robustness against the spontaneous radiations of the excited Rydberg states, and release the limiting condition for the strengths of the polychromatic driving fields. Under the feasible experimental conditions, the fidelities of three- and four-partite GHZ states can be stabilized at 99.24 % and 98.76 %, respectively.
Deep Sheep: kinship assignment in livestock from facial images

For the non-farmer folk all sheep might look the same, but they are in fact morphologically quite different; including when it comes to facial features. Image analysis has already demonstrated that computer-based facial recognition in livestock is very accurate. We investigate the viability of deep learning for assigning kinship in livestock for use in genetic evaluation- given two images of sheep faces, our proposed model predicts their genetic relationship. In this work we present two CNN models: one for face detection (reporting 80% accuracy) and one for kinship detection (reporting 68% balanced accuracy).
Quad-Approx CNNs for Embedded Object Detection Systems

Convolutional Neural Networks (CNNs) are computational-intensive and resource-consuming. To build CNNs with low resource requirements of embedded computer vision applications such as object detection, we propose quad-approx networks. Although binarized networks are good for classification tasks they are not adequate for object detection. In quad-approx networks, we first quantize the convolutional layers. Features and weights for convolution are encoded into 3 bits. On top of that, an approximate multiplier for this special quantized network is proposed. Both approximations are back annotated to the training process leading to no loss in overall precision. The hardware simulation and experimental results are presented for quad-approx CNN based on Zynq UltraScale+ MPSoC ZCU102. 5.3x compression of network and 1.20x speedup for calculation are achieved.
Self - Adaptive Load Balancing Using Live Migration of Virtual Machines in Cloud Environment

In this paper, a self-adaptive load balancing technique is proposed using live migration of heterogeneous virtual machines (VM) in a Hyper-V based cloud environment. A cloud supported plugin as a management activity within the infrastructure as a service strategy. It is proposed to assist the load balancing process in such a way so that all hypervisors are almost equally loaded once the overload status gets triggered. In the cloud computing environment, load balancing plays a major role if the large number of events triggered has a high impact on the performance of the system. The efficiency of cloud computing is based on the efficient load balancing having a self-adjustable technique using live migration of VMs across clusters of nodes. The proposed load balancing model is efficient in performance improvement by efficient resource utilization and also it helps to avoid the situation occurrence of server hanging by the cause of server overload within the infrastructure of multiple Microsoft Hyper-V hypervisors environment.
FiSSA at SemEval-2020 Task 9: Fine-tuned for Feelings

In this paper, we present our approach for sentiment classification on Spanish-English codemixed social media data in the SemEval-2020 Task 9. We investigate performance of various pre-trained Transformer models by using different fine-tuning strategies.We explore both monolingual and multilingual models with the standard fine-tuning method.Additionally, we propose a custom model that we fine-tune in two steps: once with a language modeling objective, and once with a task-specific objective.Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score with 0.537 on development data and 0.739 on test data.With this score, our team jupitter placed tenth overall in the competition.
Convolutional Neural Network-Based Interference Recognition

Interference signals are a huge threat to communication systems and interference recognition is an indispensable part in anti-interference communication. With the rapid development of artificial intelligence, interference recognition methods based on deep learning have been proposed. However, these methods do not allow networks to learn relationships among interference signals. In this paper, we propose new loss functions and a model to improve the recognition performance of interference signals without increasing complexity. Firstly, interference signals are converted into time-frequency images (TFIs) by short-time Fourier transform (STFT) and these TFIs are the input of convolutional neural network (CNN). Secondly, two CNNs with shared parameters are established and the samples of every batch in the training are divided into several pairs. Finally, through these pairs and proposed loss functions, which are Kullback-Leibler (KL) divergence and Euclidean distance of extracted features, the network can learn relationships of every category of interference and have better ability of generalization. The experiments prove our proposed approach significantly improves the recognition accuracy of interference signals.
A Unified Formal Model for Proving Security and Reliability Properties

Taint-propagation and X-propagation analyses are important tools for enforcing circuit design properties such as security and reliability. Fundamental to these tools are effective models for accurately measuring the propagation of information and calculating metadata. In this work, we formalize a unified model for reasoning about taint- and X-propagation behaviors and verifying design properties related to these behaviors. Our model are developed from the perspective of information flow and can be described using standard hardware description language (HDL), which allows formal verification of both taint-propagation (i.e., security) and X-propagation (i.e., reliability) related properties using standard electronic design automation (EDA) verification tools. Experimental results show that our formal model can be used to prove both security and reliability properties in order to uncover unintended design flaw, timing channel and intentional malicious undocumented functionality in circuit designs.
Review on the Usage of Deep Learning Models in Multi-modal Sentiment Analysis

In recent years, Sentiment Analysis is reshaping the business operations of many organizations by monitoring their brand reputation on social media and acquiring insights from customer"s feedback. Sentiment Analysis is one of the classification tools that identifies and extracts the subjective information of a product. This subjective information can be stated in different ways, such as feedback, discussions, blogs, podcasts, and video logs. This type of information generated by the empowered customers is known as user-generated content, which is traditionally in the form of words. The analysis was performed on a huge number of words using Natural Language Processing (NLP), which is a Unimodal Sentiment Analysis. With the rapid growth in the usage of the Internet, social media turned out to be a platform to share the thoughts of the individuals. This caused researchers to migrate from the traditional Unimodal analysis to Multimodal Sentiment Analysis, which includes video, audio, and images. This approach leverages the use of emotion and content and helps identify the scope and polarity of an individual’s sentiment. With the latest deep learning algorithms, Multimodal Sentiment Analysis can solve the problem of sarcasm identification. Multi-Modal Sentiment Analysis generates more accurate results compared to Uni Modal Sentiment Analysis. Therefore, this study aimed to define Sentiment Analysis and review the approaches and techniques in Sentiment Analysis from conventional Unimodal to Multimodal. In addition, this paper discusses a Multimodal Sentiment Analysis architecture using a transformers attention net.
Creativity in machines: Music composition using artificial intelligence

In this paper we propose a framework to take the next step towards making creative machines. Taking cue from Turing’s Mind Paper (1950) to more recent studies by Riedl in ‘’The Lovelace 2.0 test of artificial creativity and intelligence’ we try to examine a very creative area of human creativity – music. We have summarized the different works published on artificial intelligence and machine learning implemented for algorithmic music composition. Comparison of different algorithms-techniques including key features, advantages, disadvantages, common issues, trade-off and future aspects are discussed in detail. We then propose our own framework of how machines can be made to learn creativity.
Large Displacement Optical Flow Estimation Based on Robust Interpolation of Sparse Correspondences

Recently, the interpolation of correspondences method has been widely used in optical flow estimation, because it produces an accurate flow field and costs little runtimes.However, most of the existing matching-based optical flow methods are usually susceptible to non-rigid motion and large displacements.We propose in this article a large displacement optical flow estimation method based on robust interpolation of sparse correspondences, named Riscflow.First, we utilize the deep matching model to achieve an initial matching result of two consecutive frames, and then we exploit a grid-based motion statistics optimization scheme to remove the outliers from the initial matching field.Second, we propose a random forest-based motion boundary extraction model and construct a sparse-to-dense interpolation method by using the boundary information to prevent the dense matching field from edge-blurring.Third, we design a global optical flow estimation method by using an energy function to optimize the dense matching field.Finally, we respectively run the proposed method on the MPI-Sintel and UCF101 databases to conduct a comprehensive comparison with some state-of-the-art optical flow approaches including the variational methods, the matching-based methods, and the deep learning-based methods.The comparison results demonstrate that the proposed method has high accuracy and good robustness of optical flow estimation, and especially gains the benefit of edge-preserving under non-rigid motion and large displacements.
Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System

Conventional neural generative models tend to generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system.To generate relevant responses, we propose a method that employs two types of constraints -topical constraint and semantic constraint.Under the hypothesis that a response and its context have higher relevance when they share the same topics, the topical constraint encourages the topics of a response to match its context by conditioning response decoding on topic words' embeddings.The semantic constraint, which encourages a response to be semantically related to its context by regularizing the decoding objective function with semantic distance, is proposed.Optimal transport is applied to compute a weighted semantic distance between the representation of a response and the context.Generated responses are evaluated by automatic metrics, as well as human judgment, showing that the proposed method can generate more topic-relevant and content-rich responses than conventional models.
Understanding Decoupled and Early Weight Decay

Weight decay (WD) is a traditional regularization technique in deep learning, but despite its ubiquity, its behavior is still an area of active research. Golatkar et al. have recently shown that WD only matters at the start of the training in computer vision, upending traditional wisdom. Loshchilov et al. show that for adaptive optimizers, manually decaying weights can outperform adding an l2 penalty to the loss. This technique has become increasingly popular and is referred to as decoupled WD. The goal of this paper is to investigate these two recent empirical observations. We demonstrate that by applying WD only at the start, the network norm stays small throughout training. This has a regularizing effect as the effective gradient updates become larger. However, traditional generalizations metrics fail to capture this effect of WD, and we show how a simple scale-invariant metric can. We also show how the growth of network weights is heavily influenced by the dataset and its generalization properties. For decoupled WD, we perform experiments in NLP and RL where adaptive optimizers are the norm. We demonstrate that the primary issue that decoupled WD alleviates is the mixing of gradients from the objective function and the l2 penalty in the buffers of Adam (which stores the estimates of the first-order moment). Adaptivity itself is not problematic and decoupled WD ensures that the gradients from the l2 term cannot "drown out" the true objective, facilitating easier hyperparameter tuning.
KDEhumor at SemEval-2020 Task 7: A Neural Network Model for Detecting Funniness in Dataset Humicroedit

This paper describes our contribution to SemEval-2020 Task 7: Assessing Humor in Edited News Headlines.Here we present a method based on a deep neural network.In recent years, quite some attention has been devoted to humor production and perception.Our team KdeHumor employs recurrent neural network models including Bi-Directional LSTMs (BiLSTMs).Moreover, we utilize the state-of-the-art pre-trained sentence embedding techniques.We analyze the performance of our method and demonstrate the contribution of each component of our architecture.