Reviewerly/exHarmony huggingface dataset:
Lightningnet: Fast and Accurate Semantic Segmentation for Autonomous Driving Based on 3D LIDAR Point Cloud

Semantic segmentation is A series of earlier works have demonstrated that 3D LiDAR point cloud segmentation is a promising approach. However, these methods usually rely on pretrained models. And their requiring improvements in either speed or accuracy prevent them to be applied to mobile platforms. To address these problems, a smaller Convolutional Neural Network (CNN) architecture, namely, LightningNet is presented. Furthermore, we propose a lightweight pipeline with Feature Refinement Modules to boost the performance of real-time 3D LiDAR point cloud segmentation. Our model is trained on spherical images projected from LiDAR point clouds on KITTI [1] dataset. Experiments show accuracy improvements of 4.1-8.2% in small objects and 4% in terms of mIoU over the state-of-the-art of spherical-image based method with 111 fps on a single 1080Ti GPU and 14fps on a Jetson TX2, which enables deployment for real-time semantic segmentation in autonomous driving. We achieve superior performance on another large dataset called SemanticKITTI (illustrated in Figure 1) both in speed and accuracy also.
Anytime automatic algorithm selection for knapsack

In this paper, we present a new approach for Automatic Algorithm Selection. In this new procedure, we feed the predictor of the best algorithm choice with a runtime limit for the solvers. Hence, the machine learning model should consider and learn from the Anytime Behavior of the solvers, together with features characterizing each instance. For this purpose, we propose a general Framework and apply it to the Knapsack problem. Thus, we created a large and diverse dataset of 15,000 instances, recorded the anytime behavior of 8 solvers on them and trained and tested three machine learning strategies, collecting the results for different machine learning algorithms. Our results show that, for the majority of the tuples <instance, time>, the solver that computes the best objective value can be predicted. We also make this data publicly available, as a challenge for the community to work in this problem and propose new and better machine learning models and solvers.
Multi-Fidelity Neural Architecture Search With Knowledge Distillation

Neural architecture search (NAS) targets at finding the optimal architecture of a neural network for a problem or a family of problems. Evaluations of neural architectures are very time-consuming. One of the possible ways to mitigate this issue is to use low-fidelity evaluations, namely training on a part of a dataset, fewer epochs, with fewer channels, etc. In this paper, we propose a Bayesian multi-fidelity method for neural architecture search: MF-KD. The method relies on a new approach to low-fidelity evaluations of neural architectures by training for a few epochs using a knowledge distillation. Knowledge distillation adds to a loss function a term forcing a network to mimic some teacher network. We carry out experiments on CIFAR-10, CIFAR-100, and ImageNet-16-120. We show that training for a few epochs with such a modified loss function leads to a better selection of neural architectures than training for a few epochs with a logistic loss. The proposed method outperforms several state-of-the-art baselines.
Designing dyadic caregiver-child personas for interactive digital media use

This paper provides a framework that adapts a design tool known as personas to better capture the ways that caregivers mediate their children's use of interactive media. Interactive digital media has become a more pervasive part of families' lives and tensions have increased around children's engagement with digital media. Historically, caregivers have enacted various tactics to mediate their children's practices around digital media. However, the design of technologies for children fails to account for different approaches to caregiving and the relationships between caregivers and children, instead focusing on the caregiver and the child as separate entities. The goal of our framework is to enable designers to consider the caregiver-child relationship by adapting the persona design tool to account for the relationship between caregivers and children. Drawing from prior research from user experience on persona development and communication on parental mediation theory, the framework outlines five phases to be used as a guide to develop caregiver-child dyadic personas. A dyadic approach to persona design explicitly highlights the relationship between two individuals (in this case, caregiver and child). We suggest that designing with dyadic personas enables designers to be more aware of nuances in caregiver-child relationships and can surface opportunities to facilitate collaboration between caregivers and children around interactive media.
A New Framework of IND-CCA Secure Public Key Encryption with Keyword Search

Abstract In the era of cloud computing, public key encryption with keyword search (PEKS) is an extremely useful cryptographic tool for searching on encryption data, whose strongest security notion is indistinguishability encryption against chosen ciphertext attack (ind-cca). Adballa et al. presented a transformation from identity based encryption (IBE) to PEKS in the Theory of Cryptography Conference 2010. This paper proposes a new framework of ind-cca secure PEKS in the standard model. Our main technical tool is a newly introduced notion of smooth projective hash function with key mapping, in which the hash key hk is mapped into another mapping projection key mhp besides the classical projection key hp. Finally, we provide an instantiation of our framework based on symmetric eXternal Diffie–Hellman assumption.
Explore Efficient LUT-based Architecture for Quantized Convolutional Neural Networks on FPGA

The vast computations of the convolutional neural network have limited the speed of the forward inference running in hardware. In recent years, network quantization technique has made it possible to quantize network into low bit-wide and retain the original performance simultaneously, while the complexity of the quantized network is still considerable. FPGA is a highly parallelized platform, which contains a mass of configurable logic resources. We study on the feasibility of implementing convolution calculation based on pure LUTs, introduce the shift multipliers and addition trees, and propose an efficient architecture for QNN on FPGA. With the optimization of Winograd algorithm for QNN, we demonstrate that our scheme significantly reduces the number of multipliers and saves the usage of LUT resources by $2.25 \times $ at least without using DSP resources. As a result, our LUT-based architecture for QNN shortens the latency up to $19.3 \times $ and represents more effective performance compared to other methods.
Decision Support System of Best Teacher Selection using Method MOORA and SAW

One method in the decision support system for the selection of the best teacher is used Multi-Objective optimization by Ratio Analysis (MOORA). Previous studies were not weighted on various criteria but only normalized. To obtain more optimal or accurate results, this paper uses analysis that starts with MOORA and ends with SAW. Weight (W) value on each Criterion (C) is adjusted to the needs of the research object. This research shows that MOORA & SAW give better result than MOORA, because of the previous research, the MOORA method was not given the weight of each criterion. The 4 best alternatives out of 24 alternatives with value alternative 1=0.524, Alternative 4=0.478, alternative 3= 0.476, alternative 2=0.47S are the results of the MOORA and SAW Method and the best alternative order of the MOORA results in previous studies is Alternative 1=9.95, Alternative 2 =9.50, Alternative 4=9.31, Alternative 3=9.20.
DeepFaceFlow: In-the-Wild Dense 3D Facial Motion Estimation

Dense 3D facial motion capture from only monocular in-the-wild pairs of RGB images is a highly challenging problem with numerous applications, ranging from facial expression recognition to facial reenactment. In this work, we propose DeepFaceFlow, a robust, fast, and highly-accurate framework for the dense estimation of 3D non-rigid facial flow between pairs of monocular images. Our DeepFaceFlow framework was trained and tested on two very large-scale facial video datasets, one of them of our own collection and annotation, with the aid of occlusion-aware and 3D-based loss function. We conduct comprehensive experiments probing different aspects of our approach and demonstrating its improved performance against state-of-the-art flow and 3D reconstruction methods. Furthermore, we incorporate our framework in a full-head state-of-the-art facial video synthesis method and demonstrate the ability of our method in better representing and capturing the facial dynamics, resulting in a highly-realistic facial video synthesis. Given registered pairs of images, our framework generates 3D flow maps at 60 fps.
Dual Embedding Expansion for Vehicle Re-identification

Vehicle re-identification plays a crucial role in the management of transportation infrastructure and traffic flow. However, this is a challenging task due to the large view-point variations in appearance, environmental and instance-related factors. Modern systems deploy CNNs to produce unique representations from the images of each vehicle instance. Most work focuses on leveraging new losses and network architectures to improve the descriptiveness of these representations. In contrast, our work concentrates on re-ranking and embedding expansion techniques. We propose an efficient approach for combining the outputs of multiple models at various scales while exploiting tracklet and neighbor information, called dual embedding expansion (DEx). Additionally, a comparative study of several common image retrieval techniques is presented in the context of vehicle re-ID. Our system yields competitive performance in the 2020 NVIDIA AI City Challenge with promising results. We demonstrate that DEx when combined with other re-ranking techniques, can produce an even larger gain without any additional attribute labels or manual supervision.
Deep Selective Feature Learning for Action Recognition

Soft-attention mechanism has attracted a lot of attention in recent years due to its ability to capture the most discriminative image features for understanding actions. However, soft-attention tends to focus on fine-grained parts on images and ignores global information, which can lead to totally wrong classification results. To address this issue, we propose a novel deep selective feature learning network (DSFNet), which can automatically learn the feature maps with both fine-grained and global information. Specially, DSFNet is designed to have the ability to learn to adjust the actions for feature map selection by maximizing the cumulative discounted rewards. Moreover, the DSFNet is an easy-to-use extension of state-of-the-art base architectures of multiple tasks. Extensive experiments show that the proposed method has achieved superior performance on two standard action recognition benchmarks across still images (PPMI) and videos (HMDB51).
The Curious Case of IR Explainability: Explaining Document Scores within and across Ranking Models

It is often useful for an IR practitioner to analyze the similarity function of an IR model, or for a non-technical search engine user to understand why a document was shown at a certain rank, in terms of the three fundamental aspects of a similarity function, namely the a) frequency of a term in a document, b) frequency of a term in a collection and c) the length of a document. We propose a general methodology of approximating an IR model as the coefficients of a linear function of these three fundamental aspects (and an additional aspect of semantic similarity between terms for neural models), which potentially can help IR practitioners to optimize the relative importance of each aspect on specific document collection and types of queries. Our analysis shows that the coefficients, which represent the relative importance of the three fundamental aspects, are useful to compare a model's different parametric instantiations or compare across different models.
Reinforcement Learning for Interactive QoS-Aware Services Composition

An important and challenging research problem in web of things is how to select an appropriate composition of concrete services in a dynamic and unpredictable environment.The main goal of this article is to select from all possible compositions the optimal one without knowing a priori the users' quality of service (QoS) preferences.From a theoretical point of view, we give bounds on the problem search space.As the QoS user's preferences are unknown, we propose a vector-valued MDP approach for finding the optimal QoS-aware services composition.The algorithm alternatively solves MDP with dynamic programming and learns the preferences via direct queries to the user.An important feature of the proposed algorithm is that it is able to get the optimal composition and, at the same time, limits the number of interactions with the user.Experiments on a real-world large size dataset with more than 3500 web services show that our algorithm finds the optimal composite services with around 50 interactions with the user.Index Terms-Quality of services (QoS), reinforcement learning (RL), services composition, web of things (WoT). I. INTRODUCTIONT HE web of things (WoT) opens the way for the develop- ment of new intelligent applications that can be used to provide innovative and valuable services in several domains, such as smart cities, smart homes, ambient assisted living, and connected cars [11], [35].One of the WoT challenges is to deal with services composition to ensure suitable services flexibility and customization.This is crucial to meet the evolving needs of users, which can be expressed through several parameters of quality of service (QoS) (such as response time, throughput, availability, price, popularity, etc.).Following the web service paradigm [4] services, composition problems are specified as a workflow involving abstract and concrete services.A concrete service refers to an executable service, where an abstract service describes the abstract functionality of
Generation of OPC UA Companion Specification with Eclipse Modeling Framework

Open Platform Communications Unified Architecture (OPC UA) is a relevant technology in the field of machine-to-machine communication that defines data transport protocols and standardizes information modeling. Instead of defining a generalized world model, domain-specific models enable interoperability. These domain-specific models can be developed and released as Companion Specifications. More than 40 Companion Specifications are currently in development or already released. In this paper the current approaches to generate an OPC UA information model or an OPC UA Companion Specification are analyzed. A new modeling tooling based on the Eclipse Modeling Framework (EMF) is presented. This tooling demonstrates the potential of a model-driven approach for the creation of the information model and the structured part of the descriptive text document of a Companion Specification.
NestedVAE: Isolating Common Factors via Weak Supervision

Fair and unbiased machine learning is an important and active field of research, as decision processes are increasingly driven by models that learn from data.Unfortunately, any biases present in the data may be learned by the model, thereby inappropriately transferring that bias into the decision making process.We identify the connection between the task of bias reduction and that of isolating factors common between domains whilst encouraging domain specific invariance.To isolate the common factors we combine the theory of deep latent variable models with information bottleneck theory for scenarios whereby data may be naturally paired across domains and no additional supervision is required.The result is the Nested Variational AutoEncoder (NestedVAE).Two outer VAEs with shared weights attempt to reconstruct the input and infer a latent space, whilst a nested VAE attempts to reconstruct the latent representation of one image, from the latent representation of its paired image.In so doing, the nested VAE isolates the common latent factors/causes and becomes invariant to unwanted factors that are not shared between paired images.We also propose a new metric to provide a balanced method of evaluating consistency and classifier performance across domains which we refer to as the Adjusted Parity metric.An evaluation of NestedVAE on both domain and attribute invariance, change detection, and learning common factors for the prediction of biological sex demonstrates that NestedVAE significantly outperforms alternative methods.
Image Denoising Using Generative Adversarial Network

Image denoising is one of the most important and fundamental research areas in the digital image-processing field. A noisy image can mislead image processing-based research. Therefore, image denoising is a critical area of research. In the recent advancement of computer vision, deep learning becomes most powerful tool. Deep learning is solving most of the problems, usually, which were earlier solved by various conventional techniques. The progress of deep learning encourages researchers to apply deep learning-based methods into image denoising also. In recent years, generative adversarial network (GAN) becomes a new avenue in computer vision research. The GANs are adversarial networks with generative capability, and the network has a very vast area of applications. In this chapter, we concentrate on a specific area of application of GAN—image denoising. At first, the traditional denoising techniques are highlighted. Then, we state the underlying architecture of GAN and its modifications. Then, we discuss the way GANs are applied in the area of image denoising. We survey all recent works of GANs in image denoising and categories those work according to the type of input images. In the end, we propose some research directions in this area. The compilation and discussions presented in this chapter regarding image denoising using GAN are new inclusion, and similar survey work is not available for the community.
Automatic Seat Adjustment using Face Recognition

The existing mechanism of driver seat adjustment in the automobile industry is outdated. It adversely affects the body of the driver when he/she adjusts the seat for a comfortable position. The comfortable seat position is different for each person. This research work explains about reducing manual effort in seat adjustment by automating the seat adjustment process by using face recognition. The seat adjustment process deals with the movement of the seat in horizontal, vertical and inclination directions by rotating the motors fitted within the seat. The comfort seat position of a driver contains three parameters for three directions of seat movement. Those parameters are different for different drivers and they are stored in the memory. When the camera recognizes a face, the seat is adjusted to his/her comfortable position which is saved in the database.
A study of lexical function detection with word2vec and supervised machine learning

In this work, we report the results of our experiments on the task of distinguishing the semantics of verb-noun collocations in a Spanish corpus. This semantics was represented by four lexical functions of the Meaning-Text Theory. Each lexical function specifies a certain universal semantic concept found in any natural language. Knowledge of collocation and its semantic content is important for natural language processing, as collocation comprises the restrictions on how words can be used together. We experimented with word2vec embeddings and six supervised machine learning methods most commonly used in a wide range of natural language processing tasks. Our objective was to study the ability of word2vec embeddings to represent the context of collocations in a way that could discriminate among lexical functions. A difference from previous work with word embeddings is that we trained word2vec on a lemmatized corpus after stopwords elimination, supposing that such vectors would capture a more accurate semantic characterization. The experiments were performed on a collection of 1,131 Excelsior newspaper issues. As the experimental results showed, word2vec representation of collocations outperformed the classical bag-of-words context representation implemented in a vector space model and fed into the same supervised learning methods.
Cross-modal learning with prior visual relation knowledge

Visual relational reasoning is a central component in recent cross-modal analysis tasks, which aims at reasoning about the visual relationships between objects and their properties. These relationships provide rich semantics and help to enhance the visual representation for improving cross-modal learning. Previous works have succeeded in modeling latent visual relationships or rigid-categorized visual relationships. However, these kinds of methods leave out the problem of ambiguity inherent in the visual relationships because of the diverse relational semantics of different visual appearances. In this work, we explore to model the visual relationships by context-aware representations based on human prior knowledge. Based on such representations, we novelly propose a plug-and-play visual relational reasoning module to enhance image encoding. Specifically, we design an Anisotropic Graph Convolution to utilize the information of relation embeddings and relation directionality between objects for generating relation-aware image representations. We demonstrate the effectiveness of the relational reasoning module by applying it to both Visual Question Answering (VQA) and Cross-Modal Information Retrieval (CMIR) tasks. Extensive experiments are conducted on VQA 2.0 and CMPlaces datasets and superior performance is reported when comparing with state-of-the-art works.
The Effects of Mobile Learning in an EFL Class on Self-Regulated Learning and School Achievement

The objective of this research was to find out whether the implementation of mobile learning pedagogies in a high school classroom can educate high school students to become independent learners, who are able to regulate their learning, improve their achievements. The findings have been analyzed by taking into consideration the self-regulation scales of motivation and learning strategies, together with the significance of the students' scores. The results of this study show that the implementation of a mobile learning teaching unit does not affect the self-regulation learning abilities of the students. However, it does reveal an increase in external and internal motivation together with a shift in the application of learning strategies. As a result, a two-stage model has been proposed aiming to educate teenagers to become agents of their learning while they practice mobile learning as well as implementation of mobile seamless learning to promote a self-regulated learning.