Reviewerly/exHarmony huggingface dataset:
Robustly Learning Composable Options in Deep Reinforcement Learning

Hierarchical reinforcement learning (HRL) is only effective for long-horizon problems when high-level skills can be reliably sequentially executed. Unfortunately, learning reliably composable skills is difficult, because all the components of every skill are constantly changing during learning. We propose three methods for improving the composability of learned skills: representing skill initiation regions using a combination of pessimistic and optimistic classifiers; learning re-targetable policies that are robust to non-stationary subgoal regions; and learning robust option policies using model-based RL. We test these improvements on four sparse-reward maze navigation tasks involving a simulated quadrupedal robot. Each method successively improves the robustness of a baseline skill discovery method, substantially outperforming state-of-the-art flat and hierarchical methods.
Contrastive Model Invertion for Data-Free Knolwedge Distillation

Model inversion, whose goal is to recover training data from a pre-trained model, has been recently proved feasible. However, existing inversion methods usually suffer from the mode collapse problem, where the synthesized instances are highly similar to each other and thus show limited effectiveness for downstream tasks, such as knowledge distillation. In this paper, we propose Contrastive Model Inversion (CMI), where the data diversity is explicitly modeled as an optimizable objective, to alleviate the mode collapse issue. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. To this end, we introduce in CMI a contrastive learning objective that encourages the synthesizing instances to be distinguishable from the already synthesized ones in previous batches. Experiments of pre-trained models on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI not only generates more visually plausible instances than the state of the arts, but also achieves significantly superior performance when the generated data are used for knowledge distillation. Code is available at https://github.com/zju-vipa/DataFree.
Local Representation is Not Enough: Soft Point-Wise Transformer for Descriptor and Detector of Local Features

Significant progress has been witnessed for the descriptor and detector of local features, but there still exist several challenging and intractable limitations, such as insufficient localization accuracy and non-discriminative description, especially in repetitive- or blank-texture regions, which haven't be well addressed. The coarse feature representation and limited receptive field are considered as the main issues for these limitations. To address these issues, we propose a novel Soft Point-Wise Transformer for Descriptor and Detector, simultaneously mining long-range intrinsic and cross-scale dependencies of local features. Furthermore, our model leverages the distinct transformers based on the soft point-wise attention, substantially decreasing the memory and computation complexity, especially for high-resolution feature maps. In addition, multi-level decoder is constructed to guarantee the high detection accuracy and discriminative description. Extensive experiments demonstrate that our model outperforms the existing state-of-the-art methods on the image matching and visual localization benchmarks.
Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy

3D human pose estimation is a fundamental problem in artificial intelligence, and it has wide applications in AR/VR, HCI and robotics. However, human pose estimation from point clouds still suffers from noisy points and estimated jittery artifacts because of handcrafted-based point cloud sampling and single-frame-based estimation strategies. In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences. To sample effective point clouds from input, we design a differentiable point cloud sampling method built on density-guided attention mechanism. To avoid the jitter caused by previous 3D human pose estimation problems, we adopt temporal information to obtain more stable results. Experiments on the ITOP dataset and the NTU-RGBD dataset demonstrate that all of our contributed components are effective, and our method can achieve state-of-the-art performance.
On Self-Distilling Graph Neural Network

Recently, the teacher-student knowledge distillation framework has demonstrated its potential in training Graph Neural Networks (GNNs). However, due to the difficulty of training over-parameterized GNN models, one may not easily obtain a satisfactory teacher model for distillation. Furthermore, the inefficient training process of teacher-student knowledge distillation also impedes its applications in GNN models. In this paper, we propose the first teacher-free knowledge distillation method for GNNs, termed GNN Self-Distillation (GNN-SD), that serves as a drop-in replacement of the standard training process. The method is built upon the proposed neighborhood discrepancy rate (NDR), which quantifies the non-smoothness of the embedded graph in an efficient way. Based on this metric, we propose the adaptive discrepancy retaining (ADR) regularizer to empower the transferability of knowledge that maintains high neighborhood discrepancy across GNN layers. We also summarize a generic GNN-SD framework that could be exploited to induce other distillation strategies. Experiments further prove the effectiveness and generalization of our approach, as it brings: 1) state-of-the-art GNN distillation performance with less training cost, 2) consistent and considerable performance enhancement for various popular backbones.
On Learning Sets of Symmetric Elements (Extended Abstract)

Learning from unordered sets is a fundamental learning setup, recently attracting increasing attention. Research in this area has focused on the case where elements of the set are represented by feature vectors, and far less emphasis has been given to the common case where set elements themselves adhere to their own symmetries. That case is relevant to numerous applications, from deblurring image bursts to multi-view 3D shape recognition and reconstruction. In this paper, we present a principled approach to learning sets of general symmetric elements. We first characterize the space of linear layers that are equivariant both to element reordering and to the inherent symmetries of elements, like translation in the case of images. We further show that networks that are composed of these layers, called Deep Sets for Symmetric Elements layers (DSS), are universal approximators of both invariant and equivariant functions, and that these networks are strictly more expressive than Siamese networks. DSS layers are also straightforward to implement. Finally, we show that they improve over existing set-learning architectures in a series of experiments with images, graphs, and point clouds.
Proposal for an Implementation Guide for a Computer Security Incident Response Team on a University Campus

Currently, society is going through a health event with devastating results. In their desire to control the 2019 coronavirus disease, large organizations have turned over the execution of their activities to the use of information technology. These tools, adapted to the use of the Internet, have been presented as an effective solution to the measures implemented by the majority of nations where quarantines are generalized. However, the solution given by information technologies has several disadvantages that must be solved. The most important in this regard is with the serious security incidents that exist, where many organizations have been compromised and their data has been exposed. As a solution, this work proposes the design of a guide that allows for the implementation of a computer incident response team on a university campus. Universities are optimal environments for the generation of new technologies; they also serve as the ideal test bed for the generation of security policies and new treatments for incidents in an organization. In addition, with the implementation of the computer incident response team in a university, it is proposed to be part of a response group to any security incident at the national level.
Exploiting Scene Graphs for Human-Object Interaction Detection

Human-Object Interaction (HOI) detection is a fundamental visual task aiming at localizing and recognizing interactions between humans and objects. Existing works focus on the visual and linguistic features of the humans and objects. However, they do not capitalise on the high-level and semantic relationships present in the image, which provides crucial contextual and detailed relational knowledge for HOI inference. We propose a novel method to exploit this information, through the scene graph, for the Human-Object Interaction (SG2HOI) detection task. Our method, SG2HOI, incorporates the SG information in two ways: (1) we embed a scene graph into a global context clue, serving as the scene-specific environmental context; and (2) we build a relation-aware message-passing module to gather relationships from objects' neighborhood and transfer them into interactions. Empirical evaluation shows that our SG2HOI method outperforms the state-of-the-art methods on two benchmark HOI datasets: V-COCO and HICO-DET. Code will be available at https://github.com/ht014/SG2HOI.
Identity Theft Detection Using Machine Learning

Abstract: Online Identity Theft is known to be quite possibly the most genuine and developing dangers to people and organizations for the past decade because of the colossal financial harm caused by it. It is a critical form of cybercrime which uses information such a victim’s name, bank details, email address, passwords, passport or identification details, and other valuable information to gain access to accounts. This aim of this research is to make use of Machine Learning algorithms for the detection of such crimes. Keywords: identity theft; cybercrime; Machine Learning; detection; algorithms
Contrastive Explanations of Plans through Model Restrictions

In automated planning, the need for explanations arises when there is a mismatch between a proposed plan and the user’s expectation. We frame Explainable AI Planning as an iterative plan exploration process, in which the user asks a succession of contrastive questions that lead to the generation and solution of hypothetical planning problems that are restrictions of the original problem. The object of the exploration is for the user to understand the constraints that govern the original plan and, ultimately, to arrive at a satisfactory plan. We present the results of a user study that demonstrates that when users ask questions about plans, those questions are usually contrastive, i.e. “why A rather than B?”. We use the data from this study to construct a taxonomy of user questions that often arise during plan exploration. Our approach to iterative plan exploration is a process of successive model restriction. Each contrastive user question imposes a set of constraints on the planning problem, leading to the construction of a new hypothetical planning problem as a restriction of the original. Solving this restricted problem results in a plan that can be compared with the original plan, admitting a contrastive explanation. We formally define model-based compilations in PDDL2.1 for each type of constraint derived from a contrastive user question in the taxonomy, and empirically evaluate the compilations in terms of computational complexity. The compilations were implemented as part of an explanation framework supporting iterative model restriction. We demonstrate its benefits in a second user study.
Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems

The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.
Supporting Student-Initiated Mobile Device Use in Online Learning

Understanding the experiences of students who self-initiate mobile device use for online courses or course-related activities provides institutions with valuable insights. In this study, we report how students enrolled in online courses in higher education voluntarily used mobile devices for their coursework and course-related activities, the challenges in using these devices, and how they managed those challenges. We surveyed 103 college students enrolled in one or more fully online courses regarding their habits in using mobile devices for online learning. Findings reveal most participants use mobile devices for convenience, portability, and overall ease of use. The way the devices are used for course-related activities varies, however, with reasons ranging from taking notes and reading course materials, to downloading those materials, communicating, socializing, and other purposes. Challenges when using these devices often relate to access issues and overall limitations of the technology. Yet, despite these limitations, some reported that, depending on the task, even though using a mobile device often took longer compared to a laptop or PC, the convenience of using it was a greater benefit. Participants were surveyed about their experiences prior to the COVID-19 pandemic, providing additional perspectives for possible future research focused on emergency circumstances to remote teaching or alternative forms of instruction. Additionally, this study provides a foundation of how and why students choose to use mobile devices for coursework and in what ways they may need support from their institutions related to their use.
Is diversity optimization always suitable? Toward a better understanding of diversity within recommendation approaches

The diversity of the item list suggested by recommender systems has been proven to impact user satisfaction significantly. Most of the existing diversity optimization approaches re-rank the list of candidate items during a post-processing step. However, the diversity level of the candidate list strongly depends on the recommender system used. Hence, applying the same post-processing diversification strategy may not be as effective for different recommendation approaches. Moreover, individual users' diversity needs are usually ignored in the diversification post-processing. This article aims at providing an in-depth analysis of the diversity performances of different recommender systems. To the best of our knowledge, it is the first study to systematically compare diversity performances of the main types of recommendation models using benchmark datasets in different domains (movie, anime and book). Semantics related to items may be considered a key factor in measuring diversity within recommender systems. In this study, we leverage support from the knowledge engineering domain and take advantage of resources such as Linked Data and knowledge graphs, to assert the diversity of recommendations. We also propose a variant of the classic diversification post-processing objective that allows to take into account specific users' diversity needs. We measure the adequacy between the diversity levels a recommender system suggests to its users and those of users' profiles with the R2 coefficient of determination. Our study indicates that: (1) none of the tested recommender systems, even the most recent ones, provides items with levels of diversity that suit user profiles (R2<0.2); (2) the classic post-processing diversification approach may lead to over-diversification compared to users' diversity needs and (3) the diversity adjustment that accounts for user profiles has more benefits (greater R2 and smaller accuracy loss). All the source code and datasets used in our study are available to ensure the reproductibility of the study.
Traffic vehicle detection algorithm based on YOLOv3

Vehicle detection based on machine vision is an important part of urban intelligent transportation, and vehicle detection technology combined with deep learning is the mainstream method. In order to overcome the low detection accuracy of traditional YOLOv3 algorithm for small vehicle targets. In this paper, we add a larger convolution layer on the basis of the traditional three convolution layers of YOLOv3, and use k-means++ clustering algorithm to get 12 anchor frames again. In this method, the newly added 104*104 feature scale is more suitable for small target detection than the original feature scale of YOLOv3, and it is easier to get the global optimal anchor point by using k-means++ algorithm, so as to improve the detection accuracy. The final experimental results show that the mean average precision (mAP) of the improved YOLOv3 is 91.01%, which is 2.93% higher than that of the traditional YOLOv3.
Deep Gated Recurrent Unit for Smartphone-Based Image Captioning

Expressing the visual content of an image in natural language form has gained relevance due to technological and algorithmic advances together with improved computational processing capacity. Many smartphone applications for image captioning have been developed recently as built-in cameras provide advantages of easy-operation and portability, resulting in capturing an image whenever or wherever needed. Here, an encoder-decoder framework based new image captioning approach with a multi-layer gated recurrent unit is proposed. The Inception-v3 convolutional neural network is employed in the encoder due to its capability of more feature extraction from small regions. The proposed recurrent neural network-based decoder utilizes these features in the multi-layer gated recurrent unit to produce a natural language expression word-by-word. Experimental evaluations on the MSCOCO dataset demonstrate that our proposed approach has the advantage over existing approaches consistently across different evaluation metrics. With the integration of the proposed approach to our custom-designed Android application, named “VirtualEye+”, it has great potential to implement image captioning in daily routine.
Experimental characterization of a non-Markovian quantum process

Most noise-characterization methods for quantum technologies assume Markovianity, meaning that the environment and the system have no memory of their interactions with each other, because it is inefficient and computationally demanding to take temporal correlations into account. Here, the authors propose a more efficient machine learning method for estimating non-Markovian noise and implement it in a proof-of-principle quantum optics experiment.
A LSB Based Image Steganography Using Random Pixel and Bit Selection for High Payload

Security in digital communication is becoming more important as the number of systems is connected to the internet day by day.It is necessary to protect secret message during transmission over insecure channels of the internet.Thus, data security becomes an important research issue.Steganography is a technique that embeds secret information into a carrier such as images, audio files, text files, and video files so that it cannot be observed.In this paper, based on spatial domain, a new image steganography method is proposed to ensure the privacy of the digital data during transmission over the internet.In this method, least significant bit substitution is proposed where the information embedded in the random bit position of a random pixel location of the cover image using Pseudo Random Number Generator (PRNG).The proposed method used a 3-3-2 approach to hide a byte in a pixel of a 24 bit color image.The method uses Pseudo Random Number Generator (PRNG) in two different stages of embedding process.The first one is used to select random pixels and the second PRNG is used select random bit position into the R, G and B values of a pixel to embed one byte of information.Due to this randomization, the security of the system is expected to increase and the method achieves a very high maximum hiding capacity which signifies the importance of the proposed method.
Performance evaluation and implementations of MFCC, SVM and MLP algorithms in the FPGA board

One of the most difficult speech recognition tasks is accurate recognition of human-to-human communication. Advances in deep learning over the last few years have produced major speech improvements in recognition on the representative Switch-board conversational corpus. Word error rates that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This raises two issues - what is human performance, and how far down can we still drive speech recognition error rates? The main objective of this article is the development of a comparative study of the performance of Automatic Speech Recognition (ASR) algorithms using a database made up of a set of signals created by female and male speakers of different ages. We will also develop techniques for the Software and Hardware implementation of these algorithms and test them in an embedded electronic card based on a reconfigurable circuit (Field Programmable Gate Array FPGA). We will present an analysis of the results of classifications for the best Support Vector Machine architectures (SVM) and Artificial Neural Networks of Multi-Layer Perceptron (MLP). Following our analysis, we created NIOSII processors and we tested their operations as well as their characteristics. The characteristics of each processor are specified in this article (cost, size, speed, power consumption and complexity). At the end of this work, we physically implemented the architecture of the Mel Frequency Cepstral Coefficients (MFCC) extraction algorithm as well as the classification algorithm that provided the best results.