Reviewerly/exHarmony huggingface dataset:
Adaptive Neuroevolution With Genetic Operator Control and Two-Way Complexity Variation

Topology and weight evolving artificial neural network (TWEANN) algorithms optimize the structure and weights of artificial neural networks (ANNs) simultaneously. The resulting networks are typically used as policy models for solving control and reinforcement learning (RL) type problems. This paper presents a neuroevolution algorithm that aims to address the typical stagnation and sluggish convergence issues present in other neuroevolution algorithms. These issues are often caused by inadequacies in population diversity preservation, exploration/exploitation balance, and search flexibility. This new algorithm, called the Adaptive Genomic Evolution of Neural-Network Topologies (AGENT), builds on the neuroevolution of augmenting topologies (NEAT) concept. Novel mechanisms for adapting the selection and mutation operations are proposed to favorably control population diversity and exploration/exploitation balance. The former is founded on a fundamentally new way of quantifying diversity by taking a graph-theoretic perspective of the population of genomes and inter-genomic differences. Further advancements to the NEAT paradigm occur through the incorporation of variable neuronal properties and new mutation operations that uniquely allow both the growth and pruning of ANN topologies during evolution. Numerical experiments with benchmark control problems adopted from the OpenAI Gym illustrate the competitive performance of AGENT against standard RL methods and adaptive HyperNEAT, and superiority over the original NEAT algorithm. Further parametric analysis provides key insights into the impact of the new features in AGENT. This is followed by evaluation on an unmanned aerial vehicle collision avoidance problem where maneuver planning models are learnt by AGENT with 33% reward improvement over 15 generations.
User Profiling for Web Personalization

This study tackles the issue of delivering personalized information on the Web based on user profile. Personalized data distribution built on customer& document reporting is an important stage toward building computers more useful and reachable to a larger audience. The technique of adapting Web sites to a particular user’s profile in order to make Web browsing easier is known as web customization. The ability of a user to easily identify relevant things or contents is referred to as Web browsing. The main goal of user profiling is to make user actions more efficient by giving more personalized information.
Scalable Online Coverage Path Planning for Multi-Robot Systems

Online coverage path planning to explore an unknown workspace with multiple homogeneous robots could be either centralized or distributed. While distributed planners are computationally faster, centralized planners can produce more efficient paths, reducing the duration of completing a coverage mission significantly. To exploit the power of a centralized framework, we propose a receding horizon centralized online multi-robot planner. In each planning horizon, it generates collision-free paths that guide the robots to visit some obstacle-free locations (aka goals) not visited so far, which in turn help them explore some new regions with their laser rangefinders. We formally prove that, under reasonable conditions, it enables the robots to cover a workspace completely and subsequently analyze its time complexity. We evaluate our planner for ground and aerial robots by performing experiments with up to 128 robots on six 2D grid-based benchmark obstacle maps, establishing scalability. We also perform Gazebo simulations with 10 quadcopters and real experiments with 2 four-wheel ground robots, demonstrating its practical feasibility. Further-more, a comparison with a state-of-the-art distributed planner establishes its superiority in coverage completion time.
Ontology-Based Natural Language Texts Generation from Knowledge Base

In the knowledge-based intelligent system, the natural language interface is responsible for implementing communication between end-users and intelligent system using natural language. However the internal information of intelligent system is represented in a certain kind of specific format (knowledge base). When intelligent system need to exchange information with the end-users, it is necessary for natural language interface to obtain the ability to convert fragment of the knowledge base into natural language text. Due to the diversity and complexity of natural language, generating fluent, appropriate natural language text is still a significant challenge. By comparing other methods converting structured data into natural language text, this article describes the characteristics of fragment of knowledge base and the difficulties in natural language generation. The article proposed a unified semantic model to development of natural language generation component of natural language interface. The development of natural language generation component requires a combination of various types of knowledge about linguistics and various problem solving models oriented on natural language generation. In this article the main novelty is that in the unified semantic model the various approaches and linguistic knowledge about natural language generation can be integrated to generate natural language text from the fragment of knowledge base represented in graphical form.
Coordination With Humans Via Strategy Matching

Human and robot partners increasingly need to work together to perform tasks as a team. Robots designed for such collaboration must reason about how their task-completion strategies interplay with the behavior and skills of their human team members as they coordinate on achieving joint goals. Our goal in this work is to develop a computational framework for robot adaptation to human partners in human-robot team collaborations. We first present an algorithm for autonomously recognizing available task-completion strategies by observing human-human teams performing a collaborative task. By transforming team actions into low dimensional representations using hidden Markov models, we can identify strategies without prior knowledge. Robot policies are learned on each of the identified strategies to construct a Mixture-of-Experts model that adapts to the task strategies of unseen human partners. We evaluate our model on a collaborative cooking task using an Overcooked simulator. Results of an online user study with 125 participants demonstrate that our framework improves the task performance and collaborative fluency of human-agent teams, as compared to state of the art reinforcement learning methods.
Fuzzy Clustering of Infrared Image Features Based on Lazy Snapping Algorithm

Substation equipment is an important part of the power grid, which undertakes the function of power transmission and conversion and directly affects the operation status of the whole substation and power system. When the substation equipment is in an abnormal working state, the temperature will change. Therefore, the temperature information of the substation equipment is used as the judgment basis to complete the judgment of the working state of the equipment, which can realize the fault diagnosis of the substation equipment and ensure that the power system works in a safe and reliable environment. In this paper, according to the characteristics of the transformer equipment shape stability, the invariant moment is used to extract the infrared image feature of the transformer equipment. The support vector machine is used to complete the classification and recognition of the image. Hu invariant moments and Zernike invariant moments are used to extracting features respectively, and the results of feature extraction are used as training samples to train support vector machines for recognition. Using the Lazy Snapping algorithm to complete the infrared image segmentation processing of substation equipment, the target region is extracted from the background completely, and the segmentation image information is completed. In the experimental test, through the test of the recognition model constructed by invariant moments, it is proved that the recognition accuracy of Zernike invariant moments combined with support vector machine in this paper is higher, and the Lazy Snapping algorithm has obvious advantages in segmentation quality compared with other methods. Therefore, the method of intelligent identification and diagnosis of the thermal fault of substation equipment studied in this paper has important practical significance for the establishment of an online diagnosis system.
The Employment of Testing DOJO as a Collaborative Learning Methodology for Teaching Failure Analysis: An Experience Report

Training IT professionals in a new function are a big challenge, primarily when its context and function are particular, requiring specific qualifications. This paper describes an experience of training new Quality Assurance (QA) professionals using the Testing DOJO collaborative learning technique in the mobile software industry context. We evaluate the influence of Testing DOJO related to motivation, collaboration, and learning perception in QA failure analysis, and to achieve this objective based on three steps: a basic literature review, QA activity definition, and employee perception evaluation. The results demonstrated that Testing DOJO positively contributed to test developers' motivation, collaboration, and learning perception, enabling improvements in interaction, knowledge exchange, and collaboration among participants. The use could be an exciting alternative for training new QA failure analysis team members.
Decentralized Blockchain Transaction Scheme Based on Digital Commitment

As a framework based on data transaction between blocks, blockchain has a high degree of transparency and decentralization. Zhang et al. proposed a transaction framework that combines digital commitment and cryptographic algorithm, which can hide the transaction amount during transaction process. However, there are two problems we should consider: the transactions recording on ledger are operated by trusted full-functional accounting nodes, so the decentralization of the blockchain cannot be realized completely; there is also a risk of data leakage during transaction process as the transaction parameters and transaction amounts are required to be transmitted multiple times. In order to solve the above problems, this paper proposes a multi-key homomorphic encryption transaction scheme based on digital commitment with every node records transaction data. The transaction amount hidden in the commitment can be directly updated in the encrypted state and relevant parameters do not need to be transported multiple times. The transaction scheme proposed in this paper is with better efficiency and provable security, which provides a novel idea for the secure transaction framework in the blockchain.
Long-term Spatio-temporal Contrastive Learning framework for Skeleton Action Recognition

Recent years have been witnessing significant developments in research in human action recognition based on skeleton data. The graphical representation of the human skeleton, available with the dataset, provides opportunity to apply Graph Convolutional Networks (GCN), to avail efficient analysis of deep spatial-temporal information from the joint and skeleton structure. Most of the current works in skeleton action recognition use the temporal aspect of the video in short-term sequences, ignoring the long-term information present in the evolving skeleton sequence. The proposed long-term Spatio-temporal Contrastive Learning framework for Skeleton Action Recognition uses an encoder-decoder module. The encoder collects deep global-level (long-term) information from the complete action sequence using efficient self-supervision. The proposed encoder combines knowledge from the temporal domain with high-level information of the relative joint and structure movements of the skeleton. The decoder serves two purposes: predicting the human activity and predicting skeleton structure in the future frames. The decoder primarily uses the high-level encodings from the encoder to anticipate the action. For predicting skeleton structure, we extract an even deeper correlation in the Spatio-temporal domain and merge it with the original frame of the video. We apply a contrastive framework in the frame prediction part so that similar actions have similar predicted skeleton structure. The use of the contrastive framework throughout the proposed model helps achieve exemplary performance while employing a self-supervised aspect to the model. We test our model on the NTU-RGB-D-60 dataset and achieve state-of-the-art performance. The codes related to this work are available at: https://github.com/AnshulRustoai/Long-Term-Spatio-Temporal-Framework.
Deep Gesture Generation for Social Robots Using Type-Specific Libraries

Body language such as conversational gesture is a powerful way to ease communication. Conversational gestures do not only make a speech more lively but also contain semantic meaning that helps to stress important information in the discussion. In the field of robotics, giving conversational agents (humanoid robots or virtual avatars) the ability to properly use gestures is critical, yet remain a task of extraordinary difficulty. This is because given only a text as input, there are many possibilities and ambiguities to generate an appropriate gesture. Different to previous works we propose a new method that explicitly takes into account the gesture types to reduce these ambiguities and generate human-like conversational gestures. Key to our proposed system is a new gesture database built on the TED dataset that allows us to map a word to one of three types of gestures: “Imagistic” gestures, which express the content of the speech, “Beat” gestures, which emphasize words, and “No gestures.” We propose a system that first maps the words in the input text to their corresponding gesture type, generate type-specific gestures and combine the generated gestures into one final smooth gesture. In our comparative experiments, the effectiveness of the proposed method was confirmed in user studies for both avatar and humanoid robot.
ODG-Q: Robust Quantization via Online Domain Generalization

Quantizing neural networks to low-bitwidth is important for model deployment on resource-limited edge hardware. Although a quantized network has a smaller model size and memory footprint, it is fragile to adversarial attacks. However, few methods study the robustness and training efficiency of quantized networks. To this end, we propose a new method by recasting robust quantization as an online domain generalization problem, termed ODG-Q, which generates diverse adversarial data at a low cost during training. ODG-Q consistently outperforms existing works against various adversarial attacks. For example, on CIFAR-10 dataset, ODG-Q achieves 49.2% average improvements under five common white-box attacks and 21.7% average improvements under five common black-box attacks, with a training cost similar to that of natural training (viz. without adversaries). To our best knowledge, this work is the first work that trains both quantized and binary neural networks on ImageNet that consistently improves robustness under different attacks. We also provide a theoretical insight of ODG-Q that accounts for the bound of model risk on attacked data.
Variance of Local Contribution: an Unsupervised Image Quality Assessment for Face Recognition

In recent years, Face Image Quality Assessment (FIQA) plays an important role in the face recognition system. However, how to define face image quality is still an open question. In this work, we argue that a high-quality face image should have more identity-related information than a low-quality face image. Thus, we propose a novel unsupervised Face Image Quality Assessment with the variance of local contribution (VLC-FIQA). In our approach, we alternately mask partial pixels of the face image, then quantify the importance of these pixels and compute the variation of the importance of different parts as the quality of the image. Extensive experiments show that our VLC-FIQA outperforms state-of-the-art approaches on LFW. Our approach can be easily used for any recognition system and be extended to other recognition tasks such as person re-identification.
Preventing Coherence State Side Channel Leaks Using TimeCache

Cache side channel attacks in the presence of shared memory have been used to extract cryptographic keys and enclave data, and are used by Spectre variants for leaking speculatively loaded data. Timing side channels exist in shared caches due to the difference in response latency of cached and uncached data. In prior work, we presented TimeCache, a cache design that prevents side channel exploits from reuse of shared memory. In this work, we extend TimeCache to also defend against attacks that exploit coherence states. TimeCache allows all running applications to use the entire cache, avoiding the need for partitioning in order to effect timing isolation. A per-process caching context prevents cache hits on data filled by another process. A novel bit-serial timestamp-parallel comparison logic allows low-overhead update of stale caching contexts. The defense is suited to all caches levels, and defends against an attacker running on any core. We evaluate TimeCache using the gem5 simulator to show that it is capable of preventing both reuse attacks and an attack based on coherence state leak. The average performance overhead for SPEC2006 is 1.13%, and for PARSEC and SPLASH is 0.46%.
Saber Simulation Research of Flyback Switching Power Supply

In this paper, the principle circuit of the DC-DC convert for a Flyback switching power supply is constructed using the Saber Simulating platform. The implementation method on Flyback switching transformer is also given and the circuit working characteristics of the switching convert in two states of CCM or DCM are analyzed and calculated. Finally all simulation waveforms under two states are provided by using TR simulation tools of Saber.
Overview of a new course on Autonomous Vehicle Systems

Autonomous vehicle systems (a.k.a. self-driving cars) are becoming ubiquitous and are going to be a part of our high-technology future. At Santa Clara University, we have introduced a new class for senior undergraduates/graduate students to teach the basic principles involved in autonomous vehicle systems. This paper describes the outline and main components of the course, findings from the recent offerings of the course and some lessons learned so far. We hope this can help other institutions planning to develop similar course.
Statistical analysis of learners’ ubiquity and autonomy: Sustaining the development of learning management system (LMS) for L2 proficiency

AbstractIn recent times learning ubiquity and autonomy have emerged as prominent e-learning models as they equip learners with the access of resources at any place and any time which implies taking charge of one’s learning as per the need and purpose. The present study takes stock of ubiquitous (UL) and autonomous learning (LA) as influencing factors for assessing students’ willingness to adopt (WA) LMS for enhancing L2 proficiency. The target population for this study consisted 302 professional students. Data is analyzed using SPSS ver.21. Regression model indicates that both LA (p=.000, β=.289) and UL (p=.000, β=.461) significantly contribute to students’ adoption of LMS. Independent sample t-test results indicate that no significant variation is found in test performance between male and female students. The findings of the proposed model will prove beneficial to assess the reception of LMS among students and will enable the educational institutions to concentrate on LMS’s effective implementation so that they can invest in e-learning technology wisely.Subject Classification: Primary 00A79Keywords: E-learningLearning management systemUbiquityAutonomyL2 proficiencySustainability
Evaluation of a Virtual Working Environment via Psychophysiological Indices

Many studies have suggested that natural landscapes could relieve stressful sentiments regardless of how the virtual nature is presented. Thus, we investigated the effect of a virtual working environment on workers’ mental and physiological states using psychophysiological measures and subjective assessments. Twenty-two healthy male students (22.0 ± 0.9 yrs.) voluntary participated in the study. The results indicated that the finger plethysmogram (PTG) amplitude measured in a forest environment was significantly lower than that measured in an office environment. However, the differences in subjective fatigue and stress between the two environments were negligible. Therefore, we assumed that the forest environment could enhance sympathetic activity owing to an excited-pleasurable working experience. However, other more sophisticated measures should be introduced. Furthermore, it is necessary to investigate the subtle effects of nature using more sensitive methods.
Improving Deepfake Detection by Mixing Top Solutions of the DFDC

The falsification of faces in videos is a growing phenomenon over the years. One of the most popular ways to tamper a face in a video is known as “deepfake”, Today, many tools exist to allow anyone to create a deepfake to discredit an individual or usurp an identity. Fortunately, the detection of deepfakes is an increasing topic of interest for the scientific community. As a result, many efforts have been made to develop mechanisms to automatically identify deepfake videos. In addition, several public deepfakes datasets have been built to help researchers to develop more effective detection methods. The most recent and also the most complete of these datasets is the one built by Facebook as part of the international DeepFake Detection Challenge (DFDC). Thousands of different frameworks, mainly based on deep learning, have been proposed during this challenge. The best solution that has been proposed obtains the accuracy of 82% on the DFDC dataset. However, the accuracy of this method is only 65% on unseen videos from the Internet. In this paper we analyse the five best methods of the DFDC and their complementarity. In addition, we experimented different assembly strategies (boosting, bagging and stacking) among these solutions. We show that we can achieve a large improvement <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(+ 41\%$</tex> on log loss and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$+2.26\%$</tex> on accuracy) when we carefully choose the models to be assembled with the most appropriate right merging method to use.