Reviewerly/exHarmony huggingface dataset:
Minimizing Link Generation in Constraint Checking for Context Inconsistency Detection

Adaptive applications rely on conditions about their environments (or contexts) to deliver smart services, e.g., location-aware services. Due to inherent noises in environmental sensing and interpretation, there is an increasing demand for guarding the consistency of contexts to avoid application misbehavior, and at the same time minimizing the guarding cost. Existing work has tried to reduce the cost by speeding up the kernel constraint checking module inside the consistency guarding process. Most efforts have been spent on reusing previous checking results or checking constraints in parallel, while leaving untouched one central problem of link generation, the step that consumes a substantially large part of the total time cost for explaining why constraints have been violated. In this paper, we propose a novel technique, MG, to automatically identify and remove redundant link generation, without harming any checking result. We show that MG is sound (always checking correctly) and complete (removing all redundancy). Our experiments with synthesized and real-world consistency constraints reported that compared with existing work, MG achieved significant efficiency improvements on the link generation (tens to hundreds times speedup), and could reduce the total constraint checking time up to 45.4%.
CACRM: Cross-Attention Based Image-Text CrossModal Retrieval

Cross-modal retrieval aims to match instance from one modality with instance from another modality. Since the learned low-level features of different modalities are heterogeneous and the high-level semantics are related, it is difficult to learn correspondence between them. Recently, the fine-grained matching methods by aggregating the similarities from all possible region-word pairs have shown advance. However, the local alignment is hard to achieve with mutual interference between different regions in the modalities. To tackle this problem, a Cross Attention for Cross-Modal Retrieval Method (CACRM) is proposed, which aims to construct a Cross Attention Model (CAM) by introducing multiple independent Transformer modules, extracting the interactive features between each image Region of Interest (ROI) and text. Then, the output of multiple Transformer modules is applied to construct a similarity matrix, making fine-grained alignment. Finally, the global similarity is obtained by pooling the similarity matrix. Besides, in view of the imbalanced distribution characteristics of the sample data, the corresponding weights are assigned to different image sub-regions according to the similarity score differentiation. Extensive experiments are performed on the public datasets Flicker30k, MS-COCO and dam inspection log dataset. Experimental results show that the proposed method significantly surpasses previous methods, demonstrating the effectiveness.
Learning Time-Vertex Dictionaries for Estimating Time-Varying Graph Signals

In this work, we study the problem of learning time-vertex dictionaries for the modeling and estimation of time-varying graph signals. We consider a setting with a collection of partially observed time-varying graph signals, and propose a solution for the estimation of the missing signal observations by learning time-vertex dictionaries from the available observations. We adopt a time-vertex dictionary model defined through a set of joint time-vertex spectral kernels, each of which captures a different spectral component of the signals in their joint time-vertex spectrum. The kernel parameters are optimized along with the representations of the signals so as to be coherent with the available signal observations. Experimental results show that the proposed method yields promising estimation performance in comparison with non-adaptive graph dictionary models and baseline classical graph regression methods.
A Study on Depth Estimation from Single Image Using Neural Networks

Depth estimation is fundamental in upcoming technology advancements like scene understanding, robot vision, intelligent driver assistance systems, and many new technologies. Estimating the depth of objects from a viewport can be achieved using various mathematical, geometrical, and stereo concepts, but the process is unaffordable and erroneous. Depth estimation from a single can be accurately done using neural networks. Although this is a challenging task, researchers around the globe have published various works. The works include different neural network standards like CNN, GANs, Encoder-Decoder. The paper analyses and examines famous works in this field of study. Later in the paper, a comparative survey of depth estimation approaches using neural networks is done.
Self-calibration of multiple-line-lasers based on coplanarity and Epipolar constraints for wide area shape scan using moving camera

High-precision three-dimensional scanning systems have been intensively researched and developed. Recently, for acquisition of large scale scene with high density, simultaneous localisation and mapping (SLAM) technique is preferred because of its simplicity; a single sensor that is moved around freely during 3D scanning. However, to integrate multiple scans, captured data as well as position of each sensor must be highly accurate, making these systems difficult to use in environments not accessible by humans, such as underwater, internal body, or outer space. In this paper, we propose a new, flexible system with multiple line lasers that reconstructs dense and accurate 3D scenes. The advantages of our proposed system are (1) no need of synchronization nor precalibration between lasers and a camera, and (2) the system can reconstruct 3D scenes in extreme conditions, such as underwater. We propose a new self-calibration method leveraging coplanarity and Epipolar constraints is proposed. We also propose a new bundle adjustment (BA) technique that is tailored to the system for a dense integration of multiple line laser scans. Experimental evaluation in both air and underwater environments confirms the advantages of the proposed method.
Teaching Estimation and Control via Probabilistic Graphical Models – An Intuitive and Problem-Based Approach

This contribution discusses an approach to teaching estimation and control via probabilistic graphical models, more specifically via Forney-style factor graphs, combined with a problem-based learning exercise model. The course's overall rationale follows the idea that factor graphs present a unifying perspective on a vast range of topics in estimation and control and may thus present a substantially more intuitive access to the subject matter. Factor graphs allow for a rigorous approach to algorithm design via mixing and matching graphical representations of algorithms or parts thereof. Even though there is a substantial overhead in explaining and understanding the way algorithms can be presented on graphical models via message passing, we postulate that this additional effort pays off by highlighting similarities, enabling easier modifications, and presenting a seamless framework combining approaches from classical control and modern machine learning. Accordingly, the course is set up to combine two goals: It (i) presents canonical topics, such as advanced Kalman filtering, parameter estimation and control, while (ii) simultaneously preparing students to continue research in the domain of probabilistic inference. To promote the latter, the exercises associated with the course adopt a problem-based learning approach, asking students to utilize and possibly extend a toolbox for factor graphs when attempting to solve relevant and mostly application-driven challenges.
A Demonstration of Blabrecs, an AI-Based Wordgame

Blabrecs is an AI-based modification to the popular wordgame Scrabble. In Blabrecs, English dictionary words may not be played; instead, players may only play nonsense words that are approved by a classifier trained on a list of English dictionary words. Gameplay therefore revolves around inventing plausibly English-sounding nonsense words and learning how to fool the classifier. In this paper, we briefly introduce our design goals for Blabrecs; describe the process by which Blabrecs was designed; and present two distinct implementations of the game's AI judge.
IMCN: Identifying Modal Contribution Network for Multimodal Sentiment Analysis

Multimodal sentiment analysis (MSA) aims to obtain the emotional polarity of language by analyzing multiple forms of human language, facial expressions, and vocal intonation. The traditional MSA model focuses on the fusion between modalities, ignoring the different contributions of language, visual, and acoustic. Thus different information of modality possesses different importance. To further explore the contributions of different modalities, we propose a highly generalized identifying modal contributions network(IMCN), which contains modality interaction module, modality fusion, and modality joint learning units in the framework. Specifically, we first designed a language modality gain detection module to make reasonable use of visual and acoustic information and reduce the noise of modal information. Secondly, crossmodal attention is used to enrich modal information. Finally, we perform joint learning of unimodal and multimodal modalities to explore the optimal solution for multimodal output. We compared with other popular multimodal sentiment analysis models and obtained better sentiment classification results on CMU-MOSI and CMU-MOSEI benchmark datasets. We also further validated the effectiveness of different modules of IMCN through ablation experiments and discussed the ideas of IMCN design.
Entangling Superconducting Qubits over Optical Fiber – Towards Optimization and Implementation

A quantum network linking superconducting quantum computers would enable highly sought-after applications such as distributed quantum computing. Optical photons are the natural way of encoding information for long distance communication; however, exchanging quantum information between optical and microwave domains requires a quantum transducer. There are many ways transducers could be integrated into a network which leads to the question: Which network is most tolerant of current transducers which are far from perfect? A logical metric for optimizing the first quantum networks is the entanglement threshold which is necessary and sufficient condition on network parameters for microwave-microwave entanglement. Here we first address the problem of finding the network with the least restrictive entanglement threshold on physical parameters. For tractability we restrict our study to networks containing only gaussian states, operations, and measurements which are among the most readily experimentally accessible options. Within this vast space of possible network configurations, we identify a small set of topologies which we believe to be optimal. We find that with previously reported technology some of these topologies are capable of entangling remote microwave modes, despite imperfections in current transduction technology. Finally, progress towards a physical implementation of one of these topologies is discussed.
A Self-Supervised Purification Mechanism for Adversarial Samples

Deep learning based techniques are broadly used in a variety of applications such as image recognition, natural language processing, etc., which express leading performance than traditional methods. However, adversarial samples can cause severe problems for deep learning models, especially for vision-based deep neural networks. Adversarial training is a commonly adopted strategy to defend adversarial samples, but it lacks generalization capability due to the transferability of adversarial samples to work on cross-task predictions. In addition, the input processing method of deep learning pipeline can improve generalization capability for cross-task predictions, whereas processing newly generated adversarial samples is the major bottleneck that limits its performance. To deal with these issues, this paper proposes a purification mechanism that combines both adversarial training-based and input processing-based methods to defend the threats of adversarial samples. The proposed mechanism has a strong generalization capability for various vision-based deep learning tasks by leveraging the advantage of input processing-based methods and maintaining the defense efficiency of adversarial training-based methods. Comprehensive experiments are conducted on multiple datasets that demonstrate the superiority of the proposed approach against the state-of-the-art methods on defending adversarial samples.
Expert-led research on the educational environment of a higher education institution: findings and analysis

The importance of the educational environment in personal growth, education, and building is realized via the analysis of scientific resources. One of the most crucial elements of such processes, according to some, is the educational environment. The terms "educational environment" and "educational environment of institution of higher education" are defined by the author. The writers promote the idea that the educational setting is a network of resources for individual education. The environmental approach to education, which aims to create and develop an educational environment that must fulfill a student's educational needs, is changing the emphasis on teaching, it is highlighted. The author's method of assessing the quality of the educational environment at a higher education institution is supported and approved, and the rating scale for this method is introduced. The method recommends doing a professional assessment of the learning environment of a higher education institution using qualitative (modality) and quantitative (professional breadth, professional saturation, sociocultural intensity, congruence, openness, mobility, informativeness) factors. A higher education institution's typology of the educational environment is offered (innovative-professional, formal-professional, pragmatically-oriented, formal general cultural educational environment). The analysis and explanation of the experiment's findings at Borys Grinchenko Kyiv University's University College are provided. Three expert groups, which included student, teacher, and parent representatives, evaluated the college's educational setting. Even while assessments made by several expert groups are comparable, it becomes clear that there are important disparities, which has been demonstrated with the help of the right statistical techniques. It has been concluded that there is tremendous potential for the validation of the entire monitoring system of the educational environment of institution of higher education and associated diagnostic instruments.
Effect Verification of a Feature Extraction Method Based on Graph Convolutional Networks

Designing effective and efficient neural network architecture is critical to the success of Neural Architecture Search (NAS). One of the critical issues we encounter in Neural Architecture Search is the neural architecture performance evaluation, and a vital step during the search progress is the neural architecture's feature extraction method. The feature vector from the input neural network was mapped to the latent space in the previous work. This feature vector is used to predict the performance of the neural architecture through a trained accuracy decoder. Previous research has employed vector-based ways to predict the neural architecture performance. The encoding scheme converts the neural architecture into an encoded vector, then forward the vector into the model for training to obtain the final prediction result. Therefore, this method tends to ignore the spatial information contained in the structure. According to specific research, each neural architecture may be treated as a graph-based data structure, allowing us to use Graph Convolutional Networks (GCN) to extract features from neural architectures. The neural architecture does not need to be converted into vectors in GCN. Instead of losing structural space information during vector translation, we can extract features straightly from the graph. Experimental results on the NASBench-101 dataset show that GCN methods can predict the accuracy of neural architectures better more accurately than state-of-the-art methods, and our method can evaluate the performance of specific neural architectures effectively and efficiently.
A temperature query device based on speech recognition

With the advancement of science and technology and society, people’s requirements for the family living environment are gradually increasing, and people expect a comfortable, safe and intelligent home environment. Based on the literature review, this paper determines the research status of speech recognition technology at home and abroad, organizes the development history of domestic speech recognition technology, and compares the advantages of foreign technologies, and designs a temperature query device based on speech recognition technology. This paper mainly introduces the technical factors of the scheme design, completes the overall scheme design of the system, and demonstrates the reliability of the technology. After the scheme comparison, in the design part, the device has carried out the design of the speech recognition chip circuit and the design of the main control unit, and designed the speech pronunciation circuit design to realize the speech recognition function. The system introduced in this paper takes Arduino Mega2560 as the control core, DS18B20 as the temperature sensor of the device, realizes human-computer interaction through the speech recognition chip, and finally uses the SYN6288 speech synthesis module to perform voice broadcast to realize real-time temperature monitoring and real-time display, and complete The detailed design of the system software is carried out to realize the whole process control, and finally the test analysis is completed to verify that the query success rate of the device is not less than 90%.
Blockchain-Governed Federated Transfer Learning for Secure Internet of Drones Networks

Internet of Drones (IoD) is a critical paradigm of future networks used for complex cooperative tasks. Due to its heterogeneity, high mobility, and exposed connectivity, it inevitably suffers from the crucial security threats of nodes failure, malicious tampering, and anonymous attacks. Specifically, federated transfer learning (FTL) and blockchain are seen as promising technologies for network security. FTL utilizes the distinctive transfer of prior knowledge to facilitate security applications in various networks. It strengthens privacy protection and the resilience of AI, which will be crucial in more complicated circumstances. Blockchain (BC) technology attracts attention due to its inherent characteristics of security authentication and tamper-resistant storage. In this article, the superiority of FTL and BC are intergraded to overcome the cyber-security concerns associated with IoD networks. We propose FTLchain, a blockchain-empowered FTL architecture. To significantly boost the cyber-security of collaborative AI while preserving privacy, we empower the architecture with lightweight safety technology and detail the relationship between the critical components in the security design. Then, we devise a hierarchical consensus mechanism integrating the FTL aggregation with the blockchain consensus to ensure the architecture's scalability. The FTLchain's robustness is explained and proved with numerical simulations.
Path Planning for Mobile Robots Based on Improved RRT Algorithm

Aiming at the disadvantages of the traditional RRT* algorithm, such as the weak random searching ability, the calculation result is usually not the shortest path, and the searching path is not smooth. The new algorithm proposed in this paper introduces the adaptive target virtual gravity method to ensure the balance between target location and surrounding space exploration. The expansion radius idea is introduced to make the mobile robot safely avoid narrow channels, and the path smoothing method based on Minimum Snap is proposed. The mobile robot can reach the target point quickly, stably, and safely along the globally optimal path. A lot of simulation results show the feasibility and effectiveness of the improved algorithm.
DecoLeaves: Exploring NFC technology for Energy Harvesting and Concealed Input on Interactive Fashion with Dynamic Graphics

We present the design and prototype of the DecoLeaves shirt, a battery-free wraparound style shirt that gives the autonomy to its wearer for modifying her appearance by moving a smartphone secretly in the shirt’s pocket. It is designed to express emotions, status or the wearer’s self in general, without being too over the top and can be worn on casual occasions. To achieve its functionality, it utilizes two NFC-based wireless energy harvesting components inside to pocket as a secret input technique to switch leaf-shaped electrochromic (EC) display embellishments on the shoulder without needing an embedded battery on the shirt. Our work contributes the utilization of NFC technology as an energy harvesting technique, as well as a secret input modality for interactive fashion to alter the visual state of the garment.
Automated Isovist Computation for Minecraft

Procedural content generation for games is a growing trend in both research and industry, even though there is no consensus on how good content looks, nor how to automatically evaluate it. A number of metrics have been developed in the past, usually focused on the artifact as a whole, and mostly lacking grounding in human experience. In this study, we develop a new set of automated metrics, motivated by ideas from architecture, namely isovists, which have a track record of capturing the human experience of space. These metrics can be computed for a specific game state, from the player’s perspective, and take into account their embodiment in the game world. We show how to apply those metrics to the 3d blockworld of Minecraft. We use a dataset of generated settlements from the GDMC Settlement Generation Challenge in Minecraft and establish several rank-based correlations between the isovist properties and the rating human judges gave those settlements. We also produce a range of heat maps that demonstrate the location-based applicability of the approach, which allows for the development of those metrics as measures for a game experience at a specific time and space.
SecureAstraea: A Self-balancing Privacy-preserving Federated Learning Framework

Federated learning is an emerging fundamental AI technology originally used to solve the problem of updating models locally by Android cell phone end-users. As a distributed approach, its design goal is to carry out efficient and secure machine learning among multiple participants or multiple computing nodes. Specifically, federated learning can somewhat protect information security during data exchange, protect endpoint data and personal data privacy, and ensure legal compliance, which is also applicable to edge computing systems. However, unlike the common training dataset, the data distribution of the edge computing system is imbalanced which will introduce biases in the model training and cause a decrease in accuracy of federated learning applications. In this work, we analyze the privacy leakage problem of existing solutions. To address this problem, we construct a self-balancing federated learning framework by designing multiple protocols in the semi-honest and server collusion scenarios, respectively. The proposed framework enables privacy protection by transmitting data as ciphertext as well as computing to protect the privacy of the participants. Compared with FedAvg, the state-of-the-art FL algorithm, our scheme has a substantial improvement in accuracy on imbalanced MNIST dataset; and compared with the prior solutions, our scheme is able to avoid the privacy leakage of participants.