Reviewerly/exHarmony huggingface dataset:
Dynamic Metric Learning with Cross-Level Concept Distillation

A good similarity metric should be consistent with the human perception of similarities: a sparrow is more similar to an owl if compared to a dog but is more similar to a dog if compared to a car. It depends on the semantic levels to determine if two images are from the same class. As most existing metric learning methods push away interclass samples and pull closer intraclass samples, it seems contradictory if the labels cross semantic levels. The core problem is that a negative pair on a finer semantic level can be a positive pair on a coarser semantic level, so pushing away this pair damages the class structure on the coarser semantic level. We identify the negative repulsion as the key obstacle in existing methods since a positive pair is always positive for coarser semantic levels but not for negative pairs. Our solution, cross-level concept distillation (CLCD), is simple in concept: we only pull closer positive pairs. To facilitate the cross-level semantic structure of the image representations, we propose a hierarchical concept refiner to construct multiple levels of concept embeddings of an image and then pull closer the distance of the corresponding concepts. Extensive experiments demonstrate that the proposed CLCD method outperforms all other competing methods on the hierarchically labeled datasets. Code is available at: https://github.com/wzzheng/CLCD .
Multimodal Aspect-Level Sentiment Analysis based on Deep Neural Networks

Aspect-level sentiment analysis is a fine-grained task of sentiment analysis that aims to identify the sentiment polarity of specific aspect words in a sentence. However, most existing approaches rely mainly on text content and ignore other potentially useful data (e.g., images) that can complement the text content and thus more accurately represent the user's sentiment. Therefore, we focus on Aspect-level Multi-model Sentiment Analysis(AMSA). Analyzing the current research status of AMSA, explore the independence and correlation between multiple modal data, capture their deep interaction information, and improve the data feature analysis and fusion capability from feature extraction methods and feature fusion strategies, respectively. Finally, the corresponding modeling scheme is proposed for aspect-level multimodal aspect-level sentiment analysis.
Data Mining and Reasoning of Radar Radiation Sources Based on Knowledge Graph

This paper mainly considers the data inadaptability issue of radar radiation sources, and proposes a corresponding similarity analysis method based on the knowledge graph, which helps to improve the reasoning performance of the knowledge graph module. In addition, an event-based knowledge graph reasoning framework is established for massive radar radiation source data and some effective information increment is obtained. Finally, simulation experiments verify the effectiveness of the proposed scheme.
LiDAR-Based Pedestrian Detection Using Multiple Features and Dimensionality Reduction Scheme

In recent years, the development of LiDAR received plenty of attention in autopilot filed because of its long-distant detection and 360-degree vision which make self-driving safer under harsh situation. However, because point cloud is sparse and has inhomogeneous representation, it may lead to error detection, especially when detecting distant or small objects (e.g., pedestrians, traffic signs). In order to mitigate lack of feature information which is triggered by above issues, some researchers proposed multi-views or multi sensor fusion methods to increase dimensionality of feature to complement missing information, but it would result in an unnecessary computation because of feature redundancy and imbalance. In this paper, we proposed a scheme of dimensionality reduction based on variational autoencoder (VAE). Through its encoder, the high-dimensional feature is refined and mapped into a meaningful low-dimensional feature space that still retains representative features. We designed a pedestrian detection framework using our method and from the experimental results, our method achieved better performance compared to previous work.
Adaptivity Gaps for the Stochastic Boolean Function Evaluation Problem

We consider the Stochastic Boolean Function Evaluation (SBFE) problem where the task is to efficiently evaluate a known Boolean function f on an unknown bit string x of length n. We determine f(x) by sequentially testing the variables of x, each of which is associated with a cost of testing and an independent probability of being true. If a strategy for solving the problem is adaptive in the sense that its next test can depend on the outcomes of previous tests, it has lower expected cost but may take up to exponential space to store. In contrast, a non-adaptive strategy may have higher expected cost but can be stored in linear space and benefit from parallel resources. The adaptivity gap, the ratio between the expected cost of the optimal non-adaptive and adaptive strategies, is a measure of the benefit of adaptivity. We present lower bounds on the adaptivity gap for the SBFE problem for popular classes of Boolean functions, including read-once DNF formulas, read-once formulas, and general DNFs. Our bounds range from $$\varOmega (\log n)$$ to $$\varOmega (n/\log n)$$ , contrasting with recent O(1) gaps shown for symmetric functions and linear threshold functions.
Content Transfer Across Multiple Screens with Combined Eye-Gaze and Touch Interaction - A Replication Study

In this paper, we describe the results of replicating one of our studies from two years ago [1] which compares two techniques for transfer-ring content across multiple screens in VR. Results from the previous study have shown that a combined gaze and touch input can outper-form a bimanual touch-only input in terms of task completion time, simulator sickness, task load and usability. Except for the simulator sickness, these findings could be validated by the replication. The difference with regards to simulator sickness and variations in abso-lute scores of the other measures could be explained by a different set of user with less VR experience.
Segmentation of Aorta with Aortic Dissection based on Centerline and Boundary Distance

Segmentation of the aorta is important for the diagnosis and treatment of aortic disease. However, low image contrast and blurred boundaries between the aortic region and surrounding tissues can significantly affect segmentation performance. Based on 3D-UNet with spatial attention module, this paper proposes a multi-branch shape-aware segmentation network named CDM-Net, which transforms the traditional segmentation problem into a regression problem of distance transformation map and centerline heatmap. A new inference method based on regression is also proposed, the prediction of our network can be combined with the predictions of other networks. Without changing other segmentation metrics (Dice, ASD), the clDice of the combined method improves by 1.5%. Our proposed method can improve the connectivity of aorta segmentation results, paving the way for accurate centerline extraction and multiplanar reconstruction in the future.
Latent State Estimation Method for Students Using Knowledge Tracing Combining with IRT

The Intelligent Tutoring Systems (ITSs) are a system that provides an efficient learning environment by assigning questions that are suitable to the learner's skill state. To improve the performance of ITSs, conventional studies have proposed student models that can estimate skill states with high accuracy. However, these models were based on the assumption that the log data was correct. In other words, they do not take into account the possibility of students cheating or memorizing answers, assuming that the test was conducted fairly and impartially. In recent years, many examinations have been conducted online as a countermeasure against COVID-19 (coronavirus) infections, and students may be able to obtain hints for the examinations using the internet or books. In this way, it is important to know how students approach learning in order to estimate their skill state. In this study, we propose a method for estimating the latent state of learners using a model that combines Knowledge Tracing, the de-facto standard for student modeling method, and Item Response Theory.
A Differentiable Approach to the Maximum Independent Set Problem Using Graph-Based Neural Network Structures

The problem of finding a Maximum Independent Set (MIS) in a given graph is a known NP-hard problem with many applications in various domains. In this work, we propose a novel approach to the MIS problem which rests on a reduction of the graph to a Neural Network (MISNN) whose structure is derived from the connectivity of the underlying graph. The input to the MISNN is obtained as a solution to a formulated box-constrained non-linear optimization program, then the MIS is obtained from a defined mapping of the minimizing input. Our experimental results using graph generated from various models demonstrate that the proposed method outperforms the approximate solver of the well-known NetworkX python library, both in the size of the found MIS and the run-time.
Quantum Computing and Information Specialization in Electrical Engineering Master Degree

In this paper, we discuss the Quantum Computing and Information (QCI) Specialization introduced in Fall 2021 in the Department of Electrical Engineering at San Jose State University (SJSU). SJSU is a Minority and Hispanic Serving Institution at the heart of Silicon Valley. Its engineering graduates have been the major workforce supply in the semiconductor industry in the last few decades. As quantum technology is becoming more and more prominent in the industry, it is important to train the students to be quantum-aware. In the course of developing quantum technology classes, the balance needs to be taken by considering the education and social-economic backgrounds of the students and the "return-on-investment" (ROI) from both the student and school resource perspectives. Therefore, to achieve the balance, a QCI specialization was introduced with three classes, namely, Introduction to Quantum Computing, Cryogenic Nanoelectronics, and Quantum Computing Architectures. We will discuss the design of the courses and how they provide the minimal knowledge engineering students need to learn to work in the quantum technology industry.
English Education Software Assisted Teaching based on Big Data

In the era of big data, we should seize the opportunity, actively explore, and use good software to assist teaching. The combination of computer-assisted teaching methods and information technology has become a more popular teaching method in today's society. The purpose of this article to study English education software-assisted teaching is to improve the application of modern network technology and provide English learners with a better platform for English learning. This article mainly uses the questionnaire survey method and the interview method to analyze the students' attitude towards English education software-assisted teaching, and conducts relevant research on English education software through practical actions. The survey results found that 76.5% of people maintain an agreeable attitude towards educational software. This shows that most people are positive about the auxiliary teaching of software.
Chinese Sentence-level Event Factuality Identification with Recursive Neural Network

Sentence-level event factuality identification (SEFI) aims to identify the factuality of an event presented in a sentence. Recent neural network-based approaches have demonstrated the efficacy of the shortest dependency path, but these methods lack semantic information compared with continuous text fragments and may lead to the omission of useful information. In addition, dependency paths are relatively flat. So far, most previous work focused on English datasets, and neglected Chinese tasks. And the existing Chinese SEFI methods ignore the syntactic information. To overcome the above issues, we propose a Chinese event factuality identification model based on dependency trees. We adopt a recursive neural network-based module that fuses event selected predicates, degree words, negative words, and event triggers to capture long-range relations among them. Experimental results on the Chinese event factuality corpus show that our proposed method outperforms other baselines.
More About the Corpus of Involutions From Two-to-One Mappings and Related Cryptographic S-Boxes

Permutation polynomials have been extensively studied for their applications in cryptography, coding theory, combinatorial design, etc. An important subfamily of permutations is the class of involutions (those permutations are equal to their compositional inverse). Elements of this class have been used frequently for block cipher designs and coding theory. In this article, we further investigate this corpus using new approaches, specifically from two-to-one (2-to-1) functions and (in some cases) using the graph indicators introduced by Carlet in 2020. In our constructions of involutions over the finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{2^{n}}$ </tex-math></inline-formula> of order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n}$ </tex-math></inline-formula> , we shall intensively use 2-to-1 mappings over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{2^{n}}$ </tex-math></inline-formula> . More specifically, we present a new constructive method to design involutions from 2-to-1 mappings through their graph indicator and derive new involutions from known 2-to-1 mappings. Besides, we also propose several new classes of 2-to-1 mappings, including 2-to-1 hexanomials, 2-to-1 mappings of the form <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(x^{2^{k}}+x+\delta)^{s_{1}}+(x^{2^{k}}+x+\delta)^{s_{2}}+cx$ </tex-math></inline-formula> , and 2-to-1 mappings from linear 2-to-1 mappings. We also exhibit the corresponding involutions of the constructed 2-to-1 mappings. Furthermore, an infinite family of involutions with differential uniformity at most 4 (EA-inequivalent to the inverse function) is obtained. Finally, we highlight that all our derived families of involutions have no fixed point, further accentuating their cryptographic interest.
Design of a YOLO Model Accelerator Based on PYNQ Architecture

The application requirements of object detection models based on deep learning are very extensive. However, high computing power requirements often seriously restrict the application of these models on resource-constrained devices with high energy efficiency requirements. To address this problem, a YOLO model accelerator architecture is proposed based on PYNQ. Based on the FPGA hardware platform, the hardware accelerator is designed by making full use of pipeline, loop unrolling, data reordering and other methods to accelerate the computationally intensive units in the YOLOv2 model such as the convolution and pooling layers. In order to reduce the delay in the data transmission process, the multi-channel transmission architecture combined with the ping-pong buffer is designed, and block-by-block reading strategy is adopted to read the off-chip data. The proposed YOLO model accelerator has been implemented and verified on Xilinx PYNQ-z2 platform. The experimental results show that the system has high detection accuracy and far lower power consumption than CPU and GPU. It can also be deployed on mobile devices to detect the surrounding environment.
A Multi-Layer Attention Network for Visual Commonsense Reasoning

Visual Commonsense Reasoning (VCR) is a challenging multimodal task involving several research fields such as vision, cognition, and reasoning, which combines images and natural language for reasoning. Existing VCR methods focus on global attention or use pre-training models, but these methods lack attention to local features of visual and language. In this paper, a multi-layer attention network is proposed for the VCR task, including an intra-modal attention module and an inter-modal attention module. The intra-modal attention module complements important features of visual and language modalities with fine-grained visual attention to improve the relevance of visual and language. The inter-modal attention module captures the internal dependencies between visual and language. Finally, the two modules are integrated into an end-to-end reasoning framework. Experiments on the VCR large-scale dataset show that the proposed method exhibits a decent improvement in the VCR task and illustrates the effectiveness of the method on three subtasks.
SeeThroughNet: Resurrection of Auxiliary Loss by Preserving Class Probability Information

Auxiliary loss is additional loss besides the main branch loss to help optimize the learning process of neural networks. In order to calculate the auxiliary loss between the feature maps of intermediate layers and the ground truth in the field of semantic segmentation, the size of each feature map must match the ground truth. In all studies using the auxiliary losses with the segmentation models, from what we have investigated, they either use a down-sampling function to reduce the size of the ground truth or use an up-sampling function to increase the size of the feature map in order to match the resolution between the feature map and the ground truth. However, in the process of selecting representative values through down-sampling and up-sampling, information loss is inevitable. In this paper, we introduce Class Probability Preserving (CPP) pooling to alleviate information loss in down-sampling the ground truth in semantic segmentation tasks. We demonstrated the superiority of the proposed method on Cityscapes, Pascal VOC, Pascal Context, and NYU-Depth-v2 datasets by using CPP pooling with auxiliary losses based on seven popular segmentation models. In addition, we propose See-Through Network (SeeThroughNet) that adopts an improved multi-scale attention-coupled decoder structure to maximize the effect of CPP pooling. SeeThroughNet shows cutting-edge results in the field of semantic understanding of urban street scenes, which ranked #1 on the Cityscapes benchmark.
Spatial Data Publication Under Local Differential Privacy

Local differential privacy (LDP), which has been applied in Google Chrome and Apple iOS, provides strong privacy assurance to users when collecting data from users. We focus on the sensitive spatial data collection, with the goal of obtaining high result utility while satisfying LDP. The existing methods for this problem mostly target at the task of range queries. They combine the frequency estimation technology and spatial decomposition method to publish the number of users located in some sub-spaces. However, these methods cannot well support distance-related applications such as k-means clustering, since they treat the sub-spaces not containing the user equally and do not consider the distances between the sub-space and user data. Motivated by this, we propose dimension-correlated piecewise mechanism (DCPM), a novel LDP perturbation mechanism with a well-designed probability density, in which the distance between the published value and the true one is considered. Extensive experiments on real-world data and synthetic data demonstrate that DCPM achieves significantly higher result utility compared to previous solutions.
Computational intelligence paradigms for audio-based video summarization

The exponential growth in video content generated via multiple sources has led to the development of video summarization mechanisms, where smaller summaries are generated from comparatively longer videos. Video summarization plays a significant role in a diverse range of applications including secured surveillance, biometrics, education, sports, news, movies, etc. Several criteria are used for video summarization based on certain events or objects present in the input video for generating respective small summaries. However, audio is a key parameter that may be used to obtain a small summary of a video where some sound or voice is noticed. This article aims to expound on an analysis of audio-based video summarization (ABVS) via data-driven techniques such as a machine or deep learning. We also present a novel framework for ABVS using handcrafted or automatic feature engineering. An evaluation methodology including performance metrics and datasets for assessing the effectiveness of various ABVS approaches is also illustrated. The study reveals several key issues related to ABVS that need further investigation from the active researchers in this active field. One of the critical issues involves exploring effective audio features and the scarcity of benchmark datasets for an audio-based summary generation.
Development of a Handover Approach in Design for Dementia

Designing for people living with dementia (People Living with Dementia, referring to people with dementia and their (in)formal caregiver network (Brankaert 2016).) (PLWD) is challenging. The benefits of involving PLWD in the design process are widely represented in literature. However, ethical and practical considerations can make it difficult to include PLWD. In these cases there’s a need for approaches to transfer unique experiences with PLWD from one designer who had experiences with PLWD to members of the design team who are unable to meet PLWD. The aim of the doctoral research is to develop accessible handover approaches to be used in the professional design context. This paper describes three concerns that emerged from research thus far (i.e. a literature review and ethnographic research); (1) the designers’ interpretation, (2) the uniqueness of each experience, and (3) the search for methods able to transfer these experiences. These concerns provide a framework for the development of handover approaches, validated with student-designers. These student cases are now in the process of being analysed in order to make conclusions for future research to build on, however the first insights gained from four past student cases are shared in this paper.
Recommender system designed using an ensemble approach to semi-supervised learning

In traditional machine learning approaches only the labeled data are used for training the model but labeled data isnot easily available and also the manual labeling of data requires a considerable amount of time, effort and expense. This is the reason why most of the research remains untouched and this problem is addressed by a new technique called semi-supervised learning. Semi-supervised learning limits the use of labeled data by efficiently using an unlabeled data, which is relatively easily available as compared to labeled data. In this paper, our idea is to use the semi-supervised learning technique in the field of regression. We have proposed a model to predict the label for unlabeled data with few labeled data using an ensemble approach to semi-supervised learning. Furthermore, we have also made a comparison of the result obtained when we use a model with and without semi supervised learning and have also shown how semi supervised learning increases the predictive performance