Reviewerly/exHarmony huggingface dataset:
From children's ideas to prototypes for the internet of things: a case study of cross-generational end-user design

A smart bracelet that reacts to a person's heartbeat. A smart bench that invites passers-by to sit close. These and others are smart things, part of the Internet of Things (IoT) and people's lives. However, people are mainly IoT consumers and rarely given the possibility of becoming IoT creators. This paper presents a case study concerning the design of smart things for outdoor environments, with end users as the main creators. Ideas of smart things were collaboratively conceptualised by child end-users with a card-based board game. Their ideas were taken up in the form of inspiration cards within a bachelor's first-year course, by students coming from different high schools. Students started from children's ideas as inspiration triggers and collaboratively evolved some of them into interactive smart-thing prototypes. The paper concludes by reflecting on its results and drawing lessons for future editions of cross-generational workshops related to IoT design with end users.
A Novel Recurrent and Convolutional Neural Network Technique for Generating Handwriting from Voice

This paper presents a way for generating online handwriting using voice. To build this tool, two broad steps are required: Voice Recognition using Google Speech-to-text API and Handwritten Recognition using a combination of Recurrent and Convolutional neural networks (RCNN). The model is evaluated on IAM and Electronic Fonts datasets that contains handwritten images. This research work has reported the result of training data based on Connectionist Temporal Classification (CTC) loss. CTC also has a function named decoder to predict vector data generated by RCNN into understandable text.
Robust 2D/3D Vehicle Parsing in Arbitrary Camera Views for CVIS

We present a novel approach to robustly detect and perceive vehicles in different camera views as part of a cooperative vehicle-infrastructure system (CVIS). Our formulation is designed for arbitrary camera views and makes no assumptions about intrinsic or extrinsic parameters. First, to deal with multi-view data scarcity, we propose a part-assisted novel view synthesis algorithm for data augmentation. We train a part-based texture inpainting network in a self-supervised manner. Then we render the textured model into the background image with the target 6-DoF pose. Second, to handle various camera parameters, we present a new method that produces dense mappings between image pixels and 3D points to perform robust 2D/3D vehicle parsing. Third, we build the first CVIS dataset for bench-marking, which annotates more than 1540 images (14017 instances) from real-world traffic scenarios. We combine these novel algorithms and datasets to develop a robust approach for 2D/3D vehicle parsing for CVIS. In practice, our approach outperforms SOTA methods on 2D detection, in-stance segmentation, and 6-DoF pose estimation by 3.8%, 4.3%, and 2.9%, respectively.
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations

Unsupervised pretraining is of great significance for visual representation. Especially, contrastive learning has achieved great success recently, but existing approaches mostly ignored spatial information which is often crucial for visual representation. Strong semantic embedding has an inherent advantage for classification, but dense prediction tasks require more spatial and low-level representation. This paper presents <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous contrastive learning</b> (HCL), an effective approach that adds spatial information to the encoding stage to alleviate the learning inconsistency between the contrastive objective and strong data augmentation operations. We demonstrate the effectiveness of HCL by showing that (i) it achieves higher accuracy in instance discrimination, (ii) it surpasses existing pre-training methods in a series of downstream tasks (iii) and it shrinks the pre-training costs by half for almost 800 GPU-hours. More importantly, we show that our approach achieves higher efficiency in visual representations, and thus delivers a key message to inspire the future research of self-supervised visual representation learning.
GCP: Graph Encoder With Content-Planning for Sentence Generation From Knowledge Bases

A knowledge base is a large repository of facts usually represented as triples, each consisting of a subject, a predicate, and an object. The triples together form a graph, i.e., a knowledge graph. The triple representation in a knowledge graph offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of triples (i.e., a graph) into natural sentences. We take an encoder-decoder based approach. Specifically, we propose a Graph encoder with Content-Planning capability (GCP) to encode an input graph. GCP not only works as an encoder but also serves as a content-planner by using an entity-order aware topological traversal to encode a graph. This way, GCP can capture the relationships between entities in a knowledge graph as well as providing information regarding the proper entity order for the decoder. Hence, the decoder can generate sentences with a proper entity mention ordering. Experimental results show that GCP achieves improvements over state-of-the-art models by up to 3.6%, 4.1%, and 3.8% in three common metrics BLEU, METEOR, and TER, respectively. The code is available at (https://github.com/ruizhang-ai/GCP/).
Observational Learning: Imitation Through an Adaptive Probabilistic Approach

This paper proposes an adaptive method to enable imitation learning from expert demonstrations in a multi-agent context. The proposed system employs the inverse reinforcement learning method to a coupled Dynamic Bayesian Network to facilitate dynamic learning in an interactive system. This method studies the interaction at both discrete and continuous levels by identifying inter-relationships between the objects to facilitate the prediction of an expert agent. We evaluate the learning procedure in the scene of learner agent based on probabilistic reward function. Our goal is to estimate policies that predict matched trajectories with the observed one by minimizing the Kullback-Leiber divergence. The reward policies provide a probabilistic dynamic structure to minimise the abnormalities.
Trajectory Planning With Deep Reinforcement Learning in High-Level Action Spaces

This article presents a technique for trajectory planning based on parameterized high-level actions. These high-level actions are subtrajectories that have variable shape and duration. The use of high-level actions can improve the performance of guidance algorithms. Specifically, we show how the use of high-level actions improves the performance of guidance policies that are generated via reinforcement learning (RL). RL has shown great promise for solving complex control, guidance, and coordination problems but can still suffer from long training times and poor performance. This work shows how the use of high-level actions reduces the required number of training steps and increases the path performance of an RL-trained guidance policy. We demonstrate the method on a space-shuttle guidance example. We show the proposed method increases the path performance (latitude range) by 18% compared with a baseline RL implementation. Similarly, we show the proposed method achieves steady state during training with approximately 75% fewer training steps. We also show how the guidance policy enables effective performance in an obstacle field. Finally, this article develops a loss function term for policy-gradient-based deep RL, which is analogous to an antiwindup mechanism in feedback control. We demonstrate that the inclusion of this term in the underlying optimization increases the average policy return in our numerical example.
A Novel on Conditional Min Pooling and Restructured Convolutional Neural Network

There is no doubt that CNN has made remarkable technological developments as the core technology of computer vision, but the pooling technique used for CNN has its own issues. This study set out to solve the issues of the pooling technique by proposing conditional min pooling and a restructured convolutional neural network that improved the pooling structure to ensure efficient use of the conditional min pooling. Some Caltech 101 and crawling data were used to test the performance of the conditional min pooling and restructured convolutional neural network. The pooling performance test based on Caltech 101 increased in accuracy by 0.16~0.52% and decreased in loss by 19.98~28.71% compared with the old pooling technique. The restructured convolutional neural network did not have a big improvement in performance compared to the old algorithm, but it provided significant outcomes with similar performance results to the algorithm. This paper presents the results that the loss rate was reduced rather than the accuracy rate, and this result was achieved without the improvement of convolution.
A Data Mining System for Potential Customers Based on One-Class Support Vector Machine

Abstract Commodity purchase data is usually severely skewed, which is reflected in the fact that there are far more negative data than positive data. This phenomenon makes it difficult for the binary classification model to obtain satisfactory results. Hence, we transform the binary classification problem into a one-class novelty detection problem. Specifically, this work proposes a potential customer mining system based on the One-class Support Vector Machine (OCSVM) and demonstrates its effectiveness for classification, prediction, and potential customer mining. This system allows merchants to focus on unpurchased customers with the strongest purchase intentions and to change their purchase decisions with minimal sale costs, which enables merchants to maximize their benefits.
Digital library studies: The subject and definition

The author demonstrates that the problems of library process automation have been put forward in 1970s. He refers to the book by Gerard Salton (1927–1995) (well-known author and designer of SMART – System for the Mechanical Analysis and Retrieval of Text): «Dynamic information and library processing» (1973). In his monograph, Salton reviewed the libraries and predicted their future as electronic institutions; he also proposed the model of efficient library collection circulation and discussed the issues of collection quality, file organization and storage, technology of computer-aided classification, etc. He concluded the monograph with the paragraph on the collection growth and document removal. In fact, it was one of the first and significant works in electronic collection studies. Russian e-librarianship dates back to 2001 when the first edition of textbook by Andrey I. Zemskov and Ya. L. Shrayberg was published. The priority of theoretical conceptualization of electronic processes was given to IT professionals. They were enthusiastic about new technologies but rather depreciative about the time-honored provisions of general and applied library science. They have been thinking that they are to design the library of special kind and, correspondingly, that the library science of special kind bears no relation to traditional library studies. Empirically they have adopted the approach of their predecessors of the dawn of civilization. They have been interpreting the subject of electronic library studies etymologically – as the book depository (i.e. the system of two elements: e-documents and facilities). The e-library, however, has got the same structure as any other library, and the structure of library science is also the same; it comprises the rovisions related to the whole e-library and each of its four elements of its primary contour, i.e. library collection, user/reader contingent, facilities and resources, and library staff. Each of the provisions is specified in the article.
Efficient NIZKs for Algebraic Sets

Significantly extending the framework of (Couteau and Hartmann, Crypto 2020), we propose a general methodology to construct NIZKs for showing that an encrypted vector \(\boldsymbol{\chi }\) belongs to an algebraic set, i.e., is in the zero locus of an ideal \(\mathcal {I}\) of a polynomial ring. In the case where \(\mathcal {I}\) is principal, i.e., generated by a single polynomial F, we first construct a matrix that is a “quasideterminantal representation” of F and then a NIZK argument to show that \(F (\boldsymbol{\chi }) = 0\). This leads to compact NIZKs for general computational structures, such as polynomial-size algebraic branching programs. We extend the framework to the case where \(\mathcal {I}\) is non-principal, obtaining efficient NIZKs for R1CS, arithmetic constraint satisfaction systems, and thus for \(\mathsf {NP}\). As an independent result, we explicitly describe the corresponding language of ciphertexts as an algebraic language, with smaller parameters than in previous constructions that were based on the disjunction of algebraic languages. This results in an efficient GL-SPHF for algebraic branching programs.
Phonology Recognition in American Sign Language

Inspired by recent developments in natural language processing, we propose a novel approach to sign language processing based on phonological properties validated by American Sign Language users. By taking advantage of datasets composed of phonological data and people speaking sign language, we use a pretrained deep model based on mesh reconstruction to extract the 3D coordinates of the signers keypoints. Then, we train standard statistical and deep machine learning models in order to assign phonological classes to each temporal sequence of coordinates.Our paper introduces the idea of exploiting the phonological properties manually assigned by sign language users to classify videos of people performing signs by regressing a 3D mesh. We establish a new baseline for this problem based on the statistical distribution of 725 different signs. Our best-performing models achieve a micro-averaged F1-score of 58% for the major location class and 70% for the sign type using statistical and deep learning algorithms, compared to their corresponding baselines of 35% and 39%.
Accelerating Deep Neural Networks Using FPGAs and ZYNQ

This article aims at implementing a Deep Neural Network (DNN) using Field Programmable Gate Arrays (FPGAs) for real time deep learning inference in embedded systems. In now days DNNs are widely used where high accuracy is required. However, due to the structural complexity, deep learning models are highly computationally intensive. To improve the system performance, optimization techniques such as weight quantization and pruning are commonly adopted. Another approach to improve the system performance is by applying heterogeneous architectures. Processor with Graphics Processing Unit (GPU) architectures are commonly used for deep learning training and inference acceleration. However, GPUs are expensive and consume much power that not a perfect solution for embedded systems. In this paper, we implemented a deep neural network on a Zynq SoC which is a heterogenous system integrated of ARM processor and FPGA. We trained the model with MNIST database, quantized the model’s 32-bit floating point weights and bias into integer and implemented model to inference in FPGA. As a result, we deployed a network on an embedded system while maintaining inference accuracy and accelerated the system performance with using less resources.
Memory Mapping and Parallelizing Random Forests for Speed and Cache Efficiency

Memory mapping enhances decision tree implementations by enabling constant-time statistical inference, and is particularly effective when memory mapped tables fit in processor cache. However, memory mapping is more challenging when applied to random forests—ensembles of many trees—as the table sizes can easily outstrip cache capacity. We argue that careful system design for parallel and cache efficiency can make memory mapping effective for random forests. Our preliminary results show memory-mapped forests can speed up inference latency by a factor of up to 30 × .
Domain Discrepancy Elimination and Mean Face Representation Learning for NIR-VIS Face Recognition

Due to its potential application in criminal cases, security systems and multimedia information retrieval, Near InfraRed (NIR) to VISible (VIS) face recognition has attracted increasing research attention in the field of computer vision. However, it is still a challenging task because of the large intra-class variations including spectrum, occlusion, lighting, blurry, expression and pose. To address the above problem, we propose a novel Domain discrepancy Elimination and Mean face Representation learning (DEMR) for NIR-VIS face recognition. The DEMR consists of two key components comprising Class-wise Domain Discrepancy Elimination (CDDE) and Cross-modal Mean Face Alignment (CMFA). Specifically, two-branch modality-specific networks are designed to extract features for VIS images and NIR images, respectively. Considering that distribution variations of cross-modal images will decrease recognition performance, we present CDDE to eliminate modality gap by narrowing distribution differences of VIS images and NIR images in a category-by-category manner. Moreover, to reduce the intra-class discrepancies and obtain compact feature representation, the CMFA is designed to achieve representation alignment between cross-domain images and VIS prototypes (i.e., VIS mean face representations), through optimizing a quadruplet constraint. Extensive experiments on multiple challenging NIR-VIS databases validate that our DEMR is effective for cross-modal face recognition task.
Exploring Painting Synthesis with Diffusion Models

As a significant composition of art, fine art painting is becoming a research hotspot in machine learning community. With unique aesthetic value, paintings have quite different representations from natural images, making them irreplaceable. Meanwhile, the lack of training data is common in painting-related machine learning tasks. Therefore, the synthesis of fine art painting is meaningful and challenging work. There are two main types of generative models for image synthesis: generative adversarial networks (GANs) and likelihood-based models. GAN-based models can obtain high-quality samples but usually sacrifice diversity and training stability. Diffusion models are a class of likelihood-based models and have recently been shown to achieve state-of-the-art quality on the image synthesis tasks. In this paper, we explore generating fine art paintings by using diffusion models. We carried out the experiments on the partial impression paintings from the Wikiart dataset. The results demonstrate that the diffusion model can generate high-quality samples, and it is easy to train to cover more target distribution than the GAN-based methods.
On diagonal equations over finite fields

Let Fq be a finite field with q=pn elements. In this paper, we study the number of solutions of equations of the form a1x1d1+…+asxsds=b with xi∈Fpti, where ai,b∈Fq and ti|n for all i=1,…,s. In our main results, we employ results on quadratic forms to give an explicit formula for the number of solutions of diagonal equations with restricted solution sets satisfying certain natural restrictions on the exponents. As a consequence, we present conditions for the existence of solutions. In the second part of the paper, we focus on the case t1=…=ts=n. A classic well-known result from Weil yields a bound for such number of solutions. In the case d1=…=ds, we present necessary and sufficient conditions for the number of solutions of a diagonal equation being maximal and minimal with respect to Weil's bound. In particular, we completely characterize maximal and minimal Fermat type curves. We also discuss further questions concerning equations and present some open problems.
A Privacy Preserving Scheme with Dimensionality Reduction for Distributed Machine Learning

To obtain useful results in machine learning, it is required to collect data from multiple institutions and learn with larger-scale data. However, data collected from multiple institutions may contain a lot of personal information and should not be explicitly shared. The existing research has proposed various methods to protect privacy by using encryption or anonymization, but encryption causes large computational costs, and anonymization may greatly reduce the usefulness of data. In this research, we propose a privacy protection method using dimensionality reduction that is difficult to reverse while maintaining the high usefulness of data. The main idea of our method is that combining dimensionality reduction algorithms with noise addition is useful for privacy-preserving data analysis with high accuracy. Furthermore, we evaluate the effectiveness and security of this method and show the utility of the proposed method.
Encouraging Improvisation in Piano LearningUsing Adaptive Visualisations and Spatiotemporal Models

The process of learning the piano for novices is usually difficult and time-consuming. Several approaches in augmented reality such as piano-roll visualizations have been explored but have not garnered enough success and adoption. These piano roll prototypes have introduced several features and modules that assist novices on aspects such in sight reading, timing and many others. However, improvisation, the act of allowing the piano user to incorporate their personal touch into their performance, and personalised learning have not been much explored in this domain. In this PhD, we are going to explore how we can encourage piano learners to improvise with the use of adaptive piano roll visualisations. Specifically, we are going to investigate how heuristics defined by experts and spatiotemporal models can be used to design visualisations that motivate and encourage learners based on their personalised learning patterns. Using these models and inputs, we will design and build a piano roll training system integrated with adaptive visualisations that serve as intervention helping learners. We will evaluate and compare these visualisations in various user studies where they get to play piano pieces and develop their improvisation skills. We intend to uncover whether these adaptive visualisations will be helpful in the overall training of piano learners. Additionally, we wish to explore whether these adaptive visualisations will allow us to discover affordances that can potentially improve piano learning in general.
Video motions classification based on CNN

There are more and more videos appearing on the internet these years, new ways should be developed to recognize and manage them. Since video is composed of images, this work builds a CNN network to do video classification. The work uses the UCF 101 dataset, which contains 101 different categories, to train the model. Then a simple CNN network containing five layers is built with PyTorch and trained with UCF 101 dataset on GPU. The result shows that it's underfitting and its accuracy won't be improved much by changing parameters. However, adding more layers, including the dropout layer and batchnorm layer can greatly improve its accuracy. Then a C3D method is also applied to improve the accuracy. Finally, the highest accuracy reaches 69 percentage. In this work, a simple and effective way to recognize actions in a small video is developed to help people supervise and manage the video resources online.