Reviewerly/exHarmony huggingface dataset:
Enhanced Optimization with Composite Objectives and Novelty Pulsation

An important benefit of multi-objective search is that it maintains a diverse population of candidates, which helps in deceptive problems in particular. Not all diversity is useful, however: candidates that optimize only one objective while ignoring others are rarely helpful. A recent solution is to replace the original objectives by their linear combinations, thus focusing the search on the most useful tradeoffs between objectives. To compensate for the loss of diversity, this transformation is accompanied by a selection mechanism that favors novelty. This paper improves this approach further by introducing novelty pulsation, i.e. a systematic method to alternate between novelty selection and local optimization. In the highly deceptive problem of discovering minimal sorting networks, it finds state-of-the-art solutions significantly faster than before. In fact, our method so far has established a new world record for the 20-line sorting network with 91 comparators. In the real-world problem of stock trading, it discovers solutions that generalize significantly better on unseen data. Composite Novelty Pulsation is therefore a promising approach to solving deceptive real-world problems through multi-objective optimization.
Blind Image Deblurring via Superpixel Segmentation Prior

We present an effective blind image deblurring algorithm based on superpixel segmentation prior (SSP). The motivation of this work is an interesting observation that the more rough the segmentation boundaries are, the clearer the image will be. Intuitively, the blurry images have less image details, which results in more smooth segmentation boundaries. The clear images have more vivid textural details and obtain more rough boundaries. The segmentation roughness could be defined as the length of image segmentation boundaries. However, the segmentation boundary length is not differentiable, making it difficult to integrate into existing joint optimization framework. Therefore, we transform the segmentation boundary length into the segmentation entropy to guide the process of image deblurring. With the image becomes clearer, its boundary becomes more rough, while the segmentation entropy is much smaller. The analysis of relationship between segmentation entropy and segmentation boundaries is detailed. Benefiting from the convexity of segmentation entropy, we propose a novel algorithm by integrating half-quadratic split and gradient descent to alternately minimize energy function. Extensive experiments show that the proposed method achieves best performance with the state-of-the-art blind deblurring methods on natural and face image deblurring.
Outliers and Extreme Values

Testing for outliers is difficult, especially if the distribution is not normal. If it is, Grubb's test is often used. Sometimes outliers are due to slippage or contamination. The larger question is, if outliers are identified, how should we deal with them? In some applications, we want to know what the greatest or least value might be in a sample, and there are fairly simple ways of determining these.
Incompressible Encodings

An incompressible encoding can probabilistically encode some data m into a codeword c, which is not much larger. Anyone can decode the codeword c to recover the original data m. However, the codeword c cannot be efficiently compressed, even if the original data m is given to the decompression procedure on the side. In other words, c is an efficiently decodable representation of m, yet is computationally incompressible even given m. An incompressible encoding is composable if many encodings cannot be simultaneously compressed. The recent work of Damgård, Ganesh and Orlandi (CRYPTO '19) defined a variant of incompressible encodings as a building block for "proofs of replicated storage". They constructed incompressible encodings in an ideal permutation model, but it was left open if they can be constructed under standard assumptions, or even in the more basic random-oracle model. In this work, we undertake the comprehensive study of incompressible encodings as a primitive of independent interest and give new constructions, negative results and applications:
Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation

Image classification is a fairly easy task for humans, but for machines it is something that is very complex and is a major problem in the field of Computer Vision which has long been sought for a solution. There are many algorithms used for image classification, one of which is Convolutional Neural Network, which is the development of Multi Layer Perceptron (MLP) and is one of the algorithms of Deep Learning. This method has the most significant results in image recognition, because this method tries to imitate the image recognition system in the human visual cortex, so it has the ability to process image information. In this research the implementation of this method is done by using the Keras library with the Python programming language. The results showed the percentage of accuracy with K = 5 cross-validation obtained the highest level of accuracy of 80.36% and the highest average accuracy of 76.49%, and system accuracy of 72.02%. For the lowest accuracy obtained in the 4th and 5th testing with an accuracy value of 66.07%. The system that has been made has also been able to predict with the highest average prediction of 60.31%, and the highest prediction value of 65.47%.
Estimating the Pose of a Medical Manikin for Haptic Augmentation of a Virtual Patient in Mixed Reality Training

Virtual medical emergency training provides complex while safe interactions with virtual patients. Classical non-virtual medical training includes the use of medical manikins. Haptically integrating such a manikin into virtual training has the potential to improve the interaction with a virtual patient (e.g. regarding social presence) and the resulting training experience. This work analyzes user requirements and several technological approaches. The presented system estimates the pose of a medical manikin in order to haptically augment a 3D human model in a virtual reality (VR) training environment, allowing users to physically touch a virtual patient. The system uses a Convolutional Neural Networks-based (CNN) body keypoint detector to locate relevant keypoints of the manikin in the images of the stereo camera built into a head-mounted display (HMD). The system retrieves manikin position, orientation and joint angles using non-linear optimization. A manual precision analysis reports a mean error distance of 48 mm. Unless further optimized, we recommend applying this method for haptically augmented social interaction with virtual patients but not for practicing precise medical treatments.
An Extensive Study and Comparison of the Various Approaches to Object Detection using Deep Learning

Smart spaces are specialized environments developed to enable the automatic monitoring of events in a monitored setting. Smart surveillance uses deep learning for object detection, to detect any hazards or predict potential threats in the designated smart space. Deep learning improves the accuracy of the dataset and even humans in tasks like image classification, speech recognition, and predictive tasks. In smart spaces, deep learning can be used for actions like voice recognition, to identify trends in collected data and smart surveillance. Deep learning algorithms are capable of locating a region of interest in a frame and predicting a label for the object in the region of interest. There are a wide variety of architectures available, each with its advantages and limitations. This paper aims to provide a study of deep learning architecture performance tuning. After an extensive comparison, considering the given evaluation metrics and time constraints of a real-time smart surveillance system, the YOLO architecture and its variants are found to be the most efficient. This architecture has been implemented on a smart space dataset and the results have been documented.
Quantum Private Comparison Protocol without a Third Party

This paper presents a quantum private comparison (QPC) protocol based on Bell states. The proposed QPC protocol can secretly compare information of the two participants without the help of a third party (TP). The proposed protocol employs some decoy state photons and quantum SWAP gates to resist various outside attacks and internal attacks. This paper compares the presented quantum private comparison (QPC) protocol with other schemes in terms of different indicators. The results show that the proposed protocol has some advantages different from previous protocols.
Use of Mobile Devices for Learning and Student Support in the Pacific Region

The Pacific Island communities face challenges such as digital divide, shoestring budgets, and escalating costs in the delivery of quality education to their people. In addition, the widely scattered nature of islands and communities requires proper communication infrastructure for learners to access education. This chapter heralds the emergence of mobile learning (mLearning) initiatives in higher education in the Pacific region. As a case study, the study focuses on the introduction of mLearning at the University of the South Pacific (USP) in the Pacific which developed a fit-for-purpose mLearning infrastructure that primarily utilized the short message service (SMS) for its delivery and services. The USP hosts an in-house setup open-source SMS gateway that facilitates its custom-made SMS services such as course notifications, exam timetable information, and online quizzes. More recently with the proliferation of smartphones in the Pacific, USP has invested largely on web-based services and mobile apps. The chapter presents the recent journey into the use of smartphones and tablets to access education and promotes lifelong and life-wide learning in the Pacific.
Investigating the Role of Image Retrieval for Visual Localization

Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for both of them. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes which often differs from the requirements of visual localization. In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms. First, we introduce a novel benchmark setup and compare state-of-the-art retrieval representations on multiple datasets using localization performance as metric. Second, we investigate several definitions of "ground truth" for image retrieval. Using these definitions as upper bounds for the visual localization paradigms, we show that there is still significant room for improvement. Third, using these tools and in-depth analysis, we show that retrieval performance on classical landmark retrieval or place recognition tasks correlates only for some but not all paradigms to localization performance. Finally, we analyze the effects of blur and dynamic scenes in the images. We conclude that there is a need for retrieval approaches specifically designed for localization paradigms. Our benchmark and evaluation protocols are available at https://github.com/naver/kapture-localization .
On clustering for cell-phenotyping in multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) data

Abstract Objective Multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) images are usually phenotyped using a manual thresholding process. The thresholding is prone to biases, especially when examining multiple images with high cellularity. Results Unsupervised cell-phenotyping methods including PhenoGraph, flowMeans, and SamSPECTRAL, primarily used in flow cytometry data, often perform poorly or need elaborate tuning to perform well in the context of mIHC and MIBI data. We show that, instead, semi-supervised cell clustering using Random Forests, linear and quadratic discriminant analysis are superior. We test the performance of the methods on two mIHC datasets from the University of Colorado School of Medicine and a publicly available MIBI dataset. Each dataset contains a bunch of highly complex images.
Evolution of activation functions for deep learning-based image classification

Activation functions (AFs) play a pivotal role in the performance of neural networks. The Rectified Linear Unit (ReLU) is currently the most commonly used AF. Several replacements to ReLU have been suggested but improvements have proven inconsistent. Some AFs exhibit better performance for specific tasks, but it is hard to know a priori how to select the appropriate one(s). Studying both standard fully connected neural networks (FCNs) and convolutional neural networks (CNNs), we propose a novel, three-population, co-evolutionary algorithm to evolve AFs, and compare it to four other methods, both evolutionary and non-evolutionary. Tested on four datasets---MNIST, FashionMNIST, KMNIST, and USPS---coevolution proves to be a performant algorithm for finding good AFs and AF architectures.
Quantum computation of nuclear observables involving linear combinations of unitary operators

We present the quantum computation of nuclear observables where the operators of interest are first decomposed in terms of the linear combination of unitaries. Then we utilize the Hadamard test and the linear combination of unitaries based methods to compute the expectation values. We apply these methods to calculate the electric quadrupole moment of the deuteron. The results are compared for the Jordan-Wigner transformation and Gray code encoding. We discuss the versatility of our approach that can be utilized in general to calculate several observables on a quantum computer.
QueryProp: Object Query Propagation for High-Performance Video Object Detection

Video object detection has been an important yet challenging topic in computer vision. Traditional methods mainly focus on designing the image-level or box-level feature propagation strategies to exploit temporal information. This paper argues that with a more effective and efficient feature propagation framework, video object detectors can gain improvement in terms of both accuracy and speed. For this purpose, this paper studies object-level feature propagation, and proposes an object query propagation (QueryProp) framework for high-performance video object detection. The proposed QueryProp contains two propagation strategies: 1) query propagation is performed from sparse key frames to dense non-key frames to reduce the redundant computation on non-key frames; 2) query propagation is performed from previous key frames to the current key frame to improve feature representation by temporal context modeling. To further facilitate query propagation, an adaptive propagation gate is designed to achieve flexible key frame selection. We conduct extensive experiments on the ImageNet VID dataset. QueryProp achieves comparable accuracy with state-of-the-art methods and strikes a decent accuracy/speed trade-off. Code is available at https://github.com/hf1995/QueryProp.
Almost complete solution for the NP-hard separability problem of Bell diagonal qutrits

With a probability of success of 95% we solve the separability problem for Bell diagonal qutrit states with positive partial transposition (PPT). The separability problem, i.e. distinguishing separable and entangled states, generally lacks an efficient solution due to the existence of bound entangled states. In contrast to free entangled states that can be used for entanglement distillation via local operations and classical communication, these states cannot be detected by the Peres-Horodecki criterion or PPT criterion. We analyze a large family of bipartite qutrit states that can be separable, free entangled or bound entangled. Leveraging a geometrical representation of these states in Euclidean space, novel methods are presented that allow the classification of separable and bound entangled Bell diagonal states in an efficient way. Moreover, the classification allows the precise determination of relative volumes of the classes of separable, free and bound entangled states. In detail, out of all Bell diagonal PPT states [Formula: see text] are determined to be separable while [Formula: see text] are bound entangled and only [Formula: see text] remain unclassified. Moreover, our applied criteria are compared for their effectiveness and relation as detectors of bound entanglement, which reveals that not a single criterion is capable to detect all bound entangled states.
Bodily Behaviors in Social Interaction

Body language is an eye-catching social signal and its automatic analysis can significantly advance artificial intelligence systems to understand and actively participate in social interactions. While computer vision has made impressive progress in low-level tasks like head and body pose estimation, the detection of more subtle behaviors such as gesturing, grooming, or fumbling is not well explored. In this paper we present BBSI, the first set of annotations of complex Bodily Behaviors embedded in continuous Social Interactions in a group setting. Based on previous work in psychology, we manually annotated 26 hours of spontaneous human behavior in the MPIIGroupInteraction dataset with 15 distinct body language classes. We present comprehensive descriptive statistics on the resulting dataset as well as results of annotation quality evaluations. For automatic detection of these behaviors, we adapt the Pyramid Dilated Attention Network (PDAN), a state-of-the-art approach for human action detection. We perform experiments using four variants of spatial-temporal features as input to PDAN: Two-Stream Inflated 3D CNN, Temporal Segment Networks, Temporal Shift Module and Swin Transformer. Results are promising and indicate a great room for improvement in this difficult task. Representing a key piece in the puzzle towards automatic understanding of social behavior, BBSI is fully available to the research community.
Learning on Graphs with Out-of-Distribution Nodes

Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs. While existing GNNs have shown great performance on various tasks related to graphs, little attention has been paid to the scenario where out-of-distribution (OOD) nodes exist in the graph during training and inference. Borrowing the concept from CV and NLP, we define OOD nodes as nodes with labels unseen from the training set. Since a lot of networks are automatically constructed by programs, real-world graphs are often noisy and may contain nodes from unknown distributions. In this work, we define the problem of graph learning with out-of-distribution nodes. Specifically, we aim to accomplish two tasks: 1) detect nodes which do not belong to the known distribution and 2) classify the remaining nodes to be one of the known classes. We demonstrate that the connection patterns in graphs are informative for outlier detection, and propose Out-of-Distribution Graph Attention Network (OODGAT), a novel GNN model which explicitly models the interaction between different kinds of nodes and separate inliers from outliers during feature propagation. Extensive experiments show that OODGAT outperforms existing outlier detection methods by a large margin, while being better or comparable in terms of in-distribution classification.
Towards the Classification of Self-Sovereign Identity Properties

Self-Sovereign Identity (SSI) is a novel and emerging, decentralized digital identity approach that enables entities to control and manage their digital identifiers and associated identity data fully while enhancing trust, privacy, security, and the many other properties identified and analyzed in this paper. The paper provides an overview and classification of the SSI properties, focusing on an in-depth analysis, furthermore, presenting a comprehensive collection of SSI properties that are important for the implementation of the SSI system. In addition, it explores the general SSI process flow, and highlights the steps in which individual properties are important. After the initial purification and classification phase, we then validated properties among experts in the field of Decentralized and Self-Sovereign Identity Management using an online questionnaire, which resulted in a final set of classified and verified SSI properties. The results can be used for further work on definition and standardization of the SSI field.
Designing felt experiences with movement-based, wearable musical instruments: From inclusive practices toward participatory design

Abstract Inclusive musical instruments benefit from incorporating wearable interfaces into digital musical instrument design, creating opportunities for bodily felt experiences and movement-based interactions. In this article, we discuss the evolution of our inclusive design approach behind the design and performance practices of three wearable musical instruments. By focusing on the embodied, somatic, and tacit dimensions of movement-based musical interaction, we evaluate these case studies, combining the third and first-person perspectives. The design and implementation of the wearable sensing, utilizing the additive manufacturing techniques, are discussed for each instrument and its performer in specific cases of musical expression. This article further discusses how our approach integrates music performance as a crucial step into design and evaluation, utilizing these performance practices and such collaborative settings for improved diversity and inclusion. Finally, we examine how our design approach evolves from user-centered design to more participatory practices, offering people with diverse abilities a shared music performance space.
Real-Time Model for Dynamic Hand Gestures Classification based on Inertial Sensor

Hand Gesture Recognition (HGR) is one of the vital topics of research in health and human-machine interaction in recent years. HGR is required for sensitive applications. Unlike static recognition, real-time gesture recognition needs the successive analysis of gestures. The design of effective gesture recognition and prediction systems using machine learning methods has attracted considerable attention. Most of the proposed solutions were not satisfied in terms of accuracy and the real-time testing process for dynamic gesture recognition. The aim of this work is to design a real-time Artificial Neural Network (ANN) model to classify dynamic gestures based on an Inertial sensor. This model is founded on a full control system with high resolution, real-time response, wireless, compact, and high sensitivity ensured with Wi-Fi communication with the ESP32 board. Based on the implemented smart system, one subject participates in collecting the dataset and performing four dynamic gestures. The developed ANN model successfully classified four dynamic gestures with an overall accuracy of 96%. Moreover, online testing and validation steps were added, and overall accuracy of 67% was achieved in 6 seconds, producing 213 real-time predictions. The overall performance of online testing has to be investigated by adding more subjects and sensors to increase the real-time prediction rate.