Reviewerly/exHarmony huggingface dataset:
Evaluating a Software Project Management Course Collaboration Framework at a Second Institution

Authors:
raw_author_name: Stefan Christov
raw_affiliation_string: Quinnipiac University
raw_author_name: Mark Hoffman
raw_affiliation_string: Quinnipiac University

Abstract: Abstract Previous work has described and evaluated a collaboration between a software project management (SPM) course and an introductory software development course. That collaboration has successfully supported the experiential learning of SPM skills as well as skills to effectively interact with a project manager. This paper further evaluates that collaboration framework by reporting its application at a second institution for two semesters. Overall, the learning outcomes and student experiences achieved at the second institution were similarly positive to those achieved at the first institution. The major components of the framework were fully implemented at the second institution with specific aspects customized to meet the institution's educational outcomes and setting. In that customization, three factors were identified for consideration when adapting the collaboration framework: the existence of a course structure to support such collaboration, the ratio between the students in the two courses, and the ability to schedule regular interactions between these students.
Fairness-Driven Private Collaborative Machine Learning

Authors:
raw_author_name: Tamir Tassa
raw_affiliation_string: The Open University, Ra'anana, Israel
raw_author_name: Erez Shmueli
raw_affiliation_string: Tel-Aviv University, Tel-Aviv, Israel

Abstract: The performance of machine learning algorithms can be considerably improved when trained over larger datasets. In many domains, such as medicine and finance, larger datasets can be obtained if several parties, each having access to limited amounts of data, collaborate and share their data. However, such data sharing introduces significant privacy challenges. While multiple recent studies have investigated methods for private collaborative machine learning, the fairness of such collaborative algorithms was overlooked. In this work we suggest a feasible privacy-preserving pre-process mechanism for enhancing fairness of collaborative machine learning algorithms. Our experimentation with the proposed method shows that it is able to enhance fairness considerably with only a minor compromise in accuracy.
Quantum algorithms to compute the neighbour list of N-body simulations

Authors:
raw_author_name: Elías Fernández-Combarro Álvarez
raw_author_name: Ignacio Fernández Rúa
raw_author_name: F. Orts
raw_author_name: G. Ortega
raw_author_name: A. M. Puertas

Abstract: Abstract One of the strategies to reduce the complexity of N -body simulations is the computation of the neighbour list. However, this list needs to be updated from time to time, with a high computational cost. This paper focuses on the use of quantum computing to accelerate such a computation. Our proposal is based on a well-known oracular quantum algorithm (Grover). We introduce an efficient quantum circuit to build the oracle that marks pairs of closed bodies, and we provide three novel algorithms to calculate the neighbour list under several hypotheses which take into account a-priori information of the system. We also describe a decision methodology for the actual use of the proposed quantum algorithms. The performance of the algorithms is tested with a statistical simulation of the oracle, where a fixed number of pairs of bodies are set as neighbours. A statistical analysis of the number of oracle queries is carried out. The results obtained with our simulations indicate that when the density of bodies is low, our algorithms clearly outperform the best classical algorithm in terms of oracle queries.
AdjointBackMapV2: Precise reconstruction of arbitrary CNN unit’s activation via adjoint operators

Authors:
raw_author_name: Qing Wan
raw_author_name: Yoonsuck Choe

Abstract: Adjoint operators have been found to be effective in the exploration of CNN’s inner workings (Wan and Choe, 2022). However, the previous no-bias assumption restricted its generalization. We overcome the restriction via embedding input images into an extended normed space that includes bias in all CNN layers as part of the extended space and propose an adjoint-operator-based algorithm that maps high-level weights back to the extended input space for reconstructing an effective hypersurface. Such hypersurface can be computed for an arbitrary unit in the CNN, and we prove that this reconstructed hypersurface, when multiplied by the original input (through an inner product), will precisely replicate the output value of each unit. We show experimental results based on the CIFAR-10 and CIFAR-100 data sets where the proposed approach achieves near 0 activation value reconstruction error.
Model Change Active Learning in Graph-Based Semi-supervised Learning

Authors:
raw_author_name: Andrea L. Bertozzi
raw_affiliation_string: Department of Mathematics, University of California, Los Angeles, 520 Portola Plaza, Los Angeles, CA, 90095, USA

Abstract: Abstract Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to identify which points to label to best improve performance while limiting the number of new labels. “Model Change” active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s). We pair this idea with graph-based semi-supervised learning (SSL) methods, that use the spectrum of the graph Laplacian matrix, which can be truncated to avoid prohibitively large computational and storage costs. We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution. We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.
Deep Unsupervised Active Learning on Learnable Graphs

Authors:
raw_author_name: Handong Ma
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Changsheng Li
raw_affiliation_string: School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
raw_author_name: Xinchu Shi
raw_affiliation_string: Meituan Group, Beijing, China
raw_author_name: Ye Yuan
raw_affiliation_string: School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
raw_author_name: Guoren Wang
raw_affiliation_string: School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

Abstract: Recently, deep learning has been successfully applied to unsupervised active learning. However, the current method attempts to learn a nonlinear transformation via an auto-encoder while ignoring the sample relation, leaving huge room to design more effective representation learning mechanisms for unsupervised active learning. In this brief, we propose a novel deep unsupervised active learning model via learnable graphs, named ALLGs. ALLG benefits from learning optimal graph structures to acquire better sample representation and select representative samples. To make the learned graph structure more stable and effective, we take into account k -nearest neighbor graph as a priori and learn a relation propagation graph structure. We also incorporate shortcut connections among different layers, which can alleviate the well-known over-smoothing problem to some extent. To the best of our knowledge, this is the first attempt to leverage graph structure learning for unsupervised active learning. Extensive experiments performed on six datasets demonstrate the efficacy of our method.
Bio inspired Boolean artificial bee colony based feature selection algorithm for sentiment classification

Authors:
raw_author_name: K. Anuradha
raw_affiliation_string: Centurion University of Technology & Management, Odisha, India; Dr. L. B. College of Engineering for Women, Visakhapatnam, India
raw_author_name: M. VamsiKrishna
raw_affiliation_string: Aditya Engineering College, Surampalem, India
raw_author_name: Banitamani Mallik
raw_affiliation_string: Centurion University of Technology & Management, Odisha, India

Abstract: Over the past few years, sentiment analysis has become most predominant aspect to be extracted in the modern digital era. Due to digitization all over the world, the number of existing digital documents increasing exponentially day by day. Analyzing such documents to extract the exact meaning itself becomes a tedious task. And analyzing those documents in sentimental point of view has increased the modality of difficultness to another level. In this research work, an effective bio-inspired filter-based feature selection algorithm is proposed to effectively handle the documents from sentiment point of view. A popular bio inspired algorithm called Artificial Bee Colony algorithm is modified using Boolean operators to effectively handle feature selection problem in classifying the documents from sentimental aspect. A total of 9 different documents instances are taken for evaluating the performance of proposed model. A total of 5 different filter-based measures are used to quantify the working model of the algorithm. The outputs are analyzed to state-of-the-art algorithms, and the comparative results demonstrate a substantial improvement over the existing models.
Compressing Features for Learning With Noisy Labels

Authors:
raw_author_name: Chen, Yingyi
raw_affiliation_string: Department of Electrical Engineering (ESAT), KU Leuven, Leuven, STADIUS, Belgium
raw_author_name: Hu, Shell Xu
raw_affiliation_string: Samsung AI Center, Cambridge, U.K.
raw_author_name: Shen, Xi
raw_affiliation_string: Tencent AI Lab, Shenzhen, China
raw_author_name: Suykens, Johan A. K.
raw_affiliation_string: Department of Electrical Engineering (ESAT), KU Leuven, Leuven, STADIUS, Belgium

Abstract: Supervised learning can be viewed as distilling relevant information from input data into feature representations. This process becomes difficult when supervision is noisy as the distilled information might not be relevant. In fact, recent research shows that networks can easily overfit all labels including those that are corrupted, and hence can hardly generalize to clean datasets. In this article, we focus on the problem of learning with noisy labels and introduce compression inductive bias to network architectures to alleviate this overfitting problem. More precisely, we revisit one classical regularization named Dropout and its variant Nested Dropout. Dropout can serve as a compression constraint for its feature dropping mechanism, while Nested Dropout further learns ordered feature representations with respect to feature importance. Moreover, the trained models with compression regularization are further combined with co-teaching for performance boost. Theoretically, we conduct bias variance decomposition of the objective function under compression regularization. We analyze it for both single model and co-teaching. This decomposition provides three insights: 1) it shows that overfitting is indeed an issue in learning with noisy labels; 2) through an information bottleneck formulation, it explains why the proposed feature compression helps in combating label noise; and 3) it gives explanations on the performance boost brought by incorporating compression regularization into co-teaching. Experiments show that our simple approach can have comparable or even better performance than the state-of-the-art methods on benchmarks with real-world label noise including Clothing1M and ANIMAL-10N. Our implementation is available at https://yingyichen-cyy.github.io/CompressFeatNoisyLabels/.
Product Recognition for Unmanned Vending Machines

Authors:
raw_author_name: Chengxu Liu
raw_affiliation_string: School of Information and Communication Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Zongyang Da
raw_affiliation_string: School of Information and Communication Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Yuanzhi Liang
raw_affiliation_string: School of Software Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Yao Xue
raw_affiliation_string: School of Information and Communication Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Guoshuai Zhao
raw_affiliation_string: School of Software Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Xueming Qian
raw_affiliation_string: Ministry of Education Key Laboratory of Intelligent Networks and Network Security, School of Information and Communication Engineering, Xi&#x2019;an, China

Abstract: Recently, the emerging concept of "unmanned retail" has drawn more and more attention, and the unmanned retail based on the intelligent unmanned vending machines (UVMs) scene has great market demand. However, existing product recognition methods for intelligent UVMs cannot adapt to large-scale categories and have insufficient accuracy. In this article, we propose a method for large-scale categories product recognition based on intelligent UVMs. It can be divided into two parts: 1) first, we explore the similarities and differences between products through manifold learning, and then we build a hierarchical multigranularity label to constrain the learning of representation; and 2) second, we propose a hierarchical label object detection network, which mainly includes coarse-to-fine refine module (C2FRM) and multiple granularity hierarchical loss (MGHL), which are used to assist in capturing multigranularity features. The highlights of our method are mine potential similarity between large-scale category products and optimization through hierarchical multigranularity labels. Besides, we collected a large-scale product recognition dataset GOODS-85 based on the actual UVMs scenario. Experimental results and analysis demonstrate the effectiveness of the proposed product recognition methods.
Rapid exchange cooling with trapped ions

Authors:
raw_author_name: Spencer D. Fallek
raw_affiliation_string: Georgia Tech Research Institute, Atlanta, 30332, GA, USA
raw_author_name: Holly N. Tinkey
raw_affiliation_string: Georgia Tech Research Institute, Atlanta, 30332, GA, USA
raw_author_name: Craig R. Clark
raw_affiliation_string: Georgia Tech Research Institute, Atlanta, 30332, GA, USA
raw_author_name: Kenton R. Brown
raw_affiliation_string: Georgia Tech Research Institute, Atlanta, 30332, GA, USA

Abstract: Abstract The trapped-ion quantum charge-coupled device (QCCD) architecture is a leading candidate for advanced quantum information processing. In current QCCD implementations, imperfect ion transport and anomalous heating can excite ion motion during a calculation. To counteract this, intermediate cooling is necessary to maintain high-fidelity gate performance. Cooling the computational ions sympathetically with ions of another species, a commonly employed strategy, creates a significant runtime bottleneck. Here, we demonstrate a different approach we call exchange cooling. Unlike sympathetic cooling, exchange cooling does not require trapping two different atomic species. The protocol introduces a bank of “coolant" ions which are repeatedly laser cooled. A computational ion can then be cooled by transporting a coolant ion into its proximity. We test this concept experimentally with two 40 Ca + ions, executing the necessary transport in 107 μ s, an order of magnitude faster than typical sympathetic cooling durations. We remove over 96%, and as many as 102(5) quanta, of axial motional energy from the computational ion. We verify that re-cooling the coolant ion does not decohere the computational ion. This approach validates the feasibility of a single-species QCCD processor, capable of fast quantum simulation and computation.
A New Approach to Capacity Scaling Augmented with Unreliable Machine Learning Predictions

Authors:
raw_author_name: Daan Rutten
raw_affiliation_string: H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
raw_author_name: Debankur Mukherjee
raw_affiliation_string: H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

Abstract: Modern data centers suffer from immense power consumption. As a result, data center operators have heavily invested in capacity-scaling solutions, which dynamically deactivate servers if the demand is low and activate them again when the workload increases. We analyze a continuous-time model for capacity scaling, where the goal is to minimize the weighted sum of flow time, switching cost, and power consumption in an online fashion. We propose a novel algorithm, called adaptive balanced capacity scaling (ABCS), that has access to black-box machine learning predictions. ABCS aims to adapt to the predictions and is also robust against unpredictable surges in the workload. In particular, we prove that ABCS is [Formula: see text] competitive if the predictions are accurate, and yet, it has a uniformly bounded competitive ratio even if the predictions are completely inaccurate. Finally, we investigate the performance of this algorithm on a real-world data set and carry out extensive numerical experiments, which positively support the theoretical results. Funding: This work was partially supported by the Division of Computing and Communication Foundations [Grant 2113027]. The authors also acknowledge financial support for this project from the Algorithm and Randomness Center–Transdisciplinary Research Institute for Advancing Data Science Fellowship at Georgia Tech.
Neural Generalization of Multiple Kernel Learning

Authors:
raw_author_name: Kamaledin Ghiasi-Shirazi
raw_affiliation_string: Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
raw_author_name: Reza Monsefi
raw_affiliation_string: Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
raw_author_name: Mohammadreza Qaraei
raw_affiliation_string: Department of Computer Science, Aalto University, Helsinki, Finland

Abstract: Abstract Multiple Kernel Learning (MKL) is a conventional way to learn the kernel function in kernel-based methods. MKL algorithms enhance the performance of kernel methods. However, these methods have a lower complexity compared to deep models and are inferior to them regarding recognition accuracy. Deep learning models can learn complex functions by applying nonlinear transformations to data through several layers. In this paper, we show that a typical MKL algorithm can be interpreted as a one-layer neural network with linear activation functions. By this interpretation, we propose a Neural Generalization of Multiple Kernel Learning (NGMKL), which extends the conventional MKL framework to a multi-layer neural network with nonlinear activation functions. Our experiments show that the proposed method, which has a higher complexity than traditional MKL methods, leads to higher recognition accuracy on several benchmarks.
Asymptotically optimal procedures for sequential joint detection and estimation

Authors:
raw_author_name: Michael Fauß
raw_affiliation_string: Department of Electrical Engineering, Princeton University, NJ 08544, Princeton, USA
raw_author_name: Abdelhak M. Zoubir
raw_affiliation_string: Technische Universität Darmstadt, 64283, Darmstadt, Germany

Abstract: We investigate the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution in a sequential setup. The aim is to jointly infer the true hypothesis and the true parameter while using on average as few samples as possible and keeping the detection and estimation errors below predefined levels. Based on mild assumptions on the underlying model, we propose an asymptotically optimal procedure, i.e., a procedure that becomes optimal when the tolerated detection and estimation error levels tend to zero. The implementation of the resulting asymptotically optimal stopping rule is computationally cheap and, hence, applicable for high-dimensional data. We further propose a projected quasi-Newton method to optimally choose the coefficients that parameterize the instantaneous cost function such that the constraints are fulfilled with equality. The proposed theory is validated by numerical examples.
The Needs and Challenges of Workforce Development in Quantum Computing

Authors:
raw_author_name: Mohammad N Amin
raw_affiliation_string: National University
raw_author_name: Ronald P. Uhlig
raw_affiliation_string: National University
raw_author_name: Pradip Peter Dey
raw_affiliation_string: National University
raw_author_name: Bhaskar Sinha
raw_affiliation_string: National University
raw_author_name: Shatha Jawad
raw_affiliation_string: National University

Abstract: Abstract Quantum computing is one of the most interesting and demanding topics in the field of computer science and engineering. The potential of quantum computing in many areas such as computer security, big data, finance, health science, and many other fields are now clear because of its incredible capacity and high processing speed. In a traditional computing system, all data and information are represented with 0 and 1 bits. But quantum computers operate in a different way, using Quantum Bits, or qubits. These qubits are created and manipulated using quantum principles of superposition and entanglement and can perform certain types of complex and large calculations on vast amounts of data very rapidly. A few examples of potential applications of quantum computing include much more secure quantum encryption, tremendous speed up of database searches using Grover’s database search algorithm, protein folding, and modeling of electrons in materials and molecules. Many scientists including Christopher Monroe at the University of Maryland and IonQ believe, within the next five years, a quantum computer will be capable of calculations that could never be run on traditional computing machines. In order to foster and expedite quantum computing research and development, the United States House of Representatives recently passed “The National Quantum Initiative Act (H.R. 6227)” which includes an emphasis on workforce development. In the USA, a number of startups and big tech companies are now involved in building quantum computing machines using different mechanisms of quantum physics, including ions trapped in electrical fields, superconducting circuits, quantum cloud service, spins of a single electron, etc.. The computer industries are working hard to use their existing semiconductor resources and technologies combined with quantum principles to build the desired computing systems. In parallel, there is an urgent need for a new generation of engineers, scientists, and programmers who can use quantum computers to solve real-world problems. This paper discusses some opportunities and challenges in quantum computing and preparing college students to fulfill the workforce demand. It further discusses how complex quantum computing courses can be integrated into the curricula of computer science, engineering, and related programs to address future workforce development issues. A multifaceted strategy for boosting enrollment, retention and successful graduation in quantum computing is proposed in order to address workforce development issues because we need to succeed in the tough competition we are facing from other countries.