Reviewerly/exHarmony huggingface dataset:
End-to-End Transformer Based Model for Image Captioning

CNN-LSTM based architectures have played an important role in image captioning, but limited by the training efficiency and expression ability, researchers began to explore the CNN-Transformer based models and achieved great success. Meanwhile, almost all recent works adopt Faster R-CNN as the backbone encoder to extract region-level features from given images. However, Faster R-CNN needs a pre-training on an additional dataset, which divides the image captioning task into two stages and limits its potential applications. In this paper, we build a pure Transformer-based model, which integrates image captioning into one stage and realizes end-to-end training. Firstly, we adopt SwinTransformer to replace Faster R-CNN as the backbone encoder to extract grid-level features from given images; Then, referring to Transformer, we build a refining encoder and a decoder. The refining encoder refines the grid features by capturing the intra-relationship between them, and the decoder decodes the refined features into captions word by word. Furthermore, in order to increase the interaction between multi-modal (vision and language) features to enhance the modeling capability, we calculate the mean pooling of grid features as the global feature, then introduce it into refining encoder to refine with grid features together, and add a pre-fusion process of refined global feature and generated words in decoder. To validate the effectiveness of our proposed model, we conduct experiments on MSCOCO dataset. The experimental results compared to existing published works demonstrate that our model achieves new state-of-the-art performances of 138.2% (single model) and 141.0% (ensemble of 4 models) CIDEr scores on 'Karpathy' offline test split and 136.0% (c5) and 138.3% (c40) CIDEr scores on the official online test server. Trained models and source code will be released.
Federated Learning in Medical Imaging: Part II: Methods, Challenges, and Considerations

Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client’s data. Federated learning is instrumental in medical imaging because of the privacy considerations of medical data. Setting up federated networks in hospitals comes with unique challenges, primarily because medical imaging data and federated learning algorithms each have their own set of distinct characteristics. This article introduces federated learning algorithms in medical imaging and discusses technical challenges and considerations of real-world implementation of them.
A Novel Blockchain-Based Healthcare System Design and Performance Benchmarking on a Multi-Hosted Testbed

As a result of the proliferation of digital and network technologies in all facets of modern society, including the healthcare systems, the widespread adoption of Electronic Healthcare Records (EHRs) has become the norm. At the same time, Blockchain has been widely accepted as a potent solution for addressing security issues in any untrusted, distributed, decentralized application and has thus seen a slew of works on Blockchain-enabled EHRs. However, most such prototypes ignore the performance aspects of proposed designs. In this paper, a prototype for a Blockchain-based EHR has been presented that employs smart contracts with Hyperledger Fabric 2.0, which also provides a unified performance analysis with Hyperledger Caliper 0.4.2. The additional contribution of this paper lies in the use of a multi-hosted testbed for the performance analysis in addition to far more realistic Gossip-based traffic scenario analysis with Tcpdump tools. Moreover, the prototype is tested for performance with superior transaction ordering schemes such as Kafka and RAFT, unlike other literature that mostly uses SOLO for the purpose, which accounts for superior fault tolerance. All of these additional unique features make the performance evaluation presented herein much more realistic and hence adds hugely to the credibility of the results obtained. The proposed framework within the multi-host instances continues to behave more successfully with high throughput, low latency, and low utilization of resources for opening, querying, and transferring transactions into a healthcare Blockchain network. The results obtained in various rounds of evaluation demonstrate the superiority of the proposed framework.
Experimental Quantum State Measurement with Classical Shadows

A crucial subroutine for various quantum computing and communication algorithms is to efficiently extract different classical properties of quantum states. In a notable recent theoretical work by Huang, Kueng, and Preskill [Nat. Phys. 16, 1050 (2020)NPAHAX1745-247310.1038/s41567-020-0932-7], a thrifty scheme showed how to project the quantum state into classical shadows and simultaneously predict M different functions of a state with only O(log_{2}M) measurements, independent of the system size and saturating the information-theoretical limit. Here, we experimentally explore the feasibility of the scheme in the realistic scenario with a finite number of measurements and noisy operations. We prepare a four-qubit GHZ state and show how to estimate expectation values of multiple observables and Hamiltonians. We compare the measurement strategies with uniform, biased, and derandomized classical shadows to conventional ones that sequentially measure each state function exploiting either importance sampling or observable grouping. We next demonstrate the estimation of nonlinear functions using classical shadows and analyze the entanglement of the prepared quantum state. Our experiment verifies the efficacy of exploiting (derandomized) classical shadows and sheds light on efficient quantum computing with noisy intermediate-scale quantum hardware.
Single electrons on solid neon as a solid-state qubit platform

Progress towards the realization of quantum computers requires persistent advances in their constituent building blocks—qubits. Novel qubit platforms that simultaneously embody long coherence, fast operation and large scalability offer compelling advantages in the construction of quantum computers and many other quantum information systems1–3. Electrons, ubiquitous elementary particles of non-zero charge, spin and mass, have commonly been perceived as paradigmatic local quantum information carriers. Despite superior controllability and configurability, their practical performance as qubits through either motional or spin states depends critically on their material environment3–5. Here we report our experimental realization of a qubit platform based on isolated single electrons trapped on an ultraclean solid neon surface in vacuum6–13. By integrating an electron trap in a circuit quantum electrodynamics architecture14–20, we achieve strong coupling between the motional states of a single electron and a single microwave photon in an on-chip superconducting resonator. Qubit gate operations and dispersive readout are implemented to measure the energy relaxation time T1 of 15 μs and phase coherence time T2 over 200 ns. These results indicate that the electron-on-solid-neon qubit already performs near the state of the art for a charge qubit21. A solid-state single-electron qubit platform is demonstrated based on trapping and manipulating isolated single electrons on an ultraclean solid neon surface in vacuum, which performs near the state of the art for a charge qubit.
Applying Machine Learning Techniques to Predict the Maintainability of Open-Source Software

Software maintainability is a vital quality aspect as per ISO standards. This has been a concern since decades and even today, it is of top priority. At present, majority of the software applications, particularly open source software are being developed using Object-Oriented methodologies. Researchers in the earlier past have used statistical techniques on metric data extracted from software to evaluate maintainability. Recently, machine learning models and algorithms are also being used in a majority of research works to predict maintainability. In this research, we performed an empirical case study on an open source software jfreechart by applying machine learning algorithms. The objective was to study the relationships between certain metrics and maintainability.
YOLOv5-Fog: A Multiobjective Visual Detection Algorithm for Fog Driving Scenes Based on Improved YOLOv5

With the rapid development of deep learning in recent years, the level of automatic driving perception has also increased substantially. However, automatic driving perception under adverse conditions, such as fog, remains a significant obstacle. The existing fog-oriented detection algorithms are unable to simultaneously address the detection accuracy and detection speed. Based on improved YOLOv5, this work provides a multi-object detection network for fog driving scenes. We construct a synthetic fog dataset by using the dataset of a virtual scene and the depth information of the image. Second, we present a detection network for driving in fog based on improved YOLOv5. The ResNeXt model, which has been modified by structural re-parameterization, serves as the model&#x2019;s backbone. We build a new feature enhancement module (FEM) in response to the lack of features in fog scene images, and use the attention mechanism to help the detection network pay more attention to the more useful features in the fog scenes. The test results show that the proposed fog multi-target detection network outperforms the original YOLOv5 in terms of detection accuracy and speed. The accuracy on the RTTS public dataset is 77.8%, and the detection speed is 31 fps, which is 14 frames faster as compared to the original YOLOv5.
Translating knowledge into innovation capability: An exploratory study investigating the perceptions on distance learning in higher education during the COVID-19 pandemic - the case of Mexico

The COVID-19 pandemic represented a significant challenge to higher education and forced academic institutions worldwide to abruptly shift to remote teaching. Higher education institutions are continuously confronted with the difficulties of creating satisfactory online learning experiences that adhere to the new norms. This paper presents a study concerning the perceptions of the quality and efficiency of the educational process in higher education concerning distance learning at the Instituto Politécnico Nacional of Mexico during the COVID-19 pandemic. The data analysis identified the level of satisfaction with distance learning education separately for students and professors and the lived experience for future education models. Data analysis consisted of a stepwise regression model to identify the most important variables related to the satisfaction aspect of e-learning for students and professors. The main findings are associated with the perception of one's own skills as the most crucial variable to define the aspects of e-learning satisfaction. Therefore, these skills involve the knowledge of e-learning platforms and the adoption rate of new technologies. The study found that the students emphasized that the variable "low sense of community" negatively impacts e-learning due to the loss of personal contact with the academic workspace. The key contribution of our research is a knowledge translation into a sustainable innovation capability model.
Designing virtual reality based 3D modeling and interaction technologies for museums

<h2>Abstract</h2> In China, museums are of great historical significance, which can greatly improve the country's cultural standards. With the advent of new media and economic times, people's behavior and way of thinking have changed, so they are less and less interested in traditional museum displays. How to create a museum moving image that meets the aesthetic and experiential requirements of the general audience has become critical. The purpose of this paper was to study the design of moving image displays using virtual reality (VR) in museums. This paper proposed a VR-based 3D modeling technology and human-computer interaction algorithm. Both of these technologies were an important part of VR technology. It can manage museums digitally and display objects clearly in two-dimensional and three-dimensional spaces. According to the experimental results of this paper, among the 80 participants, 40% were very satisfied with the exhibition hall experience of Chengde Mountain Resort Museum, and 35% were only moderately satisfied. It can be seen that most people find it very attractive to integrate VR technology into the showroom experience. Therefore, it is very important to integrate VR technology into the dynamic image display of the museum.
Reconstruction of Threaded Conversations in Online Discussion Forums

Online discussion boards, or Internet forums, are a signiﬁcant part of the Internet. People use Internet forums to post questions, provide advice and participate in discussions. These online conversations are represented as threads, and the conversation trees within these threads are important in understanding the behaviour of online users. Unfortunately, the reply structures of these threads are generally not publicly accessible or not maintained. Hence, in this paper, we introduce an efﬁcient and simple approach to reconstruct the reply structure in threaded conversations. We contrast its accuracy against three baseline algorithms, and show that our algorithm can accurately recreate the in and out degree distributions of forum reply graphs built from the reconstructed reply structures.
Learning to be a Bot: Reinforcement Learning in Shooter Games

This paper demonstrates the applicability of reinforcement learning for first person shooter bot artificial intelligence. Reinforcement learning is a machine learning technique where an agent learns a problem through interaction with the environment. The Sarsa(&amp;lambda;) algorithm will be applied to a first person shooter bot controller to learn the tasks of (1) navigation and item collection, and (2) combat. The results will show the validity and diversity of reinforcement learning in a first person shooter environment.
Collecting and Analyzing Japanese Splogs based on Characteristics of Keywords

This paper focuses on analyzing (Japanese) splogs based on various characteristics of keywords contained in them. We estimate the behavior of spammers when creating splogs from other sources by analyzing the characteristics of keywords contained in splogs. Since splogs often cause noises in word occurrence statistics in the blogosphere, we assume that we can efficiently collect splogs by sampling blog homepages containing keywords of a certain type on the date with its most frequent occurrence. We manually examine various features of collected blog homepages regarding whether their text content is excerpt from other sources or not, as well as whether they display affiliate advertisement or out-going links to affiliated sites. Among various informative results, it is important to note that more than half of the collected splogs are created by a very small number of spammers.
Supervised Probabilistic Robust Embedding with Sparse Noise

Many noise models do not faithfully reflect the noise processes introduced during data collection in many real-world applications. In particular, we argue that a type of noise referred to as sparse noise is quite commonly found in many applications and many existing works have been proposed to model such sparse noise. However, all the existing works only focus on unsupervised learning without considering the supervised information, i.e., label information. In this paper, we consider how to model and handle sparse noise in the context of embedding high-dimensional data under a probabilistic formulation for supervised learning. We propose a supervised probabilistic robust embedding (SPRE) model in which data are corrupted either by sparse noise or by a combination of Gaussian and sparse noises. By using the Laplace distribution as a prior to model sparse noise, we devise a two-fold variational EM learning algorithm in which the update of model parameters has analytical solution. We report some classification experiments to compare SPRE with several related models.
Is Pair Programming An Effective Way To Learn Computer Architecture?

Pair programming is a concept where two programmers work side by side at the same computer, writing code jointly.One of them, called the driver, is in control of the keyboard and mouse.The other, called the navigator, observes what the driver is doing and offers advice.It is the driver's job to write the code.The navigator has a chance to observe the larger picture, evaluating the driver's code for correctness of design and implementation.Studies have shown that pair programming is very effective.Two programmers can finish a task in little over half the elapsed time that a single programmer takes.And the quality of the code-measured in terms of absence of defects-is much higher.In the past few years, pair programming has made inroads into industry and into programming courses.However, it has not typically been used in courses that teach subjects other than programming or software engineering, nor has it been used in the analysis of experimental results.This paper reports on an experiment in a combined senior/masters level computer architecture class, using Hennessy & Patterson's Computer Architecture: A Quantitative Approach as a text.Students were required to implement three projects simulating various aspects of a microarchitecture (cache, branch predictor, dynamic instruction scheduler).Then they engaged in an experimental analysis to find the best configuration in a design space.They were encouraged to pair-program, and data were gathered on their experience.
Abstracting Abstraction in Search II: Complexity Analysis

Modelling abstraction as a function from the original state space to an abstract state space is a common approach in combinatorial search. Sometimes this is too restricted, though, and we have previously proposed a framework using a more flexible concept of transformations between labelled graphs. We also proposed a number of properties to describe and classify such transformations. This framework enabled the modelling of a number of different abstraction methods in a way that facilitated comparative analyses. It is of particular interest that these properties can be used to capture the concept of refinement without backtracking between levels; how to do this has been an open question for at least twenty years. In this paper, we continue our previous research by analysing the complexity of testing the various transformation properties for both explicit and implicit graph~representations.
Towards Rational Deployment of Multiple Heuristics in A* (Extended Abstract)

In this paper we discuss and experiment with Lazy A*, a variant of A* where heuristics are evaluated lazily and with Rational Lazy A*, which decides whether to compute the more expensive heuristics at all, based on a myopic value of information estimate. Full version appears in IJCAI-2013.
Exploring Abductive Event Binding for Opportunistic Storytelling

We present a prototype game system using opportunistic storytelling, a particular commitment in the space of interactive narrative. It uses abductive event binding to deliver authored stories about the actions that the player is already taking to achieve game play goals. We show results of a simulation experiment that characterizes efficiency in delivering story event content, in terms of parameterized player motivation to follow storylines and the constraints placed on the events. Results show the value of two reasoning features in the system, incentive and look-ahead.
Applying Learning by Observation and Case-Based Reasoning to Improve Commercial RTS Game AI

This document summarises my research in the area of Real-Time Strategy (RTS) video game Artificial Intelligence (AI). The main objective of this research is to increase the quality of AI used in commercial RTS games, which has seen little improvement over the past decade. This objective will be addressed by investigating the use of a learning by observation, case-based reasoning agent, which can be applied to new RTS games with minimal development effort. To be successful, this agent must compare favourably with standard commercial RTS AI techniques: it must be easier to apply, have reasonable resource requirements, and produce a better player. Currently, a prototype implementation has been produced for the game StarCraft, and it has demonstrated the need for processing large sets of input data into a more concise form for use at run-time.
Anytime Tree-Restoring Weighted A* Graph Search

Incremental graph search methods reuse information from previous searches in order to minimize redundant computation and to find solutions to series of similar search queries much faster than it is possible by solving each query from scratch. In this work, we present a simple, but very effective, technique for performing incremental weighted A* graph search in an anytime fashion. On the theoretical side, we show that our anytime incremental algorithm preserves the strong theoretical guarantees provided by the weighted A* algorithm, such as completeness and bounds on solution cost sub-optimality. We also show that our algorithm can handle a variety of changes to the underlying graph, such as both increasing and decreasing edge costs, and changes in the heuristic. On the experimental side, we demonstrate the effectiveness of our algorithm in the context of (x, y, z, yaw) navigation planning for an unmanned aerial vehicle and compare our algorithm to popular incremental and anytime graph search algorithms.
Interactive Modules For Electric Circuits

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1432 Interactive Modules for Electric Circuits Mahmood Nahvi, Professor California Polytechnic State University Abstract Digital simulators are familiar tools in the undergraduate teaching and learning environments. In both inanimate and live forms, simulators are used as tools for design, concept development, demonstrations, and supplements to laboratory experiments. We have developed computer-based modules for the circuits, signals and systems courses that provide maximum man-machine interaction with minimum effort. For maximum learning effectiveness, the modules i) are specialized, ii) have reaction speed close to the real-time operation of the physical system and iii) use open-loop movement of finger and hand as opposed to closed sensory-motor loops for variable modification. This paper presents circuit modules covering resistive circuits, network theorems, amplifiers, RC-RL-RLC circuits, resonance, and energy and power. Each module focuses on a specific topic. It gives the user the choice of circuit and signals within a family and allows him/her to rapidly modify element values and initial states. Circuit variables such as voltages, currents, and power are displayed on the monitor. The modules, as an integral part of the courseware, can play an important role in helping students develop concepts, practice, analyze, simulate and design. 1. Introduction Among tools and approaches used in undergraduate engineering education are a) paper/pencil exercises b) computational engines (e.g., mathematical tables, abacus, nomograms, slide rules, calculators, programmable computers), c) simulations, and d) laboratories. Traditionally, paper/pencil (a) and laboratories (d) carry the banner, Fig. 1-a. Contemporary approaches incorporate computational engines (b) and simulators (c) within the environment, Fig. 1-b1, 2. Paper/ Lab Pencil Paper/ Lab Pencil Computer Simulation Analysis a) b) Figure 1 - Two Approaches to Undergraduate Engineering Education