Reviewerly/exHarmony huggingface dataset:
ELight: Enabling Efficient Photonic In-Memory Neurocomputing with Life Enhancement

With the recent advances in optical phase change material (PCM), photonic in-memory neurocomputing has demonstrated its superiority in optical neural network (ONN) designs with near-zero static power consumption, time-of-light latency, and compact footprint. However, photonic tensor cores require massive hardware reuse to implement large matrix multiplication due to the limited single-core scale. The resultant large number of PCM writes leads to serious dynamic power and overwhelms the fragile PCM with limited write endurance. In this work, we propose a synergistic optimization framework, ELight, to minimize the overall write efforts for efficient and reliable optical in-memory neurocomputing. We first propose write-aware training to encourage the similarity among weight blocks, and combine it with a post-training optimization method to reduce programming efforts by eliminating redundant writes. Experiments show that ELight can achieve over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$20\times$</tex> reduction in the total number of writes and dynamic power with comparable accuracy. With our ELight, photonic in-memory neurocomputing will step forward towards viable applications in machine learning with preserved accuracy, order-of-magnitude longer lifetime, and lower programming energy.
Nonuniform piecewise membership function approximation methods based robust tracking control design of T‐S fuzzy systems

Abstract For the stability analysis issue of a T‐S fuzzy system, improved piecewise membership functions dependent approach is proposed to reduce conservatism. Based on a least‐square method, the nonlinear membership functions are approximated by a series of decoupled polynomial functions. Then the piecewise vertices are chosen by a newly proposed derivative‐integral nonuniform division approach. For the robust tracking control task, a series of convex robust stable conditions are obtained. Finally, a mass‐spring‐damper (MSD) simulation example is adopted to illustrate the effectiveness of the designed method.
Image transformers for classifying acute lymphoblastic leukemia

Cancer is the leading cause of death by disease in American children. Each year, nearly 16,000 children in the United States and over 300,000 children globally are diagnosed with cancer. Leukemia is a form of blood cancer that originates in the bone marrow and accounts for one-third of pediatric cancers. This disease occurs when the bone marrow contains 20% or more immature white blood cell blasts. Acute lymphoblastic leukemia is the most prevalent leukemia type found in children, with half of all annual cases in the U.S. diagnosed for subjects under 20 years of age. To diagnose acute lymphoblastic leukemia, pathologists often conduct a morphological bone marrow assessment. This assessment determines whether the immature white blood cell blasts in bone marrow display the correct morphological characteristics, such as size and appearance of nuclei. Pathologists also use immunophenotyping via multi-channel flow cytometry to test whether certain antigens are present on the surface of blast cells; the antigens are used to identify the cell lineage of acute lymphoblastic leukemia. These manual processes require well-trained personnel and medical professionals, thus being costly in time and expenses. Computerized decision support via machine learning can accelerate the diagnosis process and reduce the cost. Training a reliable classification model to distinguish between mature and immature white blood cells is essential to the decision support system. Here, we adopted the Vision Transformer model to classify white blood cells. The Vision Transformer achieved superb classification performance compared to state-of-the-art convolutional neural networks while requiring less computational resources for training. Additionally, the latent self-attention architecture provided attention maps for a given image, providing clues as to which portion(s) of the image were significant in decision-making. We applied the Vision Transformer model and a convolutional neural network model to an acute lymphoblastic leukemia classification dataset of 12,528 samples and achieved accuracies of 88.4% and 86.2%.
The Effect of Serious Mobile Games on Student English Vocabulary Acquisition and Attitude Toward English

The main aim of this study was to explore the effect of the serious mobile game Jumo the Jumper on the vocabulary acquisition of primary school English as a Foreign Language (EFL) students and their attitudes toward an EFL course. The main research design of the study was a quasi-experimental design with nonequivalent control groups that included pre-test and post-test control groups. Twenty students in the experimental group played the serious mobile game aimed at teaching English vocabulary about the “animals” unit for four weeks, while 20 students in the control group received traditional, curriculum-based tuition. Based on the analysis of the independent sample t-test, the use of the serious mobile game Jumo the Jumper in primary school classrooms had a significant effect on the vocabulary acquisition of the students compared to traditional instruction. On the other hand, the serious mobile game did not have a significant effect on the students’ attitudes toward the EFL course.
Noise fingerprints in quantum computers: Machine learning software tools

In this paper we present the high-level functionalities of a quantum-classical machine learning software, whose purpose is to learn the main features (the fingerprint) of quantum noise sources affecting a quantum device, as a quantum computer. Specifically, the software architecture is designed to classify successfully (more than 99% of accuracy) the noise fingerprints in different quantum devices with similar technical specifications, or distinct time-dependences of a noise fingerprint in single quantum machines.
Interpretable Semantic Photo Geolocation

Planet-scale photo geolocalization is the complex task of estimating the location depicted in an image solely based on its visual content. Due to the success of convolutional neural networks (CNNs), current approaches achieve superhuman performance. However, previous work has exclusively focused on optimizing geolocalization accuracy. Due to the black-box property of deep learning systems, their predictions are difficult to validate for humans. State-of-the-art methods treat the task as a classification problem, where the choice of the classes, that is the partitioning of the world map, is crucial for the performance. In this paper, we present two contributions to improve the interpretability of a geolocalization model: (1) We propose a novel semantic partitioning method which intuitively leads to an improved understanding of the predictions, while achieving state-of-the-art results for geolocational accuracy on benchmark test sets; (2) We introduce a metric to assess the importance of semantic visual concepts for a certain prediction to provide additional interpretable information, which allows for a large-scale analysis of already trained models. Source code and dataset are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Attention Mechanism Based on Improved Spatial-Temporal Convolutional Neural Networks for Traffic Police Gesture Recognition

Human action recognition has attracted extensive research efforts in recent years, in which traffic police gesture recognition is important for self-driving vehicles. One of the crucial challenges in this task is how to find a representation method based on spatial-temporal features. However, existing methods performed poorly in spatial and temporal information fusion, and how to extract features of traffic police gestures has not been well researched. This paper proposes an attention mechanism based on the improved spatial-temporal convolutional neural network (AMSTCNN) for traffic police gesture recognition. This method focuses on the action part of traffic police and uses the correlation between spatial and temporal features to recognize traffic police gestures, so as to ensure that traffic police gesture information is not lost. Specifically, AMSTCNN integrates spatial and temporal information, uses weight matching to pay more attention to the region where human action occurs, and extracts region proposals of the image. Finally, we use Softmax to classify actions after spatial-temporal feature fusion. AMSTCNN can strongly make use of the spatial-temporal information of videos and select effective features to reduce computation. Experiments on AVA and the Chinese traffic police gesture datasets show that our method is superior to several state-of-the-art methods.
Generating Relevant and Informative Questions for Open-Domain Conversations

Recent research has highlighted the importance of mixed-initiative interactions in conversational search. To enable mixed-initiative interactions, information retrieval systems should be able to ask diverse questions, such as information-seeking, clarification, and open-ended ones. question generation (QG) of open-domain conversational systems aims at enhancing the interactiveness and persistence of human-machine interactions. The task is challenging because of the sparsity of question generation (QG)-specific data in conversations. Current work is limited to single-turn interaction scenarios. We propose a context-enhanced neural question generation (CNQG) model that leverages the conversational context to predict question content and pattern, then perform question decoding. A hierarchical encoder framework is employed to obtain the discourse-level context representation. Based on this, we propose Review and Transit mechanisms to respectively select contextual keywords and predict new topic words to further construct the question content. Conversational context and the predicted question content are used to produce the question pattern, which in turn guides the question decoding process implemented by a recurrent decoder with a joint attention mechanism. To fully utilize the limited QG-specific data to train our question generator, we perform multi-task learning with three auxiliary training objectives, i.e., question pattern prediction, Review , and Transit mechanisms. The required additional labeled data is obtained in a self-supervised way. We also design a weight decaying strategy to adjust the influences of various auxiliary learning tasks. To the best of our acknowledge, we are the first to extend the application of QG to the multi-turn open-domain conversational scenario. Extensive experimental results demonstrate the effectiveness of our proposal and its main components on generating relevant and informative questions, with robust performance for contexts with various lengths.
FineCodeAnalyzer: Multi-Perspective Source Code Analysis Support for Software Developer Through Fine-Granular Level Interactive Code Visualization

Source code analysis is one of the important activities during the software maintenance phase that focuses on performing the tasks including bug localization, feature location, bug/feature assignment, and so on.However, handling the aforementioned tasks on a manual basis (i.e.finding the location of buggy code from a large application) is an expensive, time-consuming, tedious, and challenging task.Thus, the developers seek automated support in performing the software maintenance tasks through automated tools and techniques.However, the majority of the reported techniques are limited to textual analysis where the real developers' concerns are not properly considered.Moreover, existing solutions seem less useful for the developers.This work proposes a tool (called as FineCodeAnalyzer) that supports an interactive source code analysis grounded on structural and historical relations at fine granular-level between the source code elements.To evaluate the performance of FineCodeAnalyzer, we consider 74 developers that assess three main facets: (i) usefulness, (ii) cognitive-load, and (iii) time efficiency.For usefulness concern, the results show that FineCodeAnalyzer outperforms the developers' self-adopted strategies in locating the code elements in terms of Precision, Recall, and F1-Measure of accurately locating the code elements.Specifically, FineCodeAnalyzer outperforms the developers' strategies up to 47%, 76%, and 61% in terms of Precision, Recall, and F1-measure, respectively.Additionally, FineCodeAnalyzer takes 5% less time than developers' strategies in terms of minutes of time.For cognitive-load, the developers found FineCodeAnalyzer to be 72% less complicated than manual strategies, in terms of the NASA Tool Load Index metric.Finally, the results indicate that FineCodeAnalyzer allows effectively locating the code elements than the developer's adopted strategies.
PressPIN: Enabling Secure PIN Authentication on Mobile Devices via Structure-Borne Sounds

PIN authentication is widely used on mobile devices due to its usability and simplicity. However, it is known to be susceptible to shoulder surfing attacks, where an adversary spies the user’s PIN by direct human observation or camera-based recording. This paper proposes PressPIN, a novel enhanced PIN authenticator on mobile devices by sensing pressures from the user’s finger. Since pressure-sensitive touch screens are unavailable on most phones, we leverage the structure-borne propagation of sounds to estimate the pressure on the screen. When the user inputs the PINs, the pressure is extracted from each number to form the <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -bit pressure code, where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> corresponds to the length of the PIN sequence. The pressure code is difficult to be inferred by snooping or videotaping, and increases the entropy of passwords. In this way, PressPIN provides a low-cost, user-friendly, and more secure solution resistant to shoulder surfing attacks. Our extensive experiments with 30 participants and three types of smartphones demonstrate that PressPIN can authenticate legitimate users with high accuracy (e.g., as high as 96.7% within two trials), and is robust to various types of attacks (e.g., only 2.5% attack success rate even when the adversary can observe the legitimate user’s PIN sequence and finger pressing clearly). Additionally, PressPIN requires no additional hardware (e.g., the pressure sensor) and can be readily integrated into existing authentication systems of mobile devices.
A Low Power Hard Decision Decoder for BCH Codes

In decoding of Bose-Chaudhuri-Hocquenghem codes, Peterson’s algorithm is more efficient for codes with single, double and triple error correcting capabilities. Numerous methods were proposed to reduce the hardware complexity caused due to the inversion operation involved in the Peterson’s algorithm. In this paper, a low complex hardware BCH decoder using inversion-less Peterson’s algorithm presented in literature is designed and its performance is verified with the Matlab results. An attempt is made to design a low power version of this low complex BCH decoder by replacing the parallel Chien search architecture in the decoder with the two-step p-parallel Chien search approach that is originally used in literature with the Berlekamp-Massey Algorithm. For use with the inversion-less Peterson’s algorithm the parallel Chien search architecture has been modified and the resultant decoder has shown a power reduction of up to 42 percentage with a moderate increase in area by 10 percentage.
EHR-Sec: A Blockchain Based Security System for Electronic Health

The technological advancements in healthcare management have resulted in increased digitization of health records, however, issues relating to lack of interoperability, security and proper infrastructure seem to cause a hinderance in complete and seamless delivery of healthcare services. Electronic Health Record (EHR) management systems developed on principles of secure data sharing and storage can help healthcare professionals for better collaboration and in making sound decisions regarding patient diagnosis and treatment. We propose an EHR management system based on objectives such as secure storage, increased availability, effective sharing and trust between stakeholders in the healthcare ecosystem like patients, doctors and government agencies. The proposed EHR system works in a permissioned blockchain framework based on the Hyperledger Fabric platform. Patients and medical professionals can interact with the EHR system using a web-based interface for managing EHRs.
Recent Advances in Representation Learning for Electronic Health Records: A Systematic Review

Abstract Representation Learning (RL) aims to convert data into low-dimensional and dense real-valued vectors, so as to realize reasoning in vector space. RL is one of the important research contents in the analysis of health data. This paper systematically reviews the latest research on Electronic Health Records (EHR) RL. We searched the Web of Science, Google Scholar, and Association for Computing Machinery Digital Library for papers involving EHR RL. On the basis of literature review, we propose a new taxonomy to categorize the state-of-the-art EHR RL methods into three categories: statistics learning-based RL methods, knowledge RL methods and graph RL methods. We analyze and summarize their characteristics according to the input data form and underlying learning mechanisms. In addition, we provide evaluation strategies to verify the quality of EHR representations from both intrinsic and extrinsic perspectives. Finally, we put forward three promising research directions to promote future research. Overall, this survey aims to provide a profound overview of state-of-the-art developments in the field of EHR RL and to help researchers find the most appropriate methods.
Fish Control Process and Traceability for Value Creation Using Blockchain Technology

Currently, the globe and, more specifically, corporate processes are evolving towards digitization and the elimination of time-consuming procedures. As origin knowledge and safety assurance of products became more important in establishing end-consumer confidence, traceability solutions are acute. A Blockchain (BC) is one of the most exciting disruptive technologies emerging today. The purpose of this study is to analyze the possibility of using BC to allow end-to-end supply chain (SC) management traceability and to provide a proposition of a BC application for fish tracking and control in SC contexts. The proposition demonstrates that BC provides the use of a single information system by several players across SC, resulting in more secure and efficient data sharing. A future understanding of the significance of BC technology usage as a traceability supplier and a detailed testing of a proposed architecture is identified as a diretion for future study. We, therefore, study current applications in supply chain management, with a big emphasis on the fish control process.
Perceptual redundancy model for compression of screen content videos

Screen content video (SCV) consists primarily of text areas, computer graphics and other computer-generated content and possesses unique perceptual characteristics. To compress SCVs more effectively with less reduction in subjective quality, perceptual characteristics of SCVs are analyzed and a perceptual redundancy (PR) model for SCV compression is proposed, including spatial PR (SPR), temporal PR (TPR) and foveated PR (FPR) model. In SPR modeling, the SCV is divided into sharp edge (SE) areas and non-SE areas, then SPR is estimated separately. In TPR modeling, both inter-frame luminance adaptation effect and motion masking effect are taken into account. In FPR modeling, each frame of SCV is classified into abrupt frames, relative motion frames or static frames. Then fixation points of different kinds of frames are predicted using different methods, and FPR is modeled considering foveated masking effect and visual attention. Finally, the perceptual redundancy of SCV is estimated based on the product of SPR, TPR and FPR. It is experimentally demonstrated that compared to the state-of-the-art models, the authors' model could obtain more accurate estimates of PR. Moreover, the model is incorporated into SCV compression with an adaptive perceptual quantizer. An average of 7.42% bits could be saved with less decline in subjective quality.
Improving Fractal Pre-training

The deep neural networks used in modern computer vision systems require enormous image datasets to train them. These carefully-curated datasets typically have a million or more images, across a thousand or more distinct categories. The process of creating and curating such a dataset is a monumental undertaking, demanding extensive effort and labelling expense and necessitating careful navigation of technical and social issues such as label accuracy, copyright ownership, and content bias.What if we had a way to harness the power of large image datasets but with few or none of the major issues and concerns currently faced? This paper extends the recent work of Kataoka et al. [15], proposing an improved pre-training dataset based on dynamically-generated fractal images. Challenging issues with large-scale image datasets become points of elegance for fractal pre-training: perfect label accuracy at zero cost; no need to store/transmit large image archives; no privacy/demographic bias/concerns of inappropriate content, as no humans are pictured; limitless supply and diversity of images; and the images are free/open-source. Perhaps surprisingly, avoiding these difficulties imposes only a small penalty in performance. Leveraging a newly-proposed pre-training task—multi-instance prediction—our experiments demonstrate that fine-tuning a network pre-trained using fractals attains 92.7-98.1% of the accuracy of an ImageNet pre-trained network. Our code is publicly available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Neural Network Pruning and Fast Training for DRL-based UAV Trajectory Planning

Deep reinforcement learning (DRL) has been applied for optimal control of autonomous UAV trajectory generation. The energy and payload capacity of small UAVs impose constraints on the complexity and size of the neural network. While Model compression has the potential to optimize the trained neural network model for efficient deployment on em-bedded platforms, pruning a neural network for DRL is more difficult due to the slow convergence in the training before and after pruning. In this work, we focus on improving the speed of DRL training and pruning. New reward function and action exploration are first introduced, resulting in convergence speedup by 34.14%. The framework that integrates pruning and DRL training is then presented with an emphasize on how to reduce the training cost. The pruning does not only improve computational performance of inference, but also reduces the training effort with-out compromising the quality of the trajectory. Finally, experimental results are presented. We show that the integrated training and pruning framework reduces 67.16% of the weight and improves trajectory success rate by 1.7%. It achieves a 4.43x reduction of the floating-point operations for the inference, resulting a measured 41.85% run time reduction.