Reviewerly/exHarmony huggingface dataset:
Melon

On-device learning is a promising technique for emerging privacy-preserving machine learning paradigms. However, through quantitative experiments, we find that commodity mobile devices cannot well support state-of-the-art DNN training with a large enough batch size, due to the limited local memory capacity. To fill the gap, we propose Melon, a memory-friendly on-device learning framework that enables the training tasks with large batch size beyond the physical memory capacity. Melon judiciously retrofits existing memory saving techniques to fit into resource-constrained mobile devices, i.e., recomputation and micro-batch. Melon further incorporates novel techniques to deal with the high memory fragmentation and memory adaptation. We implement and evaluate Melon with various typical DNN models on commodity mobile devices. The results show that Melon can achieve up to 4.33× larger batch size under the same memory budget. Given the same batch size, Melon achieves 1.89× on average (up to 4.01×) higher training throughput, and saves up to 49.43% energy compared to competitive alternatives. Furthermore, Melon reduces 78.59% computation on average in terms of memory budget adaptation.
Entanglement Detection with Imprecise Measurements

We investigate entanglement detection when the local measurements only nearly correspond to those intended. This corresponds to a scenario in which measurement devices are not perfectly controlled, but nevertheless operate with bounded inaccuracy. We formalise this through an operational notion of inaccuracy that can be estimated directly in the lab. To demonstrate the relevance of this approach, we show that small magnitudes of inaccuracy can significantly compromise several renowned entanglement witnesses. For two arbitrary-dimensional systems, we show how to compute tight corrections to a family of standard entanglement witnesses due to any given level of measurement inaccuracy. We also develop semidefinite programming methods to bound correlations in these scenarios.
A CNN-Based Framework for Predicting Public Emotion and Multi-Level Behaviors Based on Network Public Opinion

Analysis of network public opinion can help to effectively predict the public emotion and the multi-level government behaviors. Due to the massive and multidimensional characteristics of network public opinion data, the in-depth value mining of public opinion is one of the research bottlenecks. Based on Term Frequency-Inverse Document Frequency (TF-IDF) and deep learning technologies, this paper proposes an advanced TF-IDF mechanism, namely TF-IDF-COR, to extract text feature representations of public opinions and develops a CNN-based prediction model to predict the tendency of publics' emotion and mental health. The proposed method can accurately judge the emotional tendency of network users. The main contribution of this paper is as follows: (1) based on the advantages of TF-IDF mechanism, we propose a TF-IDF-COR mechanism, which integrates the correlation coefficient of word embeddings to TF-IDF. (2) To make the extracted feature semantic information more comprehensive, CNN and TF-IDF-COR are combined to form an effective COR-CNN model for emotion and mental health prediction. Finally, experiments on Sina-Weibo and Twitter opinion data sets show that the improved TF-IDF-COR and the COR-CNN model have better classification performance than traditional classification models. In the experiment, we compare the proposed COR-CNN with support vector machine, k-nearest neighbors, and convolutional neural network in terms of accuracy and F1 score. Experiment results show that COR-CNN performs much better than the three baseline models.
Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem

Ant Colony Optimization (ACO) is a practical and well-studied bio-inspired algorithm to generate feasible solutions for combinatorial optimization problems such as the Traveling Salesman Problem (TSP). ACO is inspired by the foraging behavior of ants, where an ant selects the next city to visit according to the pheromone on the trail and the visibility heuristic (inverse of distance). ACO assigns higher heuristic desirability to the nearest city without considering the issue of returning to the initial city or starting point once all the cities are visited. This study proposes an improved ACO-based method, called ACO with Adaptive Visibility (ACOAV), which intelligently adopts a generalized formula of the visibility heuristic associated with the final destination city. ACOAV uses a new distance metric that includes proximity and eventual destination to select the next city. Including the destination in the metric reduces the tour cost because such adaptation helps to avoid using longer links while returning to the starting city. In addition, partial updates of individual solutions and 3-Opt local search operations are incorporated in the proposed ACOAV. ACOAV is evaluated on a suite of 35 benchmark TSP instances and rigorously compared with ACO. ACOAV generates better solutions for TSPs than ACO, while taking less computational time; such twofold achievements indicate the proficiency of the individual adoption techniques in ACOAV, especially in AV and partial solution update. The performance of ACOAV is also compared with the other ten state-of-the-art bio-inspired methods, including several ACO-based methods. From these evaluations, ACOAV is found as the best one for 29 TSP instances out of 35 instances; among those, optimal solutions have been achieved in 22 instances. Moreover, statistical tests comparing the performance revealed the significance of the proposed ACOAV over the considered bio-inspired methods.
ENOS: Energy-Aware Network Operator Search in Deep Neural Networks

This work proposes a novel <i>Energy-aware Network Operator Search</i> (<i>ENOS</i>) approach to address the energy-accuracy trade-offs of a deep neural network (DNN) accelerator. In recent years, novel hardware-friendly inference operators such as binary-weight, multiplication-free, and deep-shift have been proposed to improve the computational efficiency of a DNN accelerator. However, simplifying DNN operators invariably comes at lower accuracy, especially on complex processing tasks. While prior works generally implement the same inference operator throughout the neural architecture, the proposed ENOS framework allows an <i>optimal layer-wise integration of inference operators with optimal precision</i> to maintain high prediction accuracy and high energy efficiency. The search in ENOS is formulated as a continuous optimization problem, solvable using gradient descent methods, thereby minimally increasing the training cost when learning <i>both</i> layer-wise inference operators and weights. Utilizing ENOS, we discuss multiply-accumulate (MAC) cores for digital spatial architectures that can be reconfigured to different operators and varying computing precision. ENOS training methods with single and bi-level optimization objectives are discussed and compared. We also discuss a sequential operator assignment strategy in ENOS that only learns the assignment for one layer in one training step. Furthermore, a stochastic mode of ENOS is also presented. ENOS is characterized on ShuffleNet and SqueezeNet using CIFAR10 and CIFAR100. Compared to the conventional uni-operator approaches, under the same energy budget, ENOS improves accuracy by 10&#x2013;20%. ENOS also outperforms the accuracy of comparable mixed-precision uni-operator implementations by 3-5% for the same energy budget.
Designing Effective Sparse Expert Models

Scale has opened new frontiers in natural language processing -- but at a high cost. In response, Mixture-of-Experts (MoE) and Switch Transformers have been proposed as an energy efficient path to even larger and more capable language models. But advancing the state-of-the-art across a broad set of natural language tasks has been hindered by training instabilities and uncertain quality during fine-tuning. Our work focuses on these issues and acts as a design guide. We conclude by scaling a sparse model to 269B parameters, with a computational cost comparable to a 32B dense encoder-decoder Transformer (Stable and Transferable Mixture-of-Experts or ST-MoE-32B). For the first time, a sparse model achieves state-of-the-art performance in transfer learning, across a diverse set of tasks including reasoning (SuperGLUE, ARC Easy, ARC Challenge), summarization (XSum, CNN-DM), closed book question answering (WebQA, Natural Questions), and adversarially constructed tasks (Winogrande, ANLI R3).
Virtual Reality as an Immersive Teaching Aid to Enhance the Connection between Education and Practice

Maximizing knowledge transfer is one of the main factors of modern education. It is important to raise the probability that students can successfully apply acquired knowledge into practice. This article deals with virtual reality (VR) as an alternative method to provide students with a more immersive approach to semester assignments and their completion. In addition to information on paper, they are provided with an immersive virtual environment that interactively visualizes problems. To test this approach, a case study took place at the Department of Industrial Engineering, Faculty of Mechanical Engineering at the University of Žilina. Students were given a standard assignment for workplace analysis and optimization. However, in addition to papers containing all the necessary information about the workplace and its processes (a drilling workplace), the students could also put on a VR headset and walk through a virtual copy of the assigned workplace. Instead of relying on a 2D layout and a few photos, the students observed every detail of the workplace from any angle. Moreover, the immersive virtual workplace was interactive, and the students could interact with machine tools and replicate the real manufacturing process. With this new addition, the students completed the assignment and then filled out a short questionnaire questioning their satisfaction with the chosen approach. With positive feedback, the implementation of VR into the teaching process could further motivate students and make the transfer of knowledge into their future jobs easier.
Quantum-brachistochrone approach to the conversion from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>W</mml:mi></mml:math> to Greenberger-Horne-Zeilinger states for Rydberg-atom qubits

Using the quantum-brachistochrone formalism, we address the problem of finding the fastest possible (time-optimal) deterministic conversion between $W$ and Greenberger-Horne-Zeilinger (GHZ) states in a system of three identical and equidistant neutral atoms that are acted upon by four external laser pulses. Assuming that all four pulses are close to being resonant with the same internal (atomic) transition -- the one between the atomic ground state and a high-lying Rydberg state -- each atom can be treated as an effective two-level system ($gr$-type qubit). Starting from an effective system Hamiltonian, which is valid in the Rydberg-blockade regime and defined on a four-state manifold, we derive the quantum-brachistochrone equations pertaining to the fastest possible $W$-to-GHZ state conversion. By numerically solving these equations, we determine the time-dependent Rabi frequencies of external laser pulses that correspond to the time-optimal state conversion. In particular, we show that the shortest possible $W$-to-GHZ state-conversion time is given by $T_{\textrm{QB}}= 6.8\:\hbar/E$, where $E$ is the total laser-pulse energy used, this last time being significantly shorter than the state-conversion times previously found using a dynamical-symmetry-based approach [$T_{\textrm{DS}}=(1.33-1.66)\:T_{\textrm{QB}}$].
Quantum simulation of quantum relativistic diffusion via quantum walks

Two models are first presented, of one-dimensional discrete-time quantum walk (DTQW) with temporal noise on the internal degree of freedom (i.e., the coin): (i) a model with both a coin-flip and a phase-flip channel, and (ii) a model with random coin unitaries.It is then shown that both these models admit a common limit in the spacetime continuum, namely, a Lindblad equation with Dirac-fermion Hamiltonian part and, as Lindblad jumps, a chirality flip and a chirality-dependent phase flip, which are two of the three standard error channels for a two-level quantum system.This, as one may call it, Dirac Lindblad equation, provides a model of quantum relativistic spatial diffusion, which is evidenced both analytically and numerically.This model of spatial diffusion has the intriguing specificity of making sense only with original unitary models which are relativistic in the sense that they have chirality, on which the noise is introduced: The diffusion arises via the byconstruction (quantum) coupling of chirality to the position.For a particle with vanishing mass, the model of quantum relativistic diffusion introduced in the present work, reduces to the well-known telegraph equation, which yields propagation at short times, diffusion at long times, and exhibits no quantumness.Finally, the results are extended to temporal noises which depend smoothly on position.
Deep learning enhancing banking services: a hybrid transaction classification and cash flow prediction approach

Abstract Small Medium Enterprises (SMEs) are vital to the global economy and all societies. However, they face a complex and challenging environment, as in most sectors they are lagging behind in their digital transformation. Banks, retaining a variety of data of their SME customers to perform their main activities, could offer a solution by leveraging all available data to provide a Business Financial Management (BFM) toolkit to their customers, providing value added services on top of their core business. In this direction, this paper revolves around the development of a smart, highly personalized hybrid transaction categorization model, interconnected with a cash flow prediction model based on Recurrent Neural Networks (RNNs). As the classification of transactions is of great significance, this research is extended towards explainable AI, where LIME and SHAP frameworks are utilized to interpret and illustrate the ML classification results. Our approach shows promising results on a real-world banking use case and acts as the foundation for the development of further BFM banking microservices, such as transaction fraud detection and budget monitoring.
Image-based Automatic Dial Meter Reading in Unconstrained Scenarios

The replacement of analog meters with smart meters is costly, laborious, and far from complete in developing countries.The Energy Company of Paraná (Copel) (Brazil) performs more than 4 million meter readings (almost entirely of non-smart devices) per month, and we estimate that 850 thousand of them are from dial meters.Therefore, an image-based automatic reading system can reduce human errors, create a proof of reading, and enable the customers to perform the reading themselves through a mobile application.We propose novel approaches for Automatic Dial Meter Reading (ADMR) and introduce a new dataset for ADMR in unconstrained scenarios, called UFPR-ADMR-v2.Our best-performing method combines YOLOv4 with a novel regression approach (AngReg), and explores several postprocessing techniques.Compared to previous works, it decreased the Mean Absolute Error (MAE) from 1,343 to 129 and achieved a meter recognition rate (MRR) of 98.9% -with an error tolerance of 1 Kilowatt-hour (kWh).
Fluid space: Digitisation of cultural heritage and its media dissemination

From the mechanical reproduction outlined by Benjamin to the virtual reality depicted by Baudrillard. With the advent of the information society and the digital age, the dissemination of cultural heritage is gradually moving away from the traditional face-to-face embodied presence toward digital communication that transcends the limits of space and time. Our visual experience has also shifted from the traditional creation and reception of visual images to the invention and immersion in new worlds of spectacle. Virtual reality is a representative new technology in the digital media era, which not only realises the digital simulation of cultural heritage from physical space to cyberspace but also incorporates the characteristics of virtual visual culture into it, freeing heritage sites from the inherent material bulkiness and becoming a lighter, more fluid space, releasing the unlimited possibilities of spectacle simulation. Virtual reality technology has not only reshaped the technology of image viewing and production, but also the technology of image dissemination. Taking cultural heritage sites as an example, this paper will explain the role and impact of virtual technology on the digitisation of cultural heritage and its media dissemination from three aspects: interactivity, spatial perception and cultural value presentation. Based on the analysis above, the involvement of virtual reality in the digitalization of cultural heritage has expanded the types of images in visual culture. Besides pictures, video and scene, it may offer a potential dimension in the quadrants of dynamic and 3D.
Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India

The COVID-19 pandemic has a significant impact on the global economy and health. While the pandemic continues to cause casualties in millions, many countries have gone under lockdown. During this period, people have to stay within walls and become more addicted towards social networks. They express their emotions and sympathy via these online platforms. Thus, popular social media (Twitter and Facebook) have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues. We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases. The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus. India-specific COVID-19 tweets have been annotated, for analysing the sentiment of common public. To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35% for Lockdown and 83.33% for Unlock data set. The suggested method outperforms many of the contemporary approaches (long short-term memory, Bi-directional long short-term memory, Gated Recurrent Unit etc.). This study highlights the public sentiment on lockdown and stepwise unlocks, imposed by the Indian Government on various aspects during the Corona outburst.
Deep Learning Approaches in Histopathology

The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartiality and to minimize the workload combined with the time consumed, which affects the accuracy of the decision taken. Regrettably, there are already certain obstacles to overcome connected to artificial intelligence deployments, such as the applicability and validation of algorithms and computational technologies, in addition to the ability to train pathologists and doctors to use these machines and their willingness to accept the results. This review paper provides a survey of how machine learning and deep learning methods could be implemented into health care providers’ routine tasks and the obstacles and opportunities for artificial intelligence application in tumor morphology.
Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms

Detecting breast cancer (BC) at the initial stages of progression has always been regarded as a lifesaving intervention. With modern technology, extensive studies have unraveled the complexity of BC, but the current standard practice of early breast cancer screening and clinical management of cancer progression is still heavily dependent on tissue biopsies, which are invasive and limited in capturing definitive cancer signatures for more comprehensive applications to improve outcomes in BC care and treatments. In recent years, reviews and studies have shown that liquid biopsies in the form of blood, containing free circulating and exosomal microRNAs (miRNAs), have become increasingly evident as a potential minimally invasive alternative to tissue biopsy or as a complement to biomarkers in assessing and classifying BC. As such, in this review, the potential of miRNAs as the key BC signatures in liquid biopsy are addressed, including the role of artificial intelligence (AI) and machine learning platforms (ML), in capitalizing on the big data of miRNA for a more comprehensive assessment of the cancer, leading to practical clinical utility in BC management.
MILES: Visual BERT Pre-training with Injected Language Semantics for Video-Text Retrieval

Dominant pre-training work for video-text retrieval mainly adopt the “dual-encoder” architectures to enable efficient retrieval, where two separate encoders are used to contrast global video and text representations, but ignore detailed local semantics. The recent success of image BERT pre-training with masked visual modeling that promotes the learning of local visual context, motivates a possible solution to address the above limitation. In this work, we for the first time investigate masked visual modeling in video-text pre-training with the “dual-encoder” architecture. We perform Masked visual modeling with Injected LanguagE Semantics (MILES) by employing an extra snapshot video encoder as an evolving “tokenizer” to produce reconstruction targets for masked video patch prediction. Given the corrupted video, the video encoder is trained to recover text-aligned features of the masked patches via reasoning with the visible regions along the spatial and temporal dimensions, which enhances the discriminativeness of local visual features and the fine-grained cross-modality alignment. Our method outperforms state-of-the-art methods for text-to-video retrieval on four datasets with both zero-shot and fine-tune evaluation protocols. Our approach also surpasses the baseline models significantly on zero-shot action recognition, which can be cast as video-to-text retrieval.
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos

For weakly supervised anomaly detection, most existing work is limited to the problem of inadequate video representation due to the inability of modeling long-term contextual information. To solve this, we propose a novel weakly supervised adaptive graph convolutional network (WAGCN) to model the complex contextual relationship among video segments. By which, we fully consider the influence of other video segments on the current one when generating the anomaly probability score for each segment. Firstly, we combine the temporal consistency as well as feature similarity of video segments to construct a global graph, which makes full use of the association information among spatial-temporal features of anomalous events in videos. Secondly, we propose a graph learning layer in order to break the limitation of setting topology manually, which can extract graph adjacency matrix based on data adaptively and effectively. Extensive experiments on two public datasets (i.e., UCF-Crime dataset and ShanghaiTech dataset) demonstrate the effectiveness of our approach which achieves state-of-the-art performance.