Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning-based Mobile Edge Computing and Transmission Scheduling for Video Surveillance

Video surveillance plays an important role in Industrial Internet of Things (IIOT). The entire system is demanded to output useful surveillance information in a timely and accurate manner, thus having high requirements of enough computation and communication resources. In this work, we propose a mobile edge computing (MEC) based video surveillance network for face recognition applications, consisting of multiple camera sensors, relays, and the mobile edge server. Considering the limited computation and communication resources, we make joint decision for task offloading, wireless channel allocation, and image compression rate selection to obtain high average recognition accuracy and low average process delay. We adopt different image recognition algorithms for both camera sensors and the MEC server according to their own computation ability, and utilize the deep Q-network (DQN) algorithm as the decision-making module. Besides, we propose a two-layer hierarchical learning framework, i.e., DQN and layers based on back propagation neural network (DQN+NN) algorithm, to reduce the curse of dimensionality. Experimental results show that both the DQN and DQN+NN algorithms can efficiently handle multiple computation tasks with limited computation and communication resources in intelligence video surveillance scenarios, and the DQN+NN algorithm performs better in terms of the convergence and training efficiency.
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation

One of the most challenging topics in Natural Language Processing (NLP) is visuallygrounded language understanding and reasoning.Outdoor vision-and-language navigation (VLN) is such a task where an agent follows natural language instructions and navigates a real-life urban environment.Due to the lack of human-annotated instructions that illustrate intricate urban scenes, outdoor VLN remains a challenging task to solve.This paper introduces a Multimodal Text Style Transfer (MTST) learning approach and leverages external multimodal resources to mitigate data scarcity in outdoor navigation tasks.We first enrich the navigation data by transferring the style of the instructions generated by Google Maps API, then pre-train the navigator with the augmented external outdoor navigation dataset.Experimental results show that our MTST learning approach is model-agnostic, and our MTST approach significantly outperforms the baseline models on the outdoor VLN task, improving task completion rate by 8.7% relatively on the test set. 1 1 Our code and dataset is released at https://github.com/VegB/VLN-Transformer.Google Maps API Vanderbilt Ave turns right and becomes E 43rd St. Speaker You ' ll have a red brick building with a red awning on your right .Go forward until you reach the next intersection , and turn right. MTST modelTurn right again and stop just past the orange and white construction barriers.Orient yourself so that the red deli awning is on your right.Turn left at the intersection.
An ultra-low power, ±0.3 V supply, fully-tunable Gaussian function circuit architecture for radial-basis functions analog hardware implementation

An ultra-low power (3.9nW), low supply-voltage (0.6V) and fully-tunable Gaussian-Bump circuit architecture for hardware-friendly implementation of Kernel functions is presented. The proposed architecture can be used to form hidden neuron cells for analog implementation of Radial Basis Functions in Neural Networks. It consists of only eleven transistors, all operating in sub-threshold. The Gaussian’s center, height and width are independently and electronically controlled. The proposed architecture is used as a building block to construct a 2-D Gaussian cascaded Bump structure, demonstrating its dimensional scalability. Proper operation, sensitivity and accuracy are confirmed via theoretical analysis and post-layout simulation results. The presented architectures were realized in TSMC 90nm CMOS process and were simulated using the Cadence IC Suite.
Improving qubit coherence using closed-loop feedback

Superconducting qubits are a promising platform for building a larger-scale quantum processor capable of solving otherwise intractable problems. In order for the processor to reach practical viability, the gate errors need to be further suppressed and remain stable for extended periods of time. With recent advances in qubit control, both single- and two-qubit gate fidelities are now in many cases limited by the coherence times of the qubits. Here we experimentally employ closed-loop feedback to stabilize the frequency fluctuations of a superconducting transmon qubit, thereby increasing its coherence time by 26% and reducing the single-qubit error rate from (8.5 ± 2.1) × 10-4 to (5.9 ± 0.7) × 10-4. Importantly, the resulting high-fidelity operation remains effective even away from the qubit flux-noise insensitive point, significantly increasing the frequency bandwidth over which the qubit can be operated with high fidelity. This approach is helpful in large qubit grids, where frequency crowding and parasitic interactions between the qubits limit their performance.
Health Natural Language Processing: Methodology Development and Applications

With the rapid growth of information technology, the necessity for processing substantial amounts of health data using advanced information technologies is increasing. A large amount of valuable data exists in natural text such as diagnosis text, discharge summaries, online health discussions, and eligibility criteria of clinical trials. Health natural language processing, as an interdisciplinary field of natural language processing and health care, plays a substantial role in a wide scope of both methodology development and applications. This editorial shares the most recent methodology innovations of health natural language processing and applications in the medical domain published in this JMIR Medical Informatics special theme issue entitled "Health Natural Language Processing: Methodology Development and Applications".
Fringe analysis: single-shot or two-frames? Quantitative phase imaging answers

Conditions of the digital recording of the fringe pattern determine the phase reconstruction procedure, which in turn directly shapes the final accuracy and throughput of the full-field (non-scanning) optical measurement technique and defines the system capabilities. In this way, the fringe pattern analysis plays a crucial role in the ubiquitous optical measurements and thus is under constant development focused on high temporal/spatial resolution. It is especially valuable in the quantitative phase imaging technology, which emerged in the high-contrast label-free biomedical microscopy. In this paper, I apply recently blossomed two-frame phase-shifting techniques to the QPI and merge them with advanced adaptive interferogram pre-filtering algorithms. Next, I comprehensively test such frameworks against classical and adaptive single-shot methods applied for phase reconstruction in dynamic QPI enabling highest phase time-space-bandwidth product. The presented study systematically tackles important question: what is the gain, if any, in QPI realized by recording two phase-shifted interferograms? Counterintuitively, the results show that single-shot demodulation exhibited higher phase reconstruction accuracy than two-frame phase-shifting methods in low and medium interferogram signal-to-noise ratio regimes. Thus, the single-shot approach is promoted due to not only high temporal resolution but also larger phase-information throughput. Additionally, in the majority of scenarios, the best option is to shift the paradigm and employ two-frame pre-filtering rather than two-frame phase retrieval. Experimental fringe analysis in QPI of LSEC/RWPE cell lines successfully corroborated all novel numerical findings. Hence, the presented numerical-experimental research advances the important field of fringe analysis solutions for optical full-field measurement methods with widespread bio-engineering applications.
Designing Menstrual Technologies with Adolescents

Starting to menstruate can restrict adolescents' movements due to physiological changes and societal stigma. We present a participatory soma design project advocating for young adolescents to listen to and care for their newly-menstruating bodies, specifically focusing on participation in sport. We designed Menarche Bits, an open-ended prototyping toolkit consisting of shape-changing actuators and heat pads, and used it in two design workshops with seven participants aged 16-18, as part of collaboration and menstrual advocacy in their sports clubs and high school. The participants designed menstrual technologies that respond to menstrual cramps and depressive, anxious feelings before menstruating. We contribute findings on designing menstrual technologies with adolescents using participatory soma design. We found that a toolkit approach to the design of menstrual technologies can allow for pluralist experiences of menstrual cycles. In addition, we found that participatory design with adolescents benefits from drawing on qualities of embodiment and participants' own body literacy.
Trace Reconstruction: Generalized and Parameterized.

In the beautifully simple-to-state problem of trace reconstruction, the goal is to reconstruct an unknown binary string $x$ given random “traces” of $x$ where each trace is generated by deleting each coordinate of $x$ independently with probability $p . The problem is well studied both when the unknown string is arbitrary and when it is chosen uniformly at random. For both settings, there is still an exponential gap between upper and lower sample complexity bounds and our understanding of the problem is still surprisingly limited. In this paper, we consider natural parameterizations and generalizations of this problem in an effort to attain a deeper and more comprehensive understanding. Perhaps our most surprising results are: 1) We prove that $\exp (O(n^{1/4} \sqrt {\log n}))$ traces suffice for reconstructing arbitrary matrices. In the matrix version of the problem, each row and column of an unknown $\sqrt {n}\times \sqrt {n}$ matrix is deleted independently with probability $p$ . Our results contrasts with the best known results for sequence reconstruction where the best known upper bound is $\exp (O(n^{1/3}))$ . 2) An optimal result for random matrix reconstruction: we show that $\Theta (\log n)$ traces are necessary and sufficient. This is in contrast to the problem for random sequences where there is a super-logarithmic lower bound and the best known upper bound is $\exp ({O}(\log ^{1/3} n))$ . 3) We show that $\exp (O(k^{1/3}\log ^{2/3} n))$ traces suffice to reconstruct $k$ -sparse strings, providing an improvement over the best known sequence reconstruction results when $k = o(n/\log ^{2}\,\,n)$ . 4) We show that $\textrm {poly}(n)$ traces suffice if $x$ is $k$ -sparse and we additionally have a “separation” promise, specifically that the indices of 1 ’s in $x$ all differ by $\Omega (k \log n)$ .
Usage of Internet by University Students of Hispanic Countries: Analysis Aimed at Digital Literacy Processes in Higher Education

One of several scientific disciplines' significant objectives is to determine the integration of information and communication technologies (ICTs) in the academic setting.This research studies the use of ICTs, especially the Internet, by university students in Hispanic countries.The methodology used is descriptive and quantitative, based on data mining, through a validated and highly reliable instrument.The sample was composed of students from six countries (N = 1893).The results show that the primary interests in using ICTs and the Internet are primarily for consuming social networks, obtaining information, and leisure, above and beyond their use for academic and university purposes.This indicates that there is still a lack of sufficient training for the optimal use of these technologies by higher education students.It is urgent to carry out digital literacy processes that allow them to develop a critical sense in using ICTs, nowadays configured as powerful means of communication.Furthermore, this study has been determined that the Hispanic common space has solid cultural roots and everyday practices that lead to quite similar general interests.
Pareto-like sequential sampling heuristic for global optimisation

In this paper, we propose a simple global optimisation algorithm inspired by Pareto's principle. This algorithm samples most of its solutions within prominent search domains and is equipped with a self-adaptive mechanism to control the dynamic tightening of the prominent domains while the greediness of the algorithm increases over time (iterations). Unlike traditional metaheuristics, the proposed method has no direct mutation- or crossover-like operations. It depends solely on the sequential random sampling that can be used in diversification and intensification processes while keeping the information-flow between generations and the structural bias at a minimum. By using a simple topology, the algorithm avoids premature convergence by sampling new solutions every generation. A simple theoretical derivation revealed that the exploration of this approach is unbiased and the rate of the diversification is constant during the runtime. The trade-off balance between the diversification and the intensification is explained theoretically and experimentally. This proposed approach has been benchmarked against standard optimisation problems as well as a selected set of simple and complex engineering applications. We used 26 standard benchmarks with different properties that cover most of the optimisation problems' nature, three traditional engineering problems, and one real complex engineering problem from the state-of-the-art literature. The algorithm performs well in finding global minima for nonconvex and multimodal functions, especially with high dimensional problems and it was found very competitive in comparison with the recent algorithmic proposals. Moreover, the algorithm outperforms and scales better than recent algorithms when it is benchmarked under a limited number of iterations for the composite CEC2017 problems. The design of this algorithm is kept simple so it can be easily coupled or hybridised with other search paradigms. The code of the algorithm is provided in C++14, Python3.7, and Octave (Matlab).
Digitizing a sustainable future

Digital technologies have a crucial role in facilitating transitions toward a sustainable future. Yet there remain challenges to overcome and pitfalls to avoid. This Voices asks: how do we leverage the digital transformation to successfully support a sustainability transition?
PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos

The end-to-end Human Mesh Recovery (HMR) approach has been successfully used for 3D body reconstruction. However, most HMR-based frameworks reconstruct human body by directly learning mesh parameters from images or videos, while lacking explicit guidance of 3D human pose in visual data. As a result, the generated mesh often exhibits incorrect pose for complex activities. To tackle this problem, we propose to exploit 3D pose to calibrate human mesh. Specifically, we develop two novel Pose Calibration frameworks, i.e., Serial PC-HMR and Parallel PC-HMR. By coupling advanced 3D pose estimators and HMR in a serial or parallel manner, these two frameworks can effectively correct human mesh with guidance of a concise pose calibration module. Furthermore, since the calibration module is designed via non-rigid pose transformation, our PC-HMR frameworks can flexibly tackle bone length variations to alleviate misplacement in the calibrated mesh. Finally, our frameworks are based on generic and complementary integration of data-driven learning and geometrical modeling. Via plug-and-play modules, they can be efficiently adapted for both image/video-based human mesh recovery. Additionally, they have no requirement of extra 3D pose annotations in the testing phase, which releases inference difficulties in practice. We perform extensive experiments on the popular benchmarks, i.e., Human3.6M, 3DPW and SURREAL, where our PC-HMR frameworks achieve the SOTA results.
Ruddit: Norms of Offensiveness for English Reddit Comments

Warning: This paper contains comments that may be offensive or upsetting.On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds.Automatic methods to detect offensive language have largely relied on datasets with categorical labels.However, comments can vary in their degree of offensiveness.We create the first dataset of English language Reddit comments that has finegrained, real-valued scores between -1 (maximally supportive) and 1 (maximally offensive).The dataset was annotated using Best-Worst Scaling, a form of comparative annotation that has been shown to alleviate known biases of using rating scales.We show that the method produces highly reliable offensiveness scores.Finally, we evaluate the ability of widely-used neural models to predict offensiveness scores on this new dataset.
Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction

Maps have been a unique source of knowledge for centuries. Such historical documents provide invaluable information for analyzing the complex spatial transformation of landscapes over important time frames. This is particularly true for urban areas that encompass multiple interleaved research domains (social sciences, economy, etc.). The large amount and significant diversity of map sources call for automatic image processing techniques in order to extract the relevant objects under a vectorial shape. The complexity of maps (text, noise, digitization artifacts, etc.) has hindered the capacity of proposing a versatile and efficient raster-to-vector approaches for decades. We propose a learnable, reproducible, and reusable solution for the automatic transformation of raster maps into vector objects (building blocks, streets, rivers). It is built upon the complementary strength of mathematical morphology and convolutional neural networks through efficient edge filtering. Evenmore, we modify ConnNet and combine with deep edge filtering architecture to make use of pixel connectivity information and built an end-to-end system without requiring any post-processing techniques. In this paper, we focus on the comprehensive benchmark on various architectures on multiple datasets coupled with a novel vectorization step. Our experimental results on a new public dataset using COCO Panoptic metric exhibit very encouraging results confirmed by a qualitative analysis of the success and failure cases of our approach. Code, dataset, results and extra illustrations are freely available at https://github.com/soduco/ICDAR-2021-Vectorization.
Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations

The sequential recommendation (also known as the next-item recommendation), which aims to predict the following item to recommend in a session according to users’ historical behavior, plays a critical role in improving session-based recommender systems. Most of the existing deep learning-based approaches utilize the recurrent neural network architecture or self-attention to model the sequential patterns and temporal influence among a user's historical behavior and learn the user's preference at a specific time. However, these methods have two main drawbacks. First, they focus on modeling users’ dynamic states from a user-centric perspective and always neglect the dynamics of items over time. Second, most of them deal with only the first-order user-item interactions and do not consider the high-order connectivity between users and items, which has recently been proved helpful for the sequential recommendation. To address the above problems, in this article, we attempt to model user-item interactions by a bipartite graph structure and propose a new recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) for the sequential recommendation. PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator. Also, it realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions. To demonstrate the effectiveness of PTGCN, we carried out a comprehensive evaluation of PTGCN on three real-world datasets of different sizes compared with a few competitive baselines. Experimental results indicate that PTGCN outperforms several state-of-the-art sequential recommendation models in terms of two commonly-used evaluation metrics for ranking. In particular, it can make a better trade-off between recommendation performance and model training efficiency, which holds great potential for online session-based recommendation scenarios in the future.
Recurrent neural network pruning using dynamical systems and iterative fine-tuning

Network pruning techniques are widely employed to reduce the memory requirements and increase the inference speed of neural networks. This work proposes a novel RNN pruning method that considers the RNN weight matrices as collections of time-evolving signals. Such signals that represent weight vectors can be modelled using Linear Dynamical Systems (LDSs). In this way, weight vectors with similar temporal dynamics can be pruned as they have limited effect on the performance of the model. Additionally, during the fine-tuning of the pruned model, a novel discrimination-aware variation of the L2 regularization is introduced to penalize network weights (i.e., reduce the magnitude), whose impact on the output of an RNN network is minimal. Finally, an iterative fine-tuning approach is proposed that employs a bigger model to guide an increasingly smaller pruned one, as a steep decrease of the network parameters can irreversibly harm the performance of the pruned model. Extensive experimentation with different network architectures demonstrates the potential of the proposed method to create pruned models with significantly improved perplexity by at least 0.62% on the PTB dataset and improved F1-score by 1.39% on the SQuAD dataset, contrary to other state-of-the-art approaches that slightly improve or even deteriorate models' performance.
Embedding of complete graphs in broken Chimera graphs

In order to solve real world combinatorial optimization problems with a D-Wave quantum annealer it is necessary to embed the problem at hand into the D-Wave hardware graph, namely Chimera or Pegasus. Most hard real world problems exhibit a strong connectivity. For the worst case scenario of a complete graph, there exists an efficient solution for the embedding into the ideal Chimera graph. However, since real machines almost always have broken qubits it is necessary to find an embedding into the broken hardware graph. We present a new approach to the problem of embedding complete graphs into broken Chimera graphs. This problem can be formulated as an optimization problem, more precisely as a matching problem with additional linear constraints. Although being NP-hard in general it is fixed parameter tractable in the number of inaccessible vertices in the Chimera graph. We tested our exact approach on various instances of broken hardware graphs, both related to real hardware as well as randomly generated. For fixed runtime, we were able to embed larger complete graphs compared to previous, heuristic approaches. As an extension, we developed a fast heuristic algorithm which enables us to solve even larger instances. We compared the performance of our heuristic and exact approaches.