Reviewerly/exHarmony huggingface dataset:
UPS Node-Based Workload Management for Data Centers Considering Flexible Service Requirements

As the IT sector is rapidly growing all over the world, more and more energy is consumed by data centers, which leads to the continuous increase of operating costs. Among other factors, the load management among uninterruptible power supply (UPS) nodes should be considered in the energy management model of the data center since its energy efficiency is correlated with the load ratio. In this article, an energy management scheme of the data center, which focuses on the workload scheduling among UPS nodes, is proposed to improve its operation efficiency. The flexible service requirement of batch workloads is also modeled in the proposed scheme to illustrate the cost of flexibility. In the proposed scheme, the efficiency of the UPS node is modeled as a function of the load ratio. Meanwhile, the cost of inconvenience is introduced to represent the flexible service requirements of batch workloads. A stochastic formulation is established to address the uncertainties involved in the scheduling process. Simulated case studies are provided, and the results demonstrate the effectiveness of the proposed approach.
AF-Net: A Medical Image Segmentation Network Based on Attention Mechanism and Feature Fusion

Medical image segmentation is an important application field of computer vision in medical image processing.Due to the close location and high similarity of different organs in medical images, the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation.To address these challenges, we propose a medical image segmentation network (AF-Net) based on attention mechanism and feature fusion, which can effectively capture global information while focusing the network on the object area.In this approach, we add dual attention blocks (DA-block) to the backbone network, which comprises parallel channels and spatial attention branches, to adaptively calibrate and weigh features.Secondly, the multi-scale feature fusion block (MFF-block) is proposed to obtain feature maps of different receptive domains and get multi-scale information with less computational consumption.Finally, to restore the locations and shapes of organs, we adopt the global feature fusion blocks (GFF-block) to fuse high-level and low-level information, which can obtain accurate pixel positioning.We evaluate our method on multiple datasets(the aorta and lungs dataset), and the experimental results achieve 94.0% in mIoU and 96.3% in DICE, showing that our approach performs better than U-Net and other state-of-art methods.
Speechformer: Reducing Information Loss in Direct Speech Translation

Transformer-based models have gained increasing popularity achieving state-of-the-art performance in many research fields including speech translation.However, Transformer's quadratic complexity with respect to the input sequence length prevents its adoption as is with audio signals, which are typically represented by long sequences.Current solutions resort to an initial sub-optimal compression based on a fixed sampling of raw audio features.Therefore, potentially useful linguistic information is not accessible to higher-level layers in the architecture.To solve this issue, we propose Speechformer, an architecture that, thanks to a reduced memory usage in the attention layers, avoids the initial lossy compression and aggregates information only at a higher level according to more informed linguistic criteria.Experiments on three language pairs (en→de/es/nl) show the efficacy of our solution, with gains of up to 0.8 BLEU on the standard MuST-C corpus and of up to 4.0 BLEU in a low resource scenario.
A Trade-off-centered Framework of Content Moderation

Content moderation research typically prioritizes representing and addressing challenges for one group of stakeholders or communities in one type of context. While taking a focused approach is reasonable or even favorable for empirical case studies, it does not address how content moderation works in multiple contexts. Through a systematic literature review of 86 content moderation papers that document empirical studies, we seek to uncover patterns and tensions within past content moderation research. We find that content moderation can be characterized as a series of trade-offs around moderation actions, styles, philosophies, and values. We discuss how facilitating cooperation and preventing abuse, two key elements in Grimmelmann's definition of moderation, are inherently dialectical in practice. We close by showing how researchers, designers, and moderators can use our framework of trade-offs in their own work, and arguing that trade-offs should be of central importance in investigating and designing content moderation.
Piecewise linear neural networks and deep learning

As a powerful modelling method, piecewise linear neural networks (PWLNNs) have proven successful in various fields, most recently in deep learning. To apply PWLNN methods, both the representation and the learning have long been studied. In 1977, the canonical representation pioneered the works of shallow PWLNNs learned by incremental designs, but the applications to large-scale data were prohibited. In 2010, rectified linear units (ReLU) advocated the prevalence of PWLNNs in deep learning. Ever since, PWLNNs have been successfully applied to many tasks and achieved excellent performance. In this Primer, we systematically introduce the methodology of PWLNNs by grouping the works into shallow and deep networks. First, different PWLNN representation models are constructed with elaborated examples. With PWLNNs, the evolution of learning algorithms for data is presented and fundamental theoretical analysis follows up for in-depth understandings. Then, representative applications are introduced together with discussions and outlooks. Piecewise linear neural networks (PWLNNs) are a powerful modelling method, particularly in deep learning. In this Primer, Tao et al. introduce the methodology and theoretical analysis of PWLNNs and some of their applications.
Generative Steganography Network

Steganography usually modifies cover media to embed secret data. A new steganographic approach called generative steganography (GS) has emerged recently, in which stego images (images containing secret data) are generated from secret data directly without cover media. However, existing GS schemes are often criticized for their poor performances. In this paper, we propose an advanced generative steganography network (GSN) that can generate realistic stego images without using cover images. We firstly introduce the mutual information mechanism in GS, which helps to achieve high secret extraction accuracy. Our model contains four sub-networks, i.e., an image generator (G), a discriminator (D), a steganalyzer (S), and a data extractor (E). D and S act as two adversarial discriminators to ensure the visual quality and security of generated stego images. E is to extract the hidden secret from generated stego images. The generator G is flexibly constructed to synthesize either cover or stego images with different inputs. It facilitates covert communication by concealing the function of generating stego images in a normal generator. A module named secret block is designed to hide secret data in the feature maps during image generation, with which high hiding capacity and image fidelity are achieved. In addition, a novel hierarchical gradient decay (HGD) skill is developed to resist steganalysis detection. Experiments demonstrate the superiority of our work over existing methods.
Deep reinforcement learning applied to an assembly sequence planning problem with user preferences

Deep reinforcement learning (DRL) has demonstrated its potential in solving complex manufacturing decision-making problems, especially in a context where the system learns over time with actual operation in the absence of training data. One interesting and challenging application for such methods is the assembly sequence planning (ASP) problem. In this paper, we propose an approach to the implementation of DRL methods in ASP. The proposed approach introduces in the RL environment parametric actions to improve training time and sample efficiency and uses two different reward signals: (1) user’s preferences and (2) total assembly time duration. The user’s preferences signal addresses the difficulties and non-ergonomic properties of the assembly faced by the human and the total assembly time signal enforces the optimization of the assembly. Three of the most powerful deep RL methods were studied, Advantage Actor-Critic (A2C), Deep Q-Learning (DQN), and Rainbow, in two different scenarios: a stochastic and a deterministic one. Finally, the performance of the DRL algorithms was compared to tabular Q-Learnings performance. After 10,000 episodes, the system achieved near optimal behaviour for the algorithms tabular Q-Learning, A2C, and Rainbow. Though, for more complex scenarios, the algorithm tabular Q-Learning is expected to underperform in comparison to the other 2 algorithms. The results support the potential for the application of deep reinforcement learning in assembly sequence planning problems with human interaction.
MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation

Recent works have shown that optical flow can be learned by deep networks from unlabelled image pairs based on brightness constancy assumption and smoothness prior.Current approaches additionally impose an augmentation regularization term for continual self-supervision, which has been proved to be effective on difficult matching regions.However, this method also amplify the inevitable mismatch in unsupervised setting, blocking the learning process towards optimal solution.To break the dilemma, we propose a novel mutual distillation framework to transfer reliable knowledge back and forth between the teacher and student networks for alternate improvement.Concretely, taking estimation of off-the-shelf unsupervised approach as pseudo labels, our insight locates at defining a confidence selection mechanism to extract relative good matches, and then add diverse data augmentation for distilling adequate and reliable knowledge from teacher to student.Thanks to the decouple nature of our method, we can choose a stronger student architecture for sufficient learning.Finally, better student prediction is adopted to transfer knowledge back to the efficient teacher without additional costs in real deployment.Rather than formulating it as a supervised task, we find that introducing an extra unsupervised term for multi-target learning achieves best final results.Extensive experiments show that our approach, termed MDFlow, achieves state-of-the-art real-time accuracy and generalization ability on challenging benchmarks.
GHz Rate Neuromorphic Photonic Spiking Neural Network With a Single Vertical-Cavity Surface-Emitting Laser (VCSEL)

Vertical-Cavity Surface-Emitting Lasers (VCSELs) are highly promising devices for the construction of neuromorphic photonic information processing systems, due to their numerous desirable properties such as low power consumption, high modulation speed, and compactness. Of particular interest is the ability of VCSELs to exhibit neuron-like spiking responses at ultrafast sub-nanosecond rates; thus offering great prospects for high-speed light-enabled spike-based processors. Recent works have shown spiking VCSELs are capable of tackling pattern recognition and image processing problems, but additionally, VCSELs have been used as nonlinear elements in photonic reservoir computing (RC) implementations, yielding state of the art operation. This work introduces and experimentally demonstrates for the first time a new GHz-rate photonic spiking neural network (SNN) built with a single VCSEL neuron. The reported system effectively implements a photonic VCSEL-based spiking reservoir computer, and demonstrates its successful application to a complex nonlinear classification task. Importantly, the proposed system benefits from a highly hardware-friendly, inexpensive realization (a single VCSEL device and off-the-shelf fibre-optic components), for high-speed (GHz-rate inputs) and low-power (sub-mW optical input power) photonic operation. These results open new pathways towards future neuromorphic photonic spike-based processing systems based upon VCSELs (or other laser types) for novel ultrafast machine learning and AI hardware.
The <scp>computer‐aided</scp> design margin: Ex vivo <scp>3D</scp> specimen mapping to improve communication between surgeons and pathologists

Abstract Background Numerous challenges exist in determining surgical margin status. Communication between surgeons and pathologists is crucial for specimen orientation and accurate margin assessment. Methods A prospective study to determine feasibility of incorporating three‐dimensional (3D) scanning into surgical pathology workflow was performed. A structured‐light 3D scanner captured the photorealistic surface topography of fresh surgical specimens. Computer‐aided design (CAD) software was used to document sites of margin sampling and sectioning. Surveys were distributed among faculty and staff stakeholders to assess feasibility. Results A series of 40 cases were 3D‐scanned. Median image acquisition time was 8 min. The majority of respondents agreed that the experimental 3D system helped achieve clearer communication. 3D specimen maps assisted in the communication of a focally positive or close margin in 4 of 17 cases. Conclusions Routine 3D scanning and specimen mapping is feasible and represents an innovative approach to intraoperative and final pathology documentation, margin analysis, and surgeon–pathologist communication.
Using Computational Models to Test Syntactic Learnability

We studied the learnability of English filler-gap dependencies and the “island” constraints on them by assessing the generalizations made by autoregressive (incremental) language models that use deep learning to predict the next word given preceding context. Using factorial tests inspired by experimental psycholinguistics, we found that models acquire not only the basic contingency between fillers and gaps, but also the unboundedness and hierarchical constraints implicated in the dependency. We evaluated a model’s acquisition of island constraints by demonstrating that its expectation for a filler-gap contingency is attenuated within an island environment. Our results provide empirical evidence against the argument from the poverty of the stimulus for this particular structure.
An explainable knowledge distillation method with XGBoost for ICU mortality prediction

Mortality prediction is an important task in intensive care unit (ICU) for quantifying the severity of patients’ physiological condition. Currently, scoring systems are widely applied for mortality prediction, while the performance is unsatisfactory in many clinical conditions due to the non-specificity and linearity characteristics of the used model. As the availability of the large volume of data recorded in electronic health records (EHRs), deep learning models have achieved state-of-art predictive performance. However, deep learning models are hard to meet the requirement of explainability in clinical conditions. Hence, an explainable Knowledge Distillation method with XGBoost (XGB-KD) is proposed to improve the predictive performance of XGBoost while supporting better explainability. In this method, we first use outperformed deep learning teacher models to learn the complex patterns hidden in high-dimensional multivariate time series data. Then, we distill knowledge from soft labels generated by the ensemble of teacher models to guide the training of XGBoost student model, whose inputs are meaningful features obtained from feature engineering. Finally, we conduct model calibration to obtain predicted probabilities reflecting the true posterior probabilities and use SHapley Additive exPlanations (SHAP) to obtain insights about the trained model. We conduct comprehensive experiments on MIMIC-III dataset to evaluate our method. The results demonstrate that our method achieves better predictive performance than vanilla XGBoost, deep learning models and several state-of-art baselines from related works. Our method can also provide intuitive explanations. Our method is useful for improving the predictive performance of XGBoost by distilling knowledge from deep learning models and can provide meaningful explanations for predictions.
Limitations of Semidefinite Programs for Separable States and Entangled Games

Semidefinite programs (SDPs) are a framework for exact or approximate optimization with widespread application in quantum information theory. We introduce a new method for using reductions to construct integrality gaps for SDPs, meaning instances where the SDP value is far from the true optimum. These are based on new limitations on the sum-of-squares (SoS) hierarchy in approximating two particularly important sets in quantum information theory, where previously no $${\omega(1)}$$ -round integrality gaps were known: Integrality gaps for the $${2\rightarrow 4}$$ norm had previously been sought due to its connection to Small-Set Expansion (SSE) and Unique Games (UG). In both cases no-go theorems were previously known based on computational assumptions such as the Exponential Time Hypothesis (ETH) which asserts that 3-SAT requires exponential time to solve. Our unconditional results achieve the same parameters as all of these previous results (for separable states) or as some of the previous results (for quantum correlations). In some cases we can make use of the framework of Lee–Raghavendra–Steurer (LRS) to establish integrality gaps for any SDP extended formulation, not only the SoS hierarchy. Our hardness result on separable states also yields a dimension lower bound of approximate disentanglers, answering a question of Watrous and Aaronson et al. These results can be viewed as limitations on the monogamy principle, the PPT test, the ability of Tsirelson-type bounds to restrict quantum correlations, as well as the SDP hierarchies of Doherty–Parrilo–Spedalieri, Navascues–Pironio–Acin, and Berta–Fawzi–Scholz. Indeed a wide range of past work in quantum information can be described as using an SDP on one of the above two problems and our results put broad limits on these lines of argument.
Use of Mobile Immersion in Foreign Language Teaching to Enhance Target Language Vocabulary Learning

Smartphones have an essential role in day-to-day lives. Mobile phone applications have several and wide ranging features. The use of these smartphone applications provides new insights for innovation and opportunities in language teaching and learning process. The present study intended to examine the efficiency of a mobile application on teaching 40 phrases from Nation and Martinez phrase test 1-5 compared to conventional activities. Quasi-experimental research design with pre-test and post-test was employed to determine the differences between the scores of the control group (N=20) and the experimental group (N=20) developed by using random sampling. Results indicated that participants in the experimental group performed significantly better in the post-test, demonstrating the effectiveness of the mobile application used in this study on learning idioms. The results showed that learners in the experimental group achieved considerably better development in the post-test, representing the efficiency of the smartphone application that was involved in the study. Suggestions have been given for using mobile phone applications more effectively for vocabulary learning.
Spamdoop: A Privacy-Preserving Big Data Platform for Collaborative Spam Detection

Spam has become the platform of choice used by cyber-criminals to spread malicious payloads such as viruses and trojans. In this paper, we consider the problem of early detection of spam campaigns. Collaborative spam detection techniques can deal with large scale e-mail data contributed by multiple sources; however, they have the well-known problem of requiring disclosure of e-mail content. Distance-preserving hashes are one of the common solutions used for preserving the privacy of e-mail content while enabling message classification for spam detection. However, distance-preserving hashes are not scalable, thus making large-scale collaborative solutions difficult to implement. As a solution, we propose Spamdoop, a Big Data privacy-preserving collaborative spam detection platform built on top of a standard Map Reduce facility. Spamdoop uses a highly parallel encoding technique that enables the detection of spam campaigns in competitive times. We evaluate our system's performance using a huge synthetic spam base and show that our technique performs favorably against the creation and delivery overhead of current spam generation tools.
The semantics of evaluational adjectives

Abstract We apply the Natural Semantic Metalanguage (NSM) approach ( Goddard &amp; Wierzbicka 2014 ) to the lexical-semantic analysis of English evaluational adjectives and compare the results with the picture developed in the Appraisal Framework ( Martin &amp; White 2005 ). The analysis is corpus-assisted, with examples mainly drawn from film and book reviews, and supported by collocational and statistical information from WordBanks Online. We propose NSM explications for 15 evaluational adjectives, arguing that they fall into five groups, each of which corresponds to a distinct semantic template. The groups can be sketched as follows: “First-person thought-plus-affect”, e.g. wonderful ; “Experiential”, e.g. entertaining ; “Experiential with bodily reaction”, e.g. gripping ; “Lasting impact”, e.g. memorable ; “Cognitive evaluation”, e.g. complex, excellent . These groupings and semantic templates are compared with the classifications in the Appraisal Framework’s system of Appreciation. In addition, we are particularly interested in sentiment analysis, the automatic identification of evaluation and subjectivity in text. We discuss the relevance of the two frameworks for sentiment analysis and other language technology applications.
Estimating Audience Engagement to Predict Movie Ratings

While watching movies, audience members exhibit both subtle and coarse gestures (e.g., smiles, head-pose change, fidgeting, stretching) which convey sentiment (i.e., engaged or disengaged) during feature length movies. Noticing these behaviors using computer vision systems is a very challenging problem-especially in a movie theatre environment. The environment is dark and contains views of people at different scales and viewpoints. Feature length movies typically run 80-120 minutes, and tracking people uninterrupted for this duration is still an unsolved problem. Facial expressions of audience members are subtle, short, and sparse; making it difficult to detect and recognize activities. Finally, annotating audience sentiment at the frame-level is prohibitively time consuming. To circumvent these issues, we use an infrared illuminated test-bed to obtain a visually uniform input of audiences watching feature length movies. We present a method which can automatically detect the change in behavior (key-gestures) using “key-frames”, which can convey audience sentiment. As the number of key-frames are many orders of magnitudes lower than the number of frames, the annotation problem is reduced to assigning a sentiment label for each key-frame. Using these discovered key-gestures, we create a movie rating classifier from crowd-sourced ratings and demonstrate its predictive capability. Our dataset consists of over 50 hours of audience behavior collected across 237 subjects.