Reviewerly/exHarmony huggingface dataset:
Power-Driven DNN Dataflow Optimization on FPGA

Deep neural networks (DNNs) have been proven to achieve unprecedented success on modern artificial intelligence (AI) tasks, which have also greatly motivated the rapid developments of novel DNN models and hardware accelerators. Many challenges still remain towards the design of power efficient DNN accelerator due to the intrinsically intensive data computation and transmission in DNN algorithms. However, most existing efforts in the domain have taken latency as the sole optimization objective, which may often result in sub-optimality in power consumption. In this paper, we propose a framework to optimize the power efficiency of DNN dataflow on FPGA while maximally minimizing the impact on latency. We first propose power and latency models that are built upon different dataflow configurations. Then a power-driven dataflow formulation is proposed, which enables a hierarchical exploration strategy on the dataflow configurations, leading to efficient power consumption at limited latency loss. Experimental results have demonstrated the effectiveness of our proposed models and exploration strategies, where power improvement has shown up to 31% with latency degradation of no worse than 6.5%.
COMPUTING FUZZY INTEGRAL OF THE BASIS OF FUZZY MESURE

Abstract The fuzzy integral of a function with a fuzzy measure is a general estimate in the form of a nonlinear convolution of particular estimates of information elements, and the possibility of interconnection of information elements is not excluded. Here is an approach that allows the expert to best formalize his fuzzy ideas, transforming the language of words into a language of quantitative estimates.
Research on cloud computing service based on trust access control

Recently, more and more network fraud incidents have damaged the interests of cloud service traders. To enhance mutual trust and win–win cooperation between the users and the cloud service provider, in the article, we construct a trust access control model for cloud services. First, we propose a trust evaluation method based on direct trust, trust risk, feedback trust, reward penalty, and obligation trust to express the complexity and uncertainty of trust relationship. Second, we propose trust evaluation and weight algorithm of trust factor by information entropy and maximum dispersion; therefore, our model has a better scientific and higher practical application value. Finally, we design related comparative experiments of three models to verify the efficiency, success rate, accuracy of trust evaluation, and privacy disclosure date, and these results show that our research performance is quite superior.
Visions that change. Articulating the politics of participatory design

Visions that change
An Improved Atom Search Optimization With Cellular Automata, a Lévy Flight and an Adaptive Weight Strategy

Atom search optimization (ASO) is a newly developed metaheuristic algorithm inspired by molecular basis dynamics. The paramount challenge in ASO is that it is prone to stagnation in local optima and premature convergence. To solve these issues, this paper uses a cellular automata structure, a Lévy flight and adaptive weight strategies to help ASO balance exploration and exploitation; the proposed method is named CALFASO. First, the cellular automata structure provides more information exchange for the population and enriches the diversity of the population. Second, a Lévy flight has the characteristics of a random walk, its short-step long-term search can improve the global search capability of the algorithm, and its short-term long-step mutations can help the algorithm jump out of local optima. Third, adaptive weights can effectively improve convergence performance. Finally, the proposed CALFASO was evaluated on the CEC2017 test set and real-world engineering problems and compared with classical and novel evolutionary algorithms to verify its performance. The experimental results and statistical analysis show that the performance of the proposed CALFASO algorithm is better than that of the other selected algorithms.
Fine Grained Named Entity Recognition via Seq2seq Framework

Fine-grained Named entity recognition (NER) is crucial to natural language processing (NLP) applications like relation extraction and knowledge graph construction. Most existing fine-grained NER systems suffer from inefficiency problem as they use manually annotated training datasets. To address such issue, our NER system could automatically generate datasets from Wikipedia in distant supervision paradigm through mapping hyperlinks in Wikipedia documents to Freebase. In addition, previous NER models can not effectively process fine-grained labels with more than 100 types. So we introduce a `BIO' tagging strategy which can identify the position and type attributes simultaneously. Such tagging scheme transfers NER problem into a sequence-to-sequence (seq2seq) based issue. We propose a seq2seq framework to comprehend the input sentence in a comprehensive way. Specifically, we adopt a Bi-LSTM as the encoder to equally process the past and future information of the input. Then we add a self-attention mechanism to handle the long-term dependency problem in a long sequence. When classifying the entity tags, we choose CRF model as it adds more constraints to avoid position logical problem. Experiments are performed on large-scale datasets for fine-grained NER tasks. Experimental results verify the effectiveness of FSeqC, and it outperforms other state-of-the-art alternatives consistently and significantly.
Development of a Novel Robust Control Method for Formation of Heterogeneous Multiple Mobile Robots With Autonomous Docking Capability

Multiple mobile robots in formation are often required to dock to each other to overcome the limitations, such as battery failure, transportation capacity, and maneuverability on rough terrains; however, it is challenging to design a single controller that navigates the robots to dock to each other, maintains the other robots in formation, and is applicable to both docked and nondocked robots, while it is also robust to uncertainties and disturbances. This article proposes a novel robust subsumption architecture for nonholonomic mobile robots in formation with docking capability. In addition to docking, the robots, i.e., all the nondocked robots and the front-docked robots, maintain a formation that can also be switched automatically to other configurations when necessary and avoid collisions with other robots and dynamic obstacles. The proposed subsumption control architecture takes into account each follower's desired goal as well as its docking condition to synthesize a control law as a velocity control signal that is then used to determine the robust input torque for each follower using the robots' dynamics. The Lyapunov stability of the controller is also proved. We also develop strategies for efficient centralized motion planning of the followers to achieve various goals, e.g., formation keeping/switching, docking, and collision avoidance. The effectiveness of our proposed methodology was verified in simulations as well as implementations on a virtual robot environment. Note to Practitioners - Multiple mobile robots, especially when operating as a formation, are able to perform tasks that are beyond the capabilities of individual robots. Existing formation control approaches neglect some realistic limitations of mobile robots, such as battery failure, limited transportation capacity, and maneuverability, to name a few. This article was motivated by these realistic limitations of mobile robots when operating in formation, and it suggests a new approach for navigation of such robots by docking some (or all) of these robots to each other and pursue a variety of goals. The goal includes autonomous docking, formation keeping/switching, and collision avoidance in dynamic environments. We include robot dynamics and system uncertainties in our algorithm and provide a robust control methodology. Therefore, the developed methodologies in this article can be adopted in real applications that require robots to be supplied with sufficient battery or having a large payload capacity, e.g., agricultural robotics.
Domain-Specific Image Caption Generator with Semantic Ontology

Image captioning is the task of generating textual descriptions of a given image, requiring techniques of computer vision and natural language processing. Recent models have utilized deep learning techniques for this task to gain performance improvement. However, these models can neither fully use information included in a given image such as object and attribute, nor generate a domain-specific caption because existing methods use open dataset such as MSCOCO which include general images. To overcome these limitations, this paper proposes a domain-specific image caption generator, which generates a caption based on attention mechanism with object and attribute information, and reconstruct a generate caption using a semantic ontology to provide natural language description for given specific-domain. To show the effectiveness of the proposed model, we evaluate the image caption generator with a dataset, MSCOCO, quantitatively and qualitatively.
WoodFisher: Efficient second-order approximations for model compression.

Second-order information, in the form of Hessian- or Inverse-Hessian-vector products, is a fundamental tool for solving optimization problems. Recently, there has been significant interest in utilizing this information in the context of deep neural networks; however, relatively little is known about the quality of existing approximations in this context. Our work examines this question, identifies issues with existing approaches, and proposes a method called WoodFisher to compute a faithful and efficient estimate of the inverse Hessian. Our main application is to neural network compression, where we build on the classic Optimal Brain Damage/Surgeon framework. We demonstrate that WoodFisher significantly outperforms popular state-of-the-art methods for one-shot pruning. Further, even when iterative, gradual pruning is considered, our method results in a gain in test accuracy over the state-of-the-art approaches, for pruning popular neural networks (like ResNet-50, MobileNetV1) trained on standard image classification datasets such as ImageNet ILSVRC. We examine how our method can be extended to take into account first-order information, as well as illustrate its ability to automatically set layer-wise pruning thresholds and perform compression in the limited-data regime. The code is available at the following link, this https URL.
Zero-shot Neural Retrieval via Domain-targeted Synthetic Query Generation.

Deep neural scoring models have recently been shown to improve ranking quality on a number of benchmarks (Guo et al., 2016; Daiet al., 2018; MacAvaney et al., 2019; Yanget al., 2019a). However, these methods rely on underlying ad-hoc retrieval systems to generate candidates for scoring, which are rarely neural themselves (Zamani et al., 2018). Re-cent work has shown that the performance of ad-hoc neural retrieval systems can be competitive with a number of baselines (Zamani et al.,2018), potentially leading the way to full end-to-end neural retrieval. A major road-block to the adoption of ad-hoc retrieval models is that they require large supervised training sets to surpass classic term-based techniques, which can be developed from raw corpora. Previous work shows weakly supervised data can yield competitive results, e.g., click data (Dehghaniet al., 2017; Borisov et al., 2016). Unfortunately for many domains, even weakly supervised data can be scarce. In this paper, we pro-pose an approach to zero-shot learning (Xianet al., 2018) for ad-hoc retrieval models that relies on synthetic query generation. Crucially, the query generation system is trained on general domain data, but is applied to documents in the targeted domain. This allows us to create arbitrarily large, yet noisy, query-document relevance pairs that are domain targeted. On a number of benchmarks, we show that this is an effective strategy for building neural retrieval models for specialised domains.
CREATION OF A NEURAL NETWORK ALGORITHM FOR AUTOMATED COLLECTION AND ANALYSIS OF STATISTICS OF EXCHANGE QUOTES GRAPHICS

Currently, the problem of automated data analysis and statistics collection from stock quotation charts has not been fully resolved.Most of the analysis of visual data falls on the physical work of the analyst, or on obsolete software solutions.The process of summarizing the information received from financial markets still requires physical attention and labor, which increases the risks associated primarily with the human factor and corresponding errors.An algorithm has been developed and tested for the automated collection of statistics from graphs of stock quotes, including data on the development and context of various figures (patterns) of technical analysis, as well as an improved adaptation and tracking system for the trend.The modeling process, analysis and the results of applying the analysis algorithm and statistics collection are presented.The developed algorithm works in conjunction with the previously created neural network pattern detector, which allows to automatically search for the exact boundaries of technical analysis figures of various sizes,
A Robust Extended Kalman Filter Applied to Ultrawideband Positioning

Ultrawideband (UWB) is well-suited for indoor positioning due to its high resolution and good penetration through objects. The observation model of UWB positioning is nonlinear. As one of nonlinear filter algorithms, extended Kalman filter (EKF) is widely used to estimate the position. In practical applications, the dynamic estimation is subject to the outliers caused by gross errors. However, the EKF cannot resist the effect of gross errors. The innovation will become abnormally large and the performance and the reliability of the filter algorithm are inevitably influenced. In this study, a robust EKF (REKF) method accompanied by hypothesis test and robust estimation is proposed. To judge the validity of model, the global test based on Mahalanobis distance is implemented to assess whether the test statistical term exceeds the threshold for outlier detection. To reduce and eliminate the effects of the individual outlier, the robust estimation using scheme III of the Institute of Geodesy and Geophysics of China (IGGIII) based on local test of the normalized residual is performed. Meanwhile, three kinds of stochastic models for outliers are expressed by modeling the contaminated distributions. Furthermore, the simulation and measurement experiments are performed to verify the effectiveness and feasibility of the proposed REKF for resisting the outliers. Simulation experiment results are given to demonstrate that the outliers following all the three kinds of contaminated distributions can be detected. The proposed REKF can effectively control the influences of the outliers being treated as systematic errors and large variance random errors. When the outliers come from the thick-tailed distribution, the robust estimation does not play a role, and the REKF are equivalent to the EKF method. The measured experiment results show that the outliers will be generated in the nonline-of-sight environment whose impact is abnormally serious. The robust estimation can provide relatively reliable optimized residuals and control the influences of the outliers caused by gross errors. We can believe that the proposed REKF is effective to resist the effects of outliers and improves the positioning accuracy compared with least-squares (LS) and EKF method. Moreover, the adaptive filter and ranging error model should be considered to compensate the state model errors and ranging systematic errors respectively. Then, the measurement outliers will be detected more correctly, and the robust estimation will be used effectively.
No More One Liners

As emojis are increasingly used in everyday online communication such as messaging, email, and social networks, various techniques have attempted to improve the user experience in communicating emotions and information through emojis. Emoji recommendation is one such example in which machine learning is applied to predict which emojis the user is about to select, based on the user’s current input message. Although emoji suggestion helps users identify and select the right emoji among a plethora of emojis, analyzing only a single sentence for this purpose has several limitations. First, various emotions, information, and contexts that emerge in a flow of conversation could be missed by simply looking at the most recent sentence. Second, it cannot suggest emojis for emoji-only messages, where the users use only emojis without any text. To overcome these issues, we present Reeboc (Recommending emojis based on context), which combines machine learning and k -means clustering to analyze the conversation of a chat, extract different emotions or topics of the conversation, and recommend emojis that represent various contexts to the user. To evaluate the effectiveness of our proposed emoji recommendation system and understand its effects on user experience, we performed a user study with 17 participants in eight groups in a realistic mobile chat environment with three different modes: (i) a default static layout without emoji recommendations, (ii) emoji recommendation based on the current single sentence, and (iii) our emoji recommendation model that considers the conversation. Participants spent the least amount of time in identifying and selecting the emojis of their choice with Reeboc (38% faster than the baseline). They also chose emojis that were more highly ranked with Reeboc than with current-sentence-only recommendations. Moreover, participants appreciated emoji recommendations for emoji-only messages, which contributed to 36.2% of all sentences containing emojis.
Efficient Management of Ultrasound Images using Digital Watermarking

Nowadays, the administration of medical images can be compromised by several information security issues. Among others, a first problem consists in authenticating the source that originated the medical image and verifying that it corresponds to the correct patient; a second problem is aimed to avoid detachment between an electronic patient record and its corresponding images. A promising solution is the use of digital watermarking techniques applied to medical images considering in the design requirements of imperceptibility-robustness of the field of medical imaging. In this paper a digital watermarking method for an efficient administration of ultrasound images is proposed. To avoid the detachment between the record and images, we use an invisible watermarking in the frequency domain of the image. To authenticate the source of origin of the medical image, we use a visible-imperceptible watermarking in the spatial domain of the image. The experimental results show an efficient robustness and imperceptibility of the embedded watermark signals into the medical image. A comparison of performance with related works previously reported in the literature is included.
Few-shot Domain Adaptation by Causal Mechanism Transfer

We study few-shot supervised domain adaptation (DA) for regression problems, where only a few labeled target domain data and many labeled source domain data are available. Many of the current DA methods base their transfer assumptions on either parametrized distribution shift or apparent distribution similarities, e.g., identical conditionals or small distributional discrepancies. However, these assumptions may preclude the possibility of adaptation from intricately shifted and apparently very different distributions. To overcome this problem, we propose mechanism transfer, a meta-distributional scenario in which a data generating mechanism is invariant among domains. This transfer assumption can accommodate nonparametric shifts resulting in apparently different distributions while providing a solid statistical basis for DA. We take the structural equations in causal modeling as an example and propose a novel DA method, which is shown to be useful both theoretically and experimentally. Our method can be seen as the first attempt to fully leverage the structural causal models for DA.
Weighted QMIX: Expanding Monotonic Value Function Factorisation.

QMIX is a popular $Q$-learning algorithm for cooperative MARL in the centralised training and decentralised execution paradigm. In order to enable easy decentralisation, QMIX restricts the joint action $Q$-values it can represent to be a monotonic mixing of each agent's utilities. However, this restriction prevents it from representing value functions in which an agent's ordering over its actions can depend on other agents' actions. To analyse this representational limitation, we first formalise the objective QMIX optimises, which allows us to view QMIX as an operator that first computes the $Q$-learning targets and then projects them into the space representable by QMIX. This projection returns a representable $Q$-value that minimises the unweighted squared error across all joint actions. We show in particular that this projection can fail to recover the optimal policy even with access to $Q^*$, which primarily stems from the equal weighting placed on each joint action. We rectify this by introducing a weighting into the projection, in order to place more importance on the better joint actions. We propose two weighting schemes and prove that they recover the correct maximal action for any joint action $Q$-values, and therefore for $Q^*$ as well. Based on our analysis and results in the tabular setting we introduce two scalable versions of our algorithm, Centrally-Weighted (CW) QMIX and Optimistically-Weighted (OW) QMIX and demonstrate improved performance on both predator-prey and challenging multi-agent StarCraft benchmark tasks.
Dynamic displacement measurement beyond half-wavelength in phase-modulated optical interferometer

We demonstrate an interference signal-processing method that extends the measurement range of dynamic displacement beyond half the wavelength of light without deteriorating the measurement accuracy in a phase-modulated optical interferometer. The measurement range extension is realized by the algorithm focusing on peak direction of the interference signal waveform. Deterioration of measurement accuracy is avoided by the data processing that removes noisy data sets. Performance of the proposed system is evaluated by the experiments using a pseudo-vibrator composed of a phase modulator. In the experiments using a lead zirconate titanate (PZT) device as a sample, we successfully measure the dynamic displacement of up to 3127 nm, which is larger than the light source wavelength of 1537 nm. Proof-of-principle simulations, including the evaluation of the measurement error, are also conducted, the results of which show that the measurement error of the proposed method is small in principle.