Reviewerly/exHarmony huggingface dataset:
SemSUM: Semantic Dependency Guided Neural Abstractive Summarization

In neural abstractive summarization, the generated summaries often face semantic irrelevance and content deviation from the input sentences. In this work, we incorporate semantic dependency graphs about predicate-argument structure of input sentences into neural abstractive summarization for the problem. We propose a novel semantics dependency guided summarization model (SemSUM), which can leverage the information of original input texts and the corresponding semantic dependency graphs in a complementary way to guide summarization process. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. Experiments show that the proposed model improves semantic relevance and reduces content deviation, and also brings significant improvements on automatic evaluation ROUGE metrics.
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance

In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations. Most of existing RLfD methods require demonstrations to be perfect and sufficient, which yet is unrealistic to meet in practice. To work on imperfect demonstrations, we first define an imperfect expert setting for RLfD in a formal way, and then point out that previous methods suffer from two issues in terms of optimality and convergence, respectively. Upon the theoretical findings we have derived, we tackle these two issues by regarding the expert guidance as a soft constraint on regulating the policy exploration of the agent, which eventually leads to a constrained optimization problem. We further demonstrate that such problem is able to be addressed efficiently by performing a local linear search on its dual form. Considerable empirical evaluations on a comprehensive collection of benchmarks indicate our method attains consistent improvement over other RLfD counterparts.
Privacy-Preserving Gradient Boosting Decision Trees

The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. Sensitivity and privacy budget are two key design aspects for the effectiveness of differential private models. Existing solutions for GBDT with differential privacy suffer from the significant accuracy loss due to too loose sensitivity bounds and ineffective privacy budget allocations (especially across different trees in the GBDT model). Loose sensitivity bounds lead to more noise to obtain a fixed privacy level. Ineffective privacy budget allocations worsen the accuracy loss especially when the number of trees is large. Therefore, we propose a new GBDT training algorithm that achieves tighter sensitivity bounds and more effective noise allocations. Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds. Furthermore, we design a novel boosting framework to allocate the privacy budget between trees so that the accuracy loss can be further reduced. Our experiments show that our approach can achieve much better model accuracy than other baselines.
A fuzzy cognitive mapping-system dynamics approach to energy-change impacts on the sustainability of small and medium-sized enterprises

Although large firms have already begun addressing energy efficiency issues and adopting "cleaner" types of energy, companies with fewer resources, such as small and medium-sized enterprises, are more reluctant to implement these measures. Given that strategies leading to energy sustainability have become a key aspect of business competitiveness, decision makers in small and medium-sized enterprises could clearly benefit from a new approach that provides a better understanding of energy-change impacts on their business activities. The present study combined fuzzy cognitive mapping and the system dynamics approach to develop an informed, transparent model for use in analyses of energy-change effects on the sustainability of small and medium-sized enterprises. This dual methodology facilitated an examination of cause-and-effect relationships between decision criteria and the clarification of dynamic interactions between variables from a holistic, real-world perspective, thereby enriching the extant literature. The results of the proposed decision-support system were validated by two senior staff members of the Directorate for Energy Sustainability Services of Portugal's Directorate General for Energy and Geology. The advantages, limitations, and implications of our constructivist, process-oriented framework for research and practice are also discussed.
Generation and symmetry control of quantum frequency combs

Abstract Quantum frequency combs are a useful resource for parallel quantum communication and processing, given the robustness and easy handling offered by the frequency degree of freedom. In this work, we propose a method to generate broadband biphoton frequency combs and control their symmetry under particle exchange, based on purely passive optical components, such as a cavity and an optical delay line. We experimentally demonstrate our method using an integrated AlGaAs semiconductor platform producing quantum frequency combs, working at room temperature and compliant with electrical injection. We show the generation and manipulation of biphoton frequency combs, spreading over more the 500 peaks. These results open interesting perspectives for the development of massively parallel and reconfigurable systems for complex quantum operations.
Fast and Incremental Loop Closure Detection Using Proximity Graphs

Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems. The frequently used bag-of-words (BoW) models can achieve high precision and moderate recall. However, the requirement for lower time costs and fewer memory costs for mobile robot applications is not well satisfied. In this paper, we propose a novel loop closure detection framework titled FILD' (Fast and Incremental Loop closure Detection), which focuses on an on-line and incremental graph vocabulary construction for fast loop closure detection. The global and local features of frames are extracted using the Convolutional Neural Networks (CNN) and SURF on the GPU, which guarantee extremely fast extraction speeds. The graph vocabulary construction is based on one type of proximity graph, named Hierarchical Navigable Small World (HNSW) graphs, which is modified to adapt to this specific application. In addition, this process is coupled with a novel strategy for real-time geometrical verification, which only keeps binary hash codes and significantly saves on memory usage. Extensive experiments on several publicly available datasets show that the proposed approach can achieve fairly good recall at 100% precision compared to other state-of-the-art methods. The source code can be downloaded at https://github.comlAnshanTJU/FILD for further studies.
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models

This paper investigates the use of target-speaker automatic speech recognition (TS-ASR) for simultaneous speech recognition and speaker diarization of single-channel dialogue recordings. TS-ASR is a technique to automatically extract and recognize only the speech of a target speaker given a short sample utterance of that speaker. One obvious drawback of TS-ASR is that it cannot be used when the speakers in the recordings are unknown because it requires a sample of the target speakers in advance of decoding. To remove this limitation, we propose an iterative method, in which (i) the estimation of speaker embeddings and (ii) TS-ASR based on the estimated speaker embeddings are alternately executed. We evaluated the proposed method by using very challenging dialogue recordings in which the speaker overlap ratio was over 20%. We confirmed that the proposed method significantly reduced both the word error rate (WER) and diarization error rate (DER). Our proposed method combined with i-vector speaker embeddings ultimately achieved a WER that differed by only 2.1 % from that of TS-ASR given oracle speaker embeddings. Furthermore, our method can solve speaker diarization simultaneously as a by-product and achieved better DER than that of the conventional clustering-based speaker diarization method based on i-vector.
Parallel Three-Dimensional Distance Transform for Railway Alignment Optimization Using OpenMP

Railway alignment optimization is a large-scale and time-consuming civil engineering problem. To solve it, a three-dimensional distance transform (3D-DT) algorithm, which is a variant of the three-dimensional Euclidean distance transform (3D-EDT), was previously designed. However, that algorithm is quite computationally intensive. In addition, the 3D-DT is inherently sequential, and it is thus challenging to parallelize. Thus, this study focuses on improving the sequential 3D-DT by transforming it into a parallel one. First, existing representative parallel EDT methods are reviewed and assessed. Then the railway alignment optimization model and the sequential 3D-DT are described. After that, critical execution properties of the 3D-DT that significantly influence its parallelization are explored in depth. Lastly, a novel so-called parallel linkage method is presented. This parallel implementation, which is developed using the OpenMP library, is highly effective and scalable by fully exploiting the parallelism of the algorithm. Using this parallel 3D-DT method, a large-scale, real-world railway case is tested and analyzed in detail. The outcomes verify that the proposed parallel method can accelerate the optimization process significantly without reducing the quality of computation results.
Differential Diagnosis of Atypical Hepatocellular Carcinoma in Contrast-Enhanced Ultrasound Using Spatio-Temporal Diagnostic Semantics

Atypical Hepatocellular Carcinoma (HCC) is very hard to distinguish from Focal Nodular Hyperplasia (FNH) in routine imaging. However little attention was paid to this problem. This paper proposes a novel liver tumor Computer-Aided Diagnostic (CAD) approach extracting spatio-temporal semantics for atypical HCC. With respect to useful diagnostic semantics, our model automatically calculates three types of semantic feature with equally down-sampled frames based on Contrast-Enhanced Ultrasound (CEUS). Thereafter, a Support Vector Machine (SVM) classifier is trained to make the final diagnosis. Compared with traditional methods for diagnosing HCC, the proposed model has the advantage of less computational complexity and being able to handle the atypical HCC cases. The experimental results show that our method obtained a pretty considerable performance and outperformed two traditional methods. According to the results, the average accuracy reaches 94.40%, recall rate 94.76%, F1-score value 94.62%, specificity 93.62% and sensitivity 94.76%, indicating good merit for automatically diagnosing atypical HCC cases.
Energy Efficient Decentralized Authentication in Internet of Underwater Things Using Blockchain

In recent years, there has been rapid growth in developing smart cities. Nearly 70% of the Earthâ€™s surface is covered by water and a large proportion of underwater environments are still unknown and have not been explored. In this context, Internet of things (IoT) is one of the most important technologies used in smart cities. Due to the growth of IoT and its influence in all areas of human life, including the underwater environment, a new class of IoT, called Internet of underwater things (IoUT) has emerged. IoUT includes a network of underwater smart devices that are connected to each other and has applications in environmental monitoring, underwater exploration, disaster prevention and military. In autonomous interactions of underwater devices, objects must be authenticated and securely interconnected to avoid security risks by malicious nodes. Most authentication methods and security mechanisms are centralized and often require a trustful third party in communications, which may well increase the computation cost and energy consumption due to the subsequent overhead, especially for underwater communications. On the other hand, there are restrictions on devices in the underwater environment, the most important of which are energy constraints. In this paper, we propose a robust, transparent, and energy-efficient decentralized authentication mechanism for IoUT using blockchain technology. We show through results that the proposed method is suitable for underwater devices with limited memory, energy, and computational power. The proposed modelâ€™s decentralized authentication in a cluster network has a significant effect on reducing the energy consumption of the devices by 74.63% compared to classic authentication methods. Moreover, using the proposed method allows a savings of more than 41.9% in end-to-end delay and increases delivery rate by 21.6%.
Probabilistic Generation Model of Solar Irradiance for Grid Connected Photovoltaic Systems Using Weibull Distribution

Around the world, countries are integrating photovoltaic generating systems to the grid to support climate change initiatives. However, solar power generation is highly uncertain due to variations in solar irradiance level during different hours of the day. Inaccurate modelling of this variability can lead to non-optimal dispatch of system resources. Therefore, accurate characterization of solar irradiance patterns is essential for effective management of renewable energy resources in an electrical power grid. In this paper, the Weibull distribution based probabilistic model is presented for characterization of solar irradiance patterns. Firstly, Weibull distribution is utilized to model inter-temporal variations associated with reference solar irradiance data through moving window averaging technique, and then the proposed model is used for irradiance pattern generation. To achieve continuity of discrete Weibull distribution parameters calculated at different steps of moving window, Generalized Regression Neural Network (GRNN) is employed. Goodness of Fit (GOF) techniques are used to calculate the error between mean and standard deviation of generated and reference patterns. The comparison of GOF results with the literature shows that the proposed model has improved performance. The presented model can be used for power system planning studies where the uncertainty of different resources such as generation, load, network, etc., needs to be considered for their better management.
Leveraging Passage-level Cumulative Gain for Document Ranking

Document ranking is one of the most studied but challenging problems in information retrieval (IR) research. A number of existing document ranking models capture relevance signals at the whole document level. Recently, more and more research has begun to address this problem from fine-grained document modeling. Several works leveraged fine-grained passage-level relevance signals in ranking models. However, most of these works focus on context-independent passage-level relevance signals and ignore the context information, which may lead to inaccurate estimation of passage-level relevance. In this paper, we investigate how information gain accumulates with passages when users sequentially read a document. We propose the context-aware Passage-level Cumulative Gain (PCG), which aggregates relevance scores of passages and avoids the need to formally split a document into independent passages. Next, we incorporate the patterns of PCG into a BERT-based sequential model called Passage-level Cumulative Gain Model (PCGM) to predict the PCG sequence. Finally, we apply PCGM to the document ranking task. Experimental results on two public ad hoc retrieval benchmark datasets show that PCGM outperforms most existing ranking models and also indicates the effectiveness of PCG signals. We believe that this work contributes to improving ranking performance and providing more explainability for document ranking.
Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation

Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved to be an efficient method of handling concept drift. However, the best way to create and maintain ensemble diversity with evolving streams is still a challenging problem. In contrast to estimating diversity via inputs, outputs, or classifier parameters, we propose a diversity measurement based on whether the ensemble members agree on the probability of a regional distribution change. In our method, estimations over regional distribution changes are used as instance weights. Constructing different region sets through different schemes will lead to different drift estimation results, thereby creating diversity. The classifiers that disagree the most are selected to maximize diversity. Accordingly, an instance-based ensemble learning algorithm, called the diverse instance-weighting ensemble (DiwE), is developed to address concept drift for data stream classification problems. Evaluations of various synthetic and real-world data stream benchmarks show the effectiveness and advantages of the proposed algorithm.
Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

Semantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems' inter-operability, which requires that the sensor ontologies themselves are inter-operable. Therefore, it is necessary to match the sensor ontologies by establishing the meaningful links between semantically related sensor information. Since the Swarm Intelligent Algorithm (SIA) represents a good methodology for addressing the ontology matching problem, we investigate a popular SIA, that is, the Firefly Algorithm (FA), to optimize the ontology alignment. To save the memory consumption and better trade off the algorithm's exploitation and exploration, in this work, we propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA), which combines the compact encoding mechanism with the co-Evolutionary mechanism. Our proposal utilizes the Gray code to encode the solutions, two compact operators to respectively implement the exploiting strategy and exploring strategy, and two Probability Vectors (PVs) to represent the swarms that respectively focuses on the exploitation and exploration. Through the communications between two swarms in each generation, CcFA is able to efficiently improve the searching efficiency when addressing the sensor ontology matching problem. The experiment utilizes the Conference track and three pairs of real sensor ontologies to test our proposal's performance. The statistical results show that CcFA based ontology matching technique can effectively match the sensor ontologies and other general ontologies in the domain of organizing conferences.
Large-Universe Attribute-Based Encryption With Public Traceability for Cloud Storage

Attribute-based encryption (ABE) can be utilized to achieve both data security and fine-grained access control in a cloud computing environment. However, we need to consider the risks of key abuse and key escrow in such a setting. Specifically, the former risk category includes the illegal sharing of user's keys (i.e., user key abuse) and illegal key distribution by an authority (i.e., authority key abuse), and the latter includes the scenario where some ciphertext is decrypted by the authority without the user's approval. Hence, in this article, we seek to address both key abuse and key escrow concerns when deploying ABE in a cloud computing environment. In our construction, two authorities [i.e., a key generation center (KGC) and an attribute authority (AA)] participate in the generation of the user's secret key. Both KGC and AA will not know the full decryption key or have the capability to forge one. As a result, neither KGC nor AA can illegally distribute the user's private key to unauthorized users or decrypt user's ciphertexts without the user's approval. In addition, in our scheme, any private keys modified by malicious users cannot be successfully used for decryption. In the event that some user illegally shares his/her original private key, the scheme has in place a mechanism to trace the abused private key (since the user's identity information is embedded in the private key). Hence, our scheme supports public traceability, key abuse, and key escrow. In addition, our scheme is based on prime order bilinear groups, and is shown to be selectively secure in the standard model.
7.2 A 12nm Programmable Convolution-Efficient Neural-Processing-Unit Chip Achieving 825TOPS

Convolutional neural networks (CNN) represent a key application in data centers, which calls for accelerators that are: 1) efficient for CNN computations; 2) having high throughput to be cost-efficient; and, 3) with adequate programming flexibility for algorithm upgrades. Lacking of the availability of such a chip in the market, we designed our own. Matrix multiplication (MM) and convolution (CONV) are the top-2 deep learning (DL) operations requiring intensive computation. Most existing accelerators, like GPUs [6], [7], TPU [9], and a few new AI chips [3], [4] are architected for GEMM. Computing CONV on a GEMM engine, one needs the img2col() transformation to flatten images into general matrixes. This introduces huge data inflation, leading to unnecessary extra computation and storage, but also decreasing arithmetic intensity and bounding performance towards I/O and memory. Although some accelerators such as [5] exploit the CONV architecture directly, integrating larger but balanced computing power into a single chip is quite challenging. Moreover, with the fast evolution of DL algorithms, it is critical to design a programmable neural processing unit (NPU) instead of a dedicated ASIC for data center scenarios. To satisfy the above requirements, our NPU is architected to be CONV-efficient under the control of operation-fused coarse-grained instructions. It integrates as much computing power as possible via squeezed computation with a large SRAM-only design. Also, it delivers programming flexibility via an instruction set architecture (ISA) with coverage for anticipated forward-looking functionality.