Reviewerly/exHarmony huggingface dataset:
Distribution Consistent Neural Architecture Search

Recent progress on neural architecture search (NAS) has demonstrated exciting results on automating deep network architecture designs. In order to overcome the unaffordable complexity of training each candidate architecture from scratch, the state-of-the-art one-shot NAS approaches adopt a weight-sharing strategy to improve training efficiency. Although the computational cost is greatly reduced, such oneshot process introduces a severe weight coupling problem that largely degrades the evaluation accuracy of each candidate. The existing approaches often address the problem by shrinking the search space, model distillation, or fewshot training. Instead, in this paper, we propose a novel distribution consistent one-shot neural architecture search algorithm. We first theoretically investigate how the weight coupling problem affects the network searching performance from a parameter distribution perspective, and then propose a novel supernet training strategy with a Distribution Consistent Constraint that can provide a good measurement for the extent to which two architectures can share weights. Our strategy optimizes the supernet through iteratively inferring network weights and corresponding local sharing states. Such joint optimization of supernet's weights and topologies can diminish the discrepancy between the weights inherited from the supernet and the ones that are trained with a stand-alone model. As a result, it enables a more accurate model evaluation phase and leads to a better searching performance. We conduct extensive experiments on benchmark datasets with multiple searching spaces. The resulting architecture achieves superior performance over the current state-of-the-art NAS algorithms with comparable search costs, which demonstrates the efficacy of our approach.
A Distributed Data Fabric Architecture based on Metadate Knowledge Graph

Just like talent, capital and land, data is becoming one of the market-oriented elements and driving profound intelligent changes in traditional industries. Data governance is a key process to improve the availability, integrity, and security of data. For large group enterprises like telecom operators, it is not simple to manage the massive data scattered in different departments, different facilities, different regions, and different systems. This paper creatively proposes a distributed data fabric architecture based on metadate knowledge graph to solve this problem. The functional architecture consists of six layers, including acquisition storage layer, data management layer, knowledge graph layer, data directory layer, model application layer, user request layer, etc. A feasible framework is designed to guide how to implement this architecture, and several key enabling technologies have been studied. Instead of storing the full amount of data in a centralized data lake, we choose to centralize the metadata into the data warehouse. This can avoid the problems of data privacy disclosure, long data turnover time, wrong data entry into the lake and high operation and maintenance cost. Specifically, the enhanced data knowledge directory derived from the lower-level global metadata knowledge map is constructed, including static data resource directory, static business resource directory, and dynamic enhanced knowledge directory automatically generated based on user input. The proposed architecture has been used in the practical application in China Telecom Group and alleviates the difficulty of massive data query to a certain extent.
Speeding up POMDP Planning via Simplification

In this paper, we consider online planning in par-tially observable domains. Solving the corresponding POMDP problem is a very challenging task, particularly in an online setting. Our key contribution is a novel algorithmic approach, Simplified Information Theoretic Belief Space Planning (SITH-BSP), which aims to speed up POMDP planning considering belief-dependent rewards, without compromising the solution's accuracy. We do so by mathematically relating the simplified el-ements of the problem to the corresponding counterparts of the original problem. Specifically, we focus on belief simplification and use it to formulate bounds on the corresponding original belief-dependent rewards. These bounds in turn are used to perform branch pruning over the belief tree, in the process of extracting the optimal policy from this existing belief tree. We further introduce the notion of adaptive simplification, while re-using calculations between different simplification levels, and exploit it to prune, at each level in the belief tree, all branches but one. Therefore, our approach is guaranteed to find the optimal solution (policy) that corresponds to the given belief tree but with substantial speedup. As a second key contribution, we derive novel analytical bounds for differential entropy, considering a sampling-based belief representation, which we believe are of interest on their own. We validate our approach in simulation using these bounds and where simplification corresponds to reducing the number of samples, exhibiting a significant computational speedup while yielding the optimal solution for the given belief tree.
Novice Type Error Diagnosis with Natural Language Models

Strong static type systems help programmers eliminate many errors without much burden of supplying type annotations. However, this flexibility makes it highly non-trivial to diagnose ill-typed programs, especially for novice programmers. Compared to classic constraint solving and optimization-based approaches, the data-driven approach has shown great promise in identifying the root causes of type errors with higher accuracy. Instead of relying on hand-engineered features, this work explores natural language models for type error localization, which can be trained in an end-to-end fashion without requiring any features. We demonstrate that, for novice type error diagnosis, the language model-based approach significantly outperforms the previous state-of-the-art data-driven approach. Specifically, our model could predict type errors correctly 62% of the time, outperforming the state-of-the-art Nate ’s data-driven model by 11%, under a more rigorous accuracy metric. Furthermore, we also apply structural probes to explain the performance difference between different language models.
Exploiting Quantum Assertions for Error Mitigation and Quantum Program Debugging

An assertion is a predicate that should be evaluated true during program execution. In this paper, we present the development of quantum assertion schemes and show how they are used for hardware error mitigation and software debugging. Compared to assertions in classical programs, quantum assertions are challenging due to the no-cloning theorem and potentially destructive measurement. We discuss how these challenges can be circumvented such that certain properties of quantum states can be verified non-destructively during program execution. Furthermore, we show that besides detecting program bugs, dynamic assertion circuits can mitigate noise effects via post-selection of the assertion results. Our case studies demonstrate the use of quantum assertions in various quantum algorithms.
Critical Review of Blockchain Consensus Algorithms: challenges and opportunities

Blockchain is a distributed ledger in which transactions are grouped in blocks linked by hash pointers. Blockchain-based solutions provide trust and privacy because of the resistance to the inconsistency of data and advanced cryptographic features. In various fields, blockchain technology has been implemented to ensure transparency, verifiability, interoperability, governance, and management of information systems. Processing large volumes of data being generated through emerging technologies is a big issue. Many researchers have used Blockchain in various fields integrated with IoT, i.e., industry 4.0, biomedical, health, genomics, etc. Blockchain has the attributes of decentralization, solidness, security, and immutability with a possibility to secure the system design for transmission and storage of data. The purpose of the consensus protocols is to keep up the security and effectiveness of the blockchain network. Utilizing the correct protocol enhances the performance of the blockchain applications. This article presents essential principles and attributes of consensus algorithms to show the applications, challenges, and opportunities of blockchain technology. Moreover, future research directions are also presented to choose an appropriate consensus algorithm to enhance the performance of Blockchain based applications
MESDeceiver: Efficiently Generating Natural Language Adversarial Examples

The research of adversarial attacks in the text domain attracts many interests, and many methods in a black-box scenario with a high attack success rate have been proposed. However, these attack methods are inefficient as they require lots of queries for the victim models when crafting text adversarial examples. In this paper, a novel attack model based on an improved beam search algorithm with different attack strategies in a black-box scenario is proposed, its attack success rate surpasses the benchmark attack methods, but more importantly, it is more efficient than the benchmark attack methods. The novel method is empirically evaluated by attacking WordCNN, LSTM, BiLSTM, and BERT on four benchmark datasets. For instance, it achieves a 100% attack success rate and surpasses all the baseline methods when attacking BERT and BiLSTM on IMDB, but the number of queries for the victim models is much lower than the baseline methods. Also, further experiments show the novel method has a good transferability to the generated adversarial examples.
DEANet: A Real-Time Image Semantic Segmentation Method Based on Dual Efficient Attention Mechanism

Image semantic segmentation is the basis of performing various tasks in computer vision. It has been widely used in medical imaging, robotics and many other fields. However, the existing image semantic segmentation technology cannot improve the segmentation speed while ensuring the segmentation accuracy, and cannot meet the requirements of real-time applications. Therefore, this paper proposes a real-time image semantic segmentation method based on dual efficient attention mechanism (DEANet). Pyramid sampling is introduced into the channel dimension to extract multi-scale information, and higher resolution aggregation features are adopted as the input of the spatial dimension. It can achieve high efficiency and accuracy of image semantic segmentation. The proposed DEANet was tested on two classic datasets. On the Cityscapes dataset, when the input size is 512 × 1024, the segmentation accuracy reaches 74.90% mIoU, and the segmentation speed reaches 99.91FPS. On the CamVid dataset, when the input size is 360 × 480, the segmentation accuracy reaches 70.07% mIoU and the segmentation speed reaches 142.72 FPS.
Research on Dynamic Trajectory Planning Algorithm for Safe Obstacle Avoidance of Automatic Forklift

Aiming at the problem of lower lateral stability and overturning caused by excessive centripetal acceleration in Obstacle Avoidance Trajectory Planning of automatic forklift, a method of Optimal Avoidance Safety Boundary (OASB) is proposed in this paper. Firstly, the kinematic and dynamic models of the vehicle are derived. Secondly, the centripetal acceleration and obstacle avoidance time generated by obstacle avoidance maneuver are used as the optimization objectives, and the optimization problem is established to delimit the boundary of minimum centripetal acceleration generated by obstacle avoidance. Finally, a trajectory planner based on model predictive control (MPC) is designed to make the forklift plan the driving trajectory reasonably and legally in the face of obstacles. The simulation results show that the centripetal acceleration of the vehicle when obstacle avoidance turning is within the safe range, which verifies the feasibility of the algorithm.
Estimation of Soiling Losses in Unlabeled PV Data

We provide a methodology for estimating the losses due to soiling for photovoltaic (PV) systems. We focus this work on estimating the losses from historical power production data that are unlabeled, i.e. power measurements with time stamps, but no other information such as site configuration or meteorological data. We present a validation of this approach on a small fleet of typical rooftop PV systems. The proposed method differs from prior work in that the construction of a performance index is not required to analyze soiling loss. This approach is appropriate for analyzing the soiling losses in field production data from fleets of distributed rooftop systems and is highly automatic, allowing for scaling to large fleets of heterogeneous PV systems.
Hybrid Consensus Protocols and Security Analysis for Blockchain

Blockchain shows the potential for various applications, thanks to its property of decentralization and its security. Consensus protocols play an important role in the blockchain system, especially in the security of the blockchain system. Moreover, hybrid consensus protocols were introduced to tackle the weaknesses of one consensus protocol. Therefore, this paper conducts a security performances analysis of the hybrid protocols, starting with the classification of mainstream consensus protocols, such as Proof-of-Work, Proof-of-Stake, Delegated-Proof-of-Stake, and Practical-Byzantine-Fault-Tolerance. This survey paper analyzes several common consensus security attacks and their target consensus protocols. Furthermore, this paper presents some hybrid protocol cases and explains common problems and the working mechanism of hybrid protocols. This paper further summarizes the security analysis of each case. The result indicates that hybrid protocols can handle particular security issues quite well since hybrid protocols can inherit the strengths from another consensus protocol that is not troubled by a particular security attack. Some future study directions are highlighted regarding hybrid protocol security performance in this paper, which are the great contribution made in this paper and will be valuable references for the related researchers.
State-Space Partitioning Schemes in Multiple Particle Filtering for Improved Accuracy

Multiple particle filtering was proposed as an alternative to particle filtering when tracking states in systems of high dimensions. Multiple particle filters are comprised of a network of particle filters assigned to track subsets of the state, and require each other's obtained information to carry out the filtering. Many improvements of multiple particle filtering have been proposed, however, there have been only a few efforts studying the effects of state partitioning on the filtering performance. In this paper, we propose two novel partitioning schemes for improved accuracy of multiple particle filtering based on: i) random permutations, and ii) the connectedness of the states, i.e. the topology of the system. Computer simulations show that the filter significantly benefits from state permutations, especially when driven by the information in the topology.
Cognitive Complexity and Graph Convolutional Approach Over Control Flow Graph for Software Defect Prediction

The software engineering community is working to develop reliable metrics to improve software quality.It is estimated that understanding the source code accounts for 60% of the software maintenance effort.Cognitive informatics is important in quantifying the degree of difficulty or the efforts made by developers to understand the source code.Several empirical studies were conducted in 2003 to assign cognitive weights to each possible basic control structure of software, and these cognitive weights are used by several researchers to evaluate the cognitive complexity of software systems.In this paper, an effort has been made to categorize the Control Flow Graphs (CFGs) nodes according to their node features.In our case, we extracted seven unique features from the program, and each unique feature was assigned an integer value that we evaluated through Cognitive Complexity Measures (CCMs).We then incorporated CCMs' results as a node feature value in CFGs and generated the same based on the node connectivity for a graph.In order to obtain the feature representation of the graph, a node vector matrix is then created for the graph and passed to the Graph Convolutional Network (GCN).We prepared our data sets using GCN output and then built Deep Neural Network Defect Prediction (DNN-DP) and Convolutional Neural Network Defect Prediction (CNN-DP) models to predict software defects.The Python programming language is used, along with Keras and TensorFlow.Three hundred twenty Python programs were written by our talented UG and PG students, and all experiments were carried out during laboratory classes.Together with three skilled lab programmers, they compiled and ran each individual program and detected defect/no-defect programs before categorizing them into three different classes, namely Simple, Medium, and Complex programs.Accuracy, Receiver Operating Characteristics (ROC), Area Under Curve (AUC), F-measure, Precision and hyperparameter tuning procedures are used to evaluate the approaches.The experimental results show that the proposed models outperformed state-of-the-art methods such as Nave Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) in all evaluation criteria.
DiMOpt: a Distributed Multi-robot Trajectory Optimization Algorithm

This paper deals with Multi-robot Trajectory Planning, that is, the problem of computing trajectories for multiple robots navigating in a shared space while minimizing for control energy.Approaches based on trajectory optimization can solve this problem optimally.However, such methods are hampered by complex robot dynamics and collision constraints that couple robot's decision variables.We propose a distributed multirobot optimization algorithm (DiMOpt) that addresses these issues by exploiting (1) consensus optimization strategies to tackle coupling collision constraints, and (2) a single-robot sequential convex programming method for efficiently handling non-convexities introduced by dynamics.We compare DiMOpt with a baseline centralized multi-robot sequential convex programming algorithm (SCP).We empirically demonstrate that DiMOpt scales well for large fleets of robots while computing solutions faster and with lower costs.Finally, DiMOpt is an iterative algorithm that finds feasible trajectories before converging to a locally optimal solution, and results suggest the quality of such fast initial solutions is comparable to a converged solution computed via SCP.
Nonlinear Transforms in Learned Image Compression From a Communication Perspective

Recently, remarkable progress has been made in learned image compression (LIC), in which nonlinear transforms (NTs) play a crucial role. Although there are many NT methods for improving the rate distortion performance, all the existing methods sacrifice the computational complexity and the number of parameters of the transformation. This paper provides a fundamental novel viewpoint on nonlinear transforms from a communication perspective, and shows how this idea can be extended to design efficient NT methods. In particular, the nonlinear transforms are inferred as signal modulation modules. Under this extrapolation, the current NTs are generalized as amplitude modulation that only varies the amplitude of the carrier wave. Therefore, a nonlinear modulation-like transform (NMLT) which varies the phase angle of the carrier is proposed. Moreover, this concept is extended by introducing In-phase/Quadrature (IQ) modulation, which is a boosting technique in communication field, in order to enhance NMLT. Furthermore, the Bit-interleaved technique in communication is used to guide the optimization of NTML with IQ. The experimental results on different datasets and backbone architectures verify the efficiency and robustness of the proposed methods. For example, when backbone architecture is hyperprior model, our method achieves 19.37% BD-rate reduction over GDN on the Kodak dataset. In addition, our method with channel wise autoregressive model leads to the state-of-the-art rate-distortion performance.
Lightweight Model Uncertainty Estimation for Deep Neural Object Detection

Quantifying model uncertainty of Deep Neural Network (DNN) is important to understand the reliability of the model prediction and avoid risks in safety critical applications. Various approaches, including Bayesian neural networks, Monte-Carlo dropout, and ensembles, are suggested to measure the model uncertainty; but with huge computational cost. We present ModelNet, an Artificial Neural Network (ANN) that can estimate spatial/semantic model uncertainties of a DNN based object detection with less computation overhead. ModelNet is a deterministic ANN that distills the predictive distribution of stochastic DNN. Experimental results show that ModelNet can learn the uncertainty estimation from stochastic DNN in various architectures. ModelNet can perform as a probabilistic object detector with 39x-179x less number of operations, or as an uncertainty assistant to a task network with 1.4x more parameters and 38x less number of operations compared to stochastic DNN. Moreover, a case study of uncertainty driven adaptive sensor using ModelNet is presented.
HPMA-Saber: High-Performance Polynomial Multiplication Accelerator for KEM Saber

The recent research in post-quantum cryptography (PQC) field has gradually switched to efficient implementation of PQC algorithms on hardware platforms. As polynomial multiplication is typically one of the critical operations within lattice-based PQC, its hardware acceleration has drawn significant attention from the research community recently. We propose a high-speed processing strategy to construct a new High-performance Polynomial Multiplication Accelerator (HPMA) for key encapsulation mechanism (KEM) Saber. Firstly, we have given a detailed mathematical derivation to obtain a low-latency processing algorithm for Saber polynomial multiplication. Then, we have innovatively used the derived the proposed algorithm to construct a new structure HPMA for FPGA implementation. Lastly, we have demonstrated the superior performance of the proposed HPMA-Saber by comparing with state-of-the-art works. The proposed design strategy is highly efficient and the obtained results can be useful for the PQC research community.
Downstream transformer generation of question-answer pairs with preprocessing and postprocessing pipelines

We present a method to perform a downstream task of transformers on generating question-answer pairs (QAPs) from a given article. We first finetune pretrained transformers on QAP datasets. We then use a preprocessing pipeline to select appropriate answers from the article, and feed each answer and the relevant context to the finetuned transformer to generate a candidate QAP. Finally we use a postprocessing pipeline to filter inadequate QAPs. In particular, using pretrained T5 models as transformers and the SQuAD dataset as the finetruning dataset, we obtain a finetuned T5 model that outperforms previous models on standard performance measures over the SQuAD dataset. We then show that our method based on this finetuned model generates a satisfactory number of QAPs with high qualities on the Gaokao-EN dataset assessed by human judges.