Reviewerly/exHarmony huggingface dataset:
An Edge Cloud Data Integrity Protection Scheme Based on Blockchain

The publicly accessible feature of edge servers leads to the threat of malicious access to the data stored on the server and a series of security problems such as the leakage of user data privacy and the destruction of integrity. Data custody causes the separation of user ownership and management rights and brings potential security risks of data theft and destruction. Among them, for the integrity of the data uploaded by the terminal, the current protection mechanism mostly verifies the identity of the visitor or encrypts the data, but the role of verification is mostly assumed by the server, and it is impossible to avoid the collusion of edge servers with malicious intruders. In this paper, a distributed virtual machine agent (VMA) is designed and implemented, an edge cloud data integrity monitoring framework is built, and the verification protocol based on blockchain is proposed, which achieves trusted verification without relying on a trusted third party. Also, a prototype system of edge cloud data integrity protection based on blockchain is constructed to prevent data corruption. The results of security proof and experimental verification show that the mechanism based on blockchain technology can defend against three attacks of cloud service providers, has superior computation, and reduces the storage costs to protect the integrity of user data.
Bash in the Wild: Language Usage, Code Smells, and Bugs

The Bourne-again shell (Bash) is a prevalent scripting language for orchestrating shell commands and managing resources in Unix-like environments. It is one of the mainstream shell dialects that is available on most GNU Linux systems. However, the unique syntax and semantics of Bash could easily lead to unintended behaviors if carelessly used. Prior studies primarily focused on improving the reliability of Bash scripts or facilitating writing Bash scripts; there is yet no empirical study on the characteristics of Bash programs written in reality, e.g., frequently used language features, common code smells, and bugs. In this article, we perform a large-scale empirical study of Bash usage, based on analyses over one million open source Bash scripts found in Github repositories. We identify and discuss which features and utilities of Bash are most often used. Using static analysis, we find that Bash scripts are often error-prone, and the error-proneness has a moderately positive correlation with the size of the scripts. We also find that the most common problem areas concern quoting, resource management, command options, permissions, and error handling. We envision that these findings can be beneficial for learning Bash and future research that aims to improve shell and command-line productivity and reliability.
Gradient-Based Algorithms for Convex Discrete Optimization via Simulation

Solving Convex Discrete Optimization via Simulation via Stochastic Search Algorithms Many decision-making problems in operations research and management science require the optimization of large-scale complex stochastic systems with high-dimensional discrete decision variables. For a number of applications, the objective function exhibits convexity in the discrete decision variables, or the problem can be transformed into a convex one. In “Gradient-Based Algorithms for Convex Discrete Optimization via Simulation,” Zhang, Zheng, and Lavaei propose provably efficient simulation–optimization algorithms for general convex optimization via simulation problems with high-dimensional discrete decision space. By utilizing the convex structure, the developed algorithms demonstrate a polynomial dependence on the dimension and scale of the decision space. In addition, information-theoretic lower bounds are derived to show the limit of algorithm design. In the case when possibly biased gradient estimators are available, they propose simulation–optimization algorithms to achieve optimality guarantees with a reduced dependence on the dimension.
High-Magnification Super-Resolution Reconstruction of Image with Multi-Task Learning

Single-image super-resolution technology has made great progress with the development of the convolutional neural network, but most of the current super-resolution methods do not attempt high-magnification image super-resolution reconstruction; only reconstruction with ×2, ×3, ×4 magnification is carried out for low-magnification down-sampled images without serious degradation. Based on this, this paper proposed a single-image high-magnification super-resolution method, which extends the scale factor of image super-resolution to high magnification. By introducing the idea of multi-task learning, the process of the high-magnification image super-resolution process is decomposed into different super-resolution tasks. Different tasks are trained with different data, and network models for different tasks can be obtained. Through the cascade reconstruction of different task network models, a low-resolution image accumulates reconstruction advantages layer by layer, and we obtain the final high-magnification super-resolution reconstruction results. The proposed method shows better performance in quantitative and qualitative comparison on the benchmark dataset than other super-resolution methods.
Smart Home Design Based on Computer Intelligent Simulation Analysis

With the continuous development of the social economy, people from all walks of life attach great importance to the smart home design of artificial intelligence technology. In the context of the information age, people’s requirements for the quality of life are also gradually upgrading, and the requirements for home furnishing have also been upgraded from the traditional requirements of only living places to the expectation that a more intelligent, convenient, smart home has gradually been brought into life by people. The smart home system can integrate all control terminals into one system and implement on-site or remote control of home equipment using the computer terminal and mobile phone terminal, to realize the function of managing home equipment more conveniently and flexibly. Artificial intelligence technology as one of the important links adopts a structured design scheme to control and complete the design of the smart home system. Compared with most smart home systems on the market, this system also focuses on the safety precautions of the residential environment. It adopts infrared detectors for doors and windows and gravity sensing devices for indoor floors. The space-time barriers between home systems optimize and enhance the user’s living experience. The article first analyzes the architecture, mainstream technology, and characteristics of computer technology and then discusses the design and implementation of my country’s smart home system, providing strong support for subsequent research.
GENERATION AND ROBUSTNESS OF QUANTUM MEMORY-ASSISTED ENTROPIC UNCERTAINTY AND UNCERTAINTY-INDUCED NONLOCALITY OF TWO NITROGEN-VACANCY CENTERS COUPLED BY OPEN TWO NANOCAVITIES

In this paper, we investigate the dynamics of quantum memory-assisted entropic uncertainty, uncertainty-induced nonlocality and log-negativity entanglement of two nitrogen-vacancy centers (NVC). In particular, we are interested in analyzing two nitrogen-vacancy centered qubits coupled with two coherent nanocavities. We consider two different configurations of NVC qubits: when the qubits are prepared initially in separable and maximally entangled states. We demonstrate that coherent nanocavities can be successfully used to generate nonlocality and entanglement in the two NVC qubits while suppressing the associated entropic uncertainty. When the system is considered initially in separable two-NVC qubits, we show that the coupled coherent nanocavities successfully generate nonlocality and entanglement while avoiding the associated entropic uncertainty. The parameter’s adjustment determines the degree of entanglement, non-locality generation, and preservation. In comparison, we find entanglement more vulnerable to the decoherence and entropic uncertainty action between the two NVC qubits and nanocavities than the uncertainty-induced nonlocality. Furthermore, the NVC-qubits coupled with two coherent nanocavities promote entanglement’s sudden birth and death phenomena. This study contributes to the quantum mechanical protocols requiring the robust practical generation of entanglement and nonlocality as well as the related information preservation for the increased efficiency of quantum devices.
Toward Register Spilling Security Using LLVM and ARM Pointer Authentication

Modern reduced instruction set computer processors are based on a load/store architecture, where all computations are performed on register operands. Compilers therefore allocate registers based on demand, and when occupancy is at maximum, register contents are spilled onto the stack and then retrieved later as data is needed. This phenomenon has security implications that cannot be ignored, as data on the stack is subject to well-known memory corruption attacks. Moreover, works presented so far are mainly targeting protection of pointers to code (e.g., return addresses), but are ineffective for protecting other context data in the stack. This article presents a security solution for spilled registers, generalizing the use of ARM pointer authentication (PA) for this purpose. The protection is enforced by the LLVM compiler via additional compiler passes and modifications. The solution provides guarantees for both integrity and confidentiality protection, and also addressing reuse attack problems associated with PA usage. Experimental data collected demonstrates the effectiveness of the solution against corruption and eavesdropping. We test our solution using SPEC CPU 2017, which confirms the functional viability of our solution. Additionally, we expose real-world performance overhead metrics of our protection design on a ARM-PA-enabled processor.
An MILP Encoding for Efficient Verification of Quantized Deep Neural Networks

Quantized deep neural networks (DNNs) have the potential to find wide applications in safety-critical cyber–physical systems implemented on processors supporting only integer arithmetic. The significant challenge therein is to ensure the correctness of the operation of the network with its approximated computation. To address this verification challenge formally, we present a methodology to encode the verification problem into a mixed-integer linear programming (MILP) problem. Our encoding is based on the bit-precise semantics of quantized neural networks, which ensures the soundness of our method. We implement our verification methodology using the Gurobi MILP solver and evaluate it on several widely used DNN benchmarks. We compare our method with state-of-the-art bit-vector encodings, which are outperformed by our MILP-based verification methodology by an order of magnitude in most cases. These experimental results establish our MILP-based verification technique as a powerful tool for developing formally verified safety-critical systems with quantized DNNs as a component.
CMQ: Crossbar-Aware Neural Network Mixed-Precision Quantization via Differentiable Architecture Search

The RRAM-based accelerators have become very popular candidates for neural network acceleration due to they perform matrix-vector multiplication in-memory with high storage density and low latency. Many related works have used fixed-precision quantization to achieve the model compression and enhance the tolerance of process variation, but these methods still suffer a large accuracy degradation and poor robustness to nonideal effects. In this work, we propose a crossbar-aware mixed-precision quantization scheme, which enables to search for the optimal precision of each part of the network as a way to improve the accuracy and robustness to noise. First, we introduce a group quantization strategy that can flexibly adjust the group size dynamically according to the crossbar size. Then, we propose a detailed mixed-precision search flow to search for the optimal precision set of the network. Finally, we give a noise injection adaption training method to enhance the tolerance of noise. Experimental results show that our proposed method can improve inference accuracy by at least 2.04% compared to the fixed-precision quantization under the same resource cost. The searched architecture with the highest accuracy most accurate (MA) could achieve an accuracy of 92.39% and a resource saving of 93.30% compared to the full precision model. The searched architecture with the biggest resource savings most efficient (ME) could achieve an accuracy of 91.11% and a resource saving of 95.57% compared to the full precision model. And, the average precision of the ME mixed-precision architecture is only 1.4 bits. Besides, the results show the mixed-precision network with noise adaption training is more robust to noise than the fixed-precision network with noise adaption training.
Photonic Reconfigurable Accelerators for Efficient Inference of CNNs With Mixed-Sized Tensors

Photonic microring resonator (MRR)-based hardware accelerators have been shown to provide disruptive speedup and energy-efficiency improvements for processing deep convolutional neural networks (CNNs). However, previous MRR-based CNN accelerators fail to provide efficient adaptability for CNNs with mixed-sized tensors. One example of such CNNs is depthwise separable CNNs. Performing inferences of CNNs with mixed-sized tensors on such inflexible accelerators often leads to low hardware utilization, which diminishes the achievable performance and energy efficiency from the accelerators. In this article, we present a novel way of introducing reconfigurability in the MRR-based CNN accelerators, to enable dynamic maximization of the size compatibility between the accelerator hardware components and the CNN tensors that are processed using the hardware components. We classify the state-of-the-art MRR-based CNN accelerators from prior works into two categories, based on the layout and relative placements of the utilized hardware components in the accelerators. We then use our method to introduce reconfigurability in accelerators from these two classes, to consequently improve their parallelism, the flexibility of efficiently mapping tensors of different sizes, speed, and overall energy efficiency. We evaluate our reconfigurable accelerators against three prior works for the area proportionate outlook (equal hardware area for all accelerators). Our evaluation for the inference of four modern CNNs indicates that our designed reconfigurable CNN accelerators provide improvements of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.8\times $ </tex-math></inline-formula> in frames-per-second (FPS) and up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.5\times $ </tex-math></inline-formula> in FPS/W, compared to an MRR-based accelerator from prior work.
Sum squeezing, entanglement and quantum teleportation of the superposition of photon-added pair coherent state

Abstract In this paper, we study some non-classical properties of the superposition of photon-added pair coherent state (SPAPCS) such as two-mode sum squeezing and entanglement. The obtained results show that this state has two-mode sum squeezing behavior but the pair coherent state does not. The degree of squeezing of the SPAPCS is enhanced as more and more photons are added to the two modes of the original pair coherent state. Besides, this state is an entangled state and the entanglement degree can be enhanced by adding photons to both modes of the state. When using the SPAPCS as an entangled resource for a coherent state quantum teleportation, it is shown that the quantum teleportation process is successful and the average fidelity of the process can be higher than that of the case where the original pair coherent state is used if the parameters are selected appropriately.
Effect of the Choice of Connectives on the Relation between Classical Logic and Intuitionistic Logic

In this paper, we study general propositional connectives characterized by truth functions and provide a necessary and sufficient condition for intuitionistic logic that coincides with classical logic.
PRE: A Precision-Recall-Effort Optimization Framework for Query Simulation

We study how to develop an interpretable query simulation framework that can potentially explain the process a real user might have used to formulate a query and propose a novel interpretable optimization framework (PRE) for simulating query formulation and reformulation uniformly based on a user's knowledge state, where the three high-level objectives are to maximize the precision and recall of the anticipated retrieval results and minimize the user effort. We propose probabilistic models to model how a user might estimate precision and recall for a candidate query and derive multiple specific query formulation algorithms. Evaluation results show that the major assumptions made in the PRE framework appear to be reasonable, matching the observed empirical result patterns. PRE provides specific hypotheses about a user's query formulation process that can be further examined via user studies, enables simulation of meaningful variations of users without requiring extra training data, and serves as a roadmap for systematic exploration and derivation of new interpretable query simulation methods.
Towards Deeper Understanding of Variational Auto-encoders for Binary Collaborative Filtering

Recommendation systems are an integral component of machine learning, wherein collaborative filtering (CF) is among the most prominent algorithms employed. Recently, variational auto-encoders (VAEs) with multinomial likelihood and weighted Kullback-Leibler (KL) regularization (referred to as Mult-VAE) provide state-of-the- art performance for collaborative filtering of binary data. To gain deeper insight into the objective function of Mult-VAE, we build a connection between the reconstruction term of Mult-VAE objective and the objective function of the probabilistic n-Choose-k model for ranking prediction. In particular, we theoretically demonstrate that the negative reconstruction error of Mult-VAE is a lower bound to the log-likelihood of the binary n-Choose-k model. Hence, Mult-VAE can be interpreted as an approximate proxy to the n-Choose-k model. We also empirically show the essential role of this reconstruction term of evidence lower bound in the context of collaborative filtering on multiple real-world datasets. Finally, inspired by the role of the weighted KL term in maximizing mutual information between observed ratings and latent variables, we propose a semi-implicit VAE framework with superior performance in terms of ranking metrics.
Agglomerative Clustering with Threshold Optimization via Extreme Value Theory

Clustering is a critical part of many tasks and, in most applications, the number of clusters in the data are unknown and must be estimated. This paper presents an Extreme Value Theory-based approach to threshold selection for clustering, proving that the “correct” linkage distances must follow a Weibull distribution for smooth feature spaces. Deep networks and their associated deep features have transformed many aspects of learning, and this paper shows they are consistent with our extreme-linkage theory and provide Unreasonable Clusterability. We show how our novel threshold selection can be applied to both classic agglomerative clustering and the more recent FINCH (First Integer Neighbor Clustering Hierarchy) algorithm. Our evaluation utilizes over a dozen different large-scale vision datasets/subsets, including multiple face-clustering datasets and ImageNet for both in-domain and, more importantly, out-of-domain object clustering. Across multiple deep features clustering tasks with very different characteristics, our novel automated threshold selection performs well, often outperforming state-of-the-art clustering techniques even when they select parameters on the test set.
Stochastic Retrieval-Conditioned Reranking

The multi-stage cascaded architecture has been adopted by many search engines for efficient and effective retrieval. This architecture consists of a stack of retrieval and reranking models in which efficient retrieval models are followed by effective (neural) learning-to-rank models. The optimization of these learning-to-rank models is loosely connected to the early stage retrieval models. This paper draws theoretical connections between the early stage retrieval and late stage reranking models by deriving expected reranking performance conditioned on the early stage retrieval results. Our findings shed light on optimization of both retrieval and reranking models. As a result, we also introduce a novel loss function for training reranking models that leads to significant improvements on multiple public benchmarks. Our findings provide theoretical and empirical guidelines for developing multi-stage cascaded retrieval models.
Filtering out Outliers in Learning to Rank

Outlier data points are known to affect negatively the learning process of regression or classification models, yet their impact in the learning-to-rank scenario has not been thoroughly investigated so far. In this work we propose SOUR, a learning-to-rank method that detects and removes outliers before building an effective ranking model. We limit our analysis to gradient boosting decision trees, where SOUR searches for outlier instances that are incorrectly ranked in several iterations of the learning process. Extensive experiments show that removing a limited number of outlier data instances before re-training a new model provides statistically significant improvements, and that SOUR outperforms state-of-the-art de-noising and outlier detection methods.
A Model Selection criterion for the Mixture Reduction problem based on the Kullback - Leibler Divergence

In order to be properly addressed, many practical problems require an accurate stochastic characterization of the involved uncertainties. In this regard, a common approach is the use of mixtures of parametric densities which allow, in general, to arbitrarily approximate complex distributions by a sum of simpler elements. Nonetheless, in contexts like target tracking in clutter, where mixtures of densities are commonly used to approximate the posterior distribution, the optimal Bayesian recursion leads to a combinatorial explosion in the number of mixture components. For this reason, many mixture reduction algorithms have been proposed in the literature to keep limited the number of hypotheses, but very few of them have addressed the problem of finding a suitable model order for the resulting approximation. The commonly followed approach in those algorithms is to reduce the mixture to a fixed number of components, disregarding its features which may vary over time. In general, finding an optimal number of mixture components is a very difficult task: once a meaningful optimality criterion is identified, potentially burdensome computational procedures must be devised to reach the optimum. In this work, by exploiting the optimal transport theory, an efficient and intuitive model selection criterion for the mixture reduction problem is proposed.
Extended Target Tracking with a Particle Filter Using State Dependent Target Measurement Models

This paper presents an extended object tracking algorithm. We model visibility constraints in the measurement process. A sequential importance sampling and resampling particle filter is used, which permits a flexible modeling of the target shape. We compare our method to the standard approach of just rotating and translating the target extent model. Our approach is applicable for both radar and LiDAR sensors. Results are presented in a a simulated scenario with LiDAR data. A proof of concept is conducted in a real world scenario with radar data.