Reviewerly/exHarmony huggingface dataset:
Symmetric-Approximation Energy-Based Estimation of Distribution (SEED) Algorithm for Solving Continuous High-Dimensional Global Optimization Problems

The Symmetric-approximation Energy-based Estimation of Distribution (SEED) is a continuous optimization algorithm based on the univariate Estimation of Distribution Algorithms (EDAs). Generally speaking, SEED uses Boltzmann selection and a symmetric divergence measure to guide the evolutionary process toward the optimum by constructing and sampling explicit probabilistic models of promising candidate solutions. SEED has been compared with other univariate EDAs, showing better results for low-dimensional continuous problems, and converging faster than the compared algorithms. In this paper, we propose to use SEED for solving continuous high-dimensional global optimization problems, and the results are compared with those of other representative evolutionary algorithms from the state-of-the-art such as Differential Evolution and Particle Swarm Optimization. To stress the algorithms, the search domain is extended from $$x\in {\left[-1000, 1000\right]}^{n}$$ for several benchmark functions whose dimension ranges from 500 to 10,000. Finally, to know the best performing algorithm, the convergence results of all algorithms are statistically compared using the Page’s Trend Test.
COBE: A Natural Language Code Search Robustness Benchmark

Benchmark frameworks and datasets allow us to analyze and understand the knowledge that NLP models capture about the world they were trained on. Several Transformer models have recently been adapted to code-related tasks such as code search, in which the goal is to find the most semantically relevant code given a query written in natural language. To achieve satisfactory performance, the retrieval models heavily rely on the quality of the query. In this paper, we introduce the Natural Language Code Search Robustness Benchmark (COBE), which provides a more holistic evaluation of the state-of-the-art models considering several aspects of the retrieval models, such as: (i) retrieval capabilities measured in multiple ranking metrics; (ii) robustness to a plethora of input perturbations; (iii) efficiency in terms of training and retrieval times; and (iv) stability across fine-tuning runs. We shed a light over important questions showing that simply computing performance-based retrieval metrics does not suffice to evaluate this kind of model. The proposed benchmark introduces novel metrics and measurement strategies that allow a rigorous quantitative analysis of input-query robustness while providing an understanding of model generalization behavior. We perform an extensive set of experiments using state-of-the-art models such as CodeBert, GraphCodeBert, and CodeT5. Those models are fine-tuned over many different scenarios in six programming languages. Several models trained in this study outperform their state-of-the-art counterparts, which provides evidence that the standard fine-tuning approach used in code search related work is sub-optimal. The proposed benchmark is a powerful tool to evaluate code search models, providing insights on how they behave during fine-tuning and how they are interpreting the input queries.
STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos

Recognizing human-object interactions is challenging due to their spatio-temporal changes. We propose the Spatio-Temporal Interaction Transformer-based (STIT) network to reason such changes. Specifically, spatial transformers learn humans and objects context at specific frame time. Temporal transformer then learns the relations at a higher level between spatial context representations at different time steps, capturing long-term dependencies across frames. We further investigate multiple hierarchy designs in learning human interactions. We achieved superior performance on Charades, Something-Something v1 and CAD-120 datasets, comparing to baseline models without learning human-object relations, or with prior graph-based networks. We also achieved state-of-the-art accuracy of 95.93% on CAD-120 dataset [1] by employing RGB data only.
A Cloth-Irrelevant Harmonious Attention Network for Cloth-Changing Person Re-identification

Cloth-changing person re-identification (CC-ReID) is a challenging task that aims at retrieving the target person across large spatial and temporal spans, with a high probability of changing clothes. To explicitly alleviate the impact of the person changing clothes on re-identification, this paper presents a cloth-irrelevant harmonious attention network (CIHANet) that learns cloth-irrelevant knowledge. Firstly, with the help of human parsing, the color information of human clothing is removed to generate black clothes images. Secondly, the raw person images are used to learn features with more color-based appearance knowledge, while the black clothes images are used to learn features with more cloth-irrelevant knowledge. Then, to fuse the knowledge of two distinct streams, we propose the harmonious attention module, including mutual learning attention and salience guided attention mechanisms. The mutual learning attention mechanism adaptively selects identity-relevant features across feature channels to make two streams interact with each other. The salience guided attention mechanism highlights the cloth-irrelevant areas by transferring the spatial knowledge from the black clothes stream to the raw images stream. Finally, quantitative and qualitative results on three CC-ReID datasets validate the superiority of our method on the CC-ReID task.
Bilingual Parallel Corpora: A Major Resource for Developing Computational Tools for Automatic Processing of Hindi-Dogri Language Pair

Dogri is a language with low computational resources. There are hardly any digital resources available for this language. The major hindrance to developing these resources is the lack of corpora. This paper describes the authors' attempt to create a bilingual parallel corpus of 0.2 million Hindi to Dogri sentences to develop various natural language processing tools like statistical & neural machine translation systems, summarization, classification, romanization, etc. These tools require a sufficient amount of corpus as a dataset when developed using deep neural models. The corpus was created for the Hindi-Dogri language pair, with a significant emphasis on ambiguous words to resolve ambiguity using deep learning models. The paper also highlights the limitations of the available rule-based Hindi to Dogri machine translation system with respect to wrong translation results and ambiguity in the output, emphasizing the need to develop systems by training on a corpus with ambiguous data using deep learning techniques.
Boosting Item Coverage in Session-Based Recommendation

Traditional recommender systems that rely heavily on user profiles or historical consumption of users are more susceptible to the cold start and model drift limitations due to dynamic user preferences. Recent advances in recommendations have seen a shift towards session-based recommender systems, which provide recommendations solely based on a user’s interactions in an ongoing session. As a result, the focus is placed on sequential learning, and existing algorithms are heavily impacted by accidental clicks, which ultimately limits an item’s coverage. In this work, we propose a two-stage approach to boosting item’s coverage in session-based recommendations. First, we train a skip-gram model with negative sampling to generate candidate items that co-occur with a given query set. We then apply weighting to mitigate the effects of accidental clicks during a session. Next, we use a multi-armed bandit approach to boost recommendation coverage by balancing the exploration-exploitation trade-off. Experiments with three real-world datasets show that our model’s performance is comparable to existing state-of-the-art methods and outperforms them in recommendation coverage.
Position Attention Network for Object Tracking

In recent years, Siamese-based trackers have achieved excellent performance, most of them usually calculate the similarity of each position on the search region to the object through the cross-correlation layer for tracking object. To solve the problem that the above method neglects the correspondence of the local information between the object and the search region and cannot adapt to the object deformation well, we propose a Siamese network-based tracker with position attention network (SiamPA). First, we use Siamese backbone network to extract template and search region features. Secondly, we adopt the box-guided object feature selection strategy to avoid similarity calculations for background information. In addition, we introduce the position attention network instead of the cross-correlation layer to learn the part-level relationship between the object and the search region features. Finally, the classification-regression sub-network is used to decode the similarity respond map obtained by the position attention network and predict the position of the object. Our contribution, one is to propose a box-guided method for refining object features, and the other is to introduce a position attention network for information embedding. Experiments on three challenging benchmarks including GOT-10k, UAVI23 and OTB-100 demonstrate that our SiamPA achieves excellent tracking performance with a real-time speed.
Multi-Cloud Service Brokers for Selecting the Optimal Data Center in Cloud Environment

Cloud computing provides different services through data centers that are often located in different geographical locations. The users are faced with a wide variety of services to choose from. Also, with the increasing number of serviced applications, overloading might occur on service brokers for balancing and serving the requests. Consequently, maximizing the number of entry points and considering the maximum number of factors that affect the performance for balancing the workload is very important for the quality of service. This paper proposes a model named multi-cloud service brokers (MCSB) for selecting the optimal DC using multiple entry points. The developed service broker policy shares information about the requests considering some new performance factors. This extension is added to the CloudAnalyst simulator tool which is used in this work, and the results are evaluated and compared to other existing policies from the literature.
An Attention-Based Token Pruning Method for Vision Transformers

Recently, vision transformers have achieved impressive success in computer vision tasks. Nevertheless, these models suffer from heavy computational cost for the quadratic complexity of the self-attention mechanism, especially when dealing with high-resolution images. Previous literature has illustrated the sparsity of attention, which suggests that uninformative tokens could be discarded to accelerate the model with limited influence to precision. As a natural indicator of token importance, attention scores can be intuitively used to extract the discriminative regions in images. Inspired by these facts, we propose an attention-based token pruning framework to address the issue of inefficiency for vision transformers. We divide the transformer blocks in the model into pruning stages, where the integrated weights in multi-attention heads are fused to estimate the importance of token. The computational cost of the model is reduced by dropping redundant patches progressively after each pruning stage. Experiments conducted on ImageNet1k verify the effectiveness of our method, where the models pruned by our module outperform other state-of-the-art models with similar FLOPs. For fine-grained image recognition, our framework also improves both accuracy and efficiency of ViT on CUB200-2011. More significantly, the proposed attention-based pruning module could be simply plugged in to any vision transformer that contains the class token by fine-tuning only 10 epochs or a single epoch, making a reasonable trade-off between accuracy and cost.
A Formal Approach to Design and Security Verification of Operating Systems for Intelligent Transportation Systems Based on Object Model

Operating system in intelligent transportation systems (ITSs) is a complex software system whose correctness and security are not obvious. There are advances in formal description and verification of operating systems in ITSs recently and they mainly focus on bottom-up proofs in which the source codes satisfy certain expected properties expressed by logic formulae. In this paper, we propose a layered object model for operating systems in ITSs. This model includes functionality layer, refinement layer and concrete layer. We consider the operating system object model as a logic system ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L)$</tex-math> </inline-formula> with variables representing the objects of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L$</tex-math> </inline-formula> , and a series of logic formulae for security and functional configurations in security of ITSs. We establish a mathematical structure as a domain of discourse for operating system in ITSs and accordingly, construct a mapping from operating system objects to the domain. In this way, we propose a formal method to verify the operating system security properties and configurations in ITSs. We use the virtual memory management part of our self-designed operating system VSOS as an example to illustrate the model and show that the claimed security properties can be rigorously proven for ITSs. The evaluation and verification of VSOS indicate that the proposed model implementation is feasible and achieves the security goals.
Improved Spatial Information Sampling Algorithm

Spatial information sampling algorithm (SIS) is an evolutionary algorithm that uses intelligent solutions for decision making. In SIS, the shape of solution space of the population is normalized to a hypercube distribution. The SIS always searches around the best individuals, and thus suffers from a local optimum trapping problem. Therefore, we propose an improved spatial information sampling algorithm (ISIS). We innovatively propose a dynamic adjustment of the population structure strategy and improve on the original search strategy so that the ISIS algorithm can search around multiple good individuals simultaneously. We compare the results of ISIS on 29 benchmark problems from CEC2017 with other state-of-the-art algorithms, and our algorithm has significant advantages.
Deception Detection Towards Multi-turn Question Answering with Context Selector Network

Deception occurring in multi-turn question answering (QA) circumstances such as interviews, court depositions, and online marketplaces, can cause serious consequences. Due to the lack of proper datasets and difficulty of finding deceptive signals, existing deception detection methods haven’t utilized the QA contexts to detect deception. Previous methods that mainly focus on context-free deception detection cannot be applied to text-based QA contexts. Therefore, we design a novel Context Selector Network (CSN) to address the challenge of modeling the context-sensitive dependencies implied in multi-turn QA. We utilize BERT to obtain sentence embeddings first and then design a context selector to explore crucial deceptive signals implied in the QA contexts automatically. Towards real-life scenarios, we collect a high-quality dataset containing multi-turn QAs that consist of sequential dependent QA pairs. Compared with several state-of-the-art baselines, experimental results show the impressive effectiveness of the proposed model.
A dynamic analysis tool for memory safety based on smart status and source-level instrumentation

Memory errors may lead to program crashes and security vulnerabilities. In this paper, we present Movec, a dynamic analysis tool that can automatically detect memory errors at runtime. To address the three major challenges faced by existing tools in detecting memory errors, low effectiveness, optimization sensitivity and platform dependence, Movec leverages a smart-status-based monitoring algorithm and performs its intrumentation at the source-level. Our extensive evaluation shows that Movec is capable of finding a wide range of memory errors with moderate and competitive overheads.
PH-CF: A Phased Hybrid Algorithm for Accelerating Subgraph Matching Based on CPU-FPGA Heterogeneous Platform

Nowadays, more data are represented and stored by a graph structure, and subgraph matching is a fundamental problem in a variety of scientific machine learning and industrial applications, such as remote sensing image registration, industrial inspection, etc. Due to the nondeterministic polynominal hard (NP-hard) problem of subgraph matching, the explosive growth of graph data, the disadvantages of high energy consumption, and the high overhead of CPU and graphics processing unit (GPU) platforms, computing subgraph matching is becoming more and more challenging. To alleviate this problem, we propose a phased hybrid algorithm to accelerate the enumeration task of subgraph matching, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PH-CF</monospace> , based on the CPU-field programmable gate arrays (FPGA) heterogeneous platform. This approach can make full use of the pipeline and data flow mechanism, low power consumption, and configurable characteristics of FPGA. First, the matching order of query vertices automatically selects GraphQL (GQL) or RI methods according to the sparsity of the data (query) graph. Second, a candidate vertex auxiliary data structure set partitioning method is designed to effectively realize the load balance of multiple computing units at the FPGA and CPU host sides. Third, FPGA's pipeline and data flow mechanism is used to accelerate the enumeration phase of subgraph matching. Experimental results on real-world and synthetic datasets show that the performance of the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PH-CF</monospace> outperforms the state-of-the-arts. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PH-CF</monospace> can obtain the average performance improvement 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">$16.07\times$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$38.61\times$</tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$11.46\times$</tex-math></inline-formula> over CFL, CECI, and DP-iso, respectively. Moreover, our approach has good stability and robustness on various datasets.
Non-parametric Bayesian updating and windowing with kernel density and the kudzu algorithm

The concept of 'updating' parameter estimates and predictions as more data arrive is an important attraction for people adopting Bayesian methods, and essential in big data settings. Implementation via the hyperparameters of a joint prior distribution is challenging. This paper considers non-parametric updating, using a previous posterior sample as a new prior sample. Streaming data can be analysed in a moving window of time by subtracting old posterior sample(s) with appropriate weights. We evaluate three forms of kernel density, a sampling importance resampling implementation, and a novel algorithm called kudzu, which smooths density estimation trees. Methods are tested for distortion of illustrative prior distributions, long-run performance in a low-dimensional simulation study, and feasibility with a realistically large and fast dataset of taxi journeys. Kernel estimation appears to be useful in low-dimensional problems, and kudzu in high-dimensional problems, but careful tuning and monitoring is required. Areas for further research are outlined.
A TOPSIS based Self-Organizing Double Loop Recurrent Broad Learning System for Uncertain Nonlinear Systems

This study proposes an efficient intelligent control structure for uncertain nonlinear systems. The controller is implemented by a sliding mode control framework including a modified broad leaning network (BLS) with a double-loop recurrent structure. In addition, the proposed BLS involves a self-organizing mechanism to increase or decrease the size of the BLS. The technique for order of preference by similarity to ideal solution (TOPSIS) method is used to build the self-organizing mechanism. Moreover, two dynamic thresholds of TOPSIS are automatically determined according to the stability of the controller. One dynamic threshold is used to consider whether to retain or remove existing network neurons in the BLS; and the other is used to generate new neurons, so as to meet the requirements of different control states and save computing resources. To improve the network's dynamic characteristics, a double-loop recurrent structure is further introduced into the self-organizing BLS. The Lyapunov stability function is used to ensure the stability of the control system. The proposed controller is applied to the simulation control of a nonlinear chaotic system and a three-link robot manipulator. The experimental results show that the proposed controller can achieve better control performance against other network-based controllers. The source code of this work is placed at https://github.com/wzhuang-xmu/SODLRBLS
Mathematical modeling of the stress-strain state of a soil during the construction of the underground part of unique buildings

Recently, the technology of construction of underground parts of buildings and "top-down" structures has become more and more popular. The effectiveness of this technology has been noted by many authors. Underground fencing and spacer elements of the structure perceive significant loads not only during the exploitation period, but also during the construction of all levels of the underground part. When designing, it is necessary to take into account the change in the stress-strain state of the fastening of the walls of a deep pit. This paper presents the results of researching of changes in the stress-strain state of anchoring the walls of a deep foundation pit by the “wall in the soil” method in the numerical modeling of floor excavation using the “top-down” technology. A volumetric finite element model of the underground part has been developed, which provides for sequential excavation to a depth of one floor with an estimate of the SSS parameters at each stage of the work.