Reviewerly/exHarmony huggingface dataset:
SDGIN: Structure-aware dual-level graph interactive network with semantic roles for visual dialog

Visual Dialog aims to answer an appropriate response based on a multi-round dialog history and a given image. Existing methods either focus on semantic interaction, or implicitly capture coarse-grained structural interaction (e.g., pronoun co-references). The fine-grained and explicit structural interaction feature for dialog history is seldom explored, resulting in insufficient feature learning and difficulty in capturing precise context. To address these issues, we propose a structure-aware dual-level graph interactive network (SDGIN) that integrates verb-specific semantic roles and co-reference resolution to explicitly capture context structural features for discriminative and generative tasks in visual dialog. Specifically, we create a novel structural interaction graph that injects syntactic knowledge priors into dialog by introducing semantic role labeling that imply which words are sentence stems. Furthermore, considering the single perspective limitation of previous algorithms, we design a dual-perspective mechanism that learns fine-grained token-level context structure features and coarse-grained utterance-level interactions in parallel. It possess an elegant view to explore precise context interactions, realizing the mutual complementation and enhancement of different granularity features. Experimental results show the superiority of our proposed approach. Compared to other task-specific models, our SDGIN outperforms previous models and achieves a significant improvement on the benchmark dataset VisDial v1.0.
Sensor fault diagnosis and correction for data center cooling system using hybrid multi-label random Forest and Bayesian Inference

The measurement biases in working sensors significantly hampers the effective operation and control of cooling systems in the data center. However, previous studies only focus on sensor fault diagnosis or sensor bias correction, neglecting the simultaneous detection, diagnosis and correction of sensor faults. This study proposed a novel method, combining Multi-Label Random Forest and Bayesian Inference (HMLRF-BI), to simultaneously detect, diagnose and correct sensor faults. Case studies were conducted involving eight single sensors and six multiple sensor fault cases in the Computer Room Air Handler (CRAH) to comprehensively evaluate the diagnostic and correction performance of the proposed method. Simulation results demonstrate that the method accurately diagnoses sensor fault types with a diagnostic accuracy of 97.42 % and an F1 score exceeding 97.90 %. Moreover, the proposed method also performs well in single/multiple sensor bias correction scenarios, with a correction accuracy exceeding 96.81 % and 97.10 %, respectively. Furthermore, a novel cycle mechanism is proposed, which utilized the MLRF fault diagnosis model to re-diagnose the corrected fault sensor to complete the closed-loop cycle of the HMLRF-BI method with an overall accuracy of 96.17 %. This study successfully filled the knowledge gap in the simultaneous detection, diagnosis and correction of sensor faults in data center CRAH, providing a comprehensive solution to restore sensor measurement performance, which is a promising application method.
Deep Contrastive Clustering via Hard positive sample Debiased

Deep graph clustering aims to reveal the underlying information of the graph and provide accurate embedding for the node clustering task, in which contrastive learning plays an important role. However, the commonly used contrastive loss function incorrectly classifies elements outside the diagonal of the cross view as negative samples, which contain a large number of positive sample pairs. In order to overcome the above problems, we propose a new deep graph contrastive clustering method, which combines hard positive sample debiasing and sample pair weighting, and improves the recognition ability of the network by removing the potential positive sample pairs and hard sample pair weighting in the negative sample pair of the loss function. More specifically, we developed a symmetric graph neural network to encode node representations. Using two sets of node representations, the correctness of negative cases is increased by clustering to generate high-confidence pseudo-label pairs for labels and confidence. The similarity distribution differences are weighted by adapting to different dataset samples to improve the sample recognition ability. To verify the efficacy of our DCHD, we compare it to existing state-of-the-art methods for node clustering tasks on six real-world datasets. Overall, the experimental results show that our proposed method outperforms current state-of-the-art graph clustering methods.
Fragility, robustness and antifragility in deep learning

We propose a systematic analysis of deep neural networks (DNNs) based on a signal processing technique for network parameter removal, in the form of synaptic filters that identifies the fragility, robustness and antifragility characteristics of DNN parameters. Our proposed analysis investigates if the DNN performance is impacted negatively, invariantly, or positively on both clean and adversarially perturbed test datasets when the DNN undergoes synaptic filtering. We define three filtering scores for quantifying the fragility, robustness and antifragility characteristics of DNN parameters based on the performances for (i) clean dataset, (ii) adversarial dataset, and (iii) the difference in performances of clean and adversarial datasets. We validate the proposed systematic analysis on ResNet-18, ResNet-50, SqueezeNet-v1.1 and ShuffleNet V2 x1.0 network architectures for MNIST, CIFAR10 and Tiny ImageNet datasets. The filtering scores, for a given network architecture, identify network parameters that are invariant in characteristics across different datasets over learning epochs. Vice-versa, for a given dataset, the filtering scores identify the parameters that are invariant in characteristics across different network architectures. We show that our synaptic filtering method improves the test accuracy of ResNet and ShuffleNet models on adversarial dataset when only the robust and antifragile parameters are selectively retrained at any given epoch, thus demonstrating applications of the proposed strategy in improving model robustness.
Texture and motion aware perception in-loop filter for AV1

Lossy compression introduces artifacts, and many conventional in-loop filters have been adopted in the AV1 standard to reduce these artifacts. Researchers have explored deep learning-based filters to remove artifacts in the compression loop. However, the high computational complexity of CNN-based filters remains a challenge. In this paper, a Texture- and Motion-Aware Perception (TMAP) in-loop filter is proposed to addresses this issue by selectively applying CNNs to texture-rich and high-motion regions, while utilizing non-learning methods to detect these regions. The proposed method introduces a new CNN structure, the Dense-Dual-Field Network (DDFN), which leverages a larger receptive field to enhance the quality of reconstructed frames by incorporating more contextual information. Furthermore, to improve perceptual quality, a novel loss function integrating wavelet-based perceptual information is presented. Experimental results demonstrate the superiority of our proposed models over other lightweight CNN models, and the effectiveness of the perceptual loss function is validated using the VMAF metric.
Machine learning based software effort estimation using development-centric features for crowdsourcing platform

Crowd-Sourced software development (CSSD) is getting a good deal of attention from the software and research community in recent times. One of the key challenges faced by CSSD platforms is the task selection mechanism which in practice, contains no intelligent scheme. Rather, rule-of-thumb or intuition strategies are employed, leading to biasness and subjectivity. Effort considerations on crowdsourced tasks can offer good foundation for task selection criteria but are not much investigated. Software development effort estimation (SDEE) is quite prevalent domain in software engineering but only investigated for in-house development. For open-sourced or crowdsourced platforms, it is rarely explored. Moreover, Machine learning (ML) techniques are overpowering SDEE with a claim to provide more accurate estimation results. This work aims to conjoin ML-based SDEE to analyze development effort measures on CSSD platform. The purpose is to discover development-oriented features for crowdsourced tasks and analyze performance of ML techniques to find best estimation model on CSSD dataset. TopCoder is selected as target CSSD platform for the study. TopCoder’s development tasks data with development-centric features are extracted, leading to statistical, regression and correlation analysis to justify features’ significance. For effort estimation, 10 ML families with 2 respective techniques are applied to get broader aspect of estimation. Five performance metrices (MSE, RMSE, MMRE, MdMRE, Pred (25) and Welch’s statistical test are incorporated to judge the worth of effort estimation model’s performance. Data analysis results show that selected features of TopCoder pertain reasonable model significance, regression, and correlation measures. Findings of ML effort estimation depicted that best results for TopCoder dataset can be acquired by linear, non-linear regression and SVM family models. To conclude, the study identified the most relevant development features for CSSD platform, confirmed by in-depth data analysis. This reflects careful selection of effort estimation features to offer good basis of accurate ML estimate.
A content-based recommender system using stacked LSTM and an attention-based autoencoder

Recommender systems (RS) are popular in many areas, such as movies, music, news, books, research articles, search queries, and social tagging. Proprietary recommender systems are used by e-commerce websites like eBay, Amazon, and Alibaba to better match customers with products they are likely to purchase. This study suggests a recommender system that combines stacked long short-term memory (LSTM) and an attention-based autoencoder. This system would be used in a self-supervised learning paradigm, and the Amazon product datasets were used to run simulations. The results showed that the proposed method is more accurate, uses less computing power, and can be used on a large scale. No matter how big the data is, it can capture low-dimensional fixed latent representations and use the bare minimum of information already in the items, whether they are new or old. Several evaluation metrics show that the method works and that the cold start problem has been solved.
Dynamic ε-Multilevel Hierarchy Constraint Optimization With Adaptive Boundary Constraint Handling Technology

Real-world optimization problems are often difficult to solve because of the complexity of the objective function and the large number of constraints that accompany it. To solve such problems, we propose Adaptive Dynamic ε-Multilevel Hierarchy Constraint Optimization (εMHCO). Firstly, we propose the dynamic constraint tolerance factor ε which can change dynamically with the feasible ratio and the number of iterations in the current population. This ensures a reasonable proportion of virtual feasible solutions in the population. Secondly, we propose adaptive boundary constraint handling technology (ABCHT). It can reshape the current individual position adaptively according to the size of constraint violation and increase the diversity of the population. Finally, we propose multi-level hierarchy optimization, whose multiple population structure is beneficial to solve real-world constraint optimization problems (COPs). To validate and analyze the performance of εMHCO, numerical experiments are conducted on the latest real-world test suite CEC’2020, which contains a set of 57 real-world COPs, and compared with four state-of-the-art algorithms. The results show that εMHCO is significantly superior to, or at least comparable to the state-of-the-art algorithms in solving real-world COPs. Meanwhile, the effectiveness and feasibility of εMHCO are verified on the real-world problem of the pipeline inner detector speed control.
Trajectory Planning for Hopping Rover on Small Bodies Under Pre-Collision Attitude Adjustment Modulation

A novel pre-collision attitude adjustment (PCAA) modulation based on angular velocity and quaternions control is developed to more controllable and reliably adjust the non-spherical hopping rover's trajectory on small bodies. This corresponds to a hopping trajectory planning (HTP) framework that addresses inherent technical challenges, including attitude-trajectory coupled features, hard-to-depicting near-truth gravity and continues dynamical contact models, difficult-to-predetermine collision times with the small bodies’ surface, and complex judgments regarding whether the period of attitude adjustment is sufficient in current hops. Two main contributions are proposed, focusing on modeling and planning. To achieve a more realistic modeling of the rover's motion, its attitude-trajectory coupling dynamic model is researched, including a ground-truth irregular gravity model and continuous dynamical contact model. Additionally, a collision detection method based on the local surface smoothing technique is proposed for rapid and exact switching between the ballistic and contact motions for the subsequent planning purpose. On the other hand, redundancy planning and binary event-trigger techniques are combined for handling unpredictable available dimensions of optimization variables of PCAA-based HTP, which arise from the indeterminate scale of collision times and potential deficiency in ballistic flight time required for completing attitude maneuvers. Specifically, the solution space is preset to guarantee the unchanging dimensions of the design variables, which are activated or deactivated by coupling binary event variables for each dimension. These newly introduced events are triggered by comparing the realistic rebound time of the current hop with the necessary time for attitude maneuver, which is accurately computed by designing a first-order sliding mode controller. This approach enables an equivalent and easily implementable version of the problem to be obtained. Logically and numerically, the application of PCAA modulation is found to significantly enlarge or contract the area of the reachable region of the hopping rover and contribute to improving the convergence and reducing the relative final position error of the hopping trajectory planning problem.
Building knowledge graphs from technical documents using named entity recognition and edge weight updating neural network with triplet loss for entity normalization

Attempts to express information from various documents in graph form are rapidly increasing. The speed and volume in which these documents are being generated call for an automated process, based on machine learning techniques, for cost-effective and timely analysis. Past studies responded to such needs by building knowledge graphs or technology trees from the bibliographic information of documents, or by relying on text mining techniques in order to extract keywords and/or phrases. While these approaches provide an intuitive glance into the technological hotspots or the key features of the select field, there still is room for improvement, especially in terms of recognizing the same entities appearing in different forms so as to interconnect closely related technological concepts properly. In this paper, we propose to build a patent knowledge network using the United States Patent and Trademark Office (USPTO) patent filings for the semiconductor device sector by fine-tuning Huggingface’s named entity recognition (NER) model with our novel edge weight updating neural network. For the named entity normalization, we employ edge weight updating neural network with positive and negative candidates that are chosen by substring matching techniques. Experiment results show that our proposed approach performs very competitively against the conventional keyword extraction models frequently employed in patent analysis, especially for the named entity normalization (NEN) and document retrieval tasks. By grouping entities with named entity normalization model, the resulting knowledge graph achieves higher scores in retrieval tasks. We also show that our model is robust to the out-of-vocabulary problem by employing the fine-tuned BERT NER model.
Unravelling ancient drilling techniques: A case of pottery repair in the Early European Neolithic

• Photogrammetric techniques detected diagnostic traces of drilling procedures. • Mixed use of drilling techniques for pottery repair observed at Méhtelek-Nádas. • Preference for repairing small or medium-sized open vessels unveils a regime of value. • Evidence of technical adaptation to vessels' damage state.
Targeted electronic health record-based recruitment strategy to enhance COVID-19 vaccine response clinical research study enrollment

Efficient recruitment of eligible participants is a significant challenge for clinical research studies. This challenge was exacerbated during the COVID-19 pandemic when in-person recruitment was not an option. In 2020, the University of Minnesota was tasked, as part of the National Cancer Institute's Serological Sciences Network for COVID-19 (SeroNet), to recruit participants for a longitudinal serosurveillance clinical research study with a goal of characterizing the COVID-19 vaccine-elicited immune response among immunocompromised individuals, which necessitated reliance on non-traditional strategies for participant recruitment. To meet our enrollment target of 300 transplant patients, 300 cancer patients, 100 persons living with HIV, and 200 immunocompetent individuals, we utilized targeted electronic health record (EHR)-based recruitment in addition to traditional recruitment tools, which was an effective combination of recruitment strategies. A significant advantage of patient portal messaging or other digital recruitment strategies such as email communication is timing. We reached 85 % (769 out of 900) of our enrollment target within one year with a 14.3 % response rate to invitations to participate in our study. This achievement is perhaps more salient given the COVID-19 pandemic-related constraints within which we were operating. We demonstrated that the EHR can be leveraged to quickly identify potentially eligible study participants either via EHR communication or mail. We also illustrate how the online portal MyChart can be used to efficiently send targeted recruitment messages.
Sparse regularized correlation filter for UAV object tracking with adaptive contextual learning and keyfilter selection

Recently, correlation filter has been widely applied in unmanned aerial vehicle (UAV) tracking due to its high frame rates, robustness and low calculation resources. However, it is fragile because of two inherent defects, i.e, boundary effect and filter corruption. To handle them, in this work, we propose a novel ℓ1 regularization correlation filter with adaptive contextual learning and keyfilter selection for UAV tracking. Firstly, we adaptively detect the positions of effective contextual distractors by the aid of the distribution of local maximum values on the response map of current frame which is generated by using the previous correlation filter model. Next, we eliminate inconsistent labels for the tracked target by removing one on each distractor and develop a new score scheme for each distractor. Then, we can select the keyfilter from the filters pool by finding the maximal similarity between the target at the current frame and the target template corresponding to each filter in the filters pool. Finally, quantitative and qualitative experiments on three authoritative UAV datasets show that the proposed method is superior to the state-of-the-art tracking methods based on correlation filter framework.
Dynamic receptive field adaptation for scene text recognition

Scene text recognition methods of the Encoder–Decoder framework, generally assume that the proportion of characters in the same text instance are basically the same. However, this assumption does not always hold in the context of complex scene images. For adaptively revising the receptive field according to the different font in the scene text image, we propose a Dynamic Receptive Field Adaption Framework which consists of Memory Attention (MA) module and Dynamic Feature Adaptive (DFA) module. MA percepts the historical location information to adapt to the change of character position in the decoder. DFA selects the most distinguishing features from feature maps of different levels dynamically. Additionally, MA and DFA can be easily extended to the existing attention-based and transformer-based text recognition methods to improve their performance. With extension experiments on public benchmark datasets, including IIIT-5K, SVT, SVTP, CUTE80, RECTS, LSVT, and RCTW, our method has shown effectiveness and robustness.
RelJoin: Relative-cost-based Selection of Distributed Join Methods for Query Plan Optimization

Selecting appropriate distributed join methods for logical join operations in a query plan is crucial for the performance of data-intensive scalable computing (DISC). Different network communication patterns in the data exchange phase generate varying network communication workloads and significantly affect the distributed join performance. However, most cost-based query optimizers focus on the local computing cost and do not precisely model the network communication cost. We propose a cost model for various distributed join methods to optimize join queries in DISC platforms. Our method precisely measures the network and local computing workloads in different execution phases, using information on the size and cardinality statistics of datasets and cluster join parallelism. Our cost model reveals the importance of the relative size of the joining datasets. We implement an efficient distributed join selection strategy, known as RelJoin in SparkSQL, which is an industry-prevalent distributed data processing framework. RelJoin uses runtime adaptive statistics for accurate cost estimation and selects optimal distributed join methods for logical joins to optimize the physical query plan. The evaluation results on the TPC-DS benchmark show that RelJoin performs best in 62 of the 97 queries and can reduce the average query time by 21% compared with other strategies.1
Experimental evaluation of RISC-V micro-architecture against fault injection attack

Today, the use of embedded processors is increasing dramatically and they are used in all aspects from our daily life to security applications. Physical access to hardware has made the hardware security a major concern. Hardware attacks compromise the hardware security by physically accessing target devices. Among the available techniques for hardware attacks, Fault Injection Attacks (FIAs), such as clock glitching, are one of the most harmful types of non-invasive attacks that can disrupt the operation of an embedded system. Thus, it will be important and fundamental to evaluate embedded software programs before using them in critical applications and check their vulnerability against fault injection attacks. However, it is often difficult for software developers to assess vulnerabilities. In this paper, an easy-to-use platform is presented to facilitate the process of evaluating the vulnerability of programs running on embedded processors against clock glitching attacks. Our experimental results show the vulnerability window of RISC-V micro-architecture for different high-level C-functions. The results of this research can help the developers of embedded systems that are used in security applications to evaluate their system against clock glitching attacks with the least cost in a short time.