Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Correlation Analysis between the Evaluation Score of English Teaching Quality and the Knowledge Points

As one of the three main courses from primary school to senior high school, improving the quality of English teaching in and out of class has become the top priority of colleges and universities. English knowledge points are complex, and domestic scholars have studied vocabulary knowledge and grammatical awareness from various perspectives, but there is still a lack of research on the correlation between vocabulary knowledge, grammatical awareness, and cloze test scores of senior high school students. Therefore, this paper carries out empirical research from the depth of English vocabulary, English grammar, reading comprehension, cloze test, and composition, aiming at exploring the relationship between teaching quality and English knowledge points. Classroom teaching quality evaluation is the basic content of education quality evaluation, which not only needs to evaluate the effect of class hours but also needs a student's long-term learning effect. In order to improve the quality of classroom English teaching, enrich the content of classroom English, and ultimately make the quality of students' learning to a higher level, the combination model of the teaching quality evaluation index is established by combining the decision tree with the knowledge point rule association method and the evaluation results are verified by association rule analysis. This paper selects the effective indicators that affect the evaluation of English teaching quality, determines the weight of the indicators by using the analytic hierarchy process, effectively constructs the combination model of the decision tree and rule association method, and establishes the evaluation model of students' English learning ability in the classroom. Taking students as the main object and combining them with the requirements of digital teaching, the evaluation index system is formed, the index weight is determined by using the analytic hierarchy process, and the classroom teaching quality evaluation model based on the decision tree and English knowledge point correlation analysis is constructed to truly reflect the teaching level of teachers, and the correlation analysis is carried out between English teachers' own quality and students' learning effects and knowledge points. By testing the model performance of English vocabulary depth, reading comprehension, grammar, writing, and other knowledge points, we can well evaluate and analyze students' mastery of English learning and the correlation of English knowledge points.
Efficient Complete Verification of Neural Networks via Layerwised Splitting and Refinement

Safety and robustness properties are highly required for neural networks deployed in safety-critical applications. Current complete verification techniques of these properties suffer from the lack of efficiency and effectiveness. In this article, we present an efficient complete approach to verify safety and robustness properties of neural networks through incrementally determinizing activation states of neurons. The key idea is to generate constraints via layerwised splitting that make activation states of hidden neurons become deterministic efficiently. These constraints are then utilized for refining inputs systematically so that abstract analysis over the refined input can be more precise. Our approach decomposes a verification problem into a set of subproblems via layerwised input space splitting. The property is then checked on each subproblem, where the activation states of at least one hidden neurons will be determinized. Further checking is accelerated by constraint-guided input refinement. We have implemented a parallel tool called LayerSAR to verify safety and robustness properties of ReLU neural networks in a sound and complete way, and evaluated it extensively on several benchmark sets. Experimental results show that our approach is promising, compared with complete tools, such as Planet, Neurify, Marabou, ERAN, Venus, Venus2, and nnenum in verifying safety and robustness properties on the benchmarks.
Bits-Ensemble: Toward Light-Weight Robust Deep Ensemble by Bits-Sharing

Robustness and uncertainty estimation is crucial to the safety of deep neural networks (DNNs) deployed on the edge. The deep ensemble model, composed of a set of individual DNNs (namely members), has strong performance in accuracy, uncertainty estimation, and robustness to out-of-distribution data and adversarial attacks. However, the storage and memory consumption increases linearly with the number of members within an ensemble. Previous works focus on selecting better members, layer-wise low-rank approximation of ensemble parameters, and designing partial ensemble model for reducing the ensemble size, thus lowering storage and memory consumption. In this work, we pay attention to the quantization of the ensemble, which serves as the last mile of network deployment. We propose a differentiable and parallelizable bit sharing scheme that allows the members to share the less significant bits of parameters, without hurting the performance, leaving alone the more significant bits. The intuition is that, numerically, more significant bits (e.g., the bit for the sign) are more useful in distinguishing a member from other members. For real deployment of the bit-sharing scheme, we further propose an efficient encoding-decoding scheme with minimal storage overhead. The experimental results show that, BitsEnsemble reduces the storage size of ensemble for over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$22\times $ </tex-math></inline-formula> , with only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.36\times $ </tex-math></inline-formula> increase in training latency, and no sacrifice of inference latency. The code is available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ralphc1212/bitsensemble</uri> .
Multi-users interaction anomalous subgraph detection for event mining

Event detection on networks is an important research task in data mining. Most previous approaches usually detect a particular type of event that satisfies the predefined rules in the model, i.e., data-driven methods. However, few methods consider the diverse interests of multiple experts during the detection process to discover unexplored events. In this paper, we regard interactive event detection on attributed networks as Multi-users Interaction Anomalous Subgraph Detection (MIASD), where events are represented as connected subgraphs on networks. The core of our method is automatically maximizing a non-parametric scan statistic over connected subgraphs to identify the most anomalous subgraph as the event and integrating the interactions from multiple experts simultaneously, i.e., under both data-driven and user-driven paradigm. In our approach, we first define individual and shared environments of experts on attributed networks to model the interests of experts, including the interactive operations and Recommended Interaction Domain. Afterwards, we propose an efficient anomalous subgraph detection algorithm in which each expert gives feedback separately on the vertices in the Recommended Interaction Domain; then, the non-parametric scan statistic over the connected subgraph is approximately maximized by integrating various feedback from experts into the iterative subgraph expansion process. In this way, our method retrieves the most anomalous subgraph on the network as the final event, which contains the potential unexplored information. We have conducted extensive experiments on three real-world datasets and proved that our algorithm could achieve better performance compared with several competitive baselines. Furthermore, the case study shows that our method could detect global abnormal events effectively.
Simulation of the role of emphasis on scheduling in the optimal incentive scheme for marine engineering employee's routine job and information security compliance

In the organizational context of marine engineering, employee individual often prefers to concentrate herself to the day-to-day routine job, but to shirk the responsibilities of the Information Security Policies (ISPs) compliance, after she has been delegated by the employer to perform the two different tasks in the same time period. This would lead to negative influences on the security of marine information systems and the employee's routine job performance. In view of the task structures of employee's routine job and marine ISPs compliance, the variables of emphasis on scheduling are incorporated into a multi-task principal-agent model to explore the optimal incentive scheme to motivate and control the employees to select appropriate effort levels for conducting the two highly structured tasks. The role of emphasis on scheduling on the incentive intensities for the two tasks have been clarified through modeling and simulation, and the corresponding incentive tactics are suggested. The new two-task incentive scheme is expected to provide useful insight for understanding and controlling marine engineering employee's routine job and ISPs compliance behavior.
Analysis of radiation energy between light source, optical window and cryogenic sample

Abstract The cryogenic mechanical properties of materials are particularly important in cryogenic engineering fields. The digital image correlation technique is a method to measure strain and displacement with high precision without contact. The optical window is necessary for DIC technology to obtain the cryogenic mechanical properties of materials. However, the thermal radiation energy between the light source, the glass and the sample greatly affects the limit temperature of the cryogenic mechanical measurement. In this paper, the spectral radiation properties of the glass and sample surfaces for the wavelength of 0.38-25 μm were measured. The thermal radiation energy between the power density emitted by the light source, the radiation energy emitted by the glass surface and the radiation energy emitted by the sample were analyzed. Finally, the effects of illumination distances on the radiation energy density and the sample temperature were studied. The results indicate that the power density emitted by the light source accounts for the main part of the radiant energy density received by the sample, and the radiant energy on the glass and the sample surface are negligible. Moreover, increasing the illumination distance is effective to reduce the radiation energy and temperature rise of the sample.
Data Marketplaces with a Free Sampling Service

Data is currently perceived as one of the most valuable resources by the industry. In this context, data marketplaces have emerged for facilitating data trading in a coordinated manner. To facilitate open, fair, and transparent trades, it is necessary to ensure trust, both among the different participants and stakeholders, and trust in the sense of confidence to the ecosystem. Thanks to the emergent blockchain technologies, there are new decentralized data marketplaces that provide this confidence in a decentralized manner with new disruptive services such as automated conflict resolution, non repudiation and auditable accounting. In this paper, we present a decentralized data marketplace called i3-market that implements these services. We also describe a service called free data sampling that allows a consumer and a provider to securely agree on a portion of a dataset that can be checked by the consumer before committing the purchase. Finally, the paper discusses how to integrate the free sampling service in the i3-market ecosystem.
Don't Dampen the Fun: Acoustic Study of a Mechanically Modified Puck for Blind Ice Hockey

In blind ice hockey, a modified puck that provides auditory feedback to players is one of the key adaptations of the game, but it suffers from durability challenges. In this paper, we present the design and evaluation of protective rings that increase durability while minimizing acoustic dampening. Results illustrate that ring location has a larger effect on puck acoustics compared to tightness, laying the foundation for the design optimization and scalability of protective puck enclosures.
A Long-term Cloud Workload Prediction Framework for Reserved Resource Allocation

Instead of constructing their own data centers, Software-as-a-Service (SaaS) providers acquire resources from Infrastructure-as-a-Service (IaaS) providers and provide services in accordance with the customer’s service level agreements (SLA). To reduce costs in resource provision, they have to purchase reserved instances and, as a result, estimate resources required in the long term. Previously, capacity planning methods were used to solve this problem. However, these methods estimate future resource needs using history workload distribution and have difficulty dealing with the highly fluctuating cloud workload with long-term tendency.To address this issue, we propose a long-term workload prediction framework by applying a Transformer-based prediction model for reserved resource allocation. Using CEEMDAN, the framework can generate stationary Intrinsic Mode Functions (IMF) from input data and cope with unstable and random cloud workloads. The prediction result of the framework is used to estimate future resource needs and purchase reserved resources. We evaluate our framework’s efficiency using real-world Wikipedia datasets and show the framework’s efforts in minimizing the total cost of reserved resource provision.
Knowledge Management in a Healthcare Enterprise: Creation of a Digital Knowledge Repository

The functional efficiency of a healthcare enterprise is dependent on how its multiple disciplines create, share, and manage knowledge. The way in which a healthcare organization manages patient-centered knowledge is well-established. On the contrary, management of process-related knowledge is not well-established. There remains a tremendous amount of room for improvement in the realm of workflow, process, and day-to-day detail documentation, specifically regarding inter-facility variability in a large healthcare enterprise. In this work-in-progress paper, we aim to propose a technical solution for a collaborative approach to knowledge management in a multimodal healthcare enterprise.
Comparison of optimization algorithms based on swarm intelligence applied to convolutional neural networks for face recognition

In this work, a comparison of optimization techniques based on swarm intelligence to design Convolutional Neural Networks is performed. The optimization techniques used in this comparison are Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA). The algorithms design convolutional neural networks (CNNs) architectures applied to face recognition. These techniques were chosen due to their similarity in their processes to find optimal results, such as their searching of prey. The design of CNNs consists of the number of layers (convolutional and fully connected), number and size of the filters, neurons fully connected, batch size, epoch, and algorithm for the learning phase. The simulation results are compared, using a different number of images for the learning phase to know which technique has a better performance using a smaller number of images to CNN design.
Language-Preference-Based Re-ranking for Multilingual Swahili Information Retrieval

Approaches for merging results in multilingual information retrieval (MLIR) systems strive for topical relevance, regardless of whether they are heuristic or machine learning (ML)-based. However, to build on topical relevance, current MLIR results merging approaches largely ignore other factors derived from user interaction behaviours, which, if used, could potentially improve the relevance of the merged results. MLIR user behaviour studies suggest that users' language preferences differ depending on the topic of search. In this paper, we propose to use language preferences driven by search topics, i.e., topic-language (T-L) preferences. Specifically, we create a T-L-based algorithm for merging results in a multilingual Swahili IR system. The approach promotes a certain number of results in the preferred language to the top of the results list, while the remaining results in the preferred language and those in the non-preferred language are interleaved in a round-robin fashion. Using a multilingual Swahili IR data set, the evaluation results show that the T-L-based approach improves the relevance of results for T-L preference-sensitive topics in general. Our findings also show that the T-L-based approach outperforms the other approaches for queries with a strong T-L association. According to these findings, incorporating user behaviour into the merging equation in MLIR systems has the potential to improve the relevance of results for some topics.
Unsupervised person re-identification via local manifold consistency learning

In the last few years, person re-identification (re-id) has made significant progress in supervised learning for matching pedestrians across disjoint camera views in surveillance. However, it is infeasible in many new scenes without sufficient labeled images when extending a re-id system. Therefore, unsupervised methods in person re-id tasks are vital for saving labeling costs. However, cross-camera scene variation is a crucial challenge for unsupervised person re-id, such as the occlusion problem. It results in uneven pairwise similarity distributions, which degrade matching performance. To solve this issue, we propose a local manifold consistency learning (LMCL) framework that consists of a context-aware feature embedding network and a camera-aware manifold alignment strategy. To better extract comprehensive features of persons in images, we propose a saliency feature attention algorithm by cropping feature maps into regions and transforming them into context features. We optimize our model based on sub-domain alignment loss to alleviate the effect of cross-camera scene variation, which closes the distance between sub-domains composed of similar samples. Extensive experimental results and ablation experiments verify the effectiveness of our LMCL approach.
Parameter Tuning of Reranking-based Diversification Algorithms using Total Curvature Analysis

In this paper, we analyze re-ranking based recommendation diversification algorithms and observe that, commonly, such algorithms can be unified under the scheme of maximizing submodular or modular objective functions from the class of parameterized concave over modular functions. We showcase that such diversification objective functions can be expressed in a generic functional form consisting of the relevance and diversity terms. We then theoretically analyze and show that the total curvature of submodular functions provides insights about the relevance-diversity trade off. This is expected to support data analysts to seek balanced hyperparameter values and, thus, serve as a 'vehicle of validation' by checking the total curvature of submodular objective functions. Our experimental evaluation and performance assessment over benchmark datasets are aligned with our theoretical analysis. We also discuss the importance of balanced trade-off between relevance and diversity in specific application settings like news recommendations to trade-off algorithmic bias and short term user engagement.
Research on Dynamic Programming Strategy of Bayesian Network Structure Learning

Bayesian network structure learning based on dynamic programming strategy can be used to find the optimal graph structure compared with approximate search methods. The traditional dynamic programming method for Bayesian network structure learning is a depth-first-based strategy, which is inefficient. We proposed two methods to solve this problem. First, the dependency constraints were used to prune the process of calculating redundancy scores. The constraints were obtained by the conditional independence test from the observed data sets. However, it was difficult to guarantee the accuracy of the constraints, which may have led to a decrease in the accuracy of the method. Second, we proposed a breadth-first-based strategy, which enhanced efficiency greatly while also ensuring global optimality. Experimental results showed that on the standard network data sets, compared with the dynamic programming based on depth-first search (DFSDP) algorithm, dynamic programming based on constraints (CBDP) could reduce the average running time by 57.10% and that dynamic programming based on breadth-first search (BFSDP) could reduce the average running time by 50.02%. On the UCI data sets, compared with DFSDP, CBDP reduced the average running time by 40.71%, and BFSDP reduced the average running time by 81.78%.
Quantifying the privacy-vs-performance trade-offs for fine-grained wireless network measurement data

Real-world network measurements are critical to building performant and resilient networks at scale. However, access to such data exposes end-users to significant privacy risks; and this is particularly true for wireless network measurements. In this paper, we apply six state-of-the-art differentially private (DP) algorithms, that span data-independent/dependent and workload-aware/unaware classes, to privatize queries from real-world WiFi traces on a large-scale campus network. We analyze utility-vs-privacy trade-offs involved in constructing privatized queries for canonical network resource provisioning tasks. We present the following results: (1) for count and histogram queries, the utility of the Laplacian-algorithm shows comparable (or better) performance compared to more complex data-aware DP algorithms, (2) for a given query-type and DP algorithm, the utility-to-noise trade-off varies for each distinct network metric, and finally, (3) we implement a state-of-the-art DP algorithm for trajectory analysis that reveals that there exist significant challenges in accurately reconstructing privatized network mobility trajectories, for relatively small trajectory lengths, even with relaxed privacy budgets.