Reviewerly/exHarmony huggingface dataset:
mrNISC from LWE with Polynomial Modulus

Introduced by Benhamouda and Lin [TCC'20], a multi-party reusable non-interactive secure computation protocol (mrNISC) consists of a commitment phase and an unbounded number of computation phases. In the commitment phase, a number of parties first commit to their input in a single broadcast round. Later in a computation phase, any subset of the parties can compute a function on their joint input by each sending a single broadcast message. Benhamouda and Lin [TCC'20] constructed the first mrNISC for all functions based on standard hardness assumptions in pairing groups. Soon after their work, two concurrent papers by Benhamouda et al. [EUROCRYPT'21] and Ananth et al. [EUROCRYPT'21] constructed mrNISC for all functions based on the hardness of LWE with super-polynomial modulus-to-noise ratio. In this work we build the first mrNISC for all functions based solely on LWE with polynomial modulus-to-noise ratio. We thus place mrNISC in the same category as public-key encryption and leveled fully homomorphic encryption in terms of the required LWE hardness assumption. We achieve our result by carefully introducing a bootstrapping step in the construction of Behamouda et al..
Safe semi-supervised learning using a bayesian neural network

Semi-supervised learning attempts to use a large set of unlabeled data to increase the prediction accuracy of machine learning models when the amount of labeled data is limited. However, in realistic cases, unlabeled data may worsen performance because they contain out-of-distribution (OOD) data that differ from the labeled data. To address this issue, safe semi-supervised deep learning has recently been presented. This study suggests a new safe semi-supervised algorithm that uses an uncertainty-aware Bayesian neural network. Our proposed method, safe uncertainty-based consistency training (SafeUC), uses Bayesian uncertainty to minimize the harmful effects caused by unlabeled OOD examples. The proposed method improves the model’s generalization performance by regularizing the network for consistency against uncertain noise. Moreover, to avoid uncertain prediction results, the proposed method includes a practical inference tip based on a well-calibrated uncertainty. The effectiveness of the proposed method is demonstrated in the experimental results on CIFAR-10 and SVHN by showing that it achieved state-of-the-art performance for all semi-supervised learning tasks with OOD data presence rates.
ERIS: Efficiently Measuring Discord in Multidimensional Sources

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL ERIS: Efficiently Measuring Discord in Multidimensional Sources 53 Pages Posted: 3 Sep 2022 See all articles by Alberto AbelloAlberto AbelloUniversitat Politécnica de CatalunyaJames CheneyUniversity of Edinburgh Abstract Data integration is a classical problem in databases, typically decomposed into schema matching, entity matching and data fusion. To solve the latter, it is mostly assumed that ground truth can be determined, or the most repeated value is often taken. Besides being over-simplistic, in many cases, this is not useful because the data gathering processes in the different sources are imperfect and cannot provide enough quality. In the absence of ways to determine ground truth, it is important to quantify how far from being internally consistent a dataset is. We propose definitions of concordant data and define a trustworthiness measure called discordance (the more discordant are the sources, the less we can trust their data). Keywords: Data fusion, Data alignment, Discord measurement, Multidimensional database, COVID-19 data quality analysis Suggested Citation: Suggested Citation Abello, Alberto and Cheney, James, ERIS: Efficiently Measuring Discord in Multidimensional Sources. Available at SSRN: https://ssrn.com/abstract=4184515 Alberto Abello (Contact Author) Universitat Politécnica de Catalunya ( email ) James Cheney University of Edinburgh ( email ) Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 1 Abstract Views 20 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.231 seconds
ANALYSIS OF DIFFICULTIES OF FOREIGN STUDENTS IN EXTRACURRICULAR ACTIVITIES IN RUSSIAN EDUCATION ENVIRONMENT

The process of foreign students’ adaptation in a Russian educational environment is a complex multi-level phenomenon that combines educational and extracurricular areas. At the same time, extracurricular activities, as one of the components of a single educational process, are on the periphery of scientific interest. The purpose of the article is to analyze the difficulties of foreign students in extracurricular activities that contribute to the disclosure of their internal potential in the educational process and effective adaptation to the Russian educational environment. To achieve this goal, the authors used the methods of questioning, interviewing and reliability retest. The developed questionnaire is designed to identify and determine the difficulties experienced by foreign students in extracurricular activities. The calculation of correlation indicators of foreign students' answers to the questions of the questionnaire proved its reliability, since the coefficient is equal to 0.8. Foreign students were asked to indicate the problematic areas of extracurricular social activity. The study involved 100 students of various specialties from South Ural State University and Chelyabinsk State University. The results show that most of the problems are associated with insufficient knowledge of the Russian language and psychological barrier in establishing extracurricular interpersonal communications in the field of leisure activities. The data obtained can be used to improve the work of the university departments responsible for the adaptation of foreign students in the Russian speaking education environment.
FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing

Federated learning (FL) is a recently developed area of machine learning, in which the private data of a large number of distributed clients is used to develop a global model under the coordination of a central server without explicitly exposing the data. The standard FL strategy has a number of significant bottlenecks including large communication requirements and high impact on the clients’ resources. Several strategies have been described in the literature trying to address these issues. In this paper, a novel scheme based on the notion of “model growing" is proposed. Initially, the server deploys a small model of low complexity, which is trained to capture the data complexity during the initial set of rounds. When the performance of such a model saturates, the server switches to a larger model with the help of function-preserving transformations. The model complexity increases as more data is processed by the clients, and the overall process continues until the desired performance is achieved. Therefore, the most complex model is broadcast only at the final stage in our approach resulting in substantial reduction in communication cost and client computational requirements. The proposed approach is tested extensively on three standard benchmarks and is shown to achieve substantial reduction in communication and client computation while achieving comparable accuracy when compared to the current most effective strategies.
Stimulates Potential for Knowledge Distillation

In knowledge distillation, numerous methods devote to exploring effective knowledge to guide the training of the small student network. However, these approaches ignore inspiring the student network’s own capability, a small student network also has the potential to achieve comparable performance to a large teacher network. We propose a new framework named stimulates the potential for knowledge distillation (SPKD). The SPKD framework consists of two components, 1) residual-based local feature normalization (LFNR), and 2) the local feature normalized extraction (LFNE). LFNR can enhance the competitiveness of local areas of feature maps by adding to the student network and can make better use of local areas with rich information. On the other hand, the stimulated local features are more expressive. LFNE extracts local representational features from the teacher network; the obtained local features are transferred to the student to guide the student network learning. Extensive experimental results demonstrate that our SPKD has achieved significant classification results on the benchmark datasets CIFAR-10 and CIFAR-100.
g's little helpers – VOTAT and NOTAT mediate the relation between intelligence and complex problem solving

Intelligence and complex problem solving (CPS) correlate closely, but little is known about the mechanism that translates intelligence into successful CPS. Therefore, this study considered the strategic exploration behaviors VOTAT (vary-one-thing-at-a-time) and NOTAT (vary no-thing-at-a-time) as possible mediators. A sample of 495 high-school students worked on nine CPS tasks, six of which with solely direct effects and three with direct and eigendynamic effects. We expected substantial mediation effects if the applied strategic behaviors were optimal to identify the particular underlying effect types (i.e., direct effects: VOTAT; direct and eigendynamic effects: VOTAT and NOTAT). The model for tasks with only direct effects revealed VOTAT and NOTAT to be substantial mediators: Whereas VOTAT showed substantial positive relations to intelligence and CPS performance, NOTAT unexpectedly showed substantial negative relations. Both VOTAT and NOTAT resulted in significant indirect mediation effects. The model for tasks with direct and eigendynamic effects showed substantial positive relations of VOTAT and NOTAT to intelligence and CPS-performance and resulted in significant and positive indirect mediation effects. Moreover, the indirect effects differed between VOTAT and NOTAT and across the two facets of CPS performance. Overall, strategic exploration behaviors are relevant for explaining the g -CPS-relation. • Nine CPS tasks with two effects types (direct, eigendynamic) were administered. • Two different strategies were extracted from log files: VOTAT & NOTAT. • VOTAT: systematic control of variables; NOTAT: non-interfering observations. • Both strategies mediated the intelligence-CPS-relation in both task types. • Differential mediation effects were shown, depending on task type and CPS facet.
Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling

Neural architecture search (NAS) aims to automate architecture design processes and improve the performance of deep neural networks. Platform-aware NAS methods consider both performance and complexity and can find well-performing architectures with low computational resources. Although ordinary NAS methods result in tremendous computational costs owing to the repetition of model training, one-shot NAS, which trains the weights of a supernetwork containing all candidate architectures only once during the search process, has been reported to result in a lower search cost. This study focuses on the architecture complexity-aware one-shot NAS that optimizes the objective function composed of the weighted sum of two metrics, such as the predictive performance and number of parameters. In existing methods, the architecture search process must be run multiple times with different coefficients of the weighted sum to obtain multiple architectures with different complexities. This study aims at reducing the search cost associated with finding multiple architectures. The proposed method uses multiple distributions to generate architectures with different complexities and updates each distribution using the samples obtained from multiple distributions based on importance sampling. The proposed method allows us to obtain multiple architectures with different complexities in a single architecture search, resulting in reducing the search cost. The proposed method is applied to the architecture search of convolutional neural networks on the CIAFR-10 and ImageNet datasets. Consequently, compared with baseline methods, the proposed method finds multiple architectures with varying complexities while requiring less computational effort.
Neural Architecture Search for Low-Precision Neural Networks

In our work, we extend the search space of the differentiable Neural Architecture Search (NAS) by adding bitwidth. The extended NAS algorithm is performed directly with low-precision from scratch without the proxy of full-precision. With our low-precision NAS, we can search for low- and mixed-precision network architectures of Convolutional Neural Networks (CNNs) under specific constraints, such as power consumption. Experiments on the ImageNet dataset demonstrate the effectiveness of our method, where the searched models achieve better accuracy (up to 1.2 percentage point) with smaller model sizes (up to $$27\%$$ smaller) and lower power consumption (up to $$27\%$$ lower) compared to the state-of-art methods. In our low-precision NAS, sharing of convolution is developed to speed up training and decrease memory consumption. Compared to the FBNet-V2 implementation, our solution reduces training time and memory cost by nearly 3 $$\times $$ and 2 $$\times $$ , respectively. Furthermore, we adapt the NAS to train the entire supernet instead of a subnet in each iteration to address the insufficient training issue. Besides, we also propose the forward-and-backward scaling method, which addresses the issue by eliminating the vanishing of the forward activations and backward gradients.
Using Multiple Heads to Subsize Meta-memorization Problem

The memorization problem is a meta-level overfitting phenomenon in meta-learning. The trained model prefers to remember learned tasks instead of adapting to new tasks. This issue limits many meta-learning approaches to generalize. In this paper, we mitigate this limitation issue by proposing multiple supervisions through a multi-objective optimization process. The design leads to a Multi-Input Multi-Output (MIMO) configuration for meta-learning. The model has multiple outputs through different heads. Each head is supervised by a different order of labels for the same task. This leads to different memories, resulting in meta-level conflicts as regularization to avoid meta-overfitting. The resulting MIMO configuration is applicable to all MAML-like algorithms with minor increments in training computation, the inference calculation can be reduced through early-exit policy or better performance can be achieved through low cost ensemble. In experiments, identical model and training settings are used in all test cases, our proposed design is able to suppress the meta-overfitting issue, achieve smoother loss landscapes, and improve generalisation.
Hypergraph Variational Autoencoder for Multimodal Semi-supervised Representation Learning

In many real-world settings, the external environment is perceived through multi-modal information, such as visual, radar, lidar, etc. Naturally, the fact motivates us to exploit interaction intra modals and integrate multiple source information using limited labels on the multimodal dataset as a semi-supervised task. A challenging issue in multimodal semi-supervised learning is the complicated correlations under pairwise modalities. In this paper, we propose a hypergraph variational autoencoder (HVAE) which can mine high-order interaction of multimodal data and introduce extra prior knowledge for inferring multimodal fusion representation. On one hand, the hypergraph structure can represent high-order data correlation in multimodal scenes. On the other hand, a prior distribution is introduced by mask-based variational inference to enhance multi-modal characterization. Moreover, the variational lower bound is leveraged to collaborate semi-supervised learning. We conduct experiments on semi-supervised visual object recognition task, and extensive experiments on two datasets demonstrate the superiority of our method against the existing baselines.
Multi-scale Vertical Cross-layer Feature Aggregation and Attention Fusion Network for Object Detection

Scale imbalance is one of the primary limitations for object detection. To tackle such a problem, existing methods such as FPN usually integrate the features at different scales, which suffers from the inconsistence of different high-level and low-level features due to the straightforward combination. In this paper, we propose a multi-scale vertical cross-layer feature aggregation and attention fusion network which not only has bottom-up and top-down pathways with lateral connections, but also adds cross-layer paths in the vertical direction. The proposed method can boost information flow and shorten the information path between high-level and low-level features. An attention fusion module is also introduced to obtain the internal correlation between local, global and contextual information of other feature layers. In order to optimize the anchor configurations, a differential evolution algorithm is employed to reconfigure the ratios and scales of anchors. Experimental results show that the proposed method achieves superior detection performance on the public dataset PASCAL VOC.
Effect of attention and triplet loss on chart classification: a study on noisy charts and confusing chart pairs

Charts are powerful tools for visualizing and comparing data. With the increase in the presence of various chart types in scientific documents in electronic media, the development of an automatic chart classification system is becoming an important task. Existing studies on chart classification fail to address the presence of noise in charts and confusing chart class pairs. Motivated by the above observations, in this paper, we propose an attention and triplet loss based deep CNN framework to address the above issues. From various experimental results over four datasets, it is evident that the proposed framework can effectively handle noise in the charts and confusing chart samples and outperforms its counterparts.
Prediction of software defects using deep learning with improved cuckoo search algorithm

Summary The software project model needs a defect prediction model to find defect‐prone file software systems. The fault‐prone model prediction, predicting bugs, and bug removal can undertake the software industry to achieve software quality. Therefore, automatically forecasting the number of errors in software modules is important, and it may assist developers in allocating limited resources more efficiently. Several methods for detecting and repairing such flaws at a low cost have been offered. These approaches, on the other hand, need to be significantly improved in terms of performance. Hence in this article, we implement an ensemble technique for the software defect prediction and prediction of the software bug. Also, we proposed a hybrid technique to predict several defects in the software system. The proposed approach uses principle component analysis for feature extraction which is to improve further performance and control the optimization problem. Classifiers were applied to five PROMISE datasets to determine the greatest implemented classifier with respect to the prediction achievement measuring factor. Our proposed model yields greater results on solving defect prediction problems and showing enhancement toward the existing model.
Protecting Private Information for Two Classes of Aggregated Database Queries

An important direction of informatics is devoted to the protection of privacy of confidential information while providing answers to aggregated queries that can be used for analysis of data. Protecting privacy is especially important when aggregated queries are used to combine personal information stored in several databases that belong to different owners or come from different sources. Malicious attackers may be able to infer confidential information even from aggregated numerical values returned as answers to queries over large collections of data. Formal proofs of security guarantees are important, because they can be used for implementing practical systems protecting privacy and providing answers to aggregated queries. The investigation of formal conditions which guarantee protection of private information against inference attacks originates from a fundamental result obtained by Chin and Ozsoyoglu in 1982 for linear queries. The present paper solves similar problems for two new classes of aggregated nonlinear queries. We obtain complete descriptions of conditions, which guarantee the protection of privacy of confidential information against certain possible inference attacks, if a collection of queries of this type are answered. Rigorous formal security proofs are given which guarantee that the conditions obtained ensure the preservation of privacy of confidential data. In addition, we give necessary and sufficient conditions for the protection of confidential information from special inference attacks aimed at achieving a group compromise.
Context-Aware Knowledge Management as an Enabler for Human-Machine Collective Intelligence

The effectiveness of maintaining and accessing decentralized organization’s information and knowledge is crucial for the competitiveness of the company and its ability to adapt to ever-changing business environment. Most of this knowledge is typically scattered across various socio-cyber-physical systems inside the company. The concept of socio-cyber-physical system and the related reseach aims on providing a holistic view on heterogeneous systems, including physical, software and human components and their real-time interactions via multilevel connections. Context-aware knowledge management is becoming de facto one of the essential business strategies to support such systems. Its purpose is to facilitate the transfer and exchange of knowledge in the context of business structures and activities related to cultural norms. This paper discussess emerging trends (including role organization, dynamic motivation mechanisms, and multidimensional ontology) in knowledge management for socio-cyber-physical systems. These trends can contribute to the creation of an innovative IT and HR environment based on the collective intelligence of humans and machines, where information and knowledge is shared among participants and among collectives of participants who can be either humans (collective intelligence as methods used by humans to take collective action to solve problems) or software services (based on artificial intelligence models). The paper discusses examples of trends and experiences of their implementation in a global manufacturing company and also proposes a concept human-machine collective intelligence environment utilizing the discussed technologies to support human-computer collaboration in decision support scenarios.