Reviewerly/exHarmony huggingface dataset:
Multi-Domain Learning By Meta-Learning: Taking Optimal Steps In Multi-Domain Loss Landscapes By Inner-Loop Learning

We consider a model-agnostic solution to the problem of Multi-Domain Learning (MDL) for multi-modal applications. Many existing MDL techniques are model-dependent solutions which explicitly require nontrivial architectural changes to construct domain-specific modules. Thus, properly applying these MDL techniques for new problems with well-established models, e.g. U-Net for semantic segmentation, may demand various low-level implementation efforts. In this paper, given emerging multi-modal data (e.g., various structural neuroimaging modalities), we aim to enable MDL purely algorithmically so that widely used neural networks can trivially achieve MDL in a model-independent manner. To this end, we consider a weighted loss function and extend it to an effective procedure by employing techniques from the recently active area of learning-to-learn (meta-learning). Specifically, we take inner-loop gradient steps to dynamically estimate posterior distributions over the hyperparameters of our loss function. Thus, our method is model-agnostic, requiring no additional model parameters and no network architecture changes; instead, only a few efficient algorithmic modifications are needed to improve performance in MDL. We demonstrate our solution to a fitting problem in medical imaging, specifically, in the automatic segmentation of white matter hyperintensity (WMH). We look at two neuroimaging modalities (T1-MR and FLAIR) with complementary information fitting for our problem.
Observed score reliability indices in diagnostic classification models

Quantifying the reliability of latent variable estimates in diagnostic classification models has been a difficult topic, complicated by the classification-based nature of these models. In this study, we derive observed score reliability indices based on diagnostic classification models as an extension of classical test theory-based reliability. Additionally, we derive conditional observed sum- and sub-score distributions. In this manner, various conditional expectations and conditional standard error of measurement estimates can be calculated for both sum- and sub-scores of a test. The proposed methods provide a variety of expectations and standard errors for attribute estimates, which we demonstrate with an analysis of an empirical test. Moreover, a simulation study revealed the proposed sub-score-based reliability index was correlated to a previously developed attribute mastery reliability index.
Steady-state teleportation fidelity and Bell nonlocality in dissipative environments

Abstract We investigate quantum teleportation and Bell nonlocality for two channel qubits coupled via the Heisenberg interaction and subject to two independent dissipative environments. Compared with the case of two uncoupled qubits, it is shown that the interaction Hamiltonian is beneficial for enhancing the teleportation fidelity and Bell nonlocality, and remarkably, it can also be used to create nonclassical teleportation fidelity and Bell nonlocality even from the initial product states. Moreover, the interaction Hamiltonian guarantees the generation of steady-state nonclassical teleportation fidelity, which is independent of the initial state and therefore one can take any state as the initial channel state.
Testing symmetry on quantum computers

Symmetry is a unifying concept in physics. In quantum information and beyond, it is known that quantum states possessing symmetry are not useful for certain information-processing tasks. For example, states that commute with a Hamiltonian realizing a time evolution are not useful for timekeeping during that evolution, and bipartite states that are highly extendible are not strongly entangled and thus not useful for basic tasks like teleportation. Motivated by this perspective, this paper details several quantum algorithms that test the symmetry of quantum states and channels. For the case of testing Bose symmetry of a state, we show that there is a simple and efficient quantum algorithm, while the tests for other kinds of symmetry rely on the aid of a quantum prover. We prove that the acceptance probability of each algorithm is equal to the maximum symmetric fidelity of the state being tested, thus giving a firm operational meaning to these latter resource quantifiers. Special cases of the algorithms test for incoherence or separability of quantum states. We evaluate the performance of these algorithms on choice examples by using the variational approach to quantum algorithms, replacing the quantum prover with a parameterized circuit. We demonstrate this approach for numerous examples using the IBM quantum noiseless and noisy simulators, and we observe that the algorithms perform well in the noiseless case and exhibit noise resilience in the noisy case. We also show that the maximum symmetric fidelities can be calculated by semi-definite programs, which is useful for benchmarking the performance of these algorithms for sufficiently small examples. Finally, we establish various generalizations of the resource theory of asymmetry, with the upshot being that the acceptance probabilities of the algorithms are resource monotones and thus well motivated from the resource-theoretic perspective.
An Novel Selective Image Encryption Algorithm For Networked Visual Inverted Pendulum

The inverted pendulum image may be subject to various cyber attacks in the networked inverted pendulum visual servo control system (NIPVSCS), how to ensure the safe and efficient transmission of image data is a challenging issue. To solve this problem, this paper proposes a novel selective image encryption algorithm. First, since the encrypted inverted pendulum image will bring long computing time that makes the system performance worse, the image is scaled by using the bilinear difference method to initially reduce the amount of image data. Second, to further improve the computation speed to meet the real-time requirement of the NIPVSCS, Hough transform is employed to determine the swing rod of inverted pendulum image, and the encrypted area is set to further reduce the calculation complexity. Third, according to the key area, chaotic map is used to generate a small number of random rows and columns, which are cyclically shifted to break the strong correlation of adjacent pixels, then the XOR operation is used to mask the pixel values. Finally, simulation experiments confirm the feasibility and effectiveness of the proposed method.
Social skills in higher education: how to combine active learning and social skills training program

Paper aims:The objective was to propose a Social Skills Training (SST) program integrated with project-based learning (PjBL) and to describe the student's perception.Originality: Social skills are recognized for promoting peer collaboration and are important requirements in professional environment.However, there are no proposals on how to integrate SST in PjBL. Research method:The same PjBL was applied to students from different classes and at the same Production Engineering course.The first in 2017 without the SST, and in 2018, with the SST conducted by a psychologist.A survey with openended questions compiled students' perceptions in both applications. Main findings:The qualitatively analyzed students' responses indicated changes regarding the perceived benefits of the PjBL over the years and students with SST cited the benefit of social interaction and teamwork. Implications for theory and practice:The study points out that SST can be positive and brings effects in professional teams.
Parts of Speech (POS) Tagging for Dogri Language

Parts of speech tagging is an important activity of natural language processing, information extraction, language translation, speech synthesis, question understanding and many more. Parts of speech tagging is basically the problem of assigning the parts of speech tags to the words in the text. As per English grammar, there are many parts of speech tags such as noun, adjective, pronoun, verb, adverb, preposition, conjunction and interjection. The methods of assigning tags to words are categorized into rule-based, stochastic and hybrid approach. In this paper, a rule-based parts of speech tagger for Dogri (regional language of Jammu) language along with the algorithm and the modular structure of the system is presented. The proposed system is evaluated over a number of corpus with six different parts of speech tags for Dogri, and hence, the evaluation is done on five datasets of Dogri corpus, and the corresponding results are also demonstrated in the paper.
Knowledge is Power: Provide Your IT-Support with Domain-Specific High-Quality Solution Material

As more and more business processes are based on IT services the high availability of these processes is dependent on the IT-Support. Thus, making the IT-Support a critical success factor of companies. This paper presents how this department can be supported by providing the staff with domain-specific and high-quality solution material to help employees faster when errors occur. The solution material is based on previously solved tickets because these contain precise domain-specific solutions narrowed down to e.g., specific versions and configurations of hard-/software used in the company. To retrieve the solution material ontologies are used that contain the domain-specific vocabulary needed. Because not all previously solved tickets contain high-quality solution material that helps the staff to fix issues the de-signed IT-Support system separates low- from high-quality solution material. This paper presents (a) theory- and practical-motivated design requirements that describe the need for automatically retrieved solution material, (b) develops two major design principles to retrieve domain-specific and high-quality solution material, and (c) evaluates the instantiations of them as a prototype with organic real-world data. The results show that previously solved tickets of a company can be pre-processed and retrieved to IT-Support staff based on their current queries.
Importance Weight Estimation and Generalization in Domain Adaptation Under Label Shift

We study generalization under labeled shift for categorical and general normed label spaces. We propose a series of methods to estimate the importance weights from labeled source to unlabeled target domain and provide confidence bounds for these estimators. We deploy these estimators and provide generalization bounds in the unlabeled target domain.
The t-wise Independence of Substitution-Permutation Networks

Block ciphers such as the Advanced Encryption Standard (Rijndael) are used extensively in practice, yet our understanding of their security continues to be highly incomplete. This paper promotes and continues a research program aimed at proving the security of block ciphers against important and well-studied classes of attacks. In particular, we initiate the study of (almost) t-wise independence of concrete block-cipher construction paradigms such as substitution-permutation networks and key-alternating ciphers. Sufficiently strong (almost) pairwise independence already suffices to resist (truncated) differential attacks and linear cryptanalysis, and hence this is a relevant and meaningful target. Our results are two-fold. Our first result concerns substitution-permutation networks (SPNs) that model ciphers such as AES. We prove the almost pairwise-independence of an SPN instantiated with concrete S-boxes together with an appropriate linear mixing layer, given sufficiently many rounds and independent sub-keys. Our proof relies on a characterization of S-box computation on input differences in terms of sampling output differences from certain subspaces, and a new randomness extraction lemma (which we prove with Fourier-analytic techniques) that establishes when such sampling yields uniformity. We use our techniques in particular to prove almost pairwise-independence for sufficiently many rounds of both the AES block cipher (which uses a variant of the patched inverse function $$x \mapsto x^{-1}$$ as the S-box) and the MiMC block cipher (which uses the cubing function $$x \mapsto x^3$$ as the S-box), assuming independent sub-keys. Secondly, we show that instantiating a key-alternating cipher (which can be thought of as a degenerate case of SPNs) with most permutations gives us (almost) t-wise independence in $$t + o(t)$$ rounds. In order to do this, we use the probabilistic method to develop two new lemmas, an independence-amplification lemma and a distance amplification lemma, that allow us to reason about the evolution of key-alternating ciphers.
GANN: A Graph alignment neural network for video partial copy detection

With the advent of we-media era, massive videos have been uploaded by users to the Internet. Such a large volume of video data brings us various information. It, however, contains some fake information created by partial copy videos, which constitute infringement act and are harmful to original authors and common users. In this paper, we propose a graph alignment neural network (GANN) for partial copy videos detection. Through building a graph neural network based on video frame-level feature extracted by a pretrained convolutional neural network and their relationship, GANN automatically integrates the global representation of a video, and learns the intra-similarity between original and copied videos, and the inter-discriminative from other videos by the self-attention and cross-attention mechanism in the graph neural network. We perform experiments on the challenging dataset VCDB, which includes a variety of complex transformations in the real scene. Results demonstrate that our GANN has better detection performance than baseline methods, where the precision of GANN is close to 80%, and the recall rate reaches 65%.
On the Performance of Bytecode Interpreters in Prolog

The semantics and the recursive execution model of Prolog make it very natural to express language interpreters in form of AST (Abstract Syntax Tree) interpreters where the execution follows the tree representation of a program. An alternative implementation technique is that of bytecode interpreters. These interpreters transform the program into a compact and linear representation before evaluating it and are generally considered to be faster and to make better use of resources.In this paper, we discuss different ways to express the control flow of interpreters in Prolog and present several implementations of AST and bytecode interpreters. On a simple language designed for this purpose, we evaluate whether techniques best known from imperative languages are applicable in Prolog and how well they perform. Our ultimate goal is to assess which interpreter design in Prolog is the most efficient as we intend to apply these results to a more complex language. However, we believe the analysis in this paper to be of more general interest.
Towards an Implementation of a Concurrent Language for Argumentation

While agent-based modelling languages naturally implement concurrency, the currently available languages for argumentation do not allow to explicitly model this type of interaction. In this paper we introduce a concurrent language for handling process arguing and communicating using a shared argumentation framework (reminding shared constraint store as in concurrent constraint). We introduce also basic expansions, contraction and revision procedures as main bricks for enforcement, debate, negotiation and persuasion.
Continuous variable measurement-device-independent quantum key distribution based on photon subtraction and optical amplifiers

Aiming at the problems of low secret key rate, short transmission distance and low detection efficiency of continuous-variable quantum key distribution protocol, we propose a measurement-device-independent continuous-variable quantum key distribution(MDI-CVQKD) protocol based on photon subtraction and noiseless linear amplifiers (NLA). The protocol uses non-Gaussian operation, in particular, photon subtraction to improve the entanglement between quantum states, and NLA is used to amplify quantum states to compensate for channel attenuation. In this paper, the principle of the protocol is analyzed theoretically, and the relationship between the secret key rate and the secure transmission distance is derived through the covariance matrix between quantum states. Simulation results show that the proposed protocol can not only improve the entanglement between quantum states, but also extend the transmission distance of key distribution and improve the performance of key distribution system.
A network-independent tool-based usable authentication system for Internet of Things devices

In this paper, we introduce a hardware-based system with a protocol realization to authenticate Internet of Things (IoT) devices. DigitalSeal is a novel standalone network-independent authentication tool implemented using an Arduino UNO and various components. DigitalSeal ’s I/O elements read a barcode and display a barcode data and its corresponding HMAC, which are used for authentication. DigitalSeal can manage cryptographic keys securely and provide a data integrity in order to defend against Man-in-the-Middle (MitM) and Man-in-the-Browser (MitB) attacks. Moreover, DigitalSeal can be used in various applications, such as an authentication system or protocol, an online/offline transaction, a login session, and an IoT device authentication. Using DigitalSeal, we propose a new protocol for IoT device authentication, providing various security benefits and reducing the burden of key maintenance for a large number of IoT devices. Our authentication protocol realization with DigitalSeal provides a convenient method for securely managing password for multiple IoT devices, prevents unauthorized IoT devices from connecting to the user’s gateway (an IoT home/enterprise network), and secures the communication between the IoT device and the gateway. Our system and associated protocol are both cost-effective and usable. According to our experiments, most users are able to obtain the authentication credential (the HMAC) within 3 seconds with more than 93% accuracy using DigitalSeal.
Multiple Hypothesis Tracking With Integrated Cell Division Detection

Automatic tracking of proliferating cells in microscopy images is important to elucidate biological processes. We have developed a new probabilistic approach for cell tracking which is based on Multiple Hypothesis Tracking and integrates cell division detection. Our method uses information from multiple frames and formulates data association with cell division detection as graph-theoretical maximum weighted independent set problem. We evaluated our approach using synthetic data as well as data from the Cell Tracking Challenge. It turned out that our approach generally improves the results for cell tracking and cell division detection compared to previous methods.
Application of Deep Learning Model Convolution Neural Network for Effective Web Information Retrieval

Convolution neural network (CNN) is the most popular deep learning method that has been used for various applications like image recognition, computer vision, and natural language processing. In this chapter, application of CNN in web query session mining for effective information retrieval is explained. CNN has been used for document analysis to capture the rich contextual structure in a search query or document content. The document content represented in matrix form using Word2Vec is applied to CNN for convolution as well as maxpooling operations to generate the fixed length document feature vector. This fixed length document feature vector is input to fully connected neural network (FNN) and generates the semantic document vector. These semantic document vectors are clustered to group similar document for effective web information retrieval. An experiment was performed on the data set of web query sessions, and results confirm the effectiveness of CNN in web query session mining for effective information retrieval.
Robust Adversarial Example as Captcha Generator

Adversarial example as one of the weaknesses of Deep Learning, can be used to attack image recognition models. Image recognition model can be used to attack common image CAPTCHA schemes, as the AI algorithm can recognize images to pass the test. So with applying robust adversarial example to image CAPTCHA, we produced a way to synthesize a robust CAPTCHA generator that are robust and cannot be easily attacked by image recognition algorithms.
Co-Seg: An Image Segmentation Framework Against Label Corruption

Supervised deep learning performance is heavily tied to the availability of high-quality labels for training. Neural networks can gradually overfit corrupted labels if directly trained on noisy datasets, leading to severe performance degradation at test time. In this paper, we propose a novel deep learning framework, namely Co-Seg, to collaboratively train segmentation networks on datasets which include low-quality noisy labels. Our approach first trains two networks simultaneously to sift through all samples and obtain a subset with reliable labels. Then, an efficient yet easily-implemented label correction strategy is applied to enrich the reliable subset. Finally, using the updated dataset, we retrain the segmentation network to finalize its parameters. Experiments in two noisy labels scenarios demonstrate that our proposed model can achieve results comparable to those obtained from fully supervised learning trained on the noise-free labels. In addition, our framework can be easily implemented in any segmentation algorithm to increase its robustness to noisy labels.
Transductive Multiview Modeling With Interpretable Rules, Matrix Factorization, and Cooperative Learning

Multiview fuzzy systems aim to deal with fuzzy modeling in multiview scenarios effectively and to obtain the interpretable model through multiview learning. However, current studies of multiview fuzzy systems still face several challenges, one of which is how to achieve efficient collaboration between multiple views when there are few labeled data. To address this challenge, this article explores a novel transductive multiview fuzzy modeling method. The dependency on labeled data is reduced by integrating transductive learning into the fuzzy model to simultaneously learn both the model and the labels using a novel learning criterion. Matrix factorization is incorporated to further improve the performance of the fuzzy model. In addition, collaborative learning between multiple views is used to enhance the robustness of the model. The experimental results indicate that the proposed method is highly competitive with other multiview learning methods.
Fast Optimisation of Convolutional Neural Network Inference using System Performance Models

The choice of convolutional routines (or primitives) for implementing the operations in a Convolutional Neural Network (CNN) has a tremendous impact over the inference time. To optimise the execution latency for a target system, a lengthy profiling stage is needed - iterating over all the implementations of convolutional primitives in the configuration of each layer to measure their execution time on that platform. Each primitive exercises the system resources in different ways, so new profiling is currently needed when optimising for another system. In this work, we replace this prohibitively expensive profiling stage with a machine learning based approach of performance modelling. Our approach drastically speeds up the optimisation by estimating the latency of convolutional primitives in any layer configuration running on a target system. We reduce the time needed for optimising the execution of large neural networks on an ARM Cortex-A73 system from hours to just seconds. Our performance model is easily transferable across target platforms. This is demonstrated by training a performance model on an Intel platform and transferring its predictive performance to AMD and ARM systems, using very few profiled samples from the target platforms for fine-tuning the performance model.