Reviewerly/exHarmony huggingface dataset:
Suffix tree-based linear algorithms for multiple prefixes, single suffix counting and listing problems

Given two strings $T$ and $S$ and a set of strings $P$, for each string $p \in P$, consider the unique substrings of $T$ that have $p$ as their prefix and $S$ as their suffix. Two problems then come to mind; the first problem being the counting of such substrings, and the second problem being the problem of listing all such substrings. In this paper, we describe linear-time, linear-space suffix tree-based algorithms for both problems. More specifically, we describe an $O(|T| + |P|)$ time algorithm for the counting problem, and an $O(|T| + |P| + \#(ans))$ time algorithm for the listing problem, where $\#(ans)$ refers to the number of strings being listed in total, and $|P|$ refers to the total length of the strings in $P$. We also consider the reversed version of the problems, where one prefix condition string and multiple suffix condition strings are given instead, and similarly describe linear-time, linear-space algorithms to solve them.
Achieving Efficient and Privacy-Preserving (,)-Core Query over Bipartite Graphs in Cloud

Bipartite graphs have been widely adopted in applications such as e-healthcare thanks to their ability to model various real-world relationships. Meanwhile, (,)-core query services over bipartite graphs are recognized as a promising approach for finding communities, i.e., closely related sets of vertices in a bipartite graph. As the bipartite graph grows, service providers tend to outsource the services to the cloud. However, there are privacy concerns related to the dataset, queries, and results. Although many schemes have been proposed for privacy-preserving graph analysis, they cannot be directly adopted to handle accurate (,)-core queries. Aiming at the challenges, under the two-server setting, this paper constructs two privacy-preserving schemes with different security levels to handle (,)-core queries. In the proposed schemes, a graph is represented as an index containing two tables and further encrypted by a symmetric homomorphic encryption scheme, and then the servers securely traverse the index. Detailed security analysis shows that both schemes can achieve access pattern privacy, while the security-enhanced one can further protect the structure of the query requests and results. In addition, extensive performance evaluations are conducted to indicate the efficiency of our proposed schemes.
On the Generalization Mystery in Deep Learning

The generalization mystery in deep learning is the following: Why do over-parameterized neural networks trained with gradient descent (GD) generalize well on real datasets even though they are capable of fitting random datasets of comparable size? Furthermore, from among all solutions that fit the training data, how does GD find one that generalizes well (when such a well-generalizing solution exists)? We argue that the answer to both questions lies in the interaction of the gradients of different examples during training. Intuitively, if the per-example gradients are well-aligned, that is, if they are coherent, then one may expect GD to be (algorithmically) stable, and hence generalize well. We formalize this argument with an easy to compute and interpretable metric for coherence, and show that the metric takes on very different values on real and random datasets for several common vision networks. The theory also explains a number of other phenomena in deep learning, such as why some examples are reliably learned earlier than others, why early stopping works, and why it is possible to learn from noisy labels. Moreover, since the theory provides a causal explanation of how GD finds a well-generalizing solution when one exists, it motivates a class of simple modifications to GD that attenuate memorization and improve generalization. Generalization in deep learning is an extremely broad phenomenon, and therefore, it requires an equally general explanation. We conclude with a survey of alternative lines of attack on this problem, and argue that the proposed approach is the most viable one on this basis.
The Correlation Between Youth's Knowledge With Youth's Perceptions of PWHA (People With HIV/AIDS)

Students' knowledge of PWHA (People With HIV/AIDS) affects their perception of PWHA in the form of positive and negative responses. This study aims to determine the relationship between knowledge and perceptions of University of Riau students towards PWHA (People With HIV/AIDS). This study used a descriptive correlation design with a cross-sectional approach. These research subjects are 391 people who were taken based on inclusion criteria using a stratified random sampling technique—data analysis using bivariate analysis using the chi-square test. Statistical tests showed that there was a significant relationship between knowledge and perception of PWHA with a p-value (0.000) &lt; alpha (0.05). University of Riau students' knowledge of PWHA has a significant relationship with University of Riau students' perceptions of PWHA. We firmly suggest that foundations or peer support group organizations carry out educational activities for adolescents about sexually transmitted diseases, especially HIV/AIDS.
Training a special teacher: Opinions and experience of teachers in the conditions of inclusive education

In the context of the active spread of inclusive education in Kazakhstan, it seems important to modernize their training for work in inclusive schools. Accordingly, the problems of effective preparation of teachers to work with inclusive classes have acquired particular relevance. The aim of the proposed study was a comparative study of the opinions of teachers-defectologists in secondary schools of the region on the organization and implementation of practice in inclusive education. Thus, an interview was conducted with teachers in North Kazakhstan. The development of inclusively oriented professional and personal competencies among teachers largely depends on the success of the inclusion of children with special educational needs in the general educational process. Based on the study of the opinion of special teachers practicing in inclusive organizations, it is argued that inclusive education of children with special educational needs requires a change in approaches to the training of special teachers, modernization of the content, and directions of their training.&#x0D; &#x0D; Keywords: Defectologists; inclusive education; special educational needs; teacher training.
A Blockchain-Based Intelligent Machine Learning System for Smart Health Care

Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. In this paper, we proposed secure, transparent and intelligent methods in the Internate of medical things industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.
A Survey on Cross-Lingual Summarization

Cross-lingual summarization is the task of generating a summary in one language (e.g., English) for the given document(s) in a different language (e.g., Chinese). Under the globalization background, this task has attracted increasing attention of the computational linguistics community. Nevertheless, there still remains a lack of comprehensive review for this task. Therefore, we present the first systematic critical review on the datasets, approaches, and challenges in this field. Specifically, we carefully organize existing datasets and approaches according to different construction methods and solution paradigms, respectively. For each type of datasets or approaches, we thoroughly introduce and summarize previous efforts and further compare them with each other to provide deeper analyses. In the end, we also discuss promising directions and offer our thoughts to facilitate future research. This survey is for both beginners and experts in cross-lingual summarization, and we hope it will serve as a starting point as well as a source of new ideas for researchers and engineers interested in this area.
Towards property-based tests in natural language

We consider a new approach to generate tests from natural language. Rather than relying on machine learning or templated extraction from structured comments, we propose to apply classic ideas from linguistics to translate natural-language sentences into executable tests. This paper explores the application of combinatory categorial grammars (CCGs) to generating property-based tests. Our prototype is able to generate tests from English descriptions for each example in a textbook chapter on property-based testing.
An LLVM-based C++ Compiler Toolchain for Variational Hybrid Quantum-Classical Algorithms and Quantum Accelerators

Variational algorithms are a representative class of quantum computing workloads that combine quantum and classical computing. This paper presents an LLVM-based C++ compiler toolchain to efficiently execute variational hybrid quantum-classical algorithms on a computational system in which the quantum device acts as an accelerator. We introduce a set of extensions to the C++ language for programming these algorithms. We define a novel Executable and Linking Format (ELF) for Quantum and create a quantum device compiler component in the LLVM framework to compile the quantum part of the C++ source and reuse the host compiler in the LLVM framework to compile the classical computing part of the C++ source. A variational algorithm runs a quantum circuit repeatedly, each time with different gate parameters. We add to the quantum runtime the capability to execute dynamically a quantum circuit with different parameters. Thus, programmers can call quantum routines the same way as classical routines. With these capabilities, a variational hybrid quantum-classical algorithm can be specified in a single-source code and only needs to be compiled once for all iterations. The single compilation significantly reduces the execution latency of variational algorithms. We evaluate the framework's performance by running quantum circuits that prepare Thermofield Double (TFD) states, a quantum-classical variational algorithm.
Research on the Security Level of μ2 against Impossible Differential cryptanalysis

In the year 2020, a new lightweight block cipher µ 2 is proposed.It has both good software and hardware performance, and it is especially suitable for constrained resource environment.However, the security evaluation on µ 2 against impossible differential cryptanalysis seems missing from the specification.To fill this gap, an impossible differential cryptanalysis on µ 2 is proposed.In this paper, firstly, some cryptographic properties on µ 2 are proposed.Then several longest 7-round impossible differential distinguishers are constructed.Finally, an impossible differential cryptanalysis on µ 2 reduced to 10 rounds is proposed based on the constructed distinguishers.The time complexity for the attack is about 2 69.63 10-round µ 2 encryptions, the data complexity is O(2 48 ), and the memory complexity is 2 63.57Bytes.The reported result indicates that µ 2 reduced to 10 rounds can't resist against impossible differential cryptanalysis.
PM-MMUT: Boosted Phone-mask Data Augmentation using Multi-Modeling Unit Training for Phonetic-Reduction-Robust E2E Speech Recognition

Consonant and vowel reduction are often encountered in speech, which might cause performance degradation in automatic speech recognition (ASR). Our recently proposed learning strategy based on masking, Phone Masking Training (PMT), alleviates the impact of such phenomenon in Uyghur ASR. Although PMT achieves remarkably improvements, there still exists room for further gains due to the granularity mismatch between the masking unit of PMT (phoneme) and the modeling unit (word-piece). To boost the performance of PMT, we propose multi-modeling unit training (MMUT) architecture fusion with PMT (PM-MMUT). The idea of MMUT framework is to split the Encoder into two parts including acoustic feature sequences to phoneme-level representation (AF-to-PLR) and phoneme-level representation to word-piece-level representation (PLR-to-WPLR). It allows AF-to-PLR to be optimized by an intermediate phoneme-based CTC loss to learn the rich phoneme-level context information brought by PMT. Experimental results on Uyghur ASR show that the proposed approaches outperform obviously the pure PMT. We also conduct experiments on the 960-hour Librispeech benchmark using ESPnet1, which achieves about 10% relative WER reduction on all the test set without LM fusion comparing with the latest official ESPnet1 pre-trained model.
Robust H∞ Observer-based Reference Tracking Control Design of Nonlinear Stochastic Systems : HJIE-embedded Deep Learning Approach

The robust H ∞ observer-based reference tracking control design of nonlinear stochastic systems with external disturbance and measurement noise is always a very complicated and difficult problem in the control field.It needs to solve a very difficult control-observer-coupled Hamilton Jacobi Isaacs equation (HJIE) for nonlinear observer and controller in the design procedure.At present, there exists no analytic and numerical way for solving this control-observer-coupled HJIE.A novel HJIE-embedded deep learning approach is proposed as a co-design of deep learning algorithm and H ∞ observer-based tracking control scheme to directly solve the nonlinear partial differential control-observer-coupled HJIE of H ∞ observer-based reference tracking control design problem of nonlinear stochastic systems.In the off-line training phase, state estimation error and tracking error are inputed to HJIE-embedded deep neural network (DNN) to output the solution of HJIE.If not, the learning error of HJIE is fedback to train DNN to solve HJIE for H ∞ tracking control law, observer gain as well as the worst-case external disturbance and measurement noise, which will be sent back to nonlinear stochastic system model to replace the external disturbance and measurement noise and estimation error signal for next step training.The proposed DNNembedded H ∞ observer-based reference tracking scheme can achieve the theoretical H ∞ observer-based reference tracking control strategy as the deep learning algorithm converges.If free of external disturbance and measurement noise, the proposed DNN-based H ∞ observer-based reference tracking control scheme can approach to the stochastically asymptotical state estimation and reference tracking simultaneously.Finally, a design example of H ∞ observer-based reference tracking control for quadrotor UAV system with external disturbance and output measurement noise is provided to illustrate the design procedure and to validate the state estimation and reference tracking performance simultaneously of the proposed HJIE-embedded H ∞ DNN-based observer-based reference tracking control scheme of nonlinear stochastic systems.
DExIE - An IoT-Class Hardware Monitor for Real-Time Fine-Grained Control-Flow Integrity

Abstract The Dynamic Execution Integrity Engine (DExIE) is a lightweight hardware monitor that can be flexibly attached to many IoT-class processor pipelines. It is guaranteed to catch both inter- and intra-function illegal control flows in time to prevent any illegal instructions from touching memory. The performance impact of attaching DExIE to a core depends on the concrete pipeline structure. In some especially suitable cases, extending a processor with DExIE will have no performance penalty. DExIE is real-time capable, as it causes no or only up to 10.4 % additional and then predictable pipeline stalls. Depending on the monitored processor’s size and structure, DExIE is faster than software-based monitoring and often smaller than a separate guard processor. We present not just the hardware architecture, but also the automated programming flow, and discuss compact adaptable storage formats to hold fine-grained control flow information.
A Faithful and Quantitative Notion of Distant Reduction for Generalized Applications

Abstract We introduce a call-by-name lambda-calculus $$\lambda J$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>λ</mml:mi><mml:mi>J</mml:mi></mml:mrow></mml:math> with generalized applications which integrates a notion of distant reduction that allows to unblock $$\beta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>β</mml:mi></mml:math> -redexes without resorting to the permutative conversions of generalized applications. We show strong normalization of simply typed terms, and we then fully characterize strong normalization by means of a quantitative typing system. This characterization uses a non-trivial inductive definition of strong normalization –that we relate to others in the literature–, which is based on a weak-head normalizing strategy. Our calculus relates to explicit substitution calculi by means of a translation between the two formalisms which is faithful, in the sense that it preserves strong normalization. We show that our calculus $$\lambda J$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>λ</mml:mi><mml:mi>J</mml:mi></mml:mrow></mml:math> and the well-know calculus $$\varLambda J$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Λ</mml:mi><mml:mi>J</mml:mi></mml:mrow></mml:math> determine equivalent notions of strong normalization. As a consequence, $$\varLambda J$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Λ</mml:mi><mml:mi>J</mml:mi></mml:mrow></mml:math> inherits a faithful translation into explicit substitutions, and its strong normalization can be characterized by the quantitative typing system designed for $$\lambda J$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>λ</mml:mi><mml:mi>J</mml:mi></mml:mrow></mml:math> , despite the fact that quantitative subject reduction fails for permutative conversions.
Research on cross-domain recommendation algorithm based on quadratic collaborative filtering

Among the existing recommendation algorithms, the single domain recommendation algorithm has serious defects, and the cross-domain recommendation algorithm becomes a new development direction. To solve the problem of data missing in collaborative filtering algorithm, a cross-domain recommendation algorithm for quadratic collaborative filtering is proposed. The accuracy of the recommendation system can be improved by predicting the missing data with two similarity matrices. Through empirical study, experimental results show that the quadratic collaborative filtering cross-domain recommendation algorithm can achieve the purpose of cross-domain recommendation, and the performance comparison with the collaborative filtering recommendation algorithm shows that the quadratic collaborative filtering cross-domain recommendation algorithm can improve the accuracy of prediction.
Patient Similarity Measuring with Graph Embedded Learning and Triplet Network

Electronic Health Records (EHR) are fast becoming the standard for patient medical record keeping. Patient similarity measuring enabled by EHRs has demonstrated the ability to assist doctors and other medical practitioners in clinical decision-making from prognosis, to diagnosis, to treatment options and treatment. Due to the complexity and heterogeneity of medical data, it remains a challenge to integrate critical latent information to estimate patient similarity. As a result, nuances like the ability of a medical entity to have multiple meanings in different contexts and its effect on whether patients are similar or not, a key player in mitigating misdiagnosis, remain a relevant problem. We seek to take full advantage of the usually heterogeneous data of EHRs, to more carefully measure patient similarity by modeling the medical entities they contain as well as their relationships both with the patient and with other medical entities in the patient EHR scenarios through the use of heterogeneous graph embed-dings and triplet networks. We demonstrate the effectiveness of our algorithm through experiments on breast cancer diagnosis and patient medical record data from a tertiary hospital.
Realistic continuous-variable quantum teleportation using a displaced Fock state channel

We investigate ideal and non-ideal continuous-variable quantum teleportation protocols realized by using an entangled displaced Fock state resource. The characteristic function formulation is applied to measure the relative performance of displaced Fock state for teleporting squeezed and coherent states. It is found that for such single-mode input fields, the average fidelity remains at the classical threshold, suggesting that the displaced Fock states are not advantageous for teleportation. We also discuss the major decoherence effects, caused by the inaccuracy in Bell measurements and photon losses for the propagation of optical fields via fibre channels. The changes in the teleportation fidelity are described by adjusting the gain factor (g), reflectivity (R), mode damping ( $$\tau $$ ), and the number of thermal photons ( $$n_\mathrm {th}$$ ). The possibility of successful teleportation can be optimized by fixing these realistic parameters.
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection

Detecting abnormal events in video is commonly framed as a one-class classification task, where training videos contain only normal events, while test videos encompass both normal and abnormal events. In this scenario, anomaly detection is an open-set problem. However, some studies assimilate anomaly detection to action recognition. This is a closed-set scenario that fails to test the capability of systems at detecting new anomaly types. To this end, we propose UBnormal, a new supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, we introduce abnormal events annotated at the pixel level at training time, for the first time enabling the use of fully-supervised learning methods for abnormal event detection. To preserve the typical open-set formulation, we make sure to include disjoint sets of anomaly types in our training and test collections of videos. To our knowledge, UBnormal is the first video anomaly detection benchmark to allow a fair head-to-head comparison between one-class open-set models and supervised closed-set models, as shown in our experiments. Moreover, we provide empirical evidence showing that UBnormal can enhance the performance of a state-of-the-art anomaly detection framework on two prominent data sets, Avenue and ShanghaiTech. Our benchmark is freely available at https://github.com/lilygeorgescu/UBnormal.