Reviewerly/exHarmony huggingface dataset:
Extracting automata from neural networks using active learning

Deep learning is one of the most advanced forms of machine learning. Most modern deep learning models are based on an artificial neural network, and benchmarking studies reveal that neural networks have produced results comparable to and in some cases superior to human experts. However, the generated neural networks are typically regarded as incomprehensible black-box models, which not only limits their applications, but also hinders testing and verifying. In this paper, we present an active learning framework to extract automata from neural network classifiers, which can help users to understand the classifiers. In more detail, we use Angluin’s L* algorithm as a learner and the neural network under learning as an oracle, employing abstraction interpretation of the neural network for answering membership and equivalence queries. Our abstraction consists of value, symbol and word abstractions. The factors that may affect the abstraction are also discussed in the paper. We have implemented our approach in a prototype. To evaluate it, we have performed the prototype on a MNIST classifier and have identified that the abstraction with interval number 2 and block size 1 × 28 offers the best performance in terms of F 1 score. We also have compared our extracted DFA against the DFAs learned via the passive learning algorithms provided in LearnLib and the experimental results show that our DFA gives a better performance on the MNIST dataset.
Responding to Illegal Activities Along the Canadian Coastlines Using Reinforcement Learning

Machine learning (ML) algorithms can prove to be instrumental in certain complex ill-con-ditioned systems when inserted as a middle layer to interface low-level hardware, such as sensors and actuators, and high-level decision-making kernels. Such an interface provides a secondary, or supervisory, conditioning layer that would enhance the system's robustness in the face of various types of uncertainties and disturbances. This article elaborates how ML can leverage the solution of a contemporary problem related to the security of maritime domains.
Research on Multi-party Cooperative Secure Identity Authentication Mechanism Based on Blockchain

Abstract With the continuous development of information society, block chain technology can play an extremely important role in many fields. However, with the increasing degree of communication in the information society, a huge amount of data is generated. How to protect the information data generated in the process of communication has become a more serious problem at present. In its earliest stages, blockchain technology was used only for cryptocurrency transactions. Under the general directory, there are a lot of sub-accounts, and these sub-accounts are all valuable accounts, and the data is stored in different blocks in different chains. Judging from the current application of blockchain technology, the application of this technology can guarantee the security of data storage. Data storage is extremely secure in terms of security, so there will be no individual or group to destroy or tamper with the data. In blockchain technology, all data is stored in a Mosaic form in many blocks, which means that a single block does not have the function of storing information data. When making a large transaction, due to the uniqueness of blockchain technology, each member of the chain needs to authorize the data, which means all members of the chain must agree to the transaction before the transaction can be completed. Starting from the application of block chain technology, this paper analyzes the most common identity authentication mechanism in our daily life, and analyzes the application of block chain technology in daily life from multiple aspects.
AdCOFE: Advanced Contextual Feature Extraction in conversations for emotion classification

Emotion recognition in conversations is an important step in various virtual chatbots which require opinion-based feedback, like in social media threads, online support, and many more applications. Current emotion recognition in conversations models face issues like: (a) loss of contextual information in between two dialogues of a conversation, (b) failure to give appropriate importance to significant tokens in each utterance, (c) inability to pass on the emotional information from previous utterances. The proposed model of Advanced Contextual Feature Extraction (AdCOFE) addresses these issues by performing unique feature extraction using knowledge graphs, sentiment lexicons and phrases of natural language at all levels (word and position embedding) of the utterances. Experiments on emotion recognition in conversations datasets show that AdCOFE is beneficial in capturing emotions in conversations.
The conceptualization of processes for handling online requests of information resources in academic libraries in South Africa: Experience from an Open Distance e-Learning (ODeL) environment

This article aims to present the Open Distance eLearning library processes for requesting information resources using the Online Public Access Catalogue (OPAC). OPAC is known as a platform used by library clients to check the availability of information resources from the entire library collection. ODeL libraries also use the OPAC, not as a library catalogue, but as a portal for requesting information resources. The processes presented in this articles starts from when the library clients request information resources when the requests reach the library/library staff, how the online requests are handled when received, how they are sent to the requesters, how the information resources are sent back to the library after use by the requesters and the process of receiving the returned information resources.
A Study on Quality Evaluation of College English Translation Teaching Based on SERVQUAL Model

In order to solve the problem of low accuracy in traditional evaluation method of college English translation teaching, a new method of college English translation teaching quality evaluation based on SERVQUAL model is proposed in this paper. The evaluation index system of college English translation teaching quality is constructed, and the evaluation index data are processed by the method of principal component analysis and support vector machine. Based on the processed data, the comprehensive evaluation results of college English translation teaching quality are obtained by using SERVQUAL model. The experimental results show that the maximum evaluation accuracy of the proposed method is 96.3%, the minimum is 94.5%, and the average output time of the evaluation results is 75.2ms, indicating that the proposed method can achieve a rapid and accurate evaluation of the quality of college English translation teaching.
Status and prospects for the libraries of academic research institutes (as the case study of SB RAS Novosibirsk Scientific Center libraries). Part 1

The role and tasks of academic libraries are changing due to external factors, and among them is the atrophy of their function as the only information source. The authors analyze the survey of the Novosibirsk Scientific Center’s libraries related to the relevant services provided by academic libraries. The study goal is to determine the balance of traditional and innovated tasks being accomplished by the libraries. The idea of the survey was conceived in 2019 before the pandemic. In Part One, the authors describe the methods and methodology of their study. The key method is the survey questionnaire. The findings are analyzed and the conclusions are presented: to solve new problems (like establishing and maintaining the systems for monitoring researchers’ publication activity, maintenance of institutional data repository, preparation of reviews and analytical surveys, advocating institutes through social media, users support on copyright and plagiarism, consulting on grant selection and application), academic administration bring the expertise of librarians, or establish new structures; however, the librarians are insufficiently engaged in solving the new tasks of science support. The authors suggest that their findings reflect the activities of academic libraries, however after the pandemic, the situation may change with the world.
Fuzzy C-means clustering algorithm for automatically determining the number of clusters

Traditional fuzzy C-means (FCM) clustering algorithm is sensitive to initial clustering center, and the number of clusters need to be set artificially in advance. For these reasons, we propose an improved FCM algorithm (AMMF) that can determine the number of clusters automatically. Firstly, the proposed algorithm uses the affinity propagation clustering algorithm to obtain coarse number of clusters, which are taken as the upper limit of searching the best number of clusters. Secondly, by the improved maximum and minimum distance algorithm obtains some representative sample points as the initial clustering centers of the FCM algorithm. Lastly, we use Silhouette Coefficient to analyze the quality of clustering to determine the optimal number of clusters automatically. Experimental results show that the AMMF algorithm has significantly better clustering performance than other improved FCM based algorithms, and improves the stability of the clustering results.
Histogram of Marked Background (HMB) Feature Extraction Method for Arabic Handwriting Recognition

Features extraction is one of the most important steps in handwriting recognition systems. In this paper, we propose a novel features extraction method, which is adapted to the complex nature of Arabic handwriting. The proposed feature called histogram of marked background (HMB) is not considering only ink pixels in a text image, but also uses the background of the image. Each background pixel in the text image was marked according to the repartition of ink pixels in its neighborhood. Feature vectors are extracted by computing histograms from the marked images. Hidden Markov models (HMMs) with Hidden Markov model toolkit (HTK) were used in the recognition process. The experiments were performed on two datasets: IBN SINA database of historical Arabic documents and Isolated Farsi Handwritten Character Database (IFHCDB). The proposed feature in this study produced efficient and promising results for Arabic handwriting recognition, for both isolated characters and for historical documents.
Development of Platform Independent Mobile Learning Tool in Saudi Universities

The term “mobile learning” (or “m-learning”) refers to using handheld phones to learn and wireless computing as a learning tool and connectivity technology. This paper presents and explores the latest mobile platform for teaching and studying programming basics. The M-Learning tool was created using a platform-independent approach to target the largest available number of learners while reducing development and maintenance time and effort. Since the code is completely shared across mobile devices (iOS, Android, and Windows Phone), students can use any smartphone to access the app. To make the programme responsive, scalable, and dynamic, and to provide students with personalised guidance, the core application is based on an analysis design development implementation and assessment (ADDIE) model implemented in the Xamarin framework. The application’s key features are depicted in a prototype. An experiment is carried out on BS students at a university to evaluate the efficacy of the generated application. A usefulness questionnaire is administered to an experimental community in order to determine students’ expectations of the developed mobile application’s usability. The findings of the experiment show that the application is considerably more successful than conventional learning in developing students’ online knowledge assessment abilities, with an impact size of 1.96. The findings add to the existing mobile learning literature by defining usability assessment features and offering a basis for designing platform-independent m-learning applications. The current findings are explored in terms of their implications for study and teaching practice.
A Multi-Agent System for Ubiquitous Learning for Hospital Education

Healthcare information systems (HIS) have become an important area of research due to ever-increasing healthcare costs to the national economy. Many recent technological developments such as mobile technology and cloud computing have profoundly affected the current state of HIS and further facilitated the developments of ubiquitous computing and ubiquitous learning systems (ULS). The authors propose a multi-agent system for ubiquitous learning (MASUL) to facilitate various learning tasks. They use JADE (Java agent development framework) for developing the multi-agent system. MASUL provides a series of functionalities that can be used by the patients and clinicians. The system simplifies the mechanisms to access learning information via mobile devices, and it also facilitates the learning-teaching process centered on the physical surroundings of the user.
The Acropolis on the Immersive Web

We report here on an application of reality media (virtual and augmented reality) to digital cultural heritage. The particular challenge we address is: how to combine VR and AR to bridge the gap between the center (the museum housing cultural artifacts) and periphery (the heritage site where the artifacts were found) while at the same time attending to, even enhancing, the aura of both artifacts and sites? Our proposed solution is to implement the cultural heritage technique known as situated simulation (sitsim) in combination with a social virtual environment called Hubs. Our case study is a sitsim of the Acropolis in Athens, which can function on location and remotely and offers real-time conferencing capabilities for its participants.
Machine learning the dynamics of quantum kicked rotor

Using the multilayer convolutional neural network (CNN), we can detect the quantum phases in random electron systems, and phase diagrams of two and higher dimensional Anderson transitions and quantum percolations as well as disordered topological systems have been obtained. Here, instead of using CNN to analyze the wave functions, we analyze the dynamics of wave packets via long short-term memory network (LSTM). We adopt the quasi-periodic quantum kicked rotors, which simulate the three and four dimensional Anderson transitions. By supervised training, we let LSTM extract the features of the time series of wave packet displacements in localized and delocalized phases. We then simulate the wave packets in unknown phases and let LSTM classify the time series to localized and delocalized phases. We compare the phase diagrams obtained by LSTM and those obtained by CNN.
Improving the recall of biomedical named entity recognition with label re-correction and knowledge distillation

Biomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotated datasets, especially the limited knowledge contained in them.To remedy the above issue, we propose a novel Biomedical Named Entity Recognition (BioNER) framework with label re-correction and knowledge distillation strategies, which could not only create large and high-quality datasets but also obtain a high-performance recognition model. Our framework is inspired by two points: (1) named entity recognition should be considered from the perspective of both coverage and accuracy; (2) trustable annotations should be yielded by iterative correction. Firstly, for coverage, we annotate chemical and disease entities in a large-scale unlabeled dataset by PubTator to generate a weakly labeled dataset. For accuracy, we then filter it by utilizing multiple knowledge bases to generate another weakly labeled dataset. Next, the two datasets are revised by a label re-correction strategy to construct two high-quality datasets, which are used to train two recognition models, respectively. Finally, we compress the knowledge in the two models into a single recognition model with knowledge distillation.Experiments on the BioCreative V chemical-disease relation corpus and NCBI Disease corpus show that knowledge from large-scale datasets significantly improves the performance of BioNER, especially the recall of it, leading to new state-of-the-art results.We propose a framework with label re-correction and knowledge distillation strategies. Comparison results show that the two perspectives of knowledge in the two re-corrected datasets respectively are complementary and both effective for BioNER.
Development of Magnetic-Based Navigation by Constructing Maps Using Machine Learning for Autonomous Mobile Robots in Real Environments

Localization is fundamental to enable the use of autonomous mobile robots. In this work, we use magnetic-based localization. As Earth’s geomagnetic field is stable in time and is not affected by nonmagnetic materials, such as a large number of people in the robot’s surroundings, magnetic-based localization is ideal for service robotics in supermarkets, hotels, etc. A common approach for magnetic-based localization is to first create a magnetic map of the environment where the robot will be deployed. For this, magnetic samples acquired a priori are used. To generate this map, the collected data is interpolated by training a Gaussian Process Regression model. Gaussian processes are nonparametric, data-drive models, where the most important design choice is the selection of an adequate kernel function. These models are flexible and generate mean predictions as well as the confidence of those predictions, making them ideal for their use in probabilistic approaches. However, their computational and memory cost scales poorly when large datasets are used for training, making their use in large-scale environments challenging. The purpose of this study is to: (i) enable magnetic-based localization on large-scale environments by using a sparse representation of Gaussian processes, (ii) test the effect of several kernel functions on robot localization, and (iii) evaluate the accuracy of the approach experimentally on different large-scale environments.
Leveraging Blockchain technology in the Education Sector

The act of maintaining educational records both online and on paper have become a norm. With the enforcement of lockdowns due to the Covid-19 pandemic, the education sector attempted to move their entire operations online.  However with this move, various operations such as Verification of Documents, Approval of LORs have become harder to deal with in a legitimate manner. In this paper, we have compared various existing methods to deal with the problem at hand and proposed our system for the same.
Generating and detecting bound entanglement in two-qutrits using a family of indecomposable positive maps

Abstract The problem of bound entanglement detection is a challenging aspect of quantum information theory for higher dimensional systems. Here, we propose an indecomposable positive map for two-qutrit systems, which is shown to detect a class of positive partial transposed (PPT) states. A corresponding witness operator is constructed and shown to be weakly optimal and locally implementable. Further, we perform a structural physical approximation of the indecomposable map to make it a completely positive one, and find a new PPT-entangled state which is not detectable by certain other well-known entanglement detection criteria.
Measuring Prefixation and Suffixation in the Languages of the World

It has long been recognized that suffixing is more common than prefixing in the languages of the world. More detailed statistics on this tendency are needed to sharpen proposed explanations for this tendency. The classic approach to gathering data on the prefix/suffix preference is for a human to read grammatical descriptions (948 languages), which is time-consuming and involves discretization judgments. In this paper we explore two machine-driven approaches for prefix and suffix statistics which are crude approximations, but have advantages in terms of time and replicability. The first simply searches a large collection of grammatical descriptions for occurrences of the terms ‘prefix’ and ‘suffix’ (4 287 languages). The second counts substrings from raw text data in a way indirectly reflecting prefixation and suffixation (1 030 languages, using New Testament translations). The three approaches largely agree in their measurements but there are important theoretical and practical differences. In all measurements, there is an overall preference for suffixation, albeit only slightly, at ratios ranging between 0.51 and 0.68.
Zoo-Tuning: Adaptive Transfer from A Zoo of Models

With the development of deep networks on various large-scale datasets, a large zoo of pretrained models are available. When transferring from a model zoo, applying classic single-model based transfer learning methods to each source model suffers from high computational burden and cannot fully utilize the rich knowledge in the zoo. We propose \emph{Zoo-Tuning} to address these challenges, which learns to adaptively transfer the parameters of pretrained models to the target task. With the learnable channel alignment layer and adaptive aggregation layer, Zoo-Tuning \emph{adaptively aggregates channel aligned pretrained parameters} to derive the target model, which promotes knowledge transfer by simultaneously adapting multiple source models to downstream tasks. The adaptive aggregation substantially reduces the computation cost at both training and inference. We further propose lite Zoo-Tuning with the temporal ensemble of batch average gating values to reduce the storage cost at the inference time. We evaluate our approach on a variety of tasks, including reinforcement learning, image classification, and facial landmark detection. Experiment results demonstrate that the proposed adaptive transfer learning approach can transfer knowledge from a zoo of models more effectively and efficiently.
Tackling the Challenges in Scene Graph Generation With Local-to-Global Interactions

In this work, we seek new insights into the underlying challenges of the scene graph generation (SGG) task. Quantitative and qualitative analysis of the visual genome (VG) dataset implies: 1) ambiguity: even if interobject relationship contains the same object (or predicate), they may not be visually or semantically similar; 2) asymmetry: despite the nature of the relationship that embodied the direction, it was not well addressed in previous studies; and 3) higher-order contexts: leveraging the identities of certain graph elements can help generate accurate scene graphs. Motivated by the analysis, we design a novel SGG framework, Local-to-global interaction networks (LOGINs). Locally, interactions extract the essence between three instances of subject, object, and background, while baking direction awareness into the network by explicitly constraining the input order of subject and object. Globally, interactions encode the contexts between every graph component (i.e., nodes and edges). Finally, Attract and Repel loss is utilized to fine-tune the distribution of predicate embeddings. By design, our framework enables predicting the scene graph in a bottom-up manner, leveraging the possible complementariness. To quantify how much LOGIN is aware of relational direction, a new diagnostic task called Bidirectional Relationship Classification (BRC) is also proposed. Experimental results demonstrate that LOGIN can successfully distinguish relational direction than existing methods (in BRC task), while showing state-of-the-art results on the VG benchmark (in SGG task).