Reviewerly/exHarmony huggingface dataset:
Reliability-aware Dynamic Feature Composition for Name Tagging

Word embeddings are widely used on a variety of tasks and can substantially improve the performance. However, their quality is not consistent throughout the vocabulary due to the long-tail distribution of word frequency. Without sufficient contexts, rare word embeddings are usually less reliable than those of common words. However, current models typically trust all word embeddings equally regardless of their reliability and thus may introduce noise and hurt the performance. Since names often contain rare and uncommon words, this problem is particularly critical for name tagging. In this paper, we propose a novel reliability-aware name tagging model to tackle this issue. We design a set of word frequency-based reliability signals to indicate the quality of each word embedding. Guided by the reliability signals, the model is able to dynamically select and compose features such as word embedding and character-level representation using gating mechanisms. For example, if an input word is rare, the model relies less on its word embedding and assigns higher weights to its character and contextual features. Experiments on OntoNotes 5.0 show that our model outperforms the baseline model by 2.7% absolute gain in F-score. In cross-genre experiments on five genres in OntoNotes, our model improves the performance for most genre pairs and obtains up to 5% absolute F-score gain.
Incremental Few-Shot Learning for Pedestrian Attribute Recognition

Pedestrian attribute recognition has received increasing attention due to its important role in video surveillance applications. However, most existing methods are designed for a fixed set of attributes. They are unable to handle the incremental few-shot learning scenario, i.e. adapting a well-trained model to newly added attributes with scarce data, which commonly exists in the real world. In this work, we present a meta learning based method to address this issue. The core of our framework is a meta architecture capable of disentangling multiple attribute information and generalizing rapidly to new coming attributes. By conducting extensive experiments on the benchmark dataset PETA and RAP under the incremental few-shot setting, we show that our method is able to perform the task with competitive performances and low resource requirements.
Testing multi-photon interference on a silicon chip

Multi-photon interference in large multi-port interferometers is key to linear optical quantum computing and in particular to boson sampling. Silicon photonics enables complex interferometric circuits with many components in a small footprint and has the potential to extend these experiments to larger numbers of interfering modes. However, loss has generally limited the implementation of multi-photon experiments in this platform. Here, we make use of high-efficiency grating couplers to combine bright and pure quantum light sources based on ppKTP waveguides with silicon circuits. We interfere up to 5 photons in up to 15 modes, verifying genuine multi-photon interference by comparing the results against various models including partial distinguishability between photons.
MVC Architecture: A Comparative Study Between Laravel Framework and Slim Framework in Freelancer Project Monitoring System Web Based

Currently, the work of freelancers is very much in demand. Because freelancers can work anywhere and anytime without being bound by a contract with a company or person. But freelancers have difficulty managing their tasks and projects because there is no system to monitor and manage the project. Therefore, the solution is to make the project freelancer monitoring system by implementing the MVC (Model View Controller) architecture model with the PHP Laravel and Slim framework. MVC design patterns are well-known patterns and are used for interactive software system architectures. The way the MVC method works is to separate the main components such as data manipulation (model), display/interface (View) and the process (Controller) so that it is more neat, structured and easily developed. The purpose of this study also compares the MVC Laravel and Slim framework architecture with a performance comparison method on load/stress testing on the dashboard page using Apache JMeter tools with 3 scenarios from samples 1, 100, and 500. Tests are done offline and report format results of performance tests is a Summary Report. The results obtained from performance comparisons using Apache JMeter are that the Slim framework is faster and better than Laravel's framework.
High Speed 3D Shape Measurement with Temporal Fourier Transform Profilometry

A novel high-speed 3D shape measurement technology called temporal Fourier transform profilometry (TFTP for short) is proposed by combining the merits of Fourier transform profilometry (FTP) and phase-measuring profilometry (PMP). Instead of using the digital light projector, a mechanical projector is employed to generate multi-period phase-shifting fringe patterns sequentially. During the reconstruction process, the phase value of each pixel is calculated independently along the temporal axis and no spectrum filtering operation is performed in a spatial domain. Therefore, high-frequency components containing the detailed information of the measured object effectively remain. The proposed method is suitable for measuring isolated dynamic objects. Only one frame of deformed fringe pattern is required to retrieve one 3D shape of the measured object, so it has the obvious advantage if measuring the dynamic scene at a high speed. A low-cost self-made mechanical projector with fast projection speed is developed to execute the principle-proof experiments, whose results demonstrate the feasibility of measuring isolated dynamic objects.
Cloud Federations: Economics, Games and Benefits

Sharing economy is a game-changing business paradigm that is currently permeating several industrial sectors. This paper aims to build a fundamental theory of the sharing economy of the computational capacity resource of Cloud Service Providers (CSPs). CSPs aim to cost-efficient serve geographically dispersed customers that often request computational resource-demanding services. The formation of CSP federations arises as an effective means to manage these diverse and time-varying service requests. In this paper, we introduce innovative federation models and policies for profitable federations that also achieve adequate QoS for their customers. Taking in account the flexible cloud computing service model, we abstract the virtualized infrastructure of each CSP to an M/M/1 queueing system, we formulate the CSP revenue and cost functions, and we study the task forwarding-based (TF) and the capacity sharing-based (CS) federation approaches. Under TF, each CSP may forward part of its workload to other federated CSPs, while under CS each CSP may share parts of its computational infrastructure with others. For both approaches, we propose two operation modes with different degree of CSPs' cooperation: (i) the joint business mode, where the CSPs fully cooperate: they jointly decide on the federation policies that maximize the total federation profit which is shared fairly among them; (ii) the reward-driven mode, where selfinterested CSPs participate in a game: they adjust their responses to federation policies aiming to maximize their individual profits. The results reveal that our policies lead to effective federations, which are beneficial both for CSPs and for customers.
A Method to Reduce Errors of String Recognition Based on Combination of Several Recognition Results with Per-Character Alternatives

We consider the problem on recognition of a string object presented in several video stream frames.In order to maximize the output accuracy, we combine several results of the recognition.To this end, we consider a model of result of a string object recognition.The model takes into account the estimations of alternative results of per-character classification.Also, we propose an algorithm to combine results of a string recognition according to this model.The algorithm was evaluated on a MIDV-500 dataset of document images.The experimental results show that the proposed algorithm allows to achieve the high accuracy of recognition result due to an analysis of several images, and the use of the estimations of alternative results of per-character classification gives the higher results then a combination of strings that contain only the final alternatives of each character.
Spectrally pure single photons at telecommunications wavelengths using commercial birefringent optical fiber

We report a bright and tunable source of spectrally pure heralded single photons in the telecom O-Band, based on cross-polarized four wave mixing in a commercial birefringent optical fiber. The source can achieve a purity of 85%, heralding efficiency of 30% and a coincidence-to-accidentals ratio of 108. Furthermore, through the measurements of joint spectral intensities, we find that the fiber is homogeneous over at least 45 centimeters and thus can potentially realize 4 sources that can produce identical quantum states of light. This paves the way for a cost-effective fiber-optic approach to implement multi-photon quantum optics experiments.
Measuring Teachers-As-Learners’ Digital Skills and Readiness to Study Online for Successful e-Learning Experience

Abstract Previous studies have analysed the need to measure learners’ readiness for online learning. In the framework of education reform in Latvia, by the end of 2021 the National Centre for Education will implement online courses for all school teachers. However, there is a lack of studies investigating teachers-as-learners’ readiness to study online in Latvia. The aim of the study was to assess Latvian teachers’ readiness to study online with focus on the differences of respondents’ socio-demographic or professional characteristics. 1092 teachers from 100 schools filled in the self-evaluation survey form online. Statistically significant differences were found in all subscales of the survey. Differences in digital skills and in readiness to study online were found at all education levels by respondents’ gender, age group or place of living. Younger respondents, males or city inhabitants showed higher rates. E-learning course should be customized, taking into account learners’ socio-demographic parameters and online learning readiness rate.
Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the complexity of arbitrary machine learning models, we propose model-agnostic complexity measures based on functional decomposition: number of features used, interaction strength and main effect complexity. We show that post-hoc interpretation of models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity.
Exploiting Visual Semantic Reasoning for Video-Text Retrieval

Video retrieval is a challenging research topic bridging the vision and language areas and has attracted broad attention in recent years. Previous works have been devoted to representing videos by directly encoding from frame-level features. In fact, videos consist of various and abundant semantic relations to which existing methods pay less attention. To address this issue, we propose a Visual Semantic Enhanced Reasoning Network (ViSERN) to exploit reasoning between frame regions. Speciﬁcally, we consider frame regions as vertices and construct a fully-connected semantic correlation graph. Then, we perform reasoning by novel random walk rule-based graph convolutional networks to generate region features involved with semantic relations. With the beneﬁt of reasoning, semantic interactions between regions are considered, while the impact of redundancy is suppressed. Finally, the region features are aggregated to form frame-level features for further encoding to measure video-text similarity. Extensive experiments on two public benchmark datasets validate the effectiveness of our method by achieving state-of-the-art performance due to the powerful semantic reasoning.
Using BERT and Augmentation in Named Entity Recognition for Cybersecurity Domain

The paper presents the results of applying the BERT representation model in the named entity recognition task for the cybersecurity domain in Russian. Several variants of the model were investigated. The best results were obtained using the BERT model, trained on the target collection of information security texts. We also explored a new form of data augmentation for the task of named entity recognition.
Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation

Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, L1-norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.
Improving Distantly-Supervised Named Entity Recognition for Traditional Chinese Medicine Text via a Novel Back-Labeling Approach

Recent advances in deep neural networks (DNNs) have enabled us to achieve reliable named entity recognition (NER) models without handcrafting features.However, these are also some obstacles imposed by using those machine learning methods, in need of a large amount of manually labeled data.To avoid such limitations, we could replace human annotation with distant supervision, however there remain a technical challenge on the error label issue caused by ignoring the entities that are not included in the vocabulary, which should be addressed to achieve the effective NER model.Then, we propose a novel backlabeling approach and integrate it into a tagging scheme, especially, we apply this scheme to handle the NER task in traditional Chinese medicine (TCM) field.In addition, we discuss how to use distant supervision methods to achieve better performance of the NER model.We conduct some experiments and verify that our scheme can effectively improve the entity recognition on the basis of distant supervision.
The Use of Virtual Reality in Geo-Education

The paper discusses the technological capabilities of virtual reality (VR) in the field of education as a highly developed form of computer modeling. VR is con-sidered to be able to revolutionize the education field since it provides better en-gagement in learning activities than other teaching methods. In order to study the impact of VR technology on ensuring the quality of e-learning, an experiment is conducted during the Physical Geography online course. The current research in-volves 60 third-year students from the Abai Kazakh National Pedagogical Uni-versity, Moscow Pedagogical State University, and Ajman University. All the re-spondents were divided into two groups. The training of the first group is under-gone through distance learning in the Moodle e-learning system, while the educa-tional process of the second is supplemented with the technological capabilities of the latest VR services (Google Earth, Apple Maps, My Way VR, The VR Muse-um of Fine Art). During the study, students’ subjective assessment of the training is performed according to the basic, expected, and desirable criteria. The exami-nation is carried out in two stages - before and after the course. At the first stage of the assessment, the use of VR corresponds to the desired quality of the educa-tional product. However, after the course and the acquiring of virtual experience, the transfer of the students' desired training criteria into the expected can be ob-served. This indicates a high degree of adaptability of VR technology in educa-tion as well as an increase in the respondents' requirements against the quality of subsequent training courses. Consequently, a significant impact of immersive technology's evolution on the demands on the e-learning quality can be noted.
Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment

Osteosarcoma is the most common malignant primary bone tumor. Standard treatment includes pre-operative chemotherapy followed by surgical resection. The response to treatment as measured by ratio of necrotic tumor area to overall tumor area is a known prognostic factor for overall survival. This assessment is currently done manually by pathologists by looking at glass slides under the microscope which may not be reproducible due to its subjective nature. Convolutional neural networks (CNNs) can be used for automated segmentation of viable and necrotic tumor on osteosarcoma whole slide images. One bottleneck for supervised learning is that large amounts of accurate annotations are required for training which is a time-consuming and expensive process. In this paper, we describe Deep Interactive Learning (DIaL) as an efficient labeling approach for training CNNs. After an initial labeling step is done, annotators only need to correct mislabeled regions from previous segmentation predictions to improve the CNN model until the satisfactory predictions are achieved. Our experiments show that our CNN model trained by only 7 hours of annotation using DIaL can successfully estimate ratios of necrosis within expected inter-observer variation rate for non-standardized manual surgical pathology task.
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations

The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task.Existing methods using multi-task learning techniques to address the problem learn interactions among the two tasks through a shared network, where the shared information is passed into the taskspecific networks for prediction.However, such an approach hinders the model from learning explicit interactions between the two tasks to improve the performance on the individual tasks.As a solution, we design a multitask learning model which we refer to as recurrent interaction network which allows the learning of interactions dynamically, to effectively model task-specific features for classification.Empirical studies on two real-world datasets confirm the superiority of the proposed model.
Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages

Recent emergence of multilingual pre-training language model (mPLM) has enabled breakthroughs on various downstream cross-lingual transfer (CLT) tasks. However, mPLM-based methods usually involve two problems: (1) simply fine-tuning may not adapt general-purpose multilingual representations to be task-aware on low-resource languages; (2) ignore how cross-lingual adaptation happens for downstream tasks. To address the issues, we propose a meta graph learning (MGL) method. Unlike prior works that transfer from scratch, MGL can learn to cross-lingual transfer by extracting meta-knowledge from historical CLT experiences (tasks), making mPLM insensitive to low-resource languages. Besides, for each CLT task, MGL formulates its transfer process as information propagation over a dynamic graph, where the geometric structure can automatically capture intrinsic language relationships to explicitly guide cross-lingual transfer. Empirically, extensive experiments on both public and real-world datasets demonstrate the effectiveness of the MGL method.
Evaluation of Quantum Cryptanalysis on SPECK

In this work, all the versions of SPECK are evaluated against quantum adversary in terms of Grovers algorithm. We extensively study the resource requirements for quantum key search under the model of known plaintext attack and show that our estimation provides better result than the existing efforts. Further, for the first time, we explore differential cryptanalysis on SPECK in quantum framework that provides encouraging results. For both the cases, the quantum resources are evaluated in terms of several parameters, i.e., the T-depth of the circuits and the number of qubits required for the attacks. Experiments are performed in IBM-Q environment to support our claims.
Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings

Event coreference resolution aims to classify all event mentions that refer to the same real-world event into the same group, which is necessary to information aggregation and many downstream applications.To resolve event coreference, existing methods usually calculate the similarities between event mentions and between specific kinds of event arguments.However, they fail to accurately identify paraphrase relations between events and may suffer from error propagation while extracting event components (i.e., event mentions and their arguments).Therefore, we propose a new model based on Event-specific Paraphrases and Argument-aware Semantic Embeddings, thus called EPASE, for event coreference resolution.EPASE recognizes deep paraphrase relations in an event-specific context of sentences and can cover event paraphrases of more situations, bringing about a better generalization.Additionally, the embeddings of argument roles are encoded into event embedding without relying on a fixed number and type of arguments, which results in the better scalability of EPASE.Experiments on both within-and cross-document event coreference demonstrate its consistent and significant superiority compared to existing methods.
Automata and fixpoints for asynchronous hyperproperties

Hyperproperties have received increasing attention in the last decade due to their importance e.g. for security analyses. Past approaches have focussed on synchronous analyses, i.e. techniques in which different paths are compared lockstepwise. In this paper, we systematically study asynchronous analyses for hyperproperties by introducing both a novel automata model (Alternating Asynchronous Parity Automata) and the temporal fixpoint calculus H µ , the first fixpoint calculus that can systematically express hyperproperties in an asynchronous manner and at the same time subsumes the existing logic HyperLTL. We show that the expressive power of both models coincides over fixed path assignments. The high expressive power of both models is evidenced by the fact that decision problems of interest are highly undecidable, i.e. not even arithmetical. As a remedy, we propose approximative analyses for both models that also induce natural decidable fragments.