Reviewerly/exHarmony huggingface dataset:
Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data

Sentiment analysis is an important task in understanding social media content like customer reviews, Twitter and Facebook feeds etc.In multilingual communities around the world, a large amount of social media text is characterized by the presence of code-switching.Thus, it has become important to build models that can handle code-switched data.However, annotated code-switched data is scarce and there is a need for unsupervised models and algorithms.We propose a general framework called Unsupervised Self-Training and show its applications for the specific use case of sentiment analysis of code-switched data.We use the power of pre-trained BERT models for initialization and fine-tune them in an unsupervised manner, only using pseudo labels produced by zero-shot transfer.We test our algorithm on multiple code-switched languages and provide a detailed analysis of the learning dynamics of the algorithm with the aim of answering the question -'Does our unsupervised model understand the Code-Switched languages or does it just learn its representations?'.Our unsupervised models compete well with their supervised counterparts, with their performance reaching within 1-7% (weighted F1 scores) when compared to supervised models trained for a two class problem.
Sentiment Analysis about Large-Scale Social Restrictions in Social Media Twitter Using Algoritm K-Nearest Neighbor

Sentiment analysis is a data processing to recognize topics that people talk about and their sentiments toward the topics, one of which in this study is about large-scale social restrictions (PSBB). This study aims to classify negative and positive sentiments by applying the K-Nearest Neighbor algorithm to see the accuracy value of 3 types of distance calculation which are cosine similarity, euclidean, and manhattan distance for Indonesian language tweets about large-scale social restrictions (PSBB) from social media twitter. With the results obtained, the K-Nearest Neighbor accuracy by the Cosine Similarity distance 82% at k = 3, K-Nearest Neighbor by the Euclidean Distance with an accuracy of 81% at k = 11 and K-Nearest Neighbor by Manhattan Distance with an accuracy 80% at k = 5, 7, 9, 11, and 13. So, in this study the K-Nearest Neighbor algorithm with the Cosine Similarity Distance calculation gets the highest point.
Optimalisasi Parameter dengan Cross Validation dan Neural Back-propagation Pada Model Prediksi Pertumbuhan Industri Mikro dan Kecil

It is important for us to predict what will happen in future and to reduce uncertainty. Various analyzes are therefore necessary in order to optimize or improve the prediction results by several methods. The objective of this research is to analyze predictive results by optimizing the training and testing by means of cross validating parameters on the growth of micro and small-scale production in Indonesia through the exactness of the return-propagative method. The method of reproduction is used. These results are compared with results of backpropagation during training and testing without optimisation of the same architectural model. The dataset is based on the growth in production in micro and small businesses by province from the Central Statistical Agency(BPS). There were 34 records in which data from 2015-2019 for growth of production were collected. The results with optimisation have surpassed without optimisation the back propagation model by looking at RMSE, in which the best RMSE in the 3-2-1 architectural model was obtained and the side type is mixed sampling. The obtained RMSE value is 0.1526, or a difference between the best background architectural model, 3-2-1 and 0.0034. (0.157). The results of this model were 94 percent.
Entanglement versus gap, quantum teleportation, and the AKLT model

Abstract Quantum-mechanical entanglement is notoriously volatile because of its susceptibility to external disturbances. However, entanglement can be stabilized if it is present in the non-degenerate ground state of a gapped, time-independent Hamiltonian. In this paper, we devise a spin-chain Hamiltonian whose ground state contains a Bell pair, with one member of the pair at each end of the chain. We study the Hamiltonian numerically, using full numerical diagonalization and a carefully tailored mean-field theory, to show that it is gapped. Whenever the Hamiltonian is tuned to increase its gap, the fidelity of its Bell pair decreases, manifesting a fundamental contention. The form of the Hamiltonian is motivated by quantum teleportation. Comparing it to the canonical Affleck, Kennedy, Lieb, and Tasaki (AKLT) model, we find that the AKLT model exhibits a sort of ‘failed quantum teleportation’.
Morphometric and Structural Evaluations of Satellite Data from the Bosumtwi Impact Structure and Adjacent Areas in Ashanti, Ghana

The Bosumtwi impact crater in Ashanti, Ghana is one of the most thoroughly investigated impact structures in Africa. Nevertheless, by the evaluations of Sentinel 1 and 2, and Landsat data as well as by ALOS PALSAR, ASTER and SRTM digital elevation model (DEM) data further geomorphological and structural knowledge can be derived. Morphometric maps were used to visualize the concentric and radial drainage and valley pattern surrounding the impact crater. The larger valleys are arranged in a nearly concentric and radial pattern like the river Pra and its tributaries, the Ofin, Anum and Brim river, tracing the outer, southern border of the impact affected area. From the ASTER and SRTM DEM data the morphological drainage basin was calculated. The drainage basin-outline is nearly circular, discharging into the Pra river in the southern part. Structural evaluations of satellite radar and optical data reveal that linear and circum-linear features can be detected more than 80 km around the Bosumtwi impact crater. Small-scale/artisanal mining activities in this area are visible on satellite images and, thus, indirectly tracing ore deposits. As detailed mineral maps are not publicly available, the position and distributions of mining areas was used as indicator for the occurrence of mineral deposits, especially placer deposits. Their occurrence and the distribution of the mining areas follows the larger rivers. Weathering processes along the deformation pattern and erosion and sedimentation probably had an influence on placer deposit development.
QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification

Scientific claim verification is a unique challenge that is attracting increasing interest.The SCIVER shared task offers a benchmark scenario to test and compare claim verification approaches by participating teams and consists in three steps: relevant abstract selection, rationale selection and label prediction.In this paper, we present team QMUL-SDS's participation in the shared task.We propose an approach that performs scientific claim verification by doing binary classifications stepby-step.We trained a BioBERT-large classifier to select abstracts based on pairwise relevance assessments for each <claim, title of the abstract> and continued to train it to select rationales out of each retrieved abstract based on <claim, sentence>.We then propose a two-step setting for label prediction, i.e. first predicting "NOT_ENOUGH_INFO" or "ENOUGH_INFO", then label those marked as "ENOUGH_INFO" as either "SUPPORT" or "CONTRADICT".Compared to the baseline system, we achieve substantial improvements on the dev set.As a result, our team is the No. 4 team on the leaderboard.
Almost Free Semantic Draft for Neural Machine Translation

Translation quality can be improved by global information from the required target sentence because the decoder can understand both past and future information.However, the model needs additional cost to produce and consider such global information.In this work, to inject global information but also save cost, we present an efficient method to sample and consider a semantic draft as global information from semantic space for decoding with almost free of cost.Unlike other successful adaptations, we do not have to perform an EM-like process that repeatedly samples a possible semantic from the semantic space.Empirical experiments show that the presented method can achieve competitive performance in common language pairs with a clear advantage in inference efficiency.We will open all our source code on GitHub.
End-to-end ASR to jointly predict transcriptions and linguistic annotations

We propose a Transformer-based sequence-tosequence model for automatic speech recognition (ASR) capable of simultaneously transcribing and annotating audio with linguistic information such as phonemic transcripts or part-of-speech (POS) tags.Since linguistic information is important in natural language processing (NLP), the proposed ASR is especially useful for speech interface applications, including spoken dialogue systems and speech translation, which combine ASR and NLP.To produce linguistic annotations, we train the ASR system using modified training targets: each grapheme or multi-grapheme unit in the target transcript is followed by an aligned phoneme sequence and/or POS tag.Since our method has access to the underlying audio data, we can estimate linguistic annotations more accurately than pipeline approaches in which NLP-based methods are applied to a hypothesized ASR transcript.Experimental results on Japanese and English datasets show that the proposed ASR system is capable of simultaneously producing highquality transcriptions and linguistic annotations.
Why Do Document-Level Polarity Classifiers Fail?

Machine learning solutions are often criticized for the lack of explanation of their successes and failures.Understanding which instances are misclassified and why is essential to improve the learning process.This work helps to fill this gap by proposing a methodology to characterize, quantify and measure the impact of hard instances in the task of polarity classification of movie reviews.We characterize such instances into two categories: neutrality, where the text does not convey a clear polarity, and discrepancy, where the polarity of the text is the opposite of its true rating.We quantify the number of hard instances in polarity classification of movie reviews and provide empirical evidence about the need to pay attention to such problematic instances, as they are much harder to classify, for both machine and human classifiers.To the best of our knowledge, this is the first systematic analysis of the impact of hard instances in polarity detection from wellformed textual reviews.
Earthy Practice: Towards a History of Excavation in Sweden, in the 17th and 18th centuries

Excavation as a practice is commonly perceived as an outcome of the development of archaeology as a discipline during the 19th century. In line with this argument, earlier antiquarians, such as Olof Rudbeck and Olof Verelius, were exceptions. In this paper, the author stresses that excavation as a method was established in the late 17th century, only to become a natural practice in the next century. Issues that are ventilated are the circumstances behind this methodological introduction and its characteristics.
Internal shortest absent word queries in constant time and linear space

Given a string T of length n over an alphabet Σ⊂{1,2,…,nO(1)} of size σ, we are to preprocess T so that given a range [i,j], we can return a representation of a shortest string over Σ that is absent in the fragment T[i]⋯T[j] of T. We present an O(n)-space data structure that answers such queries in constant time and can be constructed in O(nlogσ⁡n) time.
The Role of Technological Pedagogical Content Knowledge (TPACK) of English Teachers on High School Learners’ Acceptance of Mobile Learning Tools

Educational technology is benefitted in diverse areas of education and mobile learning tools are one of the components of educational technology. Therefore, using technology in accordance with content and pedagogy has become more important. It is also important for teachers to know how to integrate technology into the learning and teaching process using appropriate methods and techniques. The aim of this study is to understand how students perceive technological pedagogical content knowledge (TPACK) of teachers and adaptation of mobile learning tools by students. This research was designed as a quantitative study and survey methodology was used to collect data from 352 high school students during the spring term in 2018. Participants were identified using convenience sampling method and only one state school with all students was included in the study. Two scales were used to collect the data: TPACK scale developed by Tseng (314-315) and Mobile Learning Tools Acceptance Scale (MLTAS) developed by Özer and Kılıç (586). The results showed that high school students perceived the TPACK of EFL teachers positively with all subcategories. They were also found to have positive perceptions of mobile learning tools. The study concluded that the more knowledge students perceived, the more they accept mobile learning tools.
Minimizing the Discrimination Time for Quantum States of an Artificial Atom

Fast discrimination between quantum states of superconducting artificial atoms is an important ingredient for quantum information processing. In circuit quantum electrodynamics, increasing the signal-field amplitude in the readout resonator, dispersively coupled to the artificial atom, improves the signal-to-noise ratio and increases the measurement strength. Here, we employ this effect over 2 orders of magnitude in readout power, made possible by the unique combination of a dimer-Josephson-junction-array amplifier with a large dynamic range and the fact that the readout of our granular aluminum fluxonium artificial atom remains quantum nondemolition (QND) at relatively large photon numbers in the readout resonator, up to $\overline{n}=110$. Using Bayesian inference, this allows us to detect quantum jumps faster than the readout-resonator response time $2/\ensuremath{\kappa}$, where $\ensuremath{\kappa}$ is the bandwidth of the readout resonator.
The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds

Hybrid systems are subject to multiple dynamic models, or so-called modes. To estimate the state, the sequence of modes has to be estimated, which results in an exponential growth of possible sequences. The most prominent solution to handle this is the interacting multiple model filter, which can be extended to smoothing. In this paper, we derive a novel generalization of the interacting multiple filter and smoother to manifold state spaces, e.g., quaternions, based on the boxplus-method. As part thereof, we propose a linear approximation to the mixing of Gaussians and a Rauch–Tung–Striebel smoother for single models on boxplus-manifolds. The derivation of the smoother equations is based on a generalized definition of Gaussians on boxplus-manifolds. The three, novel algorithms are evaluated in a simulation and perform comparable to specialized solutions for quaternions. So far, the benefit of the more principled approach is the generality towards manifold state spaces. The evaluation and generic implementations are published open source.
The Trend of Intelligent Education Under 5G Driven Artificial Intelligence

Under the background of actively advocating the development of educational informatization, the arrival of "5g + AI era" is leading the educational reform.In recent years, intelligence has become a new development trend in the education industry.The combination of new technologies such as voice recognition, image recognition, big data, artificial intelligence, virtual reality and the education industry has attracted much attention.As the main form of future education, intelligent education determines the development direction of future education and has a guiding role in future education.In this paper, under the background of educational informationization, the author first thinks about intelligent education, and then expounds how intelligent education can take the fast train of information technology reform in the environment of 5G technology and artificial intelligence.On this basis, how to explore the future education reform from the direction of intelligent education.Finally, from the two aspects of 5G information technology and artificial intelligence applied to intelligent education, the paper discusses the future trend of intelligent education and provides some reference for the development and research of intelligent education in the future.
FITAnnotator: A Flexible and Intelligent Text Annotation System

In this paper, we introduce FITAnnotator, a generic web-based tool for efficient text annotation.Benefiting from the fully modular architecture design, FITAnnotator provides a systematic solution for the annotation of a variety of natural language processing tasks, including classification, sequence tagging and semantic role annotation, regardless of the language.Three kinds of interfaces are developed to annotate instances, evaluate annotation quality and manage the annotation task for annotators, reviewers and managers, respectively.FITAnnotator also gives intelligent annotations by introducing task-specific assistant to support and guide the annotators based on active learning and incremental learning strategies.This assistant is able to effectively update from the annotator feedbacks and easily handle the incremental labeling scenarios.
Algorithm Substitution Attacks: State Reset Detection and Asymmetric Modifications

In this paper, we study algorithm substitution attacks (ASAs), where an algorithm in a cryptographic scheme is substituted for a subverted version. First, we formalize and study the use of state resets to detect ASAs, and show that many published stateful ASAs are detectable with simple practical methods relying on state resets. Second, we introduce two asymmetric ASAs on symmetric encryption, which are undetectable or unexploitable even by an adversary who knows the embedded subversion key. We also generalize this result, allowing for any symmetric ASA (on any cryptographic scheme) satisfying certain properties to be transformed into an asymmetric ASA. Our work demonstrates the broad application of the techniques first introduced by Bellare, Paterson, and Rogaway (Crypto 2014) and Bellare, Jaeger, and Kane (CCS 2015) and reinforces the need for precise definitions surrounding detectability of stateful ASAs.
Hybrid quantum devices: Guest editorial

An introduction to the APL Special Issue on “Hybrid Quantum Devices” by the guest editors.
Deep Feature Representation and Similarity Matrix based Noise Label Refinement Method for Efficient Face Annotation

Face annotation is a naming procedure that assigns the correct name to a person emerging from an image.Faces that are manually annotated by people in online applications include incorrect labels, giving rise to the issue of label ambiguity.This may lead to mislabelling in face annotation.Consequently, an efficient method is still essential to enhance the reliability of face annotation.Hence, in this work, a novel method named the Similarity Matrix-based Noise Label Refinement (SMNLR) is proposed, which effectively predicts the accurate label from the noisy labelled facial images.To enhance the performance of the proposed method, the deep learning technique named Convolutional Neural Networks (CNN) is used for feature representation.Several experiments are conducted to evaluate the effectiveness of the proposed face annotation method using the LFW, IMFDB and Yahoo datasets.The experimental results clearly illustrate the robustness of the proposed SMNLR method in dealing with noisy labelled faces.
Improving Cross-Lingual Sentiment Analysis via Conditional Language Adversarial Nets

Sentiment analysis has come a long way for high-resource languages due to the availability of large annotated corpora.However, it still suffers from lack of training data for lowresource languages.To tackle this problem, we propose Conditional Language Adversarial Network (CLAN), an end-to-end neural architecture for cross-lingual sentiment analysis without cross-lingual supervision.CLAN differs from prior work in that it allows the adversarial training to be conditioned on both learned features and the sentiment prediction, to increase discriminativity for learned representation in the cross-lingual setting.Experimental results demonstrate that CLAN outperforms previous methods on the multilingual multi-domain Amazon review dataset.
Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation

One of the challenges in information retrieval (IR) is the vocabulary mismatch problem, which happens when the terms between queries and documents are lexically different but semantically similar.While recent work has proposed to expand the queries or documents by enriching their representations with additional relevant terms to address this challenge, they usually require a large volume of query-document pairs to train an expansion model.In this paper, we propose an Unsupervised Document Expansion with Generation (UDEG) framework with a pretrained language model, which generates diverse supplementary sentences for the original document without using labels on querydocument pairs for training.For generating sentences, we further stochastically perturb their embeddings to generate more diverse sentences for document expansion.We validate our framework on two standard IR benchmark datasets.The results show that our framework significantly outperforms relevant expansion baselines for IR.
Malicious Traffic Flow Detection in IOT Using Ml Based Algorithms

Identifying the malicious traffic flows in Internet of things (IOT) is very important to monitor and avoid unwanted errors or the unwanted flows in the network.So, for a security to this network various machine learning algorithms (ML) has been introduced by various analyst to avoid this flow of error in the network.But, owing to the unsuitable selection of features, the ML models which introduced previously suffer from misclassify errors.So, there arises a need to study the problem of feature selection more depth to predict the accurate traffic flow observation in the network.To overcome this problem, a new structure in machine learning (ML) is introduced.So, for thisa novel features selection metric CorrAUC is suggested.So, based on this metric approach, a new feature selection algorithm CorrAUC is develop and design, it is based on wrapper technique to get features accurately by filtering to predict flow of traffic is suggested.Then, we applied multicriteria decision method called VIKOR which is used for validating the features selected for recognition the flow of traffic errors in the network.We estimate our approach by using the NSL-KDD dataset and three different ML algorithms.
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers

How much information do NLP tasks really need from a transformer's attention mechanism at application-time (inference)?From recent work, we know that there is sparsity in transformers and that the floating-points within its computation can be discretized to fewer values with minimal loss to task accuracies.However, this requires retraining or even creating entirely new models, both of which can be expensive and carbon-emitting.Focused on optimizations that do not require training, we systematically study the full range of typical attention values necessary.This informs the design of an inference-time quantization technique using both pruning and logscaled mapping which produces only a few (e.g. 2 3 ) unique values.Over the tasks of question answering and sentiment analysis, we find nearly 80% of attention values can be pruned to zeros with minimal (< 1.0%) relative loss in accuracy.We use this pruning technique in conjunction with quantizing the attention values to only a 3-bit format, without retraining, resulting in only a 0.8% accuracy reduction on question answering with fine-tuned RoBERTa.