Reviewerly/exHarmony huggingface dataset:
Multifold behavior of the information transmission by the quantum 3-switch

We uncover new behaviors of the transmission of information by three quantum channels in superposition of causal orders subject to some level of noise. We find that the transmission can exhibit three different behaviors as the level of noise is varied. This multifold behavior can be explained by the different equivalence classes of quantum switch matrices related to specific combinations of causal orders. We classify these matrices using their characteristic polynomials and matrix invariants, and we calculate analytical expressions for the Holevo information in three representative cases. Our results are a step forward to understand and harness quantum control of causal orders with different levels of noise. We also study the Holevo information as function of a continuous order parameter and analyse transitions at integer values.
An Investigation into the Influence of Blended Learning on Oral English Proficiency of Senior High School Students

We advocate the in-depth integration of information technology and education in the digital age, and we also encourage teachers of all disciplines to actively carry out online and offline blended learning. This study attempts to use an empirical research to apply the Blended Learning to the oral English teaching in the first year of senior high school. A one-semester teaching experiment is conducted to explore whether there is a significant difference in the students&amp;rsquo; oral English proficiency between the experimental class and the controlled class. The major findings of the study are as follows: (1) There are significant differences of students&amp;rsquo; oral English proficiency before and after the experiment in the experimental class and the controlled class; (2) Blended learning can improve students&amp;rsquo; oral English proficiency, among which pronunciation and intonation, range and accuracy of vocabulary and fluency of language are the most significant ones, while the accuracy and complexity of grammatical structure are insignificant.
Bug Severity Prediction using Keywords in Imbalanced Learning Environment

Reported bugs of software systems are classified into different severity levels before fixing them. The number of bug reports may not be equally distributed according to the severity levels of bugs. However, most of the severity prediction models developed in the literature assumed that the underlying data distribution is evenly distributed, which may not correct at all instances and hence, the aim of this study is to develop bug classification models from unevenly distributed datasets and tested them accordingly. To that end first, the topics or keywords of developer descriptions of bug reports are extracted using Rapid Keyword Extraction (RAKE) algorithm and then transferred them into numerical attributes, which combined with severity levels constructs datasets. These datasets are used to build classification models; Naïve Bayes, Logistic Regression, and Decision Tree Learner algorithms. The models’ prediction quality is measured using Area Under Recursive Operative Characteristics Curves (AUC) as the models learnt from more skewed environments. According to the results, the prediction quality of the Logistics Regression model is 0.65 AUC whereas the other two models recorded maximum 0.60 AUC. Though the datasets contain comparatively less number of instances from the high severity classes; Blocking and High, the Logistic Regression models predict the two classes with a decent AUC value of 0.65 AUC. Hence, this projects shows that the models can be trained from highly skewed datasets so that the models prediction quality is equally well over all the classes regardless of number of instances representing the class. Further, this project emphasizes that the models should be evaluated using the appropriate metrics when the models are trained from imbalance learning environments. Also, this work uncovers that the Logistic Regression model is also capable of classifying documents as Naïve Bayes, which is well known for this task.
A Double Phases Generation Network for Yes or No Question Generation (Student Abstract)

This paper aims to solve the task of generating yes or no questions, which generates yes/no questions based on given passages. These questions can be used for evaluation automatically. We propose a double phases generation network that can identify specific phrases related to facts from the input passage and use them as auxiliary information for generation. Specifically, the 1st-phase prediction uses the extracted phrases as assistance to generate an initial question. Then, the 2nd-phase prediction utilizes an attention network to focus on the relevant phrases related to the initial question in the passage to generate questions that are more relevant to the specific facts contained in the initial question. Extensive experiments we performed on BoolQ dataset demonstrate the effectiveness of our framework.
A Closer Look at Causal Links: Complexity Results for Delete-Relaxation in Partial Order Causal Link (POCL) Planning

Partial Order Causal Link (POCL) planning follows the principle of least commitment in that it maintains only a partial order on its actions to prevent unnecessary early commitment during search. This can reduce the search space significantly by systematically representing up to an exponential number of action sequences in just a single search node. Progress on goal achievement is represented fully by this partial order and by causal links, which represent the causal relationships between these actions as well as between the initial state and goal. Plan existence for a state in classical planning thus corresponds to plan existence for a partial plan in POCL planning. Yet almost no theoretical investigations for POCL plan existence were conducted so far. While delete-relaxation makes plan existence tractable in classical planning, we show it to be NP-hard in POCL planning unless the current plan is totally ordered or causal links are almost completely ignored.
Verifiable Decryption in the Head

In this work we present a new approach to verifiable decryption which converts a 2-party passively secure distributed decryption protocol into a 1-party proof of correct decryption. This leads to an efficient and simple verifiable decryption scheme for lattice-based cryptography, especially for large sets of ciphertexts; it has small size and lightweight computations as we reduce the need of zero-knowledge proofs for each ciphertext. We believe the flexibility of the general technique is interesting and provides attractive trade-offs between complexity and security, in particular for the interactive variant with smaller soundness. Finally, the protocol requires only very simple operations, making it easy to correctly and securely implement in practice. We suggest concrete parameters for our protocol and give a proof of concept implementation, showing that it is highly practical.
Low power threshold, ultrathin optical limiter based on a nonlinear zone plate

Ultrathin optical limiters are needed to protect light sensitive components in miniaturized optical systems. However, it has proven challenging to achieve a sufficiently low optical limiting threshold. In this work, we theoretically show that an ultrathin optical limiter with low threshold intensity can be realized using a nonlinear zone plate. The zone plate is embedded with nonlinear saturable absorbing materials that allow the device to focus low intensity light, while high intensity light is transmitted as a plane wave without a focal spot. Based on this proposed mechanism, we use the finite-difference time-domain method to computationally design a zone plate embedded with InAs quantum dots as the saturable absorbing material. The device has a thickness of just 0.5 μm and exhibits good optical limiting behavior with a threshold intensity as low as 0.45 kW/cm2, which is several orders of magnitude lower than bulk limiter counterparts based on a similar mechanism, and also performs favorably compared to current ultrathin flat-optics-based optical limiters. This design can be optimized for different operating wavelengths and threshold intensities by using different saturable absorbing materials. Additionally, the diameter and focal length of the nonlinear zone plate can be easily adjusted to fit different systems and applications. Due to its flexible design, low power threshold, and ultrathin thickness, this optical limiting concept may be promising for application in miniaturized optical systems.
Assessing the Syntactic Capabilities of Transformer-based Multilingual Language Models

Multilingual Transformer-based language models, usually pretrained on more than 100 languages, have been shown to achieve outstanding results in a wide range of crosslingual transfer tasks.However, it remains unknown whether the optimization for different languages conditions the capacity of the models to generalize over syntactic structures, and how languages with syntactic phenomena of different complexity are affected.In this work, we explore the syntactic generalization capabilities of the monolingual and multilingual versions of BERT and RoBERTa.More specifically, we evaluate the syntactic generalization potential of the models on English and Spanish tests, comparing the syntactic abilities of monolingual and multilingual models on the same language (English), and of multilingual models on two different languages (English and Spanish).For English, we use the available SyntaxGym test suite; for Spanish, we introduce SyntaxGymES, a novel ensemble of targeted syntactic tests in Spanish, designed to evaluate the syntactic generalization capabilities of language models through the SyntaxGym online platform.
Evaluating and Assuring Research Data Quality for Audiovisual Annotated Language Data

This paper presents the QUEST project and describes concepts and tools that are being developed within its framework.The goal of the project is to establish quality criteria and curation criteria for annotated audiovisual language data.Building on existing resources developed by the participating institutions earlier, QUEST also develops tools that could be used to facilitate and verify adherence to these criteria.An important focus of the project is making these tools accessible for researchers without substantial technical background and helping them produce high-quality data.The main tools we intend to provide are a questionnaire and automatic quality assurance for depositors of language resources, both developed as web applications.They are accompanied by a knowledge base, which will contain recommendations and descriptions of best practices established in the course of the project.Conceptually, we consider three main data maturity levels in order to decide on a suitable level of strictness of the quality assurance.This division has been introduced to avoid that a set of ideal quality criteria prevent researchers from depositing or even assessing their (legacy) data.
Implementation of Machine Learning Using the Convolution Neural Network Method for Aglaonema Interest Classification

One of the wealth of the Indonesian nation is the many types of ornamental plants. Ornamental plants, for example, the Aglaonema flower, which is much favored by hobbyists of ornamental plants, from homemakers, is a problem to distinguish between types of aglaonema ornamental plants with other ornamental plants. So the authors try to research with the latest technology using a deep learning convolutional neural network method. It is for calcifying aglaonema interest. This research is based on having fascinating leaves and colors. With the study results using the CNN method, the products of aglaonema flowers of Adelia, Legacy, Widuri, RedKochin, Tiara with moderate accuracy value are 56%. In contrast, the aglaonema type Sumatra, RedRuby, has the most accuracy a high of 61%.
Maintenance of Social Commitments in Multiagent Systems

We introduce and formalize a concept of a maintenance commitment, a kind of social commitment characterized by states whose truthhood an agent commits to maintain. This concept of maintenance commitments enables us to capture a richer variety of real-world scenarios than possible using achievement commitments with a temporal condition. By developing a rule-based operational semantics, we study the relationship between agents' achievement and maintenance goals, achievement commitments, and maintenance commitments. We motivate a notion of coherence which captures alignment between an agents' achievement and maintenance cognitive and social constructs, and prove that, under specified conditions, the goals and commitments of both rational agents individually and of a multiagent system are coherent.
Cricket Score Forecasting using Neural Networks

Today, Sports is not what it used to be a decade ago. Technologies like Machine Learning and Artificial Intelligence have dominated it. Now there are sensors in all types of sports equipment like cricket bats, stumps, flannels, etc., which analyse the data and provide analytics, which may or may not be helpful, but we, as spectators, thoroughly enjoy the game. The terms such as Cric-Science (Cricket + Data Science) and Cricket Analytics are the fruit of ML/AI. In the last decade alone, cricket has witnessed many changes, such as the addition of a new format like T10, which is yet to be recognised by ICC, along with the introduction of many other international leagues such as IPL, BBL, PSL, CPL, apart from the widely recognised formats like Test Match, One day International and T20. With so much cricket played, the data generated is also massive. But even with these technological advancements, run rate is conventionally used to predict a team’s score in the upcoming overs. So, in this research paper, we aim to predict a team’s score using Neural Network by using the data from past balls.
Two-Stage Clustering Pseudo-Labels Correction for Cross-Domain Person Re-Identification

The same identity of person images captured by multiple cameras may encounter appearance change significantly. In this case, most cluster-based cross-domain person re-identification(re-id) methods easily group them into different groups, which may generate lots of noise pseudo labels and limit the model performance. In this paper, we propose a Two-stage Clustering Pseudo-Labels Correction method (TCPC) based on camera ID to obtain reliable pseudo labels list. Specifically, we first cluster the samples of the same camera individually and assign them local pseudo-labels. Then, we cluster all samples to obtain global pseudo-labels. Using reliable local pseudo-labels to correct the global pseudo-labels, the impact of noisy pseudo-labels on model performance is reduced. Furthermore, we enhance the discriminativeness of pedestrian features by introducing the attention module to promote the clustering effect. We constructed a baseline and conducted comprehensive experiments on three widely used datasets, DukeMTMC-ReID, Market1501 and MSMT17, showing that our method improves the performance of the baseline. After that, we embedded the method into other the state-of-the-art frameworks to further verify its effectiveness.
On Scalar Embedding of Relative Positions in Attention Models

Attention with positional encoding has been demonstrated as a powerful component in modern neural network models, such as transformers. However, why positional encoding works well in attention models remains largely unanswered. In this paper, we study the scalar relative positional encoding (SRPE) proposed in the T5 transformer. Such an encoding method has two features. First, it uses a scalar to embed relative positions. Second, the relative positions are bucketized using a fixed heuristic algorithm, and positions in the same bucket share the same embedding. In this work, we show that SRPE in attention has an elegant probabilistic interpretation. More specifically, the positional encoding serves to produce a prior distribution for the attended positions. The resulting attentive distribution can be viewed as a posterior distribution of the attended position given the observed input sequence. Furthermore, we propose a new SRPE (AT5) that adopts a learnable bucketization protocol and automatically adapts to the dependency range specific to the learning task. Empirical studies show that the AT5 achieves superior performance than the T5's SRPE.
Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison

We consider the problem of image set comparison, i.e., to determine whether two image sets show the same unique object (approximately) from the same viewpoints. Our proposition is to solve it by a multi-stream fusion of several image recognition paths. Immediate applications of this method can be found in fraud detection, deduplication procedure, or visual searching. The contribution of this paper is a novel distance measure for similarity of image sets and the experimental evaluation of several streams for the considered problem of same-car image set recognition. To determine a similarity score of image sets (this score expresses the certainty level that both sets represent the same object visible from the same set of views), we adapted a measure commonly applied in blind signal separation (BSS) evaluation. This measure is independent of the number of images in a set and the order of views in it. Separate streams for object classification (where a class represents either a car type or a car model-and-view) and object-to-object similarity evaluation (based on object features obtained alternatively by the convolutional neural network (CNN) or image keypoint descriptors) were designed. A late fusion by a fully-connected neural network (NN) completes the solution. The implementation is of modular structure—for semantic segmentation we use a Mask-RCNN (Mask regions with CNN features) with ResNet 101 as a backbone network; image feature extraction is either based on the DeepRanking neural network or classic keypoint descriptors (e.g., scale-invariant feature transform (SIFT)) and object classification is performed by two Inception V3 deep networks trained for car type-and-view and car model-and-view classification (4 views, 9 car types, and 197 car models are considered). Experiments conducted on the Stanford Cars dataset led to selection of the best system configuration that overperforms a base approach, allowing for a 67.7% GAR (genuine acceptance rate) at 3% FAR (false acceptance rate).
Evaluation of Thematic Coherence in Microblogs

Collecting together microblogs representing opinions about the same topics within the same timeframe is useful to a number of different tasks and practitioners.A major question is how to evaluate the quality of such thematic clusters.Here we create a corpus of microblog clusters from three different domains and time windows and define the task of evaluating thematic coherence.We provide annotation guidelines and human annotations of thematic coherence by journalist experts.We subsequently investigate the efficacy of different automated evaluation metrics for the task.We consider a range of metrics including surface level metrics, ones for topic model coherence and text generation metrics (TGMs).While surface level metrics perform well, outperforming topic coherence metrics, they are not as consistent as TGMs.TGMs are more reliable than all other metrics considered for capturing thematic coherence in microblog clusters due to being less sensitive to the effect of time windows.
Automatic Rephrasing of Transcripts-based Action Items

The automated transcription of spoken language, and meetings, in particular, is becoming more widespread as automatic speech recognition systems are becoming more accurate.This trend has significantly accelerated since the outbreak of the COVID-19 pandemic, which led to a major increase in the number of online meetings.However, the transcription of spoken language has not received much attention from the NLP community compared to documents and other forms of written language.In this paper, we study a variation of the summarization problem over the transcription of spoken language: given a transcribed meeting, and an action item (i.e., a commitment or request to perform a task), our goal is to generate a coherent and self-contained rephrasing of the action item.To this end, we compiled a novel dataset of annotated meeting transcripts, including human rephrasing of action items.We use state-of-the-art supervised text generation techniques and establish a strong baseline based on BART and UniLM (two pretrained transformer models).Due to the nature of natural speech, language is often broken and incomplete and the task is shown to be harder than an analogous task over email data.Particularly, we show that the baseline models can be greatly improved once models are provided with additional information.We compare two approaches: one incorporating features extracted by coreferenceresolution.Additional annotations are used to train an auxiliary model to detect the relevant context in the text.Based on the systematic human evaluation, our best models exhibit nearhuman-level rephrasing capability on a constrained subset of the problem.
Developing Teachers’ Social and Personality Competencies in Online Teaching: A New Challenge

Since entering the 21st century, teachers have been required to have ICT knowledge and skills and then are required to be able to fully integrate them into online learning during the Covid-19 pandemic.Having ICT knowledge and skills can certainly increase the pedagogical and professional competence of teachers.This study focuses on describing the efforts of teachers to develop their social and personality competencies in online teaching at the vocational high school level in the area of East Java, Indonesia.This study found that the problems that were found when teaching online had more negative impacts on the social competence and personality of the teacher so that the teacher's efforts to develop social and personality competencies were heavier than developing pedagogic and professional competencies.Finding solutions to overcome these problems is a new challenge for teachers to develop their social and personality competencies.The efforts made by accounting teachers are teachers trying to control emotions, maintaining attitudes and actions following applicable norms and religion, and not violating the teacher's code of ethics.The teacher also tries to be more concerned with the development of their students, monitoring their work, and the attitudes and behavior of their students.Besides, teachers are also not tried to encourage their students to continue learning.This research is sufficient to provide a discourse for accounting teachers in overcoming problems faced during online teaching, especially to develop social and personality competences.