Reviewerly/exHarmony huggingface dataset:
Graph-based Semantical Extractive Text Analysis

In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them) has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. this algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as the result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework which can be used individually or as a part of generating the summary to overcome coverage problems.
Rate-Privacy-Storage Tradeoff in Federated Learning with Top $r$ Sparsification

We investigate the trade-off between rate, privacy and storage in federated learning (FL) with top $r$ sparsification, where the users and the servers in the FL system only share the most significant $r$ and $r'$ fractions, respectively, of updates and parameters in the FL process, to reduce the communication cost. We present schemes that guarantee information theoretic privacy of the values and indices of the sparse updates sent by the users at the expense of a larger storage cost. To this end, we generalize the scheme to reduce the storage cost by allowing a certain amount of information leakage. Thus, we provide the general trade-off between the communication cost, storage cost, and information leakage in private FL with top $r$ sparsification, along the lines of two proposed schemes.
Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects

Communications systems to date are primarily designed with the goal of reliable transfer of digital sequences (bits). Next generation (NextG) communication systems are beginning to explore shifting this design paradigm to reliably executing a given task such as in task-oriented communications. In this paper, wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label. Edge devices may not have sufficient processing power and may not be trusted to perform the signal classification task, whereas the transfer of signals to the gNodeB may not be feasible due to stringent delay, rate, and energy restrictions. Task-oriented communications is considered by jointly training the transmitter, receiver and classifier functionalities as an encoder-decoder pair for the edge device and the gNodeB. This approach improves the accuracy compared to the separated case of signal transfer followed by classification. Adversarial machine learning poses a major security threat to the use of deep learning for task-oriented communications. A major performance loss is shown when backdoor (Trojan) and adversarial (evasion) attacks target the training and test processes of task-oriented communications.
Integrated Quantum Optical Phase Sensor

The quantum noise of light fundamentally limits optical phase sensors. A semiclassical picture attributes this noise to the random arrival time of photons from a coherent light source such as a laser. An engineered source of squeezed states suppresses this noise and allows sensitivity beyond the standard quantum limit (SQL) for phase detection. Advanced gravitational wave detectors like LIGO have already incorporated such sources, and nascent efforts in realizing quantum biological measurements have provided glimpses into new capabilities emerging in quantum measurement. We need ways to engineer and use quantum light within deployable quantum sensors that operate outside the confines of a lab environment. Here we present a photonic integrated circuit fabricated in thin-film lithium niobate that provides a path to meet these requirements. We use the second-order nonlinearity to produce a squeezed state at the same frequency as the pump light and realize circuit control and sensing with electro-optics. Using a 26.2 milliwatts of optical power, we measure (2.7 $\pm$ 0.2 )$\%$ squeezing and apply it to increase the signal-to-noise ratio of phase measurement. We anticipate that on-chip photonic systems like this, which operate with low power and integrate all of the needed functionality on a single die, will open new opportunities for quantum optical sensing.
A Retrieve-and-Read Framework for Knowledge Graph Link Prediction

Knowledge graph (KG) link prediction aims to infer new facts based on existing facts in the KG. Recent studies have shown that using the graph neighborhood of a node via graph neural networks (GNNs) provides more useful information compared to just using the query information. Conventional GNNs for KG link prediction follow the standard message-passing paradigm on the entire KG, which leads to superfluous computation, over-smoothing of node representations, and also limits their expressive power. On a large scale, it becomes computationally expensive to aggregate useful information from the entire KG for inference. To address the limitations of existing KG link prediction frameworks, we propose a novel retrieve-and-read framework, which first retrieves a relevant subgraph context for the query and then jointly reasons over the context and the query with a high-capacity reader. As part of our exemplar instantiation for the new framework, we propose a novel Transformer-based GNN as the reader, which incorporates graph-based attention structure and cross-attention between query and context for deep fusion. This simple yet effective design enables the model to focus on salient context information relevant to the query. Empirical results on two standard KG link prediction datasets demonstrate the competitive performance of the proposed method. Furthermore, our analysis yields valuable insights for designing improved retrievers within the framework.
MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages

This paper investigates the problem of Named Entity Recognition (NER) for extreme low-resource languages with only a few hundred tagged data samples. NER is a fundamental task in Natural Language Processing (NLP). A critical driver accelerating NER systems' progress is the existence of large-scale language corpora that enable NER systems to achieve outstanding performance in languages such as English and French with abundant training data. However, NER for low-resource languages remains relatively unexplored. In this paper, we introduce Mask Augmented Named Entity Recognition (MANER), a new methodology that leverages the distributional hypothesis of pre-trained masked language models (MLMs) for NER. The <mask> token in pre-trained MLMs encodes valuable semantic contextual information. MANER re-purposes the <mask> token for NER prediction. Specifically, we prepend the <mask> token to every word in a sentence for which we would like to predict the named entity tag. During training, we jointly fine-tune the MLM and a new NER prediction head attached to each <mask> token. We demonstrate that MANER is well-suited for NER in low-resource languages; our experiments show that for 100 languages with as few as 100 training examples, it improves on state-of-the-art methods by up to 48% and by 12% on average on F1 score. We also perform detailed analyses and ablation studies to understand the scenarios that are best-suited to MANER.
Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023?

The CoNLL-2003 English named entity recognition (NER) dataset has been widely used to train and evaluate NER models for almost 20 years. However, it is unclear how well models that are trained on this 20-year-old data and developed over a period of decades using the same test set will perform when applied on modern data. In this paper, we evaluate the generalization of over 20 different models trained on CoNLL-2003, and show that NER models have very different generalization. Surprisingly, we find no evidence of performance degradation in pre-trained Transformers, such as RoBERTa and T5, even when fine-tuned using decades-old data. We investigate why some models generalize well to new data while others do not, and attempt to disentangle the effects of temporal drift and overfitting due to test reuse. Our analysis suggests that most deterioration is due to temporal mismatch between the pre-training corpora and the downstream test sets. We found that four factors are important for good generalization: model architecture, number of parameters, time period of the pre-training corpus, in addition to the amount of fine-tuning data. We suggest current evaluation methods have, in some sense, underestimated progress on NER over the past 20 years, as NER models have not only improved on the original CoNLL-2003 test set, but improved even more on modern data. Our datasets can be found at https://github.com/ShuhengL/acl2023_conllpp.
Final Results of the Qweak Experiment

sensitivity to specific examples of new physics like compositeness. From the proton’s weak charge, a result for the fundamental standard model parameter sin2θW is obtained at the energy scale of our experiment. We compare that to the few other determinations of sin2θW available in the literature, as well as the predicted behaviour based on SM input.
Developing Intelligent Assistants to Search for Content on Websites of a Certain Genre

This paper discusses an approach to automatic generation of intelligent assistants, which provide information search on the content of a website. A feature of the approach is to use genre models, developed for a given type of resource (educational, informational, etc.), on the basis of which the genre structuring and subsequent thematic clustering of the content of the target website is performed. The resulting genre structures allow us to define more precisely the boundaries of thematic clusters related to the topic of the user’s search query. The search quality evaluation for the Russian-language websites showed an F-score of 87.8% and originality of 80.9%, which exceeds the Yandex search engine results by 1.1% and 9.1%, respectively. In order to predict user information needs, a method for refining the resulting sample is proposed. It allows a user to get information implicitly, based on current and previous queries, about what the user was not satisfied with in the previous search results. A model of user’s search intentions has been developed and its computational component includes a method for evaluating query closeness based on the FRiS function. Based on the proposed methods, a chatbot was created on the Telegram messenger platform to search the websites of educational institutions. The experiments showed that the user needs the average of 1.75 qualifying questions to find the necessary information.
Comparison of Chinese and Malaysian Media Literacy: An Analytical Study

Media literacy refers to the ability to select, fully absorb, analyze, and react critically to the multitude of types of information disseminated by the media. Important components of media literacy include avoiding creating or disseminating false news and respecting other people's rights and privacy. In the era of the Internet, online media literacy is a brand-new subset of media literacy. In addition to the information use and critical literacy skills found in conventional media, online information creation, online communication, social collaboration, and social participation skills are included as well.
ATRIBUIÇÕES DO ENFERMEIRO FRENTE À AUDITORIA EM SAÚDE

Art therapy as a function is not restricted to the establishment and is based on various forms of artistic expression with applications inherent to evaluation, prevention, treatment and rehabilitation.It is extremely important to highlight that this therapeutic method does not replace the use and/or psychotherapy, but works as an auxiliary treatment.
PENINGKATAN HASIL BELAJAR TEMA 1 INDAHNYA KEBERSAMAAN MELALUI MODEL PEMBELAJARAN GROUP INVESTIGATION PADA SISWA KELAS 4 SDN 175 TANJAKAN

This study aims to improve learning outcomes in theme 1 through the application of the Group Investigation learning model to 4th grade students at SDN 175 Tanjakan Bandung City. This research is Classroom Action Research (PTK) which is carried out in two cycles consisting of 4 stages, namely the planning, implementation, observation, and reflection stages. The subjects of this study were 4th grade students at SDN 175 Tanjakan, which consisted of 30 students. The data collection technique used was a test (observation, documentation) then the data was analyzed descriptively quantitatively. The results showed an increase in the average percentage of learning outcomes from cycle I totaling 18 students (60%) included in the complete classification, 12 students (40%) included in the incomplete classification classification and cycle II as many as 26 students (86.67%) entered into the complete classification and 4 students (13.33%) in the incomplete classification. Based on the results of the study, it can be concluded that the Group Investigation learning model can improve learning outcomes for theme 1 class 4 SDN 175 Tanjakan Bandung City.
Evaluation of Identification Method of Corresponding Numerical Attributes in Heterogeneous Databases Based on Instances

Abstract This paper experimentally evaluates the instance-based schema matching method for the attributes storing numerical data. This method uses data distributions and correlations between two attributes. Experimental results show that this method could find appropriate attribute correspondences even for the attributes of different kinds of tables in databases.
A tale of two jets

We used model studies to investigate approaches to distin-guish signal and combinatorial jets.
Application Method Play Show and Tell in Increase Ability communicate Oral Early Childhood 5-6 Years in Kindergarten Creation Nation Rambai 2021/2022 Academic Year

Problem main in study this that is, application method play show and tell inside increase ability communicate oral child age early. Study this aim for knowing enhancement ability communication oral child with method playing show and tell at Karya Kindergarten Nation Rambai. Study this is Study Qualitative descriptive. Deep data collection techniques study this is observation and interview. subject study this is child aged 5-6 years with a total of 14 children. object study is ability communication oral child age early through method playing show and tell to children 5-6 years old at Karya Kindergarten Nation Rambai T. A 2021-2022. Data analyzed in a manner descriptive qualitative with interviews and observations. Research results show exists enhancement ability communication oral child increase through activity play show and tell. this proven with exists increase in average ability communication oral child through activity playing show and tell, on observation initial 45% increase to 75% on observation after he did action method play show and tell. With thereby could concluded that activity play show and tell can increase communication oral child age early in TK Karya Nation Rambai.
AHS mapping for hydrothermal alterations detection and mineral deposits exploration

The Actve Hyperspectral Sensing (AHS) application for a mineral mapping have been tested on outcrops with porphyry-copper style of mineralization in the Vlaykov Vruh and Tsar Assen deposits. As a new tool used in mineral exploration AHS reveals the advantages for mineral detection and targeting as an express new innovative technology and efficient tool for mineral prospecting.
A Generalization of NTRUencrypt

Abstract NTRU cryptosystem is a new public key cryptosystem based on lattice hard problem proposed in 1996 by three digit theorists Hoffstein, Piper and Silverman of Brown University in the United States. The essence of NTRU cryptographic design is the generalization of RSA on polynomials, so it is called the cryptosystem based on polynomial rings. Its main feature is that the key generation is very simple, and the encryption and decryption algorithm is much faster than the commonly used RSA and elliptic curve cryptography. In particular, NTRU can resist quantum computing attacks and is considered to be a potential public key cryptography that can replace RSA in the post-quantum cryptography era.
LWE Public Key Cryptosystem

Abstract In 2005, O.Regev proposed the first LWE public key cryptosystem at Tel Aviv University in Israel based on LWE distribution $$A_{s,\chi }$$ . Because of this paper, Regev won the highest award for theoretical computer science in 2018—the Godel Award. The size of public key is $$\tilde{O}(n^2)$$ bits, and the size of private key s and ciphertext is $$\tilde{O}(n)$$ bits. The plaintext encrypted each time is 1 bit. In fact, the LWE public key cryptosystem is a probabilistic cryptosystem, which depends on a high probability algorithm. Since the security of LWE problem has been clearly proved (see Chap. 3 ), the LWE cryptosystem has received extensive attention as soon as it was proposed, and it becomes the most cutting-edge research topic in the lattice-based cryptosystem study.
Fully Homomorphic Encryption

Abstract In 1978, Rivest et al. (1978) proposed the concepts of data bank and fully homomorphic encryption. Some individuals and organizations encrypt the original data and store them in the data bank for privacy protection. Data bank is also called data cloud. Therefore, the cloud stores a large amount of original data, which is obviously a huge wealth. How to use these data effectively? First of all, we must solve the problem of calculation of these encrypted data, which is called a privacy calculation problem. Rivest, Adleman and Dertouzos conjecture that if all data is fully homomorphic encryption, that is, the addition and multiplication of ciphertext are homomorphic to the corresponding addition and multiplication of plaintext, then the encrypted data can be effectively computed by elementary calculation without changing the structure of the plaintext data (under the condition of homomorphism). The RAD conjecture has been proposed for more than 30 years, but no one could solve this problem since the cryptographic structure of the fully homomorphic encryption system is too complicated. In 2009, C. Gentry, a computer scholar at Stanford University, first proposed a fully homomorphic encryption scheme in Gentry (2009b) based on ideal lattice, for which he won the 2022 highest award in theoretical computer science—the Godel Award. Based on Gentry’s work, the second and third fully homomorphic encryption schemes based on LWE distribution and trapdoor matrix technology have also been proposed; see Brakerski and Vaikuntanathan (2011a), (2011b), (2012), (2014), (2015) and Gentry et al. (2013) in 2013. The main purpose of this chapter is to systematically analyze and discuss the above three fully homomorphic encryption techniques, in order to understand the latest research trends of the post-quantum cryptography.
Learning with Error

Abstract Learning with error was proposed by O. Regev in 2005 (see Regev, 2009), which can be regarded as a dual form of SIS problem. LWE has very important applications in modern cryptography, such as LWE-based fully homomorphic encryption. The main purpose of this chapter is to explain the mathematical principles of the LWE problem in detail, especially the polynomial equivalence between the average LWE problem and the hard problems on lattice, which is one generalization of the Ajtai reduction principle and solves the computational complexity of the LWE problem effectively.
Foreign Language Instructors’ Professional Development in Times of Crisis: Challenges and Solutions

The unprecedented situation of COVID-19 compelled many universities and colleges worldwide to reconsider both the management and delivery of classes, forcing faculty to use innovative online and mobile means. The transition also caused faculty to reevaluate their professional development (PD). This qualitative exploratory study focused on the PD experienced by a group of foreign language university instructors in Colombia during this crisis. Specifically it identified the challenges encountered and how these were tackled. Data were collected through an open-ended survey and analyzed using content analysis. Results show ten main challenges, which were then categorized into three themes: (a) adaptation to emergency remote teaching, (b) promotion of student engagement, and (c) handling of emotions. Conclusions reveal that professors embraced the crisis with a positive attitude, engaging in different PD opportunities, displaying agency, responsibility, and flexibility. Therefore, the challenges and their reaction to them fostered their PD and learning. These findings suggest that institutions should allow instructors to give input into the types of PD programs that they need forspecific situations.
A Search Engine for Discovery of Scientific Challenges and Directions

Keeping track of scientific challenges, advances and emerging directions is a fundamental part of research. However, researchers face a flood of papers that hinders discovery of important knowledge. In biomedicine, this directly impacts human lives. To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery. We construct and release an expert-annotated corpus of texts sampled from full-length papers, labeled with novel semantic categories that generalize across many types of challenges and directions. We focus on a large corpus of interdisciplinary work relating to the COVID-19 pandemic, ranging from biomedicine to areas such as AI and economics. We apply a model trained on our data to identify challenges and directions across the corpus and build a dedicated search engine. In experiments with 19 researchers and clinicians using our system, we outperform a popular scientific search engine in assisting knowledge discovery. Finally, we show that models trained on our resource generalize to the wider biomedical domain and to AI papers, highlighting its broad utility. We make our data, model and search engine publicly available. https://challenges.apps.allenai.org/
Partition GHZ SLOCC class of three qubits into ten families under LU

In [Science 340:1205, (2013)], via entanglement polytopes Michael Walter et al. obtained a finite yet systematic classification of multi-particle entanglement. It is well known that under SLOCC, pure states of three (four) qubits are partitioned into six (nine) families. In this paper,we present a LU invariant and an entanglement measures for the GHZ SLOCC class of three qubits, and partition states of the GHZ SLOCC class of three qubits into ten families and each family into two subfamilies under LU. We give a necessary and sufficient condition for the uniqueness of the generalized Schmidt decomposition for the GHZ SLOCC class.