Reviewerly/exHarmony huggingface dataset:
LanguageRefer: Spatial-Language Model for 3D Visual Grounding

For robots to understand human instructions and perform meaningful tasks in the near future, it is important to develop learned models that comprehend referential language to identify common objects in real-world 3D scenes. In this paper, we introduce a spatial-language model for a 3D visual grounding problem. Specifically, given a reconstructed 3D scene in the form of point clouds with 3D bounding boxes of potential object candidates, and a language utterance referring to a target object in the scene, our model successfully identifies the target object from a set of potential candidates. Specifically, LanguageRefer uses a transformer-based architecture that combines spatial embedding from bounding boxes with fine-tuned language embeddings from DistilBert to predict the target object. We show that it performs competitively on visio-linguistic datasets proposed by ReferIt3D. Further, we analyze its spatial reasoning task performance decoupled from perception noise, the accuracy of view-dependent utterances, and viewpoint annotations for potential robotics applications.
On the effectiveness of restarting local search

Premature convergence can be detrimental to the performance of search methods, which is why many search algorithms include restart strategies to deal with it. While it is common to perturb the incumbent solution with diversification steps of various sizes with the hope that the search method will find a new basin of attraction leading to a better local optimum, it is usually unclear whether this strategy is effective. To establish a connection between restart effectiveness and properties of a problem, we introduce a new property of fitness landscapes termed Neighbours with Similar Fitness. We conjecture that this property is true for many PLS-complete problems, and we argue that the effectiveness of a restart strategy depends on this property.
An Ensemble Noise-Robust K-fold Cross-Validation Selection Method for Noisy Labels

We consider the problem of training robust and accurate deep neural networks (DNNs) when subject to various proportions of noisy labels. Large-scale datasets tend to contain mislabeled samples that can be memorized by DNNs, impeding the performance. With appropriate handling, this degradation can be alleviated. There are two problems to consider: how to distinguish clean samples and how to deal with noisy samples. In this paper, we present Ensemble Noise-robust K-fold Cross-Validation Selection (E-NKCVS) to effectively select clean samples from noisy data, solving the first problem. For the second problem, we create a new pseudo label for any sample determined to have an uncertain or likely corrupt label. E-NKCVS obtains multiple predicted labels for each sample and the entropy of these labels is used to tune the weight given to the pseudo label and the given label. Theoretical analysis and extensive verification of the algorithms in the noisy label setting are provided. We evaluate our approach on various image and text classification tasks where the labels have been manually corrupted with different noise ratios. Additionally, two large real-world noisy datasets are also used, Clothing-1M and WebVision. E-NKCVS is empirically shown to be highly tolerant to considerable proportions of label noise and has a consistent improvement over state-of-the-art methods. Especially on more difficult datasets with higher noise ratios, we can achieve a significant improvement over the second-best model. Moreover, our proposed approach can easily be integrated into existing DNN methods to improve their robustness against label noise.
How to Approximate Ontology-Mediated Queries

We introduce and study several notions of approximation for ontology-mediated queries based on the description logics ALC and ALCI. Our approximations are of two kinds: we may (1) replace the ontology with one formulated in a tractable ontology language such as ELI or certain TGDs and (2) replace the database with one from a tractable class such as the class of databases whose treewidth is bounded by a constant. We determine the computational complexity and the relative completeness of the resulting approximations. (Almost) all of them reduce the data complexity from coNP-complete to PTime, in some cases even to fixed-parameter tractable and to linear time. While approximations of kind (1) also reduce the combined complexity, this tends to not be the case for approximations of kind (2). In some cases, the combined complexity even increases.
NOTE: Solution for KDD-CUP 2021 WikiKG90M-LSC

WikiKG90M in KDD Cup 2021 is a large encyclopedic knowledge graph, which could benefit various downstream applications such as question answering and recommender systems. Participants are invited to complete the knowledge graph by predicting missing triplets. Recent representation learning methods have achieved great success on standard datasets like FB15k-237. Thus, we train the advanced algorithms in different domains to learn the triplets, including OTE, QuatE, RotatE and TransE. Significantly, we modified OTE into NOTE (short for Norm-OTE) for better performance. Besides, we use both the DeepWalk and the post-smoothing technique to capture the graph structure for supplementation. In addition to the representations, we also use various statistical probabilities among the head entities, the relations and the tail entities for the final prediction. Experimental results show that the ensemble of state-of-the-art representation learning methods could draw on each others strengths. And we develop feature engineering from validation candidates for further improvements. Please note that we apply the same strategy on the test set for final inference. And these features may not be practical in the real world when considering ranking against all the entities.
Evolution and Prospect of Educational Informatization Development

This paper explores the historical evolution of the development of education informatization, and taking the east-west Lake District of Wuhan as an example, discusses in detail the existing problems in the development of education informatization, and further probes into the future development path.
AN ANALYSIS OF READING LITERACY FOR COLLEGE STUDENTS THROUGH WEBTOON AS INTERACTIVE LEARNING

This research is about An Analysis of Reading Literacy for College Students through Webtoon as Interactive Learning. Webtoon is one of a way aimed to interest students in reading literacy. The purpose of this research is to know whether the use of webtoon can be interactive learning and increase reading literacy. Also, this research was conducted by using qualitative data with descriptive analysis approach and triangulation data. The subject of this research was the 3rd semester of As-Syafi’iyah Islamic University 2020/2021 academic year which consisted of 20 students and take 5 students randomly.&#x0D; The data were gathered through observations and interviews of the instrument. The technique of data analysis in this study is thematic analysis through the answer of interview questions then the transcript to gain the topic to become a theme. The theme used to be analyzed as a result of this study. Based on the data, the result of this research showed that webtoon can be interactive learning and increase the reading literacy of students. Learning using the webtoon more simple and easy access. In other words, webtoon can be more interested in increase reading literacy for students.&#x0D;
Improving Efficiency and Accuracy of Causal Discovery Using a Hierarchical Wrapper

Causal discovery from observational data is an important tool in many branches of science. Under certain assumptions it allows scientists to explain phenomena, predict, and make decisions. In the large sample limit, sound and complete causal discovery algorithms have been previously introduced, where a directed acyclic graph (DAG), or its equivalence class, representing causal relations is searched. However, in real-world cases, only finite training data is available, which limits the power of statistical tests used by these algorithms, leading to errors in the inferred causal model. This is commonly addressed by devising a strategy for using as few as possible statistical tests. In this paper, we introduce such a strategy in the form of a recursive wrapper for existing constraint-based causal discovery algorithms, which preserves soundness and completeness. It recursively clusters the observed variables using the normalized min-cut criterion from the outset, and uses a baseline causal discovery algorithm during backtracking for learning local sub-graphs. It then combines them and ensures completeness. By an ablation study, using synthetic data, and by common real-world benchmarks, we demonstrate that our approach requires significantly fewer statistical tests, learns more accurate graphs, and requires shorter run-times than the baseline algorithm.
The quantum condition space

In this work we first propose to exploit the fundamental properties of quantum physics to evaluate the probability of events with projection measurements. Next, to study what events can be specified by quantum methods, we introduce the concept of the condition space, which is found to be the dual space of the classical outcome space of bit strings. Just like the classical outcome space generates the quantum state space, the condition space generates the quantum condition space that is the central idea of this work. The quantum condition space permits the existence of entangled conditions that have no classical equivalent. In addition, the quantum condition space is related to the quantum state space by a Fourier transform guaranteed by the Pontryagin duality, and therefore an entropic uncertainty principle can be defined. The quantum condition space offers a novel perspective of understanding quantum states with the duality picture. In addition, the quantum conditions have physical meanings and realizations of their own and thus may be studied for purposes beyond the original motivation of characterizing events for probability evaluation. Finally, the relation between the condition space and quantum circuits provides insights into how quantum states are collectively modified by quantum gates, which may lead to deeper understanding of the complexity of quantum circuits.
Conservative Generalisation for Small Data Analytics –An Extended Lattice Machine Approach

Small data analytics is to tackle the data analysis challenges such as overfitting when the data set is small. There are different approaches to small data analytics, including knowledge-based learning, but most of these approaches need experience to use. In this paper we consider another approach, lattice machine. Lattice machine is a conservative generalisation based learning algorithm. It is a learning paradigm that "learns" by generalising data in a consistent, conservative and parsimonious way. A lattice machine model built from a dataset is a set of hyper tuples that tightly "wraps around" clusters of data, each of which is a conservative generalisation of the underlying cluster. A key feature of lattice machine, indeed any conservative generalisation based learning algorithm, is that it has high precision and low recall, limiting its applications as high recall is needed in some applications such as disease (e.g. covid-19) screening. It is thus necessary to improve lattice machine's recall whilst retaining his high precision. In this paper, we present a study on how to achieve this for lattice machine.
Book Recommendation System using Matrix Factorization

In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.
The TCP/Indeterminate Place Quartet: a Global Hyperorgan Scenario

The TCP/Indeterminate Place quartet presents a telematic performance in which two concert hall pipe organs, in two different locations in Europe, are controlled remotely using a series of different techniques.
Ultranarrow spectral line of the radiation in double qubit-cavity ultrastrong coupling system

The ultrastrongly coupling (USC) system has very important research significance in quantum simulation and quantum computing. In this paper, the ultranarrow spectrum of a circuit QED system with two qubits ultrastrongly coupled to a single-mode cavity is studied. In the regime of USC, the JC model breaks down and the counter-rotating terms in the quantum Rabi Hamiltonian leads to the level anti-crossing in the energy spectrum. Choosing a single-photon driving field at the point of avoided-level crossing, we can get an equivalent four-level dressed state model, in which the dissipation of the two intermediate states is only related to the qubits decay. Due to the electron shelving of these two metastable states, a narrow peak appears in the cavity emission spectrum. Furthermore, we find that the physical origin for the spectral narrowing is the vacuum-induced quantum interference between two transition pathways. And this interference effect couples the slowly decaying incoherent components of the density matrix into the equations of the sidebands. This result provides a possibility for the study of quantum interference effect in the USC system.
Analyzing the Performance of Anomaly Detection Algorithms

An outlier is a data observation that is considerably irregular from the rest of the dataset.The outlier present in the dataset may cause the integrity of the dataset.Implementing machine learning techniques in various real-world applications and applying those techniques to the healthcare-related dataset will completely change the particular field's present scenario.These applications can highlight the physiological data having anomalous behavior, which can ultimately lead to a fast and necessary response and help to gather more critical knowledge about the particular area.However, a broad amount of study is available about the performance of anomaly detection techniques applied to popular public datasets.But then again, have a minimal amount of analytical work on various supervised and unsupervised methods considering any physiological datasets.The breast cancer dataset is both a universal and numeric dataset.This paper utilized and analyzed four machine learning techniques and their capacity to distinguish anomalies in the breast cancer dataset.
Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation.

Sentiment analysis is a costly yet necessary task for enterprises to study the opinions of their customers to improve their products and to determine optimal marketing strategies. Due to the existence of a wide range of domains across different products and services, cross-domain sentiment analysis methods have received significant attention. These methods mitigate the gap between different applications by training cross-domain generalizable classifiers which help to relax the need for data annotation for each domain. Most existing methods focus on learning domain-agnostic representations that are invariant with respect to both the source and the target domains. As a result, a classifier that is trained using the source annotated data would generalize well in a related target domain. We introduce a new adaptation method which induces large margins between different classes in an embedding space. This embedding space is trained to be domain-agnostic by matching the data distributions across the domains. Large intraclass margins in the source help to reduce the effect of domain shift on the classifier performance in the target domain. Theoretical and empirical analysis are provided to demonstrate that the proposed method is effective.
LEARNING MOTIVATION OF DYSLEXIA STUDENTS IN OVERCOMING LEARNING DIFFICULTIES

ABSTRACTChildren with special needs (ABK) who have dyslexia can cause children to have difficulties in learning, including difficulties in spelling, reading, writing and communicating with others. The subjects in this study were dyslexic students with the initials DH at one of the Inclusive Elementary Schools in Sleman. The results showed that parents only realized dyslexic children when the children entered their teens, the environment also affects the learning motivation of dyslexic children, and the efforts made by teachers and parents in overcoming the learning difficulties of dyslexic students. From the results of the study, DH experienced dyslexia in the form of difficulty in speaking, hesitation when answering the interlocutor's questions, spelling word for word slowly with a soft or low voice or intonation, and difficulties in writing and difficulty communicating with the surrounding environment. The method used in this research is descriptive qualitative. Data collection techniques were carried out by indepth interviews, observation and literature review. By providing maximum learning assistance between teachers and parents, it will increase learning motivation for dyslexic children.Keywords: dyslexia children, learning motivation, learning difficulties
AGREEMENT AND DISAGREMENT FUNCTION IN DISCOURSE MARKER WELL IN DEBATE NIGHT AMERICA PROGRAM: A PRAGMATICANALYSIS

The research entitled “Discourse Marker Well in Debate Night in America Interview Program.” The objectives of this research are to analyze and describe two types and two functions of discourse marker well which occur in Debate Night in America Program. This marker that frequently occurs in the program demonstrates a special sort of discourse marker well in particular news interviews. This study uses description method to analyze the research. Pragmatic is used to approach the analysis, which is mainly developed by Schiffrin (1987). He called discourse markers as linguistics expressions, which can be a word or phrase. The results of this study show ten discourse markers well that frequently occur in Debate Night in America Program. In term of functions, the frequently marker proposes various functions. The marker serves its own function in guiding the hearer and the audience to interpret the intended meaning of the speaker. In addition, the functions of discourse marker well provide particular purpose by the speaker in order to influence and attract the voters. The function analyzed in the use of discourse marker well is to show agreement and disagreement.
Vacutainer serum separator sebagai alternatif tabung penampung darah pada pemeriksaan kadar ureum

ABSTRACT Serum using is preferred for urea level because it does not use anticoagulants which can interfere with activity and reaction to the results. The tubes that are widely used to collect blood into serum are vacutainer serum separator and vacutainer plain.This researche aims to determine the degree of agreement s between vacutainer serum separator and vacutainer plain usage on serum urea level result.This research was cross sectional design and hold on October 2020 with subject were taken from thirty blood samples of health analyst students which taken randomly and had no history of disease or kidney function disorder. Each student was taken 6 ml of blood drawn using a venoject with each vacutainer containing 3 ml, so we had 60 data. The data were analyzed by descriptively and inferentially using the Interclass Correlation Coefficient (ICC) statistical test. From the descriptive analysis, the difference in mean levels was 0.35 mg/dL and the ICC statistical test resulted in a degree of agreement 0.745. The data were analyzed by descriptively and inferentially using the Interclass Correlation Coefficient (ICC) statistical test. From the descriptive analysis, the difference in mean levels was 0.35 mg/dL and the ICC statistical test resulted in a degree of agreement was 0.745. The calculation of the average working time between the vacutainer serum separator and the vacutainer plain was 4 minutes 38 seconds and 35 minutes 58 seconds. The analysis concluded that the vacutainer serum separator and the vacutainer plain could be used as an alternative of blood collecting tubes for urea level testing which proved to be no significant difference in the results from this research. Keywords : Urea level, Vacutainer Serum Separator, Vacutainer Plain
Zero-Knowledge Proofs for Committed Symmetric Boolean Functions

Zero-knowledge proofs (ZKP) are a fundamental notion in modern cryptography and an essential building block for countless privacy-preserving constructions. Recent years have witnessed a rapid development in the designs of ZKP for general statements, in which, for a publicly given Boolean function $$f: \{0,1\}^n \rightarrow \{0,1\}$$ , one’s goal is to prove knowledge of a secret input $$\mathbf {x} \in \{0,1\}^n$$ satisfying $$f(\mathbf {x}) = b$$ , for a given bit b. Nevertheless, in many interesting application scenarios, not only the input $$\mathbf {x}$$ but also the underlying function f should be kept private. The problem of designing ZKP for the setting where both $$\mathbf {x}$$ and f are hidden, however, has not received much attention. This work addresses the above-mentioned problem for the class of symmetric Boolean functions, namely, Boolean functions f whose output value is determined solely by the Hamming weight of the n-bit input $$\mathbf {x}$$ . Although this class might sound restrictive, it has exponential cardinality $$2^{n+1}$$ and captures a number of well-known Boolean functions, such as threshold, sorting and counting functions. Specifically, with respect to a commitment scheme secure under the Learning-Parity-with-Noise (LPN) assumption, we show how to prove in zero-knowledge that $$f(\mathbf {x}) = b$$ , for a committed symmetric function f, a committed input $$\mathbf {x}$$ and a bit b. The security of our protocol relies on that of an auxiliary commitment scheme which can be instantiated under quantum-resistant assumptions (including LPN). The protocol also achieves reasonable communication cost: the variant with soundness error $$2^{-\lambda }$$ has proof size $$c\cdot \lambda \cdot n$$ , where c is a relatively small constant. The protocol can potentially find appealing privacy-preserving applications in the area of post-quantum cryptography, and particularly in code-based cryptography.