Reviewerly/exHarmony huggingface dataset:
A Graph Reasoning Model Based on Multiple Types of Nodes

Multi-hop machine reading comprehension (MRC) depends on the model to answer a semantically related question based on the given supporting text. In this study, inspired by previous research about Graph Neural Network(GNN), we develop a new graph covering three kinds of nodes: Sentence, Entity, Candidate. A corresponding MRC method based on graph inference is proposed, called SEC reasoner. The advantage of SECr is that it adopts different granular information as graph nodes, which can execute a more comprehensive reasoning. Our proposed model first initialize the node representations by co-attention, multi-head attention and self-attention based on context embedding. SECr then employs GNN based information transfer algorithms to collect evidences from the node encoding, and calculates a score for choosing the answer. In experiments, we evaluate the model on two popular benchmark datasets - WikiHop and MedHop. SECr reachs accuracy rates of 71.6%, 63.1% respectively, which is a competitive result. In addition, a comprehensive analysis proves the effectiveness and interpretability of the SECr.
Penyuluhan Machine Learning dan Quantum Artificial Intelligence di Era Industri 4.0

The Digital Era 4.0 has started since 2016 and two Southeast Asia countries such as Malaysia and Singapore have already adapted to the era; unfortunately, Indonesia has been struggling to adapt the era and, therefore, needs to catch up the digital competitiveness of its neighbouring countries. According to IMD World Digital Competitiveness 2020, Indonesia placed 56th of 63 countries in the digital competitiveness measurement. Despite its poor performance, Indonesia can catch up with other countries by starting from universities’ environment where Indonesia’s next generations study. Universities are prominent education institutions which prepare next generations for world digital competitiveness. According to BPS Indonesia, the unemployment of bachelor, master, and doctoral graduates reach a total number of 737.000, or 5,67% of 13 millions work force. One of the causes is the lack of technological knowledge, specifically, Artificial Intelligence (AI), from the graduates. Particularly, when they become business leaders, they are not fully prepared to create new job openings because mostly their mindsets are to find suitable jobs after study. The two webinars are results of collaboration between several universities which form NUNI (Jejaring Universitas Nusantara) whose purpose is to equip students with the knowledge of AI. Our method of counselling whose format is two webinars with both titles are Interpretable Machine Learning and Quantum Artificial Intelligence has gained appreciation in the form of average participation score which approaches excellent score (4,60 of 5,00). Additionally, these two webinars are publicly available in web blogs and Youtube videos.
Pengaruh Pembelajaran Daring Terhadap Minat Belajar Siswa/i Kelas VIII SMP Negeri 1 Telukdalam

Online learning is learning that takes place in a network where teachers and those being taught do not meet face-to-face. One of the schools that conduct online learning is SMP Negeri 1 Telukdalam. Like other schools, they carry out teaching and learning activities with online learning. Based on the results of an initial survey conducted by researchers in SMP Negeri 1 Telukdalam there are some students who state that there are many obstacles when online learning takes place. In this study, the aim is to find out how the influence of online learning on students interest in class VIII SMP Negeri 1 Telukdalam. The type of research used is quantitative with aplication assistance tools SPSS Vers. 16. Based on the results of the study showed that the relationship between the variable X with the variable Y of 0,857 indicates the rxy correlations that occurs between the independent variable and the dependet variable is in a “Very Strong” relationship. (0,80-1,000). Obtained a coefficient of determination () of 0,735 so that it can be shown that the diversity of the depedent variable (interest in learning) can be influenced by the independent variable (online learning). The value of tcount (13,308) &gt; ttable (1.668) and a significant level of 0,000 &lt; 0.05, then the decision is Ha accepted and H0 is rejected with the meaning that the online learning variable has a positive and significant effect on the variable interest in student learning in class VIII SMP Negeri 1 Telukdalam.
Effect of Emitters on Quantum State Transfer in Coupled Cavity Arrays

Over the last decade, considerable work has been done on quantum state transfer along one dimensional arrays of coupled qubits, as modeled by quantum spin chains. Protocols for perfect quantum state transfer, requiring 'full engineering' of the exchange constants $J_{i}$ have been discovered. The degradation induced by the inevitable disorder $\Delta J_{i}$ present in any experimental realization has also been discussed. In this paper, we consider an extension of such studies to quantum state transfer in a coupled cavity array including the effects of atoms in the cavities which can absorb and emit photons as they propagate down the array. Our model is equivalent to previously examined spin chains in the one-excitation sector and in the absence of emitters. We introduce a Monte Carlo approach to the inverse eigenvalue problem which allows the determination of the inter-cavity and cavity-emitter couplings resulting in near-perfect quantum state transfer fidelity, and examine the time dependent polariton wave function through exact diagonalization of the resulting Tavis-Cummings-Hubbard Hamiltonian. The effect of inhomogeneous emitter locations is also evaluated.
VL-Adapter: Parameter-Efficient Transfer Learning for  Vision-and-Language Tasks

Recently, fine-tuning language models pre-trained on large text corpora have provided huge improvements on vision-and-language (V&L) tasks as well as on pure language tasks. However, fine-tuning the entire parameter set of pre-trained models becomes impractical since the model size is growing rapidly. Hence, in this paper, we introduce adapter-based parameter-efficient transfer learning techniques to V&L models such as VL-BART and VL-T5. We evaluate our methods in a unified multi-task setup on four diverse V&L tasks: VQAv2, GQA, NLVR2 , and MSCOCO image captioning. With careful training and thorough experiments, we benchmark three popular adapter-based methods (Adapter, Hyperformer, Compacter) against the standard full fine-tuning and the recently proposed prompt-tuning approach. We also enhance the efficiency and performance of adapters by sharing their weights to attain knowledge across tasks. Our results demonstrate that training the adapter with the weight-sharing technique (4.4% of total parameters) can match the performance of fine-tuning the entire model. Lastly, we present a comprehensive analysis including the combination of adapter and task-specific prompts and the impact of V&L pre-training on adapters. Our code is available at: https://github.com/ylsung/VL_adapter.
Dual Cluster Contrastive learning for Person Re-Identification

Recently, cluster contrastive learning has been proven effective for person ReID by computing the contrastive loss between the individual feature and the cluster memory. However, existing methods that use the individual feature to momentum update the cluster memory are not robust to the noisy samples, such as the samples with wrong annotated labels or the pseudo-labels. Unlike the individual-based updating mechanism, the centroid-based updating mechanism that applies the mean feature of each cluster to update the cluster memory is robust against minority noisy samples. Therefore, we formulate the individual-based updating and centroid-based updating mechanisms in a unified cluster contrastive framework, named Dual Cluster Contrastive learning (DCC), which maintains two types of memory banks: individual and centroid cluster memory banks. Significantly, the individual cluster memory is momentum updated based on the individual feature.The centroid cluster memory applies the mean feature of each cluter to update the corresponding cluster memory. Besides the vallina contrastive loss for each memory, a consistency constraint is applied to guarantee the consistency of the output of two memories. Note that DCC can be easily applied for unsupervised or supervised person ReID by using ground-truth labels or pseudo-labels generated with clustering method, respectively. Extensive experiments on two benchmarks under supervised person ReID and unsupervised person ReID demonstrate the superior of the proposed DCC. Code is available at: https://github.com/htyao89/Dual-Cluster-Contrastive/
Understanding and Improving the Exemplar-based Generation for Open-domain Conversation

Exemplar-based generative models for open-domain conversation produce responses based on the exemplars provided by the retriever, taking advantage of generative models and retrieval models. However, due to the one-to-many problem of the open-domain conversation, they often ignore the retrieved exemplars while generating responses or produce responses over-fitted to the retrieved exemplars. To address these advantages, we introduce a training method selecting exemplars that are semantically relevant to the gold response but lexically distanced from the gold response. In the training phase, our training method first uses the gold response instead of dialogue context as a query to select exemplars that are semantically relevant to the gold response. And then, it eliminates the exemplars that lexically resemble the gold responses to alleviate the dependency of the generative models on that exemplars. The remaining exemplars could be irrelevant to the given context since they are searched depending on the gold response. Thus, our training method further utilizes the relevance scores between the given context and the exemplars to penalize the irrelevant exemplars. Extensive experiments demonstrate that our proposed training method alleviates the drawbacks of the existing exemplar-based generative models and significantly improves the performance in terms of appropriateness and informativeness.
DiPS: Differentiable Policy for Sketching in Recommender Systems

In sequential recommender system applications, it is important to develop models that can capture users' evolving interest over time to successfully recommend future items that they are likely to interact with. For users with long histories, typical models based on recurrent neural networks tend to forget important items in the distant past. Recent works have shown that storing a small sketch of past items can improve sequential recommendation tasks. However, these works all rely on static sketching policies, i.e., heuristics to select items to keep in the sketch, which are not necessarily optimal and cannot improve over time with more training data. In this paper, we propose a differentiable policy for sketching (DiPS), a framework that learns a data-driven sketching policy in an end-to-end manner together with the recommender system model to explicitly maximize recommendation quality in the future. We also propose an approximate estimator of the gradient for optimizing the sketching algorithm parameters that is computationally efficient. We verify the effectiveness of DiPS on real-world datasets under various practical settings and show that it requires up to 50% fewer sketch items to reach the same predictive quality than existing sketching policies.
Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences

Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules.To address this limitation, Linformer and Informer reduce the quadratic complexity to linear (modulo logarithmic factors) via low-dimensional projection and row selection, respectively.These two models are intrinsically connected, and to understand their connection we introduce a theoretical framework of matrix sketching.Based on the theoretical analysis, we propose Skeinformer to accelerate selfattention and further improve the accuracy of matrix approximation to self-attention with column sampling, adaptive row normalization and pilot sampling reutilization.Experiments on the Long Range Arena benchmark demonstrate that our methods outperform alternatives with a consistently smaller time/space footprint 1 .
Implicit Neural Deformation for Multi-View Face Reconstruction

In this work, we present a new method for 3D face reconstruction from multi-view RGB images. Unlike previous methods which are built upon 3D morphable models (3DMMs) with limited details, our method leverages an implicit representation to encode rich geometric features. Our overall pipeline consists of two major components, including a geometry network, which learns a deformable neural signed distance function (SDF) as the 3D face representation, and a rendering network, which learns to render on-surface points of the neural SDF to match the input images via self-supervised optimization. To handle in-the-wild sparse-view input of the same target with different expressions at test time, we further propose residual latent code to effectively expand the shape space of the learned implicit face representation, as well as a novel view-switch loss to enforce consistency among different views. Our experimental results on several benchmark datasets demonstrate that our approach outperforms alternative baselines and achieves superior face reconstruction results compared to state-of-the-art methods.
Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networks

We present a spatial graph convolution (GC) to classify signals on a graph. Existing GC methods are limited in using the structural information in the feature space. Furthermore, GCs only aggregate features from one-hop neighboring nodes to the target node in their single step. In this paper, we propose two methods to improve the performance of GCs: 1) Utilizing structural information in the feature space, and 2) exploiting the multi-hop information in one GC step. In the first method, we define three structural features in the feature space: feature angle, feature distance, and relational embedding. The second method aggregates the node-wise features of multi-hop neighbors in a GC. Both methods can be simultaneously used. We also propose graph neural networks (GNNs) integrating the proposed GC for classifying nodes in 3D point clouds and citation networks. In experiments, the proposed GNNs exhibited a higher classification accuracy than existing methods.
An unsupervised extractive summarization method based on multi-round  computation

Text summarization methods have attracted much attention all the time. In recent years, deep learning has been applied to text summarization, and it turned out to be pretty effective. However, most of the current text summarization methods based on deep learning need large-scale datasets, which is difficult to achieve in practical applications. In this paper, an unsupervised extractive text summarization method based on multi-round calculation is proposed. Based on the directed graph algorithm, we change the traditional method of calculating the sentence ranking at one time to multi-round calculation, and the summary sentences are dynamically optimized after each round of calculation to better match the characteristics of the text. In this paper, experiments are carried out on four data sets, each separately containing Chinese, English, long and short texts. The experiment results show that our method has better performance than both baseline methods and other unsupervised methods and is robust on different datasets.
Multibeam outlier detection by clustering and topological persistence approach, ToMATo algorithm

The datasets acquired during hydrographic surveys contain outliers, i.e., soundings that do not describe the sea bottom. Many algorithms are developed to identify them. Here, we study unsupervised non-parametric algorithms with a density-based approach. These algorithms make no assumption about the data and identify outliers as the data furthest away from their neighbors. We asses the ToMATo method developed by INRIA in 2009 to detect outlier soundings from multibeam echosounder data. This clustering algorithm combines a mode-seeking phase with a cluster merging phase using topological persistence. After the theoretical presentation of the ToMATo algorithm, we evaluate its performance on four data sets representing a wide variety of seabeds. We compare this method with the well-known DBSCAN and LOF algorithms. Finally, we suggest an application of the ToMATo algorithm to multibeam data acquired in extradetection mode, where topological persistence allows to form the most relevant clusters.
An Adaptive Sigmoidal Activation Function for Training Feed Forward Neural Network Equalizer

Feed for word neural networks (FFNN) have attracted a great attention, in digital communication area. Especially they are investigated as nonlinear equalizers at the receiver, to mitigate channel distortions and additive noise. The major drawback of the FFNN is their extensive training. We present a new approach to enhance their training efficiency by adapting the activation function. Adapting procedure for activation function extensively increases the flexibility and the nonlinear approximation capability of FFNN. Consequently, the learning process presents better performances, offers more flexibility and enhances nonlinear capability of NN structure thus the final state kept away from undesired saturation regions. The effectiveness of the proposed method is demonstrated through different challenging channel models, it performs quite well for nonlinear channels which are severe and hard to equalize. The performance is measured throughout, convergence properties, minimum bit error achieved. The proposed algorithm was found to converge rapidly, and accomplish the minimum steady state value. All simulation shows that the proposed method improves significantly the training efficiency of FFNN based equalizer compared to the standard training one.
STAF: A Spatio-Temporal Attention Fusion Network for Few-shot Video  Classification

We propose STAF, a Spatio-Temporal Attention Fusion network for few-shot video classification. STAF first extracts coarse-grained spatial and temporal features of videos by applying a 3D Convolution Neural Networks embedding network. It then fine-tunes the extracted features using self-attention and cross-attention networks. Last, STAF applies a lightweight fusion network and a nearest neighbor classifier to classify each query video. To evaluate STAF, we conduct extensive experiments on three benchmarks (UCF101, HMDB51, and Something-Something-V2). The experimental results show that STAF improves state-of-the-art accuracy by a large margin, e.g., STAF increases the five-way one-shot accuracy by 5.3% and 7.0% for UCF101 and HMDB51, respectively.
NL-Augmenter: A Framework for Task-Sensitive Natural Language  Augmentation

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its transformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robustness analysis results are available publicly on the NL-Augmenter repository (\url{https://github.com/GEM-benchmark/NL-Augmenter}).
“And It’s All About Him...”

The proposed essay is a memoir about Yuri Alexandrovich Sorokin – the scientific supervisor of the author’s PhD thesis during her postgraduate studies, which is accompanied by a small selection of poems by Gleb Arsenyev (the creative pseudonym of Yu.A. Sorokin).
SPECIFITY AND EFFICIENCY OF COMPUTER MONITORING OF KNOWLEDGE AS A HIGHPRIORITY METHOD OF DIAGNOSIS OF THE LEVEL OF STUDENTS’ ACADEMIC ACHIEVEMENTS

Research issues of specificity and efficiency of the computer monitoring of knowledge as a high-priority method of diagnostics of the level of students’ educational attainments are made actual. The essence of the interrelated notions such as “ computer monitoring of knowledge”, “ computer testing”, “ test assignment” is revealed.On the basis of analysis of available references the objective of computer testing is defined, the reasons of figuring out the objective results which are achieved by the means of two constituents that are proper prepared test and actionable hardware system are set. At the same time the general properties of test such as validity, complexity, reliability, stability, representativeness, significancy, truthfulness, test hypothesis and discriminant are assigned. As a rule, test consists of two parts: informational and operational. The technology of constructing test tasks is characterized, it presupposes observance of number of rules: each test task should evaluate the achievements of an important and significant educational goal; it should check the appropriate level of knowledge acquisition, as well as higher cognitive levels and the time allocated for the test task which should be spent on finding the answer, but not on understanding the condition of the question. Along with that, the efficiency of computer testing is analyzed, which depends on the variability and multiplicity of created test tasks. Based on the results of the research we have the reason to confirm that computer testing being promising direction of modern educational process becomes constituent of the modern knowledge diagnostics. It broadens the means of control and evaluation of the level of educational achievements, it is an alternative to traditional methods of monitoring, it can be carried out taking into account different types and forms of control as a tool of operational management.Computer testing enables to audit knowledge of a large number of people simultaneously in a short span of time; random selection of questions from the course unit; processing of test results on a personal computer using a rating scale.
Online Learning in the Library and Information Science Education During the Covid-19

This study aims to describe the proper online lecture practice during the pandemic Covid-19 in the Department of Library Science of UIN Sunan Kalijaga. This study uses a mixed-method approach (quantitative and qualitative). 92 students as the respondents filled out online questionnaires about their proper online learning. Not only filling in a quantitative questionnaire, these respondents were also given to provide reasons for each filled answer. Online learning in the Department of Library Science of UIN Sunan Kalijaga has been conducted since March 2020. The implementation uses different applications for each lecturer. Some of the most widely used applications are Whatsapp, Google Classroom, eLearning UIN Sunan Kalijaga, online meeting applications such as Zoom, Google Meet, and Webex, Youtube, and podcast applications such as Anchor and Spotify. Evaluation of online learning using the principle of inclusiveness is necessary.