Reviewerly/exHarmony huggingface dataset:
The Neural Process Family: Survey, Applications and Perspectives

The standard approaches to neural network implementation yield powerful function approximation capabilities but are limited in their abilities to learn meta representations and reason probabilistic uncertainties in their predictions. Gaussian processes, on the other hand, adopt the Bayesian learning scheme to estimate such uncertainties but are constrained by their efficiency and approximation capacity. The Neural Processes Family (NPF) intends to offer the best of both worlds by leveraging neural networks for meta-learning predictive uncertainties. Such potential has brought substantial research activity to the family in recent years. Therefore, a comprehensive survey of NPF models is needed to organize and relate their motivation, methodology, and experiments. This paper intends to address this gap while digging deeper into the formulation, research themes, and applications concerning the family members. We shed light on their potential to bring several recent advances in other deep learning domains under one umbrella. We then provide a rigorous taxonomy of the family and empirically demonstrate their capabilities for modeling data generating functions operating on 1-d, 2-d, and 3-d input domains. We conclude by discussing our perspectives on the promising directions that can fuel the research advances in the field. Code for our experiments will be made available at https://github.com/srvCodes/neural-processes-survey.
Actor Prioritized Experience Replay

A widely-studied deep reinforcement learning (RL) technique known as Prioritized Experience Replay (PER) allows agents to learn from transitions sampled with non-uniform probability proportional to their temporal-difference (TD) error. Although it has been shown that PER is one of the most crucial components for the overall performance of deep RL methods in discrete action domains, many empirical studies indicate that it considerably underperforms actor-critic algorithms in continuous control. We theoretically show that actor networks cannot be effectively trained with transitions that have large TD errors. As a result, the approximate policy gradient computed under the Q-network diverges from the actual gradient computed under the optimal Q-function. Motivated by this, we introduce a novel experience replay sampling framework for actor-critic methods, which also regards issues with stability and recent findings behind the poor empirical performance of PER. The introduced algorithm suggests a new branch of improvements to PER and schedules effective and efficient training for both actor and critic networks. An extensive set of experiments verifies our theoretical claims and demonstrates that the introduced method significantly outperforms the competing approaches and obtains state-of-the-art results over the standard off-policy actor-critic algorithms.
Focus-Driven Contrastive Learniang for Medical Question Summarization

Automatic medical question summarization can significantly help the system to understand consumer health questions and retrieve correct answers. The Seq2Seq model based on maximum likelihood estimation (MLE) has been applied in this task, which faces two general problems: the model can not capture well question focus and and the traditional MLE strategy lacks the ability to understand sentence-level semantics. To alleviate these problems, we propose a novel question focus-driven contrastive learning framework (QFCL). Specially, we propose an easy and effective approach to generate hard negative samples based on the question focus, and exploit contrastive learning at both encoder and decoder to obtain better sentence level representations. On three medical benchmark datasets, our proposed model achieves new state-of-the-art results, and obtains a performance gain of 5.33, 12.85 and 3.81 points over the baseline BART model on three datasets respectively. Further human judgement and detailed analysis prove that our QFCL model learns better sentence representations with the ability to distinguish different sentence meanings, and generates high-quality summaries by capturing question focus.
On the Complexity of the Storyplan Problem

Motivated by dynamic graph visualization, we study the problem of representing a graph $G$ in the form of a \emph{storyplan}, that is, a sequence of frames with the following properties. Each frame is a planar drawing of the subgraph of $G$ induced by a suitably defined subset of its vertices. Between two consecutive frames, a new vertex appears while some other vertices may disappear, namely those whose incident edges have already been drawn in at least one frame. In a storyplan, each vertex appears and disappears exactly once. For a vertex (edge) visible in a sequence of consecutive frames, the point (curve) representing it does not change throughout the sequence. Note that the order in which the vertices of $G$ appear in the sequence of frames is a total order. In the \textsc{StoryPlan} problem, we are given a graph and we want to decide whether there exists a total order of its vertices for which a storyplan exists. We prove that the problem is NP-complete, and complement this hardness with two parameterized algorithms, one in the vertex cover number and one in the feedback edge set number of $G$. Also, we prove that partial $3$-trees always admit a storyplan, which can be computed in linear time. Finally, we show that the problem remains NP-complete in the case in which the total order of the vertices is given as part of the input and we have to choose how to draw the frames.
Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Second Replication Study (GPT2SP Replication Report)

Fu and Tantithamthavorn have recently proposed GPT2SP, a Transformer-based deep learning model for SP estimation of user stories. They empirically evaluated the performance of GPT2SP on a dataset shared by Choetkiertikul et al including 16 projects with a total of 23,313 issues. They benchmarked GPT2SP against two baselines (namely the naive Mean and Median estimators) and the method previously proposed by Choetkiertikul et al. (which we will refer to as DL2SP from now on) for both within- and cross-project estimation scenarios, and evaluated the extent to which each components of GPT2SP contribute towards the accuracy of the SP estimates. Their results show that GPT2SP outperforms DL2SP with a 6%-47% improvement over MAE for the within-project scenario and a 3%-46% improvement for the cross-project scenarios. However, when we attempted to use the GPT2SP source code made available by Fu and Tantithamthavorn to reproduce their experiments, we found a bug in the computation of the Mean Absolute Error (MAE), which may have inflated the GPT2SP's accuracy reported in their work. Therefore, we had issued a pull request to fix such a bug, which has been accepted and merged into their repository at https://github.com/awsm-research/gpt2sp/pull/2. In this report, we describe the results we achieved by using the fixed version of GPT2SP to replicate the experiments conducted in the original paper for RQ1 and RQ2. Following the original study, we analyse the results considering the Medan Absolute Error (MAE) of the estimation methods over all issues in each project, but we also report the Median Absolute Error (MdAE) and the Standard accuracy (SA) for completeness.
Media Google Classroom Berbantuan Whatsapp terhadap Hasil Belajar Matematika Siswa MTs

One of the E-learning learning media that is well known to the public is Google Classroom. In order to increase the effectiveness of using Google Classroom, a familiar and frequently used supporting application is needed, namely WhatsApp. The purpose of the study was to determine the effect and magnitude of the influence of the Google Classroom learning media assisted by Whatsapp on the mathematics learning outcomes of class VII students at MTs Miftahul Huda Ngreco Kandat Kediri. This research uses a quasi-experimental (quasi-experimental) type of quantitative approach. The sampling technique is random sampling. The population of this research is class VII students as many as 120 students. The research sample is class VII A and VII B as many as 42 students. The method of data collection is through student learning outcomes tests. Data analysis using t-test with the help of SPSS 26.0. The results of this study are (1) There is a significant effect of using Google Classroom media with the help of Whatsapp on the mathematics learning outcomes of class VII students at MTs Miftahul Huda Ngreco Kandat Kediri, and (2) Based on Cohen's value the use of Google Classroom media assisted by Whatsapp on the mathematics learning outcomes of class VII students at MTs Miftahul Huda Ngreco Kandat Kediri has a big effect.
Relationship between conflict management and employees’ commitment: The mediating role of work motivations

Conflict management and work motivations play a critical role in improving the employees’ commitment which results in attaining organizational success (Robbins &amp; Judge, 2013). Unfortunately, the literature demonstrated that the relationships between these constructs have never been studied previously. Therefore, the purpose of this quantitative study was to determine the mediating effect of work motivations on the relationship between conflict management and employees’ commitment. One hundred and eighty-six participants selected randomly from twenty-four insurance companies in Jordan completed the survey questionnaire. The findings of the study revealed that both conflict management and work motivations have a positive effect on employees’ commitment. Also, the findings indicated that work motivation partially mediates the relationship between conflict management and employees’ commitment. Thus, organizations that hope to maintain highly committed employees should integrate work motivations with the conflict management process
Investigation of the applicability of natural language processing methods to problems of searching and matching of machinery drawing images

In this work it is shown that the application of the technique of local feature descriptors in its pure form to the task of searching and matching of drawings is ineffective. It is revealed that this is mainly due to the presence in the drawings of a large number of identical elements (frames, a title block, extension lines, font elements, etc.). It is proposed that this problem should be solved using a tf-idf (term frequency-inverse document frequency) method, which is widely known in natural language processing. In the study, instead of the word vectors used in the original tf-idf technique, descriptors of image feature points calculated using the ORB and BRISK algorithms were used. The study has led to the following conclusions: 1) the proposed approach offers high efficiency in finding a copy of the image-query in the database. Thus, copies of all images presented for search and having their full analogs in the database are revealed. 2) The identification rate of modified image-queries varies, depending on the algorithm used for finding keypoints and descriptors. So, the maximum percentage of identified modified analogs is 60% when using ORB and 80% when using BRISK - out of all image analogs in the database. 3) The proposed approach shows a limited efficiency in finding images that can be attributed to the same class as the image queries (for example, a drawing of an excavator, a bulldozer, or a truck crane). Here, the maximum proportion of false identification has reached 60%.
Knowledge Management System Analysis on The Admission Website for Adding Knowledge Sharing Features

&lt;p&gt;&lt;em&gt;The website for new student admission is a medium for promotion and registration at the STMIK Amik Riau campus.&lt;/em&gt; &lt;em&gt;Currently, the website already has several criteria from the knowledge management system but has never been analyzed. The criteria that already exist on the STMIK Amik Riau website are Repositories, Access and Authentication Layer, Application Layer, Collaborative Intelligence &amp;amp; Filtering. Although the STMIK Amik Riau admission website already has several knowledge management criteria, this website still finds several problems, including between users who do not know what happened when the incident occurred. Then The College Database Section, The Finance Section and The Academic &amp;amp; Student Administration Section often mismatch new student data. For this reason, it is necessary to add criteria, namely the Interfaces Layer, where it stores problems that occur on the website or on the knowledge sharing. However, before adding knowledge sharing, an analysis was carried out using SECI. From this analysis, it can be seen that the admission website users are ready to apply knowledge sharing later.&lt;/em&gt;&lt;/p&gt;
Sistem Pendukung Keputusan Penerimaan Zakat dengan Metode Weight Product di LAZISMU Yogyakarta

Zakat is one of the five pillars of Islam and is conducted by giving wealth to those who are entitled to receive it. The Lembaga Amil Zakat Infaq Sadaqah Muhammadiyah (LAZISMU) is a zakat institution in Muhammadiyah that receives and distributes zakat to its recipients. LAZISMU examines recipient data to determine several factors, including housing index, property ownership, family living conditions, worship routines, business marketing, operational operational finance, and human business resources. Based on an interview with the LAZISMU committee, it was stated that the delivery of zakat was conducted by selecting prospective zakat recipients based on pre-existing criteria. However, the LAZISMU committee uses perceptions or assumptions to select prospective zakat recipients and can make wrong decisions. This study proposes a support system for selecting zakat recipients based on the Weight Product (WP) method. This decision support system can select zakat recipients automatically by determining the parameters for each zakat criteria. The result of the decision is the ranking of prospective zakat recipients. The support system is checked using the Black Box test and User Experience Questionnaire (UEQ). The UEQ test results show that the value of attractiveness, speed, efficiency, accuracy, stimulation, and novelty gets excellents results.
Automatic generation of threat paths in internet of things‐based systems

Today, ‘security challenge’ is considered a commonly-used catchword when it comes to emerging technologies such as the internet of things (IoT). Turning a blind eye to this challenge can sometimes lead to irreparable human and financial damage in everyday life. Threat modelling, despite being time consuming, complex, and error-prone, is still recognized as a preventative solution in the design phase. The method proposed in this paper, called automatic generation of threat paths, is suggested as an automated solution to the problem of identifying and determining potential threats. The proposed method is the improved and robust development of automated modelling and the introduction of the integration of new features (such as conditional probability and security) with the techniques of previous generations (such as Petri Nets (PNs)). The proposed method is evaluated using different security scenarios, and the results show that the proposed method outperforms other manual methods in this domain in terms of both time and cost (99.9% and 98.05%, respectively).
Petrology and geochemistry of bimodal volcanic rocks of Southern Lake Hayk area, northwestern Ethiopian plateau: implication for their petrogenesis

This study presents and integrates field, petrological, and whole-rock geochemical (major and trace elements) data of the volcanic rocks from the Lake Hayk area to understand their petrogenesis. The study area's major lithological components include lower and upper basalt, rhyolitic lava, rhyolitic tuff, rhyolitic ignimbrite, and unwelded tuff. Petrographic analysis suggests that felsic rocks are dominated by quartz and well-developed sanidine (K-feldspar) phenocrysts with glassy groundmass, whereas mafic volcanic products are characterized by aphyric to porphyritic textures with the olivine and plagioclase dominant phenocryst. The area constitutes bimodal composition of flood basaltic to rhyolitic rock with scarce intermediate composition. Basalts have low Rb/Nb = 0.5-0.58, La/Nb = 0.88-1.06 and high TiO2 = 2.08-3.04, basaltic andecite have higher Rb/Nb =2.7, La/Nb = 1.81 and low TiO2= 1.96 and rhyolite Rb/Nb = 0.97-1.69, La/Nb = 0.51-1.08 and lower TiO2 = 0.41- 0.71.The positive Ba and negative K anomalies testify amphibole mantle source. The basalts are characterized by low CaO/Al2O3 ratios (0.71–0.97) and relatively less fractionated and flat hree patterns with (TbN/YbN = 1.75-2.33) chondritic values. This suggests a mantle source mostly containing spinel rather than garnet. Rhyolites are characterized by a steep negative correlation in bivariate plots of MgO, Fe2O3, TiO, and CaO against SiO2 and positive anomaly of Ta with slight Nb trough. This suggests that Fractional Crystallization is the major process for the genesis of rhyolitic rocks, rather than crustal contribution, partial melting, and assimilation, producing rhyolitic rocks.&#x0D;
A Water Gauge Scale Capturing Method in Tidal Well Based on Image Recognition

Abstract Tide level observation data is an important technical support data for the delimitation of warning tide level and disaster prevention. The performance of the tidal well is tested by regularly checking the external water gauge, so as to maintain the tidal follow-up and wave dissipation of the tidal well. The check of water gauge inside and outside the well shall be carried out once a month. It is time-consuming and labour consuming. The paper proposes a real-time borehole water gauge capturing algorithm in tidal well based on image recognition. Experiments have proved that the method can recognize the water gauge scale effectively. The comparison experiments with the other standard measurement method are done. It can be seen from the comparison chart that the error between the water gauge scale and the tide level value is ± 1cm, which meets the national standard.
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control

Recent approaches for modelling dynamics of physical systems with neural networks enforce Lagrangian or Hamiltonian structure to improve prediction and generalization. However, when coordinates are embedded in high-dimensional data such as images, these approaches either lose interpretability or can only be applied to one particular example. We introduce a new unsupervised neural network model that learns Lagrangian dynamics from images, with interpretability that benefits prediction and control. The model infers Lagrangian dynamics on generalized coordinates that are simultaneously learned with a coordinate-aware variational autoencoder (VAE). The VAE is designed to account for the geometry of physical systems composed of multiple rigid bodies in the plane. By inferring interpretable Lagrangian dynamics, the model learns physical system properties, such as kinetic and potential energy, which enables long-term prediction of dynamics in the image space and synthesis of energy-based controllers.
End-to-End Rationale Reconstruction

The logic behind design decisions, called design rationale, is very valuable. In the past, researchers have tried to automatically extract and exploit this information, but prior techniques are only applicable to specific contexts and there is insufficient progress on an end-to-end rationale information extraction pipeline. Here we outline a path towards such a pipeline that leverages several Machine Learning (ML) and Natural Language Processing (NLP) techniques. Our proposed context-independent approach, called Kantara, produces a knowledge graph representation of decisions and of their rationales, which considers their historical evolution and traceability. We also propose validation mechanisms to ensure the correctness of the extracted information and the coherence of the development process. We conducted a preliminary evaluation of our proposed approach on a small example sourced from the Linux Kernel, which shows promising results.
Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss

Convolutional Neural Networks (CNNs) are nowadays often employed in vision-based perception stacks for safetycritical applications such as autonomous driving or Unmanned Aerial Vehicles (UAVs). Due to the safety requirements in those use cases, it is important to know the limitations of the CNN and, thus, to detect Out-of-Distribution (OOD) samples. In this work, we present an approach to enable OOD detection for 2D object detection by employing the margin entropy (ME) loss. The proposed method is easy to implement and can be applied to most existing object detection architectures. In addition, we introduce Separability as a metric for detecting OOD samples in object detection. We show that a CNN trained with the ME loss significantly outperforms OOD detection using standard confidence scores. At the same time, the runtime of the underlying object detection framework remains constant rendering the ME loss a powerful tool to enable OOD detection.
Exploring Effective Information Utilization in Multi-Turn Topic-Driven Conversations

Conversations are always related to certain topics. However, it is challenging to fuse dialogue history and topic information from various sources at the same time in current dialogue generation models because of the input length limit of pre-trained language models (PLMs). In order to expand the information that PLMs can utilize, we encode topic and dialogue history information using certain prompts with multiple channels of Fusion-in-Decoder (FiD) and explore the influence of three different channel settings. In this paper, our experiments focus on a specific Chinese dataset named NaturalConv, where the conversation revolves around a piece of recent news. We thoroughly compared different dialogue models and different FiD channel settings. Empirical results show that by combining our proposed whole passage channel with additional history channel, our methods can achieve competitive performance on NaturalConv, making it possible to encode various information from excessively long texts.
Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process

This paper investigates the problem of bounding possible output from a counterfactual query given a set of observational data. While various works of literature have described methodologies to generate efficient algorithms that provide an optimal bound for the counterfactual query, all of them assume a finite-horizon causal diagram. This paper aims to extend the previous work by modifying Q-learning algorithm to provide informative bounds of a causal query given an infinite-horizon causal diagram. Through simulations, our algorithms are proven to perform better compared to existing algorithm.