Reviewerly/exHarmony huggingface dataset:
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation

Causal effect estimation from observational data is a central problem in causal inference. Methods based on potential outcomes framework solve this problem by exploiting inductive biases and heuristics from causal inference. Each of these methods addresses a specific aspect of causal effect estimation, such as controlling propensity score, enforcing randomization, etc., by designing neural network (NN) architectures and regularizers. In this paper, we propose an adaptive method called Neurosymbolic Causal Effect Estimator (NESTER), a generalized method for causal effect estimation. NESTER integrates the ideas used in existing methods based on multi-head NNs for causal effect estimation into one framework. We design a Domain Specific Language (DSL) tailored for causal effect estimation based on causal inductive biases used in literature. We conduct a theoretical analysis to investigate NESTER's efficacy in estimating causal effects. Our comprehensive empirical results show that NESTER performs better than state-of-the-art methods on benchmark datasets.
Word Order Matters when you Increase Masking

Word order, an essential property of natural languages, is injected in Transformer-based neural language models using position encoding. However, recent experiments have shown that explicit position encoding is not always useful, since some models without such feature managed to achieve state-of-the art performance on some tasks. To understand better this phenomenon, we examine the effect of removing position encodings on the pre-training objective itself (i.e., masked language modelling), to test whether models can reconstruct position information from co-occurrences alone. We do so by controlling the amount of masked tokens in the input sentence, as a proxy to affect the importance of position information for the task. We find that the necessity of position information increases with the amount of masking, and that masked language models without position encodings are not able to reconstruct this information on the task. These findings point towards a direct relationship between the amount of masking and the ability of Transformers to capture order-sensitive aspects of language using position encoding.
Review of coreference resolution in English and Persian

Coreference resolution (CR) is one of the most challenging areas of natural language processing. This task seeks to identify all textual references to the same real-world entity. Research in this field is divided into coreference resolution and anaphora resolution. Due to its application in textual comprehension and its utility in other tasks such as information extraction systems, document summarization, and machine translation, this field has attracted considerable interest. Consequently, it has a significant effect on the quality of these systems. This article reviews the existing corpora and evaluation metrics in this field. Then, an overview of the coreference algorithms, from rule-based methods to the latest deep learning techniques, is provided. Finally, coreference resolution and pronoun resolution systems in Persian are investigated.
Physics-Constrained Backdoor Attacks on Power System Fault Localization

The advances in deep learning (DL) techniques have the potential to deliver transformative technological breakthroughs to numerous complex tasks in modern power systems that suffer from increasing uncertainty and nonlinearity. However, the vulnerability of DL has yet to be thoroughly explored in power system tasks under various physical constraints. This work, for the first time, proposes a novel physics-constrained backdoor poisoning attack, which embeds the undetectable attack signal into the learned model and only performs the attack when it encounters the corresponding signal. The paper illustrates the proposed attack on the real-time fault line localization application. Furthermore, the simulation results on the 68-bus power system demonstrate that DL-based fault line localization methods are not robust to our proposed attack, indicating that backdoor poisoning attacks pose real threats to DL implementations in power systems. The proposed attack pipeline can be easily generalized to other power system tasks.
Analysis of the Advantages and Disadvantages of Using Autodesk Revit for the Dean Building of the Faculty of Education, Universitas Negeri Semarang

Abstract. Building Information Modeling (BIM) as a supporting software of the fourth phase of the industrial revolution in Indonesia in the construction sector is a digital technology mode to increase the efficiency and accuracy of construction work to avoid mistakes during the planning to the implementation period. This study uses the Autodesk Revit application to provide the output of working drawings, 3D modeling, and calculate the volume and cost of work carried out in the Dean building of the Faculty of Education, Universitas Negeri Semarang. The purpose of this study is to determine the advantages and disadvantages of using Autodesk Revit for planning the Dean building of the Faculty of Education, Universitas Negeri Semarang. The results of this study are the advantages of using Autodesk Revit for building modeling, including providing convenience with design integration in one application, and avoiding crashes between designs. Autodesk Revit also has weaknesses, such as the need for high hardware specifications on the device, high license prices, the application it is necessary to create a new material family according to the design and size required, Autodesk Revit cannot calculate the preparatory work because the calculation of the volume and cost of Autodesk Revit work is limited to data input.
FGCM: Noisy Label Learning via Fine-Grained Confidence Modeling

A small portion of mislabeled data can easily limit the performance of deep neural networks (DNNs) due to their high capacity for memorizing random labels. Thus, robust learning from noisy labels has become a key challenge for deep learning due to inadequate datasets with high-quality annotations. Most existing methods involve training models on clean sets by dividing clean samples from noisy ones, resulting in large amounts of mislabeled data being unused. To address this problem, we propose categorizing training samples into five fine-grained clusters based on the difficulty experienced by DNN models when learning them and label correctness. A novel fine-grained confidence modeling (FGCM) framework is proposed to cluster samples into these five categories; with each cluster, FGCM decides whether to accept the cluster data as they are, accept them with label correction, or accept them as unlabeled data. By applying different strategies to the fine-grained clusters, FGCM can better exploit training data than previous methods. Extensive experiments on widely used benchmarks CIFAR-10, CIFAR-100, clothing1M, and WebVision with different ratios and types of label noise demonstrate the superiority of our FGCM.
PROFILING STUDENTS’ EXPERIENCES AND PERSPECTIVES ON USING VIDEO-TELECONFERENCING APPLICATIONS AS A LEARNING PLATFORM DURING PANDEMIC

This study aims to capture students’ profiles in terms of using video-teleconferencing (VTC) applications and to identify students’ perspectives on using the video-teleconference applications as a platform for English teaching and learning during the pandemic in the English department in Universitas Riau. This mixed-method study collected data from semi-structured interviews and an online questionnaire. The entire sophomores of the English Study Program of Riau University (n=39 students) were taken as the samples of the research. An analysis model from Miles &amp; Huberman (2007) was used as the method to analyze the collected data. The results show that the majority (56.4 %) of the students used a mobile phone as a learning device and 59% of them relied on their own internet data for learning. The students had both positive and negative perspectives on the VTC applications. Most students thought the VTC-based learning was efficient for them. However, they reported some problems such as an unsupportive learning environment, lack of equipment, and unreliable internet connections. These problems imply that universities should provide more learning facilities for students to make online learning more effective.
E-BOOKS UTILISATION AND STUDENTS' ACADEMIC ACHIEVEMENT IN UNIVERSITIES IN LAGOS STATE, NIGERIA: IMPLICATION FOR EDUCATIONAL PLANNERS

This study examined e-books utilisations and students' academic achievement in universities in Lagos State: implication for educational planners. With the use of descriptive survey research methodologies, this study was driven by three research questions and three null hypotheses. All six of Lagos State's traditional universities make up the study's population. Three institutions were chosen using a straightforward random sampling method. Additionally, 150 final-year students from each of the studied institutions were chosen using a practical selection approach, resulting in 450 respondents from the three universities chosen for the study. A well-structured questionnaire titled "E-Book Utilisation and Students' Academic Achievement in Universities" was used to gather data (IEBSAAU). The test-retest reliability approach was used to validate the instrument, and it was determined to be reliable with a reliability coefficient of 0.79. The statistical technique Chi-Square was used to gather data. The findings showed significant relationship in the use of electronic books with X2 = (4, N = 450) = 246.462, P =.000; a significant relationship between computer literacy with X2 = (4, N = 450) = 206.431 , P =.000; a significant relationship between students ‘perception on the use of electronic books for academic progress with X2 = (4, N = 450) = 262.109 , P =.000 and student academic achievement. It is recommended that: E-book library should be set up and maintained in every university of study to boost interest and enlightens students and teachers on the importance of e-books to teaching and learning.
Multi-attribute fire safety evaluation of subway station based on FANP – FGRA – Cloud model

Abstract With the development of subway network, the fire safety of station is affected by many factors in the operation process. But there seem to be few studies developing quantitative models to evaluate fire safety of subway station. The aim of this study is to develop a multi-attribute hybrid method by combining Fuzzy Analytic Network Process (FANP), Fuzzy Grey Relational Analysis (FGRA) with Cloud model. This research sorts out and identifies high-frequency hazard factors associated with fire accidents of station from personal, equipment, environmental and management aspects comprehensively, and explores dependences and interactions among them simultaneously. Seven typical transfer stations of Changsha subway network in China are studied by using the proposed approach, and the results show that: (1) the fire safety order from the best to the worst determined is FBH (A2), SWGY (A7), CSRS (A6), YWZ (A1), HTL (A5), HJT (A4) and WYGC (A3); (2) through comparing the results obtained by the proposed method with that determined fuzzy TOPSIS and fuzzy sum weighted method, it is proved that the method developed in this study is reliable and effective; and (3) sensitivity analysis indicates that the ranking order of stations is influenced by fluctuation of criterion and sub-criterion weights. These findings concentrate on providing a new sight for identification of hazard factors and quantitative evaluation regarding the fire safety of stations, so as to prevent this type of security accidents and minimize damages caused by this.
An improved product quantization method applying fuzzy clustering

With the development of computer vision as well as retrieval technology, content-based image retrieval (CBIR) has been widely used. The product quantization retrieval algorithm (Product Quantization) is one kind of CBIR, which contains three steps: Cartesian integral solution of the original vector space, quantization of the lowdimensional vector space, and similarity search. The original product quantization method uses k-means clustering algorithm for quantization of low-dimensional vector space, which will be influenced by the initial point setting to reach local optimum and cause quantization error. Moreover, the features of many pictures are mediated, and the k-means algorithm for the clustering of feature vectors belongs to the hardening score, which will produce a large classification error. To address the above problems, this paper proposes to apply the improved product quantization method of fuzzy clustering (Fuzzy Product Quantization) and tests the effects of different quantization parameters on retrieval accuracy. The quantization of the low-dimensional vector space by using fuzzy clustering avoids the quantization error caused by hardening scores and makes the classification more objective. The use of weighted asymmetric distance calculation in a similar search step can further reduce the error of distance calculation and improve the accuracy of image retrieval. Experimental tests using the Oxford Building dataset show that the retrieval accuracy of the original product quantization method is 61.8% when taking the first 20 images in the retrieval results, and the retrieval accuracy of the proposed method is 64.8%, with an overall accuracy improvement of 3%, and the algorithm performance is better than the original product quantization method.
ARCHAEOLOGY AND ART IN LATVIA

The correlation between art and archaeology in Latvia can be traced in vari- ous ways. First, despite all technical innovations there are still professional artists that work on field with archaeologists and are directly involved in the recording of archaeological evidence. Furthermore, art-related work is being done in reconstruc- tions, book illustrations and museum exhibitions. In addition to those who devote their artistic skills to scientific and educational purposes under the supervision of archaeologists, we can see quite a lot of artwork that represents something archaeo- logical while being the free fruits of artistic imagination. Archaeological science is of the opinion that inaccurate stylisations of archaeological costumes in movies or arbitrary depictions of some archaeological period in literary or any other work of art can very easily lead to false impressions and misunderstandings that endure for decades. On the other hand, scientists have no right to restrain the public from interpreting its own past as it deems fit. The aim of this paper is to discuss the relations and crossroads between art and archaeology by examining how they historically have developed, coexisted and influenced one another in the territory of Latvia from the 19th century till nowadays.
MONTHLY GLOBAL-MEAN FORECASTING ANN

&lt;p&gt; &lt;/p&gt; &lt;p&gt;In conjunction with the climate summit held in Sharm El Sheikh, Arab Republic of Egypt. We present to you this research paper specialized in creating an artificial neural network to predict the global average monthly temperature.&lt;/p&gt; &lt;p&gt;We chose this topic for our research paper because of its great importance to humanity in general with its different beliefs, religions, ethnicities and political orientations. The increase in average temperatures on Earth is one of the most visible effects of global warming, and the National Oceanic and Atmospheric Administration (NOAA) indicates that the average global temperature has risen by about 0.8 degrees Celsius over the past 100 years only, and the National Oceanic and Atmospheric Administration and NASA announced 2016 was the hottest year since record keeping began in 1895, with an average temperature increase of 0.99°C above the average temperature for the entire century.&lt;/p&gt; &lt;p&gt;We are not weather specialists and we don't have much knowledge in this field, but we are technology and programming specialists in particular. Therefore, as a contribution to spreading awareness about the increasing global temperature, we designed and programmed an artificial neural network to predict the monthly global average temperature.&lt;/p&gt;
Intelligent campus score analysis method based on association rules and neural network

In order to solve the problem of insufficient ability of campus achievement analysis, this paper puts forward a complete set of intelligent campus achievement analysis methods. Firstly, the method collects and preprocesses the students' achievement data of each subject, uses the Apriori association rule algorithm to select the main pre courses that affect the students' comprehensive assessment results, and filters the irrelevant pre courses; Then, the neural network algorithm is used to construct the score prediction model, convert the scores corresponding to the main pre courses into feature vectors, take the students' comprehensive examination scores as labels, and use the feature vectors and labels to train the score prediction model; Third, use the trained model to predict the students' comprehensive examination results; Finally, if the predicted comprehensive assessment result fails, send an early warning notice of the failure of the comprehensive assessment result to the student in advance, and remind the student to strengthen the study of the main pre courses. Experiments show that the method proposed in this paper performs well in achievement analysis and prediction, and is conducive to promoting the improvement of students' achievement.
Local Scouring Visualization of Bed Channel Surface in Hydraulic Structure Using Surfer

Abstract. Water flowing in the river causes a scouring event that occurs at the bed channel. The existence of scouring will increase along with changes in the geometry of the river and obstacles such as the weir which are followed by local scouring around the weir. Local scouring often occurs downstream end of weir sill which weakens its construction. Scouring measurement was conducted to monitor the pattern of scouring that occurs. By using Surfer software, the scouring visualization through the Data Grid and 3D Surface can be seen. In this research, scour monitoring in the laboratory using open channels (flume) and MDO weir with fine aggregate material as the basis of the channel. The retrieval of scouring data was performed after installing fine aggregate and three times variations of flow discharge, then the data were processed using Surfer. The first flow used water level +3.50 with the material carried at 3.77%. The second flow used water level +4.50 with the material carried at 11.16%. The third flow used water level +5.70 with the material carried at 25.46%. So with the continuous method, the total material carried at 36.27% is equivalent to 12575.77 cm3.
Normalisasi Sungai Cilemer Kabupaten Pandeglang, Banten (Studi Kasus HM 00+00 sampai HM 153+00)

Along with changes in river conditions and population growth, rivers do not function optimally, so as a result of these changes, flooding occurs and causes many losses. Banten Province floods have impacted the community, damaging buildings, rice fields, facilities and infrastructure, and the environment, harming people's livelihoods, especially in the Cilemer River. This study aimed to determine the magnitude of the planned flood discharge for the 100-year return period, the river's capacity with the flood discharge, and the best solution to the Cilemer River's flooding problem. The flood discharge cannot be accommodated by rainfall plans of 177.137 mm, HM 00+00 to HM 153+00, with 94 cross-sectional river segments. According to the hydraulic analysis, the Cilemer River's capacity in the current conditions cannot drain the planned flood discharge, so flood control efforts are required. River normalization and embankment elevation reduced water level by 45.42 percent on average and discharged by 12.46 percent on average.
Analysis of Benefits and Barriers Factors in the Implementation of Building Information Modeling (BIM) in Building Construction for Contractor

Abstract. Everywhere in the world, especially as the fourth industrial revolution begins, technological advancements are being discussed. This technological advancement also affects the building industry, one example being the use of Building Information Modeling (BIM) technology. All technologies, particularly BIM, will advance and redevelop throughout this Covid-19 epidemic. BIM has benefits and barriers to implementation that increase with its development and sophistication, particularly for contractors. Therefore, it's important to research the advantages of using BIM as well as the barriers for contractors. The purpose of this study is to analyze the benefits and inhibiting factors in the implementation of BIM in building construction for contractor. This research was conducted by using literature from previous studies to find out what statements are the benefits and barrier factors in the implementation of BIM 4D for contractors with top and middle qualifications. Then the statement was compiled into a questionnaire and distributed to respondents via online platform. In addition to obtaining the mean value for each research variable, the questionnaire survey responses were processed utilizing validity and reliability tests. The findings of this study show that the benefits of BIM implementation for top contractors include better and more appealing building design or modeling, more accurate documentation, as well as better and more attractive building design or modeling for middle contractors. The lack of training and education in BIM itself is the main obstacle to its implementation for these top contractors, while the cost of licensing BIM software or apps is fairly high for midrange contractors.
Analysis of the Application of Green Building Concepts in Educational Buildings Case Study: Building B, Faculty of Biology UGM Yogyakarta

Abstract. The building sector is one of the world's largest energy consumers. This problem is further exacerbated by our dependence on non-renewable energy sources. More than 90% of the world's energy comes from fossil energy including oil, natural gas and coal whose availability in nature continues to decrease because they cannot be renewed. Green design is a reaction to the global environmental crisis. Green design which also leads to eco design, or design for environment is the art of designing physical objects and the surrounding environment to balance sustainable principles with economic, social, and ecological aspects. As a construction actor, you are required to be able to produce a building design that does not require too much energy consumption and also of course considers the comfort of the occupants in it. In an effort to develop the campus, Universitas Gadjah Mada seeks to make the UGM campus a Green Campus. One of the buildings at UGM and the first building at UGM to be certified as a green building by the Green Building Council Indonesia (GBCI), namely Building B, Faculty of Biology UGM with 43% energy savings points, 30% water and 65% efforts to utilize local materials . From the Energy sector, this building applies a small percentage of window/glass openings, uses reflective paint so as to minimize heat that will be transmitted into the building, external shading devices (shading), applies insulation on the roof, a variable refrigerant type HVAC system (VRF). ) with a high COP so that it can save more energy and use artificial lighting / energy-saving lamps. From the water sector, using a toilet fixture with a low flow so that it can save water. Meanwhile, the use of materials includes the use of clay tiles, hebel walls and other materials that are local products.