Reviewerly/exHarmony huggingface dataset:
IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension

Machine Reading Comprehension (MRC) has become one of the essential tasks in Natural Language Understanding (NLU) as it is often included in several NLU benchmarks (Liang et al., 2020; Wilie et al., 2020). However, most MRC datasets only have answerable question type, overlooking the importance of unanswerable questions. MRC models trained only on answerable questions will select the span that is most likely to be the answer, even when the answer does not actually exist in the given passage (Rajpurkar et al., 2018). This problem especially remains in medium- to low-resource languages like Indonesian. Existing Indonesian MRC datasets (Purwarianti et al., 2007; Clark et al., 2020) are still inadequate because of the small size and limited question types, i.e., they only cover answerable questions. To fill this gap, we build a new Indonesian MRC dataset called I(n)don'tKnow- MRC (IDK-MRC) by combining the automatic and manual unanswerable question generation to minimize the cost of manual dataset construction while maintaining the dataset quality. Combined with the existing answerable questions, IDK-MRC consists of more than 10K questions in total. Our analysis shows that our dataset significantly improves the performance of Indonesian MRC models, showing a large improvement for unanswerable questions.
FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation

Recent model-based reference-free metrics for open-domain dialogue evaluation exhibit promising correlations with human judgment. However, they either perform turn-level evaluation or look at a single dialogue quality dimension. One would expect a good evaluation metric to assess multiple quality dimensions at the dialogue level. To this end, we are motivated to propose a multi-dimensional dialogue-level metric, which consists of three sub-metrics with each targeting a specific dimension. The sub-metrics are trained with novel self-supervised objectives and exhibit strong correlations with human judgment for their respective dimensions. Moreover, we explore two approaches to combine the sub-metrics: metric ensemble and multitask learning. Both approaches yield a holistic metric that significantly outperforms individual sub-metrics. Compared to the existing state-of-the-art metric, the combined metrics achieve around 16% relative improvement on average across three high-quality dialogue-level evaluation benchmarks.
SUPR: A Sparse Unified Part-Based Human Representation

Statistical 3D shape models of the head, hands, and fullbody are widely used in computer vision and graphics. Despite their wide use, we show that existing models of the head and hands fail to capture the full range of motion for these parts. Moreover, existing work largely ignores the feet, which are crucial for modeling human movement and have applications in biomechanics, animation, and the footwear industry. The problem is that previous body part models are trained using 3D scans that are isolated to the individual parts. Such data does not capture the full range of motion for such parts, e.g. the motion of head relative to the neck. Our observation is that full-body scans provide important information about the motion of the body parts. Consequently, we propose a new learning scheme that jointly trains a full-body model and specific part models using a federated dataset of full-body and body-part scans. Specifically, we train an expressive human body model called SUPR (Sparse Unified Part-Based Human Representation), where each joint strictly influences a sparse set of model vertices. The factorized representation enables separating SUPR into an entire suite of body part models. Note that the feet have received little attention and existing 3D body models have highly under-actuated feet. Using novel 4D scans of feet, we train a model with an extended kinematic tree that captures the range of motion of the toes. Additionally, feet deform due to ground contact. To model this, we include a novel non-linear deformation function that predicts foot deformation conditioned on the foot pose, shape, and ground contact. We train SUPR on an unprecedented number of scans: 1.2 million body, head, hand and foot scans. We quantitatively compare SUPR and the separated body parts and find that our suite of models generalizes better than existing models. SUPR is available at http://supr.is.tue.mpg.de
THOR-Net: End-to-end Graformer-based Realistic Two Hands and Object Reconstruction with Self-supervision

Realistic reconstruction of two hands interacting with objects is a new and challenging problem that is essential for building personalized Virtual and Augmented Reality environments. Graph Convolutional networks (GCNs) allow for the preservation of the topologies of hands poses and shapes by modeling them as a graph. In this work, we propose the THOR-Net which combines the power of GCNs, Transformer, and self-supervision to realistically reconstruct two hands and an object from a single RGB image. Our network comprises two stages; namely the features extraction stage and the reconstruction stage. In the features extraction stage, a Keypoint RCNN is used to extract 2D poses, features maps, heatmaps, and bounding boxes from a monocular RGB image. Thereafter, this 2D information is modeled as two graphs and passed to the two branches of the reconstruction stage. The shape reconstruction branch estimates meshes of two hands and an object using our novel coarse-to-fine GraFormer shape network. The 3D poses of the hands and objects are reconstructed by the other branch using a GraFormer network. Finally, a self-supervised photometric loss is used to directly regress the realistic textured of each vertex in the hands' meshes. Our approach achieves State-of-the-art results in Hand shape estimation on the HO-3D dataset (10.0mm) exceeding ArtiBoost (10.8mm). It also surpasses other methods in hand pose estimation on the challenging two hands and object (H2O) dataset by 5mm on the left-hand pose and 1 mm on the right-hand pose.
TabMixer: Excavating Label Distribution Learning with Small-scale Features

Label distribution learning (LDL) differs from multi-label learning which aims at representing the polysemy of instances by transforming single-label values into descriptive degrees. Unfortunately, the feature space of the label distribution dataset is affected by human factors and the inductive bias of the feature extractor causing uncertainty in the feature space. Especially, for datasets with small-scale feature spaces (the feature space dimension $\approx$ the label space), the existing LDL algorithms do not perform well. To address this issue, we seek to model the uncertainty augmentation of the feature space to alleviate the problem in LDL tasks. Specifically, we start with augmenting each feature value in the feature vector of a sample into a vector (sampling on a Gaussian distribution function). Which, the variance parameter of the Gaussian distribution function is learned by using a sub-network, and the mean parameter is filled by this feature value. Then, each feature vector is augmented to a matrix which is fed into a mixer with local attention (\textit{TabMixer}) to extract the latent feature. Finally, the latent feature is squeezed to yield an accurate label distribution via a squeezed network. Extensive experiments verify that our proposed algorithm can be competitive compared to other LDL algorithms on several benchmarks.
Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets

Overparameterization in deep learning typically refers to settings where a trained neural network (NN) has representational capacity to fit the training data in many ways, some of which generalize well, while others do not. In the case of Recurrent Neural Networks (RNNs), there exists an additional layer of overparameterization, in the sense that a model may exhibit many solutions that generalize well for sequence lengths seen in training, some of which extrapolate to longer sequences, while others do not. Numerous works have studied the tendency of Gradient Descent (GD) to fit overparameterized NNs with solutions that generalize well. On the other hand, its tendency to fit overparameterized RNNs with solutions that extrapolate has been discovered only recently and is far less understood. In this paper, we analyze the extrapolation properties of GD when applied to overparameterized linear RNNs. In contrast to recent arguments suggesting an implicit bias towards short-term memory, we provide theoretical evidence for learning low-dimensional state spaces, which can also model long-term memory. Our result relies on a dynamical characterization which shows that GD (with small step size and near-zero initialization) strives to maintain a certain form of balancedness, as well as on tools developed in the context of the moment problem from statistics (recovery of a probability distribution from its moments). Experiments corroborate our theory, demonstrating extrapolation via learning low-dimensional state spaces with both linear and non-linear RNNs.
(k − 2)-linear connected components in hypergraphs of rank k

We define a q-linear path in a hypergraph H as a sequence (e_1,...,e_L) of edges of H such that |e_i ∩ e_i+1 | ∈ [[1, q]] and e_i ∩ e_j = ∅ if |i − j| &gt; 1. In this paper, we study the connected components associated to these paths when q = k − 2 where k is the rank of H. If k = 3 then q = 1 which coincides with the well-known notion of linear path or loose path. We describe the structure of the connected components, using an algorithmic proof which shows that the connected components can be computed in polynomial time. We then mention two consequences of our algorithmic result. The first one is that deciding the winner of the Maker-Breaker game on a hypergraph of rank 3 can be done in polynomial time. The second one is that tractable cases for the NP-complete problem of "Paths Avoiding Forbidden Pairs" in a graph can be deduced from the recognition of a special type of line graph of a hypergraph.
Evolutionary Combination of Subplot Patterns into Meaningful Plots

Abstract Purpose: Many of the stories we are exposed to are built from small patterns of connected events involving a set of characters -- boy meets girl leads to a relationship or crime leads to revenge. The present paper studies the computational constraints that apply to the task of putting together a story by combining a set of such patterns. This approach presents three challenges: how to mix up the elements in the different patterns,how to instantiate the characters across the patterns andhow to tell acceptable combinations from the rest. Methods: The present paper applies an evolutionary solution that relies on a genetic representation for these combinations of patterns, and applies as fitness functions a set of metrics on compatibility constraints across pattern combinations. Outputs of this procedure are evaluated by human judges in comparison with baseline solutions. Results: The proposed solution generates a population of story drafts that resemble plot descriptions for simple stories. A comparative evaluation by human judges against baselines based on random gene assignment yields positive results. Conclusion: The genetic representation of pattern combinations and the metrics on compatibility across pattern pairs provide a valid evolutionary solution for constructing simple plots.
An Online Machine Learning Approach to Sentiment Analysis in Social Media

The online learning, is one that continuously adapts to arriving data, and gets updated incrementally instance by instance. In this paper, we compare the performance of different online machine learning algorithms for the task of sentiment analysis on challenging text datasets. We assess the models using a wide range of metrics, such as microF1, macroF1, accuracy, and running time. Our experiments have revealed that these online models provide a viable alternative to traditional offline machine learning in sentiment analysis, in fraction of the time.
From Corporate Governance to Algorithm Governance

This chapter explores the changes that AI brings about in corporate law and corporate governance, especially in terms of the challenges it poses for corporations. The law scholar Jan Lieder argues that whilst there is the potential to enhance the current system, there are also risks of destabilisation. Although algorithms are already being used in the board room, lawmakers should not consider legally recognizing e-persons as directors and managers. Rather, academia should evaluate the effects of AI on the corporate duties of boards and their liabilities. By critically examining three main topics, algorithms as directors, AI in a management board, and AI in a supervisory board, the author suggests the need for transparency in a company’s practices regarding AI for awareness-raising and the enhancement of overall algorithm governance, as well as the need for boards to report on their overall AI strategy and ethical guidelines relating to the responsibilities, competencies, and protective measures they established. Additionally, the author argues that a reporting obligation should require the boards to deal with questions of individual rights and explain how they relate to them.
Feature selection or extraction decision process for clustering using PCA and FRSD

This paper concerns the critical decision process of extracting or selecting the features before applying a clustering algorithm. It is not obvious to evaluate the importance of the features since the most popular methods to do it are usually made for a supervised learning technique process. A clustering algorithm is an unsupervised method. It means that there is no known output label to match the input data. This paper proposes a new method to choose the best dimensionality reduction method (selection or extraction) according to the data scientist's parameters, aiming to apply a clustering process at the end. It uses Feature Ranking Process Based on Silhouette Decomposition (FRSD) algorithm, a Principal Component Analysis (PCA) algorithm, and a K-Means algorithm along with its metric, the Silhouette Index (SI). This paper presents 5 use cases based on a smart city dataset. This research also aims to discuss the impacts, the advantages, and the disadvantages of each choice that can be made in this unsupervised learning process.
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation

This paper adds to the fundamental body of work on benchmarking the robustness of deep learning (DL) classifiers. We innovate a new benchmarking methodology to evaluate robustness of DL classifiers. Also, we introduce a new four-quadrant statistical visualization tool, including minimum accuracy, maximum accuracy, mean accuracy, and coefficient of variation, for benchmarking robustness of DL classifiers. To measure robust DL classifiers, we created a comprehensive 69 benchmarking image set, including a clean set, sets with single factor perturbations, and sets with two-factor perturbation conditions. After collecting experimental results, we first report that using two-factor perturbed images improves both robustness and accuracy of DL classifiers. The two-factor perturbation includes (1) two digital perturbations (salt & pepper noise and Gaussian noise) applied in both sequences, and (2) one digital perturbation (salt & pepper noise) and a geometric perturbation (rotation) applied in both sequences. All source codes, related image sets, and preliminary data, figures are shared on a GitHub website to support future academic research and industry projects. The web resources locate at https://github.com/caperock/robustai
Objects and Action Detection of Human Faces through Thermal Images Using ANU-Net

Thermal cameras, as opposed to RBG cameras, work effectively in extremely low illumination situations and can record data outside of the human visual spectrum. For surveillance and security applications, thermal images have several benefits. However, due to the little visual information in thermal images and intrinsic similarity of facial heat maps, completing face identification tasks in the thermal realm is particularly difficult. It can be difficult to attempt identification across modalities, such as when trying to identify a face in thermal images using the ground truth database for the matching visible light domain or vice versa. We proposed a method for detecting objects and actions on thermal human face images, based on the classification of five different features (hat, glasses, rotation, normal, and hat with glasses) in this paper. This model is presented in five steps. To improve the results of feature extraction during the pre-processing step, initially, we resize the images and then convert them to grayscale level using a median filter. In addition, features are extracted from pre-processed images using principle component analysis (PCA). Furthermore, the horse herd optimization algorithm (HOA) is employed for feature selection. Then, to detect the human face in thermal images, the LeNet-5 method is used. It is utilized to detect objects and actions in face areas. Finally, we classify the objects and actions on faces using the ANU-Net approach with the Monarch butterfly optimization (MBO) algorithm to achieve higher classification accuracy. According to experiments using the Terravic Facial Infrared Database, the proposed method outperforms "state-of-the-art" methods for face recognition in thermal images. Additionally, the results for several facial recognition tasks demonstrate good precision.
PENERAPAN MODEL INKUIRI UNTUK MENINGKATKAN HASIL BELAJAR MATERI PENGGOLONGAN HEWAN DI KELAS IV SD TELOK BULAN

This study aims to determine the increase in learning outcomes of fourth grade students at SD Negeri Telok Bulan in the science subject subject of Classifying Animals by using the Inquiry Learning Model. The research subjects were fourth grade students of SD Negeri Telok Bulan, totaling 34 people consisting of 15 male students and 19 female students. This type of research is classroom action research. This research consists of three stages, namely 1) Planning, 2) Action, 3) Observation, and 4) Reflection. This research was conducted in three cycles, namely Pre-Cycle, Cycle I, and Cycle II. The data collection technique was carried out by making observations made by teachers and supervisors 2. From the results of the research conducted, it was seen that there was an increase in student learning outcomes from each cycle. In the pre-cycle implementation, the average value of student learning outcomes was 64.47 with a completeness of 35.29%. In the first cycle, the average value of student learning outcomes was 77.25 with 41.18% completeness. In cycle II the average value of student learning outcomes is 87.25 with 100% completeness. From the results of this learning improvement research, it can be seen that the application of the Inquiry Learning Model in the science subject of Classifying Animals can improve the learning outcomes of fourth grade students at SD Negeri Telok Bulan ABSTRAKPenelitian ini bertujuan untuk mengetahui peningkatan hasil belajar siswa kelas IV SD Negeri Telok Bulan pada mata pelajaran IPA pokok bahasan Menggolongkan Hewan dengan menggunakan Model Pembelajaran Inkuiri. Subjek penelitian adalah siswa-siswi kelas IV SD Negeri Telok Bulan yang berjumlah 34 orang yang terdiri atas 15 siswa laki-laki dan 19 siswi perempuan. Jenis penelitian ini adalah penelitian tindakan kelas. Penelitian ini terdiri dari tiga tahapan yaitu 1) Perencanaan, 2) Tindakan, 3) Observasi, dan 4) Refleksi. Penelitian ini dilakukan dalam tiga siklus, yaitu Pra Siklus, Siklus I, dan Siklus II. Teknik pengumpulan data dilakukan dengan cara melakukan observasi yang dilakukan oleh guru dan supervisor 2. Dari hasil penelitian yang dilakukan terlihat adanya peningkatan hasil belajar siswa dari setiap siklus. Pada pelaksanaan pra siklus, nilai rata-rata hasil belajar siswa 64,47 dengan ketuntasan 35,29%. Pada siklus I nilai rata-rata hasil belajar siswa 77,25 dengan ketuntasan 41,18%. Pada siklus II nilai rata-rata hasil belajar siswa 87,25 dengan ketuntasan 100%. Dari hasil penelitian perbaikan pembelajaran ini terlihat bahwa penerapan Model Pembelajaran Inkuiri pada mata pelajaran IPA pokok bahasan Menggolongkan Hewan dapat meningkatkan hasil belajar siswa kelas IV SD Negeri Telok Bulan.
Parasitological assessment of presumptive diagnosis and treatment of Malaria in some health facilities in Jos

According to World Health Organization estimates released in 2021, there were 241 million cases of malaria and 627,000 deaths worldwide in 2020. Sub – Saharan Africa and India carried almost 85% of the global burden. Nigeria is among the six countries which accounted for more than half of all malaria cases worldwide, mostly consisting of pregnant women and children under 5years. Nearly 110million clinical cases of malaria are diagnosed each year, and malaria contributes up to 60% of out – patients’ visits and 30% of hospital admissions. This study sought to establish the accuracy with which malaria is presumptively diagnosed and treated based on clinical presentations only and the cost estimates on the patient in the study facilities. A prospective study was carried out between October, 2018 to June 2019 using 356 participants who came to the health facilities to access treatment with suspected cases of malaria and were presumptively diagnosed and treated for malaria. Demographics and blood samples were collected from eligible and consented patients and screened for plasmodium parasite by microscopy. The results obtained showed 88% of the patients tested negative for plasmodium parasite while 12% tested positive. The cost of anti-malarial ranged from 500 – 1000 naira (using the median score). Authors concluded that only 12% of those treated for malaria actually needed such treatments. This underscores the need to test for malaria before treatment.
A DEEP NEURAL NETWORK FOR SPATIOTEMPORAL PREDICTION OF THEFT CRIMES

Abstract. Accurate crime prediction plays an important role in public safety, providing technical guidance and decision support for the police and government departments. Due to the dynamics and imbalance of crime distribution, it is difficult to build predictive models for it. Specifically, the fine-grained and non-linear spatiotemporal dependencies of crime data cannot be captured accurately. In this paper, a neural network model ST-ACLCrime based on ConvLSTM and SE block was proposed to predict the number of theft crimes in hotspot areas. By overlaying ConvLSTM layers, fine-grained spatiotemporal dependencies are captured while preserving spatial location information. To further enhance the global channel feature representation, SE block is used to recalibrate the channel features and enhance the channel inter-dependencies. In addition, the closeness and the period components are set to dynamically capture the dependence of different time trends. We choose the city of Chicago as the study case, and use a multi-level spatial grid to divide the whole city area. The experimental results show that the proposed model exceeds all baseline model, such as HA, CNN, LSTM, CNN-LSTM and ConvLSTM. It was effectively capturing spatiotemporal dependence and improving prediction accuracy.