Reviewerly/exHarmony huggingface dataset:
Estimation of Daily Direct Solar Radiation for Rabat

To dimension concentrated solar energy systems, it is essential to have direct solar radiation the site considered. Since the difficulty to measure direct solar radiation for all sites of a country, we often had recourse to estimate it. Most of the developed models estimate this direct component either from the sunshine fraction or from global solar radiation, which are relatively easier to measure. In the present study, three new models were developed to estimate the daily direct solar radiation in Rabat. The validation of these models was carried out through direct solar radiation measurements, which were realized in Rabat at the station of the solar energy and environment laboratory. The calculation of the statistical indicators allowed us to compare the precision of the models developed. The obtained results show that the exponential model, where the relative error does not exceed −6.5%, is the most suitable for estimating the daily direct solar radiation in Rabat. This model can be used to generate a database of direct solar radiation in Rabat.
On finite mixtures of Discretized Beta model for ordered responses

Abstract The paper discusses the specification of finite mixture models based on the Discretized Beta distribution for the analysis of ordered discrete responses, as ratings and count data. The ultimate goal of the paper is to parameterize clusters of opposite and intermediate response outcomes. After a thorough discussion on model interpretation, identifiability and estimation, the proposal is illustrated on the wake of a case study on the probability to vote for German Political Parties and with a comparative discussion with the state of the art.
CentriForce: Multiple-Domain Adaptation for Domain-Invariant Speaker Representation Learning

In the real world, speaker recognition systems usually suffer from serious performance degradation due to the domain mismatch between training and test conditions. To alleviate the harmful effect of domain shift, unsupervised domain adaptation methods are introduced to learn domain-invariant speaker representations, which focus on addressing the single-source-to-single-target domain adaptation issue. However, labeled speaker data are usually collected from multiple sources, such as different languages, genres and devices. The single-domain adaptation methods can not deal with the complex multiple-domain mismatch problem. To address this issue, we propose a multiple-domain adaptation framework named CentriForce to extract domain-invariant speaker representations for speaker recognition. Different from previous methods, CentriForce learns multiple domain-related speaker representation spaces. To mitigate the multiple-domain mismatch, CentriForce reduces the Wasserstein distance between each pair of source and target domains in their domain-related representation space and meanwhile uses the target domain as an anchor point to draw all source domains closer to each other. In our experiments, CentriForce achieves the best performance on most of the 16 challenging adaptation tasks, compared with other competing adaptation methods. Ablation study and representation visualization further demonstrate its effectiveness for learning the domain-invariant speaker embedding.
Avicenna: a challenge dataset for natural language generation toward commonsense syllogistic reasoning

Syllogism is a type of everyday reasoning. For instance, given that ‘Avicenna wrote the famous book the Canon of Medicine’ and ‘The Canon of Medicine has influenced modern medicine,’ it can be concluded that ‘Avicenna has influenced modern medicine.’ This study revolves around syllogistic natural language generation (NLG). The Avicenna corpus (https://github.com/ZeinabAghahadi/Syllogistic-Commonsense-Reasoning) was developed as a benchmark for syllogistic NLG. In this respect, once the syllogistic relation between two premises is recognised [Aghahadi, Z., & Talebpour, A. (2022). Language-based syllogistic reasoning using deep neural networks. Cognitive Semantics, 8(2)], the Avicenna-trained models learn to generate the conclusion sentence. The experiments were performed using state-of-the-art pre-trained text generative models and the accuracy was improved up to 32% when transfer learning was adopted. The model’s confusion in detecting the middle-term was one of the main categories of errors that showed up in the error analysis. This issue indicates that the model learns how to extract new facts based on the premises, but it faces a challenge in commonsense reasoning.
Forecasting Crime Event Rate with a CNN-LSTM Model

In India, the crime percentage is growing day by day. It is essential to develop different modern advanced tools and techniques to predict the rate and time of the crime events in advance. This prediction will enable the police to improve the monitoring/vigilance and strengthen intelligence in the particular district to avoid such crime events. There are several Spatiotemporal statistical methods are used to predict such events in the past. Forecasting crime event rate prediction is a central part of setting a prediction approach or taking suitable timely action to reduce the crime rate. Additionally, using this Long short-term memory (LSTM) that one can analyze the relationships among long-term data utilizing its functions. Therefore in this work, we attempted to forecast the crime rate using the CNN-LSTM model. For this research, we utilized the crime dataset taken from the NCRB for three years. We chose four features: murder, rape, theft, and offenses against property. Initially, we use CNN to excerpt the attributes from the dataset and we used LSTM to forecast the crime rate. During the experiments, we found that the CNN along with the LSTM model could provide a trustworthy crime predicting method with high forecast accurateness. This method is a new exploration idea for crime rate forecasting as well as a good prediction technique.
Some new flexible classes of normal distribution for fitting multimodal data

In this paper, we introduce two new families of distributions containing unimodal as well as bimodal and trimodal distributions. The models extend the normal model to trimodal symmetric and asymmetric situations, and typically involve fewer parameters to be estimated than the mixtures of normal distributions. Statistical properties of these new families are studied in detail. The problem of estimating parameters is addressed by considering the maximum likelihood method and Fisher information matrices are derived. A small Monte Carlo simulation study is conducted to examine the performance of the obtained estimators. The methodology developed is illustrated with three real data applications.
Unabridged adjacent modulation for clothing parsing

• Based on the encoder-decoder architecture, we propose a block-specific unabridged channel attention mechanism, such that features within each block can be recalibrated. • A top-down adjacent modulation for decoder network is proposed so that the lowlevel features substantially contain abundant semantic contexts. • We demonstrate the effectiveness of UAM-Net via two challenging benchmarks. Results declare that we can achieve a new state-of-the-art on colorful fashion parsing dataset and comparable performance on modified fashion clothing dataset with less computation overhead. Clothing parsing has made tremendous progress in the domain of computer vision recently. Most state-of-the-art methods are based on the encoder-decoder architecture. However, the existing methods mainly neglect problems of feature uncalibration within blocks and semantics dilution between blocks. In this work, we propose an unabridged adjacent modulation network (UAM-Net) to aggregate multi-level features for clothing parsing. We first build an unabridged channel attention (UCA) mechanism on feature maps within each block for feature recalibration. We further design a top-down adjacent modulation (TAM) for decoder blocks. By deploying TAM, high-level semantic information and visual contexts can be gradually transferred into lower-level layers without loss. The joint implementation of UCA and TAM ensures that the encoder has an enhanced feature representation ability, and the low-level features of the decoders contain abundant semantic contexts. Quantitative and qualitative experimental results on two challenging benchmarks ( i.e ., colorful fashion parsing and the modified fashion clothing) declare that our proposed UAM-Net can achieve competitive high-accurate performance with the state-of-the-art methods. The source codes are available at: https://github.com/ctzuo/UAM-Net .
Accelerating Graph Similarity Search via Efficient GED Computation

Computing the graph edit distance (GED) between graphs is the core operation in graph similarity search. Recent studies suggest that the existing index structures are ineffective in reducing the overall processing time of graph similarity search, and that directly verifying the GED between the query graph and every data graph in the database is still the best option. The state-of-the-art algorithm for GED verification is the recently proposed AStar-LSa. However, AStar-LSa may consume an extremely large amount of main memory or even run out-of-memory, when the graphs become larger and/or the GED threshold becomes larger. In this paper, we aim to improve the efficiency of GED verification and simultaneously lower the main memory consumption. To achieve that, we propose a new estimation for the lower bounds of partial mappings between graphs. We formally prove that our new lower bound is tighter than the one used in AStar-LSa. Moreover, we also propose efficient algorithms to compute the lower bounds, as well as optimization techniques to improve the efficiency. Empirical studies on real datasets demonstrate that our newly proposed algorithm AStar-BMao runs faster, and at the same time consumes much less main memory, than AStar-LSa.
Machine Learning Methods for Predicting <i>League of Legends</i> Game Outcome

The video game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">League of Legends</i> has several professional leagues and tournaments that offer prizes reaching several million dollars, making it one of the most followed games in the Esports scene. This article addresses the prediction of the winning team in professional matches of the game, using only pregame data. We propose to improve the accuracy of the models trained with the features offered by the game application programming interface (API). To this end, new features are built to collect interesting information, such as the skills of a player handling a certain champion, the synergies between players of the same team or the ability of a player to beat another player. Then, we perform feature selection and train different classification algorithms aiming at obtaining the best model. Experimental results show classification accuracy above 0.70, which is comparable to the results of other proposals presented in the literature, but with the added benefit of using few samples and not requiring the use of external sources to collect additional statistics.
Learner experience in artificial intelligence-scaffolded argumentation

Writing academic arguments is a complex and demanding task, even for proficient tertiary students. At the same time, providing prompt support for individual students working on discipline-specific arguments is often challenging for instructors. Artificial Intelligence (AI) techniques enable automated and adaptive educational scaffolding. In this study, we leveraged Natural Language Processing (NLP) AI techniques in an AI-Supported Scaffolding (AISS) system to evaluate written arguments and present alternative writing examples that human experts might write. To evaluate a pilot version of AISS, we gathered mixed-method data from 14 students enrolled in two sections of the same graduate-level online course (6 students in Cohort 1 and 8 students in Cohort 2). We used the Tool for the Automatic Analysis of Cohesion (TAACO) to track revisions in written arguments. TAACO indices demonstrated that the students used AI-generated scaffolding to build stronger claims with more elaborate and cohesive ideas. In written reflections, students revealed the perceived value of AISS, and visual inspection of their writing indicates that students used AISS feedback to improve their arguments. The findings demonstrate the potential of AI to provide personalised scaffolding for academic argument composition.
A Deep Autoencoder-Based Hybrid Recommender System

Recommender systems build their suggestions on rating data, given explicitly or implicitly by users on items. These ratings create a huge sparse user-item rating matrix which opens two challenges for researchers on the field. The first challenge is the sparsity of the rating matrix and the second one is the scalability of the data. This article proposes a hybrid recommender system based on deep autoencoder to learn the user interests and reconstruct the missing ratings. Then, SVD++ decomposition is employed, in parallel, to hold information of correlation between different features factors. Additionally, the authors gave a deep analysis of the top-N recommender list from the user's perspective to ensure that the proposed model can be used for practical application. Experiments showed that the proposed model performed better with high-dimensional datasets, and outperformed various hybrid algorithms in terms of RMSE, MAE, and in terms of the final top-N recommendation list.
Behind Bars But Not Sentenced: The Role of Computerized Central Repository in Addressing Awaiting-Trial Problems in Ebonyi State, Nigeria

Quick dispensation of justice is necessary for every democratic society to succeed. It is encouraging to see studies, concerns and active agitations from people on the promotion of quick justice in Nigeria. Unfortunately, these studies and agitations have not yielded any concrete results. This article examines how a computerized central repository system can address awaiting-trial problems in Ebonyi State. Utilizing quantitative and qualitative approaches of research, a sample of 1,498 respondents were drawn from Ebonyi State. Purposive and multi-stage sampling techniques were used to reach the respondents. Questionnaires and in-depth interviews served as instruments for data collection. After the administration of instruments, data were sorted, coded, and analyzed using descriptive statistics, Chi-square, Pearson Product Moment Correlation Coefficient, and multiple linear regressions. Findings confirmed that while there is an uptick of awaiting-trial problems in Nigeria, there are no meaningful provisions to address it despite the fact that provisions exist within the Nigerian legal framework. Regression analysis revealed the Information and Computer Technology (ICT) method facilitates pretrial detention and brings it to quick conclusion as against the manual method. Based on the findings, the study calls for the creation of a computerized platform to automate all the Criminal Justice bureaucratic processes and make them faster through a centrally based computerized centrally repository system that is accessible to all agencies involved in Criminal Justice administration.
A Text-Guided Generation and Refinement Model for Image Captioning

A high-quality image description requires not only the logic and fluency of language but also the richness and accuracy ofcontent. However, due to the semantic gap between vision and language, most existing image captioning approaches thatdirectly learn the cross-modal mapping from vision to language are difficult to meet these two requirements simultaneously. Inspired by the progressive learning mechanism, we trace the “generating + refining” route and propose a novel Text-GuidedGeneration and Refinement (dubbed as TGGAR) model with assistance from the guide text to improve the quality of captions.The guide text is selected from the training set according to content similarity, then utilized to explore salient objects andextend candidate words. Specifically, we follow the encoderdecoder architecture, and design a Text-Guided Relation Encoder(TGRE) to learn the visual representation that is more consistent with human visual cognition. Besides, we divide the decoderpart into two sub-modules: a Generator for the primary sentence generation and a Refiner for the sentence refinement.Generator, consisting of a standard LSTM and a Gate on Attention (GOA) module, aims to generate the primary sentencelogically and fluently. Refiner contains a caption encoder module, an attentionbased LSTM and a GOA module, whichiteratively modifies the details in the primary caption to make captions rich and accurate. Extensive experiments on theMSCOCO captioning dataset demonstrate our framework with fewer parameters remains comparable to transformer-basedmethods, and achieves state-of-the-art performance compared with other relevant approaches.
A Real-Time 1280 × 720 Object Detection Chip With 585 MB/s Memory Traffic

Memory bandwidth has become the real-time bottleneck of current deep learning accelerators (DLA), particularly for high definition (HD) object detection. Under resource constraints, this paper proposes a low memory traffic DLA chip with joint hardware and software optimization. To maximize hardware utilization under memory bandwidth, we morph and fuse the object detection model into a group fusion-ready model to reduce intermediate data access. This reduces the YOLOv2's feature memory traffic from 2.9 GB/s to 0.15 GB/s. To support group fusion, our previous DLA based hardware employes a unified buffer with write-masking for simple layer-by-layer processing in a fusion group. When compared to our previous DLA with the same PE numbers, the chip implemented in a TSMC 40nm process supports 1280x720@30FPS object detection and consumes 7.9X less external DRAM access energy, from 2607 mJ to 327.6 mJ.
Ensemble Making Few-Shot Learning Stronger

Abstract Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks. Many few-shot models have been widely used for relation learning tasks. However, each of these models has a shortage of capturing a certain aspect of semantic features, for example, CNN on long-range dependencies part, Transformer on local features. It is difficult for a single model to adapt to various relation learning, which results in a high variance problem. Ensemble strategy could be competitive in improving the accuracy of few-shot relation extraction and mitigating high variance risks. This paper explores an ensemble approach to reduce the variance and introduces fine-tuning and feature attention strategies to calibrate relation-level features. Results on several few-shot relation learning tasks show that our model significantly outperforms the previous state-of-the-art models.
Transfer Learning for Boosted Relational Dependency Networks Through Genetic Algorithm

Machine learning aims at generalizing from observations to induce models that aid decisions when new observations arrive. However, traditional machine learning methods fail at finding patterns from several objects and their relationships. Statistical relational learning goes a step further to discover patterns from relational domains and deal with data under uncertainty. Most machine learning methods, SRL included, assume the training and test data come from the same distribution. Nonetheless, in several scenarios, this assumption does not hold. Transfer learning aims at acting on scenarios like that, leveraging learned knowledge from a source task to improve the performance in a target task when data is scarce. A costly challenge associated with transfer learning in relational domains is mapping from the source and target background knowledge language. This paper proposes GROOT, a framework that applies genetic algorithm-based solutions to discover the best mapping between the source and target tasks and adapt the transferred model. GROOT relies on a set of relational dependency trees built from the source data as a starting point to build the models for the target data. Over generations, individuals carry a possible mapping. They are submitted to genetic operators that recombine subtrees and revise the initial structure tree, enabling a prune or expansion of the branches. Experimental results conducted on Cora, IMDB, UW-CSE, and NELL datasets show that GROOT reaches results better than the baselines in most cases.
Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning

We consider information-theoretic bounds on expected generalization error for statistical learning problems in a networked setting. In this setting, there are K nodes, each with its own independent dataset, and the models from each node have to be aggregated into a final centralized model. We consider both simple averaging of the models as well as more complicated multi-round algorithms. We give upper bounds on the expected generalization error for a variety of problems, such as those with Bregman divergence or Lipschitz continuous losses, that demonstrate an improved dependence of 1/K on the number of nodes. These "per node" bounds are in terms of the mutual information between the training dataset and the trained weights at each node, and are therefore useful in describing the generalization properties inherent to having communication or privacy constraints at each node.
AESOP: Abstract Encoding of Stories, Objects, and Pictures

Visual storytelling and story comprehension are uniquely human skills that play a central role in how we learn about and experience the world. Despite remarkable progress in recent years in synthesis of visual and textual content in isolation and learning effective joint visual-linguistic representations, existing systems still operate only at a superficial, factual level. With the goal of developing systems that are able to comprehend rich human-generated narratives, and co-create new stories, we introduce AESOP: a new dataset that captures the creative process associated with visual storytelling. Visual panels are composed of clip-art objects with specific attributes enabling a broad range of creative expression. Using AESOP, we propose foundational storytelling tasks that are generative variants of story cloze tests, to better measure the creative and causal reasoning ability required for visual storytelling. We further develop a generalized story completion framework that models stories as the co-evolution of visual and textual concepts. We benchmark the proposed approach with human baselines and evaluate using comprehensive qualitative and quantitative metrics. Our results highlight key insights related to the dataset, modelling and evaluation of visual storytelling for future research in this promising field of study.
High-precision four-dimensional hyperspectral imager integrating fluorescence spectral detection and 3D surface shape measurement

A four-dimensional hyperspectral imager (FDHI) that combines fluorescence spectral detection and 3D surface morphology measurement is proposed. The FDHI consists of a hyperspectral line-scanner, a line structured light stereo vision system, and a line laser. The line laser is used as both the excitation light for the fluorescence and the scanning light line for the 3D profiling. At each scanning step, the system collects both fluorescent and 3D spatial data of the irradiated line region, which are fused to 4D data points based on a line mapping relationship between the datasets, and by scanning across the measurement object, a complete 4D dataset is obtained. The FDHI shows excellent performance with spatial and spectral resolution of 26.0 µm and 3 nm, respectively. The reported FDHI system and its applications provide a solution for 4D detection and analysis of fluorescent objects in meters measurement range, with advantage of high integration as two imaging modules sharing a same laser source.