Reviewerly/exHarmony huggingface dataset:
A TDRL Model for the Emotion of Regret

To better understand the nature, function and elicitation conditions of emotion it is important to approach studying emotion from a multidisciplinary perspective involving psychology, neuroscience and affective computing. Recently, the TDRL Theory of Emotion has been proposed. It defines emotions as variations of temporal difference assessments in reinforcement learning. In this paper we present new evidence for this theory. We show that regret - a negative emotion that signifies that an alternative action should have been taken given new outcome evidence - is modelled by a particular form of TD error assessment. In our model regret is attributed to each action in the state-action trace of an agent for which - after new reward evidence - an alternative action becomes the best action in that state (the new argmax) after adjusting the action value of the chosen action in that state. Regret intensity is modeled as the difference between this new best action and the adjusted old best action, reflecting the additional amount of return that could have been received should that alternative have been chose. We show in simulation experiments how regret varies depending on the amount of adjustment as well as the adjustment mechanism, i.e. Q-trace, Sarsa-trace, and Monte Carlo (MC) re-evaluation of action values. Our work shows plausible regret attribution to actions, when this model of regret is coupled with MC action value update. This is important evidence that regret can be seen as a particular variation of TD error assessment involving counterfactual thinking.
Neural Nets via Forward State Transformation and Backward Loss Transformation

This article studies (multilayer perceptron) neural networks with an emphasis on the transformations involved — both forward and backward — in order to develop a semantic/logical perspective that is in line with standard program semantics. The common two-pass neural network training algorithms make this viewpoint particularly fitting. In the forward direction, neural networks act as state transformers, using Kleisli composition for the multiset monad — for the linear parts of network layers. In the reverse direction, however, neural networks change losses of outputs to losses of inputs, thereby acting like a (real-valued) predicate transformer. In this way, backpropagation is functorial by construction, as shown in other works recently. We illustrate this perspective by training a simple instance of a neural network.
Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques

Dimension measurement is of utmost importance in the logistics industry. This work studies a hand-held structured light vision system for boxes. This system measures dimension information through laser triangulation and deep learning using only two laser-box images from a camera and a cross-line laser projector. The structured edge maps of the boxes are detected by a novel end-to-end deep learning model based on a trimmed-holistically nested edge detection network. The precise geometry of the box is calculated by the 3D coordinates of the key points in the laser-box image through laser triangulation. An optimization method for effectively calibrating the system through the maximum likelihood estimation is then proposed. Results show that the proposed key point detection algorithm and the designed laser-vision-based visual system can locate and perform dimension measurement of measured boxes with high accuracy and reliability. The experimental outcomes show that the system is suitable for portable automatic box dimension online measurement.
A Three-Dimensional Cartesian Mesh Generation Algorithm Based on the GPU Parallel Ray Casting Method

Robust and efficient Cartesian mesh generation for large-scale scene is of great significance for fluid dynamics simulation and collision detection. High-quality and large-scale mesh generation task in a personal computer is hard to achieve. In this paper, a parallel Cartesian mesh generation algorithm based on graphics processing unit (GPU) is proposed. The proposed algorithm is optimized based on the traditional ray casting method in computer graphics, and is more efficient and stable for large-scale Cartesian mesh generation. In the process of mesh generation, the geometries represented by triangular facets are transformed into a mesh composed of orthogonal hexahedrons. A parallel ray generation method is proposed to reduce the data exchange between the host memory and device memory. A parallel primitives searching method based on lattice grid is adopted to search the triangular facets for intersection calculation between rays and triangles. The parallel Cartesian mesh generation algorithm has been implemented using CUDA library. The performance of parallel Cartesian mesh generation algorithm has been promoted enormously compared with the traditional the sequential algorithm, which is shown in different numerical experiments. Through some tests, the performance of parallel algorithm is analyzed, and the results show that the parallel computing power of the GPU is fully utilized. Finally, examples of Cartesian mesh generation are presented.
Designing a Blockchain Based Framework for IoT Data Trade

With its application ranging from smart homes and wearables to smart vehicles and smart cities, Internet of Things(IoT) devices have become an integral part of our lives. These devices are enabled with sensing and networking facilities and generate an enormous amount of data, which can be highly valuable for various stakeholders of the system. Enabling the regulated exchange of IoT generated data can enhance the overall efficiency of the IoT based eco-system. However, the current state of the art data exchange frameworks is highly centralized, making it prone to a single point of failure and security breaches. Moreover, the cloud-based systems lack transparency and provide the owners of the IoT devices limited control over the data being exchanged, thus raising the privacy concerns of an individual. Also, the device owners, most of the time, are deprived of receiving proper incentives in exchange for their shared data. Blockchain Technology, with its inherent characteristics of being transparent, decentralized, always available along with smart contracts, can be one of the potential solutions for the problems mentioned above. In this work, we propose, analyze, and discuss a blockchain-based decentralized framework for IoT data trade.
Exploring the Intersection Between Speaker Verification and Emotion Recognition

Many scenarios in practical applications require the use of speaker verification systems using audio with high emotional content (e.g., calls from 911, forensic analysis of threatening recordings). For these cases, it is important to explore the intersection between speaker and emotion recognition tasks. A key challenge to address this problem is the lack of resources, since current emotional databases are commonly limited in size and number of speakers. This paper (1) creates the infrastructure to study this challenging problems, and (2) presents an exploratory analysis to evaluate the accuracy of state-of-the-art speaker and emotion recognition systems to automatically retrieve specific emotional behaviors from target speakers. We collected a pool of sentences from multiple speakers (132,930 segments), where some of these speaking turns belong to 146 speakers in the MSP-Podcast database. Our framework trains speaking verification models, which are used to retrieve candidate speaking turns from the pool of sentences. The emotional content in these sentences are detected using state-of-the-art emotion recognition algorithms. The experimental evaluation provides promising results, where most of the retrieved sentences belong to the target speakers and has the target emotion. The results highlight the need for emotional compensation in speaker recognition systems, especially if these models are intended for commercial applications.
A novel change-detection scheduler for a network of depth sensors

In many monitoring applications such as smart home and surveillance, deployment of multiple depth sensors increases monitoring area and offers better occlusion handling which is not sensitive to illumination condition in comparison with RGB sensors. However, multiple sensors also increase the volume of data associated with signal processing alongside the associated computational complexity and power consumption. In order to address these drawbacks, this paper proposes a novel change detection algorithm that can be used as a part of a sensor scheduler in a centralized (e.g. star) network configuration. Initially, each sensor in the network performs a unique single scan of the common environment in order to detect any incremental changes in the sensed depth signal. This initial change detection is then used as a basis for several follow-up tasks such as foreground segmentation, background detection, target detection, and tracking for monitoring tasks. Here, instead of processing a complete depth frame, we proposed to utilize a collection of 1D scans of the depth frames. A confidence function is defined that can be used to estimate the reliability of the detected changes in each sensor and to reduce any false positive events which can be triggered by the noise and outliers. Analysis of the proposed confidence function is carried out through performance analysis in the presence of sensor noise and other parameters which can affect the reliability of the sensed data of each sensor. Finally, a score function is defined based on the confidence of the detected parameters and sensor resolution in order to rank and match sensors with the associated objects to be tracked. It results in tracking target(s) by a sensor (or sensors) that offer a high tracking score. This approach offers many advantages such as decreasing the overall system power consumption by placing the sensors with a low confidence value on standby mode and reducing the overall computational overheads.
Troubleshooting deep-learner training data problems using an evolutionary algorithm on Summit

Architectural and hyperparameter design choices can influence deep-learner (DL) model fidelity but can also be affected by malformed training and validation data. However, practitioners may spend significant time refining layers and hyperparameters before discovering that distorted training data were impeding the training progress. We found that an evolutionary algorithm (EA) can be used to troubleshoot this kind of DL problem. An EA evaluated thousands of DL configurations on Summit that yielded no overall improvement in DL performance, which suggested problems with the training and validation data. We suspected that contrast limited adaptive histogram equalization enhancement that was applied to previously generated digital surface models, for which we were training DLs to find errors, had damaged the training data. Subsequent runs with an alternative global normalization yielded significantly improved DL performance. However, the DL intersection over unions still exhibited consistent subpar performance, which suggested further problems with the training data and DL approach. Nonetheless, we were able to diagnose this problem within a 12-hour span via Summit runs, which prevented several weeks of unproductive trial-and-error DL configuration refinement and allowed for a more timely convergence on an ultimately viable solution.
MDS and I-perfect poset block codes

We obtain the Singleton bound for poset block codes and define a maximum distance separable poset block code (MDS (P,π)-code) as a code meeting this bound. We extend the concept of I-balls to poset block metric and describe r-perfect and MDS (P,π)-codes in terms of I-perfect codes. As a special case when all the blocks have the same dimension, we establish that MDS (P,π)-codes are same as I-perfect codes for I∈In−km(P). We show that the duality result also holds for this case. Further, we determine the weight enumerator of an MDS (P,π)-code.
A Versatile Online System for Person-specific Facial Expression Recognition

In this paper, we introduce an online facial expression recognition (FER) model, which infers the emotional states in real time. This model enables the computer to interact more intelligently with the user. Our proposed mechanism identifies the frontal face along with the region of interest (ROI), extracts discriminating features from suitable facial landmarks, and classifies the facial expressions. Histogram of oriented gradient (HOG) is implemented to extract features and facial landmark positions from active facial regions, which enhances the system performance against all the possible scale and pose variations. The system speed improved with appropriate integration of detection and tracking algorithms. Further, support vector machine (SVM) classifier is used to classify the detected face into neutral or six universal emotions. For achieving the best results with new user faces, the system extracts the neutral features of the user during the time of execution and uses them to train the classifiers. To validate the performance of the proposed algorithm is validated using CK+ and RafD databases.
Evaluation of Long-Term Performance of a Solar Home System (SHS) Monitoring System on Harsh Environments

Dataloggers installed in rural regions of developing countries need to be autonomous, robust, and have good recording capacity as they may be exposed to harsh environmental conditions. An extremely hot, dry, and dusty climate can imply additional wear and tear toequipment. A test procedurewas designed and run in a confined space to control climate conditions to test the datalogger. An outdoor campaign lasting more than three years was performed at the Instituto Madrileño de Estudios Avanzados (IMDEA) Water Institute, in Alcalá de Henares (Madrid, Spain) and at the Escuela Politécnica Superior (EPS) Linares (Jaén, Spain) to test the low-cost datalogger under real conditions. The results demonstrated that it was robust and endured extreme weather conditions. In order to avoid the loss of data, a new version with a redundant system based on an SD card was implemented and tested under real conditions.
TempCourt: evaluation of temporal taggers on a new corpus of court decisions

Abstract The extraction and processing of temporal expressions (TEs) in textual documents have been extensively studied in several domains; however, for the legal domain it remains an open challenge. This is possibly due to the scarcity of corpora in the domain and the particularities found in legal documents that are highlighted in this paper. Considering the pivotal role played by temporal information when it comes to analyzing legal cases, this paper presents TempCourt, a corpus of 30 legal documents from the European Court of Human Rights, the European Court of Justice, and the United States Supreme Court with manually annotated TEs. The corpus contains two different temporal annotation sets that adhere to the TimeML standard, the first one capturing all TEs and the second dedicated to TEs that are relevant for the case under judgment (thus excluding dates of previous court decisions). The proposed gold standards are subsequently used to compare ten state-of-the-art cross-domain temporal taggers, and to identify not only the limitations of cross-domain temporal taggers but also limitations of the TimeML standard when applied to legal documents. Finally, the paper identifies the need for dedicated resources and the adaptation of existing tools, and specific annotation guidelines that can be adapted to different types of legal documents.
Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs

Graph-based semi-supervised learning (SSL) assigns labels to initially unlabelled vertices in a graph. Graph neural networks (GNNs), esp. graph convolutional networks (GCNs), are at the core of the current-state-of-the art models for graph-based SSL problems. GCNs have recently been extended to undirected hypergraphs in which relationships go beyond pairwise associations. There is a need to extend GCNs to directed hypergraphs which represent more expressively many real-world data sets such as co-authorship networks and recommendation networks. Furthermore, labels of interest in these applications are most naturally represented by probability distributions. Motivated by these needs, in this paper, we propose a novel GNN-based method for directed hypergraphs, called Directed Hypergraph Network (DHN) for semi-supervised learning of probability distributions (Soft SSL). A key contribution of this paper is to establish generalisation error bounds for GNN-based soft SSL. In fact, our theoretical analysis is quite general and has straightforward applicability to DHN as well as to existing hypergraph methods. We demonstrate the effectiveness of our method through detailed experimentation on real-world datasets. We have made the code available.
A joint neural network model for combining heterogeneous user data sources: An example of at‐risk student prediction

Abstract Information service providers often require evidence from multiple, heterogeneous information sources to better characterize users and offer personalized service. In many cases, statistic information (for example, users' profiles) and sequentially dynamic information (for example, logs of interaction with information systems) are two prominent sources that can be combined to achieve optimized results. Previous attempts in combining these two sources mainly exploited models designed for either static or sequential information, but not both. This study aims to fill the gap by proposing a novel joint neural network model that can naturally fit both static and sequential user data. To evaluate the effectiveness of the proposed method, this study uses the problem of at‐risk student prediction as an example where both static data (personal profiles) and sequential data (event logs) are involved. A thorough evaluation was conducted on an open data set, with comparisons to a range of existing approaches including both static and sequential models. The results reveal superb performances of the proposed method. Implications of the findings on further research and applications of joint models are discussed.
Keywords Extraction from Arabic Documents Using Centrality Measures

In this paper, we propose a graph-based approach to extract keywords from Arabic documents. We represent the original text as a collocation graph, whose vertices represent original words and the edges represent the co-occurrence relation between all consecutive words. Then, a set of centrality measures are calculated and considered as features set. The vertices in the collocation graph are candidates to be keywords. We demonstrate the effectiveness of the proposed approach on news article documents. Four different machine learning algorithms are used in the experiments including Multi-layer Perceptron, Naïve Bayes, Random Forest, and OneR. The experimental results showed that the performance of the proposed approach achieved good results with comparison to other state-of-the-art results in terms of precision, recall, and F-score.
A high accurate automated first‐break picking method for seismic records from high‐density acquisition in areas with a complex surface

ABSTRACT As the application of high‐density high‐efficiency acquisition technology becomes more and more wide, the areas with complex surface conditions gradually become target exploration areas, and the first‐break picking work of massive low signal‐to‐noise ratio data is a big challenge. The traditional method spends a lot of manpower and time to interactively pick first breaks, a large amount of interactive work affects the accuracy and efficiency of picking. In order to overcome the shortcoming that traditional methods have weak anti‐noise to low signal‐to‐noise ratio primary wave, this paper proposes a high accurate automated first‐break picking method for low signal‐to‐noise ratio primary wave from high‐density acquisition in areas with a complex surface. Firstly, this method determines first‐break time window using multi‐azimuth spatial interpolation technology; then it uses the improved clustering algorithm to initially pick first breaks and then perform multi‐angle comprehensive quality evaluation to first breaks according to the following sequence: ‘single trace → spread → single shot → multiple shots’ to identify the abnormal first breaks; finally it determines the optimal path through the constructed evaluation function and using the ant colony algorithm to correct abnormal first breaks. Multi‐azimuth time window spatial interpolation technology provides the base for accurately picking first‐break time; the clustering algorithm can effectively improve the picking accuracy rate of low signal‐to‐noise ratio primary waves; the multi‐angle comprehensive quality evaluation can accurately and effectively eliminate abnormal first breaks; the ant colony algorithm can effectively improve the correction quality of low signal‐to‐noise ratio abnormal first breaks. By example analysis and comparing with the commonly used Akaike Information Criterion method, the automated first‐break picking theory and technology studied in this paper has high picking accuracy and the ability to stably process low signal‐to‐noise ratio seismic data, has a significant effect on seismic records from high‐density acquisition in areas with a complex surface and can meet the requirements of accuracy and efficiency for massive data near‐surface modelling and statics calculation.
Multithresholding Approach for Segmenting Plasmodium Parasites

Malaria has become one of the deadliest diseases in the world. The main method in diagnosing malaria is manually conducted by pathologists using a microscope. This process is time-consuming and prone to error due to human factor. These facts encourage the development of system that consistently yielded more objective results regardless of the condition on the field. In this research work, a novel method to segment infected erythrocytes using threshold and morphological is proposed. The proposed method was tested in a database consisting of 30 images with varying condition. The experimental results showed that the proposed method achieved 96.74 ± 0.7075 %, 76.77 ± 2.1441 %, 99.74 ± 0.1397 %, 97.84 ± 1.2514 % and 96.61 ± 0.8021 % of accuracy, sensitivity, specificity, prediction value positive and prediction value negative, respectively. In conclusion, the proposed method provides a consistent result for segmenting parasite in infected erythrocytes image. This result indicates that this proposed scheme is proper to assist the pathologists in detecting Plasmodium parasites.
A comparative analysis of detection mechanisms for emotion detection

Abstract This paper compared the performance of emotion detection mechanisms using dataset crawled from Facebook diabetes support group pages. To be specific, string-based Multinomial Naïve Bayes algorithm, NRC Emotion Lexicon (Emolex) and Indico API were used to detect five emotions present in 2475 Facebook posts, namely, fear, joy, sad, anger and surprise. Both accuracy and F-score measures were used to assess the effectiveness of the algorithms in detecting the emotions. Findings indicate string-based Multinomial Naïve Bayes to outperform both Emolex (i.e. 82% vs. 78%) and Indico API (i.e. 82% vs. 50%). Further analysis also revealed emotions such as joy, fear and sadness to be of the highest frequencies for the diabetes community. Implications of the findings and emotions detected are further discussed in this paper.