Reviewerly/exHarmony huggingface dataset:
Modeling Working Memory Using Convolutional Neural Networks for Knowledge Tracing

Recently, working memory models have been used in knowledge tracing to improve prediction performance, which is a cognitive system with limited processing capacity enabling short-term storage of information. However, existing versions do not model limited capacity adequately or consider individual differences in processing capacity. To resolve these problems, we simulate working memory for knowledge tracking (MCKT) by utilizing a Convolutional Neural Network (CNN). To this end, multiple CNNs with convolution kernels of different scales are used to process a single interaction sequence, where each scale of convolution kernels represents a certain processing capacity of the current learner. MCKT utilizes a multi-scale convolution kernel approach to achieve personalized simulation of processing capacities of different learners. Moreover, long-term memory is simulated to predict future learner responses. The results of experiments on several benchmark datasets from the real world reveals that MCKT performs better than multiple classical models.
A Kernel Density Estimation Based Quality Metric for Quality Assessment of Obstetric Ultrasound Video

Simplified ultrasound scanning protocols (sweeps) have been developed to reduce the high skill required to perform a regular obstetric ultrasound examination. However, without automated quality assessment of the video, the utility of such protocols in clinical practice is limited. An automated quality assessment algorithm is proposed that applies an object detector to detect fetal anatomies within ultrasound videos. Kernel density estimation is applied to the bounding box annotations to estimate a probability density function of certain bounding box properties such as the spatial and temporal position during the sweeps. This allows quantifying how well the spatio-temporal position of anatomies in a sweep agrees with previously seen data as a quality metric. The new quality metric is compared to other metrics of quality such as the confidence of the object detector model. The source code is available at: https://github.com/kwon-j/KDE-UltrasoundQA .
LNPB: A Light Node for Public Blockchain with Constant-Size Storage

In the blockchain system, the light node uses a simplified transaction verification method. Its storage overhead increases linearly with the size of the blockchain, which can quickly become prohibitive for mobile and IoT low-end devices in the blockchain system. However, most existing schemes only achieve limited storage compression, apply only to UTXO scenarios, or cannot verify the transactions independently. This paper proposes a light node for public blockchain with constant-size storage called LNPB. LNBP fuses the blockchain verification and the simple payment verification protocols to achieve a more succinct and secure transaction verification protocol with constant-size storage. It applies to UTXO and non-UTXO scenarios. To this end, we re-design the block header, which contains a constant-size summary and an RSA modulus. Then we use an RSA encryption accumulator to calculate this summary. The light node only stores the summary of the latest block when verifying the transaction. In generating the proof of the transaction by full node, we employ the Merkle Mountain Range to store the intermediate results of the summary, which makes the proof generating faster. In addition, we conduct simulation experiments and analysis on LNPB, and compare it with existing schemes. The results indicate that LNPB achieves the expected goals, and can save the storage and computation overheads.
A Firefly Algorithm Based on Prediction and Hybrid Samples Learning

The firefly algorithm is an optimization algorithm developed based on the interactive behavior of fireflies in nature. It is based on the simulation of the mutual attraction and flickering behavior between fireflies to obtain the optimal solution by optimizing the objective function in the problem. To address the slow convergence speed, high time complexity, and easy trapping in local optima of the firefly algorithm during the search process, this paper proposes a firefly algorithm based on prediction and hybrid sample learning (PHSFA). Firstly, fireflies with insufficient progress space will be eliminated, and new fireflies will replace them to search for a better optimal value of the search function. Secondly, a hybrid sample is designed to enable each generation of fireflies to learn and enhance the convergence speed of the algorithm while reducing time complexity. In addition, a new adaptive step size strategy is adopted to adapt to the proposed algorithm. To verify the performance of PHSFA, experiments are conducted on CEC2020 test functions. The experimental results show that PHSFA performs the best on most test functions and has better solution accuracy.
Fireworks Algorithm for Dimensionality Resetting Based on Roulette

In order to enhance the local search ability and global search ability of the fireworks algorithm (FWA), this paper proposes a fireworks algorithm for dimensionality resetting based on roulette (FWADRR). First, the algorithm improves the exponential decay explosion and mapping rules in EDFWA, avoid the interference in the process of the algorithm using regional information, and enhance the local search ability of the algorithm. Then, a new firework reset method is proposed, which uses roulette to reset a certain dimension of the optimal firework based on the change of the optimal firework position, and uses this position as the position for resetting the firework. Change the explosion range of the reset firework to the explosion range of the optimal firework to ensure that the reset firework and the optimal firework are in the same search stage. This method ensures that other fireworks can play a role until the algorithm converges, and improves the global search ability of the algorithm. Tested on the CEC2020 benchmark suite, the experimental results show that FWADRR significantly outperforms previous fireworks algorithms.
Open-World Few-Shot Object Detection

General object detection has made significant progress under the close-set setting. However, the detector can only support a fixed set of categories and fail to identify unknown objects in real-world scenarios. Therefore, class-agnostic object detection (CAOD) has recently attracted much attention, aiming to localize both known and unknown objects in the image. Since CAOD utilizes the binary label to train the detector, lacking multi-class classification information, and is also incapable to further generalize quickly to the unknown objects of interest, the scalability of this task is limited in more downstream applications. In this paper, we propose a new task termed Open-World Few-Shot Object Detection (OFOD), extending class-agnostic object detection with the few-shot learning ability. Compared with CAOD, OFOD can accurately detect unknown objects with only a few examples. Besides, we propose a new model termed OFDet, built upon a class-agnostic object detector under the two-stage fine-tuning paradigm. OFDet consists of three key components, Class-agnostic Localization Module (CALM) that generates class-agnostic proposals, Base Classification Module (BCM) that classifies objects from classes features, and Novel Detection Module (NDM) that learns to detect novel objects. OFDet detects the novel classes in NDM and localizes the potential unknown proposals in CALM. Furthermore, an Unknown Proposals Selection algorithm is proposed to select more accurate unknown objects. Extensive experiments are conducted on PASCAL VOC and COCO datasets under multiple tasks, CAOD, few-shot object detection (FSOD) and OFOD. The results show that OFDet performs well on the traditional FSOD and CAOD settings as well as the proposed OFOD setting. Specifically for OFOD, OFDet achieves state-of-the-art results on the average recall of unknown classes (32.5%) and obtains high average precision of novel classes (15.7%) under the 30-shot setting of COCO's unknown set 1.
Self-supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-Grained Time Resolution

Accurate time prediction of patients’ critical events is crucial in urgent scenarios where timely decision-making is important. Though many studies have proposed automatic prediction methods using Electronic Health Records (EHR), their coarse-grained time resolutions limit their practical usage in urgent environments such as the emergency department (ED) and intensive care unit (ICU). Therefore, in this study, we propose an hourly prediction method based on self-supervised predictive coding and multi-modal fusion for two critical tasks: mortality and vasopressor need prediction. Through extensive experiments, we prove significant performance gains from both multi-modal fusion and self-supervised predictive regularization, most notably in far-future prediction, which becomes especially important in practice. Our uni-modal/bi-modal/bi-modal self-supervision scored 0.846/0.877/0.897 (0.824/0.855/0.886) and 0.817/0.820/0.858 (0.807/0.81/0.855) with mortality (far-future mortality) and with vasopressor need (far-future vasopressor need) prediction data in AUROC, respectively.
Smart Service Dimensions: Exploring Customer Experience and Participation using Stimulus-Organism-Response Model

Today, the emergence of smart service technologies has an impact on customer experience and customer participation in the service industry. Artificial intelligence (AI) appears likely to influence marketing strategies, including business models, sales processes, and customer service options, as well as customer behaviors. Technology has been regarded as a game changer in the service context. As consumers become more accustomed to smart technology and robotic services, technology will continue to play an essential role in the service industry to better serve customers. While using smart technology, customers themselves will be involved in the service process. They will participate in the service with no or limited involvement with the staff. Therefore, it is very crucial to emphasis the technology-enhanced experience from a customers’ perspective to have better understanding of the actual content and operational application of smart service and robotic experience by examining the specific dimensions of the smart service. Therefore, this proposed study aims to provide an intense discussion and comprehensive conceptualization of the smart service dimensions that may stimulate customer experience and participation by using Stimulus-Organism-Response (S-O-R) Model.
Executive summary

Hate speech is a virulent form of discrimination that targets and undermines the human rights of persons and peoples based on their –presumed – identity and serves as a driver of populist narratives and violent extremist ideologies. In view of the increasing prevalence of hate speech, online and offline, it is imperative that local, regional and national governments and international organizations prioritize the development of effective strategies to counter it while maintaining a balance with the fundamental human right to freedom of expression.
Short summary

Hate speech is spreading faster and further than ever before as a result of social media user growth and the rise of populism. Both online and offline, hate speech targets people and groups based on who they are. It has the potential to ignite and fuel violence, spawn violent extremist ideologies, including atrocity crimes and genocide. It discriminates and infringes on individual and collective human rights, and undermines social cohesion.
Fairness Index Measures to Evaluate Bias in Biometric Recognition

The demographic disparity of biometric systems has led to serious concerns regarding their societal impact as well as applicability of such systems in private and public domains. A quantitative evaluation of demographic fairness is an important step towards understanding, assessment, and mitigation of demographic bias in biometric applications. While few, existing fairness measures are based on post-decision data (such as verification accuracy) of biometric systems, we discuss how pre-decision data (score distributions) provide useful insights towards demographic fairness. In this paper, we introduce multiple measures, based on the statistical characteristics of score distributions, for the evaluation of demographic fairness of a generic biometric verification system. We also propose different variants for each fairness measure depending on how the contribution from constituent demographic groups needs to be combined towards the final measure. In each case, the behavior of the measure has been illustrated numerically and graphically on synthetic data. The demographic imbalance in benchmarking datasets is often overlooked during fairness assessment. We provide a novel weighing strategy to reduce the effect of such imbalance through a non-linear function of sample sizes of demographic groups. The proposed measures are independent of the biometric modality, and thus, applicable across commonly used biometric modalities (e.g., face, fingerprint, etc.).
Class-Balanced Affinity Loss for Highly Imbalanced Tissue Classification in Computational Pathology

Early detection of cancer, and breast cancer in particular, can have a positive impact on the survival rate of cancer patients. However, visual inspection by expert pathologists of whole-slide-images is subjective and error-prone given the lack of skilled pathologists. To overcome this limitation, many researchers have proposed deep learning driven approaches to detect breast cancer from histopathology images. However, these datasets are often highly imbalanced as patches belonging to the cancerous category is minor in comparison to the healthy cells. Therefore, when trained, the classification performance of the conventional Convolutional Neural Network (CNN) models drastically decreases, particularly for the minor class, which is often the main target of detection. This paper proposes a class balanced affinity loss function which can be injected at the output layer to any deep learning classifier model to address the imbalance learning. In addition to treating the imbalance, the proposal also builds uniformly spread class prototypes to address the fine-grained classification challenge in histopathology datasets, which conventional softmax loss cannot address. We validate our loss function performance by using two publicly available datasets with different levels of imbalance, namely the Invasive Ductal Carcinoma (IDC) and Colorectal cancer (CRC) datasets. In both cases, our method results in better performance, especially for the minority. We also observe a better 2D feature projection in multi-class classification with the proposed loss function, making it more apt to handle imbalanced fine-grained classification challenges.
Understanding the Properties and Limitations of Contrastive Learning for Out-of-Distribution Detection

A recent popular approach to out-of-distribution (OOD) detection is based on a self-supervised learning technique referred to as contrastive learning. There are two main variants of contrastive learning, namely instance and class discrimination, targeting features that can discriminate between different instances for the former, and different classes for the latter. In this paper, we aim to understand the effectiveness and limitation of existing contrastive learning methods for OOD detection. We approach this in 3 ways. First, we systematically study the performance difference between the instance discrimination and supervised contrastive learning variants in different OOD detection settings. Second, we study which in-distribution (ID) classes OOD data tend to be classified into. Finally, we study the spectral decay property of the different contrastive learning approaches and examine how it correlates with OOD detection performance. In scenarios where the ID and OOD datasets are sufficiently different from one another, we see that instance discrimination, in the absence of fine-tuning, is competitive with supervised approaches in OOD detection. We see that OOD samples tend to be classified into classes that have a distribution similar to the distribution of the entire dataset. Furthermore, we show that contrastive learning learns a feature space that contains singular vectors containing several directions with a high variance which can be detrimental or beneficial to OOD detection depending on the inference approach used.
Case-Based Adaptation of Argument Graphs with WordNet and Large Language Models

Finding information online is hard, even more so once you get into the domain of argumentation. There have been developments around the specialized argumentation machines that incorporate structural features of arguments, but all current approaches share one pitfall: They operate on a corpora of limited sizes. Consequently, it may happen that a user searches for a rather general term like cost increases, but the machine is only able to serve arguments concerned with rent increases. We aim to bridge this gap by introducing approaches to generalize/specialize a found argument using a combination of WordNet and Large Language Models. The techniques are evaluated on a new benchmark dataset with diverse queries using our fully featured implementation. Both the dataset and the code are publicly available on GitHub.
Form of Language Errors in The Syntastic Order When the Essay Writing Process Personal Experience

This study aims to describe errors at the syntactic level which include aspects of phrases and sentences in writing personal experiences. This study used a qualitative research design with a descriptive research type. The results of this study indicate that students make many mistakes in sentence structure which includes ten aspects. These findings prove that students pay little attention to the use of grammar in writing, the lack of vocabulary that students have, the influence of the use of regional languages, and the teacher pays little attention to the use of grammar in writing. The method used in this research is descriptive qualitative method. Based on the results of the research, the researchers found data on language errors in the syntactical aspect, including: 8 errors in the word aspect, 1 error in the phrase aspect, and 34 errors in the sentence aspect. From the findings of these data researchers chose 3 data to be analyzed. These data are: 1 word aspect error, 1 phrase aspect error, and 1 sentence aspect error.&#x0D; Keywords: Language, Sentence, Syntastic
Human Right and Stable Economic Growth: Implications for Corporate Social Responsibility

"Stable economic growth is usually a factor of several indices. Modern economists generally believe that economic growth is principally and primarily influenced by factors around it thus, the paper studied the relationship between human rights and stable economic growth observing that these variables could affect directly on corporate social responsibility initiatives. The study examined the nexus between human rights, stable economic growth and corporate social responsibilities. Explorative research design was adopted. The study adopted secondary sources of data. The study sample size was the 19 countries in the population of G-20. The study explanatory variable is human right, measured by Best Countries Index, while the dependent variable is stable economic growth, measured by Gross Domestic Product per capital and Human Development Index. Ordinary least square regression analysis was adopted. The findings of the study exposed that measuring a country’s human rights does not immediately translate to an improved economy. There is a need to improve the measurement criteria of human rights and make it more economical and quantitative to aid scientific testing. The study concludes that a country that monitors and improves its human rights indicators and encourages formal and informal businesses to embark on corporate social responsibilities would improve its ratings. The study recommends that countries should make a conscious effort to improve the defense of human rights to reflect the country’s perception and its image. The study would assist policymakers in measuring human rights and formulate policies that would assist the government in pursuing these factors deliberately to improve economic growth."
The Implication of Cooperative Integrated Reading and Composition (CIRC) Technique on English Reading Comprehension for Madrasah Aliyah Students

The objective of this study was to find out whether or not there was an effect of the Cooperative Integrated Reading and Composition (CIRC) Technique on Students Reading Comprehension in understanding the Narrative text at MA Pancasila Bengkulu City. In this study, researcher used a Quasi Experimental research nonequivalent pretest-posttest control group. The population of this research was the tenth grade students at MA Panacasila Bengkulu. The sample was X MIPA.1 which was treated as experimental class and taught by using CIRC Technique and X MIPA.2 was treated as control class and taught by using conventional strategy. There were 30 students who were sampled as 19 experimental classes and 19 control classes. To determine the sample, the researcher used Total sampling technique. In the process of data collection, researcher used test and documentation as research instruments. The test take form of multiple choice containing 20 questions. Test is given to both experimental and control classes as pre-test and post-test. The data from the pre- test and post-test were examined using the t-test. The result of this research showed that students reading comprehension test for the experimental class's mean score was 54.21, while the control class was 44.47, and for post-test findings reveal that the experimental class has an average value of 80.79, while the control class has an average value of 71.58. The hypothesis test results show that the output of the independent sample t-test (sig. 2-tailed) is 0.001 which is lower than 0.05. So, Ho was rejected and Ha was accepted. In brief, it can be concluded that Cooperative Integrated Reading and Composition (CIRC) Technique can be one of effective ways in teaching reading for tenth grade students in Madrasah Aliyah level.&#x0D; Keywords: Cooperative Integrated Reading and Composition (CIRC), Madrasah Aliyah, Narrative Text Reading Comprehension
Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents

Recent advances in the healthcare industry have led to an abundance of unstructured data, making it challenging to perform tasks such as efficient and accurate information retrieval at scale. Our work offers an all-in-one scalable solution for extracting and exploring complex information from large-scale research documents, which would otherwise be tedious. First, we briefly explain our knowledge synthesis process to extract helpful information from unstructured text data of research documents. Then, on top of the knowledge extracted from the documents, we perform complex information retrieval using three major components- Paragraph Retrieval, Triplet Retrieval from Knowledge Graphs, and Complex Question Answering (QA). These components combine lexical and semantic-based methods to retrieve paragraphs and triplets and perform faceted refinement for filtering these search results. The complexity of biomedical queries and documents necessitates using a QA system capable of handling queries more complex than factoid queries, which we evaluate qualitatively on the COVID-19 Open Research Dataset (CORD-19) to demonstrate the effectiveness and valueadd.