Reviewerly/exHarmony huggingface dataset:
Unsupervised Hashing with Semantic Concept Mining

Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is based on the similarities between image features extracted by a pre-trained CNN model. However, most of these methods tend to ignore high-level abstract semantic concepts contained in images. Intuitively, concepts play an important role in calculating the similarity among images. In real-world scenarios, each image is associated with some concepts, and the similarity between two images will be larger if they share more identical concepts. Inspired by the above intuition, in this work, we propose a novel Unsupervised Hashing with Semantic Concept Mining, called UHSCM, which leverages a VLP model to construct a high-quality similarity matrix. Specifically, a set of randomly chosen concepts is first collected. Then, by employing a vision-language pretraining (VLP) model with the prompt engineering which has shown strong power in visual representation learning, the set of concepts is denoised according to the training images. Next, the proposed method UHSCM applies the VLP model with prompting again to mine the concept distribution of each image and construct a high-quality semantic similarity matrix based on the mined concept distributions. Finally, with the semantic similarity matrix as guiding information, a novel hashing loss with a modified contrastive loss based regularization item is proposed to optimize the hashing network. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task.
Together is Better: Heavy Hitters Quantile Estimation

Stream monitoring is fundamental in many data stream applications, such as financial data trackers, security, anomaly detection, and load balancing. In that respect, quantiles are of particular interest, as they often capture the user's utility. For example, if a video connection has high tail (e.g., 99'th percentile) latency, the perceived quality will suffer, even if the average and median latencies are low. In this work, we consider the problem of approximating the per-item quantiles. Elements in our stream are (ID, value) tuples, and we wish to track the quantiles for each ID. Existing quantile sketches are designed for a plain number stream (i.e., containing just a value). While one could allocate a separate sketch instance for each ID, this may require an infeasible amount of memory. Instead, we consider tracking the quantiles for the heavy hitters (most frequent items), which are often considered particularly important, without knowing them beforehand. We first present a couple of simple and effective algorithms that serve as baselines, a sampling approach and a sketching approach. Then, we present SQUAD, an algorithm that combines sampling and sketching while improving the asymptotic space complexity. Intuitively, SQUAD uses a background sampling process to capture the behaviour of the quantiles of an item before it is allocated with a sketch, thereby allowing us to use fewer samples and sketches. The algorithms are rigorously analyzed, and we demonstrate SQUAD's superiority using extensive~simulations on real-world traces.
Narrowing the gap: expected versus deployment performance

Successful model development requires both an accurate a priori understanding of future performance and high performance on deployment. Optimistic estimations of model performance that are unrealized in real-world clinical settings can contribute to nonuse of predictive models. This study used 2 tasks, predicting ICU mortality and Bi-Level Positive Airway Pressure failure, to quantify: (1) how well internal test performances derived from different methods of partitioning data into development and test sets estimate future deployment performance of Recurrent Neural Network models and (2) the effects of including older data in the training set on models' performance.The cohort consisted of patients admitted between 2010 and 2020 to the Pediatric Intensive Care Unit of a large quaternary children's hospital. 2010-2018 data were partitioned into different development and test sets to measure internal test performance. Deployable models were trained on 2010-2018 data and assessed on 2019-2020 data, which was conceptualized to represent a real-world deployment scenario. Optimism, defined as the overestimation of the deployed performance by internal test performance, was measured. Performances of deployable models were also compared with each other to quantify the effect of including older data during training.Longitudinal partitioning methods, where models are tested on newer data than the development set, yielded the least optimism. Including older years in the training dataset did not degrade deployable model performance. Using all available data for model development fully leveraged longitudinal partitioning by measuring year-to-year performance.
Application of interactive information system in college personnel management by using BP neural network algorithm

BP neural network algorithm has the ability of flexible modeling and data parallel processing. When working with data, it learns through self-learning, adaptive algorithms, and associative functions. In the computational mode algorithm, this paper carries on the forward wizard to the data signal and the reverse wizard to the obtained value. The content of BP algorithm involved in the data is transmitted in the mode of forward guidance, and according to the analysis of the three-layer and above BP neuron model data, the form of the re-adjustment algorithm is obtained, and the interval data are analyzed and summarized, so as to study the trigger event of its application. In this paper, starting from the problem idea, the numerical analysis of the system processed as the starting point, starting from the personnel status quo, personnel form, analysis of the characteristics of the current stage of personnel management, and the difference between the previous personnel form, and summed up the characteristics of modern personnel management mode according to the principle, and analyzed the theoretical knowledge, from the concept, logic, and physical structure of the data analysis and design; an important development direction has been formed in terms of talent.
Prompt and Instruction-Based Tuning for Response Generation in Conversational Question Answering

In recent years, prompt-based tuning and instruction-based tuning have emerged as popular approaches for natural language processing. In this paper, we investigate the application of prompt and instruction-based tuning approaches for response generation in conversational question answering. We approach this task from both extractive and generative angles, where we adopt prompt-based tuning for the extractive angle and instruction-based tuning for the generative angle. Additionally, we utilize multi-task learning to integrate these two angles. To evaluate the performance of our proposed approaches, we conduct experiments on the GPT-2 model. The results show that the approaches improve performance by $$18\%$$ on F1 score over the baseline. We share our codes and data for reproducibility. ( https://github.com/yujie-xing/Multi-Turn_QA_Prompt ).
Time-Aware Smart City Services Based on QoS Prediction: A Contrastive Learning Approach

Smart cities are designed to satisfy the needs of residents and improve their quality of life by providing a wide range of smart city services. One of the keys to the efficient operation of smart city services is the accurate forecast of the missing Quality of Service (QoS). Presently, many approaches utilize the context information of users and services, such as geographic location and network location, to somewhat increase the prediction accuracy and forecast the missing QoS values. However, because the network conditions and server status are unpredictable, time is also considered as one of the important factors affecting QoS prediction, which brings more challenges as follows: higher data dimension, more complex data characteristics, and higher data sparsity. To overcome these challenges, we propose an approach for time-aware Web service QoS prediction based on contrastive learning (named CLpred). CLpred utilizes a sequential data input format for QoS data and models these QoS sequences through transformer encoder with CLpred framework. Therefore, it can downscale QoS data and extract a more efficient representation in complex QoS data. Furthermore, it makes it possible to apply data augmentation methods to address the problems of data sparsity. In order to prove the superiority of the proposed approach, particularly inside the presence of extremely high-data sparsity, extensive experiments are conducted on the well-known service QoS data set WSDREAM.
Dynamic and Nonlinear Programming for Trajectory Planning

Direct optimal control techniques, relying on numerical methods for constrained optimization, are typically used in trajectory planning tasks in high-dimensional spaces. However, general-purpose solvers often fail to find a feasible solution when facing cluttered environments. Sampling-or graph-based methods, instead, can explore complex configuration spaces but struggle with dynamic constraints. Here, we propose to combine dynamic programming (DP) and derivative-based methods to reliably solve trajectory planning problems. Specifically, we exploit DP to generate a sequence of waypoints in a lowdimensional space, which are then encoded as pointwise path constraints for a high-dimensional trajectory, whose constraint violations are then represented as a penalty within the Bellman equation to recompute the waypoints. This iterative approach, alternating path and trajectory optimization, avoids both the curse of dimensionality for DP and problematic nonconvexities (such as obstacles) for motion planning. We demonstrate our strategy using numerical experiments on a six-degree-of-freedom robotic manipulator moving in a confined space.
Knowledge Distillation in Granular Fuzzy Models by Solving Fuzzy Relation Equations

A knowledge distillationKnowledge distillation method is proposed that uses inverse inference to deploy a granular-fuzzy classifier on devices with limited computing resources. The System of Fuzzy Relation Equations (SFRE) serves as the carrier of teacher knowledge. The self-organized set of rules is integrated into the hierarchical distillation structure based on granular solutions of the SFRE. At the first stage, knowledge is transferred from the granular teacher model to a compact relational model to identify semantic trends in data. At the second stage, knowledge is transferred from the relation-based teacher model to the granular student model. To control the granularity level, knowledge is distilled in the form of interval or constrained solutionsConstrained solutions of the SFRE. The completeness and accuracy of the rule set is ensured by the genetic-neural technology for solvingSolving fuzzy relation equations the SFRE. The sequential data and knowledge distillationKnowledge distillation ensures a competitive compression performance. After compressing the training data, the relational model is trained using the distilled expert dataset. Incorporation of expert knowledge in the form of interval rules makes it possible to replace the granular teacher model with a compact relational model. The multitask distillation scheme ensures transferring distributed knowledge into the granular student model. Deployment of the student model can be parallelized when solving the SFRE for each output class. The self-organized set of rules based on solutions of the SFRE is autonomously identified using the incremental learning scheme. Compression of the student model is achieved through quantization at the level of granular or constrained solutionsConstrained solutions of the SFRE.
The prospect of digital human communication for organizational purposes

Introduction There has been a great deal of excitement and discussion about the potential for artificial intelligence (AI) to improve business processes by providing more effective delivery options in cost-effective ways. One such way for many companies is the use of chatbots (AI tools to talk to customers) as their first line of customer service communication. Using digital human technology (AI with a digital human) such as a chatbot to improve communication engagement is increasing in many companies. Digital human communication has advantages over other communication modes, such as being available at any time for employees to consume and always having correctly, vetted information to share. Even though digital human technology offers great promise for organizational communication, the potential utility will be impacted by how employees perceive communication. For example, in-person conversations with business leaders or human resources may not be readily available, creating a positive perception of the communication availability of digital humans. However, if employees have a negative bias toward digital human communication, they may not perceive it as an effective source of communication. Method This experimental exploratory study was conducted to better understand the satisfaction and perceived effectiveness of digital human communication. It was assessed by using mixed methods across three groups of respondents. One group heard a company announcement in person, the second group read the same announcement in the form of an email, and the last group heard the message from an AI-generated digital human. The digital human created was the same as the in person human and the words were exactly the same. After the communication, the respondents took a survey assessing their satisfaction and perception of the communication modality they received. Results It was found that users were satisfied with all 3 modes of communication, but the email was the most effective tool for most aspects of communication, even surpassing the human one for most aspects of satisfaction and effectiveness. Additionally, there was no perceived difference in organizational support for the 3 groups. Discussion When subjects are exposed to something new, they immediately make an appraisal based on their perception of the usability and gain of that interaction which impacts their willingness to accept new technologies. As per our study, firms can start using Digital humans for communication for corporate announcements and even better would be an interactive digital human.
Emoji as a Social Presence Tool Among Arab Digital Media Users: Do the Demographic Variables of the Sender Play a Role?

To contribute to the current knowledge, this research was conducted, perhaps for the first time, among 1354 Arab users of digital media platforms to investigate emoji as a social presence tool and how the variables of the gender, generation, and the sender’s relationship to the receiver affect the usage and interpretation of the appropriateness of these graphical icons. Among the significant results of this study, generation and gender explained a significant amount of the variance in the frequency and motivation index. Interestingly, there was a significant, three-way interaction among senders’ gender, raters’ gender, and salience. The analysis also showed that the generation and relationship of the sender had a statistically significant effect on appropriateness ratings.
Active learning-based pedestrian and road sign detection

For autonomous driving, pedestrian and road signs detection are key elements. There is much existing literature available addressing this issue successfully. However, the autonomous system requires a large and diverse set of training samples and labeling in real-world environments. Manual annotation of these samples is somewhat challenging and time-consuming. In this paper, our goal is to get better detection accuracy with minimal training data. For this, we have employed the active learning algorithm. Active learning is a useful method that selects only the effective portion of the dataset for training and reduces annotation costs. Though it uses only a small amount of the training data, it provides a high detection accuracy. In this work, we have chosen the deep active learning model for object detection via the probabilistic model of Choi et al. and modified the depth scale of different layers in the backbone. As real-world data may contain noise, motion, or other disruptions, we modified the original model to obtain improved detection results. In this experiment, we create a customized dataset that contains pedestrians, road signs, traffic lights, and zebra (or pedestrian) crossings to deploy the active learning algorithm. The experimental results show that the active learning model can produce good detection outcomes by accurately detecting and classifying pedestrians, road signs, traffic light, and zebra (or pedestrian) crossings.
Iterative multiple instance learning for weakly annotated whole slide image classification

Objective.Whole slide images (WSIs) play a crucial role in histopathological analysis. The extremely high resolution of WSIs makes it laborious to obtain fine-grade annotations. Hence, classifying WSIs with only slide-level labels is often cast as a multiple instance learning (MIL) problem where a WSI is regarded as a bag and tiled into patches that are regarded as instances. The purpose of this study is to develop a novel MIL method for classifying WSIs with only slide-level labels in histopathology analysis.Approach.We propose a novel iterative MIL (IMIL) method for WSI classification where instance representations and bag representations are learned collaboratively. In particular, IMIL iteratively finetune the feature extractor with selected instances and corresponding pseudo labels generated by attention-based MIL pooling. Additionally, three procedures for robust training of IMIL are adopted: (1) the feature extractor is initialized by utilizing self-supervised learning methods on all instances, (2) samples for finetuning the feature extractor are selected according to the attention scores, and (3) a confidence-aware loss is applied for finetuning the feature extractor.Main results.Our proposed IMIL-SimCLR archives the optimal classification performance on Camelyon16 and KingMed-Lung. Compared with the baseline method CLAM, IMIL-SimCLR significantly outperforms it by 3.71% higher average area under curve (AUC) on Camelyon16 and 4.25% higher average AUC on KingMed-Lung. Additionally, our proposed IMIL-ImageNet achieve the optimal classification performance on TCGA-Lung with the average AUC of 96.55% and the accuracy of 96.76%, which significantly outperforms the baseline method CLAM by 1.65% higher average AUC and 2.09% higher average accuracy respectively.Significance.Experimental results on a public lymph node metastasis dataset, a public lung cancer diagnosis dataset and an in-house lung cancer diagnosis datasets show the effectiveness of our proposed IMIL method across different WSI classification tasks compared with other state-of-the-art MIL methods.
Predicting the Persuasiveness of Influence Strategies From Student Online Learning Behaviour Using Machine Learning Methods

There is a dearth of knowledge on how persuasiveness of influence strategies affects students’ behaviours when using online educational systems. Persuasiveness is a term used in describing a system’s capability to motivate desired behaviour. Most existing approaches for assessing the persuasiveness of a system are based on subjective measures (questionnaires) which are static and do not allow for automatic measurement of systems persuasiveness at run-time. Being able to automatically predict a system’s persuasiveness at run-time is essential for dynamic and continuous adaptation of the system to reflect each individual user’s state. In this study, we investigate the links between persuasiveness of influence strategies and students’ behaviour in an online educational system for a course. We implemented and tested Machine Learning (ML) classification models to determine whether persuasiveness had a significant impact on students’ usage of a learning system. Our findings revealed that students learning data can be applied to predict the persuasiveness of different influence strategies. The implications are that by using machine learning classifiers powered with learning sessions data, online educational systems would be able to automatically adapt their persuasive strategies to improve students’ engagement and learning.
Combining CNN and transformers for full-reference and no-reference image quality assessment

Most deep learning approaches for image quality assessment use regression from deep features extracted by CNN (Convolutional Neural Networks). However, non-local information is usually neglected in existing methods. Motivated by the recent success of transformers in modeling contextual information, we propose a hybrid framework that utilizes a vision transformer backbone to extract features and a CNN decoder for quality estimation. We propose a shared feature extraction scheme for both FR and NR settings. A two-branch structured attentive quality predictor is devised for quality prediction. Evaluation experiments on various IQA datasets, including LIVE, CSIQ and TID2013, LIVE-Challenge, KADID-10 K, and KONIQ-10 K, show that our proposed models achieve outstanding performance for both FR and NR settings.
Classroom engagement evaluation using 360-degree view of the camera with deep learning techniques

Measuring classroom engagement is an important but challenging task in education. In this paper, we present an automated method for the assessment of the degree of classroom engagement using computer vision techniques that integrate data from multiple sensors, including the front and back of the student's seating arrangement. The students' engagement is evaluated based on attributes such as facial expression, gesture, head position, and distractions visible from the frontal view of the students. Moreover, using the videos from the back of the classroom, the professor's teaching content as well as their alignment with student engagement, are calculated. We leverage deep learning methods to extract emotion and behavior features to aid in the evaluation of engagement. These AI methods will quantify the classroom engagement process.
Visible light small object detection based on YOLOv5

The traditional object detection algorithm is difficult to extract its characteristic information due to its own features such as low resolution and small coverage area of small objects, resulting in the inability to achieve effective and reliable recognition accuracy. Aiming at the problem of small object detection, this paper proposes a method of visible light small object detection based on deep learning YOLOv5 algorithm. First of all, a total of 4000 visible light small object dataset is created at noon and low light under the background of sunny and cloudy weather, and then YOLOv5 is used for training, of which the mAP@0.5 of 100 and 200 times are trained to reach about 95% and 96%, respectively. Finally, the 500 pure sky background visible light small object images outside the dataset are tested using the trained model, and the recognition rate in sunny weather reached 99%. However, in cloudy weather, due to the interference of clouds, false detection and missed detection occur, and the recognition rate is about 97%. For the phenomenon of false detection, the moving object detection algorithm are combined to exclude. First of all, a small amount of large particles of pretzel noise is added, combined with the moving object detection algorithm, the motion trajectory is plotted for the continuously moving visible small objects, so as to exclude the noise that is far away from the motion trajectory, the coarse filtration rate reaches 79.5%, and the remaining target point collection is further filtered out by DBSCAN clustering algorithm, and the noise filtering rate can reach 100%.