Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-label Image Classification Model via Label Correlation Matrix

Multi-label classification is one of the most essential tasks of computer vision. In the multi-label image classification model, using the correlation between labels is a powerful method for improving the classification effectiveness of the model. However, common methods ignore the interrelationship between the label pairs. On the other hand, introducing a spatial attention mechanism into the model could also improve the classification effectiveness of the model. However, most methods that use the attention mechanism module do not use the correlation information between labels. To solve these issues, we propose a novel multi-label image classification model using the label correlation in the paper. Our model generates label word vectors based on the BERT model that can describe the potential relationship between labels. And then we combine these vectors with static statistics information on labels to construct a new label correlation matrix. Moreover, we introduce label semantic information into the spatial attention mechanism. With the semantic information, the generated spatial attention map could focus on the image feature regions with label correlation, and complete the accurate classification of the model. On the Microsoft COCO data set, this model achieves the best score of 84.3% on mAP, which shows the effectiveness of our model.
Neighbor Reward with Optimal Reciprocal Collision Avoidance for Swarm Agents*

Navigating in an unknown area safely is counted as the underlying work which can support swarm agents for more complex tasks. When available information of search regions are lacking, agents make real-time action decisions according to surrounding environments they have perceived. For swarm agent system, connectivity maintenance and collision avoidance are both essential. Based on optimal Reciprocal Collision Avoidance (ORCA) algorithm, we proposed a method that agents can provide assistances to surrounding agents by spreading the status information of themselves, which is the neighbor reward method (NRM). This kind of status information contains ambient information and perceptions of the task which are transferred to reward data for convenient and uniform distributions. In other words, individuals utilize inter-neighbor interactions to achieve the same high-level goal, as well as result in an intelligent independent swarm agents system. This method solves the velocity selection problem of ORCA and optimizes the obstacle avoidance of the original NRM. The algorithm has been integrated in ROS framework and simulated on GAZEBO. In the tested scenario, our method is efficient for swarm agents collision avoidance in decentralized way.
Joint Measurement of Multi-channel Sound Event Detection and Localization Using Deep Neural Network

For joint sound event localization and detection (SELD), a multi-channel sound event method based on deep learning is proposed. This paper uses CRNN model training with datasets of maximum two overlapping sound events. The difficulty of the polyphonic SELD is the combination of SED and DOA estimation in the same network. Using multi-channel audio can better identify these overlapping sound events. The input of the proposed model is a series of continuous spectrograms, which are then output to two branches respectively. As the first branch, SED performs multi-label classification in each time segment. As the second branch, 3-D Cartesian coordinates are used to represent the DOA estimate of each sound event. This paper extracts the phase feature and amplitude feature of the sound spectrum from each audio channel, avoiding feature extraction limited by other microphone arrays.
SPI-CGAN: Single-pixel imaging based on conditional generative adversarial network

Imaging quality at extremely sampling ratios is a key research topic in the field of single-pixel imaging (SPI). Although conventional methods can be used to reconstruct the object images at low sampling ratios, the reconstructed image is still visually unclear. To solve this problem, an SPI model based on a conditional generative adversarial network (SPI-CGAN) is proposed to achieve an end-to-end reconstruction of the object images and improve the image quality of the reconstruction at extremely low sampling ratios. To improve the stability of the training model, the objective function is composed of multiple loss functions. The validity of the model is verified through simulation data, which do not require a long time to collect. The optimized SPI-CGAN can reconstruct a sharp image edge at extremely low sampling ratios. Compared with a linear iterative method and a nonlinear iterative method, the proposed method performs better in terms of the quantitative indicators used.
Particle swarm optimization gray wolf fusion algorithm for solving the optimal solution of function

In order to improve the local difference problem of wolf swarm algorithm, which is easy to fall into local optimization in solving function optimization, a particle swarm grey wolf fusion algorithm is proposed based on the characteristics of particle swarm algorithm, such as fast search speed, strong ability and easy implementation. Firstly, this paper compares particle swarm optimization and wolf swarm algorithm, and introduces particle swarm optimization gray wolf fusion algorithm by analyzing the root of the problem. The experimental results show that the particle swarm wolf swarm algorithm is feasible, and the search accuracy, stability and the optimal value of the objective function are significantly improved in the solution process.
A Method of Robot Grasping Based on Reinforcement Learning

In this paper, we propose a method for robot grasping based on reinforcement learning. A six-degree-of-freedom robot is used to execute the grasping action, and an RGB-D camera is used as a sensing module to collect the information. Different from previous model-based methods, our whole system is data-driven. In order to better grasp a bunch of randomly placed objects, the pushing action is added to spare more space for grasping. The whole process is described as a Markov decision process (MDP). The training is carried out in simulation. Experimental results show that our method can complete the grasping work efficiently, especially in those complicated and difficult scenarios. At the same time, the model can be directly applied in actual grasping tasks which greatly reduce the risk of training in the physical world.
Onchip digital calibrated 2 mW 12-bit 25 MS/s SAR ADC with reduced input capacitance

We present a 12-bits asynchronous SAR ADC with a low complexity digital on-chip calibration and just 2 pF of total array capacitance. The ADC architecture utilizes a redundant weighting switching of 2 fF MOM capacitors consuming 14 clock-cycles to complete the conversion. Taking advantage of redundancy, the weights of the MSB capacitors are estimated using the LSB array, thus it is possible to digitally compensate for the mismatch non-linearity directly over the ADC output. The circuit consumes 2 mW at 25 MS/s on a core area of 300 μm × 500 μm in 180 nm CMOS technology. ENOB improvements of 0.85 bits were post-layout simulated after calibration. Sample characterization is ongoing.
Channel Attention Module for Efficient Action Recognition

For action recognition, 3D CNNs can achieve satisfactory performance, but they are parameter-rich and computationally intensive. Although conventional 2D CNNs have a small computational burden, they do not capture the channel relationships well. To mitigate this issue, we propose a plug-and-play channel attention module that is simple yet effective. Our CA module can perform adaptive refinement of the feature for the given input feature map by capturing channel-wise dependencies. Since the CA module is generic and lightweight, it can be easily integrated into regular convolutional neural networks. We embedded the CA module into TSM and evaluated it by conducting extensive experiments on the commonly used action recognition dataset: Kinetics-400 and gesture recognition dataset: Jester. For Kinetics-400, our CA module was able to improve the top-1 accuracy by 1.24%, 1.46%, 1.43% and 1.53% when using one crop, three crops, five crops and ten crops, respectively. Our experimental results showed that the CA module brings consistent performance improvements, proving the effectiveness of the CA module.
Twitter Sentiment Analysis: Global Attitudes Towards COVID-19 Policies

This project aims to capture widespread sentiment about COVID-19 and COVID-19 related policies on Twitter—the world’s most popular text-based social media platform. To accomplish this task, we fine-tuned a pretrained BERT model: Twitter-RoBERTa-Base-Sentiment [1] on COVID-19-related Tweets labelled for 3 sentiment classes. After training, this model achieves a test accuracy of 65 . 47% , out-performing the baseline Twitter-RoBERTa-Base-Sentiment by 19 . 47% . Then, we applied our model to classify Twitter sentiment globally in English speaking nations (United Kingdom, United States, and Canada) related to two of the most prevalent COVID-19-related policies: digital contact tracing (DCT) and vaccine mandates. Our results show that, overall, people viewed digital contact tracing more favorably than vaccine mandates, and that both DCT and vaccines are more supported by Twitter users in the United Kingdom than those in the United States.
Portfolio allocation and borrowing constraints

We explore the empirical relationship between borrowing constraints and household ﬁnancial portfolio allocation. To motivate our analysis we develop a mean-variance model of portfolio allocation with three tradable asset classes deﬁned by increasing risk, and establish a link between borrowing restrictions and ﬁnancial portfolio allocation at the household level. Under non-restrictive assumptions the proportion of wealth allocated to the medium-risk asset is ambiguous. We also demonstrate that in the presence of both correlated background risk and borrowing constraints the domain of the non-binding risk-return space will be a function of background risk. We then analyse the US Survey of Consumer Finances with a view to empirically exploring the predictions of our theoretical framework. The distribution of medium-risk assets in US households is remarkably similar to that for high-risk assets, and suggests the presence of a more general ‘risk puzzle’, which our proxies for borrowing constraints partially explain. Our ﬁndings indicate that such constraints are inversely related to the proportion of ﬁnancial wealth allocated to both high-risk and medium-risk assets, but are positively related to low-risk asset holdings. In light of our ﬁndings, further work aimed at accounting for the allocation of medium-risk assets in US households is considered expedient. it would also be unable to handle boundary observations of 0 or 1 shares; and would likely embody heteroskedasticity in u ij . We have also explored the use of a multi-nominal fractional response model, see for example, Becker In this setting, the inherent risk ordering of asset classes is not accounted for in the estimation strategy, instead the multi-nominal probit model is used as the foundation of the estimation strategy. We obtain similar results to those presented when we adopt this alternative modelling strategy.
IID SQuAD track: Comparing QANet with BiDAF

In the task of reading comprehension or question answering, a model will be given a paragraph and a question about that paragraph as inputs, and the goal is to answer the question correctly. This is an interesting task as it could be viewed as how well a model can “understand” text. Current end-to-end machine reading and question answering models such as BiDAF [1] and QANet [2] can achieve relatively good results. In this work, we (1) improved on the given BiDAF-based model, (2) implemented QANet, (3) explored data augmentation and model ensembling to further improve the performance of the QA system, and (4) investigated and analyzed the difference between BiDAF and QANet. For our final ensemble model, we achieved an F1 score of 70.09, an EM score of 67.50, and an AvNA score of 74.36 on dev set - all three of these metrics showed around 10 % improvement compared to the baseline model. We released the source code of our project on GitHub.
Comparison of mobile QoE: an unsupervised field study in Argentina and Cuba

This article presents the findings of an unsupervised field study conducted over 31 days in Argentina and Cuba in order to explore mobile QoE. We also analyzed the correspondence between the objective observations and users’ opinions to identify contextual conditions that could have influenced the study’s results. Using their own mobile devices in everyday contexts, 95 users from Argentina and Cuba interacted with CovidInfo app, thereby recording values for eight objective metrics as well as their opinions. The total data set collected consisted of 41,144 records. Analysis of the CovidInfo application yielded positive QoE results in both countries, as 88% and 70% of the objective metrics in Argentina and Cuba, respectively, were optimal. The main difference between the two countries is the type of connection: In Argentina, WiFi networks are predominant, while the trend in Cuba is mobile data connections.
Improving Multi-label Malevolence Detection in Dialogues through Multi-faceted Label Correlation Enhancement

A dialogue response is malevolent if it is grounded in negative emotions, inappropriate behavior, or an unethical value basis in terms of content and dialogue acts. The detection of malevolent dialogue responses is attracting growing interest. Current research on detecting dialogue malevolence has limitations in terms of datasets and methods. First, available dialogue datasets related to malevolence are labeled with a single category, but in practice assigning a single category to each utterance may not be appropriate as some malevolent utterances belong to multiple labels. Second, current methods for detecting dialogue malevolence neglect label correlation. Therefore, we propose the task of multi-label dialogue malevolence detection and crowdsource a multi-label dataset, multi-label dialogue malevolence detection (MDMD) for evaluation. We also propose a multi-label malevolence detection model, multi-faceted label correlation enhanced CRF (MCRF), with two label correlation mechanisms, label correlation in taxonomy (LCT) and label correlation in context (LCC). Experiments on MDMD show that our method outperforms the best performing baseline by a large margin, i.e., 16.1%, 11.9%, 12.0%, and 6.1% on precision, recall, F1, and Jaccard score, respectively.
Run-off-road and recovery – state estimation and vehicle control strategies

ABSTRACT Despite many advances in vehicle safety technology, traffic fatalities remain a devastating burden on society. With over two-thirds of all fatal single-vehicle crashes occurring off the roadway, run-off-road (ROR) crashes have become the focus of much roadway safety research. Current countermeasures, including roadway infrastructure modifications and some on-board vehicle safety systems, remain limited in their approach as they do not directly address the critical factor of driver behaviour. It has been shown that ROR crashes are often the result of poor driver performance leading up to the crash. In this study, the performance of two control algorithms, sliding control and linear quadratic control, was investigated for use in an autonomous ROR vehicle recovery system. The two controllers were simulated amongst a variety of ROR conditions where typical driver performance was inadequate to safely operate the vehicle. The sliding controller recovered the fastest within the nominal conditions but exhibited large variability in performance amongst the more extreme ROR scenarios. Despite some small sacrifices in lateral error and yaw rate, the linear quadratic controller demonstrated a higher level of consistency and stability amongst the various conditions examined. Overall, the linear quadratic controller recovered the vehicle 25% faster than the sliding controller while using 70% less steering, which combined with its robust performance, indicates its high potential as an autonomous ROR countermeasure.
A minibatch stochastic Quasi-Newton method adapted for nonconvex deep learning problems

In this study, we develop a limited memory nonconvex Quasi-Newton (QN) method, tailored to deep learning (DL) applications. Since the stochastic nature of (sampled) function information in minibatch processing can affect the performance of QN methods, three strategies are utilized to overcome this issue. These involve a novel progressive trust-region radius update (suitable for stochastic models), batched evaluation instead of the entire data set, for selecting gradient batch-size and a restart strategy when quasi-Netwon approximation accuracy deteriorates. We analyze the convergence properties of our proposed method and provide the required theoretical analysis for different components of our algorithm. The numerical results illustrate that our proposed methodology with the new adjustments outperforms the previous similar methods, and is competitive with the best tuned stochastic first-order methods, in cases where large batch-size is required. Finally, we empirically show that our method is robust to the choices of hyper-parameters, thus, requiring less tuning compared to Stochastic Gradient Descent (SGD) method.
Standard Attention versus Attention-Based Recurrent Networks for Reading Comprehension

This paper describes an Attention-Based Recurrent Network model containing the self-attention mechanism from Microsoft Research Asia’s R-Net (2017) with the encoding and output layers similar to Seo et al.’s Bi-Directional Attention Flow (BiDAF) model. Given an input context paragraph and question about the context paragraph, the model locates the answer to the question within the context paragraph by predicting the start token and end token of the answer string within the context paragraph. The paper compares the attention mechanisms of BiDAF and R-Net, discussing their upsides and drawbacks. This paper also analyzes the challenges of implementing an Attention-Based Recurrent Network model with minimal information loss and quick training time. The model attains an EM score of 55.790 and an F1 score of 59.537 on the Stanford Question Answering Dataset 2.0 (SQuAD 2.0) (Rajpurkar et al., 2016). This metric shows that the Attention-Based Recurrent Network model does not perform much better than the BiDAF baseline, although there are specific improvements that could be made to the model as discussed in section 6.
Application of Mixture of Experts in Domain-agnostic Question Answering

The goal of this project is to explore question answering models that are able to transfer what it learned in certain domains to unknown domains. Inspired by the intuition that given a particular target dataset, a specialized model trained on a similar dataset will outperform a general model, we employ a method to ensemble a mixture of dataset experts which turns out to be effective in improving both in-domain and out-of-domain predicition performance.
Detecting User Initiative in Social Chatbots

Mixed initiative, or two parties taking turns leading the conversation, is an important feature of human-human conversations, but one that social chatbots have difficulty with. In this project, we train a neural model that detects the user’ initiative level, as a basis for the future task of conditioning bot response on user initiative. We have annotated our own data and finetuned two pretrained models, BERT and DialogRPT, the latter with stacking ensemble learning. Our models achieve moderate improvement over the baseline. We discuss the limitations of our models in detail and lay out a plan for future work
Does volunteering improve well-being and physical health?

Doesvolunteeringcausallyimprove well-being? To empiricallytestthis question, we examined one instantiation of volunteering that is com- mon at post-secondary institutions across North America: community service learning (CSL). CSL is a form of experiential learning that combines volunteer work with intentional learning goals and active re ﬂ ection. We partnered with an academic program that randomly assigns interested students to participate in a CSL program or to a wait-list. As part of this CSL program, students are required to engage in 10 – 12 h of formal volunteering each week in addition to completing related coursework. To assess the well-being bene ﬁ ts of formal volunteering through CSL participation, we examined the subjective well-being (SWB) of students from both groups over a six-month period. Using Bayesian statistics, and comparing a null model to a model specifying a small to moderate bene ﬁ t of CSL participation, we found conclusive evidence in support of the null model. These ﬁ ndings diverge from previous correlational research in this area by providing no evidence for the causal bene ﬁ ts of volunteering on SWB. These ﬁ ndings highlight the critical importance of using experimental meth-odology to establish the causal bene ﬁ ts of volunteer work, such as the experiences provided by CSL programs, on SWB.
Dynamic and Static Chunk Reader (DCR/SCR) on SQuAD 2.0 Task

Question answering has been attempted by numerous models of many different architectures. The Dynamic Chunk Reader model proposed by Yu et al. [1], contrary to most, has attempted to frame the task as the prediction of chunks of text rather than independent prediction of start and end indices. We implement this model and put it to test under the updated SQuAD 2.0 dataset. We also discuss the shortcomings of the model, related with how it chooses to generate the answer chunks to be considered as candidates. To this end, we propose a variation, the Static Chunk Reader model, that aims to overcome the limitations of DCR by making use of the structure of the context paragraphs from which the questions will be answered, based on these paragraphs’ dependency trees. We also refine the word embeddings fed into the model by including the character-level embeddings, following the CNN approach by Kim [2]. We find that this augmentation improves the performance of the BiDAF [3] model that we use as baseline. Our findings also show that DCR performs poorly on the SQuAD 2.0 dataset, and SCR follows a similar pattern, although leaving more room for improvement.
Sepsis outcomes in patients with pre-existing liver disease

Aim of the study To determine the characteristics, including the use of various diagnostic criteria, outcomes and treatment strategies in septic patients treated outside of the critical care area with pre-existing liver disease (LD). Material and methods The study population included patients recruited into two annual 24-hour prospective point-prevalence studies on the general wards and emergency departments across all Welsh acute hospitals in 2016 and 2017. Data were collected on patient demographics, observations and SIRS, SOFA and qSOFA scores. Results Out of 839 recruited patients, 24 (2.9%) had a past medical history of LD. 12/24 (50%) had a SIRS score ≥ 2, 21/24 (87.5%) a SOFA score ≥ 2 and 3/24 (12.5%) a qSOFA score ≥ 2. Patients with LD had 2.4 times higher odds (95% CI = 1.07-5.53, p = 0.03) of mortality after the sepsis episode. LD patients were younger than non-LD patients (p = 0.04) but not significantly different in frailty, do not attempt cardiopulmonary resuscitation (DNA-CPR) status or ceiling of care (p = 0.78, p = 0.54, p = 0.06, respectively). Conclusions The 90-day mortality was greater in patients with LD than the rest of the population. Management of sepsis in LD patients poses a challenge with current therapeutic bundles being underused and of unclear significance in improving patient outcome.