Reviewerly/exHarmony huggingface dataset:
Non-Anchor-Based Vehicle Detection for Traffic Surveillance Using Bounding Ellipses

Cameras for traffic surveillance are usually pole-mounted and produce images that reflect a birds-eye view. Vehicles in such images, in general, assume an ellipse form. A bounding box for the vehicles usually includes a large empty space when the vehicle orientation is not parallel to the edges of the box. To circumvent this problem, the present study applied bounding ellipses to a non-anchor-based, single-shot detection model (CenterNet). Since this model does not depend on anchor boxes, non-max suppression (NMS) that requires computing the intersection over union (IOU) between predicted bounding boxes is unnecessary for inference. The SpotNet that extends the CenterNet model by adding a segmentation head was also tested with bounding ellipses. Two other anchor-based, single-shot detection models (YOLO4 and SSD) were chosen as references for comparison. The model performance was compared based on a local dataset that was doubly annotated with bounding boxes and ellipses. As a result, the performance of the two models with bounding ellipses exceeded that of the reference models with bounding boxes. When the backbone of the ellipse models was pretrained on an open dataset (UA-DETRAC), the performance was further enhanced. Several data augmentation schemes also improved the performance of the proposed models. As a result, the best mAP score of a CenterNet exceeds 0.95 when augmenting heatmaps with bounding ellipses.
A Survey Of zero shot detection: Methods and applications

Zero shot learning (ZSL) is aim to identify objects whose label is unavailable during training. This learning paradigm makes classifier has the ability to distinguish unseen class. The traditional ZSL method only focuses on the image recognition problems that the objects only appear in the central part of images. But real-world applications are far from ideal, which images can contain various objects. Zero shot detection (ZSD) is proposed to simultaneously localizing and recognizing unseen objects belongs to novel categories. We propose a detailed survey about zero shot detection in this paper. First, we summarize the background of zero shot detection and give the definition of zero shot detection. Second, based on the combination of traditional detection framework and zero shot learning methods, we categorize existing zero shot detection methods into two different classes, and the representative methods under each category are introduced. Third, we discuss some possible application scenario of zero shot detection and we propose some future research directions of zero-shot detection.
The Effect of Training in Virtual Reality on the Precision of Hand Movements

The main point of the work was to use virtual reality to discover its benefits on training, specifically on the precision of hand movements in specific settings, and then evaluate its effects both for virtual reality and the transfer of the results to the real world. A virtual reality simulation was created using the Unity3D game engine and real-world experimental material was also prepared. A total of 16 participants took part in the training, which lasted for approximately one month. Once the data was gathered from both the virtual reality and real-world tests, we carried out in-depth statistical analysis. The results suggest positive outcomes in most aspects in virtual reality training productivity, but only partial transfer of the training benefits to the real world scenario. The possible reasons for this are described in the work and suggestions are given to duplicate the study with different variables to try to achieve different results.
Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers

The increasing use of machine learning in practice and legal regulations like EU’s GDPR cause the necessity to be able to explain the prediction and behavior of machine learning models. A prominent example of particularly intuitive explanations of AI models in the context of decision making are counterfactual explanations. Yet, it is still an open research problem how to efficiently compute counterfactual explanations for many models. In this contribution, we investigate how to efficiently compute counterfactual explanations for an important class of models, prototype-based classifiers such as learning vector quantization models. In particular, we derive specific convex and non-convex programs depending on the used metric. Typically counterfactual explanations deliver a feedback in terms of changes of the input features which lead to a different output – one application scenario is the link of these required changes to actionable items to change the desired outcome. Yet, rather than minimum changes of the input, it is interesting to address minimum changes of the model itself, which are required to lead to a different result. rather than a change of its inputs. We phrase this question as a counterfactual of the model prescription rather than the data points. We focus on distance-based classifiers (in particular learning vector quantization models), where model changes correspond to changes of metric parameters, and we develop efficient optimization techniques to generate such counterfactual metric changes depending on the chosen model.
Variables are valuable: making a case for deductive modeling

Abstract Following the quantitative turn in linguistics, the field appears to be in a methodological “wild west” state where much is possible and new frontiers are being explored, but there is relatively little guidance in terms of firm rules or conventions. In this article, we focus on the issue of variable selection in regression modeling. It is common to aim for a “minimal adequate model” and eliminate “non-significant” variables by statistical procedures. We advocate an alternative, “deductive modeling” approach that retains a “full” model of variables generated from our research questions and objectives. Comparing the statistical model to a camera, i.e., a tool to produce an image of reality, we contrast the deductive and predictive (minimal) modeling approaches on a dataset from a corpus study. While a minimal adequate model is more parsimonious, its selection procedure is blind to the research aim and may conceal relevant information. Deductive models, by contrast, are grounded in theory, have higher transparency (all relevant variables are reported) and potentially a greater accuracy of the reported effects. They are useful for answering research questions more directly, as they rely explicitly on prior knowledge and hypotheses, and allow for estimation and comparison across datasets.
A Study into Pre-Training Strategies for Spoken Language Understanding on Dysarthric Speech

End-to-end (E2E) spoken language understanding (SLU) systems avoid an intermediate textual representation by mapping speech directly into intents with slot values. This approach requires considerable domain-specific training data. In low-resource scenarios this is a major concern, e.g., in the present study dealing with SLU for dysarthric speech. Pretraining part of the SLU model for automatic speech recognition targets helps but no research has shown to which extent SLU on dysarthric speech benefits from knowledge transferred from other dysarthric speech tasks. This paper investigates the efficiency of pre-training strategies for SLU tasks on dysarthric speech. The designed SLU system consists of a TDNN acoustic model for feature encoding and a capsule network for intent and slot decoding. The acoustic model is pre-trained in two stages: initialization with a corpus of normal speech and finetuning on a mixture of dysarthric and normal speech. By introducing the intelligibility score as a metric of the impairment severity, this paper quantitatively analyzes the relation between generalization and pathology severity for dysarthric speech.
Natural Residual Reinforcement Learning for Bicycle Robot Control

This work focuses on motion control of the bicycle robot by using the proposed NRRL algorithm. Unlike the traditional RL algorithm, decomposing the main tasks into subtasks manually and introducing qualitative prior knowledge to the agent have been applied in the NRRL algorithm. Simulation results show that better performance and better sample efficiency of the proposed NRRL algorithm have been achieved in terms of balance control and path tracking of bicycle robot. It's believed that the NRRL algorithm is available on the real physical bicycle robot, and the deployment of the algorithm will be realized soon, as the real physical bicycle robot has been constructed currently.
Leveraging bilingual-view parallel translation for code-switched emotion detection with adversarial dual-channel encoder

Code-switched emotion detection, a task analyzing the emotion in code-switched texts, has gain increasing research attention within recent years. Prior works utilize various neural models with sophisticated features to pursuit high performance of the task, while they still overlook some crucial characteristics of the code-switched texts. In this work, we present an innovative approach for improving code-switched emotion detection. We first consider a bilingual-view parallel translation for code-switched text enhancement, i.e., translating the code-switched texts into two languages. Then we propose an adversarial dual-channel encoder architecture, where two private encoders take as inputs the parallel texts in two languages, respectively. The private encoders and the shared encoder work collaboratively, and effectively retrieve the features from monolingual and bilingual perspectives under adversarial training. We conduct extensive experiments on five code-switched benchmark datasets. Results show that our model outperforms the strongly-performing baselines that leverage external code-switched or bilingual word embedding with over 1.5% F1 score on the Chinese–English, Spanish–English and Hindi–English code-mixed data, becoming the new state-of-the-art system. Further analyses including ablation, qualitative and error studies, demonstrate the effectiveness of our proposed encoder for code-switched texts, as well as the bilingual-view parallel translation strategy.
Multi‐agent reinforcement learning via knowledge transfer with differentially private noise

In multi-agent reinforcement learning, transfer learning is one of the key techniques used to speed up learning performance through the exchange of knowledge among agents. However, there are three challenges associated with applying this technique to real-world problems. First, most real-world domains are partially rather than fully observable. Second, it is difficult to pre-collect knowledge in unknown domains. Third, negative transfer impedes the learning progress. We observe that differentially private mechanisms can overcome these challenges due to their randomization property. Therefore, we propose a novel differential transfer learning method for multi-agent reinforcement learning problems, characterized by the following three key features. First, our method allows agents to implement real-time knowledge transfers between each other in partially observable domains. Second, our method eliminates the constraints on the relevance of transferred knowledge, which expands the knowledge set to a large extent. Third, our method improves robustness to negative transfers by applying differentially exponential noise and relevance weights to transferred knowledge. The proposed method is the first to use the randomization property of differential privacy to stimulate the learning performance in multi-agent reinforcement learning system. We further implement extensive experiments to demonstrate the effectiveness of our proposed method.
Arousing a wide range of emotions within educational virtual reality simulation about major depressive disorder affects knowledge retention

Abstract The experience of using an educational application, concerning a major depressive disorder simulation, could be anything but pleasant, so the challenges of creating such an application are ample. In this research, the effects of the emotional experience of the players, deriving from the positive ending of the virtual reality (VR) simulation’s embedded narrative or the lack of it, are evaluated. Alongside the investigation of a possible link between the emotional impact of the simulation and information retention, the overall effect of the application in relation to VR presence and body ownership is appraised. Thirty participants over 18 years old tested the application, using an Oculus Rift head-mounted display with a joystick, and their data were recorded by a pre- and a post-questionnaire. The 30 participants have been separated into groups of 15, where the positive ending was accessible to only one of the two groups. The group which experienced the positive ending reported a significant correlation of emotional impact and knowledge retention.
A new phase to height model in fringe projection profilometry by considering radial distortion of camera lens

In fringe projection profilometry, the straight-shaped fringe patterns predefined in computer are projected onto the object surface and the distorted fringe patterns reflected from the surface are captured by camera. The phase information obtained by analysis of the modulated fringe pattern is converted to the height information by a certain phase to height model. Here, the accuracy of the height information is affected by the lens distortion. So it arises the problems to consider the lens distortion during capturing the fringe pattern and to use correct phase to height model. In this paper a phase to height model is proposed, which uses 9 coefficients, and a new method for removing the radial distortion of camera lens is suggested by ignoring the tangential distortion and approximating the total lens distortion model to a 5th-order equation. Simulation and experiment results demonstrate that the proposed method is available and effective in three-dimensional shape measurement.
Emerging trends: Deep nets for poets

Abstract Deep nets have done well with early adopters, but the future will soon depend on crossing the chasm. The goal of this paper is to make deep nets more accessible to a broader audience including people with little or no programming skills, and people with little interest in training new models. A github is provided with simple implementations of image classification, optical character recognition, sentiment analysis, named entity recognition, question answering (QA/SQuAD), machine translation, speech to text (SST), and speech recognition (STT). The emphasis is on instant gratification. Non-programmers should be able to install these programs and use them in 15 minutes or less (per program). Programs are short (10–100 lines each) and readable by users with modest programming skills. Much of the complexity is hidden behind abstractions such as pipelines and auto classes, and pretrained models and datasets provided by hubs: PaddleHub, PaddleNLP, HuggingFaceHub, and Fairseq. Hubs have different priorities than research. Research is training models from corpora and fine-tuning them for tasks. Users are already overwhelmed with an embarrassment of riches (13k models and 1k datasets). Do they want more? We believe the broader market is more interested in inference (how to run pretrained models on novel inputs) and less interested in training (how to create even more models).
Discovering Composite Lifestyle Biomarkers With Artificial Intelligence From Clinical Studies to Enable Smart eHealth and Digital Therapeutic Services

Discovery of biomarkers is a continuous activity of the research community in the clinical domain that recently shifted its focus toward digital, non-traditional biomarkers that often use physiological, psychological, social, and environmental data to derive an intermediate biomarker. Such biomarkers, by triggering smart services, can be used in a clinical trial framework and eHealth or digital therapeutic services. In this work, we discuss the APACHE trial for determining the quality of life (QoL) of cervical cancer patients and demonstrate how we are discovering a biomarker for this therapeutic area that predicts significant QoL variations. To this extent, we present how real-world data can unfold a big potential for detecting the cervical cancer QoL biomarker and how it can be used for novel treatments. The presented methodology, derived in APACHE, is introduced by Healthentia eClinical solution, and it is beginning to be used in several clinical studies.
A Performant Protocol for Distributed Health Records Databases

Electronic Health Records (EHR) have a distributed nature and can be managed by distinct affinity domains.Sharing patient health information across distinct organisations helps to deliver a wellinformed diagnosis, improving the quality of healthcare service.The federation of those information systems can take the form of a distributed database where data are partitioned and possibly replicated across distinct computational systems.However, the benefits of having a distributed system, such as consistency, availability, and data protection, are mostly absent.This article proposes a distributed database consensus protocol designed to improve the performance of EHR insertion operations, a particularly critical issue in medical imaging cases due to the data volume.It explores the personal and non-transferable nature of EHR and the proposed methodology reduces the data contention through data isolation, improving the overall retrieval performance and detection of misbehaving parties.Furthermore, the proposal follows the recent European General Data Protection Regulation (GDPR), which states that appropriate mechanisms should be used in order to protect data against accidental loss, destruction, or damage, using appropriate technical or organisational measures.
Master Face Attacks on Face Recognition Systems

Face authentication is now widely used, especially on mobile devices, rather than authentication using a personal identification number or an unlock pattern, due to its convenience. It has thus become a tempting target for attackers using a presentation attack. Traditional presentation attacks use facial images or videos of the victim. Previous work has proven the existence of master faces, i.e., faces that match multiple enrolled templates in face recognition systems, and their existence extends the ability of presentation attacks. In this paper, we report an extensive study on latent variable evolution (LVE), a method commonly used to generate master faces. An LVE algorithm was run under various scenarios and with more than one database and/or face recognition system to identify the properties of master faces and to clarify under which conditions strong master faces can be generated. On the basis of analysis, we hypothesize that master faces originate in dense areas in the embedding spaces of face recognition systems. Last but not least, simulated presentation attacks using generated master faces generally preserved the false matching ability of their original digital forms, thus demonstrating that the existence of master faces poses an actual threat.
Meaning-Preserving Translations of Non-classical Logics into Classical Logic: Between Pluralism and Monism

Abstract In order to prove the validity of logical rules, one has to assume these rules in the metalogic. However, rule-circular ‘justifications’ are demonstrably without epistemic value (sec. 1). Is a non-circular justification of a logical system possible? This question attains particular importance in view of lasting controversies about classical versus non-classical logics. In this paper the question is answered positively, based on meaning-preserving translations between logical systems. It is demonstrated that major systems of non-classical logic, including multi-valued, paraconsistent, intuitionistic and quantum logics, can be translated into classical logic by introducing additional intensional operators into the language (sec. 2–5). Based on this result it is argued that classical logic is representationally optimal. In sec. 6 it is investigated whether non-classical logics can be likewise representationally optimal. The answer is predominantly negative but partially positive. Nevertheless the situation is not symmetric, because classical logic has important ceteris paribus advantages as a unifying metalogic.
RRT-GoalBias and Path Smoothing Based Motion Planning of Mobile Manipulators with Obstacle Avoidance

Mobile manipulator combines the mobility of mobile platform and the dexterity of manipulator, which has the great application potential in industry scenarios. In practical applications, efficiently planning a path in a given environment is the main objective, but the high degrees of redundancy, which is caused by the coupling of platform and manipulator, will make the planning problem more challenging. Therefore, the motion planning based on the RRT-GoalBias algorithm and path smoothing is proposed in this paper, where the kinematics of mobile manipulator is modeled, and the constraints of configuration and obstacle avoidance are analyzed. Subsequently, the RRT-GoalBias algorithm is applied and modified to generate an initial path for mobile manipulator in a given site, and a novel post-processing algorithm is proposed to smooth and optimize the initial path. Comparative Simulations are carried out with MATLAB and V-REP to verify the effectiveness of the proposed path planning algorithm for mobile manipulator under the given environment.
The effectiveness of zoom touchscreen gestures for authentication and identification and its changes over time

This paper focuses on how zoom touchscreen gestures can be used to continuously authenticate and identify smartphone users. The zoom gesture is critically under-researched as a behavioral biometric despite richness of data found in this gesture. Furthermore, analysing how the zoom gesture performs over time is a novel line of inquiry. Zoom samples from three different data collection sessions were sourced. In these sessions, each participant zoomed in and out on three images. Eighty-five features were extracted from each gesture. The classification models used were Support Vector Machine (SVM), Random Forest (RF), and K-nearest Neighbor (KNN). The best authentication performance of AUC 0.937 and EER 10.6% were achieved using the SVM classifier. The best identification performance of 65.5% accuracy, 69.6% precision, and 67.9% recall were achieved using the RF classifier. In terms of stability over time, SVM proved to be the most stable classifier, with an AUC degradation of only 0.007 after two weeks had elapsed. This analysis proves that zoom gestures demonstrate promise for use in continuous smartphone authentication and identification applications.