Miracle-dz/UniFAR-FTUs huggingface dataset:
Semidefinite Relaxation Method for Moving Object Localization Using a Stationary Transmitter at Unknown Position

This paper addresses the multistatic localization of a moving object in position and velocity using time delay (TD) and Doppler frequency shift (DFS) measurements, where the position of the transmitter is unknown and has not yet been synchronized with the receivers. Based on the TD and DFS measurements from the direct and indirect paths, we formulate a non-convex weighted least squares (WLS) minimization problem, and then apply semidefinite relaxation (SDR) to relax the WLS problem into a convex semidefinite program. Simulation results show that the proposed SDR method is able to achieve the Cramer-Rao lower bound accuracy under mild Gaussian noise condition and outperforms the existing method.
Wikitag: Wikipedia-Based Knowledge Embeddings Towards Improved Acoustic Event Classification

Acoustic event classification (AEC) is the task of determining whether certain events occur in an audio clip. Inspired by previous research [1], [2], [3] that embeddings from event labels can be leveraged to facilitate the learning of new detectors with no or limited audio samples, we introduce Wikipedia-based text embeddings as auxiliary information to improve AEC. We describe how to extract label embeddings from multiple Wikipedia texts, and formulate the multi-view aligned AEC problem based on VGGish model. We show that our "wikiTAG" embeddings encode rich semantic information and are more informative than label embeddings for AEC tasks. Compared to a supervised baseline on AudioSet, the multi-view model with "wikiTAG" embeddings achieves 7.3% and 1.3% relative improvement in mean average precision (mAP) using 10% and full AudioSet for training, respectively. To the author’s knowledge, this is the first work in the AEC domain on building large-scale label representations by leveraging Wikipedia data in a systematic fashion.
Evolutionary Neural Architecture Design of Liquid State Machine for Image Classification

As a recurrent spiking neural network, liquid state machine (LSM) has attracted more and more attention in neuromorphic computing due to its biological plausibility, computation power, and hardware implementation. However, the neural architecture of LSM, such as hidden neuron number, synaptic density, percentage connectivity, and connection state, has significant impact on its model performance. Manually defining a neural architecture will be ineffective and laborious in most cases. Therefore, based on a state-of-the-art differential evolution algorithm, an evolutionary neural architecture design methodology is proposed to automatically build suitable model topologies for LSM in this study, without any prior knowledge. The effectiveness of the proposed method has been validated on commonly-used image classification tasks.
Contrastive Heartbeats: Contrastive Learning for Self-Supervised ECG Representation and Phenotyping

The non-invasive and easily accessible characteristics of electrocardiogram (ECG) attract many studies targeting AI-enabled cardiovascular-related disease screening tools based on ECG. However, the high cost of manual labels makes high-performance deep learning models challenging to obtain. Hence, we propose a new self-supervised representation learning framework, contrastive heartbeats (CT-HB), which learns general and robust electrocardiogram representations for efficient training on various downstream tasks. We employ a novel heartbeat sampling method to define positive and negative pairs of heartbeats for contrastive learning by utilizing the periodic and meaningful patterns of electrocardiogram signals. Using the CT-HB framework, the self-supervised learning model learns personalized heartbeat representations representing the specific cardiology context of a patient. Evaluations on public benchmark datasets and a private large-scale real-world dataset with multiple tasks demonstrate that the learned semantic representations result in better performance on downstream tasks and retain high performance while supervised learning suffers performance degradation with fewer supervised labels in downstream tasks.
End-to-End ASR-Enhanced Neural Network for Alzheimer’s Disease Diagnosis

This paper presents an approach to Alzheimer’s disease (AD) diagnosis from spontaneous speech using an end-to-end ASR-enhanced neural network. Under the condition that only audio data are provided and accurate transcripts are unavailable, this paper proposes a system that can analyze utterances to differentiate between AD patients, healthy controls, and individuals with mild cognitive impairment. The ASR-enhanced model comprises automatic speech recognition (ASR) with an encoder-decoder structure and the encoder followed by an AD classification network. The encoder takes a Mel spectrogram as input and transforms it into high-level acoustic features that correlate with AD. The classification network then maps intermediate acoustic features to three categories. In the training phase, the AD classification and speech recognition tasks are optimized simultaneously. Experimental results obtained from an AD recognition dataset of Chinese spontaneous speech1 illustrate the effectiveness of integrating ASR into AD diagnosis in an end-to-end manner. Further, our model has low dependency on accurate ASR transcripts. This work achieved accuracy scores of 89.1% and 82.6% for long and short utterance tracks, respectively.
Statistical, Spectral and Graph Representations for Video-Based Facial Expression Recognition in Children

Child facial expression recognition is a relatively less investigated area within affective computing. Children’s facial expressions differ significantly from adults; thus, it is necessary to develop emotion recognition frameworks that are more objective, descriptive and specific to this target user group. In this paper we propose the first approach that (i) constructs video-level heterogeneous graph representation for facial expression recognition in children, and (ii) predicts children’s facial expressions using the automatically detected Action Units (AUs). To this aim, we construct three separate length-independent representations, namely, statistical, spectral and graph at video-level for detailed multi-level facial behaviour decoding (AU activation status, AU temporal dynamics and spatio-temporal AU activation patterns, respectively). Our experimental results on the LIRIS Children Spontaneous Facial Expression Video Database demonstrate that combining these three feature representations provides the highest accuracy for expression recognition in children.
Thin Slices of Depression: Improving Depression Detection Performance Through Data Segmentation

The computing community is making major efforts towards automatic detection of depression, a serious pathology that affects roughly 4.4% of the world’s population. One of the main difficulties is the collection of data aimed at training models capable to learn differences between depressed and non-depressed people. In fact, data collection in the depression domain requires the respect of rigorous ethical constraints that, inevitably, limit the size of the corpora that can be collected. This article proposes to address the problem by using the thin slices theory, i.e., the possibility to detect the inner state of an individual (depression in this case) through very short samples of behavior. In particular, the article shows that the performance of data-driven models can be improved by segmenting the data at disposition into thin slices and then training data-driven models over them. This increases the amount of samples at disposition and allows a relative F1 Score improvement by up to 16.2%.
Graph Learning Based Autoencoder for Hyperspectral Band Selection

Hyperspectral band selection aims to identify an optimal sub-set of bands from hyperspectral images (HSIs). Most existing methods explore the relationships between pair-wise pixels in a fixed graph. However, the quality of the initial fixed graph may be influenced by noises and user-defined parameters that may not be optimal for HSI analysis. In this paper, we pro-pose a graph learning based autoencoder (GLAE) to achieve unsupervised hyperspectral band selection. Using the relationships of pair-wise pixels within HSIs, GLAE constructs the initial graph to characterize the geometric structures of HSIs and then adjusts the graph to adapt the band selection process. To solve the proposed model, we intoduce an alternative optimization algorithm. Experiments and comparisons on three HSI datasets demonstrate that the proposed GLAE achieves better results over the state-of-the-art methods.
Boost Ensemble Learning for Classification of CTG SIGNALS

During the process of childbirth, fetal distress caused by hypoxia can lead to various abnormalities. Cardiotocography (CTG), which consists of continuous recording of the fetal heart rate (FHR) and uterine contractions (UC), is routinely used for classifying the fetuses as hypoxic or non-hypoxic. In practice, we face highly imbalanced data, where the hypoxic fetuses are significantly underrepresented. We propose to address this problem by boost ensemble learning, where for learning, we use the distribution of classification error over the dataset. We then iteratively select the most informative majority data samples according to this distribution. In our work, in addition to addressing the imbalanced problem, we also experimented with features that are not commonly used in obstetrics. We extracted a large number of statistical features of fetal heart tracings and uterine activity signals and used only the most informative ones. For classification, we implemented several methods: Random Forest, AdaBoost, k-Nearest Neighbors, Support Vector Machine, and Decision Trees. The paper provides a comparison in the performance of these methods on fetal heart rate tracings available from a public database. Our results on the publicly available Czech database show that most applied methods improved their performances considerably when boost ensemble was used.
Distributed Particle Filters for State Tracking on the Stiefel Manifold Using Tangent Space Statistics

This paper introduces a novel distributed diffusion algorithm for tracking the state of a dynamic system that evolves on the Stiefel manifold. To compress information exchanged between nodes, the algorithm builds a Gaussian parametric approximation to the particles that are previously projected onto the tangent space to the Stiefel manifold and mapped to real vectors. Observations from neighboring nodes are then assimilated for a general nonlinear observation model. Performance results are compared to those of competing linear diffusion Extended Kalman Filters and other particle filters.
Multi-Scale Temporal Frequency Convolutional Network with Axial Attention for Multi-Channel Speech Enhancement

Speech quality is often degraded by background noise and reverberation. Usually, a dense prediction network is used to reconstruct clean speech. In this work, a novel backbone for speech dense-prediction is proposed. After adjusting part of the input and output, this backbone is used for multi-channel speech enhancement task in this paper. To improve the performance of the backbone, strategies such as multi-channel phase encoder, multi-scale temporal frequency processing, axial self-attention, and two-stage masking are designed. Our proposed method is evaluated based on the datasets of ICASSP 2022 L3DAS22 Challenge. The experimental results show that the proposed method outperforms previous state-of-the-art baselines by a large margin1 and ranked second in L3DAS22 Challenge. The proposed backbone is also used for mono-channel speech enhancement and ranked first in both ICASSP 2022 AEC2 and DNS Challenges(non-personal track)3.
Deep Learning Based Off-Angle Iris Recognition

Even with trained operators and cooperative subjects, it is still possible to capture off-angle iris images. Considering the recent demands for stand-off iris biometric systems and the trend towards "on-the-move-acquisition", off-angle iris recognition became a hot topic within the biometrics community. In this work, CNNs trained with the triplet loss function are applied to extract features for iris recognition. To analyze which parts of the eye are most suited for the CNN-based recognition system, experiments are carried out using image data from different parts of the eye (full eye, eye zoomed to iris, iris only, iris normalized, eye without iris). To analyze the impact of different gaze angles on the recognition performance, experiments are applied on: (1) different gaze angles separately, (2) image data with increasing differences in the gaze angles, and (3) corrected off-angle image data. The experiment results show superior performance of the CNN trained with the triplet loss on the iris images with more lateral gaze angles (≥ 30°). However, higher differences in the gaze angles between images deteriorate the network performance. Also, the results are about the same for the different parts of the eye and correcting the gaze angle did not really improve the performance of the CNN.
Study of the Null Directions on The Performance of Differential Beamformers

Null directions are important parameters for differential beamformers, which play an important role on the beamforming performance. In this paper, we investigate the performance of differential beamformers as a function of the null directions. We first derive the directivity factor (DF) as an explicit function of null and show that the DF decreases to 0 if any null approaches to the desired look direction. We then validate the theoretical analysis through simulations using the beampattern, DF and signal-to-interference gain as the performance measures. The results show that: 1) the performance of a differential beamformer degrades significantly if there is any null close to the desired look direction; 2) with a fixed null direction, increasing the order of the differential beamformer can help improve performance.
Adaptive Diffusion with Compressed Communication

We consider multi-agent networks that aim at solving, cooperatively and online, distributed optimization problems under communication constraints. We propose the ACTC (Adapt-Compress-Then-Combine) diffusion strategy, which leverages differential randomized compression to infuse the classical ATC strategy with the ability to handle compressed data. We consider the flexible setting of directed graphs and left-stochastic policies, and require strong convexity only at a network level (i.e., some agents might even have non-convex risks). We prove that each agent is able to learn the optimal solution up to a small error on the order of the step-size, achieving remarkable savings in terms of bits exchanged between neighboring agents.
CHAINME: fast decentralized finding of better supply chains

Decentralized Supply Chain Formation (SCF) appears as a highly intricate task because agents only possess local information, have limited knowledge about the capabilities of other agents, and prefer to preserve privacy. State-of-theart decentralized SCF approaches can either: (i) find Supply Chains (SC) of high value at the expense of high resources usage; or (ii) find SCs of low value with low resources usage. This work presents chainme, a novel decentralized SCF algorithm. Our results show that chainme finds SCs with higher value than state-of-the-art decentralized algorithms whilst decreasing the amount of resources required from one up to four orders of magnitude.
Analysing IoT Applications with DISSECT-CF-Fog in Simulated Fog and Cloud Environments

The latest services of the Internet are expanded to the edge of the network, which led to the emergence of Internet of Things (IoT), where sensors, actuators and smart devices are connected in order to interoperate with each other. IoT services often deal with a vast amount of raw data, therefore they collaborate by exploiting cloud resource capabilities. However, the abilities of Cloud Computing make it suitable to process and store the IoT data, real time applications or low latency services have stricter requirements. As a result, Cloud Computing was extended towards Fog Computing to resolve issues such as over-loaded communication channels or minimal delay of a service. In order to develop, research or evaluate such complex IoT-Fog-Cloud systems, stakeholders often use simulation tools, because these simulators usually deal with realistic models and behaviour. They also provide reasonable alternatives to the high operation and purchase costs of real equipment, or system failures. In this paper, we present the DISSECT-CF-Fog simulator, which aims to model IoT-Fog-Cloud systems and ensures various configuration options. Its usability is presented through representative IoT use cases. The broad functionalities make the software a proper decision for investigating trade-offs of modern networks in terms of energy, utilisation cost or computational power.
Student Research Award 1998: Clinimetric evaluation of the Sinonasal Outcome Test-16

This study describes the reliability, validity, and responsiveness of the Sinonasal Outcome Test-16 (SNOT-16), a rhinosinusitis-specific health-related quality-of-life instrument, in the University of Washington Department of Otolaryngology-Head and Neck Surgery patient Population. The SNOT-16 was completed by 47, 24, and 22 patients at weeks 0, 6, and 12, respectively. In addition, all 47 patients completed the Short-form 36-item Health Survey (SF-36) at week 0. Furthermore, an additional cohort of patients from the otology clinic who denied symptoms of rhinosinusitis or previous physician diagnoses of rhinosinusitis were asked to complete the SNOT-16. These scores were subsequently used to determine discriminant validity of the instrument. Cronbach's alpha was 0.89, indicating a high degree of homogeneity of the test items. The SNOT-16 demonstrated excellent discriminant validity, and mean total SNOT-16 scores were significantly correlated with patient-reported overall health, overall bother, and 7 of the 8 SF-36 subscales. The standardized response mean calculated between weeks 0 and 6 was 0.69, indicating moderate sensitivity to change. We conclude that the SNOT-16 is a reliable, valid, and responsive instrument for measuring rhinosinusitis-specific health-related quality of life.
Using Open Vector-Based Spatial Data to Create Semantic Datasets for Building Segmentation for Raster Data

With increasing access to open spatial data, it is possible to improve the quality of analyses carried out in the preliminary stages of the investment process. The extraction of buildings from raster data is an important process, especially for urban, planning and environmental studies. It allows, after processing, to represent buildings registered on a given image, e.g., in a vector format. With an actual image it is possible to obtain current information on the location of buildings in a defined area. At the same time, in recent years, there has been huge progress in the use of machine learning algorithms for object identification purposes. In particular, the semantic segmentation algorithms of deep convolutional neural networks which are based on the extraction of features from an image by means of masking have proven themselves here. The main problem with the application of semantic segmentation is the limited availability of masks, i.e., labelled data for training the network. Creating datasets based on manual labelling of data is a tedious, time consuming and capital-intensive process. Furthermore, any errors may be reflected in later analysis results. Therefore, this paper aims to show how to automate the process of data labelling of cadastral data from open spatial databases using convolutional neural networks, and to identify and extract buildings from high resolution orthophotomaps based on this data. The conducted research has shown that automatic feature extraction using semantic ML segmentation on the basis of data from open spatial databases is possible and can provide adequate quality of results.
Can Clicks Be Both Labels and Features?: Unbiased Behavior Feature Collection and Uncertainty-aware Learning to Rank

Using implicit feedback collected from user clicks as training labels for learning-to-rank algorithms is a well-developed paradigm that has been extensively studied and used in modern IR systems. Using user clicks as ranking features, on the other hand, has not been fully explored in existing literature. Despite its potential in improving short-term system performance, whether the incorporation of user clicks as ranking features is beneficial for learning-to-rank systems in the long term is still questionable. Two of the most important problems are (1) the explicit bias introduced by noisy user behavior, and (2) the implicit bias, which we refer to as the exploitation bias, introduced by the dynamic training and serving of learning-to-rank systems with behavior features. In this paper, we explore the possibility of incorporating user clicks as both training labels and ranking features for learning to rank. We formally investigate the problems in feature collection and model training, and propose a counterfactual feature projection function and a novel uncertainty-aware learning to rank framework. Experiments on public datasets show that ranking models learned with the proposed framework can significantly outperform models built with raw click features and algorithms that rank items without considering model uncertainty.
Constraining word alignments with posterior regularization for label transfer

Unsupervised word alignments offer a lightweight and interpretable method to transfer labels from high- to low-resource languages, as long as semantically related words have the same label across languages. But such an assumption is often not true in industrial NLP pipelines, where multilingual annotation guidelines are complex and deviate from semantic consistency due to various factors (such as annotation difficulty, conflicting ontology, upcoming feature launches etc.);We address this difficulty by constraining the alignments models to remain consistent with both source and target annotation guidelines , leveraging posterior regularization and labeled examples. We illustrate the overall approach using IBM 2 (fast_align) as a base model, and report results on both internal and external annotated datasets. We measure consistent accuracy improvements on the MultiATIS++ dataset over AWESoME, a popular transformer-based alignment model, in the label projection task (+2.7\% at word-level and +15\% at sentence-level), and show how even a small amount of target language annotations help substantially.
GENETIC TRANSFORMATION OF THE EPSPS HERBICIDE RESISTANCE GENE IN AGROBACTERIUM MEDIATED PEANUT ( ARACHIS HYPOGAEA L.) AND EFFECTIVE REVIVAL OF TRANSGENIC PLANTS

The current study was based on Agrobacterium-mediated peanut genetic transformation and effective remediation of transgenic plants. The effect of Agrobacterium tumefaciens strain LBA4404 with a binary vector of PxCSG m-YFP 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene has been evaluated on two commercial varieties and on two sources of explants (Embryo and cotyledon), each containing a CaMV 35S promoter. The comparative regeneration and transformation efficiency analyses revealed that the embryo is a better target tissue than cotyledon, and early-ripe peanut genotypes with relatively small seeds (such as Bard-479) have proven to be comparatively responsive to transformation. Explant culture in media containing 3 mg L 1 indole-3-butyric acid and 0.1 mg L 1 napthalene acetic acid resulted in strong roots of virtually all transgenic plants. More than 87% of the transplanted plants were able to produce morphologically normal blooms and pods containing viable seeds. The phenotypic and genotypic monitoring of the EPSPS gene inheritance over two generations exhibited the desired 3:1 inheritance. Our findings suggest that Agrobacterium-mediated transformation is a feasible and valuable tool for peanut breeding and functional genomics research.