Reviewerly/exHarmony huggingface dataset:
Inference and Learning in a Latent Variable Model for Beta Distributed Interval Data

Latent Variable Models (LVMs) are well established tools to accomplish a range of different data processing tasks. Applications exploit the ability of LVMs to identify latent data structure in order to improve data (e.g., through denoising) or to estimate the relation between latent causes and measurements in medical data. In the latter case, LVMs in the form of noisy-OR Bayes nets represent the standard approach to relate binary latents (which represent diseases) to binary observables (which represent symptoms). Bayes nets with binary representation for symptoms may be perceived as a coarse approximation, however. In practice, real disease symptoms can range from absent over mild and intermediate to very severe. Therefore, using diseases/symptoms relations as motivation, we here ask how standard noisy-OR Bayes nets can be generalized to incorporate continuous observables, e.g., variables that model symptom severity in an interval from healthy to pathological. This transition from binary to interval data poses a number of challenges including a transition from a Bernoulli to a Beta distribution to model symptom statistics. While noisy-OR-like approaches are constrained to model how causes determine the observables’ mean values, the use of Beta distributions additionally provides (and also requires) that the causes determine the observables’ variances. To meet the challenges emerging when generalizing from Bernoulli to Beta distributed observables, we investigate a novel LVM that uses a maximum non-linearity to model how the latents determine means and variances of the observables. Given the model and the goal of likelihood maximization, we then leverage recent theoretical results to derive an Expectation Maximization (EM) algorithm for the suggested LVM. We further show how variational EM can be used to efficiently scale the approach to large networks. Experimental results finally illustrate the efficacy of the proposed model using both synthetic and real data sets. Importantly, we show that the model produces reliable results in estimating causes using proofs of concepts and first tests based on real medical data and on images.
Modelling a Deep Learning Framework for Recognition of Human Actions on Video

In Human action recognition, the identification of actions is a system that can detect human activities. The types of human activity are classified into four different categories, depending on the complexity of the steps and the number of body parts involved in the action, namely gestures, actions, interactions, and activities []. It is challenging for video Human action recognition to capture useful and discriminative features because of the human body's variations. To obtain Intelligent Solutions for action recognition, it is necessary to training models to recognize which action is performed by a person. This paper conducted an experience on Human action recognition compare several deep learning models with a small dataset. The main goal is to obtain the same or better results than the literature, which apply a bigger dataset with the necessity of high-performance hardware. Our analysis provides a roadmap to reach the training, classification, and validation of each model.
A Survey on Randomized Mechanisms for Statistical Learning under Local Differential Privacy

High confidence computing is an emerging computing paradigm, which supports the next-generation information system. Statistical learning is a basic research direction of this paradigm, which has been widely applied to various services and applications, such as data mining and data analysis. However, data privacy is one of the most concerned challenges by multiple participants and has an important influence on the development of statistical learning. Local differential privacy is a strong privacy metric in a local setting, which has a rigorous mathematical model. There are a series of research works to propose efficient randomized mechanisms for statistical learning. However, there are few literatures to provide a systematic overview of the existing works.In this paper, we present the existing literatures for randomized mechanisms under local differential privacy. At first, we introduce the basic perturbation mechanisms satisfying local differential privacy. Then, we give a framework and the main components of locally private mechanisms. Next, we describe the detailed content of randomized mechanisms for frequency estimation, heavy hitter identification and distribution estimation. Finally, we discuss the possible trends and challenges of the future research. Our survey provides a systematic and comprehensive understanding about randomized mechanisms for statistical learning under local differential privacy.
Inventory Management of the Refrigerator's Produce Bins Using Classification Algorithms and Hand Analysis

Tracking the inventory of one's refrigerator has been a mission for consumers since the advent of the refrigerator. With the improvement of computer vision capabilities, automatic inventory systems are within reach. One inventory area with many potential benefits is the fresh food produce bins. The bins are a unique storage area due to their deep size. A user cannot easily see what is in the bins without opening the drawer. Produce items are also some of the quickest foods in the refrigerator to spoil, despite being temperature and humidity controlled to have the fruits and vegetables last longer. This research addresses the challenges presented by getting a full inventory of all items within the produce bins by observing if the hand can provide useful information. The research proposes that all items must go in or out of the refrigerator by the main door, and by using a single camera to observe the hand-object interactions, a more complete inventory list can be created. As an initial proof-of-concept, two types of produce, an apple and an orange, will be used as a testing ground. The accuracy of the hand analysis (e.g., hand with apple or orange vs. hand empty) was determined by comparing the model predictions to a 301-frame video with ground truth labels. The hand analysis system was 87% accurate for classifying an empty hand, 85% accurate on a hand holding an apple, and 74% accurate on a hand holding an orange. The system was 93% accurate at detecting what was added or removed from the refrigerator.
Promoting Self-Efficacy Through an Effective Human-Powered Nonvisual Smartphone Task Assistant

Accessibility assessments typically focus on determining a binary measurement of task performance success/failure; and often neglect to acknowledge the nuances of those interactions. Although a large population of blind people find smartphone interactions possible, many experiences take a significant toll and can have a lasting negative impact on the individual and their willingness to step out of technological comfort zones. There is a need to assist and support individuals with the adoption and learning process of new tasks to mitigate these negative experiences. We contribute with a human-powered nonvisual task assistant for smartphones to provide pervasive assistance. We argue, in addition to success, one must carefully consider promoting and evaluating factors such as self-efficacy and the belief in one's own abilities to control and learn to use technology. In this paper, we show effective assistant positively affects self-efficacy when performing new tasks with smartphones, affects perceptions of accessibility and enables systemic task-based learning.
Data Processing Strategy in Internet of Things Application System

Based on a large number of data generated by the application of the Internet of things, the data information is processed and processed. The contents and methods of data processing in three-tier IOT structure are different. Upper layer: data communication and data communication; data acquisition and application. The data processing process reflects the purpose and mode of data processing in the Internet of things system. A large number of experiments have proved that the data processing methods and forms used in this paper have practical significance.
IRF

Recommender systems are used with increasing frequency in a wide variety of domains ranging from e- commerce to tourism, healthcare and online learning. However, the interaction with these systems generally tends to be limited to shallow feedback, such as providing ratings or filtering. Allowing users to interact with the recommender systems in a conversational environment brings opportunities in which the preferences can effectively be elicited from the users while the users can feel more in control of the whole process. However, when the existing non-interactive recommender systems are considered, it may not be easy to build an interactive layer directly on top of them. This is because there is already a great deal of modelling and work invested in the underlying algorithm and the system itself. Enabling interaction could mean rebuilding the whole solution from scratch, as the current design may not be able to consume preferences and information learnt from the user interaction online. In this paper, we propose the Interactive Recommender Framework, which converts non-interactive recommender solutions to conversational recommenders. We demonstrate how Interactive Recommender Framework can successfully enable interactivity on top of non-interactive recommender systems by integrating it into two different recommender algorithms from literature, and validate our solution through offline simulation experiments and online user studies.
Max-Product Type Exponential Neural Network Operators

In the present article, we extend the theory of exponential sampling type neural network operators to the max-product setting. The approximation properties of these operators activated by the sigmoidal functions have been studied by using the moment type approach. We establish the point-wise and uniform approximation theorem for these operators along with the quantitative estimate of the order of convergence using the modulus of continuity. Consequently, we discuss the convergence of exponential sampling type quasi-interpolation operators of the max-product kind. At the end, we provide a few examples of the sigmoidal functions satisfying the assumptions of the presented theory.
All-optical Sudoku solver with photonic spiking neural network

We proposed a Sudoku solver based on photonic spiking neural network (SNN) consisting of vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSELs-SA). Here, N 2 neurons are adopted to solve the N × N Sudoku puzzle with a unique solution. Values in Sudoku are temporally spike-encoded in different spiking times. The result is also achieved according to the spike timing information. In a network, for these VCSELs-SA belong to the same row or column or quadrant, they are inhibitory connected., i.e., from X polarization (XP) mode to Y polarization (YP) mode. Otherwise, they are excitatory connected, i.e., from XP mode to XP mode. Polarization mode competition (PMC) mechanism of VCSEL-SA is utilized to ensure the priority of inhibition behavior. With numerical simulations, 3 × 3 Sudoku puzzle is successfully solved with the help of inhibitory dynamics of VCSEL-SA. The results are represented by the output spike timing of XP mode of each VCSEL-SA. Furthermore, the photonic SNN is also extended to solve 4 × 4 Sudoku puzzle. This work may be interesting for the brain-inspired photonic neuromorphic information processing.
Articulation rates’ inter-correlations and discriminating powers in an English speech corpus

• We present articulation rate and deletion statistics derived from a 100-speaker corpus of British English speech. • Five measures of articulation rate are strongly inter-correlated. • The measures yield similar discriminative powers as quantified through log-likelihood analysis. • We offer a replicable approach to gauging the impact of a choice between alternative measures on analysis results. Studies that quantify speech tempo on acoustic grounds typically use one of various rate measures. The availability of multiple measurement techniques yields ‘researcher degrees of freedom’ which call the robustness of generalisations across studies into question. However, explicit assessments of the possible impact of researchers’ choices amongst the available measures are rare. In this study we attempt such an assessment by comparing the distributions of five common rate measures―canonical and surface syllable and phone rates, and CV segment rate―calculated over fluent stretches of unscripted speech produced by 100 English speakers. We assess the measures’ inter-correlations across the corpus as a whole as well as in relevant data samples to simulate multiple analysis scenarios. We also report on deletion rates in our corpus, as they determine the relationship between canonical and surface rates; we assess the impact on rate figures of variable assumptions as to what constitutes deletion; and we compare the measures’ discriminating powers in a forensic analysis context using Bayesian likelihood ratios. Our results suggest that in a sizeable English corpus with normal deletion rates, the five rates are closely inter-correlated and have similar discriminating powers; decisions as to the segmental make-up of canonical forms also have limited impact on distributions. Therefore, for common analytical purposes and forensic applications the choice between these measures is unlikely to substantially affect outcomes.
Pedestrian re‐identification based on attribute mining and reasoning

The high-level semantic information extracted from the pedestrian attribute feature is an important element for pedestrian recognition. Pedestrian attribute recognition plays an important role in both intelligent video surveillance and pedestrian re-identification promoting the convenience of searching and performance of model. This paper tries finding a practical method to improve the performance of the pedestrian re-identification by combining pedestrian attributes and identities. The multi-task learning method combines pedestrian recognition and attribute information in a direct way that considers the correlation between pedestrian attributes and identities but ignores the principle and degree of such correlation. To solve this problem, a new pedestrian recognition framework based on attribute mining and reasoning is proposed in this paper. To enhance the expression ability of attribute features, it designs spatial channel attention module (SCAM) based on attention mechanism to extract features from every attribute. SCAM can not only locate the attributes on the feature map, but also effectively mine channel features with a higher degree of association with attributes. In addition, both spatial attention model and channel attention model are integrated by multiple groups of parallel branches, which further improve the network performance. Finally, using the semantic reasoning and information transmission function of graph convolutional network, the relationship between attribute features and pedestrian features can be mined. Besides, pedestrian features with stronger expression ability can also be obtained. Experiment work is conducted in two databases, DukeMTMC-reID and Market-1501, which are commonly used in pedestrian recognition tasks. On the Market-1501 dataset, the final effect of the algorithm model CMC-1 can reach 94.74%, and mAP can reach 87.02%; on the DukeMTMC-reID dataset, CMC-1 can reach 87.03%, and mAP can reach 77.11%. The results show that our method is at the top of the existing pedestrian recognition methods.
Two-Layer Neural Networks with Values in a Banach Space

We study two-layer neural networks whose domain and range are Banach spaces with separable preduals.In addition, we assume that the image space is equipped with a partial order, i.e. it is a Riesz space.As the nonlinearity we choose the lattice operation of taking the positive part; in case of R d -valued neural networks this corresponds to the ReLU activation function.We prove inverse and direct approximation theorems with Monte-Carlo rates for a certain class of functions, extending existing results for the finite-dimensional case.In the second part of the paper we study, from the regularisation theory viewpoint, the problem of finding optimal representations of such functions via signed measures on a latent space from a finite number of noisy observations.We discuss regularity conditions known as source conditions and obtain convergence rates in a Bregman distance for the representing measure in the regime when both the noise level goes to zero and the number of samples goes to infinity at appropriate rates.
Recognizing American Sign Language Nonmanual Signal Grammar Errors in Continuous Videos

As part of the development of an educational tool that can help students achieve fluency in American Sign Language (ASL) through independent and interactive practice with immediate feedback, this paper introduces a near real-time system to recognize grammatical errors in continuous signing videos without necessarily identifying the entire sequence of signs. Our system automatically recognizes if a performance of ASL sentences contains grammatical errors made by ASL students. We first recognize the ASL grammatical elements including both manual gestures and nonmanual signals independently from multiple modalities (i.e. hand gestures, facial expressions, and head movements) by 3D-ResNet networks. Then the temporal boundaries of grammatical elements from different modalities are examined to detect ASL grammatical mistakes by using a sliding window-based approach. We have collected a dataset of continuous sign language, ASL-HW-RGBD, covering different aspects of ASL grammars for training and testing. Our system is able to recognize grammatical elements on ASL-HW-RGBD from manual gestures, facial expressions, and head movements and successfully detect 8 ASL grammatical mistakes.
Highly Contrast Image Correction for Dim Boundary Separation of Image Semantic Segmentation

The efficiency and accuracy of the image semantic segmentation algorithm represent a trade-off relationship, and the loss of accuracy tends to increase as the model structure simplifies to improve efficiency. Developing more efficient and accurate algorithms requires methods to complement them. In this study, we applied the logarithmic-exponential mixture (LEM) function for gamma correction of images to improve the accuracy of image semantic segmentation. The basic model used in this work was produced by constructing a full convolution neural network based on MobileNetV2. To avoid the noise of input compression, we corrected training and validation images with gamma from 1/8 to 8 (7 different levels) before doing convolution. We evaluated models using Tensorflow deep-learning library based on Python. We compared models using LEM function to models using conventional gamma function. The prediction masks of the proposed model using the LEM function had relatively small fluctuations of accuracy upon gamma change. For images that have shadows overlapped on the object, the object was better distinguished in small gamma values. For dark images, the increase in accuracy was more effective. The results indicated that the proposed gamma correction could improve image segmentation accuracy in images with unclear edges. We believe that the presented results will guide further studies for accuracy improvement of image recognition algorithms applicable to future devices, such as autonomous vehicles and mobile robots.
Training Multilayer Neural Networks Analytically Using Kernel Projection

This paper proposes a kernel projection (KP) neural network that analytically determines its network parameters. The proposed network is composed of cascaded modules of 2-layer sub-networks. A technique which encodes the label information into each module has been introduced to enable a locally supervised learning. Such a supervised learning in the 2-layer module begins with a kernel projection in the first layer and determines its parameters analytically via solving a least squares problem in the second layer. We show that the analytic nature of the proposed network allows a learning process significantly faster than that of the traditional backpropagation method as it only needs to visit the dataset once. Experiments of classification tasks on various datasets are carried out, showing comparable or better results compared with several competing methods.
On the Evaluation of Generative Adversarial Networks By Discriminative Models

Generative Adversarial Networks (GANs) can accurately model complex multi-dimensional data and generate realistic samples. However, due to their implicit estimation of data distributions, their evaluation is a challenging task. The majority of research efforts associated with tackling this issue were validated by qualitative visual evaluation. Such approaches do not generalize well beyond the image domain. Since many of those evaluation metrics are proposed and bound to the vision domain, they are difficult to apply to other domains. Quantitative measures are necessary to better guide the training and comparison of different GANs models. In this work, we leverage Siamese neural networks to propose a domain-agnostic evaluation metric: (1) with a qualitative evaluation that is consistent with human evaluation, (2) that is robust relative to common GAN issues such as mode dropping and invention, and (3) does not require any pretrained classifier. The empirical results in this paper demonstrate the superiority of this method compared to the popular Inception Score and are competitive with the FID score.
Levenstein's Algorithm On English and Arabic: A Survey

Survey research is one of the important types of scientific research needed to address the advancement in specific research areas. In this paper, we aim to provide a critical review of name-matching algorithms related to Levenstein's algorithm used in Arabic and English languages. We have collected and reviewed 50 papers related to Levenstein's algorithm. Name matching algorithms are becoming increasingly important in areas such as text classification, proofreading, automatic correction, plagiarism detection, extracting and aggregating specific information from huge data. In addition to the retrieval and tracking topics as well as answering questions, recognizing handwriting, evaluating articles, and summarizing. Among the new application in the area of artificial intelligence is the conversation systems, which are programs that communicate directly with humans using different natural languages. The most important part of name-matching algorithms is finding similarities between words or terms. The application of natural language processing tasks in Arabic and English represents a very difficult process as they contain many characteristics. Nevertheless, several name-matching algorithms have been introduced in Arabic and English texts, the attention of this research is focused on reviewing the research studies related to Levenstein's algorithm besides listing down developed applications in different areas.