Reviewerly/exHarmony huggingface dataset:
Defining a species-specific K constant in American Bullfrogs (Lithobates catesbeianus) through use of computed tomography

To use CT measurements to define the body surface area (BSA) formula in American bullfrogs (Lithobates catesbeianus) and calculate the species-specific shape constant (K) to suggest chemotherapeutic doses.12 American bullfrogs owned by the North Carolina State College of Veterinary Medicine Department of Laboratory Animal Resources underwent CT scans without anesthesia or sedation in November 2022.As part of this prospective study, each American bullfrog underwent a complete physical exam and CT scan. 3-D surface models were created using CT data, and the resulting measurements were used for BSA calculations. Animals were grouped by sex. Nonlinear regression analysis of BSA versus body weight was performed, and a species-specific formula was derived for calculating BSA in American bullfrogs.The mean body weight of the bullfrogs was 354 grams. The mean CT-derived BSA was 414.92 cm2. The calculated K constant was 8.28 for the 12 American bullfrogs, and the CT-derived BSA formula was BSA in cm2 = 8.28 X (body weight in g)2/3. The K constant was 8.07 for females and 8.44 for males and was not significantly different between sexes (P = .5).Results indicated that the species-specific K constant for American bullfrogs is 8.28. This is the first calculated K constant that exists for amphibians to our knowledge.
Absolute testing of two flats based on even and odd functions without testing refractive index uniformity

A novel absolute measurement method by using two plates based on the odd-even functions is proposed. Compared to existing methods, our method requires only two plates and the refractive index uniformity of the transmitted flat is not to be measured. The principle of the proposed method is discussed. The corresponding simulations are carried out to prove the feasibility of the method. Experiments on a 100 mm Zygo-interferometer yielded the PV value of the rest error is less than 3 nm. Error term affecting the calculation accuracy was analyzed including the rotation angle and pixel misalignment errors of the coordinate axis. The proposed method was proven to effectively obtain the accurate surface error.
Transfer learning for collaborative recommendation with biased and unbiased data

In a recommender system, a user's interaction is often biased by the items' displaying positions and popularity, as well as the user's self-selection. Most existing recommendation models are built using such a biased user-system interaction data alone. In this paper, we introduce an additional specially collected unbiased data, and then have a new problem called collaborative recommendation with biased and unbiased data. We first formalize the studied problem and list three challenges, including the bias challenge, the heterogeneity challenge and the unbalance challenge. Then we propose a novel transfer learning-based AI solution, i.e., transfer via joint reconstruction (TJR), to achieve knowledge transfer and sharing between the biased data and unbiased data. Specifically, in our TJR, we use two different models to extract the users' preferences and bias information, and then refine the prediction via the latent features containing the bias information in order to obtain a more accurate and unbiased recommendation. We further integrate the two data by reconstructing their interaction in a joint learning manner. Moreover, in order to better address the unbalance challenge, we introduce a bias regularization term and integrate bidirectional knowledge distillation. Finally, we adopt four representative methods, i.e., variational autoencoders, matrix factorization, neural collaborative filtering and graph convolution network, as the backbone models of our TJR and conduct extensive empirical studies on three public datasets, showcasing the effectiveness of our transfer learning solution over some very competitive baselines.
Generating embedding spaces for re-identifying pallets from their chipwood patterns

Modern logistics rely on continuous tracking of even most basic entities like pallets. While tracking is nowadays achieved via additional markers (e.g., barcodes), pallets have a unique woodchip pattern in their blocks that can be exploited for re-identification if suitable methods are employed. This paper shows how this task can be accomplished in an open-set scenario. In this work, images of pallet blocks are transformed into vector representations using neural networks based on the ResNet-50 architecture. The networks are trained such that the Euclidean distance between image vectors of the same palette block is small. Several approaches, i.e., cross entropy, contrastive and triplet loss, to create the embedding space are compared. It is shown that Triplet Loss has advantages over other methods with a top-1 accuracy of 0.86 on the pallet-block-32965 dataset. In addition, a neighborhood-based approach to novelty detection called NSAND is presented, which outperforms a purely distance-based approach by up to 20%.
Monocular Blind Spot Estimation with Occupancy Grid Mapping

We present a low-cost method for detecting blind spots in front of the ego vehicle. In low visibility conditions, blind spot estimation is crucial to avoid the risk of pedestrians or vehicles appearing suddenly. However, most blind spot estimation methods require expensive range sensors or neural networks trained with data measured by them. Our method only uses a monocular camera throughout all phases from training to inference, since it is cheaper and more versatile. We assume that a blind spot is a depth discontinuity region. Occupancy probabilities of these regions are integrated using the occupancy grid mapping algorithm. Instead of using range sensors, we leverage the self-supervised monocular depth estimation method for the occupancy grid mapping. 2D blind spot labels are created from occupancy grids and a blind spot estimation network is trained using these labels. Our experiments show quantitative and qualitative performance and demonstrate an ability to learn with arbitrary videos.
Differential Evolution With Multivariate Gaussian Sampling For Sensor Arrangement

Sensor arrangement problems with the unknown number of sensors and obstacles in an area to be covered by sensors, which are complicated for common evolutionary algorithms to search for the global optimum. This paper presented a new evolutionary algorithm to solve the problem, which utilized hybrid mutation and a specific crossover strategy. Sampling the best individual of the population using the multivariate Gaussian distribution during the mutation process, and the individuals generated by multivariate Gaussian sampling would skip the crossover to selection. It has been proved that the new algorithm has better solution quality and faster convergent speed than classic differential evolution (DE).
Deep Filter Context Network for Click-Through Rate Prediction

The growth of e-commerce has led to the widespread use of DeepCTR technology. Among the various types, the deep interest network (DIN), deep interest evolution network (DIEN), and deep session interest network (DSIN) developed by Alibaba have achieved good results in practice. However, the above models’ use of filtering for the user’s own historical behavior sequences and the insufficient use of context features lead to reduced recommendation effectiveness. To address these issues, this paper proposes a novel article model: the deep filter context network (DFCN). This improves the efficiency of the attention mechanism by adding a filter to filter out data in the user’s historical behavior sequence that differs greatly from the target advertisement. The DFCN pays attention to the context features through two local activation units. This model greatly improves the expressiveness of the model, offering strong environment-related attributes and the adaptive capability of the model, with a significant improvement of up to 0.0652 in the AUC metric when compared with our previously proposed DICN under different datasets.
Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields

Neural Radiance Fields (NeRF) have attracted significant attention due to their ability to synthesize novel scene views with great accuracy. However, inherent to their underlying formulation, the sampling of points along a ray with zero width may result in ambiguous representations that lead to further rendering artifacts such as aliasing in the final scene. To address this issue, the recent variant mipNeRF proposes an Integrated Positional Encoding (IPE) based on a conical view frustum. Although this is expressed with an integral formulation, mip-NeRF instead approximates this integral as the expected value of a multivariate Gaussian distribution. This approximation is reliable for short frustums but degrades with highly elongated regions, which arises when dealing with distant scene objects under a larger depth of field. In this paper, we explore the use of an exact approach for calculating the IPE by using a pyramid-based integral formulation instead of an approximated conical-based one. We denote this formulation as Exact-NeRF and contribute the first approach to offer a precise analytical solution to the IPE within the NeRF domain. Our exploratory work illustrates that such an exact formulation (Exact-NeRF) matches the accuracy of mip-NeRF and furthermore provides a natural extension to more challenging scenarios without further modification, such as in the case of unbounded scenes. Our contribution aims to both address the hitherto unexplored issues of frustum approximation in earlier NeRF work and additionally provide insight into the potential future consideration of analytical solutions in future NeRF extensions.
Texts as Images in Prompt Tuning for Multi-Label Image Recognition

Prompt tuning has been employed as an efficient way to adapt large vision-language pre-trained models (e.g. CLIP) to various downstream tasks in data-limited or label-limited settings. Nonetheless, visual data (e.g., images) is by default prerequisite for learning prompts in existing methods. In this work, we advocate that the effectiveness of image-text contrastive learning in aligning the two modalities (for training CLIP) further makes it feasible to treat texts as images for prompt tuning and introduce TaI prompting. In contrast to the visual data, text descriptions are easy to collect, and their class labels can be directly derived. Particularly, we apply TaI prompting to multi-label image recognition, where sentences in the wild serve as alternatives to images for prompt tuning. Moreover, with TaI, double-grained prompt tuning (TaI-DPT) is further presented to extract both coarse-grained and fine-grained embeddings for enhancing the multi-label recognition performance. Experimental results show that our proposed TaI-DPT outperforms zero-shot CLIP by a large margin on multiple benchmarks, e.g., MS-COCO, VOC2007, and NUS-WIDE, while it can be combined with existing methods of prompting from images to improve recognition performance further. The code is released at https://github.com/guozix/TaI-DPT.
Egocentric Video Task Translation

Different video understanding tasks are typically treated in isolation, and even with distinct types of curated data (e.g., classifying sports in one dataset, tracking animals in another). However, in wearable cameras, the immersive egocentric perspective of a person engaging with the world around them presents an interconnected web of video understanding tasks—hand-object manipulations, navigation in the space, or human-human interactions—that unfold continuously, driven by the person's goals. We argue that this calls for a much more unified approach. We propose EgoTask Translation (EgoT2), which takes a collection of models optimized on separate tasks and learns to translate their outputs for improved performance on any or all of them at once. Unlike traditional transfer or multi-task learning, EgoT2's “flipped design” entails separate task-specific backbones and a task translator shared across all tasks, which captures synergies between even heterogeneous tasks and mitigates task competition. Demonstrating our model on a wide array of video tasks from Ego4D, we show its advantages over existing transfer paradigms and achieve top-ranked results on four of the Ego4D 2022 benchmark challenges. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Project webpage: https://vision.cs.utexas.edu/projects/egot2/.
iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-training for Visual Recognition

This paper presents a method that effectively combines two prevalent visual recognition methods, i.e., image classification and contrastive language-image pre-training, dubbed iCLIP. Instead of naïve multi-task learning that use two separate heads for each task, we fuse the two tasks in a deep fashion that adapts the image classification to share the same formula and the same model weights with the language-image pre-training. To further bridge these two tasks, we propose to enhance the category names in image classification tasks using external knowledge, such as their descriptions in dictionaries. Extensive experiments show that the proposed method combines the advantages of two tasks well: the strong discrimination ability in image classification tasks due to the clean category labels, and the good zero-shot ability in CLIP tasks ascribed to the richer semantics in the text descriptions. In particular, it reaches 82.9% top-1 accuracy on IN-1K, and mean-while surpasses CLIP by 1.8%, with similar model size, on zero-shot recognition of Kornblith 12-dataset benchmark. The code and models are publicly available at https://github.com/weiyx16/iCLIP.
Comparison of TDNN and Factorized TDNN Approaches for Indonesian Speech Recognition

The use of Deep Neural Networks in speech recognition development has outperformed the GMM-HMM technique and has been widely applied in various world languages. One of the DNNs traditionally used in speech recognition development is TDNN which has undergone several modifications, such as Factorized TDNN or TDNN-F. In this paper, we will compare the performance of standard TDNN with TDNN-F for developing Indonesian speech recognition. Our experiment used the KDW-BPPT-50K-ASR1 speech corpus developed by BPPT in 2013. We aim to identify which architecture suits Indonesian speech recognition applications better. Using the nnet3 recipe with the chain model in Kaldi, various variations of the TDNN architecture were tested to create an Indonesian speech recognition acoustic model. Furthermore, the acoustic model is compared with the acoustic model produced by TDNN-F. The experimental results show that TDNN-F performs very well compared to vanilla TDNN. The outcomes also indicate that alterations in the vanilla TDNN’s architecture, such as the number of layers and configurations in each layer, do not result in a substantial performance improvement.
Local Implicit Ray Function for Generalizable Radiance Field Representation

We propose LIRF (Local Implicit Ray Function), a generalizable neural rendering approach for novel view rendering. Current generalizable neural radiance fields (NeRF) methods sample a scene with a single ray per pixel and may therefore render blurred or aliased views when the input views and rendered views capture scene content with different resolutions. To solve this problem, we propose LIRF to aggregate the information from conical frustums to construct a ray. Given 3D positions within conical frustums, LIRF takes 3D coordinates and the features of conical frustums as inputs and predicts a local volumetric radiance field. Since the coordinates are continuous, LIRF renders high-quality novel views at a continuously-valued scale via volume rendering. Besides, we predict the visible weights for each input view via transformer-based feature matching to improve the performance in occluded areas. Experimental results on real-world scenes validate that our method outperforms state-of-the-art methods on novel view rendering of unseen scenes at arbitrary scales.
Small Search Engine based on Red and Black Tree

This procedure uses C + + language to achieve a small search engine each part, including search, analysis, index, query four processes. And establish corresponding data files to speed up the follow-up query. The data structure used is mainly red and black trees. The algorithms used include multi-channel merge sorting (loser tree optimization) and tf-idf algorithm.
Speaker Recognition System based on Triplet State Loss Function

The purpose of this paper is to build a model and design a speaker recognition system by comprehensively summarizing and learning the research data of speaker speech recognition models at home and abroad, and adopting a research method based on deep machine learning theory. Its main contents and proposed methods are as follows: For data processing, firstly, select and download the public data set from official website, preprocess each voice in the data set, extract Fbank features, convert it into. npy, store it in a file, process the voice into a format suitable for model input, and wait for subsequent input into the model. In practice, a ResCNN architecture based on convolution neural network is used to build a model. The model uses triplet loss function training to map speech to hyperplane, so cosine similarity is directly used to characterize the distance between two speakers. Speaker verification function provides three different ways to obtain speech, input the two acquired speech into the model, judge the similarity of the two speech and give the judgment result. For the speaker recognition model, three different ways can also be used to obtain the speech and determine which speaker the speech is in the corpus. For the speaker confirmation model, a speech is randomly played, and a speaker is randomly selected to judge whether the speech is the speaker's voice.
Conceptual Metaphor of Network Catchwords from a Cognitive Perspective

Internet catchwords, as a special expression form of network language, contain a great deal of metaphorical expressions. The emergence of the Internet catchphrase "lying flat" in 2021 is the result of conceptual metaphor. Taking "lying flat", one of the top ten Internet catchwords in China in 2021, as an example, this paper discusses the conceptual metaphor mechanism in the process of its emergence and popularity, further reveals the cognitive attributes of Internet catchwords and enriches the research process of Internet catchwords.
Study of English Grammar Teaching Practice in Senior High School under the Background of New Curriculum Standards

The English Curriculum Standards for Senior High Schools (revised in 2020, 2017 edition)(hereinafter referred to as the "New Curriculum Standards") advocates a three-dimensional dynamic grammar view of "form-meaning-use", which is oriented by language application. Under the guidance of thematic meaning, English grammar teaching in high school can try to start from the four levels of "introducing the theme, exploring the grammatical form", "exploring the theme, identifying the grammatical form", "expanding the theme, exploring the grammatical rules", and "sublimating the theme, applying the grammatical rules", so as to enable students to understand, analyze, summarize and use the grammatical rules in the context of the theme, and cultivate their comprehensive language application ability. Therefore, this paper expounds the basic principles that should be followed in grammar teaching practice under the background of the new curriculum Standards, and designs a teaching case of modal verbs in senior high school English grammar based on this principle, so as to explore the combination and application of the basic principles in the teaching of new curriculum standards and senior high school English grammar teaching.
Demystifying True Crime: A Vehicle for Building Criticality

Preview this article: Demystifying True Crime: A Vehicle for Building Criticality, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/vm/30/4/voicesfromthemiddle32569-1.gif
A Lightweight YOLOv5 Real-time Mask-wearing Detection Algorithm for the Post-pandemic Era

Since the outbreak of the COVID-19 pandemic, standardized mask-wearing has become a powerful measure to combat the epidemic. Although the epidemic has been brought under control, vigilance in densely populated areas remains essential. Manual supervision is not only inefficient but also increases the risk of infection among relevant personnel. As a result, this paper proposes a lightweight real-time mask-wearing detection algorithm to monitor mask-wearing in crowds in real time. Built upon the YOLOv5 framework, the proposed algorithm replaces the backbone feature extraction network of the original model with an improved EfficientNetV2, reducing the model's parameter count and enhancing accuracy. The introduction of the ECA module in place of the SE module in the EfficientNetV2 network, coupled with the substitution of DIoU-NMS for the weighted NMS in the original model, further reduces model parameters and improves convergence. Additionally, this approach enhances the detection of occluded objects. Experimental results based on a publicly collected mask dataset demonstrate that the proposed algorithm reduces the model's parameter count by 44.7%, achieves a mAP of 95.3%, and attains an inference speed of 270.3 FPS. The algorithm introduced in this paper effectively identifies whether individuals are wearing masks correctly. Its lightweight nature makes it suitable for deployment on resource-constrained mobile devices, aligning well with post-pandemic epidemic prevention and control efforts in the era to come.
An egocentric logic of de dicto and de re knowing who

Abstract The article proposes de dicto and de re versions of ‘knowing-who’ modalities as well as studies the interplay between them and modalities ‘knows’ and ‘for all agents’. It shows that neither of these four modalities is definable through a combination of the three others. In addition, a sound and complete logical system describing the properties of de dicto ‘knows who’, ‘knows’ and ‘for all agents’ modalities is presented.
Randomized Spectrum Transformations for Adapting Object Detector in Unseen Domains

We propose a Meta Learning on Randomized Transformations (MLRT) to learn domain invariant object detectors. Domain generalization is a problem about learning an invariant model from multiple source domains which can generalize well on unseen target domains. This problem is overlooked in object detection field, which is formally named as domain generalizable object detection (DGOD). Moreover, existing domain generalization methods have the problem of domain bias so that they can easily overfit to some specific domain (e.g., source domain). In order to alleviate the domain bias, in MLRT model, a novel randomized spectrum transformation (RST) module is proposed to increase the diversity of source domains. Specifically, RST randomizes the domain specific information of images in frequency-space, which can transform single or multiple source domains into various new domains. Besides, we observe a prior that the gradient imbalance degree among domains can also reflect the domain bias. Therefore, we further propose to alleviate the domain bias from the perspective of gradient balancing, and a novel gradient weighting (GW) module is proposed to balance the gradients over all domains via a hand-crafted weight. Finally we embed our RST and GW into a general meta learning framework and the proposed MLRT model is formalized for DGOD task. Extensive experiments are conducted on six benchmarks, and our method achieves the SOTA performance.