Reviewerly/exHarmony huggingface dataset:
PSM-PS: Part-Based Signal Modulation for Person Search

Person search (PS) is a computer vision problem that joins the two tasks of person detection and person re-identification (ReID). Previous works handle PS problem with either two-step or one-step approaches and have attained much attention due to complex challenges in the scene such as appearance variations, background clutter, and deformation. These approaches achieve significant performance but are still prone to performance degradation under complex scenes which may jeopardize the accuracy of person search methods. In this paper, we propose a novel Part-based Signal Modulation module for Person Search (PSM-PS) within a faster R-CNN-based person search framework. The proposed PSM module transforms the person parts, represented as part tokens, in a wave-like manner, where amplitude indicates the real part and phase shows the imaginary part in a complex domain. The proposed PSM module modulates the pedestrian part tokens such that it enhances the feature representation where the close parts of the person have a close phase compared to others. The experiments are performed over the two prominent person search datasets: CUHK-SYSU [23] and PRW [26]. The extensive experimental study demonstrates the effectiveness of our method and shows the state-of-the-art performance compared to other methods.
Artificial Intelligence and Machine Learning Approaches for Understanding Food and Nutrients

To avoid obesity inside the human body, a healthy diet must be maintained at a consistent level. Automated classification of food photos and estimations of food qualities are presented in this research using a unique machine-learning-based classification system. To train the prototype system's convolutional neural network, this chapter is based on understanding a deep learning approach based on a convolutional neural network. Recommendations for a huge increase in capital savings in the food sector are made by minimizing human mistakes through the use of AI in the food industry. Food establishments can benefit from the use of AI and data science to increase output and improve quality by using different algorithms for sales forecasting. AI has the potential to greatly improve packaging, shelf life, menu mix-and-matching, and food safety by streamlining the supply chain. Smart farming, robotic farming, and drones will be the future of the food industry thanks to AI and ML.
Fast Context Adaptation for Video Object Segmentation

In this paper we present an adaptation module for feature matching based Semi-automatic Video Object Segmentation methods (SVOS). Most current solutions to adapt SVOS methods during inference are slow or inefficient. Feature matching based methods use affinity between a set of reference and query features to segment a target in the current frame based on a reference. We propose an adaptation module working solely with the user supplied mask in the first frame of a video. Our adaptation of the matching module provides more reliable information to the model for segmentation in all the video frames and does not significantly increase inference time. The evaluation on both OVIS and DAVIS 17 datasets shows a significant improvement on the segmentation (respectively $$+2.9\%$$ and $$+1\%$$ of the Jaccard index). This demonstrates that our adaptation of the feature space provides a better matching between query and reference features.
Artificial Intelligence for Understanding Human Behavior and Psychology

Artificial intelligence (AI) technology provides useful tools for therapies by combining approaches such as data analysis (creating new knowledge from a deep study of huge amounts of data) and insightful analysis. AI can detect current and foreseeable issues, as well as evaluate and validate forecasts and solutions. The goals of cognitive computing psychology are related in that both learn to determine the structure of human cognition, with the former seeking to develop such mechanisms using sophisticated technology. While there are important variations between computational modeling and AI, both are essential tools for studying the nature of intelligent thought and giving insights into the emerging discipline of cognitive science.
3D Non-separable Moment Invariants

In this paper, we introduce new 3D rotation moment invariants, which are composed of non-separable Appell moments. The Appell moments can be substituted directly into the 3D rotation invariants instead of the geometric moments without violating their invariance. We show that non-separable moments may outperform the separable ones in terms of recognition power and robustness thanks to a better distribution of their zero surfaces over the image space. We test the numerical properties and discrimination power of the proposed invariants on three real datasets – MRI images of human brain, 3D scans of statues, and confocal microscope images of worms.
Fast Video Instance Segmentation via Recurrent Encoder-Based Transformers

State-of-the-art transformers-based video instance segmentation (VIS) frameworks typically utilize attention-based encoders to compute multi-scale spatio-temporal features to capture target appearance deformations. However, such an attention computation is computationally expensive, thereby hampering the inference speed. In this work, we introduce a VIS framework that utilizes a light-weight recurrent-CNN encoder to learn multi-scale spatio-temporal features from the standard attention encoders through knowledge distillation. The light-weight recurrent encoder effectively learns multi-scale spatio-temporal features and achieves improved VIS performance by reducing the over-fitting as well as increasing the inference speed. Our extensive experiments on the popular Youtube-VIS 2019 benchmark reveal the merits of the proposed framework over the baseline. Compared to the recent SeqFormer, our proposed Recurrent SeqFormer improves the inference speed by two-fold while also improving the VIS performance from 45.1% to 45.8% in terms of overall average precision. Our code and models are available at https://github.com/OmkarThawakar/Recurrent-Seqformer
RLSTM: A Novel Residual and Recurrent Network for Pedestrian Action Classification

Properly training LSTMs requires long time and extensive amount of data. To improve the training of these models, this paper proposes a novel residual and recurrent neural network, Resnet-LSTM, for spatio-temporal pedestrian action recognition from image sequences. The model includes a novel layer, called MapGrad, whose goal is improving stationarity of the feature map sequences processed by the ConvLSTM. The paper demonstrates the effectiveness of the proposed model and the MapGrad layer in the spatio-temporal classification of pedestrian actions through an ablation study and comparison with state-of-the-art methods. Overall, RLSTM achieves an accuracy value of 88% and an average precision of 94% on the JAAD dataset, which is a widely used benchmark in the field. Finally, the paper empirically analyzes the effect of increasing input sequence length on standing action recognition, showing that the proposed method yields a recall of 93%.
Data Center Control Application With Fuzzy Logic

Data centers are systems that host devices utilizing recording and communication technologies, which are expected to operate securely and accurately. Consequently, transforming data centers into smart environments for control purposes has become a significant area of focus. In this study, we monitor the cabinet environment within data centers and ensure that the control system reaches the predetermined optimal state values in the event of undesirable situations. Threshold control was implemented for humidity and flame data, while fuzzy logic theory was applied to temperature data. Fuzzy clusters can be adjusted according to the data center's location at the user's request. This approach allows users to input desired optimal and threshold values into the system, which are then evaluated based on the situation. The designed system ensures data center security with minimal personnel involvement. Additionally, all problematic events are recorded in the system, enabling them to be viewed on a webpage and communicated to designated personnel via email. In the conducted study, the fuzzy-controlled temperature value outputs are reported as heating (40%), cooling (53%), and instances where the system does not perform heating or cooling.
UN-PUNet for phase unwrapping from a single uneven and noisy ESPI phase pattern

The wrapped phase patterns of objects with varying materials exhibit uneven gray values. Phase unwrapping is a tricky problem from a single wrapped phase pattern in electronic speckle pattern interferometry (ESPI) due to the gray unevenness and noise. In this paper, we propose a convolutional neural network (CNN) model named UN-PUNet for phase unwrapping from a single wrapped phase pattern with uneven grayscale and noise. UN-PUNet leverages the benefits of a dual-branch encoder structure, a multi-scale feature fusion structure, a convolutional block attention module, and skip connections. Additionally, we have created an abundant dataset for phase unwrapping with varying degrees of unevenness, fringe density, and noise levels. We also propose a mixed loss function MS_SSIM + L2. Employing the proposed dataset and loss function, we can successfully train the UN-PUNet, ultimately realizing effective and robust phase unwrapping from a single uneven and noisy wrapped phase pattern. We evaluate the performance of our method on both simulated and experimental ESPI wrapped phase patterns, comparing it with DLPU, VUR-Net, and PU-M-Net. The unwrapping performance is assessed quantitatively and qualitatively. Furthermore, we conduct ablation experiments to evaluate the impact of different loss functions and the attention module utilized in our method. The results demonstrate that our proposed method outperforms the compared methods, eliminating the need for pre-processing, post-processing procedures, and parameter fine-tuning. Moreover, our method effectively solves the phase unwrapping problem while preserving the structure and shape, eliminating speckle noise, and addressing uneven grayscale.
Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies

Catastrophic forgetting remains an outstanding challenge in continual learning. Recently, methods inspired by the brain, such as continual representation learning and memory replay, have been used to combat catastrophic forgetting. Associative learning (retaining associations between inputs and outputs, even after good representations are learned) plays an important function in the brain; however, its role in continual learning has not been carefully studied. Here, we identified a two-layer neural circuit in the fruit fly olfactory system that performs continual associative learning between odors and their associated valences. In the first layer, inputs (odors) are encoded using sparse, high-dimensional representations, which reduces memory interference by activating nonoverlapping populations of neurons for different odors. In the second layer, only the synapses between odor-activated neurons and the odor's associated output neuron are modified during learning; the rest of the weights are frozen to prevent unrelated memories from being overwritten. We prove theoretically that these two perceptron-like layers help reduce catastrophic forgetting compared to the original perceptron algorithm, under continual learning. We then show empirically on benchmark data sets that this simple and lightweight architecture outperforms other popular neural-inspired algorithms when also using a two-layer feedforward architecture. Overall, fruit flies evolved an efficient continual associative learning algorithm, and circuit mechanisms from neuroscience can be translated to improve machine computation.
Between hunger and contagion: digital mediation and advocacy during the COVID-19 emergency in Delhi

When COVID-19 struck India in March 2020 the central government announced a nationwide lockdown to slow the spread of the virus. In Delhi, the suspension of normal economic and social life precipitated a crisis of hunger for the thousands who depend on daily wage labour to feed their families. Many of these workers were unable to access the city's Public Distribution System for subsidised food supplies because they lacked the correct paperwork. In response, the Delhi government implemented an online system, known as E-Coupons, through which those affected could apply for emergency rations. However, this digital system proved complicated to navigate for the marginalised people that it was aimed at. In the east Delhi neighbourhood in which this research took place brokers offering digital connections and online form-filling services proliferated in the crisis, but often provided unreliable or incomplete support to those in need. Recognising the need for digital mediation and support for the marginalised we argue that networks of reliable community advocates are required if welfare bureaucracies are to be digitised through mobile governance projects such as E-Coupons. The human mediation and advocacy, which underpins these schemes should be acknowledged and included in system design.
Checkerboard corner detection method based on neighborhood linear fitting

To improve the calibration accuracy of a vision measurement system, a checkerboard corner detection method based on linear fitting of the checkerboard local contour is proposed. First, by binarization and morphological dilation of the checkerboard image, the coordinates of two adjacent vertices of adjacent dark squares are obtained; the midpoint of the two vertices is taken as the reference point; the reference dotted array is obtained; and the Zernike moment subpixel method is used to obtain the checkerboard contour data points in the neighborhood of each reference point. Finally, the contour points are classified according to the orientation based on the reference points; two intersecting lines are fitted; and the intersection of the two lines is exactly the corner point that we want to find. A camera calibration experiment was conducted on the same group of checkerboard images. The results show that the calibration results of the corner points obtained based on this method are highly consistent with the OpenCV library function method and the MATLAB Toolbox calibration method, and the reprojection error is smaller. At the same time, it is robust to changes in the light source brightness.
Towards Unified Multi-Domain Machine Translation With Mixture of Domain Experts

Multi-domain machine translation (MDMT) aims to construct models with mixed-domain training corpora to switch translation between different domains. Previous studies either assume that the domain information is given and leverage the domain knowledge to guide the translation process, or suppose that the domain information is unknown and utilize the model to automatically recognize it. However, the cases are mixed in practical scenarios, which means that some sentences are labeled with domain information while others are unlabeled, which is beyond the capacity of the previous methods. In this paper, we propose a unified MDMT model with a mixture of sub-networks (experts) to address the cases with or without domain labels. The mixture of sub-networks in our MDMT model includes a shared expert and multiple domain-specific experts. For the inputs with domain labels, our MDMT model goes through the shared and the corresponding domain-specific experts. For the unlabeled inputs, our MDMT model activates all the experts, each of which makes a dynamic contribution. Experimental results on multiple diverse domains in De <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> En, Fr <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> En, and En <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> Ro demonstrate that our method can outperform the strong baselines in both scenarios with or without domain labels. Further analyses show that our model has good generalization ability when transferring into new domains.
Modeling and Evaluation of Echo-State Networks Using Spin Torque Nano-Oscillators

An echo state network (ESN), capable of processing time-series data with high accuracy, is designed and benchmarked using spin torque nano-oscillators (STNOs) with easy-plane anisotropy. An ESN belongs to the category of reservoir computers, where the reservoir comprises randomly initialized, recurrently connected, and untrained pool of neurons and acts as a high-dimensional expansion of the input signal. The readout function is used to glean a meaningful output representation. Here, we use STNOs as the basic building block of the ESN and apply the ESN to predict the Mackey-Glass (MG) time-series data. The design parameters of the STNO and the input data representation are selected to yield prediction errors as low as 4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> . We also quantify the short-term memory (STM) and the parity-check (PC) capacity of the ESN and obtain metrics that are comparable to or better than existing spintronics-based ESNs, as well as ESNs employing ‘tanh’ neurons. The peak STM is found to be approximately 8.8, while the peak PC capacity is found to be approximately 3.9. Impacts of thermal fluctuations and process variability on the ESN performance are systematically quantified. Although the ESN’s prediction and memory capability remain robust with temperature variations, a 10% variation in the dimensions of the STNO free layer can lead to around 40% increase in its prediction error for the MG time-series data.
P-Transformer: Towards Better Document-to-Document Neural Machine Translation

Directly training a document-to-document (Doc2Doc) neural machine translation (NMT) via Transformer from scratch, especially on small datasets, usually fails to converge. Our dedicated probing tasks show that 1) both the absolute position and relative position information gets gradually weakened or even vanished once it reaches the upper encoder layers, and 2) the vanishing of absolute position information in encoder output causes the training failure of Doc2Doc NMT. To alleviate this problem, we propose a position-aware Transformer (P-Transformer) to enhance both the absolute and relative position information in both self-attention and cross-attention. Specifically, we integrate absolute positional information, i.e., position embeddings, into the query-key pairs both in self-attention and cross-attention through a simple yet effective addition operation. Moreover, we also integrate relative position encoding in self-attention. The proposed P-Transformer utilizes sinusoidal position encoding and does not require any task-specified position embedding, segment embedding, or attention mechanism. Through the above methods, we build a Doc2Doc NMT model with P-Transformer, which ingests the source document and completely generates the target document in a sequence-to-sequence (seq2seq) way. In addition, P-Transformer can be applied to seq2seq-based document-to-sentence (Doc2Sent) and sentence-to-sentence (Sent2Sent) translations. Extensive experimental results of Doc2Doc NMT show that P-Transformer significantly outperforms strong baselines on the widely-used 9 document-level datasets in 7 language pairs, covering small-, middle-, and large-scales, and achieves a new state-of-the-art. Experimentation on discourse phenomena shows that our Doc2Doc NMT models improve the translation quality in both BLEU and discourse coherence. We make our code available on Github.
On the detection of local and global amoebas: theoretical insights and practical algorithms (Brief Announcement)

Let G be a graph of order n, and let e ϵ E(G) and e' ϵ (V(G)/2) \ E(G). If the graph G' = G - e + e' is isomorphic to G, we say that e → e' is a feasible edge-replacement. We call G a local amoeba if, for any two copies G1 and G2 of G on V(G), G2 can be reach from G1 by performing a sequence of edge replacements. A graph G is a global amoeba if it can be made into a local amoeba by adding some isolated vertices. Our work includes a proof that almost every graph is not an amoeba, and the identification of a special type of edge-replacement called weird-edge-replacements. Additionally, we provide an infinite family of trees that are both weird local and global amoebas. Our contributions extend to the development and implementation of several algorithms for detecting local and global amoebas, which are made available in a public repository along with multiple examples.
Freeze-Tag is NP-hard in 3D with L1 distance

The Freeze-Tag Problem (FTP) is the task of scheduling the activation of a robot swarm. The input consists of the initial locations of a set of mobile robots in some metric space. A single robot is initially “active” while the others are initially “frozen”. Active robots can move at unit speed, and upon reaching the location of a frozen robot, the latter is activated. The goal is to activate all the robots within the minimum time, minimizing the so-called makespan of the schedule. The complexity of this problem in Euclidean spaces was open until 2017, when Abel et al. [1] proved that FTP is NP-hard in the Euclidean plane with L2 distance. During that same year, Demaine and Rudoy [2] showed that it is also NP-hard in 3D Euclidean space with Lp distance for any p > 1, but left open the case with p = 1. This paper closes this gap and shows that FTP is indeed NP-hard in 3D Euclidean space with L1 distance. Furthermore, the hardness result holds in the strong sense, such that every coordinate is a rational bounded by a polynomial in the instance size.
A Memory-Efficient Federated Kernel Support Vector Machine for Edge Devices

A federated learning (FL) scheme (denoted as Fed-KSVM) is designed to train kernel support vector machines (SVMs) over multiple edge devices with low memory consumption. To decompose the training process of kernel SVM, each edge device first constructs high-dimensional random feature vectors of its local data, and then trains a local SVM model over the random feature vectors. To reduce the memory consumption on each edge device, the optimization problem of the local model is divided into several subproblems. Each subproblem only optimizes a subset of the model parameters over a block of random feature vectors with a low dimension. To achieve the same optimal solution to the original optimization problem, an incremental learning algorithm called block boosting is designed to solve these subproblems sequentially. After training of the local models, the central server constructs a global SVM model by averaging the model parameters of these local models. Fed-KSVM only increases the iterations of training the local SVM models to save the memory consumption, while the communication rounds between the edge devices and the central server are not affected. Theoretical analysis shows that the kernel SVM model trained by Fed-KSVM converges to the optimal model with a linear convergence rate. Because of such a fast convergence rate, Fed-KSVM reduces the communication cost during training by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$99\%$</tex-math> </inline-formula> compared with the centralized training method. The experimental results also show that Fed-KSVM reduces the memory consumption on the edge devices by nearly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$90\%$</tex-math> </inline-formula> while achieving the highest test accuracy, compared with the state-of-the-art schemes.