Reviewerly/exHarmony huggingface dataset:
Incorporating Human Body Shape Guidance for Cloth Warping in Model to Person Virtual Try-on Problems

The world of retail has witnessed a lot of change in the last few decades and with a size of 2.4 trillion, the fashion industry is way ahead of others in this aspect. With the blessings of technology like virtual try-on (vton), now even online shoppers can virtually try a product before buying. However, the current image-based virtual try-on methods still have a long way to go when it comes to producing realistic outputs. In general, vton methods work in two stages. The first stage warps the source cloth and the second stage merges the cloth with the person image for predicting the final try-on output. While the second stage is comparatively easier to handle using neural networks, predicting an accurate warp is difficult as replicating actual human body deformation is challenging. A fundamental issue in vton domain is data. Although lots of images of cloth are available over the internet in either social media or e-commerce websites, but most of them are in the form of a human wearing the cloth. However, the existing approaches are constrained to take separate cloth images as the input source clothing. To address these problems, we propose a model to person cloth warping strategy, where the objective is to align the segmented cloth from the model image in a way that fits the target person, thus, alleviating the need of separate cloth images. Compared to the existing approaches of warping, our method shows improvement especially in the case of complex patterns of cloth. Rigorous experiments applied on various public domain datasets establish the efficacy of this method compared to benchmark methods.
DuTriNet: Dual-Stream Triplet Siamese Network for Self-Supervised Action Recognition by Modeling Temporal Correlations

Self Supervised Learning (SSL) is the task of training a model independent of human annotations. A recent path-breaking SSL work is OpenAI's GPT-3. Very limited work has happened on SSL for Action Recognition (AR). Present SSL models either leverage only spatial data or use suboptimal frame sampling algorithms. To this end, we present a comprehensive study and propose DuTriNet for SSL in AR. We introduce the idea of temporal protraction by fusing optical-flow information with weber-maps inside parallel data-streams which share weights under a triplet Siamese architecture. We also propose flow-intensity based non-parametric frame sampling algorithm. Extensive experiments and ablations have been performed on two publicly available benchmark datasets for AR. Our findings suggest the suitability of DuTriNet for SSL.
Imagenation - A DCGAN based method for Image Reconstruction from fMRI

We propose a method to reconstruct natural grayscale images and handwritten characters from Functional Magnetic Resonance Imaging (fMRI) data and achieve a high degree of similarity to the original stimuli images. The approach utilizes a pre-trained Deep Convolutional Generative Adversarial Network (DCGAN) to reconstruct images and provide visual confirmations regarding the resemblance between the reconstructed and original images. A linear regressor is used to elicit information from the fMRI data and estimate a latent space representation for the formerly trained generative model. A composite loss function combining the Perceptual and Multi-Scale Structural Similarity Index (MS-SSIM) losses is used to train the regressor. The advantages of both functions are evident with the Perceptual loss capturing semantic information and the MS-SSIM loss carrying information about objects in a scene. With this loss function, we were able to reconstruct human objects in the stimuli to a degree of accuracy. The reconstructions obtained were then validated using the Scale Invariant Feature Transform (SIFT) method to elucidate the number of features matched between the original and recreated images. The SSIM scores for the reconstructed images are observed to be higher than state-of-the-art methods. Parallels are drawn between the distortions produced in images submerged underwater and those in the reconstructed images using the Contrast Limited Adaptive Histogram Equalization (CLAHE), an image enhancement technique. A sharp increase in the number of SIFT features matched, is observed with the application of CLAHE on the reconstructed images.
An Application of LSTM Recurrent Networks to Expanding of Search Tree Nodes in Symbolic Planning

Applying deep learning to symbolic planning is not straightforward because they differently internalize knowledge. One represents knowledge numerically while the other does symbolically. Hybrid methods that take strengths from both techniques will deliver better performance on automated planning. This paper presents a method to improve the performance of symbolic planning by applying LSTM recurrent networks to inferring the guidance knowledge for search tree node expansion. The networks learn sequential patterns across previous node expansions and generalizes the learned knowledge to predict promising actions and their probability scores at the present expansion step. The prediction consists with node cost estimation for heuristic search. A simple experiment vindicates the method of this research.
A Mining Frequent Itemsets Algorithm in Stream Data Based on Sliding Time Decay Window

In order to reduce the time and memory consumption of frequent itemsets mining in stream data, and weaken the impact of historical transactions on data patterns, this paper proposes a frequent itemsets mining algorithm SWFIUT-stream based on sliding decay time window. In this algorithm, the time attenuation factor is introduced to assign different weights to each window unit to weaken their influence on data mode. In order to realize the fast stream data mining processing, when mining the frequent itemsets, the two-dimensional table is used to scan and decompose the itemsets synchronously to mine all the frequent itemsets in the window, and the distributed parallel computing processing is carried out based on storm framework. Experimental data show that the algorithm consumes less time and consumes less memory space than conventional algorithms when mining frequent itemsets in stream data.
SLAM-based Augmented Reality System in Interactive Exhibition

A SLAM-based AR technology makes the exhibition richer and diversified. The audience can move in the real space to interact with virtual objects that are realistic in the interactive form. Besides, the integration between virtual objects and real space is also closer. This research designs an interactive solution called “AR Cross-center Interactive Device” by using buttons and combining the advantages of SLAM-based AR. The cross-center system is used for users to quickly understand the target of the interactive object and simply operate with buttons. This research uses the device in an interactive exhibition that related to the history of education in Taiwan. We expect that the introduction of this system helps users respond intuitively and provides an effective interactive solution in the SLAM-based AR exhibitions.
Hybrid popularity model for solving cold-start problem in recommendation system

This research proposes a new hybrid popularity model for solving the cold-start problem in the recommendation system. A cold-start problem arises when the target user has no rating history in the system. A hybrid popularity model combines the benefit of both the user and item popularities. The item popularity model assumes that a target user is most expected to like the top-rated items. Whereas the user popularity model presumes that a target user is likely to be influenced by the top users who have given a large number of ratings. Naturally, our proposed HPop model is built in three phases: item popularity, user popularity, and hybrid popularity. The ratio of the item and user popularities are controlled by the use of α. We use the Normalized Discounted Cumulative Gain (NDCG), as well as Precision and Recall metrics to evaluate the performance of our model and its counterparts, i.e., IPop and UPop. Using a real-world MovieLens dataset, our experiments show that the employment of the user popularity model is always more beneficial than the item popularity model. HPop performs best when α = 0.9 and worst when α = 1. The NDCG average of increases from HPop to IPop and UPop are respectively 12.22% and 8.02%. The results in terms of Precision-Recall also show a similar trend to those of NDCG. Hence, we conjecture that the performances of HPop, IPop, and UPop are stable in any evaluation metrics.
Research on time series characteristics of sports training effect based on support vector machine

Sports athletes not only exercise fast, but also suffer from the surrounding complex environment. Therefore, the video needs to be sequenced to improve processing efficiency. From the perspective of machine learning, this paper designs a spatial feature extractor based on CNN to extract time series features of sports. Moreover, this paper uses the support vector machine as the basis of the construction model to construct a feature extraction model based on support vector machine and random forest based on different situations. At the same time, this paper collects test data through the sports database and uses the swimming project as an example to analyze the model performance. Finally, the paper verifies the validity of the model by comparing and verifying methods. The research indicates that the proposed method has certain effectiveness and can provide theoretical reference for subsequent related research.
Evaluation of Neural Network Models for Performance Prediction of Scientific Applications

Performance prediction is an important and active research area. In particular, several research efforts have built empirical models using machine learning algorithms for performance prediction. These models enable us to understand the dependence on hardware components for algorithm execution, system's scaling capabilities, cross-platform prediction in multi-core systems, and many others. The user community can use this knowledge to select hardware configurations best suited for executing a given software. In recent times, neural network-based models are widely used to build empirical models that understand complex relations between independent and dependent variables of an unknown data set. This paper has studied one-layer and multi-layer neural network models for performance prediction of three algorithms with different computations and memory-access patterns. We have shown that the multi-layer model outperforms the one-layer model, especially for computationally intensive algorithms. We have also shown that computationally intensive algorithms having a higher variance in runtime due to manufacturer variability require a higher number of neurons for convergence than memory-intensive algorithms. Our multi-layer neural network with optimal configuration has a prediction accuracy of about 88% for computationally intensive algorithms and about 95% for the memory-intensive algorithm.
3D Hand Pose Estimation from Single Depth Images with Label Distribution Learning

Reliable hand pose estimation enriches the way of human-computer interaction, such as sign language recognition and virtual reality. However, the task of estimating the hand pose faces two severe challenges. To be specific, it is difficult to learn spatial information from a 2D image and regress the location of a point in 3D space. And the highly non-linear correlation between the hand feature space and the joint location makes it hard to be modeled. To deal with the above problems, we propose a deep regression network, which learns the hand feature space from the point cloud and includes a specific label distribution learning network. Due to the point cloud contains more spatial information, it is beneficial for the neural network to extract the hand spatial geometric features. Utilizing the deep network to guide label learning actively reduces the negative effects of nonlinearity. According to the experimental results, our proposed network achieves the state-of-the-art performance on MSRA dataset.
Intelligence Is beyond Learning: A Context-Aware Artificial Intelligent System for Video Understanding

Understanding video files is a challenging task. While the current video understanding techniques rely on deep learning, the obtained results suffer from a lack of real trustful meaning. Deep learning recognizes patterns from big data, leading to deep feature abstraction, not deep understanding. Deep learning tries to understand multimedia production by analyzing its content. We cannot understand the semantics of a multimedia file by analyzing its content only. Events occurring in a scene earn their meanings from the context containing them. A screaming kid could be scared of a threat or surprised by a lovely gift or just playing in the backyard. Artificial intelligence is a heterogeneous process that goes beyond learning. In this article, we discuss the heterogeneity of AI as a process that includes innate knowledge, approximations, and context awareness. We present a context-aware video understanding technique that makes the machine intelligent enough to understand the message behind the video stream. The main purpose is to understand the video stream by extracting real meaningful concepts, emotions, temporal data, and spatial data from the video context. The diffusion of heterogeneous data patterns from the video context leads to accurate decision-making about the video message and outperforms systems that rely on deep learning. Objective and subjective comparisons prove the accuracy of the concepts extracted by the proposed context-aware technique in comparison with the current deep learning video understanding techniques. Both systems are compared in terms of retrieval time, computing time, data size consumption, and complexity analysis. Comparisons show a significant efficient resource usage of the proposed context-aware system, which makes it a suitable solution for real-time scenarios. Moreover, we discuss the pros and cons of deep learning architectures.
D-ISDET: Double Intensity of Image Shadow Detection and Elimination in Autonomous Vehicle

We nominate the double intensity of image shadow detection and elimination method which is called D-ISDET. It offers to restore the invisible image information in the image which is covered by the shadows. The D-ISDET which supports the certainty of object detection in the autonomous vehicle is running well. Hence, the disparity of the shadowed space and the true non-shadowed space can be distinguished with using the double intensity and threshold methods. Likewise, in the big problem of imprecise detection of low-shadows and the perplexed shadows, the double intensity method is endorsed to reinforce the true shadow spaces, thereby the threshold method abolishes the shadow correctly. The experimental results of D-ISDET performance indicate the enhancement of the BER and RMSE indexes for shadow detection and elimination, respectively. D-ISDET outshines achievement between 0.24% and 1.85% of the shadow area detection and between 1.42% and 3.05% of the shadow-free area detection compared to the other methods. D-ISDET also works out between 4.11% and 16.59% of the shadow elimination and D-ISDET reaches between 0.60% and 16.57% of shadow-free elimination compared to the other methods. D-ISDET also carries out the first-rate performance compared with the other methods.
Partially Observable Games for Secure Autonomy

Technology development efforts in autonomy and cyber-defense have been evolving independently of each other, over the past decade. In this paper, we report our ongoing effort to integrate these two presently distinct areas into a single framework. To this end, we propose the two-player partially observable stochastic game formalism to capture both high-level autonomous mission planning under uncertainty and adversarial decision making subject to imperfect information. We show that synthesizing sub-optimal strategies for such games is possible under finite-memory assumptions for both the autonomous decision maker and the cyber-adversary. We then describe an experimental testbed to evaluate the efficacy of the proposed framework.
Privacy and Utility Trade-Off for Textual Analysis via Calibrated Multivariate Perturbations

In recent years, the problem of data leakage often appears in our lives. As of today, a number of enterprises have been fined heavily for the leakage of user data, including Facebook, Uber and Equifax. This paper makes deep research on the privacy protection of the text. We proposed a three-layer privacy protection mechanism for carrying out privacy-preserving text perturbation. This approach allows different levels of privacy protection for different parts of the text, thereby increasing the level of privacy protection without reducing utility. Extensive experiments prove that the proposed method not only provides fine-grained control over the level of privacy in that data but also improves performance.
GBEx - towards Graph-Based Explanations

This paper proposes Graph-Based Explanations (GBEx), a approach to explain machine learning models. It presents explanations in the form of a graph, where nodes represent arguments, and edges represent connections. The value of a graph node accounts for the influence of a given argument while the value of a graph edge accounts for the influence of a given connection. Contrarily to LIME, GBEx does not provide local explanations but a global explanations. And contrarily to SHAP, it can automatically explain interactions between variables. We provide an illustration on how GBEx can provide both local and global explanation.
Towards the implementation of an Attention-based Neural Machine Translation with artificial pronunciation for Nahuatl as a mobile application

There are great translation systems online. However, even though this technology is available for the majority of languages, it is not the case of Nahuatl [1]. For this reason, this paper outlines a master's degree thesis proposal which is aimed to use a Neural Network for Translation with an attention mechanism and Long Short-Term Memory (LSTM) like the one used by Google [2]. In addition, it seeks to implement an artificial Text To Speech (TTS) system trained with a Neural Network with a given dataset of Mel spectrograms in [3] from a person speaking in Nahuatl and it attempts to achieve a natural voice output as a spectrogram, then process it and obtain the sound desired. Finally, once trained, these models can be prepared for being used within mobile devices, and even taking advantage of the neural engine some of them are equipped with. In this way, this technology can reach more people and help to preserve and even spread the language. The early results showed how the limited resources of this language could cause a strong bias in the outputs and also how there could be some loss of information given the morphemes Nahuatl has, given its polysynthetic nature. This also highlights the way it can be tokenized, playing an important role in how the results turn out obtaining a BLEU score of 0.34 at best. Finally, this application and research can be an interesting framework of how a polysynthetic language can be manipulated to be used for fusional languages like Spanish or English. This research work was carried out at the “Tecnológico Nacional de México” (TecNM), campus of the “Instituto Tecnológico de Apizaco” (ITA).
Algorithms for Regional Human Resources Capacity Management System

The task of providing the needs of the regional job market by graduates of educational institutions is discussed.Considered algorithms are designed to determine the enterprises' needs for specialists, blue- and white-collar workers in different positions according to natural decline, retirements, and organizational activities are linked to changes in the staffing table. Methods for determing the conformity of educational programs with the vacancies of the job market in specific professions and positions are proposed. General structure of the system is described.
ExKaldi: A Python-based Extension Tool of Kaldi

We present ExKaldi, an automatic speech recognition (ASR) toolkit, which is implemented based on the Kaldi toolkit and Python language. While similar Kaldi wrappers are available, a key feature of ExKaldi is an integrated strategy to build ASR systems, including processing feature and alignment, training an acoustic model, training, querying N-grams language model, decoding and scoring. Primarily, ExKaldi builds a bridge between Kaldi and deep learning frameworks to help users customize a hybrid hidden Markov model-deep neural network-based ASR system. We performed benchmark experiments on the TIMIT corpus and revealed that ExKaldi could build a system from scratch with Python and achieved reasonable recognition accuracy. The toolkit is open-source and released under the Apache license.
Research Report: Formally-Verified ASN.1 Protocol C-language Stack

We describe our approach and progress in verification of a mature open-source ASN.1 compiler, ASN1C, using the Coq proof assistant. Once completed, our project will provide state-of-the-art high assurance suitable for mission-critical systems. Furthermore, since formal verification will be layered atop a well-tested ASN.1 stack, it will combine the benefits of high-performance portable stack implementation with formal correctness guarantees. As an essential step in our approach, the project will also provide a formalization of a key part of the ASN.1 standard. Such formal specification could subsequently be used by others to analyze ASN.1 properties and validate other implementations.
Enchancing Learning Experience Through Augmented Reality

There cannot be a better compliment to a book than the statement, “Between the pages of a book is a wonderful place to be”. Since forever, books have been the primary source of learning. But there has not been much of an attempt to apply creativity to them. Though the information carried by books is humongous, the intelligence derived by the readers usually seems to be minuscule. This problem can undeniably be imputed to not having developed enough techniques to effectively impart the information contained. This piqued our curiosity to attempt devising strategies to help books become more efficient in conveying the content they withhold and thus resulted in the building of our Augmented Reality (AR) powered application. This application’s range of influence is undoubtedly extensive, has now been designed to devote focus to a few chapters of a Chemistry text book specifically which required facilitation for effectively imparting content to the learners. The application employs various media like audio, image, 3D model, graphic, video and gaming to engage its users and provide an interactive learning experience.
Vehicle Speed Calculation from Drone Video Based on Deep Learning

The number of the vehicles continues to increase from year to year and cause congestion especially when there is a long holiday. The cost of installing and maintaining the recording equipment and supporting infrastructure is quite extortionate. Therefore, the calculation of speed with a flexible and inexpensive cost requires drones. The purpose of this study is to monitor the flow of traffic in areas where there is no supporting infrastructure to install traffic cameras used to calculate the speed of the vehicles during long holidays. The camera shots taken from the drones are used to detect the types of passenger cars using library of You Only Look Once (YOLO) deep learning. The detected object is then tracked by using SORT (Simple Online and Realtime Tracking) to always detect the same object and calculate the speed of the vehicle when passing the RoI (Region of Interest). On the 5, 10, and 15 meters RoI, the lowest error values are 4.79%, 4.38%, and 2.96% respectively. Each of the results was obtained at a height of 10 meters. The proposed method can prove the error calculation of speed vehicle is small when the frame rate (fps) is high, altitude is low and the distance ROI is long.