Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Encoder-Decoder Networks for Mapping Raw Images to Dynamic Movement Primitives

In this paper we propose a new approach for learning perception-action couplings. We show that by collecting a suitable set of raw images and the associated movement trajectories, a deep encoder-decoder network can be trained that takes raw images as input and outputs the corresponding dynamic movement primitives. We propose suitable cost functions for training the network and describe how to calculate their gradients to enable effective training by back-propagation. We tested the proposed approach both on a synthetic dataset and on a widely used MNIST database to generate handwriting movements from raw images of digits. The calculated movements were also applied for digit writing with a real robot.
Amar Bangladesh - a Machine Learning Based Smart Tourist Guidance System

Tourism is a very prominent sector for many third world countries around the world as a greater portion of their economy is dependent on it. In countries like Bangladesh, who are blessed with enriched natural beauty, are often lagging behind to attract more tourists both by private and government organizations. Amar Bangladesh is the first tourism-based smart phone application to introduce machine learning in its program. The mobile application uses the Google Maps API and finds out the location of all points of interest added to the database by the Admin. An user will be able to log in to this app using their basic information or Facebook details. Upon logging in, users will plan their day long trips starting from the nearest attraction. They can deselect attractions which they do not prefer. The ML program used in this application will find the most suitable routes to reach the destinations one by one. Suggestions developed through machine learning would appear each time while choosing their points of interest. Users will also be able to write notes as a mean of diary entry which will be saved on their phones offline database. Users may allow public access to those entries or restrict them from sharing too.
Modal Fields of a Triangular Microcavity Laser with a Piercing Hole

Eigenmodes of triangular microcavities with piercing holes in the center are computed as solutions of the Lasing Eigenvalue Problem using the system of Muller boundary integral equations and the Nyström method. The numerical study demonstrates that small centered holes in equilateral triangular microcavities can lead to a significant growth of the directionality of laser modes with the preservation of their low thresholds.
System- Wide Sustainability Factors and Strategies for Big Data Center

Big data centers play an important role in the current IT infrastructure, but they are also the main contributors of the carbon dioxide emissions, and their impact on resource consumption and environmental pollution will continue to increase in the coming years. At present, many scholars have been discussing how technology can reduce pollution, increase resource utilization, and predict data center energy consumption. Based on these studies, this paper analyzes the major components of energy consumption in a big data center, and proposes a system-wide energy consumption strategy to reduce its carbon emissions. The discussion of this paper can help a firm manager to effectively build and manage a sustainable big data center.
DNN-Supported Speech Enhancement With Cepstral Estimation of Both Excitation and Envelope

In this paper, we propose and compare various techniques for the estimation of clean spectral envelopes in noisy conditions. The source-filter model of human speech production is employed in combination with a hidden Markov model and/or a deep neural network approach to estimate clean envelope-representing coefficients in the cepstral domain. The cepstral estimators for speech spectral envelope-based noise reduction are both evaluated alone and also in combination with the recently introduced cepstral excitation manipulation (CEM) technique for a priori SNR estimation in a noise reduction framework. Relative to the classical MMSE short time spectral amplitude estimator, we obtain more than 2 dB higher noise attenuation, and relative to our recent CEM technique still 0.5 dB more, in both cases maintaining the quality of the speech component and obtaining considerable SNR improvement.
Human Action Recognition System based on Skeleton Data

In this paper, the proposed system aims to enhance human action recognition by using skeletal features from Kinect sensor to obtain discriminative features. Joints distance feature is used for feature extraction. Instead of using traditional (non-static) K-means, such feature is clustered based on static K-means algorithm which takes statically the initial defined centroids at the first estimates for the K centroids and reduces the randomized starting centroids at all time to increase the accuracy of postures selection. Each posture is labelled by using artificial Neural Network (ANN) which makes the system more intelligent. Recognition of human action is performed using hidden Markov Model (HMM) based on the sequence of known poses to improve performance and accuracy. The proposed system recognizes the fundamental actions (walking, sitting, standing, and bending) and evaluated on the public dataset UTKinect-Action3D. The experimental results show the better accuracy rate on the static K-means than the non-static K-means.
Empirical Study on the Wechat Mini Program Acceptance Based on UTA UT Model Take the Pearl River Delta as an Example

Users' acceptance is important to evaluate the success of an application tool. There is no exception to the Wechat Mini Program. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), we construct a model with more explanative variables to investigate the affecting factors of users' acceptance towards Wechat Mini Program. Using samples of college students in Pearl River Delta, we collected data by conducting an online questionnaire survey and applied them to test our model through a Structural Equation Modeling (SEM). The empirical results indicate that the five variables: Endeavor Expectation, Performance Expectation, Community Impact, Perceived Risk and Perceived Interference, affect users' Behavior Intention. Furthermore, Behavior Intention and Convenience Level jointly influence Usage Behavior of Wechat Mini Program. Finally, according to these results, our study proposes some suggestions on how to improve the users' acceptance towards Wechat Mini Program.
How Long is Long Enough to Induce Immersion?

In this paper, the immersiveness of three variations of spatial content was tested and compared. Content A is a high quality architectural visualization, which is characterized as purely spatial immersion. Content B is the best goal moments of real football games, which is mainly spatial immersion, with slight tactical immersion focus, compiled with mixed qualities. And content C is a high quality recorded virtual game animation, which is mainly spatial immersion, with slight emotional immersion focus. Each of the three spatial contents was cut into different lengths of 3 min, 7 min and 11 min. The participants report the ratings of their immersive experience on a 34-item questionnaire, after watching a combination of three media clips fully randomized in content types and durations, on a 10-inch tablet. Results show that overall, 7 min duration allows the users to feel significantly greater immersive experience than 3 min and 11 min durations for content A and content C. And for content B, 3 min duration stands out as the most immersive. Our study suggests that it is not the longer the more immersive, but there is an optimal duration for spatial immersion (around 7 min). After that, if there is not enough dramaturgical structure to sustain the audience interest, the immersiveness of the spatial content would significantly diminish (i.e. immersion turns into boredom). Our study also shows that realism factors also play a crucial role in inducing spatial immersion.
Macrophage migration inhibitory factor serves a pivotal role in the regulation of radiation-induced cardiac senescencethrough rebalancing the microRNA-34a/sirtuin 1 signaling pathway.

Radiotherapy significantly increases survival innumerous cancer patients, although it may have delayed adverse effects, including significant short‑ and long‑term effects on cardiovascular function, leading to significant morbidity and mortality. However, the mechanisms underlying these effects remain unclear. Cardiomyocyte senescence contributes to cardiovascular disease via impaired cardiac function. MicroRNA‑34a (miR‑34a) is a senescence‑associated miR involved in the pathology of cardiovascular diseases, while macrophage migration inhibitory factor (MIF) is a cardioprotective cytokine with an important role in cardiovascular diseases. The present study aimed to determine whether MIF has a cytoprotective effect in cardiomyocytes exposed to radiation through modulating miR‑34a. Human cardiomyocytes (HCMs) were incubated with MIF and then exposed to radiation. Cellular proliferation was measured using a Cell Counting Kit‑8, while cellular senescence was evaluated based on the senescence‑associated β‑galactosidase activity and the gene expression levels of cyclin‑dependent kinase inhibitor 1a (Cdkn1a) and Cdkn2c. Oxidative stress was evaluated by measuring the generation of reactive oxygen species and malondialdehyde, as well as the expression of antioxidant genes. In addition, HCMs were treated with small interfering RNA against sirtuin 1 (SIRT1) to examine the role of this gene in MIF‑associated rejuvenation following radiation‑associated senescence. miR‑34a was significantly increased in HCMs exposed to radiation, while MIF inhibited senescence by suppressing miR‑34a. SIRT1 was identified as a target gene of miR‑34a, mediating the anti‑senescence effect induced by MIF. Furthermore, MIF rejuvenation involved rebalancing the oxidation process disturbed by radiation. These results provided direct evidence that inhibition of miR‑34a by MIF protected against radiation‑induced cardiomyocyte senescence via targeting SIRT1. Inhibition of miR‑34a by MIF may thus be a novel strategy for combating cardiac radiation‑associated damage.
Two-Stage Identification of Locally Stationary Autoregressive Processes and its Application to the Parametric Spectrum Estimation

The problem of identification of a nonstationary autoregressive process with unknown, and possibly time-varying, rate of parameter changes, is considered and solved using the parallel estimation approach. The proposed two-stage estimation scheme, which combines the local estimation approach with the basis function one, offers both quantitative and qualitative improvements compared with the currently used single-stage methods.
Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering

We propose a novel automatic shrinkage tuning technique for the adaptive proximal forward-backward splitting (APFBS) algorithm. The shrinkage tuning aims to choose an appropriate value of the shrinkage parameter and achieve minimal system mismatch as possible. The system mismatch is approximated based on time-averaged second-order statistics. Numerical examples show that the proposed method achieves performance fairly close to that with a manually chosen shrinkage parameter for colored input signals at some signal to noise ratio (SNR).
Learning an Inverse Tone Mapping Network with a Generative Adversarial Regularizer

Transferring a low-dynamic-range (LDR) image to a high-dynamic-range (HDR) image, which is the so-called inverse tone mapping (iTM), is an important imaging technique to improve visual effects of imaging devices. In this paper, we propose a novel deep learning-based iTM method, which learns an inverse tone mapping network with a generative adversarial regularizer. In the framework of alternating optimization, we learn a U-Net-based HDR image generator to transfer input LDR images to HDR ones, and a simple CNN-based discriminator to classify the real HDR images and the generated ones. Specifically, when learning the generator we consider the content-related loss and the generative adversarial regularizer jointly to improve the stability and the robustness of the generated HDR images. Using the learned generator as the proposed inverse tone mapping network, we achieve superior iTM results to the state-of-the-art methods consistently.
A Collaborative Intrusion Detection Model using a novel optimal weight strategy based on Genetic Algorithm for Ensemble Classifier

Cybersecurity, especially intrusion detection, is becoming increasingly critical in our daily life. The intrusion detection systems (IDS) have been widely used to prevent disclosure of personal information and detect potentially suspicious attacks. Although many machine learning algorithms have been broadly applied to enhance the performance of IDS, low detection rate and high false alarm rate are still two critical problems. A collaborative and robust intrusion detection model using a novel optimal weight strategy based on Genetic Algorithm (GA) for ensemble classifier is proposed in this paper. Since network data stream can be divided into three categories according to network protocols, detectors are applied in the network protocol separately. All of the detectors can work collaboratively and efficiently. In the proposed model, GA is used to optimize the weight of each base classifier of ensemble classifier. In order to improve features quality, Principal Component Analysis (PCA) is used for dimension reduction and attribute extraction. The NSL-KDD datasets is used to test the effectiveness of the collaborative intrusion detection model. Experimental results show that the proposed model has a higher accuracy and better generalized performance than others in this field.
A Unified Random Coding Bound

In this paper, we prove a unified achievability bound that generalizes and improves random coding bounds for any combination of source coding, channel coding, joint source–channel coding, and coding for computing problems assuming blockwise node operation. As a general network setup, we consider an acyclic discrete memoryless network, where the network demands and constraints are specified by a joint-typicality constraint on the whole channel input and output sequences. For achievability, a basic building block for node operation consists of simultaneous nonunique decoding, simultaneous compression, and symbol-by-symbol mapping. Our bound can be useful for deriving random coding bounds without error analysis, especially for large and complex networks. In particular, our bound can be used for unifying and generalizing many known relaying strategies. For example, a generalized decode-compress-amplify-and-forward bound is obtained as a simple corollary of our main theorem, and it is shown to strictly outperform the previously known relaying schemes. Furthermore, by exploiting the symmetry in our bound, we formally define and characterize three types of network duality based on channel input–output reversal and network flow reversal combined with packing–covering duality.
Fast Adaptation on Deepmixture Generative Network Based Acoustic Modeling

Deep neural network (DNN) has achieved the state-of-the-art performance in automatic speech recognition (ASR). However, the meaning of parameters and neurons are hard to be interpreted in DNNs, which makes the regularizations and adaptation of DNNs difficult. In this work, we aim to do effective and efficient adaptation on a more interpretable model, deep mixture generative network (DMGN). Adapted means are first proposed to perform adaptation for DMGN. The speaker-dependent means are estimated in an unsupervised adaptation mode. Moreover, discriminative linear regression (DLR) is proposed to estimate more robust speaker-dependent means when lack of adaptation data. We evaluate our proposed methods on 50-hour subset of Switchboard. Experiments reveal that all proposed methods are better than speaker independent baseline, and a slight performance improvement is obtained compared with LHUC. In addition, we project the Gaussian mean of one senone and all inputs aligned to this senone to a 2 $D$ graph. The illustration shows that after applying DLR, the mean is indeed transferred from an average point to the speaker specific center, which demonstrates the better explanation of DMGN again.
A Deep Framework for Automatic Annotation with Application to Retail Warehouses

The paper presents a novel deep learning framework for automatic annotation and segmentation of densely cluttered objects in a warehouse application use-case as spec-iﬁed by the Amazon Robotics Challenge (ARC) 2017. This framework addresses two challenges of the competition: (1) reducing the amount of manual labour involved in generating a large number of annotated data that could be used for training a deep network and, (2) achieving good segmentation accuracy in a very limited amount of training time ( ≤ 30 minutes). These two problems are solved by proposing a deep architecture comprising of Residual Network and Feature Pyramidal based convolutional neural network that helps to retain primitive features along with higher level features obtained from each successive layer. In addition, a framework is proposed using this network to automatically generate a large annotated dataset having different degrees of clutters to carry out multi-class semantic segmentation after training with this machine generated dataset. The proposed framework is shown to provide better segmentation accuracy with lesser training time as compared to the existing state-of-the-art architectures such as PSPNet and Mask R-CNN. The overall working of the proposed architecture is explained by creating a new dataset from the objects speciﬁed by the ARC competition. An extensive experiment is also performed using the MIT-Princeton database [22]. Our TCS-ARC-Dataset [13]is made available online for the convenience of readers.
Boundary Objectness Network for Object Detection and Localization

In this paper, we present the boundary objectness network (BON), an effective convolutional neural network (CNN) for object detection. Its core contribution is to accurately localize the objects. Generally, the CNN-based localizers predict four bounding box coordinates by learning a regression function. This method shows a low Intersection-of-Union (IoU) with the ground truth box. In our work, the localization is formu-lated as a probabilistic problem. Specifically, the deep features inside the candidate proposal are mapped into a row and a column feature vector, which are called boundary object-ness. The boundary objectness indicates the existence of an object in the horizontal and vertical direction of the proposal, enabling us to elaborately localize the object. Moreover, the modules of object detection share the common convolution-al layers. Meanwhile, a multi-task loss function is designed for joint training strategy. Experimental results on the PAS-CAL VOC datasets demonstrate the competitive performance of our method. For the VGG16 model, we achieve 77.6 % mAP at a speed of 4 frame per second (FPS), thus having the potential for real-time processing.
Conversational Control Interface to Facilitate Situational Understanding in a City Surveillance Setting

In this paper we explore the use of a conversational interface to query a decision support system pro- viding information relating to a city surveillance setting. Specifically, we focus on how the use of a Controlled Natural Language (CNL) can provide a method for processing natural language queries whilst also tracking the context of the conversation with relation to past utterances. Ultimately, we pro- pose our conversational approach leads to a versa- tile tool for providing decision support with a low enough learning curve such that untrained users can operate it either within a central command location or when operating within the field (at the tactical edge). The key contribution of this paper is an il- lustration of applied concepts of CNLs as well as furthering the art of conversational context tracking whilst using such a technique. Keywords: Natural Language Processing (NLP), Conversational Systems, Situational Understanding
Titration and optimization trial for the initiation of insulin glargine 100 U/mL in patients with inadequately controlled type 2 diabetes on oral antidiabetic drugs

For patients with type 2 diabetes mellitus (T2DM) and inadequate glycaemic control, addition of basal insulin is recommended, but titration and optimization of basal insulin therapy in primary care is not well understood. We conducted an observational trial in 2470 patients with T2DM who initiated insulin glargine 100 U/L (Gla‐100) on top of oral antidiabetic drugs. Physicians were free to choose either a “Davies,” “Fritsche” or “individual” titration algorithm. We found that fasting blood glucose (FBG) and glycated haemoglobin (HbA1c) levels were effectively reduced by Gla‐100; 65.9% of patients achieved the primary endpoint (FBG ≤6.1 mmol/L (110 mg/dL) or an individual HbA1c target). There were no significant differences in efficacy and safety between the algorithms used. The mean FBG decreased by 3.2 mmol/L (59 mg/dL) over 12 months, while the mean HbA1c decreased by 15.3 mmol/mol (1.4%)%. From a starting dose of 11.7 U/d, the Gla‐100 dosage was 22.8 U/d at 12 months, with similar values in each group. Rates of hypoglycaemia were low and did not differ by titration algorithm. We conclude that Gla‐100 was effective at reducing FBG and HbA1c, independent of the titration algorithm, but observed that algorithms were inconsistently applied in clinical practice.
Using Semantic Web Technology and Data Mining for Personalized Recommender System to Online Shopping

The growth of the World Wide Web provides online users a large amount of information, which has led to the emergence of web recommendation systems to recommend items, services or pages to users. Although the recommendation systems assist in retrieving and accessing interesting items automatically by mainly rely on acquiring users' historical data and matching items with the preferences of users, but still have limitations due to many issues. The issues can be identified as content overspecialization, cold start problem, and sparsity of data structures. In addition, the current recommender systems fail to make full use of the semantic information about items and the relations among them. The aim of this paper is to improve traditional recommender systems by incorporating users' information from social networks and developing users' information ontology to make personalized recommendations. Therefore, the paper proposes a framework for semantic recommender system employing user profile ontology, and products/items ontology. The proposed model includes the using of data mining techniques for knowing which classification algorithm fit well user's data analysis, which will be next using in developing the user profile ontology. Then, building a semantic application and integrate the system for checking the accuracy of the recommendation model after the validation for each techniques built. This paper is presenting the experimental results of the first step by using data mining technique of proposed model. The results show that by analyzing users' data by applying different types of classification algorithms and based on the TPR results, the Decision table algorithm gives the highest TPR by (0.871). This means that the best classifier algorithm that can be used for building user profile recommender model.