Reviewerly/exHarmony huggingface dataset:
Fine-Grained Motion Representation For Template-Free Visual Tracking

The object tracking task requires tracking the arbitrary target in consecutive video frames. Recently, several attempts have been made to develop the template-free models to attain generality. However, current template-free paradigm only estimates the displacement to approximate the motion of the object. The displacement is insufficient to represent complex bounding box transformation, including scaling and rotation. We argue that the coarse-grained representation of object motion limits the performance of current template-free approaches. In this paper, we explore the finer-grained motion estimation to improve the accuracy of the template-free model. In respect of the image space, our method estimates the transformation for each pixel in the image. Concern on the motion representation, we represent the motion by the transformation parameterized by displacement, scaling, and rotation. By applying the differential vector operators on the optical flow, our approach estimates both displacement, scaling, and rotation for each pixel in a unified theory. To the best of our knowledge, we are the first work to model the displacement, scaling and rotation in a unified theory with the optical flow. To further improve the localization accuracy, we develop the appearance branch to introduce the appearance information into our model. Furthermore, to suppress optical flow estimation failure samples during training, we propose a novel loss function Limited L1. The experiment shows our model FGTrack achieves state-of-the-art performance on both NFS and VOT2017 datasets.
Review on Dimensionality Reduction Techniques

With the increasing use of Machine day by day, data analysts' job has increased drastically. With the data gathered from millions of machines and sensors, modern day datasets becomes wealthier in information. This makes the data to be high dimensional and it is quite common to see datasets with hundreds of features. One of the biggest problems that data analysts face is dealt with high dimensional data. Without a major loss of information, data can be effectively reduced to a much smaller number of variables. This method of reducing variables is known as Dimensionality Reduction. The objective of this paper is to review methods used for reducing Dimensionality.
Remote Control for Mobile Robots Using Gestures Captured by the RGB Camera and Recognized by Deep Learning Techniques

Mobile robotics tends to move forward with autonomous systems, but in some cases, it is not possible to use this strategy. Non-autonomous robots are controlled by some human-machine interface, mostly industrial equipment, which are often expensive. Controlling the robot using some fixed control attaches the operator to the equipment, causing it to lose mobility and agility, not to mention the lack of control accuracy. This paper proposes to solve this problem by using computer vision techniques to develop a gesture control interface. From the movement of the left index finger, the operator can send the robot linear speeds. By moving the finger of the right hand, angular movements are sent to the robot. This strategy has proven practical and easy for the operator to learn, as well as avoiding operator-attached cables and is ideal for situations that pose a risk.
An Improved Chaos Based Medical Image Encryption Using DNA Encoding Techniques

Medical Image protection is one of the primary concerns of health care services. There has been a lot of research in this field in the past decade. But unfortunately, in India, the implementation is very poor and unstructured. In this paper, a secured model for MRI image encryption and decryption using DNA Encoding and Logistic Chaotic Map is proposed. Initially a key is generated using logistic map function to generate a sequence K1,i this sequence is multiplied with a factor of 255, another sequence K2,i generated from Linear Feedback Right Shift Register (LFRSR) using an initial seed value, then both the sequences are XORed to form the key. After the key is generated the sequence is again XORed with the 8-bit image pixel of the MRI Image. The generated cipher is then converted into a DNA sequence using DNA Encoding. The generated text of DNA sequence is then passed to the network. This sequence is extremely secure and robust, the Root Mean Square Error (RMSE) value measured between the original and encrypted sequence is very high.
A new model for pattern recognition

The hidden Markov model (HMM) has recently achieved impressive success in the field of pattern recognition, but some limitations and drawbacks restrict its performance. In this study, a new simple model is proposed to overcome the restrictions of HMM with a high reduction in the computational complexity. The training algorithm of the proposed model is built without iterations. It depends on the number of occurrences of each symbol in the training array, in case the discrete data form is used. However, for the continuous form, it uses the mean and covariance of the training data. On the other hand, the log-likelihood and the Mahalanobis distance are employed in the testing algorithm for indicating the highest matching between the testing data and the training parameters. This new model has been tested for face recognition; the experiments exhibit its advantage in terms of memory usage and processing time requirements, as well as recognition rate.
Integral Assessment of Condition and Sustainability of Socio-Ecological-Economic Systems

The methodological fundamentals and methods of integral assessment of the condition and sustainability of socio-ecological-economic systems are examined. The aim of the research was to summarize the results of the development and testing of classification models of the integrated assessment of the condition and sustainability of socio-ecological-economic systems. The research focuses on socio-ecological-economic systems which include three subsystems: 1—environmental (quality of the environment), 2—economic, 3—social. In some models, a separate component is distinguished in the third subsystem: 4—morbidity rate in the population. The research topics are the following: I—theoretical and methodological aspects and methods of generation of integral condition indicators (integral indicators of the first level of development for ecological, economic, and social subsystems, as well as the integral indicators of the second level of development or summary indicators); II—sustainability of socio-ecological-economic systems or their ability to maintain the characteristics, parameters of modes and class of the condition in which the system (or its individual subsystems) had been before the start of the impact. The study examines basic definitions, fundamentals of theory and methodology of assessment of condition and sustainability of socio-ecological-economic systems, algorithms for the realization of the integral estimates, and results of the assessment research. Examples include (1)—the integral assessment of socio-ecological-economic system of Russia and comparative integral assessment of socio-ecological-economic systems of Russia and Canada, as well as Russia and Mexico; (2)—the condition of socio-ecological-economic systems of the Federal Districts of the Russian Federation; (3)—the condition of socio-ecological-economic systems of the individual regions of the Russian Federation and their subregions (the Northwest, the Center, the northern regions, etc.); (4)—the condition of socio-ecological-economic systems of the cities of the Russian Federation; (5)—the record of morbidity among the population in the integral assessment of socio-ecological-economic systems of the regions, and (6)—the study of the sustainability of regional systems in relation to the impacts on them and their subsystems. This research focuses on the development of multi-criteria and multilevel classifications (classification models) to study the condition and sustainability of socio-ecological-economic systems and the construction of integral indicators based on analysis and synthesis of Indicators in information deficit (ASPID) and Aggregated Preference Indices System (APIS) methods and models.
Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation

In spite of the accomplishments of deep learning based algorithms in numerous applications and very broad corresponding research interest, at the moment there is still no rigorous understanding of the reasons why such algorithms produce useful results in certain situations. A thorough mathematical analysis of deep learning based algorithms seems to be crucial in order to improve our understanding and to make their implementation more effective and efficient. In this article we provide a mathematically rigorous full error analysis of deep learning based empirical risk minimisation with quadratic loss function in the probabilistically strong sense, where the underlying deep neural networks are trained using stochastic gradient descent with random initialisation. The convergence speed we obtain suffers under the curse of dimensionality. However, it is presumably close to optimal in the generality of the framework we consider and, to the best of our knowledge, we establish the first full error analysis in the scientific literature for a deep learning based algorithm in the probabilistically strong sense as well as the first full error analysis in the scientific literature for a deep learning based algorithm where stochastic gradient descent with random initialisation is the employed optimisation method.
Automated discovery of scientific concepts: Replicating three recent discoveries in mechanics

Throughout history, humanity cherished new scientific discoveries; many people devoted their lives to find them in laborious manual processes. Since the emergence of artificial intelligence, about 60 years ago, the possibility of automated scientific discovery has become conceivable and has attracted significant and growing interest. Many studies of automated scientific discovery have been presented over the years. This article presents a new automated discovery approach that crosses disciplines and transfers knowledge between them. The approach requires rich and formal background knowledge to find concepts, methods, or laws not known in one discipline by using their counterpart in other disciplines, specifically disciplinary knowledge that is represented in the Interdisciplinary Engineering Knowledge Genome. Three recent discoveries in mechanics were replicated automatically through software execution, demonstrating the validity of the approach. In future studies, the goal is to discover new knowledge in mechanics and/or electronics, as well as venture into other disciplines including outside engineering.
Movie Recommender Systems Made Through Tag Interpolation

20 years of MovieLens datasets have witnessed a blossom of research that is garnering a remarkable significance with the advent of e-commerce and the whole industry. Four variations of the dataset have been downloaded hundreds of thousands of times, reflecting their popularity and distinctive contribution in the field of recommendation systems and connected subjects. This paper exploits the movie recommender system based on movies' genres and actors/actresses themselves as the input tags, or tag interpolation. The problem has not been addressed in the literature, especially for the 100K variations of the MovieLens datasets. We apply tag-based filtering and collaborative filtering that can effectively predict a list of movies that is similar to the movie that a user has been watched. Due to not depending on users' profiles, our model has eliminated the e.ect of the cold-start problem. The experimental results provide us much better recommendations to users because it utilizes the underlying relation between movies based on their similar genres and actors/actresses. A movie recommender system has been deployed to demonstrate our work.
Multi-Scale Adversarial Cross-Domain Detection with Robust Discriminative Learning

Domain shift practically exists in almost all computer vision tasks including object detection, caused by which the performance drops evidently. Most existing methods for domain adaptation are specially designed for classification. For object detection, existing methods separate domain shift into image-level shift and instance-level shift and align image-level feature and instance-level feature respectively. However, we find that there are two problems which remain unsolved yet. First, the scale of objects is not the same even in an image. Second, negative transfer can affect model performance if not handled properly. We improve the performance of cross-domain detection from three perspectives: 1) using multiple dilated convolution kernels with different dilation rate to reduce the image-level domain discrepancy; 2) removing images or instances with low transferability to weaken the influence of negative transfer; 3) diversifying distributions by keeping instances' feature away from each other, and then pull them closer to the center of each category, so that make source samples distribution more dispersed and more robust for cross-domain detection. We test our model with Cityscapes [5], Foggy Cityscape [30] and SIM 10K [18] datasets, experimental results show that our method outperforms the state-of-the-art for object detection under the setting of unsupervised domain adaptation (UDA).
Revisiting Hard Example for Action Recognition

Video-based action recognition, which needs to handle temporal motion and spatial cues simultaneously, remains a challenging task. In this paper, our motivation is to address this issue by fully utilizing temporal information. Specially, a novel light-weight Voting-based Temporal Correlation (VTC) module is proposed to enhance temporal information. Multiple branches with different temporal sampling intervals are included in this module and they are regarded as voters. The final classification result is “voted” by these branches together. VTC module integrates sparse temporal sampling strategy into feature sequences, so it mitigates the effect of redundant information and focuses more on temporal modeling. Additionally, we propose a simple and intuitive Similarity Loss (SL) to guide the training procedure of the VTC module and the backbone network. When we introduce confusion in the predicted vector intentionally, SL eases intra-class variation by discovering class-specific common motion patterns rather than sample-specific discriminative information. SL neither needs excessive parameter tuning during training nor adds significant computation overhead during test time. By combining VTC module and SL with complementary advances in the field, we clearly outperform state-of-the-art results and achieve 83.0, 98.4, 49.6 and 77.8 accuracy on HMDB51, UCF101, something-something-v1, and Kinetics respectively.
BANQUET: Balancing Quality of Experience and Traffic Volume in Adaptive Video Streaming

Bitrate-selection algorithms are key to improving the quality of experience (QoE) of adaptive video streaming. Although current bitrate selection algorithms maximize the QoE, video consumers are concerned with QoE and traffic-volume usage due to the pay-per-use or data-capped plans. To balance between the QoE and traffic volume, some commercial video-streaming services enable users to set the upper limit of the selectable bitrate. However, it is difficult for users to set an appropriate limit to obtain sufficient QoE. We propose BANQUET, a novel bitrate-selection algorithm that enables users to control intuitively the balance between the QoE and traffic volume. Assuming a user-set target QoE as a balancing parameter, BANQUET selects the bitrate that minimizes the traffic volume while maintaining the estimated mean opinion score (MOS) above the target QoE. BANQUET calculates the appropriate bitrate based on estimations of the throughput and butter transition. A trace-based simulation shows that BANQUET reduces the traffic volume by up to 47.0% compared to a baseline while maintaining the same average estimated MOS.
BLNN: An R package for training neural networks using Bayesian inference

The Bayesian Learning for Neural Networks (BLNN) package coalesces the predictive power of neural networks with a breadth of Bayesian sampling techniques for the first time in R. BLNN offers users Hamiltonian Monte Carlo (HMC) and No-U-Turn (NUTS) sampling algorithms with dual averaging for posterior weight generation. A robust implementation of hyper-parameters and optional re-estimation through the evidence procedure gives BLNN high predictive precision. BLNN is compatible with RStan diagnostic tool ShinyStan. BLNN can be used in a wide range of applications which are based on developing statistical models such as multiple linear and logistic regression, classification, and survival analysis.
Expander construction in VNC1

We give a combinatorial analysis (using edge expansion) of a variant of the iterative expander construction due to Reingold, Vadhan, and Wigderson [44], and show that this analysis can be formalized in the bounded arithmetic system VNC1 (corresponding to the “NC1 reasoning”). As a corollary, we prove the assumption made by Jeřábek [28] that a construction of certain bipartite expander graphs can be formalized in VNC1. This in turn implies that every proof in Gentzen's sequent calculus LK of a monotone sequent can be simulated in the monotone version of LK (MLK) with only polynomial blowup in proof size, strengthening the quasipolynomial simulation result of Atserias, Galesi, and Pudlák [9].
Mobile Robot Environment Map Building, Trajectory Tracking and Collision Avoidance Applications

The fundamental challenge many engineers have to overcome in robotic world is how to control robot facing real conditions. This paper presents some robotic mobile applications. Mobile robot uses the metric approach to detect obstacles and to build environmental map using the data from an ultrasonic sensor. To validate the proposed approach an experiment is conducted in an internal environment with the mobile robot Dr Robot i90. The PC applications are implemented using Matlab GUI.
Improving Robustness of Authentication System Based on Pinna Related Transfer Function

In this paper, we consider personal authentication technology using individual differences of acoustic transfer function measured from the pinna; the Pinna Related Transfer Function (PRTF). In addition, in this paper, considering the convenience in practical use, a smartphone that is not necessary to be worn on the pinna for each authentication is used to measure PRTF. However, it is known that in the case of using a smartphone, PRTF changes according to the emission direction of measurement signal toward the pinna, so the authentication rate is lower than that of using earphones and headphones. In this paper, we measure the PRTF from multiple locations and use them for learning at the same time. We demonstrate that the proposed authentication can correspond to the changes in the emission direction of each measurement and improve the robustness.
Efficient VLSI Architectures of LILLIPUT Block Cipher for Resource-constrained RFID Devices

Information security in resource-constrained devices has attracted a great number of researchers in recent years. Secure communication suffers from resource limitation. Therefore, it is difficult to choose the most satisfactory security primitive for a particular application. LILLIPUT has been popular lightweight block cipher used to overcome such issues. In this paper, different architectures of LILLIPUT block cipher are presented. One of these designs enhances the efficiency in terms of throughput at the expense of a larger area. This design transforms the data input with the speed of 1132.40 Mbps for Field-Programmable Gate Arrays (FPGAs) of xc5vlx50t-3ff1136 device. In addition, the second implementation shares several building blocks for each round. Due to this, a compact structure is achieved by using the same reprogrammable device. Therefore, this proposed design results ina good area-time product. The proposed compact structure is most appropriate for low cost active smart devices. All results are simulated and verified for various families of XilinxISE design suite.
Efficient Implementation of Non-Local Means Image Denoising Algorithm

Image acquisition has become a common feature in many consumer electronic devices. In processing the acquired images, denoising is a critical task in providing good quality images and non-local means (NLM) is one of the most effective approaches in currently available denoising algorithms. Using weights based on the similarity between image patches, NLM estimates the denoised pixel by the weighted sum of the neighboring pixels. Because the number of neighboring pixels needs to be sufficiently large so that the denoising effect of the weighted averaging operation can be fulfilled, NLM needs to calculate a large number of similarities between patches. This translates to extremely high computational cost and limits its application in consumer electronic devices. In this paper, we incorporate 3 strategies to speed up the computation. The first strategy is to use binary descriptors of patches to exclude dissimilar patches from computation. The second strategy is the pre-termination of similarity computation when the distance between patches exceeds a threshold. The third strategy is to apply a spiral trajectory instead of a zig-zag trajectory to search for similar patches. Experimental results show that applying these strategies can speed up the processing by 11.5 times and still enhance the denoising performance of NLM.
Resource Allocation for Mobile Blockchain: A Hierarchical Combinatorial Auction Approach

As a decentralized ledger to record all transaction information, blockchain can be applied to address the security and privacy issues in mobile application system. We term the blockchain applied to mobile applications as mobile blockchain. The mining process in mobile blockchain requires high computing capacity and energy which could overwhelm that mobile devices can offer. In this case, mobile edge computing servers (MESs) can be involved to offer computation services to miners in mobile blockchain. Note that the resources of MESs are also limited, MESs could further request resources from the cloud computing server (CCS). Accordingly, in this paper, both mobile edge computing and cloud computing are considered to support the mobile blockchain applications which makes the problem a hierarchical one. Naturally, the issue of hierarchical resource allocation arises. And a hierarchical combinatorial auction model is proposed to solve this problem, based on which an efficient and truthful framework is provided. Specifically, we formulate winner determination problems (WDPs) for mobile edge computing service providers and cloud computing service provider, and computationally tractable algorithms to address both problems are proposed. Finally, numerical analysis shows the effectiveness of the proposed scheme.