Reviewerly/exHarmony huggingface dataset:
Computing QoE-Relevant Adaptive Video Streaming Metrics Using Discrete-Time Analysis

HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. Splitting the video clip into small segments and providing multiple quality levels per segment allows the client to dynamically adapt the quality to current network conditions. The performance of HAS, and as a consequence the user Quality of Experience (QoE), is influenced by a multitude of parameters. This includes adjustable settings like quality switching thresholds, the initial buffer level, or the maximum buffer, as well as video characteristics like segment duration or the variation of segment sizes along the video. Finding an appropriate tuning of those parameters still remains a challenge, which is mainly tackled by performing testbed measurements or simulative analysis. Due to the large problem space and the complex interactions of the involved influence factors, a holistic comparison of a multitude of parameter settings is extremely time intensive. To address this problem, we propose to enhance a GI/GI/1 system with pq-policy, which models video buffer behavior, with the capability to switch between different quality levels. This allows to investigate all relevant QoE influence factors for HAS-based video delivery. In a first evaluation, we illustrate the impact of different quality switching thresholds on the QoE influence factors for varying network conditions.
An Improved Vehicle Detection and Tracking Model

With the continuous advancement of urbanization in China, the number of urban motor vehicles has increased exponentially, but the pressure on urban traffic and security monitoring has also increased. In recent years, the application of Intelligent Transportation System (ITS) has created good economic benefits for the transportation industry. Vehicle Detection and Tracking is the basis for subsequent vehicle information attribute calculation and statistical analysis of data and plays a leading role in ITS. This paper discusses the state-of-the-art detection and tracking algorithms and points out their shortcomings, and on this basis, we proposed an improved video vehicle detection and tracking model: a model based on Gaussian mixture model, Kalman filter + CAMshift + contour method. First, in the background extraction process, the Gaussian mixture model with different parameters is used for the foreground and the background to reduce the calculation cost and enhance the separation effect; For the extracted foreground, the part connecting in different vehicles is overlapped by the matching of the pits to distinguish the overlapping vehicles; In the tracking process, the results of CAMshift and contour tracking are used as observations of the Kalman filter to avoid the phenomenon that vehicles with similar color distribution characteristics and similar positions are misidentified as the same target. The experimental results show that our model has better robustness to complex road conditions, and the detection and tracking effect is better than other models.
Skipping Two Layers in ResNet Makes the Generalization Gap Smaller than Skipping One or No Layer

The ResNet skipping two layers (ResNet2) is known to have a smaller expected risk than that skipping one layer (ResNet1) or no layer (MLP), however, the mechanism of the small expected risk is still unclear. The expected risk is divided into the three components, the generalization gap, the optimization error, and the sample expressivity, and the last two components are known to contribute the fast convergence of ResNet. We calculated the first component, the generalization gap, in the linear case, and show that ResNet2 has a smaller generalization gap than ResNet1 or MLP. Our numerical experiments confirmed the validity of our analysis and the applicability to the case with the ReLU activation function.
WALGES: Weighted Probability Based Scoring Approach for Solving Algebraic Word Problems using Semantic Parsing

Virtual assistants like Google Assistant, Siri, Cortana, and many others, are now the major feature of smart-phones and tablets. Natural language understanding is the most promising part of those. Besides question and answering, sometimes those smart assistants face verbally stated mathematical problems. Algebraic word problems are one of the fundamental mathematical problems where financial news, election results, sports results, and many other services are also related. In this paper, we have generated solutions of the algebraic word problems. Semantic parsing is used to ground the problem text into containers, quantities, and entities to generate equation trees. From the equation trees, features are extracted to train a local and a global classifier model. After that, to find the candidate equation from the generated equation, probabilistic scores from the local and the global classifier models have used. Backpropagation neural network algorithm is used to incorporate the probabilistic scores of the local and the global classifier models with weights. Our proposed weighted probability based scoring approach increases the accuracy to predict the solution of the algebraic word problems about 9% surpassing the existing system. Our result showed that our proposed technique is useful.
A comparative study on performance of XML parser APIs (DOM and SAX) in parsing efficiency

As a semi-structure language, XML is widely used in converting unstructured data to structured data due to its simplicity, extendibility and interoperability. There are numerous XML parser APIs that perform the same function of parsing XML document. This paper compares the performance between two famous XML parser APIs, DOM and SAX, in terms of speed, memory consumption and modifiability in parsing process. This experiment concluded that DOM API takes more time, more memory with higher level of modifiability while SAX API takes less time, less memory with lower level of modifiability.
A New Gabidulin-Like Code and Its Application in Cryptography

We introduce a new rank-metric code, namely $$\varvec{\lambda }$$ -Gabidulin code by multiplying each of the columns of the generator of Gabidulin codes with entries from $$\varvec{\lambda }=(\lambda _1,\ldots ,\lambda _n) \in \mathbb {F}_{q^m}^n$$ . We discuss the motivation of introducing $$\varvec{\lambda }$$ -Gabidulin code and prove some of its properties. Then, we design a new McEliece type rank metric based encryption scheme on $$\varvec{\lambda }$$ -Gabidulin code, with a scrambler matrix depending on $$\varvec{\lambda }$$ . We show that this new cryptosystem is secure against the existing attacks on Gabidulin codes based encryption, in particularly how it resists Overbeck's structural attack, annulator polynomial attack and the Frobenius weak attack. Finally, we also propose some parameters for the new cryptosystem and show that our proposal has smaller key size than the Loi17 Encryption [29] using Gabidulin codes proposed in PQCrypto 2017.
Medical events extraction to analyze clinical records with conditional random fields

The rapid growth in the extraction of clinical events from unstructured clinical records has raised considerable challenges. In this paper, we propose the use of different features with a statical modeling method called conditional random fields, which is consider an algorithm for effectively solvi ng problems of sequence tagging. Our goal is to determine which feature selection can affect the performance of four subtasks presented in SemEval Task-12: Clinical TempEval 2016. We applied a careful preprocessing, where the proposed method was tested on real clinical records from Task-12: Clinical TempEval 2016. The comparative analyses obtained indicate that our proposal achieves good results compared to the work presented in Task-12: Clinical TempEval 2016 challenges.
Efficient Stochastic Subgradient Descent Algorithms for High-dimensional Semi-sparse Graphical Model Selection

We consider the structure learning problem of Gaussian graphical models when the underlying graph is semi-sparse. More specifically, we assume that the number of edges in the graph grows quadratically with the dimension P. Similar to the case of sparse graphs, the problem is formulated as maximizing the data log-likelihood with an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm penalty on the precision matrix (the inverse covariance matrix) that promotes sparsity. We notice that the time complexity of all existing methods is at least O(P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) under the scenario of semi-sparse graphs, thus severely hindering their applications to high-dimensional data. By contrast, the time complexity of the proposed method is only O(P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) with the help of stochastic gradients. We prove the convergence of the proposed algorithm. Numerical results show that the computational time of the proposed method is shorter than that of the state-of-the-art methods when the graph is semi-sparse.
Human Action Recognition: A Survey

In this paper, we provide a comprehensive survey in human action recognition and prediction, which has always been a universal and critical area in computer vision. Human action recognition is the first step for a machine to understand and percept the nature, which is small part in machine perception. Human action prediction is the higher layer than human action recognition that is small part in machine cognition, which would give the machine the ability of imagination and reasoning. Here, we only discuss human action recognition from two methodologies that is based on presentations and deep learning, separately. Then, 4 public datasets of human action recognition are descripted closely. Some challenges in dataset are also proposed because of the significance to the development of computer vision. Meanwhile, we compare and summarize recent-published research achievements under deep learning. In the end, we conclude about mentioned methods and future challenges to work on for computer vision.
From Quadratic Functions to Polynomials: Generic Functional Encryption from Standard Assumptions

The "all-or-nothing" notion of traditional public-key encryptions is found to be insufficient for many emerging applications in which users are only allowed to obtain a functional value of the ciphertext without any other information about the ciphertext. Functional encryption was proposed to address this issue. However, existing functional encryption schemes for generic circuits either have bounded collusions or rely on not well studied assumptions. Recently, Abdalla et al. started a new line of work that focuses on specific functions and well-known standard assumptions. Several efficient schemes were proposed for inner-product and quadratic functions. There are still a lot of unsolved problems in this direction, in particular, whether a generic FE scheme can be constructed for quadratic functions and even higher degree polynomials. In this paper, we provide affirmative answers to these questions. First, we show an IND-secure generic functional encryption scheme against adaptive adversary for quadratic functions from standard assumptions. Second, we show how to build a functional encryption scheme for cubic functions (the first in the literature in public-key setting) from a functional encryption scheme for quadratic functions. Finally, we give a generalized method that transforms an IND-secure functional encryption scheme for degree-m polynomials to an IND-secure functional encryption scheme for degree- $$(m+1)$$ polynomials.
Curiosity-driven Reinforcement Learning for Dialogue Management

In this paper we describe the use of curiosity rewards for dialogue policy learning of goal oriented dialogues via reinforcement learning. Using curiosity improves state-action space exploration and helps overcome reward sparsity. Additionally, for goal oriented dialogues it makes sense to perform inherently curious actions in order to gain knowledge about the user goal. We show that intrinsic curiosity rewards can replace random -greedy exploration and stabilize training. The best results are achieved when curiosity rewards are combined with -greedy exploration.
Lapped Transforms: A Graph-based Extension

Lapped transforms are transform coding tools with basis functions that overlap across blocks in order to reduce blocking artifacts. In this work, we take the uniform line graph model interpretation of the discrete cosine transform (DCT) and extend it to lapped transforms. We first extend the conditions of perfect reconstruction and orthogonality to lapped transforms on graphs, where different transforms are allowed for different blocks. Then, with the focus on line graphs, we design a lapped graph Fourier transform (LGFT) that has these properties, with significantly reduced blocking artifact. Experimental results show that the proposed LGFT can achieve improved transform coding gain as compared to other existing transforms.
Simulation and Analysis of Three-Dimensional Space Path Prediction for Six-Degree-of-Freedom (SDOF) Manipulator

Traditional methods are ineffective in predicting the three-dimensional path of a six-degree-of-freedom (SDOF) manipulator. In view of the above situation, this paper proposes a three-dimensional space path prediction simulation method for a SDOF manipulator. The structure of the SDOF manipulator is analyzed, and the kinematics model of the manipulator is constructed. The kinematics model of the manipulator is solved by the forward and reverse kinematics solutions. According to the inverse kinematics solutions, the method of automatic optimization of multiple solutions is obtained, which can effectively improve the performance of the manipulator. The collision is avoided in the three-dimensional motion of a SDOF manipulator. On the basis of the kinematics of the manipulator, the Cartesian space is used to predict the path trajectory. The average operation time of the path planning cycle and the deviation of the relative smooth trajectory are compared through the three-dimensional path prediction distance and the straight line distance of the SDOF manipulator. The experimental results show that the average operation time of the period of the path prediction between the two is close, and the deviation of the three-dimensional space path prediction distance of the SDOF manipulator is better than that of the straight line distance. It has certain application performance.
Cluster Center Initialization and Outlier Detection Based on Distance and Density for the K-Means Algorithm

K-means algorithm, the most classic partition-based clustering method, has its disadvantages. If there are outliers in the data sets, the K-means algorithm may lead to serious deviation of the mean value. In addition, random initialization is very sensitive to the input data parameters. In this paper, we propose initialization and outlier detection based on distance and density for the K-means algorithm (KMIDDO), an improvement method to optimize the initial center points, especially it has more effective in the case of outliers. What’s more, we extend an outlier detection method to improve the clustering effect. We hope the distance between every two center points is as far as possible and the density of the center points are as large as they can. In terms of initialization, we calculate the distance and density of points. In the outliers detection, we take the outliers as a single class based on the distance and density. Experiments are conducted to illustrate the effectiveness and accuracy of the proposed algorithms on several synthetic and real datasets.
Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities

In this paper, we develop a novel method for approximate continuous-discrete Bayesian filtering. The projection filtering framework is exploited to develop accurate approximations of posterior distributions within parametric classes of probability distributions. This is done by formulating an ordinary differential equation for the posterior distribution that has the prior as initial value and hits the exact posterior after a unit of time. Particular emphasis is put on exponential families, especially the Gaussian family of densities. Experimental results demonstrate the efficacy and flexibility of the method.
Model checking ontology‐driven reasoning agents using strategy and abstraction

Summary We present a framework for the modelling, specification, and verification of ontology‐driven multi‐agent rule‐based systems (MASs). We assume that each agent executes in a separate process and that they communicate via message passing. The proposed approach makes use of abstract specifications to model the behaviour of some of the agents in the system and exploits information about the reasoning strategy adopted by the agents. Abstract specifications are given as Linear Temporal Logic (LTL) formulas which describe the external behaviour of the agents, allowing their temporal behaviour to be compactly modelled. Both abstraction and strategy have been combined in an automated model checking encoding tool T ovrba for rule‐based multi‐agent systems which allows the system designer to specify information about agents' interaction, behaviour, and execution strategy at different levels of abstraction. The T ovrba tool generates an encoding of the system for the Maude LTL model checker, allowing properties of the system to be verified.
BitStream: An efficient framework for inference of binary neural networks on CPUs

Convolutional Neural Networks (CNN) has been well-studied and widely used in the field of pattern recognition. Many pattern recognition algorithms need features extracted from CNN models to adapt to complex tasks, such as image classification, object detection, natural language processing and so on. However, to deal with more and more complex tasks, modern CNN models are becoming larger and larger, contain large number of parameters and computation, leading to high consumption of memory, computational and power resources during inference. This makes it difficult to run CNN based applications in real time on mobile devices, where memory, computational and power resources are limited. Binarization of neural networks is proposed to reduce memory and computational complexity of CNN. However, traditional implementations of Binary Neural Networks (BNN) follow the conventional im2col-based convolution computation flow, which is widely used in floating-point networks but not friendly enough to cache when it comes to binarized neural networks. In this paper, we propose BitStream, a general architecture for efficient inference of BNN on CPUs. In BitStream, we propose a simple but novel computation flow for BNN. Unlike existing implementations of BNN, in BitStream, all the layers, including convolutional layers, binarization layers and pooling layers are all calculated in binary precision. Comprehensive analyses demonstrate that our proposed computation flow consumes less memory during inference of BNN, and it’s friendly to cache because of its continuous memory access.
Fast Adaptive Blockchain’s Consensus Algorithm via Wlan Mesh Network

This paper presents a decentralised and fast adaptive block chain’s consensus algorithm with maximum voter privacy using wlan mesh network. The algorithm is suitable for consortium blockchain and private blockchain, and is written as a smart contract for Hyperledger Fabric. Unlike previously proposed blockchain’s consensus protocols, this is the first implementation that does not rely on any trusted authority to compute the tally or to protect the voter’s privacy. Instead, the algorithm is a fast adaptive protocol, and each voter is in control of the privacy of their own vote such that it can only be breached by a full collusion involving all other voters. The execution of the protocol is enforced using the consensus mechanism that also secures the Fabric blockchain. This paper tests the implementation on Fabric’s official test network to demonstrate its feasibility. Also, this paper provides a computational breakdown of its execution cost.
Geochemical discrimination and petrogenetic affinities of dykes intruding the Ladakh batholith, NW India

Eocene dykes extending over 50 km along the southern margin of the Ladakh batholith (NW India) fall into two main groups showing different orientation as well as isotope and trace element geochemistry.Both dyke families formed in the same tectonic setting over a time span of approximately 4 Ma.However, each family is far from monolithic, and therefore we tested several statistical approaches to identify geochemical subgroups from REE data and relate them to magmatogenetic processes.Hierarchical Clustering and Multidimensional Scaling calculate similarities/dissimilarities among individuals of a population.Both statistical tools, when applied to the Ladakh dykes, reflect the east-west dichotomy.However, detailed quantification of the resulting grouping varies according to input data.Normalization to chondrites yields slightly different groupings from unnormalized concentration data.Population-internal REE normalization provides the most accurate grouping as revealed by the fact that multiple samples from the same dyke are assigned the closest relatedness.Independently from normalization, east-west dichotomy is mirrored by marked differences in degree of crustal assimilation and magma evolution, pointing to km-scale geological heterogeneity.Finally, dykes intruding the Ladakh batholith 150 km SE of the present samples are geochemically similar, but cluster as distinct group.
Effectiveness of mobile learning in college english vocabulary teaching under multimodal environment

Vocabulary is one of the three elements that constitute language. Words carry meaning and sentences are made up with different classes of words. Thus, in foreign language learning, vocabulary acquisition is crucial. Thanks to the mobile communications technology, mobile learning has developed into a new learning-mode. This paper investigates the effectiveness of applying mobile learning to college English vocabulary teaching in China. A fourteen-week's experiment proves combining mobile learning with conventional teaching has enlarged students' vocabulary effectively and their level of expression. The interviews reveal first-year college students show great enthusiasm in mobile vocabulary learning and teachers' guidance and supervision are needed during the process.
Learning Search Path for Region-level Image Matching

Finding a region of an image which matches to a query from a large number of candidates is a fundamental problem in image processing. The exhaustive nature of the sliding window approach has encouraged works that can reduce the run time by skipping unnecessary windows or pixels that do not play a substantial role in search results. However, such a pruning-based approach still needs to evaluate the non-ignorable number of candidates, which leads to a limited efficiency improvement. We propose an approach to learn efficient search paths from data. Our model is based on a CNN-LSTM architecture which is designed to sequentially determine a prospective location to be searched next based on the history of the locations attended. We propose a reinforcement learning algorithm to train the model in an end-to-end manner, which allows to jointly learn the search paths and deep image features for matching. These properties together significantly reduce the number of windows to be evaluated and makes it robust to background clutters. Our model gives remarkable matching accuracy with the reduced number of windows and run time on MNIST and FlickrLogos-32 datasets.