Reviewerly/exHarmony huggingface dataset:
Joint graph regularized dictionary learning and sparse ranking for multi-modal multi-shot person re-identification

The promising achievement of sparse ranking in image-based recognition gives rise to a number of development on person re-identification (Re-ID) which aims to reconstruct the probe as a linear combination of few atoms/images from an over-complete dictionary/gallery. However, most of the existing sparse ranking based Re-ID methods lack considering the geometric relationships between probe, gallery, and cross-modal images of the same person in multi-shot Re-ID. In this paper, we propose a novel joint graph regularized dictionary learning and sparse ranking method for multi-modal multi-shot person Re-ID. First, we explore the probe-based geometrical structure by enforcing the smoothness between the codings/coefficients, which refers to the multi-shot images from the same person in probe. Second, we explore the gallery-based geometrical structure among gallery images, which encourages the multi-shot images from the same person in the gallery making similar contributions while reconstructing a certain probe image. Third, we explore the cross-modal geometrical structure by enforcing the smoothness between the cross-modal images and thus extend our model for the multi-modal case. Finally, we design an APG based optimization to solve the problem. Comprehensive experiments on benchmark datasets demonstrate the superior performance of the proposed model. The code is available at https://github.com/ttaalle/Lhc.
Real-Time Autonomous Obstacle Avoidance for Fixed-Wing UAVs Using a Dynamic Model

This paper presents an approach for real-time autonomous obstacle avoidance for fixed-wing unmanned aerial vehicles (UAVs) for scenarios in which a UAV is required to stay close to a reference path. A key challenge is rapid trajectory generation around obstacles while accommodating vehicle constraints. A UAV model with nonlinear dynamic constraints provides more natural accommodation of the vehicle’s constraints than a kinematic model with linear constraints. This paper presents a method for using finite horizon model predictive control with a custom solver that offers low solution time. A comparative study of a high-fidelity model and a lower-fidelity counterpart is presented. Using the proposed method, the high-fidelity model provides better trajectories than the lower-fidelity counterpart, despite both having low computational requirement for onboard trajectory generation in an embedded platform.
A Generalized Proof-Theoretic Approach to Logical Argumentation Based on Hypersequents

Abstract In this paper we introduce hypersequent-based frameworks for the modelling of defeasible reasoning by means of logic-based argumentation and the induced entailment relations. These structures are an extension of sequent-based argumentation frameworks, in which arguments and the attack relations among them are expressed not only by Gentzen-style sequents, but by more general expressions, called hypersequents . This generalization allows us to overcome some of the known weaknesses of logical argumentation frameworks and to prove several desirable properties of the entailments that are induced by the extended (hypersequent-based) frameworks. It also allows us to incorporate as the deductive base of our formalism some well-known logics (like the intermediate logic , the modal logic , and the relevance logic ), which lack cut-free sequent calculi, and so are not adequate for standard sequent-based argumentation. We show that hypersequent-based argumentation yields robust defeasible variants of these logics, with many desirable properties.
Parameter Analysis and Simulation Experiment of Ant Colony Optimization on Small-scale TSP Problem

Abstract Ant colony optimization (ACO) is a simple and efficient bionic intelligent algorithm, which can be applied to various optimization problems. There are many controllable parameters in the ant colony optimization, so the parameter setting has a great impact on the efficiency of the algorithm. In order to further improve the efficiency of the algorithm, many researchers have proposed various improvements. However, the current improved method of ACO ignored the consideration of its original parameter settings. In order to analyze the effect of parameter setting on the efficiency of the ant colony optimization, this article uses the ant colony optimization to solve small-scale TSP problems as an example for simulation experiments. Explore the impact of three parameter pairs on the efficiency of algorithm optimization by setting variable pairs. The experimental results show that when the ant colony size is set to approximately 0.6 times the TSP problem size, a better algorithm efficiency can be obtained. Within the value range of [1, 10], setting the information heuristic factor to 1, and the expected heuristic factor to a range of [5, 10] can get a better efficiency. In the last experiments, the pheromone volatility coefficient is generally better to be set greater than 0.2, and in a small-scale TSP problem, totally randomness has little effect on the optimization accuracy of the algorithm, and the value of pheromone enhancement coefficient has little effect on the efficiency of the algorithm.
Stopping rules for multi‐category computerized classification testing

Computerized classification testing (CCT) aims to classify persons into one of two or more possible categories to make decisions such as mastery/non-mastery or meet most/meet all/exceed. A defining feature of CCT is its stopping criterion: the test terminates when there is enough confidence to make a decision. There is abundant research on CCT with a single cut-off, and two common stopping criteria are the sequential probability ratio test (SPRT) statistic and the generalized likelihood ratio statistic (GLR). However, there is a relative scarcity of research extending the SPRT to the multi-hypothesis case for when there is more than one cut-off. In this paper, we propose a new multi-category GLR (mGLR) statistic as well as a stochastically curtailed version of the CCT with three or more categories. A simulation study was conducted to show that the mGLR statistic outperformed the existing stopping rules by generating shorter average test length without sacrificing classification accuracy. Results also revealed that the stochastically curtailed mGLR successfully increased test efficiency in certain testing conditions.
A Survey on Privacy and Security in Mobile Cloud Computing

Mobile cloud computing (MCC) is one of the significant inventions in the field of information technology that empowers mobile applications by strengthening computational strength and storage in cloud-connected through the network. But there are several security issues while using MCC that are related to data security, partitioning, offloading, virtualization, mobile application or device, and privacy. Specifically, the security issues in educational institutions are rarely studied. This research aims to identify privacy and security issues while using MCC in educational institutions. Using Qualtrics, an online questionnaire survey is conducted for the study. The result illustrates that spam and network interruptions are the most prevalent security issues followed by viruses and phishing. The issues were found more prevalent in android operating system users and the methods being used to address the issues are not enough to maintain high-level security. Thus, a secure framework is required for safe MCC in educational institutions.
Netflix Bigdata Analytics- The Emergence of Data Driven Recommendation

Netflix is one of the largest online streaming media providers. It began its operations in 1997. Founded by two tech entrepreneur Reed Hastings and Marc Randolph. The Company’s head office is in Los Gatos, California. Netflix’s initially started selling DVDs or provide them on a rental basis. Over the period with growth of internet users and the decline of DVD sales and rental services, it changed its business model to video on demand. From 2012 onwards, it started producing its original TV-series and movies. Netflix uses bigdata analytics to understand its customers base better. By using these data, they provide better service or product to the customer. Netflix collects huge amounts of data from a vast variety of subscriber base. It collects data such as the location of a user; content watched by the user, user interests, the data searched by the user, and the time at which user watched. Based on these parameters its algorithm gives a personalized recommendation based on the user interest. Netflix has constantly focused on changing business needs they have moved their business model from DVD rental to video on demand and currently producing original shows. In this paper we analyze various business strategies of Netflix. This paper also analyzes how Netflix with the help of bigdata analytics focused on improving the subscriber’s experience and how it helped to be more customer-centric and increased its user base. Based on the SWOT and PESTLE analysis we have provided some suggestion that can be incorporated by Netflix as business strategy.
Facial Emotion Recognition Using Shallow CNN

Facial emotional recognition became an important task in the modern day scenario to understand the state of emotions of a human being by machines. With the development of computational power and deep learning techniques, facial emotion recognition (FER) became feasible, which contributed to a wide range of applications in modern day technology. In this paper, we propose a shallow convolutional neural network architecture with feature-based data, which can do this task more effectively and attained the state-of-the-art accuracy with less computational complexity (in terms of learnable parameters). The proposed architecture is shallow and gives comparable performance with all the existing approaches for FER in deep learning.
Slice-Based Cognitive Complexity Metrics for Defect Prediction

Researchers have identified several quality metrics to predict defects, relying on different information however, these approaches lack metrics to estimate the effort of program understandability of system artifacts. In this paper, novel metrics to compute the cognitive complexity based on program slicing are introduced. These metrics help identify code that is more likely to have defects due to being challenging to comprehension. The metrics include such measures as the total number of slices in a file, the size, the average number of identifiers, and the average spatial distance of a slice. A scalable lightweight slicing tool is used to compute the necessary slicing data. A thorough empirical investigation into how cognitive complexity correlates with and predicts defects in the version histories of 10 datasets of 7 open source systems is performed. The results show that the increase of cognitive complexity significantly increases the number of defects in 94% of the cases. In a comparison study to metrics that have been shown to correlate with understandability and with defects, the addition of cognitive complexity metrics shows better prediction by up to 14% in F1, 16% in AUC, and 35% in R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Proof searching in HOL4 with genetic algorithm

Proof searching and proof automation are the two most desired properties in interactive theorem provers (ITPs) as they generally require manual user guidance, which can be quite cumbersome. In this paper, we provide an evolutionary proof searching approach for the HOL4 proof assistant, where a genetic algorithm (GA) with different crossover and mutation operators is used to search and optimize the proofs in different HOL theories. Random proof sequences are first generated from a population of frequently occurring HOL4 proof steps that are discovered with sequential pattern mining. Generated proof sequences are then evolved with GA operators (three crossover and two mutation) till their fitness match the fitness of the target proof sequences. Various crossover and mutation operators are used to compare their effect on the performance of GAs in proof searching. Obtained results suggest that integrating GAs with HOL4 allows us to efficiently support proof finding and optimization.
Address Entities Extraction using Named Entity Recognition

Due to presence of large amounts of digital data, many tools for information extraction were developed in order to provide meaningful information and knowledge that could be used in text analysis and interpretation. Machine learning, artificial intelligence and data mining can help there a lot. In this paper, program for extracting address entities is presented as task of named entity recognition. The dataset for named entity recognition are USA addresses that are labeled as one of 8 labels. The model is trained in Python with Tensorflow using pretrained word vectors taken from GloVe-Global vector word embedding. The algorithm that is used is long short-term memory (LSTM) which is special type of recurrent neural network. It was very useful for this application since it takes care of context of the input data. By using this algorithm, model was able to learn how later entities are related to previous ones and thus resolve some complex examples such as differentiating between city and state with the same name.
Customer-Specific Robotic Attendant for VR Simulators

With regard to virtual reality (VR) simulators, direct guidance is needed due to the complex procedures and safety of customers compared with simple coin-op amusement games. However, it is difficult for human attendants to repeatedly guide the customer with a high level of service quality. In this article, we propose a new type of robotic attendant for VR simulators, which provides personalized high-quality services and automatically operates the VR simulators in VR theme parks. In addition, the robotic attendant improves customer satisfaction by providing personalized services based on the estimation of the customer's age, gender, and game progress. Finally, we experimentally show that the personalized services provided by the robotic attendant improve customer satisfaction. The results of the satisfaction questionnaire and independent-samples t-test validate our proposed scheme.
High-Frequency Refinement for Sharper Video Super-Resolution

A video super-resolution technique is expected to generate a `sharp' upsampled video. The sharpness in the generated video comes from the precise prediction of the high-frequency details (e.g. object edges). Thus high-frequency prediction becomes a vital sub-problem of the super-resolution task. To generate a sharp-upsampled video, this paper proposes an upsampling network architecture `HFR-Net' that works on the principle of `explicit refinement and fusion of high-frequency details'. To implement this principle and to train HFR-Net, a novel technique named 2-phase progressive-retrogressive training is being proposed. Additionally, a method called dual motion warping is also being introduced to preprocess the videos that have varying motion intensities (slow and fast). Results on multiple video datasets demonstrate the improved performance of our approach over the current state-of-the-art.
Reservoir induced activation of a quantum neuron

Artificial neural networks are one of the building blocks of artificial intelligence. And their quantum versions have a superior performance possibility. This work proposes an open quantum neuron as a unit structure of an open quantum network and demonstrates that it can be activated through the connected reservoir. It's been shown that the model successfully classifies the temperature data coming from distinct quantum thermal reservoirs and exhibits an activation through the reservoir parameters. Also, a possible physical version of the model operating in the microwave regime discussed to be three orders of magnitude faster than the classical classifiers.
Incremental Attack Synthesis

Information leakage is a signi cant problem in modern software systems. Information leaks due to side channels are especially hard to detect and analyze. In recent years, techniques have been developed for automated synthesis of adaptive side-channel attacks that recover secret values by iteratively generating inputs to reveal partial information about the secret based on the sidechannel observations. Prominent approaches of attack synthesis use symbolic execution, model counting, and meta-heuristics to maximize information gain. These approaches could bene t by reusing results from prior steps in each step. In this paper, we present an incremental approach to attack synthesis that reuses model counting results from prior iterations in each attack step to improve efficiency. Experimental evaluation demonstrates that our approach drastically improves performance, reducing the attack synthesis time by an order of magnitude.
Path Planning for Multi-Rotors UAVs Formation Based on Ant Colony Algorithm

Multiple unmanned aerial vehicles (UAVs) formation has a broad application prospects, which will be used widely in the civilian and military fields. An improved ant colony algorithm method is proposed to solve the problem of path planning in formation transformation. Firstly, we designed a location assignment based on ant colony optimization algorithm. Then a new node selection strategy is proposed to improve the optimization results. At the end, the simulation results show the effectiveness and progress of the method. The improved method proposed in this paper can avoid the results into the local optimization and the algorithm optimization speed has increased.
Fast Intent Classification for Spoken Language Understanding Systems

Spoken Language Understanding (SLU) systems consist of several machine learning components operating together (e.g. intent classification, named entity recognition and resolution). Deep learning models have obtained state of the art results on several of these tasks, largely attributed to their better modeling capacity. However, an increase in modeling capacity comes with added costs of higher latency and energy usage, particularly when operating on low complexity devices. To address the latency and computational complexity issues, we explore a BranchyNet scheme on an intent classification scheme within SLU systems. The BranchyNet scheme when applied to a high complexity model, adds exit points at various stages in the model allowing early decision making for a set of queries to the SLU model. We conduct experiments on the Facebook Semantic Parsing dataset with two candidate model architectures for intent classification. Our experiments show that the BranchyNet scheme provides gains in terms of computational complexity without compromising model accuracy. We also conduct analytical studies regarding the improvements in the computational cost, distribution of utterances that egress from various exit points and the impact of adding more complexity to models with the BranchyNet scheme.
A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It

In this paper, we propose a novel stochastic model based on the quadtree, so that our model effectively represents the variable block size segmentation of images. Then, we construct the Bayes code for the proposed stochastic model. In general, the computational cost to calculate the posterior distribution required in the Bayes code increases exponentially with respect to the data size. However, we introduce an efficient algorithm to calculate it in the polynomial order of the data size without loss of the optimality. Some experiments are performed to confirm the flexibility of the proposed stochastic model and the efficiency of the introduced algorithm.
QoE-aware auto-scaling of heterogeneous containerized services (and its application to health services)

Containerized service is currently a widely adopted solution to deploy services in the cloud. However, many companies offer a very diverse set of Web accessible services that are subjected to very distinctive workloads. Consequently, to correctly provision the right amount of resources for each of these services is a challenge. In this paper we propose the Autonomic ConTainerized Service Scaler (ACTS), an autonomic system able to horizontally and vertically scale a set of heterogeneous containerized services subjected to different workloads. The adaptation decisions depended on a set of high-level Quality of Experience (QoE) metrics centered on the services' end-user. We have applied ACTS to some of the digital services of the Shared Services of the Ministry of Health (SPMS) public company. The experimental results show that our solution is able to adequately adapt the configuration of each service, as a direct response to alterations on its workload.
Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques

Our purpose in this research is to develop a method to automatically and efficiently classify web images as Unified Modeling Language (UML) static diagrams, and to produce a computer tool that implements this function. The tool receives a bitmap file (in different formats) as an input and communicates whether the image corresponds to a diagram. For pragmatic reasons, we restricted ourselves to the simplest kinds of diagrams that are more useful for automated software reuse: computer-edited 2D representations of static diagrams. The tool does not require that the images are explicitly or implicitly tagged as UML diagrams. The tool extracts graphical characteristics from each image (such as grayscale histogram, color histogram and elementary geometric forms) and uses a combination of rules to classify it. The rules are obtained with machine learning techniques (rule induction) from a sample of 19,000 web images manually classified by experts. In this work, we do not consider the textual contents of the images. Our tool reaches nearly 95% of agreement with manually classified instances, improving the effectiveness of related research works. Moreover, using a training dataset 15 times bigger, the time required to process each image and extract its graphical features (0.680 s) is seven times lower.
A Secure Steganographic Scheme Based on Chaotic Map and DNA Computing

This paper proposes a secure steganographic scheme based on DNA computation and chaos based on random bit generation. This scheme uses a cross-coupled chaotic map to generate random DNA sequences. DNA computing is used for encoding the secret message, and the encoded secret is embedded onto a cover image using well-known LSB substitution technique. This scheme is equally effective for hiding image as well as text. The secret message is converted to a DNA sequence, and another random DNA sequence is generated using a cross-coupled chaotic system. Now, the two DNA sequences are added using DNA addition. Then, the LSBs of the cover image pixels are substituted by the secret DNA sequence. The results of the experiment and security analysis show that the algorithm devised by us is effective and also can resist statistical and differential attack.