Reviewerly/exHarmony huggingface dataset:
Instruction-guided object detection

This paper aims at instruction-guided object detection, i.e., predicting the objects associated with the implementation of a specific instruction for intelligent robots. It is not practical to solve this problem by picking out the instruction-related objects from the detection results of a general object detector due to massive annotation cost and low adaptability of training data. To address these challenges, we introduce a flexible dataset that can well adapt to the variation of the instruction set and only annotates instruction-related object samples. We then propose to amend the current detection paradigm by incorporating semantic instruction description effectively. Specifically, the relationship between an instruction and related objects is modeled by the and-or graph and is further fused into a unified neural network for solving the object detection problem constrained by instructions. Our algorithm encodes the and-or graph representation to attend related objects sensitive local features and then infer instruction-related object location. Extensive evaluation on the newly constructed dataset verifies the effectiveness of our approach.
Construction of Game-Based Teaching Environment for Animation Scene Scheduling Based on Virtual Reality

Audio-visual language is the language of film and television animation art. The scene scheduling in the audio-visual language of animation includes action design, the design of characters’ moving routes in space, and the arrangement and design between the roles and the background. Traditional teaching methods are not enough to provide virtual scenes for learners to practice. Therefore, this paper develops an immersive gamification teaching environment for the practical teaching of scene scheduling content in audio-visual animation language, which includes three aspects: role scene scheduling, shot scene scheduling, role scheduling and shot scheduling. Given this teaching system we have carried on the teaching pilot experiment, the experimental results showed that the immersive teaching environment could improve students’ learning effect, promote the construction of action program schemata, and promote the formation and maintenance of skills.
Human-in-the-Middle: Increasing Security of Two-Factor Authentication

Online security and security of personal information is an important topic in information technology. Due to the recent high-profile security breaches as a result of authentication manipulation, there has been a growing need for enhanced methods of authentication as attackers have become more skilled in defeating existing authentication mechanisms. Attackers have resulted to phishing, social engineering, token interception and other highly sophisticated attackers all to gain access to the accounts of user. These attacks have resulted in serious damages for individuals and organizations. As a result, methods, such as, Two-Factor Authentication (2FA) have been introduced to. Nevertheless, loopholes major vulnerabilities in the existing two-factor authentication implementation. In this paper, This paper analyzes the. The are listed and analyzed, and the methods of manipulation explained. This paper finally suggests a solution of a web platform service which utilizes the mobile phones of users as a form of secure confirmation of the identity of the user through a dedicated mobile application. This solution hopes to eliminate the need for communication of security parameters of the traditional telephone network.
Linear projections of the Vandermonde polynomial

An n-variate Vandermonde polynomial is the determinant of the n×n matrix where the ith column is the vector (1,xi,xi2,…,xin−1)T. Vandermonde polynomials play a crucial role in the theory of alternating polynomials and are useful in Lagrangian polynomial interpolation which arise in the theory of error correcting codes. In this work we study structural and computational aspects of linear projections of Vandermonde polynomials. First, we consider the problem of testing if a given polynomial is equivalent to the Vandermonde polynomial. We obtain a deterministic polynomial time algorithm to test if f is linearly equivalent to the Vandermonde polynomial when f is given as product of linear factors. In the case when f is given as a black-box our algorithm runs in randomized polynomial time. Exploring the structure of projections of Vandermonde polynomials further, we describe the group of symmetries of a Vandermonde polynomial and obtain a basis for the associated Lie algebra. Finally, we study arithmetic circuits built over projections of Vandermonde polynomials. We show universality property for some of the models and obtain a lower bounds against sum of projections of Vandermonde determinant.
Complex Predictive Solution for Computerized Processes in Tire Industry

Following increasing market needs of productivity, cost reduction and safety requirements, computerized industry are faced to finding optimum between economic aspects of business and safety-related risk management. Modern factories equipped with computerized processes and extended diagnostic tools to support operator do not often use of all information’s which comes from the equipment. Some of the relations between the events are also omitted or neglected. Authors after analyzing the defects of modern production lines with batch production, noticed a correlation between the defects and the historical information registered by the supervisory system controlling the production process. The problem is that with the multitude of appearing information, they are not analyzed on an ongoing basis. This article presents a new approach to increase machinery reliability, safety and quality increase through predictive data analysis.
An Experimental Analysis of Similarity Measures Effect for Identification of Software Component Composition of Services Based on Use-Cases

The current business environment made helpless to rethink that how software component is developed so that in future if similar feature required then it could be reused. For the reuse process the component-based software engineering was useful. In component-based development scenario the challenging task is to identify the software logical components. In the literature there are various clustering techniques with expert judgment are available to identify logical software components. In this context, all the previous methods use the similarity measure technique for finding the software cohesion. It has been observed that if any change has been made to similarity measure then it reflects changes in value of software cohesion. So, the goal of this paper is to show the effect of similarity measure for identify the logical software component of software system. For the validation and justification various feature-based similarity measures and standard parameter (Precision, Recall and Accuracy etc.) are used for software cohesion for the design of Online Broker System (case study).
Proper learning of k-term DNF formulas from satisfying assignments

In certain applications there may be only positive examples available to learn concepts of a class of interest. Furthermore, learning has to be done properly, i. e. the hypothesis space has to coincide with the concept class, and without false positives, i. e. the hypothesis always has to be a subset of the real concept (one-sided error). For the well studied class of k-term DNF formulas it has been known that learning is difficult. Unless RP = NP, it is not feasible to learn k-term DNF formulas properly in a distribution-free sense even if both positive and negative examples are available and even if false positives are allowed. This paper constructs an efficient algorithm that, for fixed but arbitrary k and q, if examples are drawn from q-bounded distributions, it properly learns the class of k-term DNFs without false positives from positive examples alone with arbitrarily small relative error.
Programming Language Support for Implementing Machine Learning Algorithms

Machine learning provides the system to automatically learn without human intervention and improve their performance with the help of previous experience. It can access the data and use it for learning by itself. Even though many algorithms are developed to solve machine learning issues, it is difficult to handle all kinds of inputs data in-order to arrive at accurate decisions. The domain knowledge of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory plays a major role in developing machine learning based algorithms. The key consideration in selecting a suitable programming language for implementing machine learning algorithm includes performance, concurrence, application development, learning curve. This chapter deals with few of the top programming languages used for developing machine learning applications. They are Python, R, and Java. Top three programming languages preferred by data scientist are (1) Python more than 57%, (2) R more than 31%, and (3) Java used by 17% of the data scientists.
Using interaction-based readouts to approach the ultimate limit of detection noise robustness for quantum-enhanced metrology in collective spin systems

We consider the role of detection noise in quantum-enhanced metrology in collective spin systems, and derive a fundamental bound for the maximum obtainable sensitivity for a given level of added detection noise. We then present an interaction-based readout utilising the commonly used one-axis twisting scheme that approaches this bound for states generated via several commonly considered methods of generating quantum enhancement, such as one-axis twisting, two-axis counter-twisting, twist-and-turn squeezing, quantum non-demolition measurements, and adiabatically scanning through a quantum phase transition. We demonstrate that this method performs significantly better than other recently proposed interaction-based readouts. These results may help provide improved sensitivity for quantum sensing devices in the presence of unavoidable detection noise.
Seismograph Design for Landslide Monitoring in the Andean Region Using Automatic Control Techniques and Mathematical Modeling

The countries of the Andean area, in particular Colombia, suffer great problems related to landslides of large amounts of soil. These landslides occur due to high mountain deforestation caused by inadequate agricultural practices and the large volume of rainfall in this region. Therefore, it is important to detect information about telluric movements and small vibrations to avoid ecological disasters and dangers for humanity, as well as to determine if the surface can be used to cultivate without erosive problems, thus minimizing economic risks for producers. Seismographs based on accelerometers and geophones are suitable for detecting small vibrations in the earth produced by erosion and movements of groundwater that can prevent soil instability. In this paper, a seismograph prototype is presented, which is based on the use of a high sensitivity accelerometer and a mathematically designed geophone. The proposed seismograph produces as output a spectrum of frequencies of the different types of movements that are damped by automatic control techniques, which serves as a base of information for the generation of reports on soil management and disaster prevention. In addition, the architecture of hardware and software used in the construction of the seismograph allows the connection to the network of several devices that can be distributed in different critical places.
Discovering Strategy in Navigation Problem

This paper explores ways to discover strategy from a state-action-state-reward log recorded during a reinforcement learning session. The term strategy here implies that we are interested not only in a one-step state-action but also a fruitful sequence of state-actions. Traditional RL has proved that it can successfully learn a good sequence of actions. However, it is often observed that some of the action sequences learned could be more effective. For example, an effective five-step navigation to the north direction can be achieved in thousands of ways if there are no other constraints since an agent could move in numerous tactics to achieve the same end result. Traditional RL such as value learning or state-action value learning does not directly address this issue. In this preliminary experiment, sets of state-action (i.e., a one-step policy) are extracted from 10,446 records, grouped together and then joined together forming a directed graph. This graph summarizes the policy learned by the agent. We argue that strategy could be extracted from the analysis of this graph network.
Comparative Analysis and Framework Evaluating Mimicry-Resistant and Invisible Web Authentication Schemes

Many password alternatives for web authentication proposed over the years, despite having different designs and objectives, all predominantly rely on the knowledge of some secret. This motivates us, herein, to provide the first detailed exploration of the integration of a fundamentally different element of defense into the design of web authentication schemes: a mimicry-resistance dimension. We analyze web authentication mechanisms with respect to new usability and security properties related to mimicry-resistance (augmenting the UDS framework), and in particular evaluate invisible techniques (those requiring neither user actions, nor awareness) that provide some mimicry-resistance (unlike those relying solely on static secrets), including device fingerprinting schemes, PUFs (physically unclonable functions), and a subset of Internet geolocation mechanisms.
Abnormal telephone identification via an ensemble-based classification framework

Abnormal telephone that often appeared in our daily life has nearly crazed all people. Most of existing solutions to the problem cannot identify them in time with low efficiency and poor prediction. By cooperating with a telecom company, we have collected a certain amount of telecom user data. But, by analysis on the data, we found that it is a special classification problem in the case of small-scale data with noise samples. In this paper, we propose an ensemble-based classification framework, which first generates multiple training sets by partly resampling on the original data, then build classifiers for every of the generated training sets and finally combines the classifiers in the ensemble way. We conduct experiments on real-life data to evaluate the performance of our framework in comparison with some practical classification algorithms. The empirical result and analysis demonstrate that our framework can achieve a significant increase in accuracy for such difficult prediction task, especially when having noise samples.
Analysis of Satisfaction Level with Servqual method using Artificial Neural Networks

Abstract This research aims to analyze the level of student satisfaction at a college in pattern recognition using an artificial neural network with feedforward backpropagation algorithms. Input on the network consists of 5 variables, namely Tangibles (X 1 ), Reliability (X 2 ), Responsiveness (X 3 ), Assurance (X 4 ) and Empathy (X 5 ) which are the dimensions of the Servqual method. Data processing in the Servqual method will become target data on the network. Data is obtained through multi-item questions given to students. After some training trials, the best configuration architecture consists of 5 neurons in the input layer, one hidden layer with seven neurons and one neuron in the output layer. The activation function used by a Sigmoid Binary that has a range of 0 to 1. This architecture can recognize the pattern well in the 14th iteration with MSE of 0.000192. Data testing results obtained an accuracy rate of 91.07% with an error rate of 8.93%. So that it can be concluded that the Artificial Neural Network with feedforward backpropagation algorithm in the case of analyzing the level of student satisfaction on college services can recognize the pattern well.
A parallel memory-efficient epistemic logic program solver: harder, better, faster

As the practical use of answer set programming (ASP) has grown with the development of efficient solvers, we expect a growing interest in extensions of ASP as their semantics stabilize and solvers supporting them mature. Epistemic Specifications, which adds modal operators K and M to the language of ASP, is one such extension. We call a program in this language an epistemic logic program (ELP). Solvers have thus far been practical for only the simplest ELPs due to exponential growth of the search space. We describe a solver that, at the time of its development (mid-2016), was able to solve harder problems better (e.g., without exponentially-growing memory needs w.r.t. K and M occurrences) and faster than any other known ELP solver.
A Numerical Framework for Efficient Motion Estimation on Evolving Sphere-Like Surfaces Based on Brightness and Mass Conservation Laws

In this work we consider brightness and mass conservation laws for motion estimation on evolving Riemannian 2-manifolds that allow for a radial parametrization from the 2-sphere. While conservation of brightness constitutes the foundation for optical flow methods and has been generalized to said scenario, we formulate the principle of mass conservation for time-varying surfaces that are embedded in Euclidean 3-space and derive a generalized continuity equation. The main motivation for this work is efficient cell motion estimation in volumetric fluorescence microscopy images of a living zebrafish embryo. The increasing spatial and temporal resolution of modern microscopes requires efficient analysis of such data. With this application in mind we address this need and follow an emerging paradigm in this field: dimensional reduction. In light of the ill-posedness of considered conservation laws, we employ Tikhonov regularization and propose the use of spatially varying regularization functionals that recover motion only in regions with cells. For the efficient numerical solution, we devise a mesh-free Galerkin method based on compactly supported (tangent) vectorial basis functions. Furthermore, for the fast and accurate estimation of the evolving sphere-like surface from scattered data, we utilize surface interpolation with spatio-temporal regularization. We present numerical results based on the aforementioned data featuring fluorescently labeled cells.
Remote Sensing Image Enhancement via Robust Guided Filtering

Remote sensing image enhancement methods have to preserve the original reflectance values as possible as they can, whereas emphasising the edges and increasing the contrast. In this study, a remote sensing image enhancement method based on robust guided filtering is proposed. We propose a multiscale decomposition with the robust guided filtering to obtain the approximation and detail layers of the image. Then the extracted details are amplified and added to the approximation layer to obtain the enhanced image. Both visual and quantitative comparisons show that the proposed method has a better preservation capability than the former methods, as well as a better contrast improvement along-with edge enhancement.
The implementation of RC4<sup>+</sup> and Variably Modified Permutation Composition algorithms in the three-pass protocol scheme for data security

Abstract The science and art that is used to take care of message security by encoding messages into obfuscated forms is called cryptography. In cryptography, there are two types of algorithms, which are symmetric and asymmetric algorithms. The RC4 + algorithm and the Variably Modified Permutation Composition (VMPC) algorithm are examples of symmetric algorithms in the form of stream ciphers where the process of encryption and decryption is done by using the same key. To achieve the security objectives, these two algorithms are then used and applied into the three-pass protocol scheme. The three-pass protocol is a cryptographic scheme for sending and receiving messages that allows one party to securely send messages to a second party without the need to exchange the keys. As a result, this research succeeded in securing text file with ( *.docx ) dan ( *.pdf ) and can restore the data back as before by combining the RC4 + algorithm and the Variably Modified Permutation Composition (VMPC) algorithm in the three-pass protocol scheme. The theoretical complexity of these two algorithms is θ(n) both for encryption and decryption. The actual running time is directly proportional to the length of the character of a file.
Experimental test of Bayesian saccade targeting under reversed reading direction

During reading, rapid eye movements (saccades) shift the reader’s line of sight from one word to another for high-acuity visual information processing. While experimental data and theoretical models show that readers aim at word centers, the eye-movement (oculomotor) accuracy is low compared to other tasks. As a consequence, distributions of saccadic landing positions indicate large (i) random errors and (ii) systematic over- and undershoot of word centers, which additionally depend on saccade lengths (McConkie et al. Visual Research, 28(10), 1107–1118, 1988). Here we show that both error components can be simultaneously reduced by reading texts from right to left in German language (N = 32). We used our experimental data to test a Bayesian model of saccade planning. First, experimental data are consistent with the model. Second, the model makes specific predictions of the effects of the precision of prior and (sensory) likelihood. Our results suggest that it is a more precise sensory likelihood that can explain the reduction of both random and systematic error components.
Study on Satisfaction of People’s Park in Mianzhu City under the Background of Population Aging

With the promotion of friendly cities for the elderly, all kinds of functional land in the city began to pay attention to the old age, and the city park, as the communication place in the daily life of the citizens, plays an important role in the retirement life of the elderly.In this paper, the fuzzy evaluation method is used to evaluate the satisfaction of Mianzhu People's Park.It is found that the satisfaction of the elderly to the park is generally low, and the suitability of the park needs to be strengthened.Through correlation analysis and regression analysis, it is found that age, physical condition, travel purpose and educational level are the important factors affecting the satisfaction of the elderly, among which the size of age and the level of education are different from the overall satisfaction of the elderly.The main reason is that the younger the age, the higher the education level of the elderly, the higher the overall satisfaction of the park.
Explicit Correlation Amplifiers for Finding Outlier Correlations in Deterministic Subquadratic Time

We derandomize Valiant's (J ACM 62, Article 13, 2015) subquadratic-time algorithm for finding outlier correlations in binary data. This demonstrates that it is possible to perform a deterministic subquadratic-time similarity join of high dimensionality. Our derandomized algorithm gives deterministic subquadratic scaling essentially for the same parameter range as Valiant's randomized algorithm, but the precise constants we save over quadratic scaling are more modest. Our main technical tool for derandomization is an explicit family of correlation amplifiers built via a family of zigzag-product expanders by Reingold et al. (Ann Math 155(1):157-187, 2002). We say that a function f:{-1,1}d→{-1,1}D is a correlation amplifier with threshold 0≤τ≤1 , error γ≥1 , and strength p an even positive integer if for all pairs of vectors x,y∈{-1,1}d it holds that (i) |⟨x,y⟩|<τd implies |⟨f(x),f(y)⟩|≤(τγ)pD ; and (ii) |⟨x,y⟩|≥τd implies ⟨x,y⟩γdpD≤⟨f(x),f(y)⟩≤γ⟨x,y⟩dpD .