Reviewerly/exHarmony huggingface dataset:
Re-ranking of Stereo Video Retrieval Results Based on Clustering and Density

Retrieval result re-ranking is one of the keys of content-based video retrieval and presentation. In order to better realize the display and location of returned stereo video clips, a re-ranking method of stereo video clip retrieval based on clustering and density is proposed to solve the problems of low accuracy in re-ranking and low efficiency in retrieval. According to the similarity of content, the similar clustering is implemented firstly in the method, and the correlation among classes is calculated, so the clustering ranking can be realized according to the correlation. Then, the density is calculated within the classes to measure the similarity between the elements in each class and the query clip, so as to achieve density-based ranking. In the experiment, compared with the situation without re-ranking, it can better meet the user's needs of browsing in similarity order. The experimental results show that the method can achieve better re-ranking in retrieval results of stereo video clips.
Constant-Error Pseudorandomness Proofs from Hardness Require Majority

Research in the 1980s and 1990s showed how to construct a pseudorandom generator from a function that is hard to compute on more than 99% of the inputs. A more recent line of works showed, however, that if the generator has small error, then the proof of correctness cannot be implemented in subclasses of TC 0 , and hence the construction cannot be applied to the known hardness results. This article considers a typical class of pseudorandom generator constructions, and proves an analogous result for the case of large error.
A Hybrid Bio-inspired Clustering Algorithm to Social Media Analysis

Particle swarm optimization algorithm is known as a population-based algorithm which actually maintains a population of particles. The particle plays a significant role which represents an effective solution to an optimization problem. The study proposed in the paper intends to integrate PSO with the artificial bee colony (ABC) algorithm. The research inspired from the intelligent biological behavior of swarms where it involves the merits of both the algorithm to perform experimental analysis on the social media data. The hybrid bio-inspired clustering approach is being proposed to apply on social media data which is known to be highly categorical in nature. The result shows that clustering analysis is helpful to classify high dimensional categorical data. Social media analysis effectively can be achieved through clustering which is being demonstrated in the proposed hybrid approach.
Improve the Tracking of the Target in Radar System Using the Particular Filter

In this paper we are interested to improve the state estimation of a maneuvering target, which improve the tracking of it in real time in radar system. Trajectory estimation is posed as a continuous filtering problem with a simple closed –form solution, which is used a turn to update the label cost. Many of the most successful tracking methods at present perform tracking by detection, the target is presented by an object model that can be detected in every frame independently. The advantage of using an object detector are that it naturally handles re-initialization if a target has been lost, and that it avoids excessive model drift. The detector yields the per-frame evidence for the presence or the absence of the target using the false alarm in discrete time. The aim is to improve the process of the state estimation for too random target at the given instant in order to smooth their true state path for a long time, it simplifies the process of real-time tracking. In this frame, we propose the numerical methods presented by MONTE CARLO (MC) counter-part the methods conventionally used named Extended KALMAN Filter (EKF), in order we showed that the first are more successful.
Optimizing Complete Cross-Validation for Prototype Weighting in Nearest Neighbor Classification

The Nearest Neighbor (NN) classifier is one of the most used and well-known techniques for classification. However, NN suffers from several drawbacks such as high storage requirements, low efficiency in classification response, and noise sensitivity in the neighborhood. Prototype weighting as one the effective learning methods, assigns a weight to each prototype in order to determine its importance and simultaneously removes both redundant and noisy ones as a side effect. NN has an instance-based structure, and this is why; it is one of the rare methods for which the results of cross validation can be achieved for all possible combinations called as Complete-Cross-Validation (CCV) without need to construct and train exponential number of models. Using CCV as the objective function in learning the weights of prototypes can increase generalization capability of the classifier and also decrease the randomness of the learning method. Nevertheless, it cannot be computed such efficiently to be used as a fitness function during most of learning algorithms. In this paper, an iterative learning method has been proposed, in which the objective is incrementally improved without computing CCV even once. The proposed approach was assessed through a series of experiments with UCI in comparison with basic NN and related prototype weighting scheme and could outperform them.
An Approach to Unification of Application Programming Interfaces of Gaming Platforms for Artificial Intelligence

This paper explores several existing gaming platforms for training and testing artificial intelligence, their application programming interfaces analyzed in order to discover potential complications of porting intellectual systems between them. We present an approach to unification of programming interfaces in order to overcome these complications. Advantages and potential side effects of this approach are described.
Face Detection Using Bionic Cascaded Framework

Face interaction plays an irreplaceable role in the service robots human-robot interaction, while face detection in this kind of scenario are challenging due to restrictions on computing capabilities and power, the character of real-time and requirements of the interaction pattern. Recent studies show that deep learning approaches can achieve impressive performance on these kinds of tasks. Therefore, a bionic cascaded framework adopted a cascaded structure with two stages of carefully designed face detectors is proposed in this paper, which exploits saccade and attention mechanism of human eyes to balance the performance. In addition, in the working process of the service robots, a new online sampling strategy that can improve the performance of interaction patterns is presented. In this way, the real time face detection and more natural human-robot interaction pattern can be achieved in service robot systems.
Sensitivity, Affine Transforms and Quantum Communication Complexity

In this paper, we study the Boolean function parameters sensitivity ( $${\mathsf {s}}$$ ), block sensitivity ( $${\mathsf {bs}}$$ ), and alternation ( $${\mathsf {alt}}$$ ) under specially designed affine transforms and show several applications. For a function $$f:{\mathbb {F}}_2^n \rightarrow \{0,1\}$$ , and $$A = Mx+b$$ for $$M \in {\mathbb {F}}_2^{n \times n}$$ and $$b \in {\mathbb {F}}_2^n$$ , the result of the transformation g is defined as $$\forall x \in {\mathbb {F}}_2^n, g(x) = f(Mx+b)$$ . As a warm up, we study alternation under linear shifts (when M is restricted to be the identity matrix) called the shift invariant alternation (the smallest alternation that can be achieved for the Boolean function f by shifts, denoted by $${\mathsf {salt}}(f)$$ ). By a result of Lin and Zhang [12], it follows that $${\mathsf {bs}}(f) \le O({\mathsf {salt}}(f)^2{\mathsf {s}}(f))$$ . Thus, to settle the Sensitivity Conjecture ( $$\forall ~f, {\mathsf {bs}}(f) \le {\mathsf {poly}}({\mathsf {s}}(f))$$ ), it suffices to argue that $$\forall ~f, {\mathsf {salt}}(f) \le {\mathsf {poly}}({\mathsf {s}}(f))$$ . However, we exhibit an explicit family of Boolean functions for which $${\mathsf {salt}}(f)$$ is $$2^{\varOmega ({\mathsf {s}}(f))}$$ . Going further, we use an affine transform A, such that the corresponding function g satisfies $${\mathsf {bs}}(f,0^n) \le {\mathsf {s}}(g)$$ , to prove that for $$F(x,y) {\mathop {=}\limits ^{\mathrm{def}}}f(x \wedge y)$$ , the bounded error quantum communication complexity of F with prior entanglement, $$Q^*_{1/3}(F)$$ is $$\varOmega (\sqrt{{\mathsf {bs}}(f,0^n)})$$ . Our proof builds on ideas from Sherstov [17] where we use specific properties of the above affine transformation. Using this, we show the following. Restricting back to the linear setting, we construct linear transformation A, such that the corresponding function g satisfies, $${\mathsf {alt}}(f) \le 2{\mathsf {s}}(g)+1$$ . Using this new relation, we exhibit Boolean functions f (other than the parity function) such that $$ {\mathsf {s}}(f)$$ is $$\varOmega (\sqrt{\mathsf {sparsity}(f)})$$ where $$\mathsf {sparsity}(f)$$ is the number of non-zero coefficients in the Fourier representation of f.
Bessel fringes modulation determination by directional spatial carrier phase shifting

Time averaging technique applied to different interferometric methods is one of the most commonly used measurement technique in vibration testing. The information on amplitude of vibration is encoded in so called fringe envelope function described by the J<sub>0</sub> Bessel function in case of harmonic motion. In the presented paper the author proposes novel solution for amplitude distribution evaluation. It is based on the analysis of the Bessel pattern modulation distribution. For modulation evaluation and Directional Spatial Carrier Phase Shifting (DSCPS) method are used. Conducted error analysis prove usefulness of the proposed approach.
Quantum Switch Realization by the Quantum Lyapunov Control

The quantum Lyapunov control is one of the methods presently used in the quantum state engineering. The mentioned approach allows not only generating specified quantum states but also controlling the dynamics of the operators defining the computational process in a quantum register. In this work, we present the quantum Lyapunov control scheme which implements a quantum switch (it can be used as a part of future quantum networks). However, this implementation is not based on a quantum gates circuit. It directly concerns the spins of the qubits which realize the quantum switch operation. We analyze also the whole process of the quantum switching in terms of the accuracy of the quantum Lyapunov control method for the switch’s implementation.
A Semantic and Social Structure of Platform Business

The platform business has had a profound impact on traditional industries such as real estate, telecommunications, and retail. It is a type of business that makes the most of the potential of the network effect. The authors conducted the current research to contribute to understanding public perception of platform business. In this study, tweets from Twitter and news articles were collected using LexisNexis database and examined through Semantic Network Analysis (SNA). The results of the two media did not show significant differences but we may conclude that the public is still in the process of building awareness about platform business.
Linear Regression to Determine the Cluster Radius for Fuzzy Rule Base Generation

Clustering and classification are important problems of machine learning. In this field, meta-learning is employed to discover regularities in data and select a suitable learning algorithm or set of its parameters. By analyzing data sets used to train previous classifiers, it is possible to discover parameter setting regularities for learning algorithms. In this work, the structural properties of data sets to be classified are associated with the parameters of training algorithms, and the parameter values for which classifiers exhibit good performance are determined. The rule base of the fuzzy classifier is generated based on data clustering results. As a metric for estimating the cluster radius, we use linear regression constructed on meta-features extracted.
Temporal Pattern Recognition in Large Scale Graphs

Many application domains involve monitoring the temporal evolution of large-scale graph data structures. Unfortunately, this task is not well supported by modern programming paradigms and frameworks for large-scale data processing. This paper presents ongoing work on the implementation of FlowGraph, a framework to recognize temporal patterns over properties of large-scale graphs. FlowGraph combines the programming paradigm of traditional graph computation frameworks with the temporal pattern detection capabilities of Complex Event Recognition (CER) systems. In a nutshell, FlowGraph distributes the graph data structure across multiple nodes that also contribute to the computation and store partial results for pattern detection. It exploits temporal properties to defer as much as possible expensive computations, to sustain a high rate of changes.
Proactive Data Centre &amp; Network Room Overheating Management System (DCNROMS) Utilizing Open Source Software and Tools

Data centre equipment are very sensitive and susceptible to environmental damage from excessive heat. Power outages can lead to overheated servers in a matter of minutes. It is therefore mandatory to monitor the ambient temperature of the data centre or network room continuously and take necessary action if temperature exceeds the threshold limit. Manual 24x7 monitoring requires additional man power and is not an efficient approach. The paper presents an effective and cost efficient mechanism to automate this process using open source software's and tools. We have utilized the inbuilt temperature sensing capabilities of the managed network switches to build such a system. Thus the system has been designed without the use of additional temperature monitoring sensors. The system has been implemented in our organization and has helped in reducing the failure rate of the network switches from 2% to 0%, thus helping in maintaining 99.98% uptime of the network.
Mammogram Classification with Ordered Loss

Breast radiologists inspect mammograms with the utmost consideration to capture true cancer cases. Yet, machine learning models are typically designed to perform a binary classification, by joining several severities into one positive class. In such scenarios with mixed gradings, a reliable classifier would make less mistakes between distant severities such as missing a true cancer case and calling it as normal or vise versa. To this end, we suggest a simple yet elegant formulation for training a deep learning model with ordered loss, by increasingly weighting the loss of more severe cases, to enforce importance of certain errors over others. Training with the ordered loss yields fewer severe errors and can decrease the chances of missing true cancers. We evaluated our method on mammogram classification, using a weakly supervised deep learning method. Our data set included over 16 K mammograms, with a large set of nearly 2,500 biopsy proven cancer cases. Evaluation of our proposed loss function showed a reduction in severe errors of missing true cancers, while preserving overall classification performance in the original task.
Optimization of adaptive media player algorithm for smart TV terminal

The emergence of resource conflicts and overload control problems during the playback of smart TV terminals has brought many obstacles to the operation of smart TV terminals, which has seriously affected the user experience of smart TV terminal users. In this regard, the adaptive media playback alg orithm is optimized for smart TV terminals. This method performs dynamic priority preemptive scheduling on exclusive resources according to resource characteristics and application priorities to optimize resource allocation and improve media playback. The feedback control algorithm is used to perform QoS scheduling on shared resources until QoS proportional fairness is achieved, and QoS proportional compression is used to eliminate resource overload. Finally, a DASH server on Apache and implements an analog DASH client using Python are built. In order to verify the performance of the algorithm, the research results show that the adaptive media playback algorithm has the overload control capability, which only solves the resource conflict and improves the response performance under heavy load of the system, and the algorithm consumes only 4.5% of the overall system QoS. Compared with the existing methods, it is about 30% lower, which is more suitable for resource scheduling of smart TV terminals. The research in this paper shows that the adoption of QoS scheduling mechanism contributes to the optimization of media playback resources and allocation ratio, thus making the playback process of smart TV terminals, which provides a reference for the optimization of smart TV terminal playback.
Not by Data Alone: The Promises and Pitfalls of Data Analysis in Understanding Consciousness

Since the introduction of new technologies, the deluge of neuroscientific data has been overwhelming. On one hand this new information has produced remarkable breakthroughs in our understanding of brain function and development as well as lifesaving treatments for trauma and disease. On the other hand, the lure and reward for explanations of mental phenomena in terms of simple, manipulable brain processes has led to questionable research methodologies and unsubstantiated claims. A more fundamental issue is raised by the attempt to explain consciousness by means of information, as proposed by the Information Integration Theory (IIT). While the models produced by this massive computation of data will no doubt improve our understanding of brain function and capacity, a strict information processing approach cannot address the problem of meaning. A solution to this problem demands an evolutionary, developmental, and dynamic account of an organism in its environment. Data analysis will play a role in this inclusive explanatory program, but explanation is insufficient by data alone.
Research on the Effectiveness Evaluation of Tactical Internet Based on Dynamic Entropy Method

The effectiveness evaluation of tactical internet is an important approach to inspect and improve the tactical internet planning of. At present, the evaluation is still in the development stage, lacking complete theoretical system and systematic research achievements. Through analyzing, summarizing and generalizing the characteristics of tactical internet effectiveness evaluation, the paper proposes an evaluation model based on dynamic entropy method and design program implementation method of the model Finally, it verifies the validity and reasonability of the model with the help of simulation.
A manipulator control in an unknown environment

An algorithm for a n-link manipulator movement amidst unknown static obstacles in a continuous space is presented. Given theorem stating that if the manipulator moves according to the algorithm will be discovered in a finite number of steps whether a given target configuration is reachable or not. The number, shapes and dispositions of obstacles may be arbitrary.
Based on the Predicted Blocking Virtual Machine Load Balancing Scheduling Strategy

The popularity of the Internet and the rapid development of the massive data promote the expansion of the field of cloud computing applications, which absorb the grid computing, utility computing, and other distributed technology, through the network sharing platform resources to improve the throughput of data processing speed and computing power. As the core of cloud computing virtualization technology, through the construction of multiple virtualization platforms, in order to achieve space expansion, backup, and other operations to achieve the purpose of efficient integration of resources, but in practical applications, with the increase in the size of resources, physical and virtual machine load balance contradiction has become increasingly prominent, if such problems are not a reasonable solution, both a waste of resources, but also interfere with the application performance, thus affecting the user experience. In this paper, we study the process of blocking technology proposed based on load balancing under the conditions of virtual machine scheduling strategy, and through experiments to verify its reliability and efficiency.
Aplikasi Pengolahan Data Berbasis Web Menggunakan Framework Codeigniter

was established in January 2005, in Sidoarjo with the previous name of Bengkel Sahabat serving magic turret, body repair, chassis repair, collision car repair, and painting. The process of improvement is the same as the other magic ketok that is synonymous with the culture of workmanship that is done in a closed, and in improving service Bengkel Sahabat has been equipped with facilities paint oven room, paint equipment, polish, or other equipment equivalent to workshop equipment body repair in general. CodeIgniter is an open source web application framework used to build dynamic php applications. The main purpose of Codeigniter development is to help developers to work on applications faster than writing all the code from scratch. Codeigniter provides a variety of libraries that can simplify the development The results of this research produced a web-based data processing application on body repair shops and car paint three brothers to facilitate the users in running applications.
T–S Fuzzy System Identification Using I/O Data

In this chapter, we consider the identification of T–S fuzzy models based on input–output (I/O) data. The identification of T–S fuzzy models includes two major tasks: structure identification and parameter identification. Structure identification determines the premise (input) variables, the number of fuzzy rules, and the initial positions of membership functions. Parameter identification determines a feasible set of parameters including antecedent (membership function) parameters and consequent parameters under a given structure.
Identification Of Induction Motor Parameters Using Wiener-Hammerstein Structure Based On Artificial Bee Colony Algorithm

In this paper, identification of parameters of induction motors using the Wiener-Hemerstein structure based on the colony helix algorithm. Identifying the system is actually finding a mathematical model of dynamical systems from input-output data, the results of experiments, and observations. Given the importance of induction machines in the industry, maintaining and protecting them is essential. One of the ways to keep such engines in check is to continuously monitor their health by continuous monitoring of the values of its structural parameters. In this paper, an engine model is estimated using the extracted data from the engine, which includes the effective values of the stator current and the power factor and the application of the colony helium algorithm. The results of the simulation show the high accuracy of the proposed method in identifying the parameters of induction motors in comparison with the previous methods.
Provably Secure Private Set Intersection With Constant Communication Complexity

Electronic information is increasingly shared among unreliable entities. In this context, one interesting problem involves two parties that secretly want to determine an intersection of their respective private data sets while none of them wish to disclose the whole set to the other. One can adopt a Private Set Intersection (PSI) protocol to address this problem preserving the associated security and privacy issues. In this article, the authors present the first PSI protocol that incurs constant (p(k)) communication complexity with linear computation overhead and is fast even for the case of large input sets, where p(k) is a polynomial in security parameter k. Security of this scheme is proven in the standard model against semi-honest entities. The authors combine somewhere statistically binding (SSB) hash function with indistinguishability obfuscation (iO) and space-efficient probabilistic data structure Bloom filter to design the scheme.