Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Projection for Everyday Robot Object Manipulation

In ever yday object manipulation tasks, like mak­ ing a pancake, autonomous robots are required to decide on the appropriate action parametrizations in order to achieve desired (and to avoid undesired) outcomes. For determining the right parameters for actions like pouring a pancake mix onto a pancake maker, robots need capabilities to predict the physical consequences of their own manipulation actions. In this work, we integrate a simulation-based approach for making tempo­ ral projections for robot manipulation actions into the logic programming language PROLOG. The realized system enables robots to determine action parameters that bring about cer­ tain effects by utilizing simulation-based temporal projections within PROLOG's chronological backtracking mechanism. For a set of formal parameters and their respective ranges of values, the developed s ystem translates the manipulation problems into physical simulations, monitors and logs the relevant data struc­ tures of the simulations, translates the logged data back into first-order time-interval-ba sed representations, called timelines, and eventually evaluates the individual timelines with respect to specified performance criteria. Integrating the proposed approach into robot control programs allow robots to mentall y simulate the consequences of different action parametrizations before committing to them and thereb y to reduce the number of undesired outcomes.
Intelligent Software Defined Atmospheric Effect Processing for 5th Generation (5G) Millimeter Wave (MMWave) Communication System

In this paper, we present atmospheric effect on 5th Generation (5G) millimeter wave (MMWave) communication system. Atmospheric effects for Delhi (India) based 5G communication system is calculated as per Delhi atmospheric conditions. Atmospheric impairments are major cause of degrading mmWave signal power while mmWave propagation in wireless channel. Due to Atmospheric impairments attenuation takes place and major impairments are like water vapour, oxygen, rain and fog for Delhi (India). 5G mmWave attenuation calculations are performed for the mmWave band frequencies 28 GHz, 37 GHz and 39 GHz. In this paper intelligent adaptive transmitter based on trend of the atmospheric conditions tunes to machine learning (ML) based derivation of channel capacity. The ML based transmitter is a supervised ML device and it has provision of self teaching learning machine based on data. Results are graphed for the mentioned frequencies and also intelligently software defined (SD) Shannon channel capacity calculated for Delhi (India) based 5G mmWave communication system under different atmospheric conditions
Expert systems, fuzzy logic and rule-based control explained at last

New techniques are becoming available to control and manage manufacturing plant, including rule-based systems, knowledge-based systems, fuzzy logic, etc. This paper will describe briefly what the terms mean and how they interrelate. It is argued that expert, rule-based systems for control differ fundamentally from traditional control systems because they attempt to model the skills of the human operator, rather than modelling the process itself Expert systems and intelligent knowledge-based systems will be introduced, explaining how they may be used in industrial control, for other complex tasks. The problem of knowledge acquisition is discussed, again in the context of control, and the role of fuzzy logic is explained, as a means of expressing necessarily imprecise knowledge and coping with the problems of an incomplete knowledge-base. The self-organising controller is briefly introduced as an enhancement of rule-based control The scope of rule-based control and artificial intelligence in general is discussed briefly. Existing and possible applications of the techniques are indicated, together with what their limitations likely to be.
From Shakespeare to Twitter: What are Language Styles all about?

As natural language processing research is growing and largely driven by the availability of data, we expanded research from news and small-scale dialog corpora to web and social media. User-generated data and crowdsourcing opened the door for investigating human language of various styles with more statistical power and real-world applications. In this position/survey paper, I will review and discuss seven language styles that I believe to be important and interesting to study: influential work in the past, challenges at the present, and potential impact for the future.
Compensation of thermocouple reference junction temperature variation by a silicon temperature sensor

A new method for the reference junction temperature compensation of a K type thermocouple based thermometer by using a PTC silicon resistive sensor KTY 10 is described. The thermometer is intended for the temperature measurement in the range from 20/spl deg/C to 600/spl deg/C during plasma deposition and plasma nitriding processes, but it can be successfully used in other applications where the reference junction temperature is varied during the measurement.
Probabilistic, Information-Theoretic Models for Etymological Alignment

This thesis starts out by reviewing Bayesian reasoning and Bayesian network models. We present results related to discriminative learning of Bayesian network parameters. Along the way, we explicitly identify a number of problems arising in Bayesian model class selection. This leads us to information theory and, more specifically, the minimum description length (MDL) principle. We look at its theoretic foundations and practical implications. The MDL approach provides elegant solutions for the problem of model class selection and enables us to objectively compare any set of models, regardless of their parametric structure. Finally, we apply these methods to problems arising in computational etymology. We develop model families for the task of sound-by-sound alignment across kindred languages. Fed with linguistic data in the form of cognate sets, our methods provide information about the correspondence of sounds, as well as the history and ancestral structure of a language family. As a running example we take the family of Uralic languages. Computing Reviews (1998)
Fast community detection based on distance dynamics

The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale networks. To identify the community structure of a large-scale network with high speed and high quality, in this paper, we propose a fast community detection algorithm, the F-Attractor, which is based on the distance dynamics model. The main contributions of the F-Attractor are as follows. First, we propose the use of two prejudgment rules from two different perspectives: node and edge. Based on these two rules, we develop a strategy of internal edge prejudgment for predicting the internal edges of the network. Internal edge prejudgment can reduce the number of edges and their neighbors that participate in the distance dynamics model. Second, we introduce a triangle distance to further enhance the speed of the interaction process in the distance dynamics model. This triangle distance uses two known distances to measure a third distance without any extra computation. We combine the above techniques to improve the distance dynamics model and then describe the community detection process of the F-Attractor. The results of an extensive series of experiments demonstrate that the F-Attractor offers high-speed community detection and high partition quality.
Static rules of variable scoping in Erlang

Erlang/OTP is a functional programming environment designed for building concurrent and distributed fault-tolerant systems wit h soft real-time characteristics. The dynamic nature of this environment, which partly comes from concurrency and partly from dynamic language features, offers a great challange for a refactoring tool. Refactoring is a programming technique for improving the design of a program without changing its behaviour. Many refactorings are concerned with variables in some way. This paper presents variable scoping rules for Erlang that are more suitable for describing refactoring conditions and transformations than those given in the Erlang reference manual.
MASQUERADE: Searching the full text of abstracts using automatic indexing

An interactive, in-house document storage and retrieval system named MASQUERADE is described. It employs automatic indexing techniques with word stemming. Both Boolean and natural language queries are implemented. An IDF (Inverse Document Frequency) term weighting scheme is used to rank output. Relevance feedback is accomplished with the use of a document chosen by the user as the basis for a search. Optional display formats are available. The system is user friendly both from the input and output sides. Documents are stored using an inverted file scheme employing a quadratic hash algorithm. MASQUERADE is currently being used to support several data bases.
Imaging system of single pixel camera based on compressed sensing

A single pixel camera was used to shoot different scenes by taking a sparse binary matrix as the measurement matrix.The single pixel camera based on compressed sensing theory could acquire a high resolution image by using a Digital Micromirror Device(DMD) and a single detector.It captured and compressed the picture simultaneously,so that the data amount,scale,complexity and the cost of the system were reduced greatly.In this paper,the effect of measurement matrix upon the precise of reconstructed picture was analyzed,and the condition to reconstruct the binary matrix for object picture precisely was given.An experimental platform was established and imaging experiments under three different scenes were performed for different strokes of the words "China" and complex real objects using sparse binary measurement matrix.The picture with the picture resolution of as large as the size of the DMD was reconstructed by gradient projection algorithm.Experiment results indicate that single pixel camera could reconstruct the image precisely when the number of measurements are 20%~30% of the total number of pixels for the object image.It concludes that the single pixel camera can acquire high resolution images.
The method of determining the video quality

ビデオストリーミングプレーヤーに関するパラメータ、及び、データ伝送に関するパラメータの少なくとも一方から導かれた計測データを用いたアルゴリズムを利用した方法、及び装置を開示している。 モデルにおいて、入力として用いられるデータは、例えば、ＭＯＳスコアのようなマルチメディアシーケンスの品質と対応する値を生成するように構成される。
A study on non-invasive brainwave optimization

A study on non-invasive brainwave optimization provides a system capable of detecting emotional imbalances among users and attempt to help the user achieve emotional equilibrium via music playback. From this system, a study can be done on the relationship between EEG signals with emotions and music. Four emotions are chosen for this study namely engagement, excitement, meditation and frustration. By understanding this relationship, diagnosis of an emotional imbalance can be done and brainwave optimization can be implemented in the place of medication when treating emotional imbalances. This paper shows that emotions can be distinguished by examining the EEG waveform in the power density spectrum. The difference of EEG waveforms before and after optimization for the aforementioned emotions is shown to demonstrate the effect of music on emotions.
Salient regions detection using convolutional neural networks and color volume

Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.
Transmission of layered video coding using media aware FEC

Applications such as video conferencing systems are subject to low delay constraints. In the open internet, typically an unreliable connection based on the user datagram protocol (UDP) is used to avoid extra delay introduced by retransmission. Thus, the transmission of packets using UDP is affected by packet losses caused by congestion within the network. Forward error correction (FEC) can be used to cope with such packet losses. However, to keep the overall system delay small, the FEC source block must be kept very short. Providing high quality services for multipoint video conferencing is challenging due to the different device capabilities and available throughput at the different participants. In conventional systems, each participant is connected to a central network entity, which mixes video and audio signal and may also use transcoding to adapt the video to the need of each participant. To avoid complex and inefficient transcoding operations within the network, scalable video coding (SVC) offers a new way by encoding one video stream, which incorporates multiple video qualities respectively rates. Such a SVC stream can be used for rate or quality adaptation within the network by a media aware network element (MANE) by simply dropping packets. Layer-Aware FEC (LA-FEC) is a novel scheme for layered media such as SVC. LA-FEC generates the parity information across layers within the media stream. In such a way, that the protection of less important layers additionally protect more important layers. Using LA-FEC, the protection of the enhancement layer also protects the base layer which allows to reduce the required base layer protection to keep a certain IP packet error rate. Simulations were performed in a low delay communication system. Results show, that the combination of SVC and LA-FEC gives a significant higher video quality over a given link bitrate while keeping the overall service reliability constant.
Steady-state magnetic circuit analysis of salient-pole synchronous machines considering cross-magnetization

A simple magnetic circuit of salient-pole synchronous machines is presented. A method for calculating steady-state electrical performances using the circuit is described. Magnetic saturation including the cross-magnetizing phenomenon is considered in the method. The saturated reactance, field current and load angle are calculated easily from the method without much computational effort as compared with the finite element method. Despite the simplicity of the magnetic circuit, the calculated reactances at various load conditions using the method agree with the measured and FEM values.
Renewable Energy Sources Interconnection to the Grid at Distribution Level With Power-Quality Improvement Features

This paper presents a novel control strategy for achieving maximum benefits from the grid interfacing inverters when installed in 3-phase 4-wire distribution systems. The inverter is controlled to perform as a multi-function device by incorporating active power filter functionality. The inverter can thus be utilized as: 1) power converter to inject power generated from RES to the grid, and 2) shunt APF to compensate current unbalance, load current harmonics, load reactive power demand and load neutral current. All of these functions may be accomplished either individually or simultaneously. With such a control, the combination of gridinterfacing inverter and the 3-phase 4-wire linear/non-linear unbalanced load at point of common coupling appears as balanced linear load to the grid. This new control concept is demonstrated with extensive MATLAB/ Simulink simulation studies and validated through digital signal processor based laboratory experimental results.
Water Tank Level Control System using Self-Adaptive Fuzzy-PID Control

This paper demonstrates the performance of selfadaptive fuzzy-PID controller to control level of an automatic water level control system. The traditional PID controller cannot give satisfactory response to liquid level systems, because there exists time delay in this type of systems. Therefore, a self-adaptive fuzzy control is developed by combining the advantages of fuzzy and PID controller is applied to water level systems. In this paper, mathematical model for a first order tank system with valve lag, measurement lag and time delay are considered. For the water tank level control system (WTLCS), the measurements are carried out from process plant which is located at process dynamics and control laboratory, VIT University. The performance analysis of the self-adaptive fuzzy-PID controller and conventional PID controller has been implemented in MATLAB and Simulink for the first order WTLCS. The comparison of various time domain parameters is performed to prove that the self-adaptive fuzzy-PID control is superior to conventional controllers.
Discovery of Patterns and evaluation of Clustering Algorithms in SocialNetwork Data (Face book 100 Universities) through Data Mining Techniques and Methods

Data mining involves the use of advanced data analysis tools to find out new, suitable patterns and project the relationship among the patterns which were not known prior. In data mining, association rule learning is a trendy and familiar method for ascertaining new relations between variables in large databases. One of the emerging research areas under Data mining is Social Networks. The objective of this paper focuses on the formulation of association rules using which decisions can be made for future Endeavour. This research applies Apriori Algorithm which is one of the classical algorithms for deriving association rules. The Algorithm is applied to Face book 100 university dataset which has originated from Adam D’Angelo of Face book. It contains self-defined characteristics of a person including variables like residence, year, and major, second major, gender, school. This paper to begin with the research uses only ten Universities and highlights the formation of association rules between the attributes or variables and explores the association rule between a course and gender, and discovers the influence of gender in studying a course. This paper attempts to cover the main algorithms used for clustering, with a brief and simple description of each.The previous research with this dataset has applied only regression models and this is the first time to apply association rules.
CALM: Survivable Virtual Data Center Allocation in Cloud Networks

Cloud data centers have become a popular infrastructure to host diversified application services for tenants. To provide agility and elasticity in resource usage for cloud services, the virtual data center (VDC) is proposed to allocate both virtual machines (VM) and network bandwidth. However, at cloud scale, hardware (e.g., link, server, and switch) failures are inevitable, which may lead to degradation in service performance. To address this challenge, we study the survivable virtual data center allocation problem (SVAP), which aims at allocating survivable virtual data center (SVDC) to each tenant to guarantee resource demands will always be satisfied even after failures. Our objective is to minimize the total bandwidth consumption in order to accommodate more SVDCs. We prove that SVAP is NP-hard and design the Collocation-Aware survivable VM placement and Link Mapping algorithm (CALM). CALM solves the problem in two stages, i.e., VM placement (VMP) and virtual link mapping (VLM). We further find that without an appropriate VMP strategy, VLM cannot lead to the minimum network resource usage. Therefore, we propose a polynomial-time algorithm called collocation-aware survivable placement (CASP) for VMP. For the VLM stage, we formulate a linear programming model to map flows onto the data center network in order to ensure survivability under switch failures. We evaluate the performance via simulations and show that CALM could save up to 42 percent network resource compared to the baseline algorithm. We further show that CALM uses only additional 13 percent network resource to guarantee survivability as compared to a typical VDC strategy.
Localization of multiplex networks by the optimized single-layer rewiring

We study localization properties of principal eigenvector (PEV) of multiplex networks. Starting with a multiplex network corresponding to a delocalized PEV, we rewire the network edges using an optimization technique such that the PEV of the rewired multiplex network becomes more localized. The framework allows us to scrutinize structural and spectral properties of the networks at various localization points during the rewiring process. We show that by rewiring only one layer of the multiplex network, we can achieve the PEV localization of the entire system. Our investigations reveal the sensitivity of the PEV to a single edge rewiring in the optimized multiplex network structure corresponding to the most localized PEV providing the deeper insight into the localization behavior of the multiplex networks. Furthermore, by constructing multiplex network using real-world social and biological data, we show that our simulation results for model multiplex network are in good agreement with the properties of these real-world multiplex network. The study is relevant to applications that require understanding of how perturbation propagates in multiplex networks.
An extended Kalman filter-simultaneous localization and mapping method with Harris-scale-invariant feature transform feature recognition and laser mapping for humanoid robot navigation in unknown environment

This article proposes a novel method that combines real-time image recognition with choosing target object real-time localization in unknown environment. The position information of the object recognized as a target is provided by laser, which avoids collecting all the objects position information in the environment. This can save the computation resource and improve the real time. Furthermore, this article realizes the autonomous back and forth navigation in unknown environment and companies voice prompt and intelligent searching of the target object. This will set up a basis on building a complete environment map. Finally, the experiments show the validness of the proposed method and improve the intelligence of the environment information collection.
APPLICATIONS OF SWARM BASED INTELLIGENCE ALGORITHM IN PHARMACEUTICAL INDUSTRY: A REVIEW

A swarm is a large number of homogeneous, uncomplicated agents collaborating locally among themselves, and their circumstances, with no central control to allow a global interesting behaviour to emerge. Swarm based algorithms are nature inspired, population based algorithms that are clever in producing optimal solution for several complex and combinatorial problems. In the pharmaceutical industry, the product and other process development problems usually involve a numerous of independent variables and it incorporated with multiple constraints. To solve problems related to pharmaceutical industry requires accurate result and it’s very hard to solve using traditional method. Swarm based intelligence technique is a collection of knowledge based algorithms which having capable of solving complex problems and can obtain optimal or accurate solution. This paper discusses swarm based intelligent algorithms to solve different pharmaceutical field related complex problems such as drug design, pharmacovigilance, alignment of sequence etc.