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In the span of ten years have witnessed a revolutionary change in Information Technology and have also changed the way of working for various IT companies. This rapid rise in technology has offered a better design to explore the new teaching model. As a matter of fact, technology plays a vital role in English language & Teaching (ELT). Technology has enabled teachers to adapt to various classroom activities, hence enhancing or encouraging the English learning process. English is absolutely necessary for each and every educated person. It has now become a minimum criterion in the placement cell. Learning the English language is not a piece of cake, you really have to work hard for it. Information technology may assist as the real educational structure allowing learners to learn English in a totally different way. IT continues to grow as an important tool to help teachers to facilitate English learning for their learners. In this early stage of this century, the range of technologies available for use in English language learning and teaching has become very diverse. This paper is going to explore the necessity of Information Technology to English Language Teaching (ELT) and also tries to bring out the problems faced by using these technologies. This makes English teachers aware of the strategies to use it in an effective manner and with easy methodology.

The monitoring of bearing health is difficult due to the presence of background noise in the acquired vibration signal along with non-linear and non-stationary characteristics. Targeting these factors a new bearing fault diagnosis technique is proposed based on integration of variational mode decomposition (VMD) and multi entropy fusion approach (MEFA). This methodology is proposed to increase the efficiency of the fault classification of rolling bearings. In this approach, the acquired vibration signal is initially denoised using VMD approach. The entropy values are then calculated using MEFA comprising of approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), permutation entropy (PE) and dispersion entropy (DisEn). These values are used as feature vectors for the present study. K-Nearest Neighbors (KNN) and Random Forest (RF) are used as classifiers for classifying different types of faults. The proposed methodology is tested on the bearing dataset of an electro-mechanical system, and its accuracy results are compared with the integrated local mean decomposition (LMD) with MEFA. The results from the experimental investigation prove the efficacy of the proposed method.

In this paper, we build a speech privacy attack that exploits speech reverberations from a smartphone's inbuilt loudspeaker captured via a zero-permission motion sensor (accelerometer). We design our attack Spearphone, and demonstrate that speech reverberations from inbuilt loudspeakers, at an appropriate loudness, can impact the accelerometer, leaking sensitive information about the speech. In particular, we show that by exploiting the affected accelerometer readings and carefully selecting feature sets along with off-the-shelf machine learning techniques, Spearphone can perform gender classification (accuracy over 90%) and speaker identification (accuracy over 80%) for the audio/video playback on the smartphone for our recorded dataset. We use lightweight classifiers and an off-the-shelf machine learning tool so that the attacking effort is minimized, making our attack practical. Our results with testing the attack on a voice call and voice assistant response were also encouraging, showcasing the impact of the proposed attack. In addition, we perform speech recognition and speech reconstruction to extract more information about the eavesdropped speech to an extent. Our work brings to light a fundamental design vulnerability in many currently-deployed smartphones, which may put people's speech privacy at risk while using the smartphone in the loudspeaker mode during phone calls, media playback or voice assistant interactions.

Recent advances in speaker verification and speech processing technology have seen voice authentication being adopted on a wide scale in commercial applications like online banking and customer care support and on devices such as smartphones and IoT voice assistant systems. However, it has been shown that the current voice authentication systems can be ineffective against voice synthesis attacks that mimic a user's voice to high precision. In this work, we suggest a paradigm shift from the traditional voice authentication systems operating in the audio domain but susceptible to speech synthesis attacks (in the same audio domain). We leverage a motion sensor's capability to pick up phonatory vibrations, that can help to uniquely identify a user via voice signatures in the vibration domain. The user's speech is played/echoed back by a device's speaker for a short duration (hence our method is termed EchoVib) and the resulting non-linear phonatory vibrations are picked up by the motion sensor for speaker recognition. The uniqueness of the device's speaker and its accelerometer results in a device-specific fingerprint in response to the echoed speech. The use of the vibration domain and its non-linear relationship with audio allows EchoVib to resist the state-of-the-art voice synthesis attacks, shown to be successful in the audio domain. We develop an instance of EchoVib using the onboard loudspeaker and the accelerometer embedded in smartphones, as the authenticator, based on machine learning techniques. Our evaluation shows that even with the low-quality loudspeaker and the low-sampling rate of accelerometer recordings, EchoVib can identify users with an accuracy of over 90%. We also analyze our system against state-of-art-voice synthesis attacks and show that it can distinguish between the morphed and the original speaker's voice samples, correctly rejecting the morphed samples with a success rate of 85% for voice conversion and voice modeling attacks. We believe that using the vibration domain to detect synthesized speech attacks is effective due to the hardness of preserving the unique phonatory vibration signatures and is difficult to mimic due to the non-linear mapping of the unique speaker and accelerometer response in the vibration domain to the voice in the audio domain.

In this paper, we propose Voicefox1, a defense against the threat of automated voice synthesis attacks in machine-based and human-based speaker verification applications. Voicefox is based on a hitherto undiscovered potential of speech-to-text transcription, already built into these applications. Voicefox relies on the premise that while the synthesized samples might be falsely accepted by the speaker verification systems and human listeners, they cannot be transcribed as accurately as a natural human voice by transcribers. Voicefox is not a speaker verification system, but rather an independent module that can be integrated with any speaker verification system to enhance its security against voice synthesis attacks. To test our premise and as an essential pre-requisite for building Voicefox, we ran an extensive study that measures the accuracy of off-the-shelf speech-to-text techniques when confronted with the synthesized samples generated by the state-of-the-art speech synthesis techniques. Our results show that the transcription error rate for the synthesized voices is significantly higher, on average 2-3x, than the error rate for natural voices. This study quantitatively proves our hypothesis that human voices are transcribed more accurately than synthesized voices. We further propose several post-transcription rules in designing Voicefox, including acceptance of transcribed text even if up to a certain number of words are not transcribed correctly, and ignoring the words not available in the reference dictionary. Using these rules, Voicefox can effectively reduce the false rejection rates to as low as 1.20-4.69% depending on the application and the transcriber used, and reduce the false accept rates to 0% for dictionaries with phonetically-distinct words.

Cloud computing is a rising technical paradigm shift that replaces computational tasks from local devices to non-localized servers. Due to this, cloud computing is generating various provocations for cyber-forensics professionals. Conventional digital forensics expert works in an area in which system elements are within an individual’s natural extension and control frames are adequately determined. Due to cloud, loss of control and loss of security are widely known. Traditional organization's network infrastructure is local so it is easy to receive, store, or investigate. Less consistent cloud service model makes it more difficult due to a mixture of scientific concerns against cyber-forensics in the cloud.

The present Science and Technology is sufficient enough of saving mankind’s labor and time constructively. It was observed that it is too time-taking and tedious for getting detections associated with multiple diseases or disorders. Moreover, the expenses nowadays are touching skies. Hence, using the concepts of Data Science & Machine Learning a model is been developed and deployed which can predict the occurrence of chronic heart disease or disorders based on the user entered values anytime, anywhere. The heart being one of the important organs of the human body should be kept at check on regular intervals and hence for this, a classifier is built that can predict if the patient or the person has any heart diseases or disorders or not. The target here in this project was to capture multiple patterns from different patients for any heart disorders. Further, the model is built using technologies like Numpy, Pandas, Matplotlib & Seaborn, Scikit Learn, and Scipy. Also, the model is deployed using the web services of Python which is Django.

It is a practice that users or customers intend to share their comments or reviews about any product in different social networking sites. An analyst usually processes to reviews properly to obtain any meaningful information from it. Classification of sentiments associated with reviews is one of these processing steps. The reviews framed are often made in text format. While processing the text reviews, each word of the review is considered as a feature. Thus, selection of right kind of features needs to be carried out to select the best feature from the set of all features. In this paper, the machine learning algorithm, i.e., support vector machine, is used to select the best features from the training data. These features are then given input to artificial neural network method, to process further. Different performance evaluation parameters such as precision, recall, f-measure, accuracy have been considered to evaluate the performance of the proposed approach on two different datasets, i.e., IMDb dataset and polarity dataset.

The MetaCoq project aims to provide a certified meta-programming environment in Coq. It builds on Template-Coq, a plugin for Coq originally implemented by Malecha (Extensible proof engineering in intensional type theory, Harvard University, http://gmalecha.github.io/publication/2015/02/01/extensible-proof-engineering-in-intensional-type-theory.html , 2014), which provided a reifier for Coq terms and global declarations, as represented in the Coq kernel, as well as a denotation command. Recently, it was used in the CertiCoq certified compiler project (Anand et al., in: CoqPL, Paris, France, http://conf.researchr.org/event/CoqPL-2017/main-certicoq-a-verified-compiler-for-coq , 2017), as its front-end language, to derive parametricity properties (Anand and Morrisett, in: CoqPL’18, Los Angeles, CA, USA, 2018). However, the syntax lacked semantics, be it typing semantics or operational semantics, which should reflect, as formal specifications in Coq, the semantics of Coq ’s type theory itself. The tool was also rather bare bones, providing only rudimentary quoting and unquoting commands. We generalize it to handle the entire polymorphic calculus of cumulative inductive constructions, as implemented by Coq, including the kernel’s declaration structures for definitions and inductives, and implement a monad for general manipulation of Coq ’s logical environment. We demonstrate how this setup allows Coq users to define many kinds of general purpose plugins, whose correctness can be readily proved in the system itself, and that can be run efficiently after extraction. We give a few examples of implemented plugins, including a parametricity translation and a certified extraction to call-by-value $$\lambda $$ -calculus. We also advocate the use of MetaCoq as a foundation for higher-level tools.

Machine learning(ML) is a standout in the essential area for IT Industries. Even though there are a few several broad areas where ML can be actualized, the accompanying paper is about the execution ML for financial data analysis. The design system helps commercial market share price prediction based on the existing dataset after that machine learning methods applied for high-level prediction of share prices. Comprehends the idea and join its usage with the ML model for shares price prediction. The paper likewise covers the planned framework architecture with its advantages and disadvantages pursued by future work.

Voice access technologies are widely adopted in mobile devices and voice assistant systems as a convenient way of user interaction. Recent studies have demonstrated a potentially serious vulnerability of the existing voice interfaces on these systems to "hidden voice commands". This attack uses synthetically rendered adversarial sounds embedded within a voice command to trick the speech recognition process into executing malicious commands, without being noticed by legitimate users. In this paper, we employ low-cost motion sensors, in a novel way, to detect these hidden voice commands. In particular, our proposed system extracts and examines the unique audio signatures of the issued voice commands in the vibration domain. We show that such signatures of normal commands vs. synthetic hidden voice commands are distinctive, leading to the detection of the attacks. The proposed system, which benefits from a speaker-motion sensor setup, can be easily deployed on smartphones by reusing existing on-board motion sensors or utilizing a cloud service that provides the relevant setup environment. The system is based on the premise that while the crafted audio features of the hidden voice commands may fool an authentication system in the audio domain, their unique audio-induced surface vibrations captured by the motion sensor are hard to forge. Our proposed system creates a harder challenge for the attacker as now it has to forge the acoustic features in both the audio and vibration domains, simultaneously. We extract the time and frequency domain statistical features, and the acoustic features (e.g., chroma vectors and MFCCs) from the motion sensor data and use learning-based methods for uniquely determining both normal commands and hidden voice commands. The results show that our system can detect hidden voice commands vs. normal commands with 99.9% accuracy by simply using the low-cost motion sensors that have very low sampling frequencies.

This paper explores the effectiveness of common sound masking solutions deployed for preserving speech privacy in workplace environment such as hospitals, financial institutions, lawyers offices, nursing homes and government buildings. With the increased awareness about personal privacy among the general population, we set out to examine the effectiveness of current speech privacy preserving tools. We seek to determine if the general approach used by the current masking mechanisms is adequate to provide the level of privacy desired from these solutions. In addition, we also seek to investigate preservation of speech privacy in the face of ubiquitous and less conspicuous devices like smartphones that possess the capability of sound recording with inbuilt noise cancellation technology. Our approach in this paper is to expose the vulnerability in sound masking technology in scenarios that require preserving privacy in personal spaces. We use human listeners to attack speech privacy under sound masking where we aim to identify spoken words eavesdropped under different scenarios. We also test currently available speech recognition tools to assess their performance at decoding speech in noisy environment. Our results indicate that pink noise, the commonly used technology to provide speech privacy for use in personal space, is ineffective against a dedicated eavesdropping adversary that uses commonplace devices such as smartphones to record the speech and noise reduction tools to counteract sound masking.

Information Retrieval is a process through which we obtain and present related information from a very large collection of information resources according to user’s query. Information Retrieval can vary according to user’s wish, it can be an information retrieval for documents, music, pictures, etc.In a classic search model we have a user who has to fulfil a task. And for that to happen that user needs some information and to search the information needed user should make a query based on the information. So that query will act as an input in a search box, and on the basis of that query our information will be retrieved.But it is required that no misconception happens to user which may lead user to believe that the information needed can be wrong. And if that happens it may lead to improper formation of the query, meaning the query formed on the basis of information can be wrong thus resulting in a wrong result through information retrieval. So it is important for us to pay proper attention to every step that is going to take place during information retrieval. This paper presents a basic IR model and describes how an information retrieval is done following user’s query. This paper also addresses how Information Retrieval and Data Retrieval are two different things and are often confused to be one.

Low-cost sensors (LCS) for air-quality monitoring have shown huge potential in enhancing spatial and temporal resolutions at a lower cost, greater flexibility in use and with less maintenance than fixed-air quality monitoring stations (AQMS). There are numerous reports showing sensor-based systems perform well under laboratory conditions. However, the accuracy of LCS operated under field conditions has been reported to deviate from their use in these controlled environments. Previous studies have employed various mathematical techniques to improve system performance. These include classic multivariate regression model (MLR) to incorporate ambient factors (e.g. temperature & relative humidity) and machine learning (ML) models for integrating complex non-linear sensor responses for gaseous pollutant concentration predictions. However, routine field calibrations may cause data inconsistency during measurement periods. This study first illustrates the limitations of short-term routine calibration by comparing performances of existing MLR and ML models. Further, based on a long-term evaluation period, a newly principle-based method named Temperature Look-Up (TLU) model was built and compared with the existing MLR and ML models. Measurements were taken for 8 months (August 2017 to March 2018) in SanMenXia (SMX) city, China, using 8 sets of sensor systems (Mini Air Stations, MAS). One MAS was co-located with AQMS to provide data to assess the principle, whereas the other 7 MAS were used for verification. The TLU model showed the best performance for long term validation (4 months) and high model inner coherence (R2 > 0.91). Meanwhile, the TLU model also shows the fast convergence (40 days) to reach stable prediction performance, and spatial representativeness in the sensor network. Recommendations on sensor network deployment and strategy for data maintenance based on our observations are discussed.

Producers of machined components and manufactured goods are continually challenged to reduce cost, improve quality, and minimize setup times in order to remain competitive. Frequently the answer is found with new technology solutions. In the recent years, there has been increasing interest in hard turning over grinding for machining of hardened steels in automotive, bearing, mold-die making industries. Hard turning is greatly affected by factors like machine tool, cutting tool geometry and materials, cutting parameters, and cooling methods. There are some issues in the process which should be understood and dealt with such as friction and heat generation at the cutting area that can affect the tool life and surface finish apart from other machining results to achieve successful performance. Researchers have worked upon several aspects related to hard turning and came up with their own recommendations to overcome these problems. This article presents an overview on all the factors that influences hard turning operations performance and is an attempt to give a proper understanding of the process. A summary of the hard turning techniques is outlined and further a comparison of hard turning and grinding is discussed with regard to certain evaluation criteria based on process economical efficiency.

Cloud computing is one of the highly debated topics in today’s IT as well as academic research domain. The balance between performance and security has always been a vital issue when adapting cloud-based services. Task scheduling is a major research area of cloud computing, where efficient algorithms are devised for service providers to allocate a task to a particular virtual machine, thereby enhancing the performance as well as execution time of the tasks. Though QoS has been kept in mind while designing such schemes, the aspect of security has been majorly ignored in the existing works. Here a secured task scheduling methodology has been devised by using the test environment of CloudSim 3.0.3.

International audience; Template-Coq is a plugin for Coq, originally implemented by Malecha, which provides a reifier for Coq terms and global declarations , as represented in the Coq kernel, as well as a denotation command. Initially, it was developed for the purpose of writing functions on Coq's AST in Gallina. Recently, it was used in the CertiCoq certified compiler project, as its front-end language, to derive parametricity properties, and to extract Coq terms to a CBV λ-calculus. However, the syntax lacked semantics, be it typing semantics or operational semantics, which should reflect, as formal specifications in Coq, the semantics of Coq's type theory itself. The tool was also rather bare bones, providing only rudimentary quoting and unquoting commands. We generalize it to handle the entire Calculus of Inductive Constructions (CIC), as implemented by Coq, including the kernel's declaration structures for definitions and inductives, and implement a monad for general manipulation of Coq's logical environment. We demonstrate how this setup allows Coq users to define many kinds of general purpose plugins, whose correctness can be readily proved in the system itself, and that can be run efficiently after extraction. We give a few examples of implemented plugins, including a parametricity translation. We also advocate the use of Template-Coq as a foundation for higher-level tools.

According to recent research, motion sensors available on current smartphone platforms may be sensitive to speech signals. From a security and privacy perspective, this raises a serious concern regarding sensitive speech reconstruction, and speaker or gender identification by a malicious application having unrestricted access to motion sensor readings, without using the microphone. In this paper, we revisit this important line of research and closely inspect the effect of speech on smartphone motion sensors, in particular, gyroscope and accelerometer. First, we revisit the previously studied scenario (Michalevsky et al.; USENIX Security 2014), where the smartphone shares a common surface with a loudspeaker (with subwoofer) generating speech signals. We observe some effect on the motion sensor signals, which may indeed allow speaker and gender recognition to an extent. However, we also argue that the recorded effect on the sensor readings is possibly from conductive vibrations through the shared surface instead of direct acoustic vibrations due to speech as perceived in previous work. Second, we further extend the previous work by analyzing the effect of speech produced by (1) other less powerful speakers like the in-built laptop and smartphone speakers, and (2) live humans. Our experiments show that in-built laptop speakers were only able to affect the accelerometer when the laptop and the motion sensor shared a surface. Smartphone speakers were not found to be powerful enough to invoke a response in the motion sensors through aerial vibrations. We also report that in the presence of live human speech, we did not notice any effect on the motion sensor readings. Our results have two-fold implications. First, human-rendered speech seems potentially incapacitated to trigger smartphone motion sensors within the limited sampling rates imposed by the smartphone operating systems. Second, it seems that even machine-rendered speech may not be powerful enough to affect smartphone motion sensors through the aerial medium, although it may induce vibrations through a conductive surface that these sensors, especially accelerometer, could pick up if a relatively powerful speaker is used. Overall, our results suggest that smartphone motion sensors may pose a threat to speech privacy only in some limited scenarios.

Keyboard acoustic side channel attacks to date have been mostly studied in the context of an adversary eavesdropping on keystrokes by placing a listening device near the intended victim creating a local eavesdropping scenario. However, being in close physical proximity of the victim significantly limits the applicability of the attack. In this paper, we study the keyboard acoustic side channel attacks in remote attack settings and propose countermeasures in such attack settings. Specifically, we introduce an offense-defense system that: (1) highlights the threat of a remote adversary eavesdropping on keystrokes while the victim is on a VoIP call, and (2) builds a way to mask the leakage through the use of system-generated sounds. On the offensive side, we show the feasibility of existing acoustic side channel attacks adapted to a remote eavesdropper setting against sensitive input such as random passwords, PINs etc. On the defensive side, we demonstrate a software-based approach towards masking the keystroke emanations as a defense mechanism against such attacks and evaluate its effectiveness. In particular, we study the use of white noise and fake keystrokes as masking sounds and show the latter to be an effective means to cloak such side channel attacks. Finally, we discuss a novel way of masking by virtually inserting the masking signal in remote voice calls without distracting the user.

In this work, we develop a platform that would consider people's activities in social media, and display results. This is done by grouping together like-minded people in a series of interactive data visualizations that will allow trends to be found. This work shows how the sentiment and mindset of people varies with location i.e. which areas in a country and in the entire world have people with similar thought process. There are many a times when this would be extremely useful, or provide interesting insights. For example analyzing the success of a marketing campaign, predicting the result of an election and psychological impact in one's life apart from market research. The analysis provided in results gives changing mindset and clear picture through a small window.

An interesting approach to pairing devices involves the use of a vibrational channel, over which the keying material (e.g., a short PIN) is sent. This approach is efficient (only a unidirectional transfer of PIN is needed) and simple (the sending device requires a vibration motor and receiving device requires an accelerometer). However, it has been shown to be susceptible to acoustic emanations usually produced by the vibration motor. Recent research introduced a mechanism to defeat these attacks by attempting to mask the acoustic leakage with deliberate acoustic noises. In this paper, we pursue a systematic investigation of the security of such a "noisy vibrational pairing" mechanism in a strong yet realistic adversarial model where the eavesdropper is co-located with the victim device(s).Our contributions are two-fold. First, we show that existing noisy vibrational pairing mechanisms - based on white noise as the masking signal - are vulnerable against a co-located eavesdropper (although they may defeat a distant eavesdropper). We build our attack based on standard signal processing and noise filtering techniques, and show that it can result in a complete compromise of pairing security. Second, we propose a defense that bolsters the masking signal with low-frequency audio tones. We present and address the challenges associated with producing such low-frequency sounds with current commodity hardware. We show that our defensive approach can not only resist our above attack but is also robust to more sophisticated, noise filtering and source separation methods when applicable. We also establish that the insertion of low-frequency sounds does not affect the receiving device's capability to sense the vibrations generated by the sending device. The suggested defense may therefore be used to enhance the security of noisy vibrational pairing without affecting its performance on a wide variety of devices.

Deauthentication is an important component of any computing system that promises to offer legitimate access to restricted services residing on the system. As computing devices are ubiquitous, it has underscored the need to design zero-effort deauthentication systems from a usability perspective. While the design of such deauthentication systems is geared towards making them more usable, often the security implication of these deigns overlook the physical security of the system resulting in various side channel vulnerabilities in the system. This issue highlights the need to design a defense mechanism that is capable of minimizing the threat posed by such side channel attacks while having minimal impact on the design of the system.In this paper, we aim to address the sound-based vulnerability recently introduced in the literature, against one of the prominent zero-effort deauthentication schemes, called ZEBRA, that transparently and continuously authenticates the user using a wearable device wirelessly connected with the authentication terminal. To this end, we propose YELP, a novel and practical defense mechanism based on the principle of sound masking. YELP uses two different types of masking sounds, namely "white noise", and "music" for cloaking the acoustic side channel leakage underlying the ZEBRA system. We believe that the use of such masking sounds at a reasonable volume level can hide the acoustic leakage emanating from the physical component of the system, and thereby reduce, if not eliminate, the imposed sound-based vulnerability. Indeed, our results show that white noise, as a masking sound, can effectively hide the acoustic leakage from ZEBRA system, thereby significantly reducing the attack success rate of an audio-based side channel attacker while music can moderately hide the acoustic leakage from the system. Our work therefore shows that sound masking can be used as an effective tool in improving the security of (de)authentication systems against an audio-based side channel attack without affecting its original design and without requiring additional effort from the user.

Keyboard acoustic side channel attacks have been shown to utilize the audio leakage from typing on the keyboard to infer the typed words up to a certain degree of accuracy. Researchers have continued to improve upon the accuracy of such attacks by employing different techniques and attack vectors such as feature extraction and classification, keyboard geometry and triangulation.

Pairing between wireless devices may be secured by the use of an auxiliary channel such as audio, visuals or vibrations. A simple approach to pairing involves one of the devices initiating the transmission of a key, or keying material like a short password, over the auxiliary channel to the other device. A successful pairing is achieved when the receiving device is able to decode the key without any errors while the attacker is unable to eavesdrop the key.In this paper, we focus on the security of the vibration channel when used for the key transmission. As shown in some recent work, sending the keying material over a clear vibrational channel poses a significant risk of an acoustic side channel attack. Specifically, an adversary can listen onto the acoustic sounds generated by the vibration motor of the sending device and infer the keying material with a high accuracy. To counteract this threat, we propose a novel pairing scheme, called Vibreaker (a ``Vibrating speaker''), that involves active injection of acoustic noise in order to mask the key signal. In this scheme, the sending device artificially injects noise in the otherwise clear audio channel while transmitting the keying material via vibrations. We experiment with several choices for the noise signal and demonstrate that the security of the audio channel is significantly enhanced with Vibreaker when appropriate noise is used. The scheme requires no additional effort by the user, and imposes minimum hardware requirement and hence can be applied to many different contexts, such as pairing of IoT and implanted devices, wearables and other commodity gadgets.

RGB-D cameras, which give an RGB image together with depths, are becoming increasingly popular for robotic perception. In this paper, we address the task of detecting commonly found objects in the three-dimensional (3D) point cloud of indoor scenes obtained from such cameras. Our method uses a graphical model that captures various features and contextual relations, including the local visual appearance and shape cues, object co-occurrence relationships and geometric relationships. With a large number of object classes and relations, the model’s parsimony becomes important and we address that by using multiple types of edge potentials. We train the model using a maximum-margin learning approach. In our experiments concerning a total of 52 3D scenes of homes and offices (composed from about 550 views), we get a performance of 84.06% and 73.38% in labeling office and home scenes respectively for 17 object classes each. We also present a method for a robot to search for an object using the learned model and the contextual information available from the current labelings of the scene. We applied this algorithm successfully on a mobile robot for the task of finding 12 object classes in 10 different offices and achieved a precision of 97.56% with 78.43% recall.1

In the race towards the pinnacle of evolution and development, few things have come out on top and become the crucial weapon or path towards success. Information is one such thing. With the increased importance & plausible struggles over the accumulation of information to secure dominance & success, the concept of Information Security has emerged. Information Security is the process of protecting the information from unauthorized access and/or damage or misuse. Cryptography techniques are used for the purpose of making the exchange of information secure and confidential. It converts the sensitive information into a form which can't be understood by the unintended individuals. In this paper, we have tried to come up with a new algorithm which can be put to use for cryptographic purposes. The proposed algorithm is simple yet robust and will prove hard to crack unless one is not familiar with its inner workings.

Knowing the physical location of files in a cloud system is of a great importance for any user, as is it can affect the whole service drastically. However, pinpointing the exact coordinates for the location of a server is very challenging. Providers prefer not to share the location of their data centers with public for security reasons, and this fact also adds to the complexity of this concept. Researchers have recently developed delay based schemes for cloud data geolocation, some of which use proprietary landmarks for location verification. Unfortunately, such landmark-based schemes are often impractical due to high cost and latency. In this paper, we have developed a practical scheme for landmark allocation in cloud data geolocation. We augment existing approaches with a new landmark allocation modification to get the same or often better accuracy, while decreasing the cost considerably. Our approach improves the existing state of the art by introducing the concept of publicly distributed landmarks for all delay based geolocation techniques.

An Ultra wide Band pass filter which operates from 3.45–10.35 GHz is designed and presented. The proposed filter consists of short circuited shunt stubs and circular shaped defected ground structures (CDGS).The CDGS acts as a low pass filter which rejects higher order frequencies whereas the short circuited shunt stubs act as a high pass filter. CDGS also suppresses the harmonic and improves the frequency response characteristics of the filter.

This paper presents a formalization of Nuprl’s metatheory in Coq. It includes a nominal-style definition of the Nuprl language, its reduction rules, a coinductive computational equivalence, and a Curry-style type system where a type is defined as a Partial Equivalence Relation (PER) a la Allen. This type system includes Martin-Lof dependent types, a hierarchy of universes, inductive types and partial types. We then prove that the typehood rules of Nuprl are valid w.r.t. this PER semantics and hence reduce Nuprl’s consistency to Coq’s consistency.

In the last decade or so the problem of Time Series Classification (TSC) has attained considerable research interests in both the Machine learning and Datamining community. In a TSC problem any real valued data is considered as a time series and the challenge is to find the best discriminating features based on the ordering of variables. Recently, the research interest has shifted to Time Series Shapelets which are small local patterns in a time series that are highly predictive of a class and are thus very useful features for building classifiers. Most of the work in time-series shapelet till date makes use of a supervised learning approach. One of the major drawbacks with the supervised shapelet approach is that they consider global features of the time series dataset to compute a shapelet which in turn leads to more time and space complexity issues. We believe that instead of taking global features of a time series data only the local features that are of interest to the problem at hand should be considered. How to find these local features from a given time series data set and to get the natural shaplet by an unsupervised approach is an interesting problem as such and in this paper we aim to propose solutions to this end. Here we introduce a new approach for time series, called Time Series Qlet, a novel approach for data mining, which addresses the above limitations. Qlets are quantized continuous subsequences of data obtained after a natural break (chunking) of quantized time series data set. Moreover we focus only on local features of a data-set ignoring the global features.

This paper extends the proof methods used by the Nuprl proof assistant to reason about the computational behavior of its untyped programs. We have implemented new methods to prove non-trivial bisimulations between programs and have successfully applied these methods to formally optimize distributed programs such as our synthesized and verified version of Paxos, a widely used protocol to achieve software based replication. We prove new results about the basic computational equality relation on terms, and we extend the theory of partial types as the basis for stating internal results about the computation system that were previously treated only in the meta theory of Nuprl. All the lemmas presented in this paper have been formally proved in Nuprl.