nils-herrmann/clean_scirepeval huggingface dataset:
INNOVATIVE APPROACH TO IMPROVE HYBRID CRYPTOGRAPHY BY USINGDNA STEGANOGRAPHY

There exists a big demand for innovative secure electronic communications while the expertise level of attackers increases rapidly and that causes even bigger demands and needs for an extreme secure connection. An ideal security protocol should always be protecting the security of connections in many aspects, and leaves no trapdoor for the attackers. Nowadays, one of the popular cryptography protocols is hybrid cryptosystem that uses private and public key cryptography to change secret message. In available cryptography protocol attackers are always aware of transmission of sensitive data. Even non-interested attackers can get interested to break the ciphertext out of curiosity and challenge, when suddenly catches some scrambled data over the network. First of all, we try to explain the roles of innovative approaches in cryptography. After that we discuss about the disadvantages of public key cryptography to exchange secret key. Furthermore, DNA steganography is explained as an innovative paradigm to diminish the usage of public cryptography to exchange session key. In this protocol, session key between a sender and receiver is hidden by novel DNA data hiding technique. Consequently, the attackers are not aware of transmission of session key through unsecure channel. Finally, the strength point of the DNA steganography is discussed.
Human and Fire Detection from High Altitude UAV Images

Illegal migration as well as wildfires constitute commonplace situations in southern European countries, where the mountainous terrain and thick forests make the surveillance and location of these incidents a tall task. This territory could benefit from Unmanned Aerial Vehicles (UAVs) equipped with optical and thermal sensors in conjunction with sophisticated image processing and computer vision algorithms, in order to detect suspicious activity or prevent the spreading of a fire. Taking into account that the flight height is about to two kilometers, human and fire detection algorithms are mainly based on blob detection. For both processes thermal imaging is used in order to improve the accuracy of the algorithms, while in the case of human recognition information like movement patterns as well as shadow size and shape are also considered. For fire detection a blob detector is utilized in conjunction with a color based descriptor, applied to thermal and optical images, respectively. Unlike fire, human detection is a more demanding process resulting in a more sophisticated and complex algorithm. The main difficulty of human detection originates from the high flight altitude. In images taken from high altitude where the ground sample distance is not small enough, people appear as small blobs occupying few pixels, leading corresponding research works to be based on blob detectors to detect humans. Their shadows as well as motion detection and object tracking can then be used to determine whether these regions of interest do depict humans. This work follows this motif as well, nevertheless, its main novelty lies in the fact that the human detection process is adapted for high altitude and vertical shooting images in contrast with the majority of other similar works where lower altitudes and different shooting angles are considered. Additionally, in the interest of making our algorithms as fast as possible in order for them to be used in real time during the UAV flights, parallel image processing with the help of a specialized hardware device based on Field Programmable Gate Array (FPGA) is being worked on.
Human-supervised control of the ATLAS humanoid robot for traversing doors

Door traversal is generally a trivial task for human beings but particularly challenging for humanoid robots. This paper describes a holistic approach for a full-sized humanoid robot to traverse through a door in an outdoor semi-structured environment as specified by the requirements of the DARPA Robotics Challenge. Door traversal can be broken down into four sub-tasks; door detection, walk to the door, door opening, and walk through the door. These topics are covered in detail along with the approaches used for perception and motion planning. Results from lab tests and the DRC Finals are shown. The approach presented in this paper can be further extended to a wide range of door types and configurations.
Robust performance degradation assessment methods for enhanced rolling element bearing prognostics

Bearing failure is one of the foremost causes of breakdowns in rotating machinery and such failure can be catastrophic, resulting in costly downtime. One of the key issues in bearing prognostics is to detect the defect at its incipient stage and alert the operator before it develops into a catastrophic failure. Signal de-noising and extraction of the weak signature are crucial to bearing prognostics since the inherent deficiency of the measuring mechanism often introduces a great amount of noise to the signal. In addition, the signature of a defective bearing is spread across a wide frequency band and hence can easily become masked by noise and low frequency effects. As a result, robust methods are needed to provide more evident information for bearing performance assessment and prognostics. This paper introduces enhanced and robust prognostic methods for rolling element bearing including a wavelet filter based method for weak signature enhancement for fault identification and Self Organizing Map (SOM) based method for performance degradation assessment. The experimental results demonstrate that the bearing defects can be detected at an early stage of development when both optimal wavelet filter and SOM method are used. q 2004
Improved preprocessing, labeling and scaling algorithms for the Weight-Constrained Shortest Path Problem

Much has been written on shortest path problems with weight, or resource, constraints. However, relatively little of it has provided systematic computational comparisons for a representative selection of algorithms. Furthermore, there has been almost no work showing numerical performance of scaling algorithms, although worst-case complexity guarantees for these are well known, nor has the effectiveness of simple preprocessing techniques been fully demonstrated. Here, we provide a computational comparison of three scaling techniques and a standard label-setting method. We also describe preprocessing techniques which take full advantage of cost and upper-bound information that can be obtained from simple shortest path information. We show that integrating information obtained in preprocessing within the label-setting method can lead to very substantial improvements in both memory required and run time, in some cases, by orders of magnitude. Finally, we show how the performance of the label-setting method can be further improved by making use of all Lagrange multiplier information collected in a Lagrangean relaxation first step.
Exploring the ambient assisted living domain: a systematic review

Ambient assisted living (AAL) is focused on providing assistance to people primarily in their natural environment. Over the past decade, the AAL domain has evolved at a fast pace in various directions. The stakeholders of AAL are not only limited to patients, but also include their relatives, social services, health workers, and care agencies. In fact, AAL aims at increasing the life quality of patients, their relatives and the health care providers with a holistic approach. This paper aims at providing a comprehensive overview of the AAL domain, presenting a systematic analysis of over 10 years of relevant literature focusing on the stakeholders’ needs, bridging the gap of existing reviews which focused on technologies. The findings of this review clearly show that until now the AAL domain neglects the view of the entire AAL ecosystem. Furthermore, the proposed solutions seem to be tailored more on the basis of the available existing technologies, rather than supporting the various stakeholders’ needs. Another major lack that this review is pointing out is a missing adequate evaluation of the various solutions. Finally, it seems that, as the domain of AAL is pretty new, it is still in its incubation phase. Thus, this review calls for moving the AAL domain to a more mature phase with respect to the research approaches.
Physics-based ball tracking and 3D trajectory reconstruction with applications to shooting location estimation in basketball video

The demand for computer-assisted game study in sports is growing dramatically. This paper presents a practical video analysis system to facilitate semantic content understanding. A physics-based algorithm is designed for ball tracking and 3D trajectory reconstruction in basketball videos and shooting location statistics can be obtained. The 2D-to-3D inference is intrinsically a challenging problem due to the loss of 3D information in projection to 2D frames. One significant contribution of the proposed system lies in the integrated scheme incorporating domain knowledge and physical characteristics of ball motion into object tracking to overcome the problem of 2D-to-3D inference. With the 2D trajectory extracted and the camera parameters calibrated, physical characteristics of ball motion are involved to reconstruct the 3D trajectories and estimate the shooting locations. Our experiments on broadcast basketball videos show promising results. We believe the proposed system will greatly assist intelligence collection and statistics analysis in basketball games.
A Virtual World Installation in an Art Exhibition : Providing a Shared Interaction Space for Local and Remote Visitors

Visiting museums and exhibitions represented in multi-user 3D environments can be an efficient way of learning about the exhibits in an interactive manner and socialising with other visitors. The rich educational information presented in the virtual environment and the presence of remote users could also be beneficial for the visitors of the physical exhibition space. In this paper we present the design and implementation of a virtual exhibition that allowed local and remote visitors coexist in the environment, access the interactive content and communicate with each other. The virtual exhibition was accessible to the remote users from the Web and to local visitors through an installation in the physical space. The installation projected the virtual world in the exhibition environment and let users interact with it using a handheld gesture-based device. We performed an evaluation of the 3D environment with the participation of both local and remote visitors. The evaluation results indicate that the virtual world was considered exciting and easy to use by the majority of the participants. Furthermore, according to the evaluation results, virtual museums and exhibitions seem to have significant advantages for remote visitors compared to typical museum web sites, and they can also be an important aid to local visitors and enhance their experience.
Narrative event adaptation in virtual environments

There is a tension between user and author control of narratives in multimedia systems and virtual environments. Reducing the interactivity gives the author more control over when and how users experience key events in a narrative, but may lead to less immersion and engagement. Allowing the user to freely explore the virtual space introduces the risk that important narrative events will never be experienced. One approach to striking a balance between user freedom and author control is adaptation of narrative event presentation (i.e. changing the time, location, or method of presentation of a particular event in order to better communicate with the user). In this paper, we describe the architecture of a system capable of dynamically supporting narrative event adaptation. We also report results from two studies comparing adapted narrative presentation with two other forms of unadapted presentation - events with author selected views (movie), and events with user selected views (traditional VE). An analysis of user performance and feedback offers support for the hypothesis that adaptation can improve comprehension of narrative events in virtual environments while maintaining a sense of user control.
Beam Selection in mm-Wave Multiuser MIMO Systems Using Compressive Sensing

In this paper, we study beam selection for millimeter-wave (mm-wave) multiuser multiple input multiple output (MIMO) systems where a base station (BS) and users are equipped with antenna arrays. Exploiting a certain sparsity of mm-wave channels, a low-complexity beam selection method for beamforming by low-cost analog beamformers is derived. It is shown that beam selection can be carried out without explicit channel estimation using the notion of compressive sensing (CS). Due to various reasons (e.g., the background noise and interference), some users may choose the same BS beam, which results in high inter-user interference. To overcome this problem, we further consider BS beam selection by users. Through simulations, we show that the performance gap between the proposed approach and the optimal beamforming approach, which requires full channel state information (CSI), becomes narrower for a larger number of users at a moderate/low signal-to-noise ratio (SNR). Since the optimal beamforming approach is difficult to be used due to prohibitively high computational complexity for large antenna arrays with a large number of users, the proposed approach becomes attractive for BSs and users in mm-wave systems where large antenna arrays can be employed.
Hindi Word Sense Disambiguation

Department of Computer Science and Engineering Indian Institute of Technology Bombay, Mumbai India {manish, mahesh, pb,pandey,yupu}@cse.iitb.ac.in Abstract Word Sense Disambiguation (WSD) is defined as the task of finding the correct sense of a word in a specific context. This is crucial for applications like Machine Translation and Information Extraction. While the work on automatic WSD for English is voluminous, to our knowledge, this is the first attempt for an Indian language at automatic WSD. We make use of the Wordnet for Hindi developed at IIT Bombay, which is a highly important lexical knowledge base for Hindi. The main idea is to compare the context of the word in a sentence with the contexts constructed from the Wordnet and chooses the winner. The output of the system is a particular synset number designating the sense of the word. The mentioned Wordnet contexts are built from the semantic relations and glosses, using the Application Programming Interface created around the lexical data. The evaluation has been done on the Hindi corpora provided by the Central Institute of Indian Languages and the results are encouraging. Currently the system disambiguates nouns. Work is on for other parts of speech too.
Predicting and Retrospective Analysis of Soccer Matches in a League

A common discussion subject for the male part of the population in particular, is the prediction of next weekend’s soccer matches, especially for the local team. Knowledge of offensive and defensive skills is valuable in the decision process before making a bet at a bookmaker. In this article we take an applied statistician’s approach to the problem, suggesting a Bayesian dynamic generalised linear model to estimate the time dependent skills of all teams in a league, and to predict next weekend’s soccer matches. The problem is more intricate than it may appear at first glance, as we need to estimate the skills of all teams simultaneously as they are dependent. It is now possible to deal with such inference problems using the iterative simulation technique known as Markov Chain Monte Carlo. We will show various applications of the proposed model based on the English Premier League and Division 1 1997-98; Prediction with application to betting, retrospective analysis of the final ranking, detection of surprising matches and how each team’s properties vary during the season.
Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus

In this paper, we analyze neural network-based dialogue systems trained in an end-to-end manner using an updated version of the recent Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words1. This dataset is interesting because of its size, long context lengths, and technical nature; thus, it can be used to train large models directly from data with minimal feature engineering. We provide baselines in two different environments: one where models are trained to select the correct next response from a list of candidate responses, and one where models are trained to maximize the loglikelihood of a generated utterance conditioned on the context of the conversation. These are both evaluated on a recall task that we call next utterance classification (NUC), and using vector-based metrics that capture the topicality of the responses. We observe that current end-to-end models are 1. This work is an extension of a paper appearing in SIGDIAL (Lowe et al., 2015). This paper further includes results on generative dialogue models, more extensive evaluation of the retrieval models using vector-based generative metrics, and a qualitative examination of responses from the generative models and classification errors made by the Dual Encoder model. Experiments are performed on a new version of the corpus, the Ubuntu Dialogue Corpus v2, which is publicly available: https://github.com/rkadlec/ubuntu-ranking-dataset-creator. The early dataset has been updated to add features and fix bugs, which are detailed in Section 3.  As a result of this analysis, we suggest some promising directions for future research on the Ubuntu Dialogue Corpus, which can also be applied to end-to-end dialogue systems in general.
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression

Logistic regression with `1 regularization has been proposed as a promising method for feature selection in classification problems. In this paper we describe an efficient interior-point method for solving large-scale `1-regularized logistic regression problems. Small problems with up to a thousand or so features and examples can be solved in seconds on a PC; medium sized problems, with tens of thousands of features and examples, can be solved in tens of seconds (assuming some sparsity in the data). A variation on the basic method, that uses a preconditioned conjugate gradient method to compute the search step, can solve very large problems, with a million features and examples (e.g., the 20 Newsgroups data set), in a few minutes, on a PC. Using warm-start techniques, a good approximation of the entire regularization path can be computed much more efficiently than by solving a family of problems independently.
ASAP: A Self-Adaptive Prediction System for Instant Cloud Resource Demand Provisioning

The promise of cloud computing is to provide computing resources instantly whenever they are needed. The state-of-art virtual machine (VM) provisioning technology can provision a VM in tens of minutes. This latency is unacceptable for jobs that need to scale out during computation. To truly enable on-the-fly scaling, new VM needs to be ready in seconds upon request. In this paper, We present an online temporal data mining system called ASAP, to model and predict the cloud VM demands. ASAP aims to extract high level characteristics from VM provisioning request stream and notify the provisioning system to prepare VMs in advance. For quantification issue, we propose Cloud Prediction Cost to encodes the cost and constraints of the cloud and guide the training of prediction algorithms. Moreover, we utilize a two-level ensemble method to capture the characteristics of the high transient demands time series. Experimental results using historical data from an IBM cloud in operation demonstrate that ASAP significantly improves the cloud service quality and provides possibility for on-the-fly provisioning.
Algorithms for Detecting Significantly Mutated Pathways in Cancer

Recent genome sequencing studies have shown that the somatic mutations that drive cancer development are distributed across a large number of genes. This mutational heterogeneity complicates efforts to distinguish functional mutations from sporadic, passenger mutations. Since cancer mutations are hypothesized to target a relatively small number of cellular signaling and regulatory pathways, a common practice is to assess whether known pathways are enriched for mutated genes. We introduce an alternative approach that examines mutated genes in the context of a genome-scale gene interaction network. We present a computationally efficient strategy for de novo identification of subnetworks in an interaction network that are mutated in a statistically significant number of patients. This framework includes two major components. First, we use a diffusion process on the interaction network to define a local neighborhood of "influence" for each mutated gene in the network. Second, we derive a two-stage multiple hypothesis test to bound the false discovery rate (FDR) associated with the identified subnetworks. We test these algorithms on a large human protein-protein interaction network using somatic mutation data from glioblastoma and lung adenocarcinoma samples. We successfully recover pathways that are known to be important in these cancers and also identify additional pathways that have been implicated in other cancers but not previously reported as mutated in these samples. We anticipate that our approach will find increasing use as cancer genome studies increase in size and scope.
Learning the MMSE Channel Estimator

We present a method for estimating conditionally Gaussian random vectors with random covariance matrices, which uses techniques from the field of machine learning. Such models are typical in communication systems, where the covariance matrix of the channel vector depends on random parameters, e.g., angles of propagation paths. If the covariance matrices exhibit certain Toeplitz and shift-invariance structures, the complexity of the minimum mean squared error (MMSE) channel estimator can be reduced to <inline-formula><tex-math notation="LaTeX">${\mathcal O}(M\log M)$ </tex-math></inline-formula> floating point operations, where <inline-formula><tex-math notation="LaTeX">$M$</tex-math> </inline-formula> is the channel dimension. While in the absence of structure the complexity is much higher, we obtain a similarly efficient (but suboptimal) estimator by using the MMSE estimator of the structured model as a blueprint for the architecture of a neural network. This network learns the MMSE estimator for the unstructured model, but only within the given class of estimators that contains the MMSE estimator for the structured model. Numerical simulations with typical spatial channel models demonstrate the generalization properties of the chosen class of estimators to realistic channel models.
Deep Reinforcement Fuzzing

Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows us to apply state-of-the-art deep Q-learning algorithms that optimize rewards, which we define from runtime properties of the program under test. By observing the rewards caused by mutating with a specific set of actions performed on an initial program input, the fuzzing agent learns a policy that can next generate new higher-reward inputs. We have implemented this new approach, and preliminary empirical evidence shows that reinforcement fuzzing can outperform baseline random fuzzing.