nils-herrmann/clean_scirepeval huggingface dataset:
CAPTCHA systems for disabled people

Nowadays the Internet users are from different ages and groups. The disabled people also use the Internet. Some Web sites are especially created for disabled people. Many Internet sites offers services for human users, but unfortunately some computer programs are designed to abuse these services. To solve this problem, some systems named CAPTCHA (completely automated public Turing test to tell computers and human apart) have been introduced to distinguish between human users and computer programs. CAPTCHA methods are mainly based on the weaknesses of OCR systems while using them is undesirable for human users, especially for disabled people. Therefore some CAPTCHA methods are designed especially for disabled people, but there is no method for peoples with more than one disability. In this paper, this problem is examined and some possible solutions are proposed.
Reducing Motion Estimation Time with Skin Detection

Motion estimation (ME) is one of the key parts of video compression algorithms. But, motion estimation and computation of motion vectors (MVs) are very time con-suming. In this paper, we propose a method for reducing the cost of motion estimation process. During this process, a series of candidate blocks should be searched to find the best motion vectors. In our method, we compare the skin parts of two blocks before comparing all pixel pairs of the two blocks. Having a preprocessing phase, the skin part comparison is performed quickly. This method provides a parameter that can be used to create a balance between the processing time and the motion estimation accuracy.
A New Method for Real-Time Steganography

The hidden, real-time sending and display of information, esp. in public places, has since long received attention and faced many challenges. Therefore, different methods have been proposed so far for hiding information in different cover media. In this paper I propose a new method for the real-time hiding of information without need to any storage cover media. This method is based on the real-time hiding of information in output screens of the instrument (such as screens shown by the monitor). This method of steganography is very similar to the two discussions of image steganography and video steganography. The idea suggested in this paper was simulated on a PC computer by using the Pascal programming language
Fast and scalable system for automatic artist identification

Digital music technologies enable users to create and use large collections of music. One of the desirable features for users is the ability to automatically organize the collection and search in it. One of the operations that they need is automatic identification of tracks' artists. This operation can be used to automatically classify new added tracks to a collection. Additionally, the user can use this operation to identify the artist of an unknown track. The artist name of a track can help the user find similar music. In this paper, we introduce a fast and scalable system that can automatically identify the artist of music tracks. This system is creating a signature for each track that is a compact representation of the tracks. The tracks' signatures of an artist are then used to create a signature for that artist. A similarity measure is also defined to measure the distance or dissimilarity of two signatures. This similarity measure is based on graph matching. To identify the signature of an unknown track, the signature of that track is compared with the artists' signatures and the nearest artist is selected as the artist of the track. The accuracy of the system on the artist20 dataset is 71.5% which is better than previously reported results on this dataset. In comparison to other proposed methods, the artist model creation and model updates are faster and more scalable in our system. Additionally, the search time of our system is less than other systems and only depends on the number of artist.
Localized CAPTCHA for illiterate people

Nowadays, many daily human activities such as education, commerce, talks, etc. are carried out through the Internet. In cases such as the registering in Websites, some hackers write programs to make automatic false enrolments which waste the resources of the Website while this may even stop the entire Website from working. Therefore, it is necessary to tell apart human users from computer programs which is known as CAPTCHA (completely automated public turing test to tell computers and human apart). CAPTCHA methods are mainly based on the weak points of OCR (optical character recognition) systems while using them are undesirable to human users. So the non-OCR-based CAPTCHA methods are proposed. In these systems users do not need to type anything. In this paper a new non-OCR-based CAPTCHA method is introduced. In this method some objects are chosen randomly and the pictures about these topics are downloaded from Internet. These pictures are shown on the screen. Then the user is said to choose a specific object. Instead of writing the question, it is said. Therefore people who are illiterate and have difficulty in reading, can easily pass the test. Also the question is said in the native Website language (e.g. Persian), therefore the user should not know English language. If the user chooses the right image we can guess that the user is a human and not a computer software. The main advantage of this method is its simplicity because the user does not have to read or type anything. This project has been implemented by PHP programming language.
Real-time and MPEG-1 layer III compression resistant steganography in speech

Embedding a secret message into a cover media without attracting any attention, known as steganography, is one of the methods used for hidden communication purposes. One of the cover media that can be used for steganography is speech. In this study, the authors propose a new steganography method in speech signals. In this method, the silence intervals of speech are found and the length (number of samples) of these intervals is changed to hide information. The main feature of our method is robustness to MPEG-1 layer III (MP3) compression. This method can hide information in a speech stream with very low processing time which makes it a real-time steganography method. The hiding capacity of our method is comparable with other MP3 resistance methods and the listening tests show that the degradation in speech quality is not annoying. Additionally, the effect of our method on chaotic features is negligible, so it is difficult to detect our method with chaotic-based steganalysis methods.
Motion CAPTCHA

In some Websites it is necessary to distinguish between human users and computer programs which is known as CAPTCHA (completely automated public turing test to tell computers and human apart). CAPTCHA methods are mainly based on the weaknesses of OCR systems while using them are undesirable to human users. In this paper a new CAPTCHA method is introduced on the basis of showing a movie of a personpsilas action. Then we ask the user to describe the movement of that person. The user should select the sentence which describes the motion from a list of sentences. If the user chooses the right sentence we can guess that the user is a human and not a computer program. The main advantage of this method is its simplicity and also the difficulty of computer attacks to it. This project has been implemented by PHP scripting language.
Designing and Implementing Two Internet Based Exam Systems Using Synchronous and Asynchronous Models

In this paper, two different systems for taking exams through the Internet are described and analyzed. They have been designed and executed in Yazd and Shahed Universities. These systems enable us to take the multiple-choice and descriptive exams. In these systems, the exam questions are prepared by the instructor of the concerned lesson and placed in the system database. The students who have taken that lesson can be connected to the system from wherever they want. After entering their username and password they can take part in the exam. One of these systems has been created on the basis of the synchronous model and another one has been developed on the basis of the asynchronous model. Each of them has its own advantages and disadvantages. Also in this paper we have dealt with description of the Internet-based exam systems in two Canadian universities and finally we have proceeded to review the problems and troubles existing in taking Internet-based exams especially in Iran.
Page Segmentation of Persian/Arabic Printed Text Using Ink Spread Effect

Nowadays, OCR (optical character recognition) is widely used for converting written documents to digital documents. One of the OCR phases is page segmentation. In page segmentation, text regions must be found in input image. In addition, text parts like text columns must be separated. In this paper, a new method for segmenting Persian/Arabic printed text is proposed. This method is based on ink spread effect idea, a new idea that has particular features. Main features of Persian/Arabic scripts are considered in designing this method. This method is skew resistant and can segment text within frames and tables or regions with gray background
Skin region based method for reducing motion estimation time

Video compression algorithms are time consuming processes. A key part of video compression algorithms which is also time consuming is motion estimation (ME) and computation of motion vectors (MVs). During this process, a series of candidate blocks should be searched to find the best motion vectors. In this paper, we propose a method for reducing the motion estimation process time. The idea of our method is to compare skin regions of candidate blocks with current frame block and skipping some candidate blocks during the search. We propose some conditions for comparing the skin regions of two blocks and describe how we can check these conditions quickly. Our method provides two parameters that can be used to create a balance between the processing time and the motion estimation accuracy.
Dynamic CAPTCHA

Registration Websites use CAPTCHA (completely automated public turing test to tell computers and human apart) systems to prevent the bot programs from wasting their resources. But sometimes the CAPTCHA test is difficult for human users to pass because the users have different ages, languages, etc. In this paper we propose a solution for this problem. The registration system can select a kind of CAPTCHA system among different available CAPTCHA systems regarding to the user's needs such as user language and disabilities. For example if the user has non-English language, the system chooses a CAPTCHA method which does not need English language knowledge.
Compressed domain global motion estimation using the Helmholtz Tradeoff Estimator

Several algorithms for global motion estimation in video sequences using pixel- or block-based approaches have been published. Most known pixel-based methods lack in performance while when using block-based algorithms working on motion vectors, robustness to outliers and accuracy is missing. In this paper we present the fundamentals of a significantly improved, robust block-based method for global motion estimation in compressed domain following the generic Helmholtz principle. To this aim, we use motion vector fields as provided by MPEG data streams.  PSNR values for four motion compensated test sequences show that our new method delivers results comparable to more complex algorithms.
New Real-Time Approaches for Video-Genre-Classification Using High-Level Descriptors and a Set of Classifiers

In this paper we describe in detail the recent publications related to video-genre-classification and present our improved new approaches for classifying video sequences in real-time as 'cartoon', 'commercial', 'music', 'news' or 'sport' by analyzing the content with new high-level audio-visual descriptors and classification methods. Such applications have also been discussed in the context of MPEG-7. The results demonstrate identification rates of more than 90% based on a large representative collection of 100 videos gathered from free digital TV and Internet.
Audio similarity matrices enhancement in an image processing framework

Audio similarity matrices have become a popular tool in the MIR community for their ability to reveal segments of high acoustical self-similarity and repetitive patterns. This is particularly useful for the task of music structure segmentation. The performance of such systems however relies on the nature of the studied music pieces and it is often assumed that harmonic and timbre variations remain low within musical sections. While this condition is rarely fulfilled, similarity matrices are often too complex and structural information can hardly be extracted. In this paper we propose an image-oriented pre-processing of similarity matrices to highlight the conveyed musical information and reduce their complexity. The image segmentation processing step handles the image characteristics in order to provide us meaningful spatial segments and enhance thus the music segmentation. Evaluation of a reference structure segmentation algorithm using the enhanced matrices is provided, and we show that our method strongly improves the segmentation performances.
Hierarchical image database browsing environment with embedded relevance feedback

We address the user-navigation through large volumes of image data. A tree structured K-means clustering is introduced which will hierarchically group images into similar groups. Providing the nodes of the different levels with representative image samples leads to different "image maps" similar to street maps with various resolutions of details. The user can zoom into various cluster levels to obtain more or less detail if required. Further a new query refinement method is introduced. The retrieval process is controlled by learning from positive examples from the user, often called the relevance feedback of the user. The combination of the relevance feedback and the hierarchical structure together with a three-dimensional visualization of the "image maps" leads to an intuitive browsing environment. The results obtained verify the attractiveness of the approach for navigation and retrieval applications.
Hybrid speaker-based segmentation system using model-level clustering

We present a hybrid speaker-based segmentation, which combines metric-based and model-based techniques. Without a priori information about the number of speakers and speaker identities, the speech stream is segmented in three stages: (1) the most likely speaker changes are detected; (2) to group segments of identical speakers, a two-level clustering algorithm is performed using a Bayesian information criterion (BIC) and HMM model scores - every cluster is assumed to contain only one speaker; (3) the speaker models are reestimated from each cluster by HMM. Finally a resegmentation step performs a more refined segmentation using these speaker models. To measure the performance, we compare the segmentation results of the proposed hybrid method versus metric-based segmentation. Results show that the hybrid approach using two-level clustering significantly outperforms direct metric-based segmentation.
An automatic system for real-time video-genres detection using high-level-descriptors and a set of classifiers

We present a new approach for classifying MPEG-2 video sequences as dasiacartoonpsila, dasiacommercialpsila, dasiamusicpsila, dasianewspsila or dasiasportpsila by analyzing specific, high-level audio-visual features of consecutive frames in real-time. This is part of the well-known video-genre-classification problem, where popular TV-broadcast genres are studied. Such applications have also been discussed in the context of MPEG-7 . In our method the extracted features are logically combined using a set of classifiers to produce a reliable recognition. The results demonstrate a high identification rate based on a large representative collection of 100 video sequences (20 sequences per genre) gathered from free digital TV-broadcasting in Europe.
Gaussian scale-space dense disparity estimation with anisotropic disparity-field diffusion

We present a new reliable dense disparity estimation algorithm which employs Gaussian scale-space with anisotropic disparity-field diffusion. This algorithm estimates edge-preserving dense disparity vectors using a diffusive method on iteratively Gaussian-filtered images with a scale, i.e. the Gaussian scale-space. While a Gaussian filter kernel generates a coarser resolution from stereo image pairs, only strong and meaningful boundaries are adoptively selected on the resolution of the filtered images. Then, coarse global disparity vectors are initialized using the boundary constraint. The per-pixel disparity vectors are iteratively obtained by the local adjustment of the global disparity vectors using an energy-minimization framework. The proposed algorithm preserves the boundaries while inner regions are smoothed using anisotropic disparity-field diffusion. In this work, the Gaussian scale-space efficiently avoids illegal matching on a large baseline by the restriction of the range. Moreover, it prevents the computation from iterating into local minima of ill-posed diffusion on large gradient areas e.g. shadow and texture region, etc. The experimental results prove the excellent localization performance preserving the disparity discontinuity of each object.
MPEG digital video-coding standards

The efficient digital representation of image and video signals has been the subject of considerable research over the past 20 years. Digital video-coding technology has developed into a mature field and products have been developed that are targeted for a wide range of emerging applications, such as video on demand, digital TV/HDTV broadcasting, and multimedia image/video database services. With the increased commercial interest in video communications, the need for international image- and video-compression standards arose. To meet this need, the Moving Picture Experts Group (MPEG) was formed to develop coding standards. MPEG-1 and MPEG-2 video-coding standards have attracted much attention worldwide, with an increasing number of very large scale integration (VLSI) and software implementations of these standards becoming commercially available. MPEG-4, the most recent MPEG standard that is still under development, is targeted for future content-based multimedia applications. We provide an overview of the MPEG video-coding algorithms and standards and their role in video communications. We review the basic concepts and techniques that are relevant in the context of the MPEG video-compression standards and outline MPEG-1 and MPEG-2 video-coding algorithms. The specific properties of the standards related to their applications are presented, and the basic elements of the forthcoming MPEG-4 standard are also described. We also discuss the performance of the standards and their success in the market place.
High-Speed tracking-by-detection without using image information

Tracking-by-detection is a common approach to multi-object tracking. With ever increasing performances of object detectors, the basis for a tracker becomes much more reliable. In combination with commonly higher frame rates, this poses a shift in the challenges for a successful tracker. That shift enables the deployment of much simpler tracking algorithms which can compete with more sophisticated approaches at a fraction of the computational cost. We present such an algorithm and show with thorough experiments its potential using a wide range of object detectors. The proposed method can easily run at 100K fps while outperforming the state-of-the-art on the DETRAC vehicle tracking dataset.
Multi-State Video Coding with Side Information

Multi-State Video Coding (MSVC) is a multiple description scheme where the video is splitted into two or more subsequences. Each subsequence is encoded and transmitted separately and can be decoded independently. The prediction gain decreases due to sequence splitting but error resilience of the system increases since reconstruction capabilities improve. The lost frames in one subsequence are reconstructed by using state recovery, i.e., interpolation of the past and/ future frames from the other subsequence. Unbalanced Quantized MSVC is realized by using the same scheme but coding the subsequences with different quantization stepsizes yielding different bitrates. The advantage of unbalanced operation is the increased system performance in case of unbalanced transmission channel characteristics. In our previous work, we proposed an advanced reconstruction algorithm to support the unbalanced coding of the subsequences: State recovery is not only used for the lost frames but also for received frames when state recovery yields a higher frame PSNR than using the received packet and applying motion compensation. But to figure out which reconstruction method gives a higher frame PSNR a comparison with the original sequence is necessary. Therefore the method is applicable at the decoder only if a feedback mechanism between the encoder and decoder is present. In this work, we present an alternative way, MSVC with side information (MSVCSI), for guiding the optimized reconstruction stategy by estimating the reliabilities of several possible reconstruction alternatives. The reliabilty values are calculated recursively for each frame using the loss history of the frames and the side information representing the specific sequence characteristics. We show that under unbalanced transmission conditions, MSVCSI outperforms the original MSVC method (Approach 1) and the advanced MSVC (Approach 2) upto 1 dB depending on the loss rates of the transmission channels. The gain increases as the loss rates and the unbalance rate increase.
Feature-based video key frame extraction for low quality video sequences

We present an approach to key frame extraction for structuring user generated videos on video sharing websites (e. g. YouTube). Our approach is intended to link existing image search engines to video data. User generated videos are, contrary to professional material, unstructured, do not follow any fixed rule, and their camera work is poor. Furthermore, the coding quality is bad due to low resolution and high compression. In a first step, we segment video sequences into shots by detecting gradual and abrupt cuts. Further, longer shots are segmented into subshots based on location and camera motion features. One representative key frame is extracted per subshot using visual attention features, such as lighting, camera motion, face, and text appearance. These key frames are useful for indexing and for searching similar video sequences using MPEG-7 descriptors .