nils-herrmann/clean_scirepeval huggingface dataset:
Rotation-invariant and scale-invariant Gabor features for texture image retrieval

Conventional Gabor representation and its extracted features often yield a fairly poor performance in retrieving the rotated and scaled versions of the texture image under query. To address this issue, existing methods exploit multiple stages of transformations for making rotation and/or scaling being invariant at the expense of high computational complexity and degraded retrieval performance. The latter is mainly due to the lost of image details after multiple transformations. In this paper, a rotation-invariant and a scale-invariant Gabor representations are proposed, where each representation only requires few summations on the conventional Gabor filter impulse responses. The optimum setting of the orientation parameter and scale parameter is experimentally determined over the Brodatz and MPEG-7 texture databases. Features are then extracted from these new representations for conducting rotation-invariant or scale-invariant texture image retrieval. Since the dimension of the new feature space is much reduced, this leads to a much smaller metadata storage space and faster on-line computation on the similarity measurement. Simulation results clearly show that our proposed invariant Gabor representations and their extracted invariant features significantly outperform the conventional Gabor representation approach for rotation-invariant and scale-invariant texture image retrieval.
Arabic speech recognition using Hidden Markov Model Toolkit(HTK)

In this paper we discuss the development and implementation of an Arabic automatic speech recognition engine. The engine can recognize both continuous speech and isolated words. The system was developed using the Hidden Markov Model Toolkit. First, an Arabic dictionary was built by composing the words to its phones. Next, Mel Frequency Cepstral Coefficients (MFCC) of the speech samples are derived to extract the speech feature vectors. Then, the training of the engine based on triphones is developed to estimate the parameters for a Hidden Markov Model. To test the engine, the database consisting of speech utterance from thirteen Arabian native speakers is used which is divided into ten speaker-dependent and three speaker-independent samples. The experimental results showed that the overall system performance was 90.62%, 98.01 % and 97.99% for sentence correction, word correction and word accuracy respectively.
Effects of sexual dimorphism on facial attractiveness

Testosterone-dependent secondary sexual characteristics in males may signal immunological competence and are sexually selected for in several species,. In humans, oestrogen-dependent characteristics of the female body correlate with health and reproductive fitness and are found attractive. Enhancing the sexual dimorphism of human faces should raise attractiveness by enhancing sex-hormone-related cues to youth and fertility in females,, and to dominance and immunocompetence in males,,. Here we report the results of asking subjects to choose the most attractive faces from continua that enhanced or diminished differences between the average shape of female and male faces. As predicted, subjects preferred feminized to average shapes of a female face. This preference applied across UK and Japanese populations but was stronger for within-population judgements, which indicates that attractiveness cues are learned. Subjects preferred feminized to average or masculinized shapes of a male face. Enhancing masculine facial characteristics increased both perceived dominance and negative attributions (for example, coldness or dishonesty) relevant to relationships and paternal investment. These results indicate a selection pressure that limits sexual dimorphism and encourages neoteny in humans.
Efficient Stixel-based object recognition

This paper presents a novel attention mechanism to improve stereo-vision based object recognition systems in terms of recognition performance and computational efficiency at the same time. We utilize the Stixel World, a compact medium-level 3D representation of the local environment, as an early focus-of-attention stage for subsequent system modules. In particular, the search space of computationally expensive pattern classifiers is significantly narrowed down. We explicitly couple the 3D Stixel representation with prior knowledge about the object class of interest, i.e. 3D geometry and symmetry, to precisely focus processing on well-defined local regions that are consistent with the environment model. Experiments are conducted on large real-world datasets captured from a moving vehicle in urban traffic. In case of vehicle recognition as an experimental testbed, we demonstrate that the proposed Stixel-based attention mechanism significantly reduces false positive rates at constant sensitivity levels by up to a factor of 8 over state-of-the-art. At the same time, computational costs are reduced by more than an order of magnitude.
Deep Air : Forecasting Air Pollution in Beijing , China

Air pollution in urban environments has risen steadily in the last several decades. Such cities as Beijing and Delhi have experienced rises to dangerous levels for citizens. As a growing and urgent public health concern, cities and environmental agencies have been exploring methods to forecast future air pollution, hoping to enact policies and provide incentives and services to benefit their citizenry. Much research is being conducted in environmental science to generate deterministic models of air pollutant behavior; however, this is both complex, as the underlying molecular interactions in the atmosphere need to be simulated, and often inaccurate. As a result, with greater computing power in the twenty-first century, using machine learning methods for forecasting air pollution has become more popular. This paper investigates the use of the LSTM recurrent neural network (RNN) as a framework for forecasting in the future, based on time series data of pollution and meteorological information in Beijing. Due to the sequence dependencies associated with large-scale and longer time series datasets, RNNs, and in particular LSTM models, are well-suited. Our results show that the LSTM framework produces equivalent accuracy when predicting future timesteps compared to the baseline support vector regression for a single timestep. Using our LSTM framework, we can now extend the prediction from a single timestep out to 5 to 10 hours into the future. This is promising in the quest for forecasting urban air quality and leveraging that insight to enact beneficial policy.
Visual Methods for Analyzing Time-Oriented Data

Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations. For that purpose, we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis. We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.
Perceptual Optimization of DCT Color Quantization Matrices

The JPEG baseline standard for image compression employs a block Discrete Cosine Transform (DCT) and uniform quantization. For a monochrome image, a single quantization matrix is allowed, while for a color image, distinct matrices are allowed for each color channel.. Here we describe a method, called DCTune, for design of color quantization matrices that is based on a model of the visibility of quantization artifacts. The model describes artifact visibility as a function of DCT frequency, color channel, and display resolution and brightness. The model also describes summation of artifacts over space and frequency, and masking of artifacts by the image itself. The DCTune matrices are different from the de facto JPEG matrices, and appear to provide superior visual quality at equal bit-rates.
Collaboration Using Multiple PDAs Connected to a PC

The Pebbles project is creating applications to connmt multiple Personal DigiM Assistants &DAs) to a main computer such as a PC We are cmenfly using 3Com Pd@Ilots b-use they are popdar and widespread. We created the ‘Remote Comrnandefl application to dow users to take turns sending input from their PahnPiiots to the PC as if they were using the PCS mouse and keyboard. ‘.PebblesDraw” is a shared whiteboard application we btit that allows dl of tie users to send input simtdtaneously while sharing the same PC display. We are investigating the use of these applications in various contexts, such as colocated mmtings. Keywor& Personal Digiti Assistants @DAs), PH11oc Single Display Groupware, Pebbles,
End system optimizations for high-speed TCP

Modern TCP implementations are capable of very high point-to-point bandwidths. Delivered performance on the fastest networks is often limited by the sending and receiving hosts, rather than by the network hardware or the TCP protocol implementation itself. In this case, systems can achieve higher bandwidth by reducing host overheads through a variety of optimizations above and below the TCP protocol stack, given support from the network interface. This paper surveys the most important of these optimizations, and illustrates their effects quantitatively with empirical results from a an experimental network delivering up to two gigabits per second of point-to-point TCP bandwidth.
A smart bus tracking system based on location-aware services and QR codes

When it comes to taking the public transportation, time and patience are of essence. In other words, many people using public transport buses have experienced time loss because of waiting at the bus stops. In this paper, we proposed smart bus tracking system that any passenger with a smart phone or mobile device with the QR (Quick Response) code reader can scan QR codes placed at bus stops to view estimated bus arrival times, buses' current locations, and bus routes on a map. Anyone can access these maps and have the option to sign up to receive free alerts about expected bus arrival times for the interested buses and related routes via SMS and e-mails. We used C4.5 (a statistical classifier) algorithm for the estimation of bus arrival times to minimize the passengers waiting time. GPS (Global Positioning System) and Google Maps are used for navigation and display services, respectively.
Learning Relaxed Deep Supervision for Better Edge Detection

We propose using relaxed deep supervision (RDS) within convolutional neural networks for edge detection. The conventional deep supervision utilizes the general groundtruth to guide intermediate predictions. Instead, we build hierarchical supervisory signals with additional relaxed labels to consider the diversities in deep neural networks. We begin by capturing the relaxed labels from simple detectors (e.g. Canny). Then we merge them with the general groundtruth to generate the RDS. Finally we employ the RDS to supervise the edge network following a coarse-to-fine paradigm. These relaxed labels can be seen as some false positives that are difficult to be classified. Weconsider these false positives in the supervision, and are able to achieve high performance for better edge detection. Wecompensate for the lack of training images by capturing coarse edge annotations from a large dataset of image segmentations to pretrain the model. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on the well-known BSDS500 dataset (ODS F-score of .792) and obtains superior cross-dataset generalization results on NYUD dataset.
FlexStore: A Software Defined, Energy Adaptive Distributed Storage Framework

In this paper we propose a flexible and scalable distributed storage framework called flex Store that can adapt to variations in available or consumable power and demonstrate its performance in the context of reduplicated virtual machine disks. We propose and investigate smart control techniques in order to cope with the power constraints either introduced as a result of increasing node density in the storage arrays (consumable power constraints) or introduced when a mix of renewable (green) and conventional (brown) energy sources are used to power the data enter. The key component in the proposed storage framework is the policy engine which is a software layer that provides interfaces to define performance requirements of the applications (and also energy related policies). The policy engine enforces those policies in the storage system by adjusting the allocation of storage resources. The experimental results demonstrate the ability of the framework to dynamically adapt to the changes in workload and power constraints and minimize performance impacts. Our evaluation of the prototype shows that the adaptive replication mechanisms can reduce the IO latencies by around 65% during energy plenty situations and the impact of adaptation actions on IO latencies during energy constrained situations is reduced by more than 40% compared to the case without the adaptive replication and optimized adaptation mechanisms.
Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification

Tumor classification based on gene expression levels is important for tumor diagnosis. Since tumor data in gene expression contain thousands of attributes, attribute selection for tumor data in gene expression becomes a key point for tumor classification. Inspired by the concept of gain ratio in decision tree theory, an attribute selection method based on fuzzy gain ratio under the framework of fuzzy rough set theory is proposed. The approach is compared to several other approaches on three real world tumor data sets in eywords: ttribute selection utual information uzzy rough sets ain ratio gene expression. Results show that the proposed method is effective. This work may supply an optional strategy for dealing with tumor data in gene expression or other applications.
From Pixels to Torques: Policy Learning with Deep Dynamical Models

Data-efficient learning in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. In this paper, we consider one instance of this challenge, the pixels to torques problem, where an agent must learn a closed-loop control policy from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model that uses deep autoencoders to learn a low-dimensional embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning ensures that not only static but also dynamic properties of the data are accounted for. This is crucial for long-term predictions, which lie at the core of the adaptive model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art reinforcement learning methods for continuous states and actions, our approach learns quickly, scales to highdimensional state spaces and is an important step toward fully autonomous learning from pixels to torques.
A Practical Dynamic Buffer Overflow Detector

Despite previous efforts in auditing software manually and automatically, buffer overruns are still being discovered in programs in use. A dynamic bounds checker detects buffer overruns in erroneous software before it occurs and thereby prevents attacks from corrupting the integrity of the system. Dynamic buffer overrun detectors have not been adopted widely because they either (1) cannot guard against all buffer overrun attacks, (2) break existing code, or (3) incur too high an overhead. This paper presents a practical detector called CRED (C Range Error Detector) that avoids each of these deficiencies. CRED finds all buffer overrun attacks as it directly checks for the bounds of memory accesses. Unlike the original referent-object based bounds-checking technique, CRED does not break existing code because it uses a novel solution to support program manipulation of out-of-bounds addresses. Finally, by restricting the bounds checks to strings in a program, CRED’s overhead is greatly reduced without sacrificing protection in the experiments we performed. CRED is implemented as an extension of the GNU C compiler version 3.3.1. The simplicity of our design makes possible a robust implementation that has been tested on over 20 open-source programs, comprising over 1.2 million lines of C code. CRED proved effective in detecting buffer overrun attacks on programs with known vulnerabilities, and is the only tool found to guard against a testbed of 20 different buffer overflow ]. Finding overruns only on strings impose an overhead of less This research was performed while the first author was at Stanford University, and this material is based upon work supported in part by the National Science Foundation under Grant No. 0086160. than 26% for 14 of the programs, and an overhead of up to 130% for the remaining six, while the previous state-ofthe-art bounds checker by Jones and Kelly breaks 60% of the programs and is 12 times slower. Incorporating wellknown techniques for optimizing bounds checking into CRED could lead to further performance improvements.
Augmented reality in the psychomotor phase of a procedural task

Procedural tasks are common to many domains, ranging from maintenance and repair, to medicine, to the arts. We describe and evaluate a prototype augmented reality (AR) user interface designed to assist users in the relatively under-explored psychomotor phase of procedural tasks. In this phase, the user begins physical manipulations, and thus alters aspects of the underlying task environment. Our prototype tracks the user and multiple components in a typical maintenance assembly task, and provides dynamic, prescriptive, overlaid instructions on a see-through head-worn display in response to the user's ongoing activity. A user study shows participants were able to complete psychomotor aspects of the assembly task significantly faster and with significantly greater accuracy than when using 3D-graphics-based assistance presented on a stationary LCD. Qualitative questionnaire results indicate that participants overwhelmingly preferred the AR condition, and ranked it as more intuitive than the LCD condition.
Articulated part-based model for joint object detection and pose estimation

Despite recent successes, pose estimators are still somewhat fragile, and they frequently rely on a precise knowledge of the location of the object. Unfortunately, articulated objects are also very difficult to detect. Knowledge about the articulated nature of these objects, however, can substantially contribute to the task of finding them in an image. It is somewhat surprising, that these two tasks are usually treated entirely separately. In this paper, we propose an Articulated Part-based Model (APM) for jointly detecting objects and estimating their poses. APM recursively represents an object as a collection of parts at multiple levels of detail, from coarse-to-fine, where parts at every level are connected to a coarser level through a parent-child relationship . Parts are further grouped into part-types (e.g., left-facing head, long stretching arm, etc) so as to model appearance variations . By having the ability to share appearance models of part types and by decomposing complex poses into parent-child pairwise relationships, APM strikes a good balance between model complexity and model richness. Extensive quantitative and qualitative experiment results on public datasets show that APM outperforms state-of-the-art methods. We also show results on PASCAL 2007 - cats and dogs - two highly challenging articulated object categories.
Humanoid robosoccer goal detection using hough transform

Goal posts detection is a critical robot soccer ability which is needed to be accurate, robust and efficient. A goal detection method using Hough transform to get the detailed goal features is presented in this paper. In the beginning, the image preprocessing and Hough transform implementation are described in detail. A new modification on the θ parameter range in Hough transform is explained and applied to speed up the detection process. Line processing algorithm is used to classify the line detected, and then the goal feature extraction method, including the line intersection calculation, is done. Finally, the goal distance from the robot body is estimated using triangle similarity. The experiment is performed on our university humanoid robot with the goal dimension of 225 cm in width and 110 cm in height, in yellow color. The result shows that the goal detection method, including the modification in Hough transform, is able to extract the goal features seen by the robot correctly, with the lowest speed of 5 frames per second. Additionally, the goal distance estimation is accomplished with maximum error of 20 centimeters.
TEACHERS, SCHOOLS, AND ACADEMIC ACHIEVEMENT

Considerable controversy surrounds the impact of schools and teachers on the achievement of students. This paper disentangles the separate factors influencing achievement with special attention given to the potential problems of omitted or mismeasured variables and of student and school selection. Unique matched panel data from the UTD Texas Schools Project permit the identification of teacher quality based on student performance along with the impact of specific, measured components of teachers and schools. Nonparametric lower bound estimates of the variance in teacher quality based entirely on within-school heterogeneity indicate that teachers have powerful effects on achievement, though little of the variation in teacher quality is explained by observable characteristics such as education or experience. First and second year teachers are systematically less effective than more experienced peers, and students do benefit from smaller classes, particularly in grades 4 and 5. The effects on mathematics and reading achievement of a very costly ten student reduction in class size similar to that undertaken in a variety of U.S. states are smaller than the benefit of moving one standard deviation up the teacher quality distribution, highlighting the importance of teacher effectiveness in the determination of school quality.
Review of Image Processing Technique for Glaucoma Detection

The review paper describes the application of various image processing techniques for automatic detection of glaucoma. Glaucoma is a neurodegenerative disorder of the optic nerve, which causes partial loss of vision. Large number of people suffers from eye diseases in rural and semi urban areas all over the world. Current diagnosis of retinal disease relies upon examining retinal fundus image using image processing. The key image processing techniques to detect eye diseases include image registration, image fusion, image segmentation, feature extraction, image enhancement, morphology, pattern matching, image classification, analysis and statistical measurements. KeywordsImage Registration; Fusion; Segmentation; Statistical measures; Morphological operation;  http://www.ijcsmc.com/docs/papers/November2013/V2I11201336.pdf