nils-herrmann/clean_scirepeval huggingface dataset:
Interactive TV for the Home: An Ethnographic Study on Users' Requirements and Experiences

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RUNE-Tag: A high accuracy fiducial marker with strong occlusion resilience

Over the last decades fiducial markers have provided widely adopted tools to add reliable model-based features into an otherwise general scene. Given their central role in many computer vision tasks, countless different solutions have been proposed in the literature. Some designs are focused on the accuracy of the recovered camera pose with respect to the tag; some other concentrate on reaching high detection speed or on recognizing a large number of distinct markers in the scene. In such a crowded area both the researcher and the practitioner are licensed to wonder if there is any need to introduce yet another approach. Nevertheless, with this paper, we would like to present a general purpose fiducial marker system that can be deemed to add some valuable features to the pack. Specifically, by exploiting the projective properties of a circular set of sizeable dots, we propose a detection algorithm that is highly accurate. Further, applying a dot pattern scheme derived from error-correcting codes, allows for robustness with respect to very large occlusions. In addition, the design of the marker itself is flexible enough to accommodate different requirements in terms of pose accuracy and number of patterns. The overall performance of the marker system is evaluated in an extensive experimental section, where a comparison with a well-known baseline technique is presented.
Circuit Fingerprinting Attacks: Passive Deanonymization of Tor Hidden Services

This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators passively. In particular, we show that the circuits, paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit. We propose two attacks, under two slightly different threat models, that could identify a hidden service client or operator using these weaknesses. We found that we can identify the users’ involvement with hidden services with more than 98% true positive rate and less than 0.1% false positive rate with the first attack, and 99% true positive rate and 0.07% false positive rate with the second. We then revisit the threat model of previous website fingerprinting attacks, and show that previous results are directly applicable, with greater efficiency, in the realm of hidden services. Indeed, we show that we can correctly determine which of the 50 monitored pages the client is visiting with 88% true positive rate and false positive rate as low as 2.9%, and correctly deanonymize 50 monitored hidden service servers with true positive rate of 88% and false positive rate of 7.8% in an open world setting.
Computed tomography (CT)-compatible remote center of motion needle steering robot: Fusing CT images and electromagnetic sensor data.

Lung cancer is the most common cause of cancer-related death, and early detection can reduce the mortality rate. Patients with lung nodules greater than 10 mm usually undergo a computed tomography (CT)-guided biopsy. However, aligning the needle with the target is difficult and the needle tends to deflect from a straight path. In this work, we present a CT-compatible robotic system, which can both position the needle at the puncture point and also insert and rotate the needle. The robot has a remote-center-of-motion arm which is achieved through a parallel mechanism. A new needle steering scheme is also developed where CT images are fused with electromagnetic (EM) sensor data using an unscented Kalman filter. The data fusion allows us to steer the needle using the real-time EM tracker data. The robot design and the steering scheme are validated using three experimental cases. Experimental Case I and II evaluate the accuracy and CT-compatibility of the robot arm, respectively. In experimental Case III, the needle is steered towards 5 real targets embedded in an anthropomorphic gelatin phantom of the thorax. The mean targeting error for the 5 experiments is 1.78 ± 0.70 mm. The proposed robotic system is shown to be CT-compatible with low targeting error. Small nodule size and large needle diameter are two risk factors that can lead to complications in lung biopsy. Our results suggest that nodules larger than 5 mm in diameter can be targeted using our method which may result in lower complication rate.
Grown , Printed , and Biologically Augmented : An Additively Manufactured Microfluidic Wearable , Functionally Templated for Synthetic Microbes

Despite significant advances in synthetic biology at industrial scales, digital fabrication challenges have, to date, precluded its implementation at the product scale. We present, Mushtari, a multimaterial 3D printed fluidic wearable designed to culture microbial communities. Thereby we introduce a computational design environment for additive manufacturing of geometrically complex and materially heterogeneous fluidic channels. We demonstrate how controlled variation of geometrical and optical properties at high spatial resolution can be achieved through a combination of computational growth modeling and multimaterial bitmap printing. Furthermore, we present the implementation, characterization, and evaluation of support methods for creating product-scale fluidics. Finally, we explore the cytotoxicity of 3D printed materials in culture studies with the model microorganisms, Escherichia coli and Bacillus subtilis. The results point toward design possibilities that lie at the intersection of computational design, additive manufacturing, and synthetic biology, with the ultimate goal of imparting biological functionality to 3D printed products.
Service-Oriented Architectures for a Flexible and Safe Use of Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) are becoming widely used for supporting multiple heterogeneous missions, requiring continuous evolution and adaptation of the implemented services. This scenario calls for the definition of architectural abstractions that support the integration of service-variants, in particular the notion of SOA (Service-Oriented Architecture) for services composition, to fulfill application-specified performance and dependability requirements. In this paper, we discuss the problems of using SOA (a non-deterministic approach) in UAVs (a real-time based embedded system) and the safety issues that such environment may raise. We then put forward a framework to tackle the identified challenges and propose potential solutions considering the opportunities and threats of using SOA in UAVs.
Mean and Variance of the Sampling Distribution of Particle Swarm Optimizers During Stagnation

Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, often including the assumption that the particles are deterministic. This has prevented the exact characterization of the sampling distribution of the particle swarm optimizer (PSO). In this paper we introduce a novel method that allows us to exactly determine all the characteristics of a PSO sampling distribution and explain how it changes over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the sampling distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction and other forms of PSO.
Contrast masking in human vision.

Contrast masking was studied psychophysically. A two-alternative forced-choice procedure was used to measure contrast thresholds for 2.0 cpd sine-wave gratings in the presence of masking sine-wave gratings. Thresholds were measured for 11 masker contrasts spanning three log units, and seven masker frequencies ranging +/- one octave from the signal frequency. Corresponding measurements were made for gratings with horizontal widths of 0.75 degrees (narrow fields) and 6.0 degrees (wide fields). For high contrast maskers at all frequencies, signal thresholds were related to masking contrast by power functions with exponents near 0.6. For a range of low masking contrasts, signal thresholds were reduced by the masker. For the wide fields, high contrast masking tuning functions peaked at the signal frequency, were slightly asymmetric, and had approximately invariant half-maximum frequencies that lie 3/4 octave below and 1 octave above the signal frequency. The corresponding low contrast tuning functions exhibited peak threshold reduction at the signal frequency, with half-minimum frequencies at roughly +/- 0.25 octaves. For the narrow fields, the masking tuning functions were much broader at both low and high masking contrasts. A masking model is presented that encompasses contrast detection, discrimination, and masking phenomena. Central constructs of the model include a linear spatial frequency filter, a nonlinear transducer, and a process of spatial pooling that acts at low contrasts only.
Detailed Garment Recovery from a Single-View Image

Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs from the web. As an alternative, we propose a method that is able to compute a rich and realistic 3D model of a human body and its outfits from a single photograph with little human interaction. Our algorithm is not only able to capture the global shape and geometry of the clothing, it can also extract small but important details of cloth, such as occluded wrinkles and folds. Unlike previous methods using full 3D information (i.e. depth, multi-view images, or sampled 3D geometry), our approach achieves detailed garment recovery from a single-view image by using statistical, geometric, and physical priors and a combination of parameter estimation, semantic parsing, shape recovery, and physicsbased cloth simulation. We demonstrate the effectiveness of our algorithm by re-purposing the reconstructed garments for virtual try-on and garment transfer applications, as well as cloth animation for digital characters.
Cluster Based Under-Sampling for Unbalanced Cardiovascular Data

Most medical datasets are not balanced in their class labels. Indeed in some cases it has been no ticed that the given class labels do not accurately represent characteristics of the data record. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced. This is because they aim to optimize the overall accuracy without considering the relative distribution of each class. In this paper we propose a cluster based under sampling technique that solves the class imbalance problem for our cardiovascular data. It shows significant better performance than existing methods.  class imbalance, under-sampling, oversampling, clustering, SMOTE.
Affordable Self Driving Cars and Robots with Semantic Segmentation

Image semantic segmentation is of immense interest for self-driving car research. Accurate and efficient segmentation mechanisms are required. We have evaluated Intersection over Union (IoU) metric over Cityscapes and KITTI datasets. We designed baseline softmax regression and maximum likelihood estimation, which performs quite poorly for the image segmentation task. We ran fully convolutional networks (FCN), which performed almost perfectly over the KITTI dataset. We found overfitting problem for the more complex Cityscape dataset. We conducted several experiments with regularization, dropout, data augmentation, image scaling and newer architectures. We are able to successfully mitigate overfitting by data augmentation. We also generated a confusion matrix and conducted error ablative analysis to get a deeper understanding of FCNs.
Auxiliary Loss Multimodal GRU Model in Audio-Visual Speech Recognition

Audio-visual speech recognition (AVSR) utilizes both audio and video modalities for the robust automatic speech recognition. Most deep neural network (DNN) has achieved promising performances in AVSR owing to its generalized and nonlinear mapping ability. However, these DNN models have two main disadvantages: 1) the first disadvantage is that most models alleviate the AVSR problems neglecting the fact that the frames are correlated; and 2) the second disadvantage is the feature learned by the mentioned models is not credible. This is because the joint representation learned by the fusion fails to consider the specific information of categories, and the discriminative information is sparse, while the noise, reverberation, irrelevant image objection, and background are redundancy. Aiming at relieving these disadvantages, we propose the auxiliary loss multimodal GRU (alm-GRU) model including three parts: feature extraction, data augmentation, and fusion & recognition. The feature extraction and data augmentation are a complete effective solution for the processing raw complete video and training, and precondition for later core part: fusion & recognition using alm-GRU equipped with a novel loss which is an end-to-end network combining both fusion and recognition, furthermore considering the modal and temporal information. The experiments show the superiority of our model and necessity of the data augmentation and generative component in the benchmark data sets.
RoXSum: Leveraging Data Aggregation and Batch Processing for XML Routing

Content-based routing is the primary form of communication within publish/subscribe systems. In those systems data transmission is performed by sophisticated overlay networks of content-based routers, which match data messages against registered subscriptions and forward them based on this matching. Despite their inherent complexities, such systems are expected to deliver information in a timely and scalable fashion. As a result, their successful deployment is a strenuous task. Relevant efforts have so far focused on the construction of the overlay network and the filtering of messages at each broker. However, the efficient transmission of messages has received less attention. In this work, we propose a solution that gracefully handles the transmission task, while providing performance benefits for the matching task as well. Along those lines, we design RoXSum, a message representation scheme that aggregates the routing information from multiple documents in a way that permits subscription matching directly on the aggregated content. Our performance study shows that RoXSum is a viable and effective technique, as it speeds up message routing for more than an order of magnitude.
Comparative study and Security Limitations of 4 G Network ( Case Study LTE and WIMAX )

-The advancement and migration of the broadband wireless communication technology into the next generation technology known as Fourth Generation (4G) network has indeed become the next emergent wireless revolution, as an important milestone beyond third generation. Long-Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (Wimax) are among the numerous new innovation technologies which have evolved as leading contenders for the 4G technology. The high speed capability and wider coverage has been a good achievement for 4G networks. Nevertheless, security and improved higher speed with a better quality of service (QOS) has been an issue in its network operations due to the open nature and all IP infrastructure of 4G network. This paper explores the trends in the evolution of 4G wireless technology and its security limitations. It studies the wireless standards of 4G technologies. Also, the security related architecture for the LTE and Wimax technologies are analysed. Finally, it evaluates and recommends ways of tackling the security issues in 4G network.
An investigation of current models of second language speech perception: the case of Japanese adults' perception of English consonants.

This study reports the results of two experiments with native speakers of Japanese. In experiment 1, near-monolingual Japanese listeners participated in a cross-language mapping experiment in which they identified English and Japanese consonants in terms of a Japanese category, then rated the identifications for goodness-of-fit to that Japanese category. Experiment 2 used the same set of stimuli in a categorial discrimination test. Three groups of Japanese speakers varying in English-language experience, and one group of native English speakers participated. Contrast pairs composed of two English consonants, two Japanese consonants, and one English and one Japanese consonant were tested. The results indicated that the perceived phonetic distance of second language (L2) consonants from the closest first language (L1) consonant predicted the discrimination of L2 sounds. In addition, this study investigated the role of experience in learning sounds in a second language. Some of the consonant contrasts tested showed evidence of learning (i.e., significantly higher scores for the experienced than the relatively inexperienced Japanese groups). The perceived phonetic distance of L1 and L2 sounds was found to predict learning effects in discrimination of L1 and L2 sounds in some cases. The results are discussed in terms of models of cross-language speech perception and L2 phonetic learning.
Fuzzy Identity Based Encryption

We introduce a new type of Identity-Based Encryption (IBE) scheme that we call Fuzzy Identity-Based Encryption. In Fuzzy IBE we view an identity as set of descriptive attributes. A Fuzzy IBE scheme allows for a private key for an identity, ω, to decrypt a ciphertext encrypted with an identity, ω′, if and only if the identities ω and ω′ are close to each other as measured by the “set overlap” distance metric. A Fuzzy IBE scheme can be applied to enable encryption using biometric inputs as identities; the error-tolerance property of a Fuzzy IBE scheme is precisely what allows for the use of biometric identities, which inherently will have some noise each time they are sampled. Additionally, we show that Fuzzy-IBE can be used for a type of application that we term “attribute-based encryption”. In this paper we present two constructions of Fuzzy IBE schemes. Our constructions can be viewed as an Identity-Based Encryption of a message under several attributes that compose a (fuzzy) identity. Our IBE schemes are both error-tolerant and secure against collusion attacks. Additionally, our basic construction does not use random oracles. We prove the security of our schemes under the Selective-ID security model.
Deep Subspace Clustering Networks

We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space. Our key idea is to introduce a novel self-expressive layer between the encoder and the decoder to mimic the “selfexpressiveness” property that has proven effective in traditional subspace clustering. Being differentiable, our new self-expressive layer provides a simple but effective way to learn pairwise affinities between all data points through a standard backpropagation procedure. Being nonlinear, our neural-network based method is able to cluster data points having complex (often nonlinear) structures. We further propose pre-training and fine-tuning strategies that let us effectively learn the parameters of our subspace clustering networks. Our experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised subspace clustering methods.
A Case Study: CRM Adoption Success Factor Analysis and Six Sigma DMAIC Application

With today's increasingly competitive economy, many organizations have initiated customer relationship management (CRM) projects to improve customer satisfaction, revenue growth and employee productivity gains. However, only a few successful CRM implementations have successfully completed. In order to enhance the CRM implementation process and increase the success rate, in this paper, first we present the most significant success factors for CRM implementation identified by the results of literature reviews and a survey we conducted. Then we propose a strategy to integrate Six Sigma DMAIC methodology with the CRM implementation process addressing five critical success factors (CSF). Finally, we provide a case study to show how the proposed approach can be applied in the real CRM implementation projects. We conclude that by considering the critical success factors, the proposed approach can emphasize the critical part of implementation process and provide high possibility of CRM adoption success.
Design of prototype humanoid robotics platform for HRP

This paper presents a prototype humanoid robotics platform developed for HRP-2. HRP-2 is a new humanoid robotics platform, which we have been developing in phase two of HRP. HRP is a humanoid robotics project, which has been lunched by Ministry of Economy, Trade and Industry (METI) of Japan from 1998FY to 2002FY for five years. The ability of the biped locomotion of HRP-2 is improved so that HRP-2 can cope with rough terrain in the open air and can prevent the possible damages to a humanoid robot’s own self in the event of tipping over. The ability of whole body motion of HRP-2 is also improved so that HRP-2 can get up by a humanoid robot’s own self even tough HRP-2 tips over. In this paper, the mechanisms and specifications of developed prototype humanoid robotics platform, and its electrical system are introduced.
Automatic programming assessment and test data generation a review on its approaches

Automatic programming assessment has recently become an important method in assisting lecturers and instructors of programming courses to automatically mark and grade students' programming exercises as well as to provide useful feedbacks on students' programming solutions. As part of the method, test data generation process plays as an integral part to perform a dynamic testing on students' programs. To date, various automated methods for test data generation particularly in software testing field are available. Unfortunately, they are seldom used in the context of automatic programming assessment research area. Nevertheless, there have been limited studies taking a stab to integrate both of them due to more useful features and to include a better quality program testing coverage. Thus, this paper provides a review on approaches that have been implemented in various studies with regard to automatic programming assessment, test data generation and integration of both of them. This review is aimed at gathering different techniques that have been employed to forward an interested reader the starting points for finding further information regarding its trends. In addition, the result of the review reveals the main gap that exists within the context of the considered areas which contributes to our main research topic of interest.