nils-herrmann/clean_scirepeval huggingface dataset:
Global Contrast Based Salient Region Detection

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.
Bidirectional LSTM Recurrent Neural Network for Keyphrase Extraction

To achieve state-of-the-art performance, keyphrase extraction systems rely on domain-specific knowledge and sophisticated features. In this paper, we propose a neural network architecture based on a Bidirectional Long Short-Term Memory Recurrent Neural Network that is able to detect the main topics on the input documents without the need of defining new hand-crafted features. A preliminary experimental evaluation on the well-known INSPEC dataset confirms the effectiveness of the proposed solution.
Single-Cell Exome Sequencing and Monoclonal Evolution of a JAK2-Negative Myeloproliferative Neoplasm

Tumor heterogeneity presents a challenge for inferring clonal evolution and driver gene identification. Here, we describe a method for analyzing the cancer genome at a single-cell nucleotide level. To perform our analyses, we first devised and validated a high-throughput whole-genome single-cell sequencing method using two lymphoblastoid cell line single cells. We then carried out whole-exome single-cell sequencing of 90 cells from a JAK2-negative myeloproliferative neoplasm patient. The sequencing data from 58 cells passed our quality control criteria, and these data indicated that this neoplasm represented a monoclonal evolution. We further identified essential thrombocythemia (ET)-related candidate mutations such as SESN2 and NTRK1, which may be involved in neoplasm progression. This pilot study allowed the initial characterization of the disease-related genetic architecture at the single-cell nucleotide level. Further, we established a single-cell sequencing method that opens the way for detailed analyses of a variety of tumor types, including those with high genetic complex between patients.
Chinese Microblog Topic Detection Based on the Latent Semantic Analysis and Structural Property

traditional topic detection method can not be applied to the microblog topic detection directly, because the microblog text is a kind of the short, fractional and grass-roots text. In order to detect the hot topic in the microblog text effectively, we propose a microblog topic detection method based on the combination of the latent semantic analysis and the structural property. According to the dialogic property of the microblog, our proposed method firstly creates semantic space based on the replies to the thread, with the aim to solve the data sparseness problem; secondly, create the microblog model based on the latent semantic analysis; finally, propose a semantic computation method combined with the time information. We then adopt the agglomerative hierarchical clustering method as the microblog topic detection method. Experimental results show that our proposed methods improve the performances of the microblog topic detection greatly.
Stereotypes and the Media : A Re-evaluation

It is a commonplace that the mass media are populated with stereotypes. They are readily recognized on television, where their frequency has been ceaselessly documented by researchers. Why, then, return to the problem of defining stereotypes at this time? I believe that by reevaluating and clarifying the term we can improve the way we study the media, particularly television, in the academy, in our research, and in our teaching. The study of stereotypes provides a point of intersection between quantitative and qualitative research, between social science and humanities perspectives, between the cultural studies and administrative approaches. Assumptions about stereotyping influence the way we think about media effects, uses and gratifications, and the ideological analysis of television. While television content analysis has been useful-even essential-its methods could be refined if researchers were to scrutinize their use of the concept of stereotype. Scholars in social psychology, mass communications, and popular culture have used the term diEerently and often approach diEerent areas in their research: the audience, for social psychologists; television in general, for mass communications researchers; and specific texts and genres, for popular culture critics. In each case, the definition of a
Generalized Rank Pooling for Activity Recognition

Most popular deep models for action recognition split video sequences into short sub-sequences consisting of a few frames, frame-based features are then pooled for recognizing the activity. Usually, this pooling step discards the temporal order of the frames, which could otherwise be used for better recognition. Towards this end, we propose a novel pooling method, generalized rank pooling (GRP), that takes as input, features from the intermediate layers of a CNN that is trained on tiny sub-sequences, and produces as output the parameters of a subspace which (i) provides a low-rank approximation to the features and (ii) preserves their temporal order. We propose to use these parameters as a compact representation for the video sequence, which is then used in a classification setup. We formulate an objective for computing this subspace as a Riemannian optimization problem on the Grassmann manifold, and propose an efficient conjugate gradient scheme for solving it. Experiments on several activity recognition datasets show that our scheme leads to state-of-the-art performance.
Consequences of Customer Engagement in Social Networking Sites : Employing Fuzzy Delphi Technique for Validation

The consequences of the customer engagement in the Social Networking Sites (SNS) community have direct impact on the brand. This present research was conducted to examine the cohesive mechanisms for item verification on the most influential consequences of participating the brand community and joining the electronic Word-of-Mouth (eWOM) as the manifestation of the behavior of such communities. Using Fuzzy Delphi techniques, a total of 12 heterogeneous experts are involved in the verification process through a 7-point linguistic scale of the questionnaire survey. The results show good evidence of expert consensus by reaching 75% for each consequence of the engaged customers. On the SNS platform, further aspects of the inspected effects can be expanded to be studied on relevant domains. Practitioners will be more strategic in maintaining and fostering customer relationships, and consistently influencing new customers when interacting actively through SNS brand pages. Customer engagement; fuzzy delphi; SNS; consequences; brand page
Mindfulness-based treatment of addiction: current state of the field and envisioning the next wave of research

Contemporary advances in addiction neuroscience have paralleled increasing interest in the ancient mental training practice of mindfulness meditation as a potential therapy for addiction. In the past decade, mindfulness-based interventions (MBIs) have been studied as a treatment for an array addictive behaviors, including drinking, smoking, opioid misuse, and use of illicit substances like cocaine and heroin. This article reviews current research evaluating MBIs as a treatment for addiction, with a focus on findings pertaining to clinical outcomes and biobehavioral mechanisms. Studies indicate that MBIs reduce substance misuse and craving by modulating cognitive, affective, and psychophysiological processes integral to self-regulation and reward processing. This integrative review provides the basis for manifold recommendations regarding the next wave of research needed to firmly establish the efficacy of MBIs and elucidate the mechanistic pathways by which these therapies ameliorate addiction. Issues pertaining to MBI treatment optimization and sequencing, dissemination and implementation, dose-response relationships, and research rigor and reproducibility are discussed.
An Improved Two-factor Authentication Protocol

Most recently, Yang et al proposed a new set of security requirements for two-factor smart-card-based password mutual authentication and then suggested a new scheme satisfying all their security requirements. In this paper, however, we first show one critical security weakness being overlooked, i.e., allowing key-compromise impersonation. We provide an attack to illustrate the adversary is able to masquerade any user to access the server's service in their protocol once if the long-term key of the server is compromised. Thereafter, we suggests key-compromise impersonation resilience should be added as one more important security requirement for two-factor smart-card based password mutual authentication and then propose an improved protocol to eliminate the security weakness existing in Yang et al's protocol.
Using Convolutional Neural Networks to Perform Classification on State Farm Insurance Driver Images

For the State Farm Photo Classification Kaggle Challenge, we use two different Convolutional Neural Network models to classify pictures of drivers in their cars. The first is a model trained from scratch on the provided dataset, and the other is a model that was first pretrained on the ImageNet dataset and then underwent transfer learning on the provided StateFarm dataset. With the first approach, we achieved a validation accuracy of 10.9%, which is not much better than random. However, with the second approach, we achieved an accuracy of 21.1%. Finally, we explore ways to make these models better based on past models and training techniques.
A Study of WIFI Control Wheeled Robot System with Ultrasonic Obstacle Avoidance

With the progress of science and technology, wheeled robots have been widely used in various fields to help people complete tasks. However, when performing a task, the robot and the object could be damaged if the robot collides with the object due to negligence of the operator. Therefore, the safety issue is the most important consideration. This paper presents a multifunction system, including Arduino controller, rotary ultrasonic obstacle avoidance and WIFI control system. Red warning led and buzzers on the fuselage will alert operators is time of danger and feedback warning information to the phone. The system is proved to be feasible by experiment.
Solving the molecular diagnostic testing conundrum for Mendelian disorders in the era of next-generation sequencing: single-gene, gene panel, or exome/genome sequencing

Next-generation sequencing is changing the paradigm of clinical genetic testing. Today there are numerous molecular tests available, including single-gene tests, gene panels, and exome sequencing or genome sequencing. As a result, ordering physicians face the conundrum of selecting the best diagnostic tool for their patients with genetic conditions. Single-gene testing is often most appropriate for conditions with distinctive clinical features and minimal locus heterogeneity. Next-generation sequencing–based gene panel testing, which can be complemented with array comparative genomic hybridization and other ancillary methods, provides a comprehensive and feasible approach for heterogeneous disorders. Exome sequencing and genome sequencing have the advantage of being unbiased regarding what set of genes is analyzed, enabling parallel interrogation of most of the genes in the human genome. However, current limitations of next-generation sequencing technology and our variant interpretation capabilities caution us against offering exome sequencing or genome sequencing as either stand-alone or first-choice diagnostic approaches. A growing interest in personalized medicine calls for the application of genome sequencing in clinical diagnostics, but major challenges must be addressed before its full potential can be realized. Here, we propose a testing algorithm to help clinicians opt for the most appropriate molecular diagnostic tool for each scenario.Genet Med 17 6, 444–451.
Construction of a Quantum Gas Microscope for Fermionic Atoms by

This thesis reports the construction of a novel apparatus for experiments with ultracold atoms in optical lattices: the Fermi gas microscope. Improving upon similar designs for bosonic atoms, our Fermi gas microscope has the novel feature of being able to achieve single-site resolved imaging of fermionic atoms in an optical lattice; specifically, we use fermionic potassium-40, sympathetically cooled by bosonic sodium-23. In this thesis, several milestones on the way to achieving single-site resolution are described and documented. First, we have tested and mounted in place the imaging optics necessary for achieving single-site resolution. We set up separate 3D magnetooptical traps for capturing and cooling both Na and K. These species are then trapped simultaneously in a plugged quadrupole magnetic trap and evaporated to degeneracy; we obtain a sodium Bose-Einstein condensate with about a million atoms and a degenerate potassium cloud cooled to colder than 1 μK. Using magnetic transport over a distance of 1 cm, we move the cold cloud of atoms into place under the high-resolution imaging system and capture it in a hybrid magnetic and optical-dipole trap. Further evaporation in this hybrid trap performed by lowering the optical trap depth, and the cooled atoms are immersed in an optical lattice, the setup and calibration of which is also described here. Finally, we cool the atoms with optical molasses beams while in the lattice, with the imaging optics collecting the fluoresence light for high-resolution imaging. With molasses cooling set up, single-site fluoresence imaging of bosons and fermions in the same experimental apparatus is within reach.  Martin W. Zwierlein Title: Professor of Physics
Smarter eyewear: using commercial EOG glasses for activity recognition

Smart eyewear computing is a relatively new subcategory in ubiquitous computing research, which has enormous potential. In this paper we present a first evaluation of soon commercially available Electrooculography (EOG) glasses (J!NS MEME) for the use in activity recognition. We discuss the potential of EOG glasses and other smart eye-wear. Afterwards, we show a first signal level assessment of MEME, and present a classification task using the glasses. We are able to distinguish of 4 activities for 2 users (typing, reading, eating and talking) using the sensor data (EOG and acceleration) from the glasses with an accuracy of 70 % for 6 sec. windows and up to 100 % for a 1 minute majority decision. The classification is done user-independent.
 The results encourage us to further explore the EOG glasses as platform for more complex, real-life activity recognition systems.
Artificial intelligence - a modern approach by Stuart Russell and Peter Norvig, Prentice Hall. Series in Artificial Intelligence, Englewood Cliffs, NJ

Benferhat, S, Dubois D and Prade, H, 1992. "Representing default rules in possibilistic logic" In: Proc. of the 3rd Inter. Conf. on Principles of knowledge Representation and Reasoning (KR'92), 673-684, Cambridge, MA, October 26-29. De Finetti, B, 1936. "La logique de la probabilite" Actes du Congres Inter, de Philosophic Scientifique, Paris. An introduction to possibilistic and fuzzy logics" Can we enforce full compositionality in uncertainty calculi?" The paradoxical success of fuzzy logic" IEEE Expert August, 3-8. Lehmann, D and Magidor. M, 1992. "What does a conditional knowledge base entail?" Artificial Intelligence 55 (1) 1-60. Maung, 1,1995. "Two characterizations of a minimum-information principle in possibilistic reasoning"  Pearl, J, 1990. " A natural ordering of defaults with tractable applications to default reasoning" Proc. of the 2nd Conf. on Theoretical Aspects of Reasoning about Knowledge (TARK'90) 121-135, San Francisco, CA, Morgan Karfman. Shoham, Y, 1988. Reasoning about Change MIT Press. Smets, P, 1988. "Belief functions" The combination of evidence in the transferable belief model"  Smets, P, 1990b. "Constructing the pignistic probability function in a context of uncertainty" Un certainty in Artificial Intelligence 5 (M Henrion et al., editors), 29-40, North-Holland. Smets, P, 1995. "Quantifying beliefs by belief functions: An axiomatic justification" In: Procoj the 13th Inter.  Smets, P and Kennes, R, 1994. "The transferable belief model" Artificial Intelligence 66 191-234.
48V Power Assist Recuperation System (PARS) with a permanent magnet motor, inverter and DC-DC converter

The present study proposed a novel Power Assist Recuperation System consisted of motor, inverter, DC-DC converter and 48V Li ion battery for fuel saving of conventional Internal Combustion Engine and hybrid vehicles. As a total solution of the next-generation 48V PARS, key components such as Interior Permanent Magnet Synchronous Motor, MOSFET power switching inverter and bi-directional DC-DC converter with cost competitiveness from in-house power IC's and power modules have been optimized based on a 48V Li ion battery. A deep flux weakening control has been evaluated for the IPMSM's high speed operation. The specific value of how much the proposed 48V PARS can reduce CO2 emission and fuel consumption was estimated by so-called Autonomie software using both efficiency maps of motoring and generating modes. It is noticed that the present 48V PARS gives a considerably enhanced performance in reduction of fuel consumption by as high as 17% for a commercial 1.8L engine.
Robust Subspace Clustering via Tighter Rank Approximation

Matrix rank minimization problem is in general NP-hard. The nuclear norm is used to substitute the rank function in many recent studies. Nevertheless, the nuclear norm approximation adds all singular values together and the approximation error may depend heavily on the magnitudes of singular values. This might restrict its capability in dealing with many practical problems. In this paper, an arctangent function is used as a tighter approximation to the rank function. We use it on the challenging subspace clustering problem. For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method. Extensive experiments on face clustering and motion segmentation show that the proposed method is effective for rank approximation.
A Novel Multipath Load Balancing Approach Using Fibonacci Series for Mobile Ad Hoc Networks

(MANETs). MANET is a temporary network with a group of wireless infrastructureless mobile nodes that communicate with each other within a rapidly dynamic topology. The FMLB protocol distributes transmitted packets over multiple paths through the mobile nodes using Fibonacci sequence. Such distribution can increase the delivery ratio since it reduces the congestion. The FMLB protocol's responsibility is balancing the packet transmission over the selected paths and ordering them according to hops count. The shortest path is used frequently more than other ones. The simulation results show that the proposed protocol has achieved an enhancement on packet delivery ratio, up to 21%, as compared to the Ad Hoc On-demand Distance Vector routing protocol (AODV) protocol. Also the results show the effect of nodes pause time on the data delivery. Finally, the simulation results are obtained by the well-known Glomosim Simulator, version 2.03, without any distance or location measurements devices.
Cloud Computing and Robotics for Disaster Management

Nowadays, IT community is experiencing great shift in computing and information storage infrastructures by using powerful, flexible and reliable alternative of cloud computing. The power of cloud computing may also be realized for mankind if some dedicated disaster management clouds will be developed at various countries cooperating each other on some common standards. The experimentation and deployment of cloud computing by governments of various countries for mankind may be the justified use of IT at social level. It is possible to realize a real-time disaster management cloud where applications in cloud will respond within a specified time frame. If a Real-Time Cloud (RTC) is available then for intelligent machines like robots the complex processing may be done on RTC via request and response model. The complex processing is more desirable as level of intelligence increases in robots towards humans even more. Therefore, it may be possible to manage disaster sites more efficiently with more intelligent cloud robots without great lose of human lives waiting for various assistance at disaster site. Real-time garbage collector, real-time specification for Java, multicore CPU architecture with network-on-chip, parallel algorithms, distributed algorithms, high performance database systems, high performance web servers and gigabit networking can be used to develop real-time applications in cloud.
A Taxonomy and Survey on Distributed File Systems

Applications that process large volumes of data (such as, search engines, grid computing applications, data mining applications, etc.) require a backend infrastructure for storing data. The distributed file system is the central component for storing data infrastructure. There have been many projects focused on network computing that have designed and implemented distributed file systems with a variety of architectures and functionalities. In this paper, we develop a comprehensive taxonomy for describing distributed file system architectures and use this taxonomy to survey existing distributed file system implementations in very large-scale network computing systems such as Grids, Search Engines, etc. We use the taxonomy and the survey results to identify architectural approaches that have not been fully explored in the distributed file system research.
Two-Level Volume Rendering

ÐIn this paper, we present a two-level approach for volume rendering, i.e., two-level volume rendering, which allows for selectively using different rendering techniques for different subsets of a 3D data set. Different structures within the data set are rendered locally on an object-by-object basis by either DVR, MIP, surface rendering, value integration (x-ray-like images), or nonphotorealistic rendering. All the results of subsequent object renderings are combined globally in a merging step (usually compositing in our case). This allows us to selectively choose the most suitable technique for depicting each object within the data while keeping the amount of information contained in the image at a reasonable level. This is especially useful when inner structures should be visualized together with semitransparent outer parts, similar to the focus-plus-context approach known from information visualization. We also present an implementation of our approach which allows us to explore volumetric data using two-level rendering at interactive frame rates. Index TermsÐVisualization, volume rendering, dynamical systems, medical applications.