nils-herrmann/clean_scirepeval huggingface dataset:
The Brain's concepts: the role of the Sensory-motor system in conceptual knowledge.

Concepts are the elementary units of reason and linguistic meaning. They are conventional and relatively stable. As such, they must somehow be the result of neural activity in the brain. The questions are: Where? and How? A common philosophical position is that all concepts-even concepts about action and perception-are symbolic and abstract, and therefore must be implemented outside the brain's sensory-motor system. We will argue against this position using (1) neuroscientific evidence; (2) results from neural computation; and (3) results about the nature of concepts from cognitive linguistics. We will propose that the sensory-motor system has the right kind of structure to characterise both sensory-motor and more abstract concepts. Central to this picture are the neural theory of language and the theory of cogs, according to which, brain structures in the sensory-motor regions are exploited to characterise the so-called "abstract" concepts that constitute the meanings of grammatical constructions and general inference patterns.
Matlab-based tool for ECG and HRV analysis

Delivery of electroporation pulses in electroporation-based treatments could potentially induce heartrelated effects. The objective of our work was to develop a software tool for electrocardiogram (ECG) analysis to facilitate detection of such effects in pre-selected ECGor heart rate variability (HRV) parameters. Our software tool consists of five distinct modules for: (i) preprocessing; (ii) learning; (iii) detection and classification; (iv) selection and verification; and (v) ECG and HRV analysis. Its key features are: automated selection of ECG segments from ECG signal according to specific user-defined requirements (e.g., selection of relatively noise-free ECG segments); automated detection of prominent heartbeat features, such as Q, R and T wave peak; automated classification of individual heartbeat as normal or abnormal; displaying of heartbeat annotations; quick manual screening of analyzed ECG signal; and manual correction of annotation and classification errors. The performance of the detection and classification module was evaluated on 19 two-hour-long ECG records from Long-Term ST database. On average, the QRS detection algorithm had high sensitivity (99.78%), high positive predictivity (99.98%) and low detection error rate (0.35%). The classification algorithm correctly classified 99.45% of all normal QRS complexes. For normal heartbeats, the positive predictivity of 99.99% and classification error rate of 0.01% were achieved. The software tool provides for reliable and effective detection and classification of heartbeats and for calculation of ECG and HRV parameters. It will be used to clarify the issues concerning patient safety during the electroporation-based treatments used in clinical practice. Preventing the electroporation pulses from interfering with the heart is becoming increasingly important because new applications of electroporation-based treatments are being developed which are using endoscopic, percutaneous or surgical means to access internal tumors or tissues and in which the target tissue can be located in immediate vicinity to the heart.
Factors mediating disclosure in social network sites

In this paper, we explore how privacy settings and privacy policy consumption (reading the privacy policy) affect the relationship between privacy attitudes and disclosure behaviors. We present results from a survey completed by 122 users of Facebook regarding their information disclosure practices and their attitudes about privacy. Based on our data, we develop and evaluate a model for understanding factors that affect how privacy attitudes influence disclosure and discuss implications for social network sites. Our analysis shows that the relationship between privacy attitudes and certain types of disclosures (those furthering contact) are controlled by privacy policy consumption and privacy behaviors. This provides evidence that social network sites could help mitigate concerns about disclosure by providing transparent privacy policies and privacy controls.
ClusterJoin: A Similarity Joins Framework using Map-Reduce

Similarity join is the problem of finding pairs of records with similarity score greater than some threshold. In this paper we study the problem of scaling up similarity join for different metric distance functions using MapReduce. We propose a ClusterJoin framework that partitions the data space based on the underlying data distribution, and distributes each record to partitions in which they may produce join results based on the distance threshold. We design a set of strong candidate filters specific to different distance functions using a novel bisector-based framework, so that each record only needs to be distributed to a small number of partitions while still guaranteeing correctness. To address data skewness, which is common for high dimensional data, we further develop a dynamic load balancing scheme using sampling, which provides strong probabilistic guarantees on the size of partitions, and greatly improves scalability. Experimental evaluation using real data sets shows that our approach is considerably more scalable compared to state-ofthe-art algorithms, especially for high dimensional data with low distance thresholds.
Duty-related trauma exposure in 911 telecommunicators: considering the risk for posttraumatic stress.

Peritraumatic distress may increase the risk for posttraumatic stress disorder (PTSD) in police officers. Much less is known about emotional reactions and PTSD symptomatology in 911 telecommunicators. The current study assessed duty-related exposure to potentially traumatic calls, peritraumatic distress, and PTSD symptomatology in a cross-sectional, convenience sample of 171 telecommunicators. Results showed that telecommunicators reported high levels of peritraumatic distress and a moderate, positive relationship was found between peritraumatic distress and PTSD symptom severity (r = .34). The results suggest that 911 telecommunicators are exposed to duty-related trauma that may lead to the development of PTSD, and that direct, physical exposure to trauma may not be necessary to increase risk for PTSD in this population.
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty

We propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning. The proposed method accelerates the speed of convergence by automatically deciding the constraint penalty needed for parameter consensus in each iteration. In addition, we also propose an extension of the method that adaptively determines the maximum number of iterations to update the penalty. We show that this approach effectively leads to an adaptive, dynamic network topology underlying the distributed optimization. The utility of the new penalty update schemes is demonstrated on both synthetic and real data, including an instance of the probabilistic matrix factorization task known as the structurefrom-motion problem.
Synthetic sequences and ground-truth flow field generation for algorithm validation

Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems.
Combinatorial Energy Learning for Image Segmentation

We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image. Our approach, combinatorial energy learning for image segmentation (CELIS) places a particular emphasis on modeling the inherent combinatorial nature of dense image segmentation problems. We propose efficient algorithms for learning deep neural networks to model the energy function, and for local optimization of this energy in the space of supervoxel agglomerations. We extensively evaluate our method on a publicly available 3-D microscopy dataset with 25 billion voxels of ground truth data. On an 11 billion voxel test set, we find that our method improves volumetric reconstruction accuracy by more than 20% as compared to two state-of-the-art baseline methods: graph-based segmentation of the output of a 3-D convolutional neural network trained to predict boundaries, as well as a random forest classifier trained to agglomerate supervoxels that were generated by a 3-D convolutional neural network.
Self-Presentation in Online Personals The Role of Anticipated Future Interaction , Self-Disclosure , and Perceived Success in Internet Dating

This study investigates self-disclosure in the novel context of online dating relationships. Using a national random sample of Match.com members (N = 349), the authors tested a model of relational goals, self-disclosure, and perceived success in online dating. The  provide support for social penetration theory and the social information processing and hyperpersonal perspectives as well as highlight the positive effect of anticipated future face-to-face interaction on online self-disclosure. The authors find that perceived online dating success is predicted by four dimensions of self-disclosure (honesty, amount, intent, and valence), although honesty has a negative effect. Furthermore, online dating experience is a strong predictor of perceived success in online dating. Additionally, the authors identify predictors of strategic success versus self-presentation success. This research extends existing theory on computer-mediated communication, selfdisclosure, and relational success to the increasingly important arena of mixed-mode relationships, in which participants move from mediated to face-to-face communication.
Toward a Framework for Managing Information Security for an Electric Power Utility—CIGRÉ Experiences

This paper deals with the increasingly important issue of proper handling of information security for electric power utilities. It is based on the efforts of CIGRE Joint Working Group (JWG) D2/B3/C2-01 on "Security for Information Systems and Intranets in Electric Power System" carried out between 2003 and 2006. The JWG has produced a technical brochure (TB), where the purpose to raise the awareness of information and cybersecurity in electric power systems, and gives some guidance on how to solve the security problem by focusing on security domain modeling, risk assessment methodology, and security framework building. Here in this paper, the focus is on the issue of awareness and to highlight some steps to achieve a framework for cybersecurity management. Also, technical considerations of some communication systems for substation automation are studied. Finally, some directions for further works in this vast area of information and cybersecurity are given.
A DUAL-BAND RF ENERGY HARVESTING USING FREQUENCY LIMITED DUAL-BAND IMPEDANCE MATCHING

In this paper, a novel dual-band RF-harvesting RF-DC converter with a frequency limited impedance matching network (M/N) is proposed. The proposed RF-DC converter consists of a dual-band impedance matching network, a rectifier circuit with villard structure, a wideband harmonic suppression low-pass filter (LPF), and a termination load. The proposed dual-band M/N can match two receiving band signals and suppress the out-of-band signals effectively, so the back-scattered nonlinear frequency components from the nonlinear rectifying diodes to the antenna can be blocked. The fabricated circuit provides the maximum RF-DC conversion efficiency of 73.76% and output voltage 7.09 V at 881MHz and 69.05% with 6.86V at 2.4GHz with an individual input signal power of 22 dBm. Moreover, the conversion efficiency of 77.13% and output voltage of 7.25V are obtained when two RF waves with input dual-band signal power of 22 dBm are fed simultaneously.
FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images

Sea–land segmentation and ship detection are two prevalent research domains for optical remote sensing harbor images and can find many applications in harbor supervision and management. As the spatial resolution of imaging technology improves, traditional methods struggle to perform well due to the complicated appearance and background distributions. In this paper, we unify the above two tasks into a single framework and apply the deep convolutional neural networks to predict pixelwise label for an input. Specifically, an edge aware convolutional network is proposed to parse a remote sensing harbor image into three typical objects, e.g., sea, land, and ship. Two innovations are made on top of the deep structure. First, we design a multitask model by simultaneously training the segmentation and edge detection networks. Hierarchical semantic features from the segmentation network are extracted to learn the edge network. Second, the outputs of edge pipeline are further employed to refine entire model by adding an edge aware regularization, which helps our method to yield very desirable results that are spatially consistent and well boundary located. It also benefits the segmentation of docked ships that are quite challenging for many previous methods. Experimental results on two datasets collected from Google Earth have demonstrated the effectiveness of our approach both in quantitative and qualitative performance compared with state-of-the-art methods.
Optic-Flow Based Control of a 46 g Quadrotor

We aim at developing autonomous miniature hovering flying robots capable of navigating in unstructured GPSdenied environments. A major challenge is the miniaturization of the embedded sensors and processors allowing such platforms to fly autonomously. In this paper, we propose a novel ego-motion estimation algorithm for hovering robots equipped with inertial and optic-flow sensors that runs in realtime on a microcontroller. Unlike many vision-based methods, this algorithm does not rely on feature tracking, structure estimation, additional distance sensors or assumptions about the environment. Key to this method is the introduction of the translational optic-flow direction constraint (TOFDC), which does not use the optic-flow scale, but only its direction to correct for inertial sensor drift during changes of direction. This solution requires comparatively much simpler electronics and sensors and works in environments of any geometries. We demonstrate the implementation of this algorithm on a miniature 46g quadrotor for closed-loop position control.
Texture-aware ASCII art synthesis with proportional fonts

We present a fast structure-based ASCII art generation method that accepts arbitrary images (real photograph or hand-drawing) as input. Our method supports not only fixed width fonts, but also the visually more pleasant and computationally more challenging proportional fonts, which allows us to represent challenging images with a variety of structures by characters. We take human perception into account and develop a novel feature extraction scheme based on a multi-orientation phase congruency model. Different from most existing contour detection methods, our scheme does not attempt to remove textures as much as possible. Instead, it aims at faithfully capturing visually sensitive features, including both main contours and textural structures, while suppressing visually insensitive features, such as minor texture elements and noise. Together with a deformation-tolerant image similarity metric, we can generate lively and meaningful ASCII art, even when the choices of character shapes and placement are very limited. A dynamic programming based optimization is proposed to simultaneously determine the optimal proportional-font characters for matching and their optimal placement. Experimental results show that our results outperform state-of-the-art methods in term of visual quality.
An Informatics Approach to Demand Response Optimization in Smart Grids ( Technical Report )

Power utilities are increasingly rolling out “smart” grids with the ability to track consumer power usage in near real-time using smart meters that enable bidirectional communication. However, the true value of smart grids is unlocked only when the veritable explosion of data that will become available is ingested, processed, analyzed and translated into meaningful decisions. These include the ability to forecast electricity demand, respond to peak load events, and improve sustainable use of energy by consumers, and are made possible by energy informatics. Information and software system techniques for a smarter power grid include pattern mining and machine learning over complex events and integrated semantic information, distributed stream processing for low latency response, Cloud platforms for scalable operations and privacy policies to mitigate information leakage in an information rich environment. Such an informatics approach is being used in the DoE sponsored Los Angeles Smart Grid Demonstration Project, and the resulting software architecture will lead to an agile and adaptive Los
ConfErr: A tool for assessing resilience to human configuration errors

We present ConfErr, a tool for testing and quantifying the resilience of software systems to human-induced configuration errors. ConfErr uses human error models rooted in psychology and linguistics to generate realistic configuration mistakes; it then injects these mistakes and measures their effects, producing a resilience profile of the system under test. The resilience profile, capturing succinctly how sensitive the target software is to different classes of configuration errors, can be used for improving the software or to compare systems to each other. ConfErr is highly portable, because all mutations are performed on abstract representations of the configuration files. Using ConfErr, we found several serious flaws in the MySQL and Postgres databases, Apache web server, and BIND and djbdns name servers; we were also able to directly compare the resilience of functionally-equivalent systems, such as MySQL and Postgres.
Rule-Based Phishing Attack Detection

The World Wide Web has become the hotbed of a multi-billion dollar underground economy among cyber criminals whose victims range from individual Internet users to large corporations and even government organizations. As phishing attacks are increasingly being used by criminals to facilitate their cyber schemes, it is important to develop effective phishing detection tools. In this paper, we propose a rule-based method to detect phishing webpages. We first study a number of phishing websites to examine various tactics employed by phishers and generate a rule set based on observations. We then use Decision Tree and Logistic Regression learning algorithms to apply the rules and achieve 95-99% accuracy, with a false positive rate of 0.5-1.5% and modest false negatives. Thus, it is demonstrated that our rulebased method for phishing detection achieves performance comparable to learning machine based methods, with the great advantage of understandable rules derived from experience. KeywordsPhishing attack, phishing website, rule-based, machine learning, phishing detection, decision tree
How Unique Is Your Web Browser?

We investigate the degree to which modern web browsers are subject to “device fingerprinting” via the version and configuration information that they will transmit to websites upon request. We implemented one possible fingerprinting algorithm, and collected these fingerprints from a large sample of browsers that visited our test side, panopticlick.eff.org. We observe that the distribution of our fingerprint contains at least 18.1 bits of entropy, meaning that if we pick a browser at random, at best we expect that only one in 286,777 other browsers will share its fingerprint. Among browsers that support Flash or Java, the situation is worse, with the average browser carrying at least 18.8 bits of identifying information. 94.2% of browsers with Flash or Java were unique in our sample. By observing returning visitors, we estimate how rapidly browser fingerprints might change over time. In our sample, fingerprints changed quite rapidly, but even a simple heuristic was usually able to guess when a fingerprint was an “upgraded” version of a previously observed browser’s fingerprint, with 99.1% of guesses correct and a false positive rate of only 0.86%. We discuss what privacy threat browser fingerprinting poses in practice, and what countermeasures may be appropriate to prevent it. There is a tradeoff between protection against fingerprintability and certain kinds of debuggability, which in current browsers is weighted heavily against privacy. Paradoxically, anti-fingerprinting privacy technologies can be selfdefeating if they are not used by a sufficient number of people; we show that some privacy measures currently fall victim to this paradox, but others do not.
Querying RDF data with text annotated graphs

Scientists and casual users need better ways to query RDF databases or Linked Open Data. Using the SPARQL query language requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology used, and URIs for entities of interest. Natural language query systems are a powerful approach, but current techniques are brittle in addressing the ambiguity and complexity of natural language and require expensive labor to supply the extensive domain knowledge they need. We introduce a compromise in which users give a graphical "skeleton" for a query and annotates it with freely chosen words, phrases and entity names. We describe a framework for interpreting these "schema-agnostic queries" over open domain RDF data that automatically translates them to SPARQL queries. The framework uses semantic textual similarity to find mapping candidates and uses statistical approaches to learn domain knowledge for disambiguation, thus avoiding expensive human efforts required by natural language interface systems. We demonstrate the feasibility of the approach with an implementation that performs well in an evaluation on DBpedia data.
Error patterns in MLC NAND flash memory: Measurement, characterization, and analysis

As NAND flash memory manufacturers scale down to smaller process technology nodes and store more bits per cell, reliability and endurance of flash memory reduce. Wear-leveling and error correction coding can improve both reliability and endurance, but finding effective algorithms requires a strong understanding of flash memory error patterns. To enable such understanding, we have designed and implemented a framework for fast and accurate characterization of flash memory throughout its lifetime. This paper examines the complex flash errors that occur at 30-40nm flash technologies. We demonstrate distinct error patterns, such as cycle-dependency, location-dependency and value-dependency, for various types of flash operations. We analyze the discovered error patterns and explain why they exist from a circuit and device standpoint. Our hope is that the understanding developed from this characterization serves as a building block for new error tolerance algorithms for flash memory.