nils-herrmann/clean_scirepeval huggingface dataset:
Representations of recent and remote autobiographical memories in hippocampal subfields

The hippocampus has long been implicated in supporting autobiographical memories, but little is known about how they are instantiated in hippocampal subfields. Using high-resolution functional magnetic resonance imaging (fMRI) combined with multivoxel pattern analysis we found that it was possible to detect representations of specific autobiographical memories in individual hippocampal subfields. Moreover, while subfields in the anterior hippocampus contained information about both recent (2 weeks old) and remote (10 years old) autobiographical memories, posterior CA3 and DG only contained information about the remote memories. Thus, the hippocampal subfields are differentially involved in the representation of recent and remote autobiographical memories during vivid recall.
Adaptive Spatial-Scale-Aware Deep Convolutional Neural Network for High-Resolution Remote Sensing Imagery Scene Classification

High-resolution remote sensing (HRRS) scene classification plays an important role in numerous applications. During the past few decades, a lot of remarkable efforts have been made to develop various methods for HRRS scene classification. In this paper, focusing on the problems of complex context relationship and large differences of object scale in HRRS scene images, we propose a deep CNN-based scene classification method, which not only enables to enhance the ability of spatial representation, but adaptively recalibrates channel-wise feature responses to suppress useless feature channels. We evaluated the proposed method on a publicly large-scale dataset with several state-of-the-art convolutional neural network (CNN) models. The experimental results demonstrate that the proposed method is effective to extract high-level category features for HRRS scene classification.
Segmenting Salient Objects from Images and Videos

In this paper we introduce a new salient object segmentation method, which is based on combining a saliency measure with a conditional random field (CRF) model. The proposed saliency measure is formulated using a statistical framework and local feature contrast in illumination, color, and motion information. The resulting saliency map is then used in a CRF model to define an energy minimization based segmentation approach, which aims to recover well-defined salient objects. The method is efficiently implemented by using the integral histogram approach and graph cut solvers. Compared to previous approaches the introduced method is among the few which are applicable to both still images and videos including motion cues. The experiments show that our approach outperforms the current state-of-the-art methods in both qualitative and quantitative terms.
Composite right/left-handed transmission line based compact resonant antennas for RF module integration

Several electrically small resonant antennas employing the composite right/left-handed transmission line (CRLH-TL) are presented for integration with portable RF modules. The proposed antenna designs are based on the unique property of anti-parallel phase and group velocity of the CRLH-TL at its fundamental mode. In this mode, the propagation constant increases as the frequency decreases, therefore, a small guided wavelength can be obtained at a lower frequency to provide the small lambdag/2 resonant length used to realize a compact antenna design. Furthermore, the physical size and the operational frequency of the antenna depend on the unit cell size and the equivalent transmission line model parameters of the CRLH-TL, including series inductance, series capacitance, shunt inductance and shunt capacitance. Optimization of these parameters as well as miniaturization techniques of the physical size of unit cell is investigated. A four unit-cell resonant antenna is designed and tested at 1.06 GHz. The length, width and height of the proposed antenna are 1/19lambda0, 1/23lambda0 and 1/83lambda0, respectively. In addition, a compact antenna using a 2-D three by three mushroom like unit cell arrangement is developed at 1.17 GHz, showing that an increased gain of 0.6 dB and higher radiation efficiency can be achieved over the first prototype antenna. The same design is applied in the development of a circularly polarized antenna operating at 2.46 GHz. A 116deg beamwidth with axial ratio better than 3 dB is observed. The physical size of the proposed mushroom type small antenna and the circularly polarized antenna is 1/14lambda0 by 1/14lambda0 by 1/39lambda0 and 1/10lambda0 by 1/10lambda 0 by 1/36lambda0, respectively
Performance Counters to Rescue: A Machine Learning based safeguard against Micro-architectural Side-Channel-Attacks

Micro-architectural side-channel-attacks are presently daunting threats to most mathematically elegant encryption algorithms. Even though there exist various defense mechanisms, most of them come with the extra overhead of implementation. Recent studies have prevented some particular categories of these attacks but fail to address the detection of other classes. This paper presents a generic machine learning based multi-layer detection approach targeting these micro-architectural side-channel-attacks, without concentrating on a single category. The proposed approach work by profiling low-level hardware events using Linux perf event API and then by analyzing these data with some appropriate machine learning techniques. This paper also presents a novel approach, using time-series data, to correlate the execution trace of the adversary with the secret key of encryption for dealing with false-positives and unknown attacks. The experimental results and performance of the proposed approach suggest its superiority with high detection accuracy and low performance overhead.
Active Learning in Collaborative Filtering Recommender Systems

In Collaborative Filtering Recommender Systems user’s preferences are expressed in terms of rated items and each rating allows to improve system prediction accuracy. However, not all of the ratings bring the same amount of information about the user’s tastes. Active Learning aims at identifying rating data that better reflects users’ preferences. Active learning Strategies are used to selectively choose the items to present to the user in order to acquire her ratings and ultimately improve the recommendation accuracy. In this survey article, we review recent active learning techniques for collaborative filtering along two dimensions: (a) whether the system requested ratings are personalised or not, and, (b) whether active learning is guided by one criterion (heuristic) or multiple
Investigation in Wireless Power Transmission for UAV Charging

This paper proposes application of wireless power transfer for charging of electric-powered Unmanned Air Vehicles (UAV)s. Multi-rotor systems, such as quadrotors, are light-weight and easy to operate. They are available in different sizes and with the wide range of capabilities. The main limitation of electric-powered UAVs is their range and endurance, due the limited battery capacity. Increasing battery system size is not a viable solution as its weight becomes a limiting factor. Supercapacitors are not an option, because of their low energy density. An alternative is to recharge UAV on the job, using wireless energy transfer (WET). WET was originally investigated by Nikola Tesla in the beginning of the 20 century. His patents are now common ground for any power transmission technology research, both wired and wireless. Investigations in resonance-based wireless energy transfer promise efficient wireless power transmission over several meters. This offers an ability to recharge moving vehicles, such as cars, trains and UAVs, wirelessly. For example, this technology can be applied to extend the range of UAVs used for the inspection of power transmission lines and towers. Presented project investigate capabilities and limitations of the wireless power transmission, for particular UAV application, i.e. for the infrastructure inspections.
OpenARC: open accelerator research compiler for directive-based, efficient heterogeneous computing

Combined with its high-level, extensible Intermediate Representation (IR) and rich semantic annotations, OpenARC serves as a powerful research vehicle for prototyping optimization, source-to-source transformations, and instrumentation for debugging, performance analysis, and autotuning. In fact, OpenARC is equipped with various capabilities for advanced analyses and transformations, as well as built-in performance and debugging tools. We explain the overall design and implementation of OpenARC, and we present key analysis techniques necessary to efficiently port OpenACC applications. Porting various OpenACC applications to CUDA GPUs using OpenARC demonstrates that OpenARC performs similarly to a commercial compiler, while serving as a general research framework.
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color

We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input. Our system consist of a end-to-end trainable convolutional network. Contrary to the existing methods, our system wholly utilizes free-form user input with color and shape. This allows the system to respond to the user’s sketch and color input, using it as a guideline to generate an image. In our particular work, we trained network with additional style loss  which made it possible to generate realistic results, despite large portions of the image being removed. Our proposed network architecture SC-FEGAN is well suited to generate high quality synthetic image using intuitive user inputs.
Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching them

Research on recommender systems is a challenging task, as is building and operating such systems. Major challenges include non-reproducible research results, dealing with noisy data, and answering many questions such as how many recommendations to display, how often, and, of course, how to generate recommendations most effectively. In the past six years, we built three research-article recommender systems for digital libraries and reference managers, and conducted research on these systems. In this paper, we share some experiences we made during that time. Among others, we discuss the required skills to build recommender systems, and why the literature provides little help in identifying promising recommendation approaches. We explain the challenge in creating a randomization engine to run A/B tests, and how low data quality impacts the calculation of bibliometrics. We further discuss why several of our experiments delivered disappointing results, and provide statistics on how many researchers showed interest in our recommendation dataset.
Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures

Conventional sentiment analysis usually neglects semantic information between (sub-)clauses, as it merely implements so-called bag-of-words approaches, where the sentiment of individual words is aggregated independently of the document structure. Instead, we advance sentiment analysis by the use of rhetoric structure theory (RST), which provides a hierarchical representation of texts at document level. For this purpose, texts are split into elementary discourse units (EDU). These EDUs span a hierarchical structure in the form of a binary tree, where the branches are labeled according to their semantic discourse. Accordingly, this paper proposes a novel combination of weighting and grid search to aggregate sentiment scores from the RST tree, as well as feature engineering for machine learning. We apply our algorithms to the especially hard task of predicting stock returns subsequent to financial disclosures. As a result, machine learning improves the balanced accuracy by 8.6 percent compared to the baseline.
A new miniaturized antenna for ISM 433 MHz frequency band

A new miniaturized antenna for 433MHz frequency band is introduced for sensor networks. The antenna is a magnetic monopole loaded with a capacitor, which can significantly decrease the resonant frequency of the structure. It is designed to operate in the ISM 433MHz band on a small PCB (103mm⋆55mm) and has at least 3MHz bandwidth. Moreover, its radiation efficiency is suitable for the possible applications and its pattern is omnidirectionnal.
The cognitive neuroscience of implicit category learning

There is much recent interest in the question of whether people have available a single category learning system or a number of qualitatively different systems. Most proponents of multiple systems have hypothesized an explicit, rule-based system and some type of implicit system. Although there has been general agreement about the nature of the explicit system, there has been disagreement about the exact nature of the implicit system. This chapter explores the question of whether there is implicit category learning, and if there is, what form it might take. First, we examine what the word “implicit” means in the categorization literature. Next, we review some of the evidence that supports the notion that people have available one or more implicit categorization systems. Finally, we consider the nature of implicit categorization by focusing on three alternatives: an exemplar memory-based system, a procedural memory system, and an implicit system that uses the perceptual representation memory system.
Polygon comparison using a graph representation

All of the information necessary to perform the polygon set operations (union, intersection, and difference) and therefore polygon clipping can be generated by a single application of a process called polygon comparison. This process accepts two or more input polygons and generates one or more polygons as output. These output polygons contain unique homogenous areas, each falling within the domain of one or more input polygons. Each output polygon is classified by the list of input polygons in which its area may be found. The union contour of all input is also generated, completing all of the information necessary to perform the polygon set operations.
 This paper introduces a polygon comparison algorithm which features reduced complexity due to its use of a graph data representation. The paper briefly introduces some of the possible approaches to the general problem of polygon comparison including the polygon set and clipping problems. The new algorithm is then introduced and explained in detail.
 The algorithm is sufficiently general to compare sets of concave polygons with holes. More than two polygons can be compared at one time; all information for future comparisons of subsets of the original input polygon sets is available from the results of the initial application of the process.
 The algorithm represents polygons using a graph of the boundaries of the polygons. These graphs are imbedded in a two dimensional geometric space. The use of the graph representation simplifies the comparison process considerably by eliminating many special cases from explicit consideration.
 Polygon operations like the ones described above are useful in a variety of application areas, especially those which deal with problems involving two dimensional or projected two dimensional geometric areas. Examples include VLSI circuit design, cartographic and demographic applications, and polygon clipping for graphic applications such as viewport clipping, hidden surface and line removal, detailing, and shadowing.
Privacy-Preserving Human Activity Recognition from Extreme Low Resolution

Privacy protection from unwanted video recordings is an important societal challenge. We desire a computer vision system (e.g., a robot) that can recognize human activities and assist our daily life, yet ensure that it is not recording video that may invade our privacy. This paper presents a fundamental approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution (LR) training videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classification, allowing the classifier to best take advantage of existing high-resolution videos (e.g., YouTube videos) by creating multiple LR training videos tailored for the problem. We experimentally confirm that the paradigm of inverse super resolution is able to benefit activity recognition from extreme low-resolution videos.
Scaling Textual Inference to the Web

Most Web-based Q/A systems work by finding pages that contain an explicit answer to a question. These systems are helpless if the answer has to be inferred from multiple sentences, possibly on different pages. To solve this problem, we introduce the HOLMES system, which utilizes textual inference (TI) over tuples extracted from text. Whereas previous work on TI (e.g., the literature on textual entailment) has been applied to paragraph-sized texts, HOLMES utilizes knowledge-based model construction to scale TI to a corpus of 117 million Web pages. Given only a few minutes, HOLMES doubles recall for example queries in three disparate domains (geography, business, and nutrition). Importantly, HOLMES’s runtime is linear in the size of its input corpus due to a surprising property of many textual relations in the Web corpus—they are “approximately” functional in a well-defined sense.
A Dictionary-based Approach to Racism Detection in Dutch Social Media

We present a dictionary-based approach to racism detection in Dutch social media comments, which were retrieved from tw o public Belgian social media sites likely to attract racist reactio ns. These comments were labeled as racist or non-racist by mu ltiple annotators. For our approach, three discourse dictionaries were create d: first, we created a dictionary by retrieving possibly raci st and more neutral terms from the training data, and then augmenting these with more general words to remove some bias. A second dictionary w s created through automatic expansion using a word2vec model trained on a large corpus of general Dutch text. Finall y, third dictionary was created by manually filtering out incorrect expansions. We trained multiple Support Vector Machines, using the dist ribu ion of words over the different categories in the dictionaries as f eatures. The best-performing model used the manually clean ed dictionary and obtained an F-score of 0.46 for the racist class on a test s et consisting of unseen Dutch comments, retrieved from the s ame sites used for the training set. The automated expansion of the dic tionary only slightly boosted the model’s performance, and this increase in performance was not statistically significant. The fact t hat the coverage of the expanded dictionaries did increase i ndicates that the words that were automatically added did occur in the corpus, b t were not able to meaningfully impact performance. The di ctionaries, code, and the procedure for requesting the corpus are availa ble at:https://github.com/clips/hades.
Pupil Diameter Predicts Changes in the Exploration–Exploitation Trade-off: Evidence for the Adaptive Gain Theory

The adaptive regulation of the balance between exploitation and exploration is critical for the optimization of behavioral performance. Animal research and computational modeling have suggested that changes in exploitative versus exploratory control state in response to changes in task utility are mediated by the neuromodulatory locus coeruleus–norepinephrine (LC–NE) system. Recent studies have suggested that utility-driven changes in control state correlate with pupil diameter, and that pupil diameter can be used as an indirect marker of LC activity. We measured participants' pupil diameter while they performed a gambling task with a gradually changing payoff structure. Each choice in this task can be classified as exploitative or exploratory using a computational model of reinforcement learning. We examined the relationship between pupil diameter, task utility, and choice strategy (exploitation vs. exploration), and found that (i) exploratory choices were preceded by a larger baseline pupil diameter than exploitative choices; (ii) individual differences in baseline pupil diameter were predictive of an individual's tendency to explore; and (iii) changes in pupil diameter surrounding the transition between exploitative and exploratory choices correlated with changes in task utility. These findings provide novel evidence that pupil diameter correlates closely with control state, and are consistent with a role for the LC–NE system in the regulation of the exploration–exploitation trade-off in humans.
PATTERNS OF INFORMATION SEEKING ON THE WEB: A QUALITATIVE STUDY OF DOMAIN EXPERTISE AND WEB EXPERTISE

This research examines the pattern of Web information seeking in four groups of nurses with different combinations of domain expertise and Web expertise. Protocols were gathered as the nurses carried out information-seeking tasks in the domain of osteoporosis. Domain and Web novices searched breadth-first and did little or no evaluation of the results. Domain expert/Web novices also searched breadth-first but evaluated information more thoroughly using osteoporosis knowledge. Domain novice/Web experts searched in a mixed, breadth-first/depth-first pattern and attempted to evaluate information using general criteria. Domain expert/Web experts carried out depth-first searches, following deep trails of information and evaluated information based on the most varied and sophisticated criteria. The results suggest that there are distinct differences in searching patterns related to expertise. Implications of these findings and suggestions for future research are provided. _______________________ Christine A. Jenkins is a Solution Architect with Sprint Corporation. christine_jenkin@hotmail.com. Cynthia L. Corritore is a professor at the College of Business Administration, Creighton University, Omaha, NE 68178 USA. cindy@creighton.edu. Susan Wiedenbeck is a professor at the College of Information Science and Technology, Drexel University, Philadelphia, PA 19104 USA.
Introducing Entity-Based Concepts to Business Process Modeling

The so-called Internet of Things (IoT) that comprises interconnected physical devices such as sensor networks and its technologies like Radio Frequency Identification (RFID) is increasingly adopted in many industries and thus becomes highly relevant for process modeling and execution. As BPMN 2.0 does not yet consider the idiosyncrasies of real-world entities we suggest new modeling concepts for a physical entity as well as a sensing task and an actuation task to make BPMN IoT-aware.