nils-herrmann/clean_scirepeval huggingface dataset:
Tools for the body (schema)

What happens in our brain when we use a tool to reach for a distant object? Recent neurophysiological, psychological and neuropsychological research suggests that this extended motor capability is followed by changes in specific neural networks that hold an updated map of body shape and posture (the putative "Body Schema" of classical neurology). These changes are compatible with the notion of the inclusion of tools in the "Body Schema", as if our own effector (e.g. the hand) were elongated to the tip of the tool. In this review we present empirical support for this intriguing idea from both single-neuron recordings in the monkey brain and behavioural performance of normal and brain-damaged humans. These relatively simple neural and behavioural aspects of tool-use shed light on more complex evolutionary and cognitive aspects of body representation and multisensory space coding for action.
Beyond Market Baskets: Generalizing Association Rules to Correlations

One of the most well-studied problems in data mining is mining for association rules in market basket data. Association rules, whose significance is measured via support and confidence, are intended to identify rules of the type, “A customer purchasing item A often also purchases item B.” Motivated by the goal of generalizing beyond market baskets and the association rules used with them, we develop the notion of mining rules that identify correlations (generalizing associations), and we consider both the absence and presence of items as a basis for generating rules. We propose measuring significance of associations via the chi-squared test for correlation from classical statistics. This leads to a measure that is upward closed in the itemset lattice, enabling us to reduce the mining problem to the search for a border between correlated and uncorrelated itemsets in the lattice. We develop pruning strategies and devise an efficient algorithm for the resulting problem. We demonstrate its effectiveness by testing it on census data and finding term dependence in a corpus of text documents, as well as on synthetic data.
Crowdsourcing graphical perception: using mechanical turk to assess visualization design

Understanding perception is critical to effective visualization design. With its low cost and scalability, crowdsourcing presents an attractive option for evaluating the large design space of visualizations; however, it first requires validation. In this paper, we assess the viability of Amazon's Mechanical Turk as a platform for graphical perception experiments. We replicate previous studies of spatial encoding and luminance contrast and compare our results. We also conduct new experiments on rectangular area perception (as in treemaps or cartograms) and on chart size and gridline spacing. Our results demonstrate that crowdsourced perception experiments are viable and contribute new insights for visualization design. Lastly, we report cost and performance data from our experiments and distill recommendations for the design of crowdsourced studies.
Learning Attitudes and Attributes from Multi-aspect Reviews

Most online reviews consist of plain-text feedback together with a single numeric score. However, understanding the multiple `aspects' that contribute to users' ratings may help us to better understand their individual preferences. For example, a user's impression of an audio book presumably depends on aspects such as the story and the narrator, and knowing their opinions on these aspects may help us to recommend better products. In this paper, we build models for rating systems in which such dimensions are explicit, in the sense that users leave separate ratings for each aspect of a product. By introducing new corpora consisting of five million reviews, rated with between three and six aspects, we evaluate our models on three prediction tasks: First, we uncover which parts of a review discuss which of the rated aspects. Second, we summarize reviews by finding the sentences that best explain a user's rating. Finally, since aspect ratings are optional in many of the datasets we consider, we recover ratings that are missing from a user's evaluation. Our model matches state-of-the-art approaches on existing small-scale datasets, while scaling to the real-world datasets we introduce. Moreover, our model is able to `disentangle' content and sentiment words: we automatically learn content words that are indicative of a particular aspect as well as the aspect-specific sentiment words that are indicative of a particular rating.
Mining asynchronous periodic patterns in time series data

Periodicy detection in time series data is a challenging pro blem of great importance in many applications. Most previous work focused on mining synchronous p eriodic patterns and did not recognize misaligned presence of a pattern due to the intervention of r andom noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that ma y be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parametersmin rep andmax dis are employed to specify the minimum number of repetitions th at is required within each segment of non-disrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, th e longest valid subsequence of a pattern is returned. A two phase algorithm is devised to first generate p o ntial periods by distance-based pruning followed by an iterative procedure to derive and validat e candidate patterns and locate the longest valid subsequence. We also show that this algorithm can not o nly provide linear time complexity with respect to the length of the sequence but also achieve space e ffici ncy.
Deep Learning with Sets and Point Clouds

We study a simple notion of structural invariance that readily suggests a parameter-sharing scheme in deep neural networks. In particular, we define structure as a collection of relations, and derive graph convolution and recurrent neural networks as special cases. We study composition of basic structures in defining models that are invariant to more complex “product” structures such as graph of graphs, sets of images or sequence of sets. For demonstration, our experimental results are focused on the setting where the discrete structure of interest is a set. We present results on several novel and non-trivial problems on sets, including point-cloud classification, set outlier detection and semi-supervised learning using clustering information.
Sequence Modeling via Segmentations

Segmental structure is a common pattern in many types of sequences such as phrases in human languages. In this paper, we present a probabilistic model for sequences via their segmentations. The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks. Since the segmentation of a sequence is usually unknown in advance, we sum over all valid segmentations to obtain the final probability for the sequence. An efficient dynamic programming algorithm is developed for forward and backward computations without resorting to any approximation. We demonstrate our approach on text segmentation and speech recognition tasks. In addition to quantitative results, we also show that our approach can discover meaningful segments in their respective application contexts.
RPL-based multipath Routing Protocols for Internet of Things on Wireless Sensor Networks

In the last few years, Wireless Sensor Network (WSN) emerges and appears as an essential platform for prominent concept of Internet of Things (IoT). Their application ranges from so-called “smart cities”, “smart homes” over environmental monitoring. The connectivity in IoT mainly relies on RPL (IPv6 Routing Protocol for Low Power and Lossy Network) - a routing algorithm that constructs and maintains DODAGs (Destination Oriented Directed Acyclic Graph) to transmit data from sensors to root over a single path. However, due to the resource constraints of sensor nodes and the unreliability of wireless links, single-path routing approaches cannot be considered effective techniques to meet the performance demands of various applications. In order to overcome these problems, many individuals and group research focuses on multi-path solutions for RPL routing protocol. In this paper, we propose three multipath schemes based on RPL (Energy Load Balancing-ELB, Fast Local Repair-FLR and theirs combination-ELB-FLR) and integrate them in a modified IPv6 communication stack for IoT. These schemes are implemented in OMNET++ simulator and the experiment outcomes show that our approaches have achieved better energy efficiency, better end-to-end delay, packet delivery rate and network load balance compared to traditional solution of RPL.
Similarity Breeds Proximity: Pattern Similarity within and across Contexts Is Related to Later Mnemonic Judgments of Temporal Proximity

Experiences unfold over time, but little is known about the mechanisms that support the formation of coherent episodic memories for temporally extended events. Recent work in animals has provided evidence for signals in hippocampus that could link events across temporal gaps; however, it is unknown whether and how such signals might be related to later memory for temporal information in humans. We measured patterns of fMRI BOLD activity as people encoded items that were separated in time and manipulated the presence of shared or distinct context across items. We found that hippocampal pattern similarity in the BOLD response across trials predicted later temporal memory decisions when context changed. By contrast, pattern similarity in lateral occipital cortex was related to memory only when context remained stable. These data provide evidence in humans that representational stability in hippocampus across time may be a mechanism for temporal memory organization.
Insights into the neural basis of response inhibition from cognitive and clinical neuroscience

Neural mechanisms of cognitive control enable us to initiate, coordinate and update behaviour. Central to successful control is the ability to suppress actions that are no longer relevant or required. In this article, we review the contribution of cognitive neuroscience, molecular genetics and clinical investigations to understanding how response inhibition is mediated in the human brain. In Section 1, we consider insights into the neural basis of inhibitory control from the effects of neural interference, neural dysfunction, and drug addiction. In Section 2, we explore the functional specificity of inhibitory mechanisms among a range of related processes, including response selection, working memory, and attention. In Section 3, we focus on the contribution of response inhibition to understanding flexible behaviour, including the effects of learning and individual differences. Finally, in Section 4, we propose a series of technical and conceptual objectives for future studies addressing the neural basis of inhibition.
Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning

Many typical applications of object detection operate within a prescribed false-positive range. In this situation the performance of a detector should be assessed on the basis of the area under the ROC curve over that range, rather than over the full curve, as the performance outside the prescribed range is irrelevant. This measure is labelled as the partial area under the ROC curve (pAUC). We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning. In addition, in order to achieve high object detection performance, we propose a new approach to extracting low-level visual features based on spatial pooling. Incorporating spatial pooling improves the translational invariance and thus the robustness of the detection process. Experimental results on both synthetic and realworld data sets demonstrate the effectiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the proposed structured ensemble learning method with spatially pooled features. The result is the current best reported performance on the Caltech-USA pedestrian detection dataset.
Verifiable smart packaging with passive RFID

Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes.
Super-Samples from Kernel Herding

We extend the herding algorithm to continuous spaces by using the kernel trick. The resulting “kernel herding” algorithm is an infinite memory deterministic process that learns to approximate a PDF with a collection of samples. We show that kernel herding decreases the error of expectations of functions in the Hilbert space at a rateO(1/T )which is much faster than the usual O(1/ √ T ) for iid random samples. We illustrate kernel herding by approximating Bayesian predictive distributions.
Microscopy cell segmentation via adversarial neural networks

We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair of two competitive artificial neural networks, with a unique architecture, termed Rib Cage, which are trained simultaneously and together define a min-max game resulting in an accurate segmentation of a given image. Our approach has two main strengths, similar to the GAN, the method does not require a formulation of a loss function for the optimization process. This allows training on a limited amount of annotated data in a weakly supervised manner. Promising segmentation results on real fluorescent microscopy data are presented. The code is freely available at: https://github.com/arbellea/DeepCellSeg.git
To recognize shapes, first learn to generate images.

The uniformity of the cortical architecture and the ability of functions to move to different areas of cortex following early damage strongly suggest that there is a single basic learning algorithm for extracting underlying structure from richly structured, high-dimensional sensory data. There have been many attempts to design such an algorithm, but until recently they all suffered from serious computational weaknesses. This chapter describes several of the proposed algorithms and shows how they can be combined to produce hybrid methods that work efficiently in networks with many layers and millions of adaptive connections.
Cortical cores in network dynamics

Spontaneous brain activity at rest is spatially and temporally organized in networks of cortical and subcortical regions specialized for different functional domains. Even though brain networks were first studied individually through functional Magnetic Resonance Imaging, more recent studies focused on their dynamic 'integration'. Integration depends on two fundamental properties: the structural topology of brain networks and the dynamics of functional connectivity. In this scenario, cortical hub regions, that are central regions highly connected with other areas of the brain, play a fundamental role in serving as way stations for network traffic. In this review, we focus on the functional organization of a set of hub areas that we define as the 'dynamic core'. In the resting state, these regions dynamically interact with other regions of the brain linking multiple networks. First, we introduce and compare the statistical measures used for detecting hubs. Second, we discuss their identification based on different methods (functional Magnetic Resonance Imaging, Diffusion Weighted Imaging, Electro/Magneto Encephalography). Third, we show that the degree of interaction between these core regions and the rest of the brain varies over time, indicating that their centrality is not stationary. Moreover, alternating periods of strong and weak centrality of the core relate to periods of strong and weak global efficiency in the brain. These results indicate that information processing in the brain is not stable, but fluctuates and its temporal and spectral properties are discussed. In particular, the hypothesis of 'pulsed' information processing, discovered in the slow temporal scale, is explored for signals at higher temporal resolution.
Artificial Diversity as Maneuvers in a Control Theoretic Moving Target Defense

Moving target cyber-defense systems encompass a wide variety of techniques in multiple areas of cyber-security. The dynamic system reconfiguration aspect of moving target cyber-defense can be used as a basis for providing an adaptive attack surface. The goal of this research is to develop novel control theoretic mechanisms by which a range of cyber maneuver techniques are provided such that when an attack is detected the environment can select the most appropriate maneuver to ensure a sufficient shift in the attack surface to render the identified attack ineffective. Effective design of this control theoretic cyber maneuver approach requires the development of two additional theories. First, algorithms are required for the estimation of security state. This will identify when a maneuver is required. Second, a theory for the estimation of the cost of performing a maneuver is required. This is critical for selecting the most cost-effective maneuver while ensuring that the attack is rendered fully ineffective. Finally, we present our moving target control loop as well as a detailed case study examining the impact of our proposed cyber maneuver paradigm on DHCP attacks.
0-knowledge fuzzing

Nowadays fuzzing is a pretty common technique used both by attackers and software developers. Currently known techniques usually involve knowing the protocol/format that needs to be fuzzed and having a basic understanding of how the user input is processed inside the binary. In the past since fuzzing was little-used obtaining good results with a small amount of effort was possible. Today finding bugs requires digging a lot inside the code and the user-input as common vulnerabilies are already identified and fixed by developers. This paper will present an idea on how to effectively fuzz with no knowledge of the userinput and the binary. Specifically the paper will demonstrate how techniques like code coverage, data tainting and inmemory fuzzing allow to build a smart fuzzer with no need to instrument it.
Towards The Deep Model: Understanding Visual Recognition Through Computational Models

Vision, due to its significance in surviving and socializing, is one of the most important and  extensively studied sensory functions in the human brain. In order to fully understand visual  information processing, or more specifically, visual recognition, David Marr proposed the  Tri-level Hypothesis [29], in that three levels of the system should be studied: the computational  goal of the system, the internal representation or the algorithm the system uses to achieve the  goal, and the neural substrates that implement the system. It is well-known that visual  recognition in the human brain is implemented by the ventral visual pathway [32], which  receives visual information from the retina and goes through a layered structure including V1  (also known as the primary visual cortex), V2, V4, before reaching the inferior temporal cortex  (IT). The topographic mapping between the retina and the human visual cortex follows a  log-polar transformation, in which the Cartesian coordinates of the retina are transformed to  polar coordinates (polar angle and eccentricity) in the human visual cortex. From V1 to V4, each  of the visual areas contains a complete polar angle and eccentricity map, and have a increasing  receptive field (RF) size and respond to increasing complex visual features [12,22]. In higher  level visual areas such as the ventral temporal cortex (VTC), studies using functional magnetic  resonance imaging (fMRI) and diffusion tensor imaging (DTI) have found there exists visual  category-selective regions, such as the Fusiform Face Area (FFA, [26]) for face processing, the  Parahippocampal Place Area (PPA, [13]) for scene perception, and the Lateral Occipital  Complex (LOC, ) for general object recognition. Grill-Spector and Malach [20] proposed that  layer-based hierarchical processing, together with the functional specialization, are the two  organizing principles of the human visual cortex. More recent studies have shown that the  central-biased face recognition pathway (FFA) and the peripheral-biased scene recognition  pathway (PPA) are both functionally and anatomically segregated by mid-fusiform sulcus (MFS),  which enables parallel fast processing of visual recognition tasks in the ventral pathway [18, 43,  21, 44].
Measuring User Influence in Twitter: The Million Follower Fallacy

Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user’s influence on others—a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.