nils-herrmann/clean_scirepeval huggingface dataset:
Self-recognition and the right prefrontal cortex

Although the anatomical and functional substrates subserving face recognition have been subject to extensive investigation, the underpinnings of self-face recognition are not well understood. Given the evidence that own-face recognition has been demonstrated by a select number of species, it is intriguing to speculate whether self-face recognition is accomplished via a 'self-network' or simply a 'face-network' within the brain. Furthermore, the relationship of self-recognition to other self-processes, such as self-evaluation and autobiographical retrieval, are not clearly defined. However, data from fMRI, ERPs and repetitive transcranial magnetic stimulation as well as from split-brain studies and patients with focal lesions, indicate that the prefrontal cortex, with possible right hemisphere lateralization, may be a preferential component in self-recognition. Studies using these methods, as well as PET, have indicated that the self-processes of self-evaluation and autobiographical memory preferentially engage networks within the right fronto-temporal region. Although it is highly improbable that there is a 'self-recognition' or 'self' center, it appears that there may be a bias for the processing of 'self' within the right prefrontal cortex.
The structure of the information visualization design space

Research on information visualization has reached the point where a number of successful point designs have been proposed and a variety of techniques have been discovered. It is now appropriate to describe and analyze portions of the design space so as to understand the differences among designs and to suggest new possibilities. This paper proposes an organization of the information visualization literature and illustrates it with a series of examples. The result is a framework for designing new visualizations and augmenting existing designs.
Part-based grasp planning for familiar objects

In this work, we present a part-based grasp planning approach that is capable of generating grasps that are applicable to multiple familiar objects. We show how object models can be decomposed according to their shape and local volumetric information. The resulting object parts are labeled with semantic information and used for generating robotic grasping information. We investigate how the transfer of such grasping information to familiar objects can be achieved and how the transferability of grasps can be measured. We show that the grasp transferability measure provides valuable information about how successful planned grasps can be applied to novel object instances of the same object category. We evaluate the approach in simulation, by applying it to multiple object categories and determine how successful the planned grasps can be transferred to novel, but familiar objects. In addition, we present a use case on the humanoid robot ARMAR-III.
Privacy-preserving pattern matching over encrypted genetic data in cloud computing

Personalized medicine performs diagnoses and treatments according to the DNA information of the patients. The new paradigm will change the health care model in the future. A doctor will perform the DNA sequence matching instead of the regular clinical laboratory tests to diagnose and medicate the diseases. Additionally, with the help of the affordable personal genomics services such as 23andMe, personalized medicine will be applied to a great population. Cloud computing will be the perfect computing model as the volume of the DNA data and the computation over it are often immense. However, due to the sensitivity, the DNA data should be encrypted before being outsourced into the cloud. In this paper, we start from a practical system model of the personalize medicine and present a solution for the secure DNA sequence matching problem in cloud computing. Comparing with the existing solutions, our scheme protects the DNA data privacy as well as the search pattern to provide a better privacy guarantee. We have proved that our scheme is secure under the well-defined cryptographic assumption, i.e., the sub-group decision assumption over a bilinear group. Unlike the existing interactive schemes, our scheme requires only one round of communication, which is critical in practical application scenarios. We also carry out a simulation study using the real-world DNA data to evaluate the performance of our scheme. The simulation results show that the computation overhead for real world problems is practical, and the communication cost is small. Furthermore, our scheme is not limited to the genome matching problem but it applies to general privacy preserving pattern matching problems which is widely used in real world.
The habitat function of mangroves for terrestrial and marine fauna : A review

Mangroves are defined by the presence of trees that mainly occur in the intertidal zone, between land and sea, in the (sub) tropics. The intertidal zone is characterised by highly variable environmental factors, such as temperature, sedimentation and tidal currents. The aerial roots of mangroves partly stabilise this environment and provide a substratum on which many species of plants and animals live. Above the water, the mangrove trees and canopy provide important habitat for a wide range of species. These include birds, insects, mammals and reptiles. Below the water, the mangrove roots are overgrown by epibionts such as tunicates, sponges, algae, and bivalves. The soft substratum in the mangroves forms habitat for various infaunal and epifaunal species, while the space between roots provides shelter and food for motile fauna such as prawns, crabs and fishes. Mangrove litter is transformed into detritus, which partly supports the mangrove food web. Plankton, epiphytic algae and microphytobenthos also form an important basis for the mangrove food web. Due to the high abundance of food and shelter, and low predation pressure, mangroves form an ideal habitat for a variety of animal species, during part or all of their life cycles. As such, mangroves may function as nursery habitats for (commercially important) crab, prawn and fish species, and support offshore fish populations and fisheries. Evidence for linkages between mangroves and offshore habitats by animal migrations is still scarce, but highly needed for management and conservation purposes. Here, we firstly reviewed the habitat function of mangroves by common taxa of terrestrial and marine animals. Secondly, we reviewed the literature with regard to the degree of interlinkage between mangroves and adjacent habitats, a research area which has received increasing attention in the last decade. Finally, we reviewed current insights into the degree to which mangrove litter fuels the mangrove food web, since this has been the subject of longstanding debate.
Knowlege Graph Embedding by Flexible Translation

Knowledge graph embedding refers to projecting entities and relations in knowledge graph into continuous vector spaces. State-of-the-art methods, such as TransE, TransH, and TransR build embeddings by treating relation as translation from head entity to tail entity. However, previous models can not deal with reflexive/one-to-many/manyto-one/many-to-many relations properly, or lack of scalability and efficiency. Thus, we propose a novel method, flexible translation, named TransF, to address the above issues. TransF regards relation as translation between head entity vector and tail entity vector with flexible magnitude. To evaluate the proposed model, we conduct link prediction and triple classification on benchmark datasets. Experimental results show that our method remarkably improve the performance compared with several state-of-the-art baselines.
Cascaded H-bridge multilevel converter for large-scale PV grid-integration with isolated DC-DC stage

The integration of large-scale PV systems to the grid is a growing trend in modern power systems. The cascaded H-bridge (CHB) converter is a suitable candidate for the grid interconnection due to its modular characteristics, high-quality output waveforms and capability of connecting to medium-voltage grids. However, the CHB converter requires isolated DC sources. In order to avoid the leakage currents caused by the high potential differences across the parasitic capacitance of the PV panels to ground, an isolated DC-DC conversion stage is required when the CHB topology is used. The objective of this paper is to compare two PV system configurations based on the CHB multilevel converter using two isolated DC-DC converter topologies, namely the boost-half-bridge (BHB) and the flyback, for their performance on providing isolation and achieving individual MPPT at the DC-DC power conversion stage of large-scale PV power systems. Simulation results from a 263 kW PV system based on a seven-level CHB converter with the two aforementioned isolated DC-DC converters are provided for comparison and evaluation with different input PV voltages.
An Improved Non-monotonic Transition System for Dependency Parsing

Transition-based dependency parsers usually use transition systems that monotonically extend partial parse states until they identify a complete parse tree. Honnibal et al. (2013) showed that greedy onebest parsing accuracy can be improved by adding additional non-monotonic transitions that permit the parser to “repair” earlier parsing mistakes by “over-writing” earlier parsing decisions. This increases the size of the set of complete parse trees that each partial parse state can derive, enabling such a parser to escape the “garden paths” that can trap monotonic greedy transition-based dependency parsers. We describe a new set of non-monotonic transitions that permits a partial parse state to derive a larger set of completed parse trees than previous work, which allows our parser to escape from a larger set of garden paths. A parser with our new nonmonotonic transition system has 91.85% directed attachment accuracy, an improvement of 0.6% over a comparable parser using the standard monotonic arc-eager transitions.
Global Textual Relation Embedding for Relational Understanding

Pre-trained embeddings such as word embeddings and sentence embeddings have been widely shown to be a fundamental tool to facilitate downstream tasks. In this work, we investigate the possibility of learning general-purpose embedding of textual relations, defined as the shortest dependency path between two entities, and whether it can facilitate downstream tasks requiring relational understanding of text. To learn such embeddings, we create the largest ever distant supervision dataset by linking the entire English CluWeb09 corpus to Freebase. Using the global co-occurrence statistics between textual and knowledge base relations as supervision signal, we learn the embeddings of textual relation with the Transformer model. We conduct intrinsic evaluation as well as extrinsic evaluation on two downstream tasks, relation extraction and knowledge base completion, and show that the learned textual relation embeddings serve as a good prior for relational understanding tasks and boost their performance. Our code and pre-trained model can be found at https://github.com/soon-to-be-released.
Empathy and compassion

As humans we are a highly social species: in order to coordinate our joint actions and assure successful communication, we use language skills to explicitly convey information to each other, and social abilities such as empathy or perspective taking to infer another person's emotions and mental state. The human cognitive capacity to draw inferences about other peoples' beliefs, intentions and thoughts has been termed mentalizing, theory of mind or cognitive perspective taking. This capacity makes it possible, for instance, to understand that people may have views that differ from our own. Conversely, the capacity to share the feelings of others is called empathy. Empathy makes it possible to resonate with others' positive and negative feelings alike--we can thus feel happy when we vicariously share the joy of others and we can share the experience of suffering when we empathize with someone in pain. Importantly, in empathy one feels with someone, but one does not confuse oneself with the other; that is, one still knows that the emotion one resonates with is the emotion of another. If this self-other distinction is not present, we speak of emotion contagion, a precursor of empathy that is already present in babies.
Advantages of ACT-R over Prolog for Natural Language Analysis

This paper discusses the advantages of using the ACT-R cognitive architecture over the Prolog programming language for the research and development of a large-scale, functional, cognitively motivated model of natural language analysis. Although Prolog was developed for Natural Language Processing (NLP), it lacks any probabilistic mechanisms for dealing with ambiguity and relies on failure detection and algorithmic backtracking to explore alternative analyses. These mechanisms are problematic for handling ill-formed or unexpected inputs, often resulting in an exploration of the entire search space, which becomes intractable as the complexity and variability of the allowed inputs and corresponding grammar grow. By comparison, ACT-R provides context dependent and probabilistic mechanisms which allow the model to incrementally pursue the best analysis. When combined with a nonmonotonic context accommodation mechanism that supports modest adjustment of the evolving analysis to handle cases where the locally best analysis is not globally preferred, the result is an efficient pseudo-deterministic mechanism that obviates the need for failure detection and backtracking, aligns with our basic understanding of Human Language Processing (HLP) and is scalable to broad coverage. The successful transition of the natural language analysis model from Prolog to ACT-R suggests that a cognitively motivated approach to natural language analysis may also be suitable for achieving a functional capability.
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers

In this paper, we develop novel, efficient 2D encodings for 3D geometry, which enable reconstructing full 3D shapes from a single image at high resolution. The key idea is to pose 3D shape reconstruction as a 2D prediction problem. To that end, we first develop a simple baseline network that predicts entire voxel tubes at each pixel of a reference view. By leveraging well-proven architectures for 2D pixel-prediction tasks, we attain state-of-the-art results, clearly outperforming purely voxel-based approaches. We scale this baseline to higher resolutions by proposing a memory-efficient shape encoding, which recursively decomposes a 3D shape into nested shape layers, similar to the pieces of a Matryoshka doll. This allows reconstructing highly detailed shapes with complex topology, as demonstrated in extensive experiments; we clearly outperform previous octree-based approaches despite having a much simpler architecture using standard network components. Our Matryoshka networks further enable reconstructing shapes from IDs or shape similarity, as well as shape sampling.
Python: a programming language for software integration and development.

One of the challenges in bio-computing is to enable the efficient use and inter-operation of a wide variety of rapidly-evolving computational methods to simulate, analyze, and understand the complex properties and interactions of molecular systems. In our laboratory we investigates several areas, including protein-ligand docking, protein-protein docking, and complex molecular assemblies. Over the years we have developed a number of computational tools such as molecular surfaces, phenomenological potentials, various docking and visualization programs which we use in conjunction with programs developed by others. The number of programs available to compute molecular properties and/or simulate molecular interactions (e.g., molecular dynamics, conformational analysis, quantum mechanics, distance geometry, docking methods, ab-initio methods) is large and growing rapidly. Moreover, these programs come in many flavors and variations, using different force fields, search techniques, algorithmic details (e.g., continuous space vs. discrete, Cartesian vs. torsional). Each variation presents its own characteristic set of advantages and limitations. These programs also tend to evolve rapidly and are usually not written as components, making it hard to get them to work together.
Individualism and Collectivism : Cross-Cultural Perspectives on Self-Ingroup Relationships

The individualism and collectivism constructs are theoretically analyzed and linked to certain hypothesized consequences (social behaviors, health indices). Study 1 explores the meaning of these constructs within culture (in the United States), identifying the individual-differences variable, idiocentrism versus allocentrism, that corresponds to the constructs. Factor analyses of responses to items related to the constructs suggest that UrS. individualism is reflected in (a) Self-Reliance With Competition, (b) Low Concern for Ingroups, and (c) Distance from Ingroups. A higher order factor analysis suggests that Subordination oflngroup Goals to Personal Goals may be the most important aspect of U.S. individualism. Study 2 probes the limits of the constructs with data from two collectivist samples (Japan and Puerto Rico) and one individualist sample (Illinois) of students. It is shown that responses depend on who the other is (i.e., which ingroup), the context, and the kind of social behavior (e.g., feel similar to other, attentive to the views of others). Study 3 replicates previous work in Puerto Rico indicating that allocentric persons perceive that they receive more and a better quality of social support than do idiocentric persons, while the latter report being more lonely than the former. Several themes, such as self-reliance, achievement, and competition, have different meanings in the two kinds of societies, and detailed examinations of the factor patterns show how such themes vary across cultures.
Artificial Neural Network Weight Optimization: A Review

Optimizing the weights of Artificial Neural Networks (ANNs) is a great important of a complex task in the research of machine learning due to dependence of its performance to the success of learning process and the training method. This paper reviews the implementation of meta-heuristic algorithms in ANNs’ weight optimization by studying their advantages and disadvantages giving consideration to some meta-heuristic members such as Genetic algorithim, Particle Swarm Optimization and recently introduced meta-heuristic algorithm called Harmony Search Algorithm (HSA). Also, the application of local search based algorithms to optimize the ANNs weights and their benefits as well as their limitations are briefly elaborated. Finally, a comparison between local search methods and global optimization methods is carried out to speculate the trends in the progresses of ANNs’ weight optimization in the current resrearch.
Enhancing Bitcoin Security and Performance with Strong Consistency via Collective Signing

While showing great promise, Bitcoin requires users to wait tens of minutes for transactions to commit, and even then, offering only probabilistic guarantees. This paper introduces ByzCoin, a novel Byzantine consensus protocol that leverages scalable collective signing to commit Bitcoin transactions irreversibly within seconds. ByzCoin achieves Byzantine consensus while preserving Bitcoin’s open membership by dynamically forming hash power-proportionate consensus groups that represent recently-successful block miners. ByzCoin employs communication trees to optimize transaction commitment and verification under normal operation while guaranteeing safety and liveness under Byzantine faults, up to a near-optimal tolerance of f faulty group members among 3 f + 2 total. ByzCoin mitigates double spending and selfish mining attacks by producing collectively signed transaction blocks within one minute of transaction submission. Tree-structured communication further reduces this latency to less than 30 seconds. Due to these optimizations, ByzCoin achieves a throughput higher than Paypal currently handles, with a confirmation latency of 15-20 seconds.
On the foundation of NOMA and its application to 5G cellular networks

Non-orthogonal multiple access (NOMA) is recognized today as a most promising technology for future 5G cellular networks and a large number of papers have been published on the subject over the past few years. Interestingly, none of these authors seems to be aware that the foundation of NOMA actually dates back to the year 2000, when a series of papers introduced and investigated multiple access schemes using two sets of orthogonal signal waveforms and iterative interference cancellation at the receiver. The purpose of this paper is to shed light on that early literature and to describe a practical scheme based on that concept, which is particularly attractive for machine-type communications (MTC) in future 5G cellular networks. Using this approach, NOMA appears as a convenient extension of orthogonal multiple access rather than a strictly competing technology, and most important of all, the power imbalance between the transmitted user signals that is required to make the receiver work in other NOMA schemes is not required here.
Managing Conflicts in Goal-Driven Requirements Engineering

A wide range of inconsistencies can arise during requirements engineering as goals and requirements are elicited from multiple stakeholders. Resolving such inconsistencies sooner or later in the process is a necessary condition for successful development of the software implementing those requirements. The paper first reviews the main types of inconsistency that can arise during requirements elaboration, defining them in an integrated framework and exploring their interrelationships. It then concentrates on the specific case of conflicting formulations of goals and requirements among different stakeholder viewpoints or within a single viewpoint. A frequent, weaker form of conflict called divergence is introduced and studied in depth. Formal techniques and heuristics are proposed for detecting conflicts and divergences from specifications of goals/ requirements and of domain properties. Various techniques are then discussed for resolving conflicts and divergences systematically by introduction of new goals or by transformation of specifications of goals/objects towards conflict-free versions. Numerous examples are given throughout the paper to illustrate the practical relevance of the concepts and techniques presented. The latter are discussed in the framework of the KAOS methodology for goal-driven requirements engineering. Index Terms Goal-driven requirements engineering, divergent requirements, conflict management, viewpoints, specification transformation, lightweight formal methods. ,((( 7UDQVDFWLRQV RQ 6RIWZDUH (QJLQHHULQJ 6SHFLDO ,VVXH RQ 0DQDJLQJ ,QFRQVLVWHQF\ LQ 6RIWZDUH 'HYHORSPHQW 1RY