nils-herrmann/clean_scirepeval huggingface dataset:
End-to-end 3D shape inverse rendering of different classes of objects from a single input image

In this paper a semi-supervised deep framework is proposed for the problem of 3D shape inverse rendering from a single 2D input image. The main structure of proposed framework consists of unsupervised pre-trained components which significantly reduce the need to labeled data for training the whole framework. using labeled data has the advantage of achieving to accurate results without the need to predefined assumptions about image formation process. Three main components are used in the proposed network: an encoder which maps 2D input image to a representation space, a 3D decoder which decodes a representation to a 3D structure and a mapping component in order to map 2D to 3D representation. The only part that needs label for training is the mapping part with not too many parameters. The other components in the network can be pre-trained unsupervised using only 2D images or 3D data in each case. The way of reconstructing 3D shapes in the decoder component, inspired by the model based methods for 3D reconstruction, maps a low dimensional representation to 3D shape space with the advantage of extracting the basis vectors of shape space from training data itself and is not restricted to a small set of examples as used in predefined models. Therefore, the proposed framework deals directly with coordinate values of the point cloud representation which leads to achieve dense 3D shapes in the output. The experimental results on several benchmark datasets of objects and human faces and comparing with recent similar methods shows the power of proposed network in recovering more details from single 2D images.
Collaboration: a Literature Review Research Report

Collaboration is the “mutual engagement of participants in a coordinated effort to solve a problem together.” Collaborative interactions are characterized by shared goals, symmetry of structure, and a high degree of negotiation, interactivity, and interdependence. Interactions producing elaborated explanations are particularly valuable for improving student learning. Nonresponsive feedback, on the other hand, can be detrimental to student learning in collaborative situations. Collaboration can have powerful effects on student learning, particularly for low-achieving students. However, a number of factors may moderate the impact of collaboration on student learning, including student characteristics, group composition, and task characteristics. Although historical frameworks offer some guidance as to when and how children acquire and develop collaboration skills, there is scant empirical evidence to support such predictions. However, because many researchers appear to believe children can be taught to collaborate, they urge educators to provide explicit instruction that encourages development of skills such as coordination, communication, conflict resolution, decision-making, problemsolving, and negotiation. Such training should also emphasize desirable qualities of interaction, such as providing elaborated explanations, asking direct and specific questions, and responding appropriately to the requests of others. Teachers should structure tasks in ways that will support the goals of collaboration, specify “ground rules” for interaction, and regulate such interactions. There are a number of challenges in using group-based tasks to assess collaboration. Several suggestions for assessing collaboration skills are made.
Similarity Group-By

Group-by is a core database operation that is used extensively in OLTP, OLAP, and decision support systems. In many application scenarios, it is required to group similar but not necessarily equal values. In this paper we propose a new SQL construct that supports similarity-based Group-by (SGB). SGB is not a new clustering algorithm, but rather is a practical and fast similarity grouping query operator that is compatible with other SQL operators and can be combined with them to answer similarity-based queries efficiently. In contrast to expensive clustering algorithms, the proposed similarity group-by operator maintains low execution times while still generating meaningful groupings that address many application needs. The paper presents a general definition of the similarity group-by operation and gives three instances of this definition. The paper also discusses how optimization techniques for the regular group-by can be extended to the case of SGB. The proposed operators are implemented inside PostgreSQL. The performance study shows that the proposed similarity-based group-by operators have good scalability properties with at most only 25% increase in execution time over the regular group-by.
Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks

We propose an inverse reinforcement learning (IRL) approach using Deep QNetworks to extract the rewards in problems with large state spaces. We evaluate the performance of this approach in a simulation-based autonomous driving scenario. Our results resemble the intuitive relation between the reward function and readings of distance sensors mounted at different poses on the car. We also show that, after a few learning rounds, our simulated agent generates collision-free motions and performs human-like lane change behaviour.
Markov Chain Monte Carlo Convergence Diagnostics : A Comparative Review Author ( s ) :

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Camera calibration and three-dimensional world reconstruction of stereo-vision using neural networks

Stereo-pair images obtained from two cameras can be used to compute three-dimensional (3D) world coordinates of a point using triangulation. However, to apply this method, camera calibration parameters for each camera need to be experimentally obtained. Camera calibration is a rigorous experimental procedure in which typically 12 parameters are to be evaluated for each camera. The general camera model is often such that the system becomes nonlinear and requires good initial estimates to converge to a solution. We propose that, for stereo vision applications in which real-world coordinates are to be evaluated, arti® cial neural networks be used to train the system such that the need for camera calibration is eliminated. The training set for our neural network consists of a variety of stereo-pair images and corresponding 3D world coordinates. We present the results obtained on our prototype mobile robot that employs two cameras as its sole sensors and navigates through simple regular obstacles in a high-contrast environment. We observe that the percentage errors obtained from our set-up are comparable with those obtained through standard camera calibration techniques and that the system is accurate enough for most machine-vision applications.
Computed eigenmodes of planar regions

Recently developed numerical methods make possible the highaccuracy computation of eigenmodes of the Laplacian for a variety of “drums” in two dimensions. A number of computed examples are presented together with a discussion of their implications concerning bound and continuum states, isospectrality, symmetry and degeneracy, eigenvalue avoidance, resonance, localization, eigenvalue optimization, perturbation of eigenvalues and eigenvectors, and other matters.
FACE RECOGNITION: A LITERATURE REVIEW

The task of face recognition has been actively researched in recent years. This paper provides an up-to-date review of major human face recognition research. We first present an overview of face recognition and its applications. Then, a literature review of the most recent face recognition techniques is presented. Description and limitations of face databases which are used to test the performance of these face recognition algorithms are given. A brief summary of the face recognition vendor test (FRVT) 2002, a large scale evaluation of automatic face recognition technology, and its conclusions are also given. Finally, we give a summary of the research results. Combined classifiers, face recognition, graph matching, neural networks.
Communication and the Emergence of Collective Behavior in Living Organisms: A Quantum Approach

Intermolecular interactions within living organisms have been found to occur not as individual independent events but as a part of a collective array of interconnected events. The problem of the emergence of this collective dynamics and of the correlated biocommunication therefore arises. In the present paper we review the proposals given within the paradigm of modern molecular biology and those given by some holistic approaches to biology. In recent times, the collective behavior of ensembles of microscopic units (atoms/molecules) has been addressed in the conceptual framework of Quantum Field Theory. The possibility of producing physical states where all the components of the ensemble move in unison has been recognized. In such cases, electromagnetic fields trapped within the ensemble appear. In the present paper we present a scheme based on Quantum Field Theory where molecules are able to move in phase-correlated unison among them and with a self-produced electromagnetic field. Experimental corroboration of this scheme is presented. Some consequences for future biological developments are discussed.
Analysis and design of a slotted waveguide antenna array using hollow substrate integrated waveguide

In this paper, a 6 × 6 planar slot array based on a hollow substrate integrated waveguide (HSIW) is presented. To eliminate the tilting of the main beam, the slot array is fed from the centre at the back of the HSIW, which results in a blockage area. To reduce the impact on sidelobe levels, a slot extrusion technique is introduced. A simplified multiway power divider is demonstrated to feed the array elements and the optimisation procedure is described. To verify the antenna design, a 6 × 6 planar array is fabricated and measured in a low temperature co-fired ceramic (LTCC) technology. The HSIW has lower loss, comparable to standard WR28, and a high gain of 17.1 dBi at 35.5 GHz has been achieved in the HSIW slot array.
GT-GOLEM-2013-001 , 2013 Kinematics and Inverse Kinematics for the Humanoid Robot HUBO 2 +

This paper derives the forward and inverse kinematics of a humanoid robot. The specific humanoid that the derivation is for is a robot with 27 degrees of freedom but the procedure can be easily applied to other similar humanoid platforms. First, the forward and inverse kinematics are derived for the arms and legs. Then, the kinematics for the torso and the head are solved. Finally, the forward and inverse kinematic solutions for the whole body are derived using the kinematics of arms, legs, torso, and head.
Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses

We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on phase change memory (PCM) technology. The synapse is capable of spike-timing dependent plasticity (STDP), where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.
Hybrid indoor pedestrian navigation combining an INS and a spatial non-uniform UWB-network

The importance of location based services (LBS) is steadily increasing with progressive automation and interconnectedness of systems and processes. However, a comprehensive localization and navigation solution is still part of research. Especially for dynamic and harsh indoor environments, accurate and affordable localization and navigation remains a challenge. In this paper, we present a hybrid localization system providing position information and navigation aid to pedestrian in dynamic indoor environments, like construction sites, by combining an IMU and a spatial non-uniform UWB-network. The key contribution of this paper is a hybrid localization concept and experimental results, demonstrating in an application near scenario the enhancements introduced by the combination of an inertial navigation system (INS) and a spatial non-uniform UWB-network.
Dynamic Analysis of a Nonholonomic Two-Wheeled Inverted Pendulum Robot

As a result of the increase in robots in various fields, the mechanical stability of specific robots has become an important subject of research. This study is concerned with the development of a two-wheeled inverted pendulum robot that can be applied to an intelligent, mobile home robot. This kind of robotic mechanism has an innately clumsy motion for stabilizing the robot’s body posture. To analyze and execute this robotic mechanism, we investigated the exact dynamics of the mechanism with the aid of 3-DOF modeling. By using the governing equations of motion, we analyzed important issues in the dynamics of a situation with an inclined surface and also the effect of the turning motion on the stability of the robot. For the experiments, the mechanical robot was constructed with various sensors. Its application to a two-dimensional floor environment was confirmed by experiments on factors such as balancing, rectilinear motion, and spinning motion.
Strong Federations: An Interoperable Blockchain Solution to Centralized Third Party Risks

Bitcoin, the first peer-to-peer electronic cash system, opened the door to permissionless, private, and trustless transactions. Attempts to repurpose Bitcoin’s underlying blockchain technology have run up against fundamental limitations to privacy, faithful execution, and transaction finality. We introduce Strong Federations: publicly verifiable, Byzantinerobust transaction networks that facilitate movement of any asset between disparate markets, without requiring third-party trust. Strong Federations enable commercial privacy, with support for transactions where asset types and amounts are opaque, while remaining publicly verifiable. As in Bitcoin, execution fidelity is cryptographically enforced; however, Strong Federations significantly lower capital requirements for market participants by reducing transaction latency and improving interoperability. To show how this innovative solution can be applied today, we describe Liquid: the first implementation of Strong Federations deployed in a Financial Market.
Yedroudj-Net: An Efficient CNN for Spatial Steganalysis

For about 10 years, detecting the presence of a secret message hidden in an image was performed with an Ensemble Classifier trained with Rich features. In recent years, studies such as Xu et al. have indicated that well-designed convolutional Neural Networks (CNN) can achieve comparable performance to the two-step machine learning approaches. In this paper, we propose a CNN that outperforms the state-of-the-art in terms of error probability. The proposition is in the continuity of what has been recently proposed and it is a clever fusion of important bricks used in various papers. Among the essential parts of the CNN, one can cite the use of a pre-processing filterbank and a Truncation activation function, five convolutional layers with a Batch Normalization associated with a Scale Layer, as well as the use of a sufficiently sized fully connected section. An augmented database has also been used to improve the training of the CNN. Our CNN was experimentally evaluated against S-UNIWARD and WOW embedding algorithms and its performances were compared with those of three other methods: an Ensemble Classifier plus a Rich Model, and two other CNN steganalyzers.
General Relativity in Electrical Engineering

In electrical engineering metamaterials have been developed that offer unprecedented control over electromagnetic fields. Here, we show that general relativity provides the theoretical tools for designing devices made of such versatile materials. Given a desired device function, the theory describes the electromagnetic properties that turn this function into fact. We consider media that facilitate space-time transformations and include negative refraction. Our theory unifies the concepts operating behind the scenes of perfect invisibility devices, perfect lenses, the optical Aharonov–Bohm effect and electromagnetic analogues of the event horizon, and may lead to further applications. New Journal of Physics 8 (2006)  S1367-2630(06)29513-2 1367-2630/06/010247+18$30.00 © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft 2 Institute of Physics DEUTSCHE PHYSIKALISCHE GESELLSCHAFT
New trust metric for the RPL routing protocol

Establishing trust relationships between routing nodes represents a vital security requirement to establish reliable routing processes that exclude infected or selfish nodes. In this paper, we propose a new security scheme for the Internet of things and mainly for the RPL (Routing Protocol for Low-power and Lossy Networks) called: Metric-based RPL Trustworthiness Scheme (MRTS). The primary aim is to enhance RPL security and deal with the trust inference problem. MRTS addresses trust issue during the construction and maintenance of routing paths from each node to the BR (Border Router). To handle this issue, we extend DIO (DODAG Information Object) message by introducing a new trust-based metric ERNT (Extended RPL Node Trustworthiness) and a new Objective Function TOF (Trust Objective Function). In fact, ERNT represents the trust values for each node within the network, and TOF demonstrates how ERNT is mapped to path cost. In MRTS all nodes collaborate to calculate ERNT by taking into account nodes' behavior including selfishness, energy, and honesty components. We implemented our scheme by extending the distributed Bellman-Ford algorithm. Evaluation results demonstrated that the new scheme improves the security of RPL.
FACTORS AFFECTING PURCHASE INTENTION OF ONLINE SHOPPING IN ZALORA INDONESIA

The purpose of the study examined the relationship between ease of use and consumer attitudes, the relationship between usefulness and consumer attitudes, the relationship between perceived risk and consumer attitudes, and the relationship between consumer attitudes and intention to buy Zalora. The data collection used convenience sampling technique and the execution using questionnaire to collect the data. This method is used because the chosen sample must understand about the research problems. Questionnaire spreaded to 150 online consumers who has intention to buy Zalora at Paragon Mall in Surakarta. The geographical selection based on practical reasons where Surakarta has many online consumers. The 150 usable questionnaires were analyzed with SPSS. In this study, Structural Equation Modeling (SEM) is used to analyze the hypothesis of the research. The result showed positive relationship and all of the hypothesis is supported. Original Research Article Putro and Haryanto; BJEMT, 9(1): 1-12, 2015; Article no.BJEMT.18704 2
Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All

Collective entity disambiguation, or collective entity linking aims to jointly resolve multiple mentions by linking them to their associated entities in a knowledge base. Previous works are primarily based on the underlying assumption that entities within the same document are highly related. However, the extent to which these entities are actually connected in reality is rarely studied and therefore raises interesting research questions. For the first time, this paper shows that the semantic relationships between mentioned entities within a document are in fact less dense than expected. This could be attributed to several reasons such as noise, data sparsity, and knowledge base incompleteness. As a remedy, we introduce MINTREE, a new tree-based objective for the problem of entity disambiguation. The key intuition behind MINTREE is the concept of coherence relaxation which utilizes the weight of a minimum spanning tree to measure the coherence between entities. Based on this new objective, we design Pair-Linking, a novel iterative solution for the MINTREE optimization problem. The idea of Pair-Linking is simple: instead of considering all the given mentions, Pair-Linking iteratively selects a pair with the highest confidence at each step for decision making. Via extensive experiments on 8 benchmark datasets, we show that our approach is not only more accurate but also surprisingly faster than many state-of-the-art collective linking algorithms.
Using Lexical Chains For Text Summarization

We investigate one technique to produce a summary of an original text without requiring its full semantic interpretation, but instead relying on a model of the topic progression in the text derived from lexical chains. We present a new algorithm to compute lexical chains in a text, merging several robust knowledge sources: the WordNet thesaurus, a part-of-speech tagger, shallow parser for the identification of nominal groups, and a segmentation algorithm. Summarization proceeds in four steps: the original text is segmented, lexical chains are constructed, strong chains are identified and significant sentences are extracted. We present in this paper empirical results on the identification of strong chains and of significant sentences. Preliminary results indicate that quality indicative summaries are produced. Pending problems are identified. Plans to address these short-comings are briefly presented.
Usage history of scientific literature: Nature metrics and metrics of Nature publications

In this study, we analyze the dynamic usage history of Nature publications over time using Nature metrics data. We conduct analysis from two perspectives. On the one hand, we examine how long it takes before the articles’ downloads reach 50 %/80 % of the total; on the other hand, we compare the percentage of total downloads in 7, 30, and 100 days after publication. In general, papers are downloaded most frequently within a short time period right after their publication. And we find that compared with non-Open Access papers, readers’ attention on Open Access publications are more enduring. Based on the usage data of a newly published paper, regression analysis could predict the future expected total usage counts.
Intelligent management of intelligence agencies: beyond accountability ping-pong.

The intelligence community (IC) is asked to predict outcomes that may often be inherently unpredictable-and is blamed for the inevitable forecasting failures, be they false positives or false negatives. To move beyond blame games of accountability ping-pong that incentivize bureaucratic symbolism over substantive reform, it is necessary to reach bipartisan agreements on performance indicators that are transparent enough to reassure clashing elites (to whom the IC must answer) that estimates have not been politicized. Establishing such transideological credibility requires (a) developing accuracy metrics for decoupling probability and value judgments; (b) using the resulting metrics as criterion variables in validity tests of the IC's selection, training, and incentive systems; and (c) institutionalizing adversarial collaborations that conduct level-playing-field tests of clashing perspectives.