nils-herrmann/clean_scirepeval huggingface dataset:
Long-Term Amnesia: A Review and Detailed Illustrative Case Study

Long-term amnesia is a slowly developing form of anterograde amnesia accompanied by retrograde amnesia of variable severity (Kapur, 1996; 1997) often associated with damage to the anterior temporal neocortex and epileptic seizures. The precise neural and functional deficits that underlie this condition are unknown. A patient, JL, who has this condition following a closed-head injury, is described in detail. Her injury caused bilateral anterior temporal neocortex damage that was more extensive on the left and right-sided damage to the perirhinal and orbitofrontal cortices. The hippocampus appeared to be intact bilaterally. Epilepsy developed within two years of JL's injury. Apart from her memory impairments, JL's cognitive functions, including high-level visual perception, attention, semantic memory and executive functions were well preserved. Her memory also seemed well preserved for at least 30 minutes following encoding. The one exception was the patient's relatively greater impairment at difficult visual recognition tests for which verbalization may not have been an effective strategy. This problem may have been caused by JL's right-sided perirhinal and orbitofrontal cortex damage. Her recall and recognition was clearly impaired after a three-week delay. She also showed a retrograde amnesia, which appeared to be milder than her remote post-morbid memory deficit. JL's remote memory was preserved for information first encountered in either the pre- or post-morbid period provided the information had received sufficient rehearsal over long periods of time. Her long-term amnesia may have been caused by anterior temporal neocortex damage, possibly in association with her epileptic seizures. Whether the condition is heterogeneous, involves a deficit in slow consolidation, disruption of unconsolidated memories, or blockage of maintenance or disruption of insufficiently rehearsed memories whether or not these have been slowly consolidated is discussed.
Monitoring Wind Turbine Vibration Based on SCADA Data

Three models for detecting abnormalities of wind turbine vibrations reflected in time domain are discussed. The models were derived from the supervisory control and data acquisition (SCADA) data collected at various wind turbines. The vibration of a wind turbine is characterized by two parameters, i.e., drivetrain and tower acceleration. An unsupervised data-mining algorithm, the k-means clustering algorithm, was applied to develop the first monitoring model. The other two monitoring models for detecting abnormal values of drivetrain and tower acceleration were developed by using the concept of a control chart. SCADA vibration data sampled at 10 s intervals reflects normal and faulty status of wind turbines. The performance of the three monitoring models for detecting abnormalities of wind turbines reflected in vibration data of time domain was validated with the SCADA industrial data.
Towards Safe Deep Learning: Unsupervised Defense Against Generic Adversarial Attacks

Recent advances in adversarial Deep Learning (DL) have opened up a new and largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems. We introduce a novel automated countermeasure called Parallel Checkpointing Learners (PCL) to thwart the potential adversarial attacks and significantly improve the reliability (safety) of a victim DL model. The proposed PCL methodology is unsupervised, meaning that no adversarial sample is leveraged to build/train parallel checkpointing learners. We formalize the goal of preventing adversarial attacks as an optimization problem to minimize the rarely observed regions in the latent feature space spanned by a DL network. To solve the aforementioned minimization problem, a set of complementary but disjoint checkpointing modules are trained and leveraged to validate the victim model execution in parallel. Each checkpointing learner explicitly characterizes the geometry of the input data and the corresponding high-level data abstractions within a particular DL layer. As such, the adversary is required to simultaneously deceive all the defender modules in order to succeed. We extensively evaluate the performance of the PCL methodology against the state-of-the-art attack scenarios, including Fast-Gradient-Sign (FGS), Jacobian Saliency Map Attack (JSMA), Deepfool, and Carlini&WagnerL2. Extensive proof-of-concept evaluations for analyzing various data collections including MNIST, CIFAR10, and ImageNet corroborate the effectiveness of our proposed defense mechanism against adversarial samples.
Modeling the learning progressions of computational thinking of primary grade students

We introduce the Progression of Early Computational Thinking (PECT) Model, a framework for understanding and assessing computational thinking in the primary grades (Grades 1 to 6). The model synthesizes measurable evidence from student work with broader, more abstract coding design patterns, which are then mapped onto computational thinking concepts.
 We present the results of a pilot-test study of the PECT Model in order to demonstrate its potential efficacy in detecting both differences in computational thinking among students of various ages as well as any clear overall progressions in increasing computational sophistication. Results of this sort are vital for establishing research-based and age-appropriate curricula for students in the primary grades, i.e., developing non-trivial, challenging but not overly daunting lesson plans that utilize the cognitive development stage of each grade level most effectively.
A Two-Step Computation of the Exact GAN Wasserstein Distance

Lemma 3.2 Suppose f∗ is an optimizer to Problem 4, then i) f∗(y) = infx∈X̂{f(x) + c(x, y)}, ∀y ∈ Ŷ , and ii) f∗(x) = supy∈Ŷ {f∗(y)− c(x, y)}, ∀x ∈ X̂ Proof. i) Since f∗ is the optimal solution to Problem 4, we have f∗(y) ≤ infx∈X̂{f(x) + c(x, y)}, ∀y ∈ Ŷ . We prove i) by contradiction. Suppose there exists a yi, without loss of generality, say yt, such that f(yt) < infx∈X̂{f(x)+c(x, yt)} (Note that in this case yt can not equal any xj in X̂ . If, without loss of generality, yt equals, say xs, then we have f(yt) = {f(xs)+c(xs, yt)} ≥ infx∈X̂{f(x)+c(x, yt)}). There exists another function f ′ such that f (xj) = f(xj), ∀xj ∈ X̂ , f (yi) = f (yi),∀yi ∈ Ŷ \ yt and f (yt) = infx∈X̂{f(x) + c(x, yt)}. It is easy to verify that f ′ satisfies the constraints in Problem 4 and ĥ(f ′) > ĥ(f∗), which leads to a contradiction. Therefore, f∗(y) = infx∈X̂{f(x) + c(x, y)}, ∀y ∈ Ŷ . ii) The proof is similar to i).
DF2Net: Discriminative Feature Learning and Fusion Network for RGB-D Indoor Scene Classification

This paper focuses on the task of RGB-D indoor scene classification. It is a very challenging task due to two folds. 1) Learning robust representation for indoor scene is difficult because of various objects and layouts. 2) Fusing the complementary cues in RGB and Depth is nontrivial since there are large semantic gaps between the two modalities. Most existing works learn representation for classification by training a deep network with softmax loss and fuse the two modalities by simply concatenating the features of them. However, these pipelines do not explicitly consider intra-class and interclass similarity as well as inter-modal intrinsic relationships. To address these problems, this paper proposes a Discriminative Feature Learning and Fusion Network (DFNet) with two-stage training. In the first stage, to better represent scene in each modality, a deep multi-task network is constructed to simultaneously minimize the structured loss and the softmax loss. In the second stage, we design a novel discriminative fusion network which is able to learn correlative features of multiple modalities and distinctive features of each modality. Extensive analysis and experiments on SUN RGB-D Dataset and NYU Depth Dataset V2 show the superiority of DFNet over other state-of-the-art methods in RGB-D indoor scene classification task.
Multimaterial 3D Printing of Graphene-Based Electrodes for Electrochemical Energy Storage Using Thermoresponsive Inks.

The current lifestyles, increasing population, and limited resources result in energy research being at the forefront of worldwide grand challenges, increasing the demand for sustainable and more efficient energy devices. In this context, additive manufacturing brings the possibility of making electrodes and electrical energy storage devices in any desired three-dimensional (3D) shape and dimensions, while preserving the multifunctional properties of the active materials in terms of surface area and conductivity. This paves the way to optimized and more efficient designs for energy devices. Here, we describe how three-dimensional (3D) printing will allow the fabrication of bespoke devices, with complex geometries, tailored to fit specific requirements and applications, by designing water-based thermoresponsive inks to 3D-print different materials in one step, for example, printing the active material precursor (reduced chemically modified graphene (rCMG)) and the current collector (copper) for supercapacitors or anodes for lithium-ion batteries. The formulation of thermoresponsive inks using Pluronic F127 provides an aqueous-based, robust, flexible, and easily upscalable approach. The devices are designed to provide low resistance interface, enhanced electrical properties, mechanical performance, packing of rCMG, and low active material density while facilitating the postprocessing of the multicomponent 3D-printed structures. The electrode materials are selected to match postprocessing conditions. The reduction of the active material (rCMG) and sintering of the current collector (Cu) take place simultaneously. The electrochemical performance of the rCMG-based self-standing binder-free electrode and the two materials coupled rCMG/Cu printed electrode prove the potential of multimaterial printing in energy applications.
Comparison of optical see-through head-mounted displays for surgical interventions with object-anchored 2D-display

Optical see-through head-mounted displays (OST-HMD) feature an unhindered and instantaneous view of the surgery site and can enable a mixed reality experience for surgeons during procedures. In this paper, we present a systematic approach to identify the criteria for evaluation of OST-HMD technologies for specific clinical scenarios, which benefit from using an object-anchored 2D-display visualizing medical information. Criteria for evaluating the performance of OST-HMDs for visualization of medical information and its usage are identified and proposed. These include text readability, contrast perception, task load, frame rate, and system lag. We choose to compare three commercially available OST-HMDs, which are representatives of currently available head-mounted display technologies. A multi-user study and an offline experiment are conducted to evaluate their performance. Statistical analysis demonstrates that Microsoft HoloLens performs best among the three tested OST-HMDs, in terms of contrast perception, task load, and frame rate, while ODG R-7 offers similar text readability. The integration of indoor localization and fiducial tracking on the HoloLens provides significantly less system lag in a relatively motionless scenario. With ever more OST-HMDs appearing on the market, the proposed criteria could be used in the evaluation of their suitability for mixed reality surgical intervention. Currently, Microsoft HoloLens may be more suitable than ODG R-7 and Epson Moverio BT-200 for clinical usability in terms of the evaluated criteria. To the best of our knowledge, this is the first paper that presents a methodology and conducts experiments to evaluate and compare OST-HMDs for their use as object-anchored 2D-display during interventions.
Consistent sparsification for graph optimization

In a standard pose-graph formulation of simultaneous localization and mapping (SLAM), due to the continuously increasing numbers of nodes (states) and edges (measurements), the graph may grow prohibitively too large for long-term navigation. This motivates us to systematically reduce the pose graph amenable to available processing and memory resources. In particular, in this paper we introduce a consistent graph sparsification scheme: (i) sparsifying nodes via marginalization of old nodes, while retaining all the information (consistent relative constraints) - which is conveyed in the discarded measurements - about the remaining nodes after marginalization; and (ii) sparsifying edges by formulating and solving a consistent ℓ1-regularized minimization problem, which automatically promotes the sparsity of the graph. The proposed approach is validated on both synthetic and real data.
Agile software development in theory and practice

Kalermo, Jonna Maria Rissanen, Jenni Karoliina Agile software development in theory and practice / Jonna Kalermo and Jenni Rissanen Jyväskylä, University of Jyväskylä, 2002. 188 pp. Master's thesis In turn of the millennium, new software development ideas were presented in the form of Agile Manifesto as a counteraction to the traditional, rigorous development methods and process models. Agile Manifesto consists of four values and twelve principles of which the authors of this thesis formed a conceptual framework. It assists in analysing different aspects of agile software development one by one but also as a whole. Agile software development aims at fast, light and efficient development that supports customer's business without being chaotic or following any rigorous method. The aim of this study was to analyse Agile Manifesto and its applicability. This was done by conducting a literature review and an empirical case study. Agile Manifesto gives an ideological background for agile methods, such as Extreme Programming (XP). Such methods are most feasible for small organisations developing innovative software products. We analysed Agile Manifesto and found out that literature supported the ideas of it, although no revolutionary ideas were discovered. We used an empirical case study as an example of an agile in-house software development method in corporate venturing context. The case study supported principles and values of Agile Manifesto and confirmed the assumption that agility is heavily based on tacit knowledge, skilled and motivated employees and frequent communication. This sets several requirements for management and employees working in an agile mode: they have to be communicative, social, responsible and skilled.
Interactive Indirect Illumination Using Voxel Cone Tracing

Indirect illumination is an important element for realistic image synthesis, but its computation is expensive and highly dependent on the complexity of the scene and of the BRDF of the involved surfaces. While off-line computation and pre-baking can be acceptable for some cases, many applications (games, simulators, etc.) require real-time or interactive approaches to evaluate indirect illumination. We present a novel algorithm to compute indirect lighting in real-time that avoids costly precomputation steps and is not restricted to low-frequency illumination. It is based on a hierarchical voxel octree representation generated and updated on the fly from a regular scene mesh coupled with an approximate voxel cone tracing that allows for a fast estimation of the visibility and incoming energy. Our approach can manage two light bounces for both Lambertian and glossy materials at interactive framerates (25-70FPS). It exhibits an almost scene-independent performance and can handle complex scenes with dynamic content thanks to an interactive octree-voxelization scheme. In addition, we demonstrate that our voxel cone tracing can be used to efficiently estimate Ambient Occlusion.
Efficient lineage tracking for scientific workflows

Data lineage and data provenance are key to the management of scientific data. Not knowing the exact provenance and processing pipeline used to produce a derived data set often renders the data set useless from a scientific point of view. On the positive side, capturing provenance information is facilitated by the widespread use of workflow tools for processing scientific data. The workflow process describes all the steps involved in producing a given data set and, hence, captures its lineage. On the negative side, efficiently storing and querying workflow based data lineage is not trivial. All existing solutions use recursive queries and even recursive tables to represent the workflows. Such solutions do not scale and are rather inefficient. In this paper we propose an alternative approach to storing lineage information captured as a workflow process. We use a space and query efficient interval representation for dependency graphs and show how to transform arbitrary workflow processes into graphs that can be stored using such representation. We also characterize the problem in terms of its overall complexity and provide a comprehensive performance evaluation of the approach.
A universal model for flexible item selection in conversational dialogs

Human-computer interaction and statistical natural language understanding has changed with the addition of a visual display screen in modern mobile devices, as visual rendering is used to communicate the dialog system's response. Onscreen item identification and resolution when interpreting the user utterances is one critical problem to achieve the natural and accurate human-machine communication. This problem, also called Flexible Item Selection (FIS), has been posed as a classification task to correctly identify intended on-screen item(s) from user utterances. This paper presents a universal FIS model that can be applied to dialog systems developed in different languages. We design a set of input features for the FIS model that makes it largely language-independent. We demonstrate that a single universal FIS model can be used in place of language specific FIS models with no loss in accuracy. We also show that such a model can generalize well to new unseen languages with minimal loss in accuracy on held out languages including English, French, Spanish, Italian, German, and Chinese. Eliminating the need for building and maintaining a separate FIS model for each new language, the universal FIS model helps scaling an existing dialogue system to new languages faster at a lower development cost.
The effects of Zumba training on cardiovascular and neuromuscular function in female college students.

The present study examined the effects of Zumba training (group fitness based on salsa and aerobics) on endurance, trunk strength, balance, flexibility, jumping performance and quality of life (QoL) in female college students. Thirty female participants were randomly assigned (strata: age, BMI and physical activity) to an intervention (INT, n = 15: age: 21.0 ± 2.3 years; BMI: 21.8 ± 3.0 kg/m(2); physical activity (PA): 7.6 ± 4.6 h/week) or control group (CON, n = 14: age: 21.0 ± 2.8 years; BMI: 21.0 ± 2.1 kg/m(2); PA: 7.3 ± 3.6 h/week). Instructed Zumba training was provided twice a week for 8 weeks (training attendance: 100%). QoL was assessed using the WHO-QoL-BREF questionnaire. Endurance was measured with the 6-min walking test (6MWT). Trunk flexibility was assessed with the stand-and-reach-test and lower-extremity strength with the jump-and-reach-test. The star excursion balance test (SEBT) was employed to assess dynamic balance. Trunk strength endurance was examined using the Swiss global trunk strength test in prone and lateral (left, right) positions. All testings were performed before and after the training period. We observed large statistically significant between-group effects of total QoL score (INT: +9.8%, CON: +0.4%, p < 0.001; partial eta squared , 6MWT distance (INT: +21%, CON: -2%, p < 0.001, , trunk strength endurance (prone, INT: +48%, CON: +11%, p = 0.04,  see text]; lateral-left, INT: +71%, CON: +11%, p = 0.01,  see text], lateral-right, INT: +54%, CON: +11%, p = 0.01,  and dynamic balance (all eight reaching distances of the SEBT, INT: +11-26%, CON: +1.1-3.8%, 0.001 < p < 0.04, 0.14 <  with significantly larger improvements for INT. Flexibility and jump performance were not relevantly affected (p > 0.05). Instructed Zumba training can be applied to improve well-being, aerobic fitness and neuromuscular function in female college students.
Different Watermarking Techniques & its Applications: A Review

It is the expansion of internet that has increased the availability & uses of digital data like video, audio, image to the world. Therefore copyright protection of digital data is an important research issue of information security. This paper presents a review of some influential work in the area of digital watermarking technique and main contribution in this field such as categories of digital watermarking process by which one can understand easily that which watermarking method can be used.
Incremental and Decremental Support Vector Machine Learning

An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the KuhnTucker conditions on all previously seen training data, in a number of steps each computed analytically. The incremental procedure is reversible, and decremental "unlearning" offers an efficient method to exactly evaluate leave-one-out generalization performance. Interpretation of decremental unlearning in feature space sheds light on the relationship between generalization and geometry of the data.
A Survey of Solutions to the Sybil Attack

Many security mechanisms are based on specific assumptions of identity and are vulnerable to attacks when these assumptions are violated. For example, impersonation is the well-known consequence when authenticating credentials are stolen by a third party. Another attack on identity occurs when credentials for one identity are purposely shared by multiple individuals, for example to avoid paying twice for a service. Such shared accounts are common in practice: friends exchange iTunes passwords to share purchased music; BugMeNot.com is a community that shares website registration passwords; and network address translation [29] devices allow multiple users to pay for a single IP address which is then shared among them. In this paper, we survey the impact of the Sybil attack [26], an attack against identity in which an individual entity masquerades as multiple simultaneous identities. The Sybil attack is a fundamental problem in many systems, and it has so far resisted a universally applicable solution. Many distributed applications and everyday services assume each participating entity controls exactly one identity. When this assumption is unverifiable or unmet, the sevice is subject to attack and the results of the application are questionable if not incorrect. A concrete example of this would be an online voting system where one person can vote using many online identities. Notably, this problem is currently only solved if a central authority, such as the administrator of a certificate authority, can guarantee that each person has a single identity represented by one key; in practice, this is very difficult to ensure on a large scale and would require costly manual attention.
Cascaded Random Forest for Fast Object Detection

A Random Forest consists of several independent decision trees arranged in a forest. A majority vote over all trees leads to the final decision. In this paper we propose a Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach. By introducing the cascade, 99% of the test images can be rejected by the first and second stage with minimal computational effort leading to a massively speeded-up detection framework. Three different cascade voting strategies are implemented and evaluated. Additionally, the training and classification speed-up is analyzed. Several experiments on public available datasets for pedestrian detection, lateral car detection and unconstrained face detection demonstrate the benefit of our contribution.