nils-herrmann/clean_scirepeval huggingface dataset:
Team-Based Project Design of an Autonomous Robot

In this paper, we discuss the design and engineering of the C-P30, a custom robot design at Cal Poly State University, San Luis Obispo. This robot is designed by undergraduate computer engineering students at the university. This robot project is intended to be continually developed as students enter and leave the project. In addition, working on the project allows the students to fulfill a university senior design experience requirement. As this project is a continual work-in-progress, this paper outlines the current state of the hardware and software design. This paper covers the technical aspects of the robot design, as well as the educational objectives that are achieved.
Introduction to Modern Cryptography

This book is a comprehensive, rigorous introduction to what the authors name “Modern” Cryptography, or in other words, the science — rather than the art — of cryptography, relying on: concrete definitions of security; formal, precise, as-few-as-possible assumptions; and rigorous proofs of security. In itself, this definition-based structure represents a novel approach to how cryptography is taught, replacing the older, construction-based approach. The book, though by no means complete, presents a thorough overview of the following subjects: block ciphers, symmetric encryption, message authentication, hash functions, (pseudo)randomness, hardness assumptions for asymmetric public key cryptography, key management, public key encryption schemes, digital signatures, the random oracle model, and a selection of cryptanalysis methods for factoring and finding discrete logarithms. To this end, the authors also give an overview of the necessary mathematical background and of the tools used in cryptography, amongst which I mention: probability notions, the Birthday problem, integer and modular arithmetic number-theory notions, and the problem of finding generators of cyclic groups. Though the authors mention some basic notions of side-channel attacks and general hardware issues, these topics are not considered in the scope of the book.
Location-based services for elderly and disabled people

Many techniques have been developed to perform indoor location. Each strategy has its own advantages and drawbacks, with the application demanding location information the main determinant of the system to be used. In this paper, a system is presented that serves location to innovative services for elderly and disabled people, ranging from alarm and monitoring to support for navigation and leisure. The system uses ZigBee and ultrasound to fulfill the application requirements, differing in this respect from all other existing systems. ZUPS (ZigBee and ultrasound positioning system) provides wide multicell coverage, easy extension, robustness even in crowded scenarios, different levels of precision depending on the user’s profile and service requirements (from a few centimeters to meters), limited infrastructure requirements, simple calibration, and cost-effectiveness. The system has been evaluated from the technical, functional, and usability standpoints, with satisfactory results, and its suitability has also been demonstrated in a residence for people with disabilities located in Zaragoza,
Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild

This paper integrates techniques in natural language processing and computer vision to improve recognition and description of entities and activities in real-world videos. We propose a strategy for generating textual descriptions of videos by using a factor graph to combine visual detections with language statistics. We use state-of-the-art visual recognition systems to obtain confidences on entities, activities, and scenes present in the video. Our factor graph model combines these detection confidences with probabilistic knowledge mined from text corpora to estimate the most likely subject, verb, object, and place. Results on YouTube videos show that our approach improves both the joint detection of these latent, diverse sentence components and the detection of some individual components when compared to using the vision system alone, as well as over a previous n-gram language-modeling approach. The joint detection allows us to automatically generate more accurate, richer sentential descriptions of videos with a wide array of possible content.
Review of state-of-the-arts in artificial intelligence with application to AI safety problem

Here, I review current state-of-the-arts in many areas of AI to estimate when it’s reasonable to expect human level AI development. Predictions of prominent AI researchers vary broadly from very pessimistic predictions of Andrew Ng to much more moderate predictions of Geoffrey Hinton and optimistic predictions of Shane Legg, DeepMind cofounder. Given huge rate of progress in recent years and this broad range of predictions of AI experts, AI safety questions are also discussed.
When recommendation meets mobile: contextual and personalized recommendation on the go

Mobile devices are becoming ubiquitous. People use their phones as a personal concierge discovering and making decisions anywhere and anytime. Understanding user intent on the go therefore becomes important for task completion on the phone. While existing efforts have predominantly focused on understanding the explicit user intent expressed by a textual or voice query, this paper presents an approach to context-aware and personalized entity recommendation which understands the implicit intent without any explicit user input on the phone. The approach, highly motivated from a large-scale mobile click-through analysis, is able to rank both the entity types and the entities within each type (here an entity is a local business, e.g., "I love sushi," while an entity type is a category, e.g., "restaurant"). The recommended entity types and entities are relevant to both user context (past behaviors) and sensor context (time and geo-location). Specifically, it estimates the generation probability of an entity by a given user conditioned on the current context in a probabilistic framework. A random-walk propagation is then employed to refine the estimated probability by mining the temporal patterns among entities. We deploy a recommendation application based on the proposed approach on Window Phone 7 devices. We evaluate recommendation performance on 10 thousand mobile clicks, as well as user experience through subjective user studies. We show that the application is effective to facilitate the exploration and discovery of surroundings for mobile users.
Object Detection with Discriminatively Trained Part-Based Models

• Lighting. When an object is in bright light, it looks brighter than when it’s in shadow, so a program can’t just look at image intensity values. • Within-class variation. Different instances of the same kind of object can look quite different to one another. For example, a green station wagon and a red convertible are both cars, so a program can’t simply compare a picture to one example. • Aspect. The same object can look very different when viewed at from different directions—pick up a book and compare its cover and its spine to see this effect. Again, this means that a program might need to have many examples of each type of object. • Deformation. Many objects can change their appearance significantly without their identity changing. For example, you can move your limbs around, change clothes, paint your face, or have your hair cut. You will look very different indeed, but you will still be a person.
Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation

Learning domain-invariant features is of vital importance to unsupervised domain adaptation, where classifiers trained on the source domain need to be adapted to a different target domain for which no labeled examples are available. In this paper, we propose a novel approach for learning such features. The central idea is to exploit the existence of landmarks, which are a subset of labeled data instances in the source domain that are distributed most similarly to the target domain. Our approach automatically discovers the landmarks and use them to bridge the source to the target by constructing provably easier auxiliary domain adaptation tasks. The solutions of those auxiliary tasks form the basis to compose invariant features for the original task. We show how this composition can be optimized discriminatively without requiring labels from the target domain. We validate the method on standard benchmark datasets for visual object recognition and sentiment analysis of text. Empirical results show the proposed method outperforms the state-ofthe-art significantly.
Translation Modeling with Bidirectional Recurrent Neural Networks

This work presents two different translation models using recurrent neural networks. The first one is a word-based approach using word alignments. Second, we present phrase-based translation models that are more consistent with phrasebased decoding. Moreover, we introduce bidirectional recurrent neural models to the problem of machine translation, allowing us to use the full source sentence in our models, which is also of theoretical interest. We demonstrate that our translation models are capable of improving strong baselines already including recurrent neural language models on three tasks: IWSLT 2013 German→English, BOLT Arabic→English and Chinese→English. We obtain gains up to 1.6% BLEU and 1.7% TER by rescoring 1000-best lists.
The bright-side and the dark-side of CEO personality: examining core self-evaluations, narcissism, transformational leadership, and strategic influence.

This article reports on an examination of the relationships between chief executive officer (CEO) personality, transformational and transactional leadership, and multiple strategic outcomes in a sample of 75 CEOs of Major League Baseball organizations over a 100-year period. CEO bright-side personality characteristics (core self-evaluations) were positively related to transformational leadership, whereas dark-side personality characteristics (narcissism) of CEOs were negatively related to contingent reward leadership. In turn, CEO transformational and contingent reward leadership were related to 4 different strategic outcomes, including manager turnover, team winning percentage, fan attendance, and an independent rating of influence. CEO transformational leadership was positively related to ratings of influence, team winning percentage, and fan attendance, whereas contingent reward leadership was negatively related to manager turnover and ratings of influence.
From eye movements to actions: how batsmen hit the ball

In cricket, a batsman watches a fast bowler's ball come toward him at a high and unpredictable speed, bouncing off ground of uncertain hardness. Although he views the trajectory for little more than half a second, he can accurately judge where and when the ball will reach him. Batsmen's eye movements monitor the moment when the ball is released, make a predictive saccade to the place where they expect it to hit the ground, wait for it to bounce, and follow its trajectory for 100–200 ms after the bounce. We show how information provided by these fixations may allow precise prediction of the ball's timing and placement. Comparing players with different skill levels, we found that a short latency for the first saccade distinguished good from poor batsmen, and that a cricket player's eye movement strategy contributes to his skill in the game.
Recent advances in differential evolution - An updated survey

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multifaceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE. & 2016
Autotuning Stencil-Based Computations on GPUs

Finite-difference, stencil-based discretization approaches are widely used in the solution of partial differential equations describing physical phenomena. Newton-Krylov iterative methods commonly used in stencil-based solutions generate matrices that exhibit diagonal sparsity patterns. To exploit these structures on modern GPUs, we extend the standard diagonal sparse matrix representation and define new matrix and vector data types in the PETSc parallel numerical toolkit. We create tunable CUDA implementations of the operations associated with these types after identifying a number of GPU-specific optimizations and tuning parameters for these operations. We discuss our implementation of GPU auto tuning capabilities in the Orio framework and present performance results for several kernels, comparing them with vendor-tuned library implementations.
Privacy implications of presence sharing in mobile messaging applications

Mobile messaging applications, such as WhatsApp, provide a free alternative for mobile texting on smartphones. Mobile messengers typically also share presence information about users to indicate when a user is online. We investigated the privacy implications of such presence updates, using WhatsApp as an example. We conducted a user study with two independent groups (19 participants in total), in which we collected and analyzed their presence information over four weeks of regular WhatsApp use and conducted follow-up interviews. Our results show that presence information alone is sufficient to accurately identify, for example, daily routines, deviations, times of inappropriate mobile messaging, or conversation partners. We discuss resulting privacy implications of presence information and potential solutions to mitigate these issues.
Notes on Hierarchical Splines, DCLNs and i-theory

We define an extension of classical additive splines for multivariate function approximation that we call hierarchical splines. We show that the case of hierarchical, additive, piece-wise linear splines includes present-day Deep Convolutional Learning Networks (DCLNs) with linear rectifers and pooling (sum or max). We discuss how these observations together with i-theory may provide a framework for a general theory of deep networks. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 1231216. Notes on Hierarchical Splines, DCLNs and i-theory Tomaso Poggio, Lorenzo Rosasco,Amnon Shashua, Nadav Cohen and Fabio Anselmi
Functional brain mapping of the relaxation response and meditation.

Meditation is a conscious mental process that induces a set of integrated physiologic changes termed the relaxation response. Functional magnetic resonance imaging (fMRI) was used to identify and characterize the brain regions that are active during a simple form of meditation. Significant (p<10(-7)) signal increases were observed in the group-averaged data in the dorsolateral prefrontal and parietal cortices, hippocampus/parahippocampus, temporal lobe, pregenual anterior cingulate cortex, striatum, and pre- and post-central gyri during meditation. Global fMRI signal decreases were also noted, although these were probably secondary to cardiorespiratory changes that often accompany meditation. The results indicate that the practice of meditation activates neural structures involved in attention and control of the autonomic nervous system.
Handwritten Urdu character recognition using one-dimensional BLSTM classifier

The recognition of cursive script is regarded as a subtle task in optical character recognition due to its varied representation. Every cursive script has different nature and associated challenges. As Urdu is one of cursive language that is derived from Arabic script, that is why it nearly shares the similar challenges and complexities but with more intensity. We can categorize Urdu and Arabic language on basis of its script they use. Urdu is mostly written in Nasta’liq style, whereas Arabic follows Naskh style of writing. This paper presents new and comprehensive Urdu handwritten offline database name Urdu-Nasta’liq handwritten dataset (UNHD). Currently, there is no standard and comprehensive Urdu handwritten dataset available publicly for researchers. The acquired dataset covers commonly used ligatures that were written by 500 writers with their natural handwriting on A4 size paper. UNHD is publically available and can be download form https://sites.google.com/site/researchonurdulanguage1/databases . We performed experiments using recurrent neural networks and reported a significant accuracy for handwritten Urdu character recognition.
Point Cloud GAN

Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show a straightforward extension of existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data. We propose a two fold modification to GAN algorithm for learning to generate point clouds (PC-GAN). First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process. A key component of our method is that we train a posterior inference network for the hidden variables. Second, instead of using only state-of-the-art Wasserstein GAN objective, we propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form. Thereby, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms. We validate our claims on ModelNet40 benchmark dataset. Using the distance between generated point clouds and true meshes as metric, we find that PC-GAN trained by the sandwiching objective achieves better results on test data than the existing methods. Moreover, as a byproduct, PCGAN learns versatile latent representations of point clouds, which can achieve competitive performance with other unsupervised learning algorithms on object recognition task. Lastly, we also provide studies on generating unseen classes of objects and transforming image to point cloud, which demonstrates the compelling generalization capability and potentials of PC-GAN.
Automatic global vessel segmentation and catheter removal using local geometry information and vector field integration

Vessel enhancement and segmentation aim at (binary) per-pixel segmentation considering certain local features as probabilistic vessel indicators. We propose a new methodology to combine any local probability map with local directional vessel information. The resulting global vessel segmentation is represented as a set of discrete streamlines populating the vascular structures and providing additional connectivity and geometric shape information. The streamlines are computed by numerical integration of the directional vector field that is obtained from the eigenanalysis of the local Hessian indicating the local vessel direction. The streamline representation allows for sophisticated post-processing techniques using the additional information to refine the segmentation result with respect to the requirements of the particular application such as image registration. We propose different post-processing techniques for hierarchical segmentation, centerline extraction, and catheter removal to be used for X-ray angiograms. We further demonstrate how the global approach is able to significantly improve the segmentation compared to conventional local Hessian-based approaches.
KairosVM: Deterministic introspection for real-time virtual machine hierarchical scheduling

Consolidation and isolation are key technologies that promoted the undisputed popularity of virtualization in most of the computer industry. This popularity has recently led to a growing interest in real-time virtualization, making this technology enter the real-time system market. However, it has several issues due to the strict timing guarantees contracted. Moreover supporting legacy software stacks adds another level of complexity when the software is a black box. We present KairosVM, a latency-bounded, real-time extension to Linux's KVM module. It aims to bridge the lack of communication of the real-time requirements between the guest scheduler and the host scheduler, exploiting virtual machine introspection. The hypervisor captures the real-time requirements of the guest by catching previously added undefined instructions, without the need to do any modification to the guests. Our evaluations show that KairosVM's overhead is negligible when compared to existing introspection solutions thus can be used in real-time.
A CMOS hysteresis undervoltage lockout with current source inverter structure

This paper describes a simple architecture and low power consumption undervoltage lockout (UVLO) circuit with hysteretic threshold. The UVLO circuit monitors the supply voltage and determines whether or not the supply voltage satisfies a predetermined condition. The under voltage lockout circuit is designed based on CSMC 0.5um CMOS technology, utilizing a relatively few amount of circuitry. It is realized with a current source inverter. The threshold voltage is determined by the W/L ratio of current source inverter and resistor in reference generator. The hysteresis is realized by using a feedback circuit to overcome the bad disturbance and noise rejection of the single threshold. Hysteretic threshold range is 40mV. The quiescent current is about 1uA at 3V supply voltage,while the power of circuit consumes only 3uW.
Learning to Describe E-Commerce Images from Noisy Online Data

Recent study shows successful results in generating a proper language description for the given image, where the focus is on detecting and describing the contextual relationship in the image, such as the kind of object, relationship between two objects, or the action. In this paper, we turn our attention to more subjective components of descriptions that contain rich expressions to modify objects – namely attribute expressions. We start by collecting a large amount of product images from the online market site Etsy, and consider learning a language generation model using a popular combination of a convolutional neural network (CNN) and a recurrent neural network (RNN). Our Etsy dataset contains unique noise characteristics often arising in the online market. We first apply natural language processing techniques to extract high-quality, learnable examples in the real-world noisy data. We learn a generation model from product images with associated title descriptions, and examine how e-commerce specific meta-data and fine-tuning improve the generated expression. The experimental results suggest that we are able to learn from the noisy online data and produce a product description that is closer to a man-made description with possibly subjective attribute expressions.