nils-herrmann/clean_scirepeval huggingface dataset:
Eviza: A Natural Language Interface for Visual Analysis

Natural language interfaces for visualizations have emerged as a promising new way of interacting with data and performing analytics. Many of these systems have fundamental limitations. Most return minimally interactive visualizations in response to queries and often require experts to perform modeling for a set of predicted user queries before the systems are effective. Eviza provides a natural language interface for an interactive query dialog with an existing visualization rather than starting from a blank sheet and asking closed-ended questions that return a single text answer or static visualization. The system employs a probabilistic grammar based approach with predefined rules that are dynamically updated based on the data from the visualization, as opposed to computationally intensive deep learning or knowledge based approaches.
 The result of an interaction is a change to the view (e.g., filtering, navigation, selection) providing graphical answers and ambiguity widgets to handle ambiguous queries and system defaults. There is also rich domain awareness of time, space, and quantitative reasoning built in, and linking into existing knowledge bases for additional semantics. Eviza also supports pragmatics and exploring multi-modal interactions to help enhance the expressiveness of how users can ask questions about their data during the flow of visual analysis.
Plant Leaf Disease Detection using Deep Learning and Convolutional Neural Network

When plants and crops are affected by pests it affects the agricultural p roduction of the country. Us ually farmers or experts observe the plants with naked eye for detection and identification of disease. But this method can be time processing, expens ive and inaccurate. Automatic detection using image processing techniques provide fast and accurate results. This paper is concerned with a new approach to the development of p lant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Advances in computer v ision present an opportunity to expand and enhance th e practice of precise p lant protection and extend the market of computer vision applications in the field of precision agricu lture. Novel way of training and the methodology used facilitate a quick and easy system implementation in pract ice. A ll essential steps required for implement ing this disease recognition model are fu lly described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts , a deep learning framework to perform the deep CNN train ing. This method paper is a new approach in detecting plant diseases using the deep convolutional neural network trained and fine -tuned to fit accurately to the database of a plant’s leaves that was gathered independently for d iverse plant diseases. The ad vance and novelty of the developed model lie in its simplicity; healthy leaves and background images are in line with other classes, enabling the mode l to distinguish between diseased leaves and healthy ones or from the environment by using deep CNN.
Intrinsic Motivation Systems for Autonomous Mental Development

Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development. The complexity of the robot's activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology
A neuromorphic architecture for anomaly detection in autonomous large-area traffic monitoring

The advanced sensing and imaging capability of today's sensor networks enables real time monitoring in a large area. In order to provide continuous monitoring and prompt situational awareness, an abstract-level autonomous information processing framework is developed that is able to detect various categories of abnormal traffic events with unsupervised learning. The framework is based on cogent confabulation model, which performs statistical inference in a manner inspired by human neocortex system. It enables detection and recognition of abnormal target vehicles within the context of surrounding traffic activities and previous events using likelihood-ratio test. A neuromorphic architecture is proposed which accelerates the computation for real-time detection by leveraging memristor crossbar arrays.
Supporting Iterative Cohort Construction with Visual Temporal Queries

Many researchers across diverse disciplines aim to analyze the behavior of cohorts whose behaviors are recorded in large event databases. However, extracting cohorts from databases is a difficult yet important step, often overlooked in many analytical solutions. This is especially true when researchers wish to restrict their cohorts to exhibit a particular temporal pattern of interest. In order to fill this gap, we designed COQUITO, a visual interface that assists users defining cohorts with temporal constraints. COQUITO was designed to be comprehensible to domain experts with no preknowledge of database queries and also to encourage exploration. We then demonstrate the utility of COQUITO via two case studies, involving medical and social media researchers.
VolumeDeform: Real-time Volumetric Non-rigid Reconstruction

We present a novel approach for the reconstruction of dynamic geometric shapes using a single hand-held consumer-grade RGB-D sensor at real-time rates. Our method builds up the scene model from scratch during the scanning process, thus it does not require a pre-defined shape template to start with. Geometry and motion are parameterized in a unified manner by a volumetric representation that encodes a distance field of the surface geometry as well as the non-rigid space deformation. The problem is tackled in real-time at the camera’s capture rate using a data-parallel flipflop optimization strategy. Our results demonstrate robust tracking even for fast motion and scenes that lack geometric features.
IR sensitivity enhancement of CMOS Image Sensor with diffractive light trapping pixels

We report on the IR sensitivity enhancement of back-illuminated CMOS Image Sensor (BI-CIS) with 2-dimensional diffractive inverted pyramid array structure (IPA) on crystalline silicon (c-Si) and deep trench isolation (DTI). FDTD simulations of semi-infinite thick c-Si having 2D IPAs on its surface whose pitches over 400 nm shows more than 30% improvement of light absorption at λ = 850 nm and the maximum enhancement of 43% with the 540 nm pitch at the wavelength is confirmed. A prototype BI-CIS sample with pixel size of 1.2 μm square containing 400 nm pitch IPAs shows 80% sensitivity enhancement at λ = 850 nm compared to the reference sample with flat surface. This is due to diffraction with the IPA and total reflection at the pixel boundary. The NIR images taken by the demo camera equip with a C-mount lens show 75% sensitivity enhancement in the λ = 700–1200 nm wavelength range with negligible spatial resolution degradation. Light trapping CIS pixel technology promises to improve NIR sensitivity and appears to be applicable to many different image sensor applications including security camera, personal authentication, and range finding Time-of-Flight camera with IR illuminations.
A Qualitative Analysis of Online Gaming Addicts in Treatment

Online gaming addiction is a relatively under-researched area and there have been few studies examining online gamers in treatment. This paper reports the findings from a qualitative interview study of nine players undergoing treatment for their addictive playing of Massively Multiplayer Online Role Playing Games (MMORPGs). A face-to-face interview study with nine online gaming addicts was carried out using Grounded Theory. The six most reported phenomena by the participants were: (i) entertainment search, (ii) virtual friendship, (iii) escapism and/or dissociation, (iv) game context, (v) control versus no control, and (vi) conflict. The findings suggest that players’ initial gaming motivation is because of three factors: (i) entertainment, (ii) escapism, and/or (iii) virtual friendship. MMORPG addiction appears once the playing time significantly increases, coupled with a loss of control and a narrow behavior focus. These factors lead to problems and result in psychological dependence and serious life conflicts. The consequences of MMORPG addiction are similar to the consequences of more established substance addictions including salience, mood modification, loss of control, craving, and serious adverse effects. Additionally, in some cases, tolerance and relapse may also be present.
The retrosplenial cortex and emotion: new insights from functional neuroimaging of the human brain

Little is known about the function of the retrosplenial cortex and until recently, there was no evidence that it had any involvement in emotional processes. Surprisingly, recent functional neuroimaging studies show that the retrosplenial cortex is consistently activated by emotionally salient words. A review of the functional neuroimaging literature reveals a previously overlooked pattern of observations: the retrosplenial cortex is the cortical region most consistently activated by emotionally salient stimuli. Evidence that this region is also involved in episodic memory suggests that it might have a role in the interaction between emotion and episodic memory. Recognition that the retrosplenial cortex has a prominent role in the processing of emotionally salient stimuli invites further studies to define its specific functions and its interactions with other emotion-related brain regions.
Identifying Generalization Properties in Neural Networks

While it has not yet been proven, empirical evidence suggests that model generalization is related to local properties of the optima which can be described via the Hessian. We connect model generalization with the local property of a solution under the PAC-Bayes paradigm. In particular, we prove that model generalization ability is related to the Hessian, the higher-order “smoothness” terms characterized by the Lipschitz constant of the Hessian, and the scales of the parameters. Guided by the proof, we propose a metric to score the generalization capability of the model, as well as an algorithm that optimizes the perturbed model accordingly.
Malware detection using adaptive data compression

A popular approach in current commercial anti-malware software detects malicious programs by searching in the code of programs for scan strings that are byte sequences indicative of malicious code. The scan strings, also known as the signatures of existing malware, are extracted by malware analysts from known malware samples, and stored in a database often referred to as a virus dictionary. This process often involves a significant amount of human efforts. In addition, there are two major limitations in this technique. First, not all malicious programs have bit patterns that are evidence of their malicious nature. Therefore, some malware is not recorded in the virus dictionary and can not be detected through signature matching. Second, searching for specific bit patterns will not work on malware that can take many forms--obfuscated malware. Signature matching has been shown to be incapable of identifying new malware patterns and fails to recognize obfuscated malware. This paper presents a malware detection technique that discovers malware by means of a learning engine trained on a set of malware instances and a set of benign code instances. The learning engine uses an adaptive data compression model--prediction by partial matching (PPM)--to build two compression models, one from the malware instances and the other from the benign code instances. A code instance is classified, either as "malware" or "benign", by minimizing its estimated cross entropy. Our preliminary results are very promising. We achieved about 0.94 true positive rate with as low as 0.016 false positive rate. Our experiments also demonstrate that this technique can effectively detect unknown and obfuscated malware.
Bilingualism : The good , the bad , and the indifferent ∗

The present paper summarizes research showing that bilingualism affects linguistic and cognitive performance across the lifespan. The effect on linguistic performance is generally seen as a deficit in which bilingual children control a smaller vocabulary than their monolingual peers and bilingual adults perform more poorly on rapid lexical retrieval tasks. The effect on cognitive performance is to enhance executive functioning and to protect against the decline of executive control in aging. These effects interact to produce a complex pattern regarding the effect of bilingualism on memory performance. Memory tasks based primarily on verbal recall are performed more poorly by bilinguals but memory tasks based primarily on executive control are performed better by bilinguals. Speculations regarding the mechanism responsible for these effects are described.
The Benefits of Facebook "Friends: " Social Capital and College Students' Use of Online Social Network Sites

This study examines the relationship between use of Facebook, a popular online social network site, and the formation and maintenance of social capital. In addition to assessing bonding and bridging social capital, we explore a dimension of social capital that assesses one's ability to stay connected with members of a previously inhabited community, which we call maintained social capital. Regression analyses conducted on results from a survey of undergraduate students (N=286) suggest a strong association between use of Facebook and the three types of social capital, with the strongest relationship being to bridging social capital. In addition, Facebook usage was found to interact with measures of psychological well-being, suggesting that it might provide greater benefits for users experiencing low self-esteem and low life satisfaction.
A quantum leap for AI

9 Quantum computing and AI Subhash Kak, Louisiana State University Every few years, we hear of a new technology that will revolutionize AI. After careful reflection, we find that the advance is within the framework of the Turing machine model and equivalent, in many cases, to existing statistical techniques. But this time, in quantum computing, we seem to be on the threshold of a real revolution— a “quantum” leap—because it is a true frontier beyond classical computing. But will these possibilities be realized any time soon? Classical computers work on classical logic and can be viewed as an embodiment of classical physics. Quantum computers, on the other hand, are based on the superpositional logic of quantum mechanics, which is an entirely different paradigm. Conventional explanation sees consciousness arising as an emergent property of the classical computations taking place in the circuits of the brain, but this does not address the question of how thoughts and feelings arise. If brains perform quantum processing, this might be the secret behind consciousness. Furthermore, it might explain several puzzling features of animal and human intelligence and provide a new direction to develop AI machines. In this brief survey, I present the rationale for the convergence between quantum computing and AI and discuss prospects for realizing the technology.
Optimizing neighborhood projection with relaxation factor for inextensible cloth simulation

In this paper, we propose a novel method for inextensible cloth simulation. Our method introduces a neighborhood projection optimized with a relaxation factor. The neighborhood projection enforces inextensibility by modifying particle positions with a Jacobi-style iteration, leading to conservation of linear and angular quasi momenta. The relaxation factor is estimated according to the corrections and constraints, and is used to scale the corrections while keeping convergence to a smaller number of iterations. Experimental results demonstrate that our method increases the simulation efficiency, and stably handles inextensible cloth even in overconstrained situations. In addition to the simulation of hanging cloth and draping cloth, the simulated umbrella demonstrates the characters of our method for this type of objects.
Create Your Own Internet of Things: A survey of IoT platforms.

We live in the age of After Google (AG), where information is just one click away and talking just one touch away. The near future of the AG age is the Internet of Things (IoT), where physical things connected over a network will take part in Internet activities to exchange information about themselves and their surroundings. In other words, the IoT is nothing but a computing concept in which everyday objects with embedded hardware/devices are connected to a network or are simply online.
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function

Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of view between cameras. In this paper, we present a novel multi-channel parts-based convolutional neural network (CNN) model under the triplet framework for person re-identification. Specifically, the proposed CNN model consists of multiple channels to jointly learn both the global full-body and local body-parts features of the input persons. The CNN model is trained by an improved triplet loss function that serves to pull the instances of the same person closer, and at the same time push the instances belonging to different persons farther from each other in the learned feature space. Extensive comparative evaluations demonstrate that our proposed method significantly outperforms many state-of-the-art approaches, including both traditional and deep network-based ones, on the challenging i-LIDS, VIPeR, PRID2011 and CUHK01 datasets.
Towards Efficiently Checking Compliance Against Automotive Security and Safety Standards

The growing connectivity of the systems that we rely on e.g. transportation vehicles is pushing towards the introduction of new standards aimed at providing a baseline to address cybersecurity besides safety. If the interplay of the two normative spaces is not mastered, compliance management might become more time consuming and costly, preventing engineers from dedicating their energies to system engineering. In this paper, we build on top of previous work aimed at increasing efficiency and confidence in compliance management. More specifically, we contribute to building a terminological framework needed to enable the systematization of commonalities and variabilities within ISO 26262 and SAE J3061. Then, we focus our attention on the requirements for software design and implementation and we use defeasible logic to prove compliance. Based on the compliance checking results, we reveal reuse opportunities. Finally, we draw our conclusions and sketch future research directions.
Short-term memory, working memory, and executive functioning in preschoolers: longitudinal predictors of mathematical achievement at age 7 years.

This study examined whether measures of short-term memory, working memory, and executive functioning in preschool children predict later proficiency in academic achievement at 7 years of age (third year of primary school). Children were tested in preschool (M age = 4 years, 6 months) on a battery of cognitive measures, and mathematics and reading outcomes (from standardized, norm-referenced school-based assessments) were taken on entry to primary school, and at the end of the first and third year of primary school. Growth curve analyses examined predictors of math and reading achievement across the duration of the study and revealed that better digit span and executive function skills provided children with an immediate head start in math and reading that they maintained throughout the first three years of primary school. Visual-spatial short-term memory span was found to be a predictor specifically of math ability. Correlational and regression analyses revealed that visual short-term and working memory were found to specifically predict math achievement at each time point, while executive function skills predicted learning in general rather than learning in one specific domain. The implications of the findings are discussed in relation to further understanding the role of cognitive skills in different mathematical tasks, and in relation to the impact of limited cognitive skills in the classroom environment.
Speed planning for solar-powered electric vehicles

Electric vehicles (EVs) are the trend for future transportation. The major obstacle is range anxiety due to poor availability of charging stations and long charging time. Solar-powered EVs, which mostly rely on solar energy, are free of charging limitations. However, the range anxiety problem is more severe due to the availability of sun light. For example, shadings of buildings or trees may cause a solar-powered EV to stop halfway in a trip. In this paper, we show that by optimally planning the speed on different road segments and thus balancing energy harvesting and consumption, we can enable a solar-powered EV to successfully reach the destination using the shortest travel time. The speed planning problem is essentially a constrained non-linear programming problem, which is generally difficult to solve. We have identified an optimality property that allows us to compute an optimal speed assignment for a partition of the path; then, a dynamic programming method is developed to efficiently compute the optimal speed assignment for the whole trip with significantly low computation overhead compared to the state-of-the-art non-linear programming solver. To evaluate the usability of the proposed method, we have also developed a solar-powered EV prototype. Experiments show that the predictions by the proposed technique match well with the data collected from the physical EV. Issues on practical implementation are also discussed.