nils-herrmann/clean_scirepeval huggingface dataset:
Vision Based Hand Gesture Recognition for Human Computer Interaction

— This project presents an approach to develop a real-time hand gesture recognition enabling human-computer interaction. It is " Vision Based " that uses only a webcam and Computer Vision (CV) technology, such as image processing that can recognize several hand gestures. The applications of real time hand gesture recognition are numerous, due to the fact that it can be used almost anywhere where we interact with computers ranging from basic usage which involves small applications to domain-specific specialized applications. Currently, at this level our project is useful for the society but it can further be expanded to be readily used at the industrial level as well. Gesture recognition is an area of active current research in computer vision. Existing systems use hand detection primarily with some type of marker. Our system, however, uses a real-time hand image recognition system. Our system, however, uses a real-time hand image recognition without any marker, simply using bare hands.  In today " s computer age, every individual is dependent to perform most of their day-today tasks using computers. The major input devices one uses while operating a computer are keyboard and mouse. But there are a wide range of health problems that affects many people nowadays, caused by the constant and continuous work with the computer. Direct use of hands as an input device is an attractive method for providing natural Human Computer Interaction which has evolved from text-based interfaces through 2D graphical-based interfaces, multimedia-supported interfaces, to fully fledged multi participant Virtual Environment (VE) systems. Since hand gestures are completely natural form for communication it does not adversely affect the health of the operator as in case of excessive usage of keyboard and mouse. Imagine the human-computer interaction of the future: A 3Dapplication where you can move and rotate objects simply by moving and rotating your hand-all without touching any input device. In this paper a review of vision based hand gesture recognition is presented.
Fast removal of non-uniform camera shake

Camera shake leads to non-uniform image blurs. State-of-the-art methods for removing camera shake model the blur as a linear combination of homographically transformed versions of the true image. While this is conceptually interesting, the resulting algorithms are computationally demanding. In this paper we develop a forward model based on the efficient filter flow framework, incorporating the particularities of camera shake, and show how an efficient algorithm for blur removal can be obtained. Comprehensive comparisons on a number of real-world blurry images show that our approach is not only substantially faster, but it also leads to better deblurring results.
Remembering the earthquake: direct experience vs. hearing the news.

Three groups of informants--two in California, one in Atlanta--recalled their experiences of the 1989 Loma Prieta earthquake shortly after the event and again 11/2 years later. The Californians' recalls of their own earthquake experiences were virtually perfect. Even their recalls of hearing the news of an earthquake-related event were very good: much higher than Atlantan recalls of hearing about the quake itself. Atlantans who had relatives in the affected area remembered significantly more than those who did not. These data show that personal involvement in the quake led to greatly improved recall, but do not show why. Many Californian informants reported low levels of stress/arousal during the event; arousal ratings were not significantly correlated with recall. The authors suggest that repeated narrative rehearsals may have played an important role.
A high Speed 8 Transistor Full Adder Design Using Novel 3 Transistor XOR Gates

The paper proposes the novel design of a 3T XOR gate combining complementary CMOS with pass transistor logic. The design has been compared with earlier proposed 4T and 6T XOR gates and a significant improvement in silicon area and power-delay product has been obtained. An eight transistor full adder has been designed using the proposed three-transistor XOR gate and its performance has been investigated using 0.15 m and 0.35 m technologies. Compared to the earlier designed 10 transistor full adder, the proposed adder shows a significant improvement in silicon area and power delay product. The whole simulation has been carried out using HSPICE. XOR gate, full adder, improvement in speed, area minimization, transistor count minimization.
A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials

Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard CCA method, which uses sinusoidal signals as reference signals, was first proposed for SSVEP detection without calibration. However, the detection performance can be deteriorated by the interference from the spontaneous EEG activities. Recently, various extended methods have been developed to incorporate individual EEG calibration data in CCA to improve the detection performance. Although advantages of the extended CCA methods have been demonstrated in separate studies, a comprehensive comparison between these methods is still missing. This study performed a comparison of the existing CCA-based SSVEP detection methods using a 12-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment. Classification accuracy and information transfer rate (ITR) were used for performance evaluation. The results suggest that individual calibration data can significantly improve the detection performance. Furthermore, the results showed that the combination method based on the standard CCA and the individual template based CCA (IT-CCA) achieved the highest performance.
Phytoremediation: synergistic use of plants and bacteria to clean up the environment.

Phytoremediation is a relatively new approach to removing contaminants from the environment. It may be defined as the use of plants to remove, destroy or sequester hazardous substances from the environment. Unfortunately, even plants that are relatively tolerant of various environmental contaminants often remain small in the presence of the contaminant. To remedy this situation, plant growth-promoting bacteria that facilitate the proliferation of various plants especially under environmentally stressful conditions may be added to the roots of plants. These bacteria have been selected to lower the level of growth-inhibiting stress ethylene within the plant and also to provide the plant with iron from the soil. The net result of adding these bacteria to plants is a significant increase in both the number of seeds that germinate and the amount of biomass that the plants are able to attain, making phytoremediation in the presence of plant growth-promoting bacteria a much faster and more efficient process.
A Semi-Supervised AUC Optimization Method with Generative Models

This paper presents a semi-supervised learning method for improving the performance of AUC-optimized classifiers by using both labeled and unlabeled samples. In actual binary classification tasks, there is often an imbalance between the numbers of positive and negative samples. For such imbalanced tasks, the area under the ROC curve (AUC) is an effective measure with which to evaluate binary classifiers. The proposed method utilizes generative models to assist the incorporation of unlabeled samples in AUC-optimized classifiers. The generative models provide prior knowledge that helps learn the distribution of unlabeled samples. To evaluate the proposed method in text classification, we employed naive Bayes models as the generative models. Our experimental results using three test collections confirmed that the proposed method provided better classifiers for imbalanced tasks than supervised AUC-optimized classifiers and semi-supervised classifiers trained to maximize the classification accuracy of labeled samples. Moreover, the proposed method improved the effect of using unlabeled samples for AUC optimization especially when we used appropriate generative models.
Comparison processes in social judgment: mechanisms and consequences.

This article proposes an informational perspective on comparison consequences in social judgment. It is argued that to understand the variable consequences of comparison, one has to examine what target knowledge is activated during the comparison process. These informational underpinnings are conceptualized in a selective accessibility model that distinguishes 2 fundamental comparison processes. Similarity testing selectively makes accessible knowledge indicating target-standard similarity, whereas dissimilarity testing selectively makes accessible knowledge indicating target-standard dissimilarity. These respective subsets of target knowledge build the basis for subsequent target evaluations, so that similarity testing typically leads to assimilation whereas dissimilarity testing typically leads to contrast. The model is proposed as a unifying conceptual framework that integrates diverse findings on comparison consequences in social judgment.
Channel Polarization: A Method for Constructing Capacity-Achieving Codes for Symmetric Binary-Input Memoryless Channels

A method is proposed, called channel polarization, to construct code sequences that achieve the symmetric capacity I(W) of any given binary-input discrete memoryless channel (B-DMC) W. The symmetric capacity is the highest rate achievable subject to using the input letters of the channel with equal probability. Channel polarization refers to the fact that it is possible to synthesize, out of N independent copies of a given B-DMC W, a second set of N binary-input channels {WN(i)1 les i les N} such that, as N becomes large, the fraction of indices i for which I(WN(i)) is near 1 approaches I(W) and the fraction for which I(WN(i)) is near 0 approaches 1-I(W). The polarized channels {WN(i)} are well-conditioned for channel coding: one need only send data at rate 1 through those with capacity near 1 and at rate 0 through the remaining. Codes constructed on the basis of this idea are called polar codes. The paper proves that, given any B-DMC W with I(W) > 0 and any target rate R< I(W) there exists a sequence of polar codes {Cfrn;nges1} such that Cfrn has block-length N=2n , rate ges R, and probability of block error under successive cancellation decoding bounded as Pe(N,R) les O(N-1/4) independently of the code rate. This performance is achievable by encoders and decoders with complexity O(N logN) for each.
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components

Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components can lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic (STL) specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks.
Sentiment Analysis of Conditional Sentences

This paper studies sentiment analysis of conditional sentences. The aim is to determine whether opinions expressed on different topics in a conditional sentence are positive, negative or neutral. Conditional sentences are one of the commonly used language constructs in text. In a typical document, there are around 8% of such sentences. Due to the condition clause, sentiments expressed in a conditional sentence can be hard to determine. For example, in the sentence, if your Nokia phone is not good, buy this great Samsung phone, the author is positive about “Samsung phone” but does not express an opinion on “Nokia phone” (although the owner of the “Nokia phone” may be negative about it). However, if the sentence does not have “if”, the first clause is clearly negative. Although “if” commonly signifies a conditional sentence, there are many other words and constructs that can express conditions. This paper first presents a linguistic analysis of such sentences, and then builds some supervised learning models to determine if sentiments expressed on different topics in a conditional sentence are positive, negative or neutral. Experimental results on conditional sentences from 5 diverse domains are given to demonstrate the effectiveness of the proposed approach.
MAERI: Enabling Flexible Dataflow Mapping over DNN Accelerators via Reconfigurable Interconnects

Deep neural networks (DNN) have demonstrated highly promising results across computer vision and speech recognition, and are becoming foundational for ubiquitous AI. The computational complexity of these algorithms and a need for high energy-efficiency has led to a surge in research on hardware accelerators. % for this paradigm. To reduce the latency and energy costs of accessing DRAM, most DNN accelerators are spatial in nature, with hundreds of processing elements (PE) operating in parallel and communicating with each other directly. DNNs are evolving at a rapid rate, and it is common to have convolution, recurrent, pooling, and fully-connected layers with varying input and filter sizes in the most recent topologies.They may be dense or sparse. They can also be partitioned in myriad ways (within and across layers) to exploit data reuse (weights and intermediate outputs). All of the above can lead to different dataflow patterns within the accelerator substrate. Unfortunately, most DNN accelerators support only fixed dataflow patterns internally as they perform a careful co-design of the PEs and the network-on-chip (NoC). In fact, the majority of them are only optimized for traffic within a convolutional layer. This makes it challenging to map arbitrary dataflows on the fabric efficiently, and can lead to underutilization of the available compute resources. DNN accelerators need to be programmable to enable mass deployment. For them to be programmable, they need to be configurable internally to support the various dataflow patterns that could be mapped over them. To address this need, we present MAERI, which is a DNN accelerator built with a set of modular and configurable building blocks that can easily support myriad DNN partitions and mappings by appropriately configuring tiny switches. MAERI provides 8-459% better utilization across multiple dataflow mappings over baselines with rigid NoC fabrics.
Space-Time Aware Behavioral Topic Modeling for Microblog Posts

How can we automatically identify the topics of microblog posts? This question has received substantial attention in the research community and has led to the development of different topic models, which are mathematically well-founded statistical models that enable the discovery of topics in document collections. Such models can be used for topic analyses according to the interests of user groups, time, geographical locations, or social behavior patterns. The increasing availability of microblog posts with associated users, textual content, timestamps, geo-locations, and user behaviors, offers an opportunity to study space-time dependent behavioral topics. Such a topic is described by a set of words, the distribution of which varies according to the time, geo-location, and behaviors (that capture how a user interacts with other users by using functionality such as reply or re-tweet) of users. This study jointly models user topic interest and behaviors considering both space and time at a fine granularity. We focus on the modeling of microblog posts like Twitter tweets, where the textual content is short, but where associated information in the form of timestamps, geo-locations, and user interactions is available. The model aims to have applications in location inference, link prediction, online social profiling, etc. We report on experiments with tweets that offer insight into the design properties of the papers proposal.
An investigation of data and text mining methods for real world deception detection

Uncovering lies (or deception) is of critical importance to many including law enforcement and security personnel. Though these people may try to use many different tactics to discover deception, previous research tells us that this cannot be accomplished successfully without aid. This manuscript reports on the promising results of a research study where data and text mining methods along with a sample of real-world data from a high-stakes situation is used to detect deception. At the end, the information fusion based classification models produced better than 74% classification accuracy on the holdout sample using a 10-fold cross validation methodology. Nonetheless, artificial neural networks and decision trees produced accuracy rates of 73.46% and 71.60% respectively. However, due to the high stakes associated with these types of decisions, the extra effort of combining the models to achieve higher accuracy
Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks

Representations are fundamental to artificial intelligence. The performance of a learning system depends on how the data is represented. Typically, these representations are hand-engineered using domain knowledge. Recently, the trend is to learn these representations through stochastic gradient descent in multi-layer neural networks, which is called backprop. Learning representations directly from the incoming data stream reduces human labour involved in designing a learning system. More importantly, this allows in scaling up a learning system to difficult tasks. In this paper, we introduce a new incremental learning algorithm called crossprop, that learns incoming weights of hidden units based on the meta-gradient descent approach. This meta-gradient descent approach was previously introduced by Sutton (1992) and Schraudolph (1999) for learning stepsizes. The final update equation introduces an additional memory parameter for each of these weights and generalizes the backprop update equation. From our empirical experiments, we show that crossprop learns and reuses its feature representation while tackling new and unseen tasks whereas backprop relearns a new feature representation.
New Constructions and Proof Methods for Large Universe Attribute-Based Encryption

We propose two large universe Attribute-Based Encryption constructions. In a large universe ABE construction any string can be used as an attribute and attributes need not be enumerated at system setup. Our first construction establishes a novel large universe Ciphertext-Policy ABE scheme on prime order bilinear groups, while the second achieves a significant efficiency improvement over the large universe Key-Policy ABE systems of Lewko-Waters and Lewko. Both schemes are selectively secure in the standard model under two “q-type” assumptions similar to ones used in prior works. Our work brings back “program and cancel” techniques to this problem. We provide implementations and benchmarks of our constructions in Charm; a programming environment for rapid prototyping of cryptographic primitives.
Formal Refinement Patterns for Goal-Driven Requirements Elaboration

Requirements engineering is concerned with the identification of high-level goals to be achieved by the system envisioned, the refinement of such goals, the operationalization of goals into services and constraints, and the assignment of responsibilities for the resulting requirements to agents such as humans, devices and programs. Goal refinement and operationalization is a complex process which is not well supported by current requirements engineering technology. Ideally some form of formal support should be provided, but formal methods are difficult and costly to apply at this stage.This paper presents an approach to goal refinement and operationalization which is aimed at providing constructive formal support while hiding the underlying mathematics. The principle is to reuse generic refinement patterns from a library structured according to strengthening/weakening relationships among patterns. The patterns are once for all proved correct and complete. They can be used for guiding the refinement process or for pointing out missing elements in a refinement. The cost inherent to the use of a formal method is thus reduced significantly. Tactics are proposed to the requirements engineer for grounding pattern selection on semantic criteria.The approach is discussed in the context of the multi-paradigm language used in the KAOS method; this language has an external semantic net layer for capturing goals, constraints, agents, objects and actions together with their links, and an inner formal assertion layer that includes a real-time temporal logic for the specification of goals and constraints. Some frequent refinement patterns are high-lighted and illustrated through a variety of examples.The general principle is somewhat similar in spirit to the increasingly popular idea of design patterns, although it is grounded on a formal framework here.
Painting with Polygons: A Procedural Watercolor Engine

Existing natural media painting simulations have produced high-quality results, but have required powerful compute hardware and have been limited to screen resolutions. Digital artists would like to be able to use watercolor-like painting tools, but at print resolutions and on lower end hardware such as laptops or even slates. We present a procedural algorithm for generating watercolor-like dynamic paint behaviors in a lightweight manner. Our goal is not to exactly duplicate watercolor painting, but to create a range of dynamic behaviors that allow users to achieve a similar style of process and result, while at the same time having a unique character of its own. Our stroke representation is vector based, allowing for rendering at arbitrary resolutions, and our procedural pigment advection algorithm is fast enough to support painting on slate devices. We demonstrate our technique in a commercially available slate application used by professional artists. Finally, we present a detailed analysis of the different vector-rendering technologies available.
Online planning for large MDPs with MAXQ decomposition

Markov decision processes (MDPs) provide an expressive framework for planning in stochastic domains. However, exactly solving a large MDP is often intractable due to the curse of dimensionality. Online algorithms help overcome the high computational complexity by avoiding computing a policy for each possible state. Hierarchical decomposition is another promising way to help scale MDP algorithms up to large domains by exploiting their underlying structure. In this paper, we present an effort on combining the benefits of a general hierarchical structure based on MAXQ value function decomposition with the power of heuristic and approximate techniques for developing an online planning framework, called MAXQ-OP. The proposed framework provides a principled approach for programming autonomous agents in a large stochastic domain. We have been conducting a long-term case-study with the RoboCup soccer simulation 2D domain, which is extremely larger than domains usually studied in literature, as the major benchmark to this research. The case-study showed that the agents developed with this framework and the related techniques reached outstanding performances, showing its high scalability to very large domains.