nils-herrmann/clean_scirepeval huggingface dataset:
Towards Optimal Binary Code Learning via Ordinal Embedding

Binary code learning, a.k.a., hashing, has been recently popular due to its high efficiency in large-scale similarity search and recognition. It typically maps high-dimensional data points to binary codes, where data similarity can be efficiently computed via rapid Hamming distance. Most existing unsupervised hashing schemes pursue binary codes by reducing the quantization error from an original real-valued data space to a resulting Hamming space. On the other hand, most existing supervised hashing schemes constrain binary code learning to correlate with pairwise similarity labels. However, few methods consider ordinal relations in the binary code learning process, which serve as a very significant cue to learn the optimal binary codes for similarity search. In this paper, we propose a novel hashing scheme, dubbed Ordinal Embedding Hashing (OEH), which embeds given ordinal relations among data points to learn the ranking-preserving binary codes. The core idea is to construct a directed unweighted graph to capture the ordinal relations, and then train the hash functions using this ordinal graph to preserve the permutation relations in the Hamming space. To learn such hash functions effectively, we further relax the discrete constraints and design a stochastic gradient decent algorithm to obtain the optimal solution. Experimental results on two large-scale benchmark datasets demonstrate that the proposed OEH method can achieve superior performance over the state-of-the-arts approaches. At last, the evaluation on query by humming dataset demonstrates the OEH also has good performance for music retrieval by using user’s humming or singing.
Visual Social Relationship Recognition

Social relationships form the basis of social structure of humans. Developing computational models to understand social relationships from visual data is essential for building intelligent machines that can better interact with humans in a social environment. In this work, we study the problem of visual social relationship recognition in images. We propose a Dual-Glance model for social relationship recognition, where the first glance fixates at the person of interest and the second glance deploys attention mechanism to exploit contextual cues. To enable this study, we curated a large scale People in Social Context (PISC) dataset, which comprises of 23,311 images and 79,244 person pairs with annotated social relationships. Since visually identifying social relationship bears certain degree of uncertainty, we further propose an Adaptive Focal Loss to leverage the ambiguous annotations for more effective learning. We conduct extensive experiments to quantitatively and qualitatively demonstrate the efficacy of our proposed method, which yields state-of-the-art performance on social relationship recognition.
Co-evolution of Fitness Maximizers and Fitness Predictors

We introduce an estimation of distribution algorithm (EDA) based on co-evolution of fitness maximizers and fitness predictors for improving the performance of evolutionary search when evaluations are prohibitively expensive. Fitness predictors are lightweight objects which, given an evolving individual, heuristically approximate the true fitness. The predictors are trained by their ability to correctly differentiate between good and bad solutions using reduced computation. We apply co-evolving fitness prediction to symbolic regression and measure its impact. Our results show that the additional computational investment in training the co-evolving fitness predictors can greatly enhance both speed and convergence of individual solutions while overall reducing the number of evaluations. In application to symbolic regression, the advantage of using fitness predictors grows as the complexity of models increases.
Educating the Disagreeable Extravert : Narcissism , the Big Five Personality Traits , and Achievement Goal Orientation

Despite the fact that longitudinal data have been compiled over the past 30 years among undergraduate students in higher education settings regarding narcissism, the literature is devoid of empirical investigations that explore the relationships between narcissism and learning. Because the data suggest that narcissism scores are increasing each year among this population, an exploration of the relationship between narcissism and learning is timely and warranted. Sampling from university undergraduate students, this study uses the Narcissistic Personality Inventory, the Big Five Inventory, and the Achievement Goal Questionnaire to verify the known relationships between narcissism and the Big Five personality traits of extraversion and agreeableness; to verify the known relationships between the Big Five personality traits of extraversion and agreeableness and goal orientation; and to explore a previously undocumented empirical relationship between narcissism and performance goal orientation. Results of this exploratory study indicate that while narcissism does contribute to a performance goal orientation, it is not a substantial variable in determining achievement goal orientation in general. The study addresses the implications and limitations of this research in addition to areas for additional investigation.
Comparative study of different methods of social network analysis and visualization

A Social Network is a social structure made of individuals or organizations that are linked by one or more specific types of interdependency, such as friends, kinship, terrorist relation, conflict, financial exchange, disease transmission (epidemiology), airline routes etc. Social Network Analysis is an approach to the study of human or organization social interactions. It can be used to investigate kinship patterns, community structure or the organization of other formal and informal social networks. Recently, email networks have been popular source of data for both analysis and visualization of social network of a person or organization. In this paper, three types of visualization technique to analyze social networks have been considered - Force directed layout, Spherical layout and Clustered layout. Each method was evaluated with various data sets from an organization. Force directed layout is used to view the total network structure (Overview). Spherical layout is a 3D method to reveal communication patterns and relationships between different groups. Clustered graph layout visualizes a large amount of data in an efficient and compact form. It gives a hierarchical view of the network. Among the three methods, Clustered layout is the best to handle large network and group relationship. Finally, the comparative study of these three methods has been given.
The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms

Taxi-calling apps are gaining increasing popularity for their efficiency in dispatching idle taxis to passengers in need. To precisely balance the supply and the demand of taxis, online taxicab platforms need to predict the Unit Original Taxi Demand (UOTD), which refers to the number of taxi-calling requirements submitted per unit time (e.g., every hour) and per unit region (e.g., each POI). Predicting UOTD is non-trivial for large-scale industrial online taxicab platforms because both accuracy and flexibility are essential. Complex non-linear models such as GBRT and deep learning are generally accurate, yet require labor-intensive model redesign after scenario changes (e.g., extra constraints due to new regulations). To accurately predict UOTD while remaining flexible to scenario changes, we propose LinUOTD, a unified linear regression model with more than 200 million dimensions of features. The simple model structure eliminates the need of repeated model redesign, while the high-dimensional features contribute to accurate UOTD prediction. We further design a series of optimization techniques for efficient model training and updating. Evaluations on two large-scale datasets from an industrial online taxicab platform verify that LinUOTD outperforms popular non-linear models in accuracy. We envision our experiences to adopt simple linear models with high-dimensional features in UOTD prediction as a pilot study and can shed insights upon other industrial large-scale spatio-temporal prediction problems.
Basic studies on wet adhesion system for wall climbing robots

This paper reports a vacuum-based wet adhesion system for wall climbing robots. In this adhesion system, a suction cup adheres on a wet surface. The problems addressed are an adherability on a rough surface, which is comes from the seal action of a liquid, and low friction between suction cup and adhered rough and smooth surfaces which is comes form lubricating action of a liquid. Generally, it is difficult that a vacuumed suction cup adheres on rough surface such concrete plate and hardly slidable. In this paper, the adhesion force and friction when a suction cup adheres on smooth glass plate and concrete plate are measured and compared wet condition with dry condition. The experiment result showed that a viscosity is important at the sealing performance of adhesion on rough surface. The silicon oil of a viscosity of 5000cSt allows a suction cup to adhere on concrete surface. In this condition it comes up to the adhesion force when a suction cup adheres on smooth glass with dry condition.
A global-local optimization framework for simultaneous multi-mode multi-corner clock skew variation reduction

As combinations of signoff corners grow in modern SoCs, minimization of clock skew variation across corners is important. Large skew variation can cause difficulties in multi-corner timing closure because fixing violations at one corner can lead to violations at other corners. Such "ping-pong" effects lead to significant power and area overheads and time to signoff. We propose a novel framework encompassing both global and local clock network optimizations to minimize the sum of skew variations across different PVT corners between all sequentially adjacent sink pairs. The global optimization uses linear programming to guide buffer insertion, buffer removal and routing detours. The local optimization is based on machine learning-based predictors of latency change; these are used for iterative optimization with tree surgery, buffer sizing and buffer displacement operators. Our optimization achieves up to 22% total skew variation reduction across multiple testcases implemented in foundry 28nm technology, as compared to a best-practices CTS solution using a leading commercial tool.
Identification of Design Elements for a Maturity Model for Interorganizational Integration: A Comparative Analysis

Maturity models in the domain of IOS have been developed and used for organizational design and analysis. Nevertheless, they typically provide a heterogeneous and inconsistent picture of relevant problem spaces (e.g. Supply Chain Management, interoperability problems, etc.). This effect is aggravated by the lack of a thorough model or method for self-appraisal with respect to an organization’s capabilities on technical, organizational and institutional levels to implement interorganizational processes. Our research attempts to fill this gap by constructing a maturity model for interorganizational integration that addresses the shortcomings of previous approaches. We analysed 23 maturity models in the domain of IOS to identify critical design elements for our own design process. We selected the model of Fraser et al. (2002) as our starting configuration that includes levels, characteristics, dimensions, elements and activities. From the analysis of alternative models we identified four additional design elements that we incorporated, namely class of entity, benefit, barrier and product. These extend the previously defined elements by Fraser et al. (2002).
Visual Discovery and Analysis

ÐWe have developed a flexible software environment called ADVIZOR for visual information discovery. ADVIZOR complements existing assumptive-based analyses by providing a discovery-based approach. ADVIZOR consists of five parts: a rich set of flexible visual components, strategies for arranging the components for particular analyses, an in-memory data pool, data manipulation components, and container applications. Working together, ADVIZOR's architecture provides a powerful production platform for creating innovative visual query and analysis applications. Index TermsÐInformation visualization, data analysis, visual design patterns, perspectives, linked views.
Comparing exploration strategies for Q-learning in random stochastic mazes

Balancing the ratio between exploration and exploitation is an important problem in reinforcement learning. This paper evaluates four different exploration strategies combined with Q-learning using random stochastic mazes to investigate their performances. We will compare: UCB-1, softmax, ∈-greedy, and pursuit. For this purpose we adapted the UCB-1 and pursuit strategies to be used in the Q-learning algorithm. The mazes consist of a single optimal goal state and two suboptimal goal states that lie closer to the starting position of the agent, which makes efficient exploration an important part of the learning agent. Furthermore, we evaluate two different kinds of reward functions, a normalized one with rewards between 0 and 1, and an unnormalized reward function that penalizes the agent for each step with a negative reward. We have performed an extensive grid-search to find the best parameters for each method and used the best parameters on novel randomly generated maze problems of different sizes. The results show that softmax exploration outperforms the other strategies, although it is harder to tune its temperature parameter. The worst performing exploration strategy is ∈-greedy.
Decision Trees for Mining Data Streams Based on the Gaussian Approximation

Since the Hoeffding tree algorithm was proposed in the literature, decision trees became one of the most popular tools for mining data streams. The key point of constructing the decision tree is to determine the best attribute to split the considered node. Several methods to solve this problem were presented so far. However, they are either wrongly mathematically justified (e.g., in the Hoeffding tree algorithm) or time-consuming (e.g., in the McDiarmid tree algorithm). In this paper, we propose a new method which significantly outperforms the McDiarmid tree algorithm and has a solid mathematical basis. Our method ensures, with a high probability set by the user, that the best attribute chosen in the considered node using a finite data sample is the same as it would be in the case of the whole data stream.
Design and analysis of a non-backdrivable series elastic actuator

The compliance of series elastic actuators makes them robust to environmental perturbations, and their ability to use impedance control may improve their performance in open-loop environments such as conventional prosthetic control. Series elastic actuators have historically required a backdrivable transmission to ensure adequate bandwidth. The authors have created a non-backdrivable series elastic actuator through the inclusion of a harmonic drive transmission, which contains no backlash; the dominant feature in limiting frequency resolution. The actuator has an acceptable speed and stall torque and an adequate frequency range, and would be used in the future to implement impedance control of prosthetics.
Memristor-The Missing Circuit Element LEON 0

A new two-terminal circuit element-called the memrirtorcharacterized by a relationship between the charge q(t) s St% i(7J d7 and the flux-linkage (p(t) = J-‘-m vfrj d T is introduced os the fourth boric circuit element. An electromagnetic field interpretation of this relationship in terms of a quasi-static expansion of Maxwell’s equations is presented. Many circuit~theoretic properties of memdstorr are derived. It is shown that this element exhibiis some peculiar behavior different from that exhibited by resistors, inductors, or capacitors. These properties lead to a number of unique applications which cannot be realized with RLC networks alone. I + ” -3 nl Although a physical memristor device without internal power supply has not yet been discovered, operational laboratory models have been built with the help of active circuits. Experimental results ore presented to demonstrate the properties and potential applications of memristors. (a) I. 1NTR00~cnoN I + Y -3 T HIS PAPER presents the logical and scientific basis for the existence of a new two-terminal circuit element called the memristor (a contraction for memory (b) resistor) which has every right to be as basic as the three classical circuit elements already in existence, namely, the resistor, inductor, and capacitor. Although the existence of a memristor in the form of a physical device without internal power supply has not yet been discovered, its laboratory realization in the form of active circuits will be presented in Section II.’ Many interesting circuit-theoretic properties possessed by the memristor, the most important of which is perhaps the passivity property which provides the circuit-theoretic basis for its physical realizability, will be derived in Section III. An electromagnetic field interpretation of the memristor characterization will be presented in Section IV with the help of a quasi-static expansion of Maxwell’s equations. Finally, some novel applications of memristors will be presented in Section V. 1 + ” -3
Autonomous driving in a multi-level parking structure

Recently, the problem of autonomous navigation of automobiles has gained substantial interest in the robotics community. Especially during the two recent DARPA grand challenges, autonomous cars have been shown to robustly navigate over extended periods of time through complex desert courses or through dynamic urban traffic environments. In these tasks, the robots typically relied on GPS traces to follow pre-defined trajectories so that only local planners were required. In this paper, we present an approach for autonomous navigation of cars in indoor structures such as parking garages. Our approach utilizes multi-level surface maps of the corresponding environments to calculate the path of the vehicle and to localize it based on laser data in the absence of sufficiently accurate GPS information. It furthermore utilizes a local path planner for controlling the vehicle. In a practical experiment carried out with an autonomous car in a real parking garage we demonstrate that our approach allows the car to autonomously park itself in a large-scale multi-level structure.
CricAI: A classification based tool to predict the outcome in ODI cricket

Victory is the ultimate goal in any sport. In this work we address the winning factors in the sport of One Day International (ODI) cricket. Winning an ODI cricket match depends on various factors related to scoring as well as physical strength of the two teams. Some of the factors have been described in the literature but there is scope for further research on analyzing them, especially with reference to predicting victory. Interesting factors include home game advantage, day / night effect, winning the toss and batting first. In this article, we have used artificial intelligence techniques, more specifically Bayesian classifiers in machine learning, to predict how these factors affect the outcome of an ODI cricket match. Based on the emerged results, we have developed a software tool called CricAI. This tool outputs the probability of victory in an ODI cricket match using input factors such as home game advantage available at the beginning of the match. The CricAI tool can be used in real-world applications by teams playing cricket. It can accordingly be helpful in adjusting certain factors in order to maximize the chances of winning the real game.
Illusion and Dazzle: Adversarial Optical Channel Exploits against Lidars for Automotive Applications

With the advancement in computing, sensing, and vehicle electronics, autonomous vehicles are being realized. For autonomous driving, environment perception sensors such as radars, lidars, and vision sensors play core roles as the eyes of a vehicle; therefore, their reliability cannot be compromised. In this work, we present a spoofing by relaying attack, which can not only induce illusions in the lidar output but can also cause the illusions to appear closer than the location of a spoofing device. In a recent work, the former attack is shown to be effective, but the latter one was never shown. Additionally, we present a novel saturation attack against lidars, which can completely incapacitate a lidar from sensing a certain direction. The effectiveness of both the approaches is experimentally verified against Velodyne’s VLP-16.
Lyapunov Approach for the stabilization of the Inverted Spherical Pendulum

A nonlinear controller is presented for the stabilization of the spherical inverted pendulum system. The control strategy is based on the Lyapunov approach in conjunction with LaSalle's invariance principle. The proposed controller is able to bring the pendulum to the unstable upright equilibrium point with the position of the movable base at the origin. The obtained closed-loop system has a very large domain of attraction, that can be as large as desired, for any initial position of the pendulum which lies above the horizontal plane
Classification and Regression by randomForest

Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken for prediction. In bagging, successive trees do not depend on earlier trees — each is independently constructed using a bootstrap sample of the data set. In the end, a simple majority vote is taken for prediction. Breiman (2001) proposed random forests, which add an additional layer of randomness to bagging. In addition to constructing each tree using a different bootstrap sample of the data, random forests change how the classification or regression trees are constructed. In standard trees, each node is split using the best split among all variables. In a random forest, each node is split using the best among a subset of predictors randomly chosen at that node. This somewhat counterintuitive strategy turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks, and is robust against overfitting (Breiman, 2001). In addition, it is very user-friendly in the sense that it has only two parameters (the number of variables in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values. The randomForest package provides an R interface to the Fortran programs by Breiman and Cutler (available at http://www.stat.berkeley.edu/ users/breiman/). This article provides a brief introduction to the usage and features of the R functions.
Bag of What? Simple Noun Phrase Extraction for Text Analysis

Social scientists who do not have specialized natural language processing training often use a unigram bag-of-words (BOW) representation when analyzing text corpora. We offer a new phrase-based method, NPFST, for enriching a unigram BOW. NPFST uses a partof-speech tagger and a finite state transducer to extract multiword phrases to be added to a unigram BOW. We compare NPFST to both ngram and parsing methods in terms of yield, recall, and efficiency. We then demonstrate how to use NPFST for exploratory analyses; it performs well, without configuration, on many different kinds of English text. Finally, we present a case study using NPFST to analyze a new corpus of U.S. congressional bills. For our open-source implementation, see http://slanglab.cs.umass.edu/phrases/.