nils-herrmann/clean_scirepeval huggingface dataset:
Boolean symmetric searchable encryption

In this article we tackle the issue of searchable encryption with a generalized query model. Departing from many previous works that focused on queries consisting of a single keyword, we consider the the case of queries consisting of arbitrary boolean expressions on keywords, that is to say conjunctions and disjunctions of keywords and their complement. Our construction of boolean symmetric searchable encryption BSSE is mainly based on the orthogonalization of the keyword field according to the Gram-Schmidt process. Each document stored in an outsourced server is associated with a label which contains all the keywords corresponding to the document, and searches are performed by way of a simple inner product. Furthermore, the queries in the BSSE scheme are randomized. This randomization hides the search pattern of the user since the search results cannot be associated deterministically to queries. We formally define an adaptive security model for the BSSE scheme. In addition, the search complexity is in $O(n)$ where $n$ is the number of documents stored in the outsourced server.
A Comparative Analysis of Methods for Pruning Decision Trees

In this paper, we address the problem of retrospectively pruning decision trees induced from data, according to a topdown approach. This problem has received considerable attention in the areas of pattern recognition and machine learning, and many distinct methods have been proposed in literature. We make a comparative study of six well-known pruning methods with the aim of understanding their theoretical foundations, their computational complexity, and the strengths and weaknesses of their formulation. Comments on the characteristics of each method are empirically supported. In particular, a wide experimentation performed on several data sets leads us to opposite conclusions on the predictive accuracy of simplified trees from some drawn in the literature. We attribute this divergence to differences in experimental designs. Finally, we prove and make use of a property of the reduced error pruning method to obtain an objective evaluation of the tendency to overprune/underprune observed in each method. Index Terms —Decision trees, top-down induction of decision trees, simplification of decision trees, pruning and grafting operators, optimal pruning, comparative studies. —————————— ✦ ——————————
A survey on Human Mobility and its applications

Human Mobility has attracted attentions from different fields of studies such as epidemic modeling, traffic engineering, traffic prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diffusion and flow in the network and makes it easier to estimate how much one part of the network influences another by using metrics like centrality measures. We aim to study population flow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of different data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges.
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks

Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with DAG-styled GRU layers within a gated-attention reader. However, coreference is limited in providing information for rich inference. We introduce a new method for better connecting global evidence, which forms more complex graphs compared to DAGs. To perform evidence integration on our graphs, we investigate two recent graph neural networks, namely graph convolutional network (GCN) and graph recurrent network (GRN). Experiments on two standard datasets show that richer global information leads to better answers. Our method performs better than all published results on these datasets.
Quantitating physical activity in COPD using a triaxial accelerometer.

To determine the reliability, validity, and stability of a triaxial accelerometer for walking and daily activity measurement in a COPD sample.



Cross-sectional, correlational, descriptive design.



Outpatient pulmonary rehabilitation program in a university-affiliated Veterans Affairs medical center.



Forty-seven outpatients (44 men and 3 women) with stable COPD (FEV(1), 37% predicted; SD, 16%) prior to entry into a pulmonary rehabilitation program.



Test-retest reliability of a triaxial movement sensor (Tritrac R3D Research Ergometer; Professional Products; Madison, WI) was evaluated in 35 of the 47 subjects during three standardized 6-min walks (intraclass correlation coefficient [rICC] = 0.84). Pearson correlations evaluated accelerometer concurrent validity as a measure of walking (in vector magnitude units), compared to walking distance in all 47 subjects during three sequential 6-min walks (0. 84, 0.85, and 0.95, respectively; p < 0.001). The validity of the accelerometer as a measure of daily activity over 3 full days at home was evaluated in all subjects using Pearson correlations with other indicators of functional capacity. The accelerometer correlated with exercise capacity (maximal 6-min walk, r = 0.74; p < 0.001); level of obstructive disease (FEV(1) percent predicted, r = 0.62; p < 0.001); dyspnea (Functional Status and Dyspnea Questionnaire, dyspnea over the past 30 days, r = - 0.29; p < 0.05); and activity self-efficacy (Activity Self-Efficacy Questionnaire, r = 0.43; p < 0.01); but not with self-report of daily activity (Modified Activity Recall Questionnaire, r = 0.14; not significant). Stability of the accelerometer to measure 3 full days of activity at home was determined by an rICC of 0.69.



This study provides preliminary data suggesting that a triaxial movement sensor is a reliable, valid, and stable measure of walking and daily physical activity in COPD patients. It has the potential to provide more precise measurement of everyday physical functioning in this population than self-report measures currently in use, and measures an important dimension of functional status not previously well-described.
TransG : A Generative Mixture Model for Knowledge Graph Embedding

Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence. This paper addresses a new issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples, and proposes a novel generative model for embedding, TransG. The new model can discover latent semantics for a relation and leverage a mixture of relation-specific component vectors to embed a fact triple. To the best of our knowledge, this is the first generative model for knowledge graph embedding, which is able to deal with multiple relation semantics. Extensive experiments show that the proposed model achieves substantial improvements against the state-of-the-art baselines.
A New Fast and Robust Method Based on Head Detection for People-Flow Counting System

This paper presents an implementation method for the people counting system which detects and tracks moving people using a fixed single camera. The main contribution of this paper is the novel head detection method based on body’s geometry. A novel body descriptor is proposed for finding people’s head which is defined as Body Feature Rectangle (BFR). First, a vertical projection method is used to get the line which divides touching persons into individuals. Second, a special inscribed rectangle is found to locate the neck position which describes the torso area. Third, locations of people’s heads can be got according to its neck-positions. Last, a robust counting method named MEA is proposed to get the real counts of walking people flows. The proposed method can divide the multiple-people image into individuals whatever people merge with each other or not. Moreover, the passing people can be counted accurately under the influence of wearing hats. Experimental results show that our proposed method can nearly reach to an accuracy of 100% if the number of a people-merging pattern is less than six. People Counting; Head Detection; BFR; People-flow Tracking
Optimal Discrete Power Control in Poisson-Clustered Ad Hoc Networks

Power control in a digital handset is practically implemented in a discrete fashion, and usually, such a discrete power control (DPC) scheme is suboptimal. In this paper, we first show that in a Poison-distributed ad hoc network, if DPC is properly designed with a certain condition satisfied, it can strictly work better than no power control (i.e., users use the same constant power) in terms of average signal-to-interference ratio, outage probability, and spatial reuse. This motivates us to propose an N-layer DPC scheme in a wireless clustered ad hoc network, where transmitters and their intended receivers in circular clusters are characterized by a Poisson cluster process on the plane ℝ2. The cluster of each transmitter is tessellated into N-layer annuli with transmit power Pi adopted if the intended receiver is located at the ith layer. Two performance metrics of transmission capacity (TC) and outage-free spatial reuse factor are redefined based on the N-layer DPC. The outage probability of each layer in a cluster is characterized and used to derive the optimal power scaling law Pi ∈ Θ(ηi-(α/2)), with ηi as the probability of selecting power Pi and α as the path loss exponent. Moreover, the specific design approaches to optimize Pi and N based on ηi are also discussed. Simulation results indicate that the proposed optimal N-layer DPC significantly outperforms other existing power control schemes in terms of TC and spatial reuse.
Multi-label classification by mining label and instance correlations from heterogeneous information networks

Multi-label classification is prevalent in many real-world applications, where each example can be associated with a set of multiple labels simultaneously. The key challenge of multi-label classification comes from the large space of all possible label sets, which is exponential to the number of candidate labels. Most previous work focuses on exploiting correlations among different labels to facilitate the learning process. It is usually assumed that the label correlations are given beforehand or can be derived directly from data samples by counting their label co-occurrences. However, in many real-world multi-label classification tasks, the label correlations are not given and can be hard to learn directly from data samples within a moderate-sized training set. Heterogeneous information networks can provide abundant knowledge about relationships among different types of entities including data samples and class labels. In this paper, we propose to use heterogeneous information networks to facilitate the multi-label classification process. By mining the linkage structure of heterogeneous information networks, multiple types of relationships among different class labels and data samples can be extracted. Then we can use these relationships to effectively infer the correlations among different class labels in general, as well as the dependencies among the label sets of data examples inter-connected in the network. Empirical studies on real-world tasks demonstrate that the performance of multi-label classification can be effectively boosted using heterogeneous information net- works.
A Computational Model of Perceived Agency in Video Games

Agency, being one’s ability to perform an action and have some influence over the world, is fundamental to interactive entertainment. Although much of the games industry is concerned with providing more agency to its players, what seems to matter more is how much agency each player will actually perceive. In this paper, we present a computational model of this phenomena, based on the notion that the amount of agency that one perceives depends on how much they desire the outcomes that result from their decisions. Using a structure for high-agency stories that we designed specifically for this intent, we present the results of a 141-participant user study that tests our model’s ability to select subsequent events in an original interactive story. Using a newly validated survey instrument for measuring both agency and fun, we found that, with a high degree of confidence, event sequences selected by our model result in players perceiving more agency than players who experience event sequences that our model
An optimal fuzzy PID controller

This paper introduces an optimal fuzzy proportional–integral–derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting controller does not need to execute any fuzzy rule base, and is actually a conventional PID controller with analytic formulas. The main improvement is in endowing the classical controller with a certain adaptive control capability. The constant PID control gains are optimized by using the multiobjective generic algorithm (MOGA), thereby yielding an optimal fuzzy PID controller. Computer simulations are shown to demonstrate its improvement over the fuzzy PID controller without MOGA optimization.
Towards Detecting Target Link Flooding Attack

A new class of target link flooding attacks (LFA) can cut off the Internet connections of a target area without being detected because they employ legitimate flows to congest selected links. Although new mechanisms for defending against LFA have been proposed, the deployment issues limit their usages since they require modifying routers. In this paper, we propose LinkScope, a novel system that employs both the end-to-end and the hopby-hop network measurement techniques to capture abnormal path performance degradation for detecting LFA and then correlate the performance data and traceroute data to infer the target links or areas. Although the idea is simple, we tackle a number of challenging issues, such as conducting large-scale Internet measurement through noncooperative measurement, assessing the performance on asymmetric Internet paths, and detecting LFA. We have implemented LinkScope with 7174 lines of C codes and the extensive evaluation in a testbed and the Internet show that LinkScope can quickly detect LFA with high accuracy and low false positive rate.
Object Pointing: A Complement to Bitmap Pointing in GUIs

Pointing, a cornerstone of our graphical user interfaces, has been conceptualized and implemented so far as the act of selecting pixels in bitmap displays. We show that the current technique, which we call bitmap pointing (BMP), is often sub-optimal as it requires continuous information from the mouse while the system often just needs the discrete specification of objects. The paper introduces object pointing (OP), a novel interaction technique based on a special screen cursor that skips empty spaces, thus drastically reducing the waste of input information. We report data from 1D and 2D Fitts’ law experiments showing that OP outperforms BMP and that the performance facilitation increases with the task’s index of difficulty. We discuss the implementation of OP in current interfaces.
An inquiry into the nature and causes of the wealth of internet miscreants

This paper studies an active underground economy which spec ializes in the commoditization of activities such as credit car d fraud, identity theft, spamming, phishing, online credential the ft, and the sale of compromised hosts. Using a seven month trace of logs c ollected from an active underground market operating on publi c Internet chat networks, we measure how the shift from “hacking for fun” to “hacking for profit” has given birth to a societal subs trate mature enough to steal wealth into the millions of dollars in less than one year.
A novel type of compliant and underactuated robotic hand for dexterous grasping

We built a highly compliant, underactuated, robust and at the same time dexterous anthropomorphic hand. We evaluate its dexterous grasping capabilities by implementing the comprehensive Feix taxonomy of human grasps and by assessing the dexterity of its opposable thumb using the Kapandji test. We also illustrate the hand’s payload limits and demonstrate its grasping capabilities in real-world grasping experiments. To support our claim that compliant structures are beneficial for dexterous grasping, we compare the dimensionality of control necessary to implement the diverse grasp postures with the dimensionality of the grasp postures themselves. We find that actuation space is smaller than posture space and explain the difference with the mechanic interaction between hand and grasped object. Additional desirable properties are derived from using soft robotics technology: the hand is robust to impact and blunt collisions, inherently safe, and not affected by dirt, dust, or liquids. Furthermore, the hand is simple and inexpensive to manufacture.
High-Performance Neural Networks for Visual Object Classification

We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better than more shallow ones. Learning is surprisingly rapid. NORB is completely trained within five epochs. Test error rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs, respectively.
Planning human-aware motions using a sampling-based costmap planner

This paper addresses the motion planning problem while considering Human-Robot Interaction (HRI) constraints. The proposed planner generates collision-free paths that are acceptable and legible to the human. The method extends our previous work on human-aware path planning to cluttered environments. A randomized cost-based exploration method provides an initial path that is relevant with respect to HRI and workspace constraints. The quality of the path is further improved with a local path-optimization method. Simulation results on mobile manipulators in the presence of humans demonstrate the overall efficacy of the approach.
A new look at psychological climate and its relationship to job involvement, effort, and performance.

This study investigated the process by which employee perceptions of the organizational environment are related to job involvement, effort, and performance. The researchers developed an operational definition of psychological climate that was based on how employees perceive aspects of the organizational environment and interpret them in relation to their own well-being. Perceived psychological climate was then related to job involvement, effort, and performance in a path-analytic framework. Results showed that perceptions of a motivating and involving psychological climate were related to job involvement, which in turn was related to effort. Effort was also related to work performance. Results revealed that a modest but statistically significant effect of job involvement on performance became nonsignificant when effort was inserted into the model, indicating the mediating effect of effort on the relationship. The results cross-validated well across 2 samples of outside salespeople, indicating that relationships are generalizable across these different sales contexts.
Computational Intelligence and AI in Games: A New IEEE Transactions

The author first provides an overview of computational intelligence and AI in games. Then he describes the new IEEE Transactions, which will publish archival quality original papers in all aspects of computational intelligence and AI related to all types of games. To name some examples, these include computer and video games, board games, card games, mathematical games, games that model economies or societies, serious games with educational and training applications, and games involving physical objects such as robot football and robotic car racing. Emphasis will also be placed on the use of these methods to improve performance in, and understanding of, the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It will also include using games as a platform for building intelligent embedded agents for real-world applications. The journal builds on a scientific community that has already been active in recent years with the development of new conference series such as the IEEE Symposium on Computational Intelligence in Games (CIG) and Artificial Intelligence and Interactive Digital Entertainment (AIIDE), as well as special issues on games in journals such as the IEEE Transactions on Evolutionary Computation. When setting up the journal, a decision was made to include both artificial intelligence (AI) and computational intelligence (CI) in the title. AI seeks to simulate intelligent behavior in any way that can be programmed effectively. Some see the field of AI as being all-inclusive, while others argue that there is nothing artificial about real intelligence as exhibited by higher mammals.
Random Generation of Combinatorial Structures from a Uniform Distribution

The class of problems involving the random generation of combinatorial structures from a uniform distribution is considered. Uniform generation problems are, in computational difficulty, intermediate between classical existence and counting problems. It is shown that exactly uniform generation of 'efficiently verifiable' combinatorial structures is reducible to approximate counting (and hence, is within the third level of the polynomial hierarchy). Natural combinatorial problems are presented which exhibit complexity gaps between their existence and generation, and between their generation and counting versions. It is further shown that for self-reducible problems, almost uniform generation and randomized approximate counting are inter-reducible, and hence, of similar complexity. CR Categories. F.I.1, F.1.3, G.2.1, G.3