nils-herrmann/clean_scirepeval huggingface dataset:
Emerging Trends in Design and Applications of Memory-Based Computing and Content-Addressable Memories

Content-addressable memory (CAM) and associative memory (AM) are types of storage structures that allow searching by content as opposed to searching by address. Such memory structures are used in diverse applications ranging from branch prediction in a processor to complex pattern recognition. In this paper, we review the emerging challenges and opportunities in implementing different varieties of CAM/AM structures. Beyond-CMOS silicon and nonsilicon memory technologies hold significant promise in implementing dense, fast, and energy-efficient CAM/AM structures. We describe circuit/architecture level implementations of CAM/AM using these technologies, as well as novel applications in different domains, including informatics, text analytics, data mining, and reconfigurable computing platforms.
Implementation of 3D Obstacle Compliant Mobility Models for UAV Networks in ns-3

UAV networks are envisioned to play a crucial role in the future generations of wireless networks. The mechanical degrees of freedom in the movement of UAVs provides various advantages for tactical and civilian applications. Due to the high cost of failures in system-based tests, initial analysis and refinement of designs and algorithms for UAV applications are performed through rigorous simulations. Current trend of UAV specific simulators is mainly biased towards the mechanical properties of flying. For network-centric simulations, the intended measurements on the performance of protocols in mobile scenarios are conventionally captured from general-purpose network simulators, which are not natively equipped with comprehensive models for 3D movements of UAVs. To facilitate such simulations for UAV systems, this paper presents different mobility models for emulation of the movement of a UAV. Detailed description of three mobility models (random walk, random direction, and Gauss-Markov) are presented, and their associated movement patterns are characterized. This characterization is further extended by considering the effect of large obstacles on movement patterns of nodes following the three models. The mobility models are prepared as open-source add-ons for ns-3 network simulator.
Efficiently exploring architectural design spaces via predictive modeling

Architects use cycle-by-cycle simulation to evaluate design choices and understand tradeoffs and interactions among design parameters. Efficiently exploring exponential-size design spaces with many interacting parameters remains an open problem: the sheer number of experiments renders detailed simulation intractable. We attack this problem via an automated approach that builds accurate, confident predictive design-space models. We simulate sampled points, using the results to teach our models the function describing relationships among design parameters. The models produce highly accurate performance estimates for other points in the space, can be queried to predict performance impacts of architectural changes, and are very fast compared to simulation, enabling efficient discovery of tradeoffs among parameters in different regions. We validate our approach via sensitivity studies on memory hierarchy and CPU design spaces: our models generally predict IPC with only 1-2% error and reduce required simulation by two orders of magnitude. We also show the efficacy of our technique for exploring chip multiprocessor (CMP) design spaces: when trained on a 1% sample drawn from a CMP design space with 250K points and up to 55x performance swings among different system configurations, our models predict performance with only 4-5% error on average. Our approach combines with techniques to reduce time per simulation, achieving net time savings of three-four orders of magnitude.
Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation

Social networks are known to be vulnerable to the so-called Sybil attack, in which an attacker maintains massive Sybils and uses them to perform various malicious activities. Therefore, Sybil detection in social networks is a basic security research problem. Structure-based methods have been shown to be promising at detecting Sybils. Existing structure-based methods can be classified into two categories: Random Walk (RW)-based methods and Loop Belief Propagation (LBP)-based methods. RW-based methods cannot leverage labeled Sybils and labeled benign users simultaneously, which limits their detection accuracy, and/or they are not robust to noisy labels. LBP-based methods are not scalable, and they cannot guarantee convergence. In this work, we propose SybilSCAR, a novel structure-based method to detect Sybils in social networks.  We first propose a framework to unify RW-based and LBP-based methods. Under our framework, these methods can be viewed as iteratively applying a (different) local rule to every user, which propagates label information among a social graph. Second, we design a new local rule, which SybilSCAR iteratively applies to every user to detect Sybils. We compare SybilSCAR with state-of-the-art RW-based methods and LBP-based methods both theoretically and empirically. Theoretically, we show that, with proper parameter settings, SybilSCAR has a tighter asymptotical bound on the number of Sybils that are falsely accepted into a social network than existing structure-based methods. Empirically, we perform evaluation using both social networks with synthesized Sybils and a large-scale Twitter dataset (41.7M nodes and 1.2B edges) with real Sybils, and our results show that 1) SybilSCAR is substantially more accurate and more robust to label noise than state-of-the-art RW-based methods; and 2) SybilSCAR is more accurate and one order of magnitude more scalable than state-of-the-art LBP-based methods.
New York University 2016 System for KBP Event Nugget: A Deep Learning Approach

This is the first time New York University (NYU) participates in the event nugget (EN) evaluation of the Text Analysis Conference (TAC). We developed EN systems for both subtasks of event nugget, i.e, EN Task 1: Event Nugget Detection and EN Task 2: Event Nugget Detection and Coreference. The systems are mainly based on our recent research on deep learning for event detection (Nguyen and Grishman, 2015a; Nguyen and Grishman, 2016a). Due to the limited time we could devote to system development this year, we only ran the systems on the English evaluation data. However, we expect that the adaptation of the current systems to new languages can be done quickly. The development experiments show that although our current systems do not rely on complicated feature engineering, they significantly outperform the reported systems last year for the EN subtasks on the 2015 evaluation data.
A relative similarity based method for interactive patient risk prediction

This paper investigates the patient risk prediction problem in the context of active learning with relative similarities. Active learning has been extensively studied and successfully applied to solve real problems. The typical setting of active learning methods is to query absolute questions. In a medical application where the goal is to predict the risk of patients on certain disease using Electronic Health Records (EHR), the absolute questions take the form of “Will this patient suffer from Alzheimer’s later in his/her life?”, or “Are these two patients similar or not?”. Due to the excessive requirements of domain knowledge, such absolute questions are usually difficult to answer, even for experienced medical experts. In addition, the performance of absolute question focused active learning methods is less stable, since incorrect answers often occur which can be detrimental to the risk prediction model. In this paper, alternatively, we focus on designing relative questions that can be easily answered by domain experts. The proposed relative queries take the form of “Is patient A or patient B more similar to patient C?”, which can be answered by medical experts with more confidence. These questions poll relative information as opposed to absolute information, and even can be answered by non-experts in some cases. In this paper we propose an interactive patient risk prediction method, which actively queries medical experts with the relative similarity of patients. We explore our method on both benchmark and real clinic datasets, and make several interesting discoveries including that querying relative similarities is effective in patient risk prediction, and sometimes can even yield better prediction accuracy than asking for absolute questions.
Dead Code Detection Method Based on Program Slicing

Malicious code writers often insert dead code into the source code to prevent reverse engineer analysis. There are few methods for detecting dead code used in conjunction with opaque predicates. In this paper, a method based on program slicing for dead code detection is presented, which combines static slicing analysis and dynamic slicing analysis. Further, we design a dead code detection framework in the LLVM compiler infrastructure. The proposed method is used to detect the dead code inserted in some benchmark programs. The experimental results show that the proposed method has high detection rate.
A Time-Restricted Self-Attention Layer for ASR

Self-attention - an attention mechanism where the input and output sequence lengths are the same - has recently been successfully applied to machine translation, caption generation, and phoneme recognition. In this paper we apply a restricted self-attention mechanism (with multiple heads) to speech recognition. By “restricted” we mean that the mechanism at a particular frame only sees input from a limited number of frames to the left and right. Restricting the context makes it easier to encode the position of the input - we use a I-hot encoding of the frame offset. We try introducing attention layers into TDNN architectures, and replacing LSTM layers with attention layers in TDNN+LSTM architectures. We show experiments on a number of ASR setups. We observe improvements compared to the TDNN and TDNN+LSTM baselines. Attention layers are also faster than LSTM layers in test time, since they lack recurrence.
Interrater reliability of a modified Ashworth scale of muscle spasticity.

We undertook this investigation to determine the interrater reliability of manual tests of elbow flexor muscle spasticity graded on a modified Ashworth scale. We each independently graded the elbow flexor muscle spasticity of 30 patients with intracranial lesions. We agreed on 86.7% of our ratings. The Kendall's tau correlation between our grades was .847 (p less than .001). Thus, the relationship between the raters' judgments was significant and the reliability was good. Although the results were limited to the elbow flexor muscle group, we believe them to be positive enough to encourage further trials of the modified Ashworth scale for grading spasticity.
3D hand tracking using Kalman filter in depth space

Hand gestures are an important type of natural language used in many research areas such as human-computer interaction and computer vision. Hand gestures recognition requires the prior determination of the hand position through detection and tracking. One of the most efficient strategies for hand tracking is to use 2D visual information such as color and shape. However, visual-sensor-based hand tracking methods are very sensitive when tracking is performed under variable light conditions. Also, as hand movements are made in 3D space, the recognition performance of hand gestures using 2D information is inherently limited. In this article, we propose a novel real-time 3D hand tracking method in depth space using a 3D depth sensor and employing Kalman filter. We detect hand candidates using motion clusters and predefined wave motion, and track hand locations using Kalman filter. To verify the effectiveness of the proposed method, we compare the performance of the proposed method with the visual-based method. Experimental results show that the performance of the proposed method out performs visual-based method.
Lnc2Cancer: a manually curated database of experimentally supported lncRNAs associated with various human cancers

Lnc2Cancer (http://www.bio-bigdata.net/lnc2cancer) is a manually curated database of cancer-associated long non-coding RNAs (lncRNAs) with experimental support that aims to provide a high-quality and integrated resource for exploring lncRNA deregulation in various human cancers. LncRNAs represent a large category of functional RNA molecules that play a significant role in human cancers. A curated collection and summary of deregulated lncRNAs in cancer is essential to thoroughly understand the mechanisms and functions of lncRNAs. Here, we developed the Lnc2Cancer database, which contains 1057 manually curated associations between 531 lncRNAs and 86 human cancers. Each association includes lncRNA and cancer name, the lncRNA expression pattern, experimental techniques, a brief functional description, the original reference and additional annotation information. Lnc2Cancer provides a user-friendly interface to conveniently browse, retrieve and download data. Lnc2Cancer also offers a submission page for researchers to submit newly validated lncRNA-cancer associations. With the rapidly increasing interest in lncRNAs, Lnc2Cancer will significantly improve our understanding of lncRNA deregulation in cancer and has the potential to be a timely and valuable resource.
An industrial study on the risk of software changes

Modelling and understanding bugs has been the focus of much of the Software Engineering research today. However, organizations are interested in more than just bugs. In particular, they are more concerned about managing risk, i.e., the likelihood that a code or design change will cause a negative impact on their products and processes, regardless of whether or not it introduces a bug. In this paper, we conduct a year-long study involving more than 450 developers of a large enterprise, spanning more than 60 teams, to better understand risky changes, i.e., changes for which developers believe that additional attention is needed in the form of careful code or design reviewing and/or more testing. Our findings show that different developers and different teams have their own criteria for determining risky changes. Using factors extracted from the changes and the history of the files modified by the changes, we are able to accurately identify risky changes with a recall of more than 67%, and a precision improvement of 87% (using developer specific models) and 37% (using team specific models), over a random model. We find that the number of lines and chunks of code added by the change, the bugginess of the files being changed, the number of bug reports linked to a change and the developer experience are the best indicators of change risk. In addition, we find that when a change has many related changes, the reliability of developers in marking risky changes is negatively affected. Our findings and models are being used today in practice to manage the risk of software projects.
Predicting Bitcoin Transactions with Network Analysis

Bitcoin has recently soared in popularity, attracting the attention of individuals, corporations, and governments alike. The aim of this paper is to predict the future number of transactions in the Bitcoin network based on the current network architecture. The task is similar to link prediction, but instead computes the expected number of edges in the network in a given time interval. We explored two major approaches: using handcrafted topological and non-topological features, and learning features in an unsupervised approach leveraging node2vec. We found that models learning over these features did not outperform a baseline regression based on node and edge counts, and discuss possible reasons why that was the case.
Eigenfaces for Recognition

We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
Academic performance, career potential, creativity, and job performance: can one construct predict them all?

This meta-analysis addresses the question of whether 1 general cognitive ability measure developed for predicting academic performance is valid for predicting performance in both educational and work domains. The validity of the Miller Analogies Test (MAT; W. S. Miller, 1960) for predicting 18 academic and work-related criteria was examined. MAT correlations with other cognitive tests (e.g., Raven's Matrices []; Graduate Record Examinations) also were meta-analyzed. The results indicate that the abilities measured by the MAT are shared with other cognitive ability instruments and that these abilities are generalizably valid predictors of academic and vocational criteria, as well as evaluations of career potential and creativity. These findings contradict the notion that intelligence at work is wholly different from intelligence at school, extending the voluminous literature that supports the broad importance of general cognitive ability (g).
The Q-CHAT (Quantitative CHecklist for Autism in Toddlers): a normally distributed quantitative measure of autistic traits at 18-24 months of age: preliminary report.

We report a major revision of the CHecklist for Autism in Toddlers (CHAT). This quantitative CHAT (Q-CHAT) contains 25 items, scored on a 5 point scale (0-4). The Q-CHAT was completed by parents of n = 779 unselected toddlers (mean age 21 months) and n = 160 toddlers and preschoolers (mean age 44 months) with an Autism Spectrum Condition (ASC). The ASC group (mean (SD) = 51.8 (14.3)) scored higher on the Q-CHAT than controls (26.7 (7.8)). Boys in the control group (27.5 (7.8)) scored higher than girls (25.8 (7.7)). The intraclass correlation for test-retest reliability was 0.82 (n = 330). The distribution in the control group was close to normal. Full examination of the clinical validity of the Q-CHAT and test properties is underway.
A Comparison of Techniques to Automatically Identify Complex Words

Identifying complex words (CWs) is an important, yet often overlooked, task within lexical simplification (The process of automatically replacing CWs with simpler alternatives). If too many words are identified then substitutions may be made erroneously, leading to a loss of meaning. If too few words are identified then those which impede a user’s understanding may be missed, resulting in a complex final text. This paper addresses the task of evaluating different methods for CW identification. A corpus of sentences with annotated CWs is mined from Simple Wikipedia edit histories, which is then used as the basis for several experiments. Firstly, the corpus design is explained and the results of the validation experiments using human judges are reported. Experiments are carried out into the CW identification techniques of: simplifying everything, frequency thresholding and training a support vector machine. These are based upon previous approaches to the task and show that thresholding does not perform significantly differently to the more naı̈ve technique of simplifying everything. The support vector machine achieves a slight increase in precision over the other two methods, but at the cost of a dramatic trade off in recall.
Test anxiety and direction of attention.

The literature reviewed suggests an attentional interpretation, of the adverse effects which test anxiety has on task performance. During task performance the highly test-anxious person divides his attention between self-relevant and task-relevant variables, in contrast to the low-test-anxious person who focuses his attention more fully on the task. This interpretation was supported by literature from diverse areas suggesting that (a) highly anxious persons are generally more self-preoccupied than are people low in anxiety; (b) the selffocusing tendencies of highly test-anxious persons are activated in testing situations; (c) those situational conditions in which the greatest performance differences occur are ones which elicit the self-focusing tendencies of highly test-anxious subjects, and the task-focusing tendencies of low-anxious subjects; (d) research examining the relationship between anxiety and task variables suggests that anxiety reduces the range of task cues utilized in performance; (e) "worry," an attentionally demanding cognitive activity, is more debilitating of task performance than is autonomic arousal. Treatment and research implications of this attentional interpretation of test anxiety are briefly discussed.
Thin dual-resonant stacked shorted patch antenna for mobile communications

Short-circuited microstrip patch antennas, or PIFAs, can be used as directive internal handset antennas in various communication systems. They provide advantages over traditional external whip and helix antennas in terms of increased total efficiency (when the handset is near the user’s head), decreased radiation towards the user, and increased mechanical reliability. The main disadvantage is their narrow impedance bandwidth compared to the volume occupied by the antenna structure. Typical requirements for directive internal antennas designed for cellular handsets (with a thickness ≤ 20mm) are antenna thickness ≤ 5mm and bandwidth ≥ 10%. It is difficult to design such a small, directive, low-profile handset antenna with high radiation efficiency and an impedance bandwidth greater than 10%. Small antenna design is always a compromise between size, bandwidth, and efficiency. Often, the price for improved performance is increased complexity. If the total dimensions of a patch antenna element are fixed, there are two effective ways to enhance its bandwidth: either dissipative loading , which reduces the radiation efficiency, or the addition of more resonators into the antenna structure (matching networks or parasitic elements). The latter method may increase the manufacturing complexity of the antenna. In this Letter, a thin dual-resonant stacked shorted patch antenna is presented. Previously, stacked short-circuited patch antenna elements have been reported in [2, 3]. More recently, similar elements have also been discussed in [4, 5]. The presented antenna is dual-resonant and has a very low profile. The thickness is only one fifth of that of the antenna reported in , whereas the surface area reserved for the patches is approximately equal. The small size of the antenna presented in this Letter makes it relatively easy to fit inside a cellular handset. The bandwidth is sufficient for many communication systems. With a common short circuit element which connects both patches to the ground plane, the presented antenna is the simplest example of a stacked shorted microstrip patch antenna. No extra tuning or shorting posts inside the patch area are needed, which makes its manufacturing easier.