nils-herrmann/clean_scirepeval huggingface dataset:
EMMA: A New Platform to Evaluate Hardware-based Mobile Malware Analyses

Hardware-based malware detectors (HMDs) are a key emerging technology to build trustworthy computing platforms, especially mobile platforms. Quantifying the efficacy of HMDs against malicious adversaries is thus an important problem. The challenge lies in that real-world malware typically adapts to defenses, evades being run in experimental settings, and hides behind benign applications. Thus, realizing the potential of HMDs as a line of defense – that has a small and battery-efficient code base – requires a rigorous foundation for evaluating HMDs. To this end, we introduce EMMA—a platform to evaluate the efficacy of HMDs for mobile platforms. EMMA deconstructs malware into atomic, orthogonal actions and introduces a systematic way of pitting different HMDs against a diverse subset of malware hidden inside benign applications. EMMA drives both malware and benign programs with real user-inputs to yield an HMD’s effective operating range— i.e., the malware actions a particular HMD is capable of detecting. We show that small atomic actions, such as stealing a Contact or SMS, have surprisingly large hardware footprints, and use this insight to design HMD algorithms that are less intrusive than prior work and yet perform 24.7% better. Finally, EMMA brings up a surprising new result— obfuscation techniques used by malware to evade static analyses makes them more detectable using HMDs.
Neonatal medicine in ancient art.

There are a limited number of artistic objects from ancient times with particular importance in neonatal medicine. The best examples are figurines from ancient Egypt of Isis nursing Horus, showing the importance of breastfeeding. The earliest images of the human fetus were made by the Olmecs in Mexico around 1200- 400 BCE. One of the earliest representations of congenital anomalies is a figurine of diencephalic twins thought to be the goddess of Anatolia, dated to around 6500 BCE. In addition to these figurines, three sets of twins in the ancient world have medical importance, and Renaissance artists often used them as a subject for their paintings: "direct suckling animals" (Romulus and Remus), "heteropaternal superfecundation"  Zeus, the king of the Olympian gods, and Leda's husband, Tyndareus), and "twin-to-twin transfusion" in monozygotic twins (Jacob and Esau).
Efficient feature selection filters for high-dimensional data

0167-8655/$ see front matter 2012 Elsevier B.V. A http://dx.doi.org/10.1016/j.patrec.2012.05.019 ⇑ Corresponding author at: Instituto Superior de E Portugal. E-mail address: arturj@cc.isel.ipl.pt (A.J. Ferreira). Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be computationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10 features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.
1d-SAX: A Novel Symbolic Representation for Time Series

SAX (Symbolic Aggregate ) is one of the main symbolization technique for time series. A well-known limitation of SAX is that trends are not taken into account in the symbolization. This paper proposes 1d-SAX a method to represent a time series as a sequence of symbols that contain each an information about the average and the trend of the series on a segment. We compare the efficiency of SAX and 1d-SAX in terms of i) goodness-of-fit and ii) retrieval performance for querying a time series database with an asymmetric scheme. The results show that 1d-SAX improves retrieval performance using equal quantity of information, especially when the compression rate increases.
“It’s Killing Us!” Narratives of Black Adults About Microaggression Experiences and Related Health Stress

Perceived racism contributes to persistent health stress leading to health disparities. African American/Black persons (BPs) believe subtle, rather than overt, interpersonal racism is increasing. Sue and colleagues describe interpersonal racism as racial microaggressions: "routine" marginalizing indignities by White persons (WPs) toward BPs that contribute to health stress. In this narrative, exploratory study, Black adults (n = 10) were asked about specific racial microaggressions; they all experienced multiple types. Categorical and narrative analysis captured interpretations, strategies, and health stress attributions. Six iconic narratives contextualized health stress responses. Diverse mental and physical symptoms were attributed to racial microaggressions. Few strategies in response had positive outcomes. Future research includes development of coping strategies for BPs in these interactions, exploration of WPs awareness of their behaviors, and preventing racial microaggressions in health encounters that exacerbate health disparities.
Specific impairments of planning.

An information-processing model is outlined that predicts that performance on non-routine tasks can be impaired independently of performance on routine tasks. The model is related to views on frontal lobe functions, particularly those of Luria. Two methods of obtaining more rigorous tests of the model are discussed. One makes use of ideas from artificial intelligence to derive a task heavily loaded on planning abilities. A group of patients with left anterior lesions has a specific deficit on the task. Subsidiary investigations support the inference that this is a planning impairment.
Factors effecting service oriented architecture implementation

With the advancement of computing the arena of software development life cycle and its traditional software architectural models are moving towards more flexible and agile techniques. Service oriented architecture is one of the modern architecture techniques which are composed of loosely coupled and flexible software components called services. Service oriented architecture supports usability, scalability and flexibility and each of these have their own implementation complexities. In this paper the focus is on those factors that affect service oriented architecture design and implementation. Authors aim to do a literature review for factors effecting service oriented architecture. The factors that have been identified earlier in service oriented architecture implementation are accumulated in this paper. The factors effecting service oriented architecture are categorized with a help of a proposed model. The proposed model can be used as a guideline for the SOA projects according to size of organization.
Emotion Recognition Using Physiological Signals

In this paper the problem of emotion recognition using physiological signals is presented. Firstly the problems with acquisition of physiological signals related to specific human emotions are described. It is not a trivial problem to elicit real emotions and to choose stimuli that always, and for all people, elicit the same emotion. Also different kinds of physiological signals for emotion recognition are considered. A set of the most helpful biosignals is chosen. An experiment is described that was performed in order to verify the possibility of eliciting real emotions using specially prepared multimedia presentations, as well as finding physiological signals that are most correlated with human emotions. The experiment was useful for detecting and identifying many problems and helping to find their solutions. The results of this research can be used for creation of affect-aware applications, for instance video games, that will be able to react to user's emotions.
Analyzing social and stylometric features to identify spear phishing emails

Targeted social engineering attacks in the form of spear phishing emails, are often the main gimmick used by attackers to infiltrate organizational networks and implant state-of-the-art Advanced Persistent Threats (APTs). Spear phishing is a complex targeted attack in which, an attacker harvests information about the victim prior to the attack. This information is then used to create sophisticated, genuine-looking attack vectors, drawing the victim to compromise confidential information. What makes spear phishing different, and more powerful than normal phishing, is this contextual information about the victim. Online social media services can be one such source for gathering vital information about an individual. In this paper, we characterize and examine a true positive dataset of spear phishing, spam, and normal phishing emails from Symantec's enterprise email scanning service. We then present a model to detect spear phishing emails sent to employees of 14 international organizations, by using social features extracted from LinkedIn. Our dataset consists of 4,742 targeted attack emails sent to 2,434 victims, and 9,353 non targeted attack emails sent to 5,912 non victims; and publicly available information from their LinkedIn profiles. We applied various machine learning algorithms to this labeled data, and achieved an overall maximum accuracy of 97.76% in identifying spear phishing emails. We used a combination of social features from LinkedIn profiles, and stylometric features extracted from email subjects, bodies, and attachments. However, we achieved a slightly better accuracy of 98.28% without the social features. Our analysis revealed that social features extracted from LinkedIn do not help in identifying spear phishing emails. To the best of our knowledge, this is one of the first attempts to make use of a combination of stylometric features extracted from emails, and social features extracted from an online social network to detect targeted spear phishing emails.
Social Media Processing

In the past ten years, new powerful algorithms based on efficient data structures have been proposed to solve the problem of Approximate Nearest Neighbors search (ANN). To find the nearest neighbors in probability-distribution-type data, the existing Locality Sensitive Hashing (LSH) algorithms for vector-type data can be directly used to solve it. However, these methods do not consider the special properties of probability distributions. In this paper, based on the special properties of probability distributions, we present a novel LSH scheme adapted to angular distance for ANN search in high-dimensional probability distributions. We define the specific hashing functions, and prove their localsensitivity. Also, we propose a Sequential Interleaving algorithm based on the “Unbalance Effect” of Euclidean and angular metrics for probability distributions. Finally, we compare, through experiments, our methods with the state-of-the-art LSH algorithms in the context of ANN on six public image databases. The results prove the proposed algorithms can provide far better accuracy in the context of ANN than baselines.
SemEval-2016 Task 12: Clinical TempEval

2016 evaluated temporal information extraction systems on the clinical domain. Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were trained and evaluated on a corpus of clinical and pathology notes from the Mayo Clinic, annotated with an extension of TimeML for the clinical domain. 14 teams submitted a total of 40 system runs, with the best systems achieving near-human performance on identifying events and times. On identifying temporal relations, there was a gap between the best systems and human performance, but the gap was less than half the gap of Clinical TempEval 2015.
Speaker-Invariant Training Via Adversarial Learning

We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the inter-talker feature variability while maximizing its senone discriminability so as to enhance the performance of a deep neural network (DNN) based ASR system. We call the scheme speaker-invariant training (SIT). In SIT, a DNN acoustic model and a speaker classifier network are jointly optimized to minimize the senone (tied triphone state) classification loss, and simultaneously mini-maximize the speaker classification loss. A speaker-invariant and senone-discriminative deep feature is learned through this adversarial multi-task learning. With SIT, a canonical DNN acoustic model with significantly reduced variance in its output probabilities is learned with no explicit speaker-independent (SI) transformations or speaker-specific representations used in training or testing. Evaluated on the CHiME-3 dataset, the SIT achieves 4.99% relative word error rate (WER) improvement over the conventional SI acoustic model. With additional unsupervised speaker adaptation, the speaker-adapted (SA) SIT model achieves 4.86% relative WER gain over the SA SI acoustic model.
Plural semantics for natural language understanding — a computational proof-theoretic approach

The semantics of natural language plurals poses a number of intricate problems – both from a formal and a computational perspective. In this thesis I investigate problems of representing, disambiguating and reasoning with plurals from a computational perspective. The work defines a computationally suitable representation for important plural constructions, proposes a tractable resolution algorithm for semantic plural ambiguities, and integrates an automatic reasoning component for plurals. My solution combines insights from formal semantics, computational linguistics and automated theorem proving and is based on the following main ideas. Whereas many existing approaches to plural semantics work on a model-theoretic basis using higher-order representation languages I propose a proof-theoretic approach to plural semantics based on a flat firstorder semantic representation language thus showing that a trade-off between expressive power and logical tractability can be found. The problem of automatic disambiguation of plurals is tackled by a deliberate decision to drastically reduce recourse to contextual knowledge for disambiguation but rely instead on structurally available and thus computationally manageable information. A further central aspect of the solution lies in carefully drawing the borderline between real ambiguity and mere indeterminacy in the interpretation of plural noun phrases. As a practical result of my computational proof-theoretic approach to plural semantics I can use my methods to perform automated reasoning with plurals by applying advanced firstorder theorem provers and model-generators available off-the shelf. The results are prototypically implemented within the two logic-oriented natural language understanding applications DRoPs and Attempto. DRoPs provides an automatic plural disambiguation component for uncontrolled natural language whereas Attempto works with a constructive disambiguation strategy for controlled natural language. Both systems provide tools for the automated analysis of technical texts allowing users for example to automatically detect inconsistencies, to perform question answering, to check whether a conjecture follows from a text or to find equivalences and redundancies.
Introduction to quantum information theory and outline of two applications to physics: the black hole information paradox and the renormalization group information flow

This review paper is intended for scholars with different backgrounds, possibly in only one of the subjects covered, and therefore little background knowledge is assumed. The first part is an introduction to classical and quantum information theory (CIT, QIT): basic definitions and tools of CIT are introduced, such as the information content of a random variable, the typical set, and some principles of data compression. Some concepts and results of QIT are then introduced, such as the qubit, the pure and mixed states, the Holevo theorem, the no-cloning theorem, and the quantum complementarity. In the second part, two applications of QIT to open problems in theoretical physics are discussed. The black hole (BH) information paradox is related to the phenomenon of the Hawking radiation (HR). Considering a BH starting in a pure state, after its complete evaporation only the Hawking radiation will remain, which is shown to be in a mixed state. This either describes a non-unitary evolution of an isolated system, contradicting the evolution postulate of quantum mechanics and violating the no-cloning theorem, or it implies that the initial information content can escape the BH, therefore contradicting general relativity. The progress toward the solution of the paradox is discussed. The renormalization group (RG) aims at the extraction of the macroscopic description of a physical system from its microscopic description. This passage from microscopic to macroscopic can be described in terms of several steps from one scale to another, and is therefore formalized as the action of a group. The c-theorem proves the existence, under certain conditions, of a function which is monotonically decreasing along the group transformations. This result suggests an interpretation of this function as entropy, and its use to study the information flow along the RG transformations.
Characteristics of a 1200 V CSTBT optimized for industrial applications

The design of power semiconductor chips has always involved a trade-off between switching speed, static losses, safe operating area and short-circuit withstanding capability. This paper presents an optimized structure for 1200 V IGBTs from the viewpoint of all-round performance. The new device is based on a novel wide cell pitch carrier stored trench bipolar transistor (CSTBT). Unlike conventional trench gate IGBTs, this structure simultaneously achieves both low on-state voltage and the rugged short-circuit capability desired for industrial applications.
AUTOMATIC FINGERING SYSTEM ( AFS )

One of the most important aspects in playing the piano is using the appropriate fingers to facilitate movement and transitions. The fingering arrangement depends to a ce rtain extent on the size of the musician’s hand. We hav e developed an automatic fingering system that, given a sequence of pitches, suggests which fingers should be used. The output can be personalized to agree with t he limitations of the user’s hand. We also consider this system to be the base of a more complex future system: a score reduction system that will reduce orchestra scor e to piano scores.  • “Vertical cost” model: the stretch induced by a given hand position. • “Horizontal cost” model: transition between two hand positions. • A system that computes low-cost fingering for a given piece of music. • A machine learning technique used to learn the appropriate parameters in the models.
Automatic detection of pain intensity

Previous efforts suggest that occurrence of pain can be detected from the face. Can intensity of pain be detected as well? The Prkachin and Solomon Pain Intensity (PSPI) metric was used to classify four levels of pain intensity (none, trace, weak, and strong) in 25 participants with previous shoulder injury (McMaster-UNBC Pain Archive). Participants were recorded while they completed a series of movements of their affected and unaffected shoulders. From the video recordings, canonical normalized appearance of the face (CAPP) was extracted using active appearance modeling. To control for variation in face size, all CAPP were rescaled to 96x96 pixels. CAPP then was passed through a set of Log-Normal filters consisting of 7 frequencies and 15 orientations to extract 9216 features. To detect pain level, 4 support vector machines (SVMs) were separately trained for the automatic measurement of pain intensity on a frame-by-frame level using both 5-folds cross-validation and leave-one-subject-out cross-validation. F1 for each level of pain intensity ranged from 91% to 96% and from 40% to 67% for 5-folds and leave-one-subject-out cross-validation, respectively. Intra-class correlation, which assesses the consistency of continuous pain intensity between manual and automatic PSPI was 0.85 and 0.55 for 5-folds and leave-one-subject-out cross-validation, respectively, which suggests moderate to high consistency. These findings show that pain intensity can be reliably measured from facial expression in participants with orthopedic injury.
Analysis of Dynamic Task Allocation in Multi-Robot Systems

Dynamic task allocation is an essential requirement for multi-robot systems functioning in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of task, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of individual robots and the collective behavior. We analyze the effect that the number of observations and the choice of decision functions have on the performance of the system. We validate the mathematical models on a multi-foraging scenario in a multi-robot system. We find that the model’s predictions agree very closely with experimental results from sensor-based simulations.
IIT (BHU) Submission for the ACL Shared Task on Named Entity Recognition on Code-switched Data

This paper describes the best performing system for the shared task on Named Entity Recognition (NER) on code-switched data for the language pair Spanish-English (ENG-SPA). We introduce a gated neural architecture for the NER task. Our final model achieves an F1 score of 63.76%, outperforming the baseline by 10%.
Breaking the Cycle ? Intergenerational Effects of an Anti-Poverty Program in Early Childhood ∗

Despite substantial evidence that resources and outcomes are transmitted across generations, there has been limited inquiry into the extent to which anti-poverty programs actually disrupt the cycle of bad outcomes. We explore how the effects of the United States’ largest early childhood program transfer across generations. We leverage the geographic rollout of this federally funded, means-tested preschool program to estimate the effect of early childhood exposure among mothers on their children’s long-term outcomes. We find evidence of intergenerational transmission of effects in the form of increased educational attainment, reduced teen pregnancy, and reduced criminal engagement in the second generation.