nils-herrmann/clean_scirepeval huggingface dataset:
SHERIFF: precise detection and automatic mitigation of false sharing

False sharing is an insidious problem for multithreaded programs running on multicore processors, where it can silently degrade performance and scalability. Previous tools for detecting false sharing are severely limited: they cannot distinguish false sharing from true sharing, have high false positive rates, and provide limited assistance to help programmers locate and resolve false sharing.
 This paper presents two tools that attack the problem of false sharing: Sheriff-Detect and Sheriff-Protect. Both tools leverage a framework we introduce here called Sheriff. Sheriff breaks out threads into separate processes, and exposes an API that allows programs to perform per-thread memory isolation and tracking on a per-page basis. We believe Sheriff is of independent interest.
 Sheriff-Detect finds instances of false sharing by comparing updates within the same cache lines by different threads, and uses sampling to rank them by performance impact. Sheriff-Detect is precise (no false positives), runs with low overhead (on average, 20%), and is accurate, pinpointing the exact objects involved in false sharing. We present a case study demonstrating Sheriff-Detect's effectiveness at locating false sharing in a variety of benchmarks.
 Rewriting a program to fix false sharing can be infeasible when source is unavailable, or undesirable when padding objects would unacceptably increase memory consumption or further worsen runtime performance. Protect mitigates false sharing by adaptively isolating shared updates from different threads into separate physical addresses, effectively eliminating most of the performance impact of false sharing. We show that Sheriff-Protect can improve performance for programs with catastrophic false sharing by up to 9×, without programmer intervention.
Automated classification of contact lens type in iris images

Textured cosmetic lenses have long been known to present a problem for iris recognition. It was once believed that clear, soft contact lenses did not impact iris recognition accuracy. However, it has recently been shown that persons wearing clear, soft contact lenses experience an increased false non-match rate relative to persons not wearing contact lenses. Iris recognition systems need the ability to automatically determine if a person is (a) wearing no contact lens, (b) wearing a clear prescription lens, or (c), wearing a textured cosmetic lens. This work presents results of the first attempt that we are aware of to solve this three-class classification problem. Results show that it is possible to identify with high accuracy (96.5%) the images in which a textured cosmetic contact lens is present, but that correctly distinguishing between no lenses and soft lenses is a challenging problem.
Recurrent convolutional neural network for speech processing

Different neural networks have exhibited excellent performance on various speech processing tasks, and they usually have specific advantages and disadvantages. We propose to use a recently developed deep learning model, recurrent convolutional neural network (RCNN), for speech processing, which inherits some merits of recurrent neural network (RNN) and convolutional neural network (CNN). The core module can be viewed as a convolutional layer embedded with an RNN, which enables the model to capture both temporal and frequency dependance in the spectrogram of the speech in an efficient way. The model is tested on speech corpus TIMIT for phoneme recognition and IEMOCAP for emotion recognition. Experimental results show that the model is competitive with previous methods in terms of accuracy and efficiency.
A low noise and low temperature coefficient CMOS bandgap reference for MEMS micro-accelerometer

A voltage reference with low noise and low temperature coefficient, applicable to accelerometers, was presented based on the analysis of conventional bandgap reference (BGR) in this paper. A chopper stabilized technique, which modulated the low frequency noise and offset components to higher frequencies by a mixing operation followed by filtering low-pass filtering, served effectively to reduce the low frequency noise and offset components of the proposed reference voltage circuit. To achieve a low temperature coefficient over a broad temperature range, a high-order curvature compensation technique, which used a temperature dependent resistor ratio generated by a high poly resistor and a nwell resistor, was introduced. The proposed circuitry was designed in standard 2.0µm CMOS process. The simulation results showed that the output noise spectral density was 169.68nV/Hz1/2 ranging from 1Hz to 10kHz when chopping frequency was high enough to ignore the low frequency 1/f noise. The average temperature coefficient was 6.25ppm/D in the temperature range from −40 to 80 D.
Modulation of phasic and tonic muscle synergies with reaching direction and speed.

How the CNS masters the many degrees of freedom of the musculoskeletal system to control goal-directed movements is a long-standing question. We have recently provided support to the hypothesis that the CNS relies on a modular control architecture by showing that the phasic muscle patterns for fast reaching movements in different directions are generated by combinations of a few time-varying muscle synergies: coordinated recruitment of groups of muscles with specific activation profiles. However, natural reaching movements occur at different speeds and require the control of both movement and posture. Thus we have investigated whether muscle synergies also underlie reaching at different speeds as well as the maintenance of stable arm postures. Hand kinematics and shoulder and elbow muscle surface EMGs were recorded in five subjects during reaches to eight targets in the frontal plane at different speeds. We found that the amplitude modulation of three time-invariant synergies captured the variations in the postural muscle patterns at the end of the movement. During movement, three phasic and three tonic time-varying synergies could reconstruct the time-normalized muscle pattern in all conditions. Phasic synergies were modulated in both amplitude and timing by direction and speed. Tonic synergies were modulated only in amplitude by direction. The directional tuning of both types of synergies was well described by a single or a double cosine function. These results suggest that muscle synergies are basic control modules that allow generating the appropriate muscle patterns through simple modulation and combination rules.
All your browser-saved passwords could belong to us: a security analysis and a cloud-based new design

Web users are confronted with the daunting challenges of creating, remembering, and using more and more strong passwords than ever before in order to protect their valuable assets on different websites. Password manager is one of the most popular approaches designed to address these challenges by saving users' passwords and later automatically filling the login forms on behalf of users. Fortunately, all the five most popular Web browsers have provided password managers as a useful built-in feature. Unfortunately, the designs of all those Browser-based Password Managers (BPMs) have severe security vulnerabilities. In this paper, we uncover the vulnerabilities of existing BPMs and analyze how they can be exploited by attackers to crack users' saved passwords. Moreover, we propose a novel Cloud-based Storage-Free BPM (CSF-BPM) design to achieve a high level of security with the desired confidentiality, integrity, and availability properties. We have implemented a CSF-BPM system into Firefox and evaluated its correctness and performance. We believe CSF-BPM is a rational design that can also be integrated into other popular Web browsers.
Characterization of anthocyanins in caucasian blueberries (Vaccinium arctostaphylos L.) native to Turkey.

High-performance liquid chromatography (HPLC) combined with diode array (DAD) and electrospray ionization mass spectrometric (ESI-MS) detections were used to characterize anthocyanins in the berries of Vaccinium arctostaphylos L. The dark purple-black berries were collected from five Caucasian blueberry populations in northeastern Turkey. The HPLC-DAD profile consisted of 19 anthocyanin peaks, but HPLC-ESI-MS revealed fragment ion patterns of 26 anthocyanins. Delphinidin, cyanidin, petunidin, peonidin, and malvidin were all glycosylated with four different monosaccharide moieties (galactose, glucose, arabinose, and xylose) with the first two also conjugated with rhamnose. Furthermore, anthocyanidin disaccharides, tentatively identified as anthocyanidin sambubiosides, were characteristic for these berries. The mean content of the total anthocyanins was 1420 mg/100 g dry weight. The most predominant anthocyanidins were delphinidin (41%), petunidin (19%), and malvidin (19%). Glucose was the most typical (61%) sugar moiety. This study revealed that wild Caucasian blueberries contain an abundance of bioactive anthocyanins and thus are ideal for various functional food purposes.
Looking at the brains behind figurative language—A quantitative meta-analysis of neuroimaging studies on metaphor, idiom, and irony processing

A quantitative, coordinate-based meta-analysis combined data from 354 participants across 22 fMRI studies and one positron emission tomography (PET) study to identify the differences in neural correlates of figurative and literal language processing, and to investigate the role of the right hemisphere (RH) in figurative language processing. Studies that reported peak activations in standard space contrasting figurative vs. literal language processing at whole brain level in healthy adults were included. The left and right IFG, large parts of the left temporal lobe, the bilateral medial frontal gyri (medFG) and an area around the left amygdala emerged for figurative language processing across studies. Conditions requiring exclusively literal language processing did not activate any selective regions in most of the cases, but if so they activated the cuneus/precuneus, right MFG and the right IPL. No general RH advantage for metaphor processing could be found. On the contrary, significant clusters of activation for metaphor conditions were mostly lateralized to the left hemisphere (LH). Subgroup comparisons between experiments on metaphors, idioms, and irony/sarcasm revealed shared activations in left frontotemporal regions for idiom and metaphor processing. Irony/sarcasm processing was correlated with activations in midline structures such as the medFG, ACC and cuneus/precuneus. To test the graded salience hypothesis (GSH, Giora, 1997), novel metaphors were contrasted against conventional metaphors. In line with the GSH, RH involvement was found for novel metaphors only. Here we show that more analytic, semantic processes are involved in metaphor comprehension, whereas irony/sarcasm comprehension involves theory of mind processes.
A Brief Introduction to Krylov Space Methods for Solving Linear Systems

With respect to the " influence on the development and practice of science and engineering in the 20th century " , Krylov space methods are considered as one of the ten most important classes of numerical methods . Large sparse linear systems of equations or large sparse matrix eigenvalue problems appear in most applications of scientific computing. Sparsity means that most elements of the matrix involved are zero. In particular, discretization of PDEs with the finite element method (FEM) or with the finite difference method (FDM) leads to such problems. In case the original problem is nonlinear, linearization by Newton's method or a Newton-type method leads again to a linear problem. We will treat here systems of equations only, but many of the numerical methods for large eigenvalue problems are based on similar ideas as the related solvers for equations. Sparse linear systems of equations can be solved by either so-called sparse direct solvers, which are clever variations of Gauss elimination, or by iterative methods. In the last thirty years, sparse direct solvers have been tuned to perfection: on the one hand by finding strategies for permuting equations and unknowns to guarantee a stable LU decomposition and small fill-in in the triangular factors, and on the other hand by organizing the computation so that optimal use is made of the hardware, which nowadays often consists of parallel computers whose architecture favors block operations with data that are locally stored or cached. The iterative methods that are today applied for solving large-scale linear systems are mostly preconditioned Krylov (sub)space solvers. Classical methods that do not belong to this class, like the successive overrelaxation (SOR) method, are no longer competitive. However, some of the classical matrix splittings, e.g. the one of SSOR (the symmetric version of SOR), are still used for preconditioning. Multigrid is in theory a very effective iterative method, but normally it is now applied as an inner iteration with a Krylov space solver as outer iteration; then, it can also be considered as a preconditioner. In the past, Krylov space solvers were referred to also by other names such as semi-iterative methods and polynomial acceleration methods.
Virtualization Technology: Cross-VM Cache Side Channel Attacks make it Vulnerable

Cloud computing provides an effective business model for the deployment of IT infrastructure, platform, and software services. Often, facilities are outsourced to cloud providers and this offers the service consumer virtualization technologies without the added cost burden of development. However, virtualization introduces serious threats to service delivery such as Denial of Service (DoS) attacks, Cross-VM Cache Side Channel attacks, Hypervisor Escape and Hyper-jacking. One of the most sophisticated forms of attack is the cross-VM cache side channel attack that exploits shared cache memory between VMs. A cache side channel attack results in side channel data leakage, such as cryptographic keys. Various techniques used by the attackers to launch cache side channel attack are presented, as is a critical analysis of countermeasures against cache side channel attacks.
Pulmonary embolism: risk assessment and management.

Acute pulmonary embolism (PE) poses a significant burden on health and survival. Its severity ranges from asymptomatic, incidentally discovered subsegmental thrombi to massive, pressor-dependent PE complicated by cardiogenic shock and multisystem organ failure. Rapid and accurate risk stratification is therefore of paramount importance to ensure the highest quality of care. This article critically reviews currently available and emerging tools for risk-stratifying acute PE, and particularly for distinguishing between elevated (intermediate) and low risk among normotensive patients. We focus on the potential value of risk assessment strategies for optimizing severity-adjusted management. Apart from reviewing the current evidence on advanced early therapy of acute PE (thrombolysis, surgery, catheter interventions, vena cava filters), we discuss recent advances in oral anticoagulation with vitamin K antagonists, and with new direct inhibitors of factor Xa and thrombin, which may contribute to profound changes in the treatment and secondary prophylaxis of venous thrombo-embolism in the near future.
GOSSIPY: A distributed localization system for Internet of Things using RFID technology

The popularity of smart objects in our daily life fosters a new generation of applications under the umbrella of the Internet of Things (IoT). Such applications are built on a distributed network of heterogeneous context-aware devices, where localization is a key issue. The localization problem is further magnified by IoT challenges such as scalability, mobility and the heterogeneity of objects. In existing localization systems using RFID technology, there is a lack of systems that localize mobile tags using heterogeneous mobile readers in a distributed manner. In this paper, we propose the GOSSIPY system for localizing mobile RFID tags using a group of ad hoc heterogeneous mobile RFID readers. The system depends on cooperation of mobile readers through time-constrained interleaving processes. Readers in a neighborhood share interrogation information, estimate tag locations accordingly and employ both proactive and reactive protocols to ensure timely dissemination of location information. We evaluate the proposed system and present its performance through extensive simulation experiments using ns-3.
Binaural interaction in auditory evoked potentials: Brainstem, middle- and long-latency components

Binaural interaction occurs in the auditory evoked potentials when the sum of the monaural auditory evoked potentials are not equivalent to the binaural evoked auditory potentials. Binaural interaction of the early- (0-10 ms), middle- (10-50 ms) and long-latency (50-200 ms) auditory evoked potentials was studied in 17 normal young adults. For the early components, binaural interaction was maximal at 7.35 ms accounting for a reduction of 21% of the amplitude of the binaural evoked potentials. For the middle latency auditory evoked potentials, binaural interaction was maximal at 39.6 ms accounting for a reduction of 48% of the binaural evoked potential. For the long-latency auditory evoked potentials, binaural interaction was maximal at 145 ms accounting for a reduction of 38% of the binaural evoked potential. In all of the auditory evoked potentials binaural interaction was long lasting around the maxima. The binaural interaction component extends for several milliseconds in the brainstem to tens of milliseconds in the middle- and long-latency components. Binaural interaction takes the form of a reduction of amplitude of the binaural evoked potential relative to the sum of the monaural responses, suggests that inhibitory processes are represented in binaural interaction using evoked potentials. Binaural processing in the auditory pathway is maximal in the time domain of the middle-latency components reflecting activity in the thalamo-cortical portions of the auditory pathways.
Low-rank tensor decomposition based anomaly detection for hyperspectral imagery

Anomaly detection becomes increasingly important in hyper-spectral image analysis, since it can now uncover many material substances which were previously unresolved by multi-spectral sensors. In this paper, we propose a Low-rank Tensor Decomposition based anomaly Detection (LTDD) algorithm for Hyperspectral Imagery. The HSI data cube is first modeled as a dense low-rank tensor plus a sparse tensor. Based on the obtained low-rank tensor, LTDD further decomposes the low-rank tensor using Tucker decomposition to extract the core tensor which is treated as the “support” of the anomaly spectral signatures. LTDD then adopts an unmixing approach to the reconstructed core tensor for anomaly detection. The experiments based on both simulated and real hyperspectral data sets verify the effectiveness of our algorithm.
Inattention and hyperactivity and the achievement gap among urban minority youth.

To outline the prevalence and disparities of inattention and hyperactivity among school-aged urban minority youth, causal pathways through which inattention and hyperactivity adversely affects academic achievement, and proven or promising approaches for schools to address these problems.



Literature review.



Approximately 4.6 million (8.4%) of American youth aged 6-17 have received a diagnosis of attention deficit/hyperactivity disorder (ADHD), and almost two thirds of these youth are reportedly under treatment with prescription medications. Urban minority youth are not only more likely to be affected but also less likely to receive accurate diagnosis and treatment. Causal pathways through which ADHD may affect academic achievement include sensory perceptions, cognition, school connectedness, absenteeism, and dropping out. In one study, youth with diagnosed ADHD were 2.7 times as likely to drop out (10.0% vs. 22.9%). A similar odds ratio for not graduating from high school was found in another prospective study, with an 8-year follow-up period (odds ratio = 2.4). There are many children who are below the clinical diagnostic threshold for ADHD but who exhibit signs and symptoms that interfere with learning. Evidence-based programs emphasizing functional academic and social outcomes are available.



Inattention and hyperactivity are highly and disproportionately prevalent among school-aged urban minority youth, have a negative impact on academic achievement through their effects on sensory perceptions, cognition, school connectedness, absenteeism, and dropping out, and effective practices are available for schools to address these problems. This prevalent and complex syndrome has very powerful effects on academic achievement and educational attainment, and should be a high priority in efforts to help close the achievement gap.
DNSNA: DNS name autoconfiguration for Internet of Things devices

This paper proposes a DNS Name Autoconfiguration (called DNSNA) for not only the global DNS names, but also the local DNS names of Internet of Things (IoT) devices. Since there exist so many devices in the IoT environments, it is inefficient to manually configure the Domain Name System (DNS) names of such IoT devices. By this scheme, the DNS names of IoT devices can be autoconfigured with the device's category and model in IPv6-based IoT environments. This DNS name lets user easily identify each IoT device for monitoring and remote-controlling in IoT environments. In the procedure to generate and register an IoT device's DNS name, the standard protocols of Internet Engineering Task Force (IETF) are used. Since the proposed scheme resolves an IoT device's DNS name into an IPv6 address in unicast through an authoritative DNS server, it generates less traffic than Multicast DNS (mDNS), which is a legacy DNS application for the DNS name service in IoT environments. Thus, the proposed scheme is more appropriate in global IoT networks than mDNS. This paper explains the design of the proposed scheme and its service scenario, such as smart road and smart home. The results of the simulation prove that our proposal outperforms the legacy scheme in terms of energy consumption.
Predicting User Engagement on Twitter with Real-World Events

People invest time, attention, and emotion while engaging in various activities in the real-world, for either purposes of awareness or participation. Social media platforms such as Twitter offer tremendous opportunities for people to become engaged in such real-world events through information sharing and communicating about these events. However, little is understood about the factors that affect people’s Twitter engagement in such real-world events. In this paper, we address this question by first operationalizing a person’s Twitter engagement in real-world events such as posting, retweeting, or replying to tweets about such events. Next, we construct statistical models that examine multiple predictive factors associated with four different perspectives of users’ Twitter engagement, and quantify their potential influence on predicting the (i) presence; and (ii) degree – of the user’s engagement with 643 real-world events. We also consider the effect of these factors with respect to a finer granularization of the different categories of events. We find that the measures of people’s prior Twitter activities, topical interests, geolocation, and social network structures are all variously correlated to their engagement with real-world events.