nils-herrmann/clean_scirepeval huggingface dataset:
Modelling microtubules in the brain as n-qudit quantum Hopfield network and beyond

The scientific approach to understand the nature of consciousness revolves around the study of human brain. Neurobiological studies that compare the nervous system of different species have accorded highest place to the humans on account of various factors that include a highly developed cortical area comprising of approximately 100 billion neurons, that are intrinsically connected to form a highly complex network. Quantum theories of consciousness are based on mathematical abstraction and Penrose-Hameroff Orch-OR Theory is one of the most promising ones. Inspired by Penrose-Hameroff Orch-OR Theory, Behrman et. al. (Behrman, 2006) have simulated a quantum Hopfield neural network with the structure of a microtubule. They have used an extremely simplified model of the tubulin dimers with each dimer represented simply as a qubit, a single quantum two-state system. The extension of this model to n-dimensional quantum states, or n-qudits presented in this work holds considerable promise for even higher mathematical abstraction in modelling consciousness systems.
Feature selection for spatially enhanced LBP: application to face recognition

Block-based local binary patterns a.k.a. enhanced local binary patterns (ELBPs) have proven to be a highly discriminative descriptor for face recognition and image retrieval. Since this descriptor is mainly composed by histograms, little work (if any) has been done for selecting its relevant features (either the bins or the blocks). In this paper, we address feature selection for both the classic ELBP representation and the recently proposed color quaternionic LBP (QLBP). We introduce a filter method for the automatic weighting of attributes or blocks using an improved version of the margin-based iterative search Simba algorithm. This new improved version introduces two main modifications: (i) the hypothesis margin of a given instance is computed by taking into account the K-nearest neighboring examples within the same class as well as the K-nearest neighboring examples with a different label; (ii) the distances between samples and their nearest neighbors are computed using the weighted $$\chi ^2$$ χ 2 distance instead of the Euclidean one. This algorithm has been compared favorably with several competing feature selection algorithms including the Euclidean-based Simba as well as variance and Fisher score algorithms giving higher performances. The proposed method is useful for other descriptors that are formed by histograms. Experimental results show that the QLBP descriptor allows an improvement of the accuracy in discriminating faces compared with the ELBP. They also show that the obtained selection (attributes or blocks) can either improve recognition performance or maintain it with a significant reduction in the descriptor size.
Multi-DOF counterbalance mechanism for low-cost, safe and easy-usable robot arm

Recently, various types of robot arms have been developed due to the increasing needs of the robot in various fields. However, these robot arms are still too much expensive and dangerous since high capacity motors and gear reducers are used to provide sufficient torques to support the robot arm mass. This makes robot market still limited to industrial fields. To this end, we propose a novel counterbalance mechanism based on the spring-wire and a double parallelogram mechanism, which can completely cancel out the gravitational torques applied at each joint due to the robot mass. Thus the robot would need much smaller torques, which can be delivered by cheap and low capacity actuators. Furthermore, a 6-DOF counterbalance robot arm was built to demonstrate the performance of the proposed mechanism. Simulation and experimental results showed that the proposed mechanism had effectively decreased the torques required to operate the robot arm and thereby allowing the prospective use of low capacity motors and gear reducers for high performance robot arms.
Effects of joint torque constraints on humanoid robot balance recovery in the presence of external disturbance

A humanoid robot should be able to keep balance under unexpected disturbance, and can take three strategies, i.e. ankle strategy, hip strategy and step strategy, to recover balance from biomechanics research. In this paper, the relationship between limited joint torque and balance recovery strategy is analyzed using Zero moment Point Manipulability Ellipsoid. Furthermore, during balance control, the constraints between the feet and the ground must be maintained. The satisfaction of these constraints, namely the gravity constraints, the friction constraints and the CoP constraints, imposed bounds on the control torque are investigated. Such control bounds have significant effects on designing balance recovery strategies and can be used to predict the type of falls.
LAyer Selection by ERase (LASER) With an Etch-Through-Spacer Technique in a Bit-Line Stacked 3-D nand Flash Memory Array

A novel electrical layer-selection method in a bit-line stacked 3-D nand memory array is proposed. The stacked layers are selected by using multiple source select lines with erased cells in a layer. The operation scheme and simulation results for the electrical layer selection are discussed. An etch-through-spacer technique is developed to form a terraced body for a vertical contact process.
Effect of backpack load carriage on cervical posture in primary schoolchildren.

This study examined the effects of various backpack loads on elementary schoolchildren's posture and postural compensations as demonstrated by a change in forward head position.



A convenience sample of 11 schoolchildren, aged 8-11 years participated.



Sagittal digital photographs were taken of each subject standing without a backpack, and then with the loaded backpack before and after walking 6 minutes (6MWT) at free walking speed. This was repeated over three consecutive weeks using backpacks containing randomly assigned weights of 10%, 15%, or 20% body weight of each respective subject. The craniovertebral angle (CVA) was measured using digitizing software, recorded and analyzed.



Subjects demonstrated immediate and statistically significant changes in CVA, indicating increased forward head positions upon donning the backpacks containing 15% and 20% body weight. Following the 6MWT, the CVA demonstrated further statistically significant changes for all backpack loads indicating increased forward head postures. For the 15 & 20%BW conditions, more than 50% of the subjects reported discomfort after walking, with the neck as the primary location of reported pain.



Backpack loads carried by schoolchildren should be limited to 10% body weight due to increased forward head positions and subjective complaints at 15% and 20% body weight loads.
Augmented Reality 2.0

Augmented Reality (AR) was first demonstrated in the 1960s, but only recently have technologies emerged that can be used to easily deploy AR applications to many users. Cameraequipped cell phones with significant processing power and graphics abilities provide an inexpensive and versatile platform for AR applications, while the social networking technology of Web 2.0 provides a large-scale infrastructure for collaboratively producing and distributing geo-referenced AR content. This combination of widely used mobile hardware and Web 2.0 software allows the development of a new type of AR platform that can be used on a global scale. In this paper we describe the Augmented Reality 2.0 concept and present existing work on mobile AR and web technologies that could be used to create AR 2.0 applications.
BMCLua: A Translator for Model Checking Lua Programs

Lua is a programming language designed as scripting language, which is fast, lightweight, and suitable for embedded applications. Due to its features, Lua is widely used in the development of games and interactive applications for digital TV. However, during the development phase of such applications, some errors may be introduced, such as deadlock, arithmetic overflow, and division by zero. This paper describes a novel verification approach for software written in Lua, using as backend the Efficient SMTBased Context-Bounded Model Checker (ESBMC). Such an approach, called bounded model checking - Lua (BMCLua), consists in translating Lua programs into ANSI-C source code, which is then verified with ESBMC. Experimental results show that the proposed verification methodology is effective and efficient, when verifying safety properties in Lua programs. The performed experiments have shown that BMCLua produces an ANSI-C code that is more efficient for verification, when compared with other existing approaches. To the best of our knowledge, this work is the first that applies bounded model checking to the verification of Lua programs.
Optimising sampling rates for accelerometer-based human activity recognition

Real-world deployments of accelerometer-based human activity recognition systems need to be carefully configured regarding the sampling rate used for measuring acceleration. Whilst a low sampling rate saves considerable energy, as well as transmission bandwidth and storage capacity, it is also prone to omitting relevant signal details that are of interest for contemporary analysis tasks. In this paper we present a pragmatic approach to optimising sampling rates of accelerometers that effectively tailors recognition systems to particular scenarios, thereby only relying on unlabelled sample data from the domain. Employing statistical tests we analyse the properties of accelerometer data and determine optimal sampling rates through similarity analysis. We demonstrate the effectiveness of our method in experiments on 5 benchmark datasets where we determine optimal sampling rates that are each substantially below those originally used whilst maintaining the accuracy of reference recognition systems.
Network Traffic Anomaly Detection Using Recurrent Neural Networks

We show that a recurrent neural network is able to learn a model to represent sequences of communications between computers on a network and can be used to identify outlier network traffic. Defending computer networks is a challenging problem and is typically addressed by manually identifying known malicious actor behavior and then specifying rules to recognize such behavior in network communications. However, these rule-based approaches often generalize poorly and identify only those patterns that are already known to researchers. An alternative approach that does not rely on known malicious behavior patterns can potentially also detect previously unseen patterns. We tokenize and compress netflow into sequences of “words” that form “sentences” representative of a conversation between computers. These sentences are then used to generate a model that learns the semantic and syntactic grammar of the newly generated language. We use Long-Short-Term Memory (LSTM) cell Recurrent Neural Networks (RNN) to capture the complex relationships and nuances of this language. The language model is then used predict the communications between two IPs and the prediction error is used as a measurement of how typical or atyptical the observed communication are. By learning a model that is specific to each network, yet generalized to typical computer-to-computer traffic within and outside the network, a language model is able to identify sequences of network activity that are outliers with respect to the model. We demonstrate positive unsupervised attack identification performance (AUC 0.84) on the ISCX IDS dataset which contains seven days of network activity with normal traffic and four distinct attack patterns.
HA-VMSI: A Lightweight Virtual Machine Isolation Approach with Commodity Hardware for ARM

Once compromising the hypervisor, remote or local adversaries can easily access other customers' sensitive data in the memory and context of guest virtual machines (VMs). VM isolation is an efficient mechanism for protecting the memory of guest VMs from unauthorized access. However, previous VM isolation systems either modify hardware architecture or introduce a software module without being protected, and most of them focus on the x86 architecture.
 This paper proposes HA-VMSI, a lightweight hardware-assisted VM isolation approach for ARM, to provide runtime protection of guest VMs, even with a compromised hypervisor. In the ARM TrustZone secure world, a thin security monitor is introduced as HA-VMSI's entire TCB. Hence, the security monitor is much less vulnerable and safe from attacks that can compromise the hypervisor. The key of HA-VMSI is decoupling the functions of memory isolation among VMs from the hypervisor into the security monitor. As a result, the hypervisor can only update the Stage-2 page tables of VMs via the security monitor, which inspects and approves each new mapping. It is worth noting that HA-VMSI is more secure and effective than current software approaches, and more flexible and compatible than hardware approaches. We have implemented a prototype for KVM hypervisor with multiple Linux as guest OSes on Juno board. The security assessment and performance evaluation show that HA-VMSI is effective, efficient and practical.
Effect of frequent interruptions of prolonged sitting on self-perceived levels of energy, mood, food cravings and cognitive function

While physical activity has been shown to improve cognitive performance and well-being, office workers are essentially sedentary. We compared the effects of physical activity performed as (i) one bout in the morning or (ii) as microbouts spread out across the day to (iii) a day spent sitting, on mood and energy levels and cognitive function.



In a randomized crossover trial, 30 sedentary adults completed each of three conditions: 6 h of uninterrupted sitting (SIT), SIT plus 30 min of moderate-intensity treadmill walking in the morning (ONE), and SIT plus six hourly 5-min microbouts of moderate-intensity treadmill walking (MICRO). Self-perceived energy, mood, and appetite were assessed with visual analog scales. Vigor and fatigue were assessed with the Profile of Mood State questionnaire. Cognitive function was measured using a flanker task and the Comprehensive Trail Making Test. Intervention effects were tested using linear mixed models.



Both ONE and MICRO increased self-perceived energy and vigor compared to SIT (p < 0.05 for all). MICRO, but not ONE, improved mood, decreased levels of fatigue and reduced food cravings at the end of the day compared to SIT (p < 0.05 for all). Cognitive function was not significantly affected by condition.



In addition to the beneficial impact of physical activity on levels of energy and vigor, spreading out physical activity throughout the day improved mood, decreased feelings of fatigue and affected appetite. Introducing short bouts of activity during the workday of sedentary office workers is a promising approach to improve overall well-being at work without negatively impacting cognitive performance.
Soft N-Modular Redundancy

Achieving robustness and energy efficiency in nanoscale CMOS process technologies is made challenging due to the presence of process, temperature, and voltage variations. Traditional fault-tolerance techniques such as N-modular redundancy (NMR) employ deterministic error detection and correction, e.g., majority voter, and tend to be power hungry. This paper proposes soft NMR that nontrivially extends NMR by consciously exploiting error statistics caused by nanoscale artifacts in order to design robust and energy-efficient systems. In contrast to conventional NMR, soft NMR employs Bayesian detection techniques in the voter. Soft voter algorithms are obtained through optimization of appropriate application aware cost functions. Analysis indicates that, on average, soft NMR outperforms conventional NMR. Furthermore, unlike NMR, in many cases, soft NMR is able to generate a correct output even when all N replicas are in error. This increase in robustness is then traded-off through voltage scaling to achieve energy efficiency. The design of a discrete cosine transform (DCT) image coder is employed to demonstrate the benefits of the proposed technique. Simulations in a commercial 45 nm, 1.2 V, CMOS process show that soft NMR provides up to 10× improvement in robustness, and 35 percent power savings over conventional NMR.
A New Resource Scheduling Strategy Based on Genetic Algorithm in Cloud Computing Environment

In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic algorithm. According to historical data and current state of the system and through genetic algorithm, this strategy computes ahead the influence it will have on the system after the deployment of the needed VM resources and then chooses the least-affective solution, through which it achieves the best load balancing and reduces or avoids dynamic migration. At the same time, this paper brings in variation rate to describe the load variation of system virtual machines, and it also introduces average load distance to measure the overall load balancing effect of the algorithm. The experiment shows that this strategy has fairly good global astringency and efficiency, and the algorithm of this paper is, to a great extent, able to solve the problems of load imbalance and high migration cost after system VM being scheduled. What is more, the average load distance does not grow with the increase of VM load variation rate, and the system scheduling algorithm has quite good resource utility.
A 3.9 mW 25-Electrode Reconfigured Sensor for Wearable Cardiac Monitoring System

A low power highly sensitive Thoracic Impedance Variance (TIV) and Electrocardiogram (ECG) monitoring SoC is designed and implemented into a poultice-like plaster sensor for wearable cardiac monitoring. 0.1 Ω TIV detection is possible with a sensitivity of 3.17 V/Ω and SNR > 40 dB. This is achieved with the help of a high quality (Q-factor > 30) balanced sinusoidal current source and low noise reconfigurable readout electronics. A cm-range 13.56 MHz fabric inductor coupling is adopted to start/stop the SoC remotely. Moreover, a 5% duty-cycled Body Channel Communication (BCC) is exploited for 0.2 nJ/b 1 Mbps energy efficient external data communication. The proposed SoC occupies 5 mm × 5 mm including pads in a standard 0.18 μm 1P6M CMOS technology. It dissipates a peak power of 3.9 mW when operating in body channel receiver mode, and consumes 2.4 mW when operating in TIV and ECG detection mode. The SoC is integrated on a 15 cm × 15 cm fabric circuit board together with a flexible battery to form a compact wearable sensor. With 25 adhesive screen-printed fabric electrodes, detection of TIV and ECG at 16 different sites of the heart is possible, allowing optimal detection sites to be configured to accommodate different user dependencies.
Cyber situational awareness - A systematic review of the literature

Cyber situational awareness is attracting much attention. It features prominently in the national cyber strategies of many countries, and there is a considerable body of research dealing with it. However, until now, there has been no systematic and up-to-date review of the scientific literature on cyber situational awareness. This article presents a review of cyber situational awareness, based on systematic queries in four leading scientific databases. 102 articles were read, clustered, and are succinctly described in the paper. The findings are discussed from the perspective of both national cyber strategies and science, and some directions for future research are examined.
Pattern activation/recognition theory of mind

In his 2012 book How to Create a Mind, Ray Kurzweil defines a "Pattern Recognition Theory of Mind" that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call "Pattern Activation/Recognition Theory of Mind." While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation.
6.7 A 28Gb/s digital CDR with adaptive loop gain for optimum jitter tolerance

As we move to higher data rates, the performance of clock and data recovery (CDR) circuits becomes increasingly important in maintaining low bit error rates (BER) in wireline links. Digital CDRs are popular in part for their robustness, but their use of bang-bang phase detectors (BB-PD) makes their performance sensitive to changes in jitter caused by PVT variations, crosstalk or power supply noise. This is because the gain of a BB-PD depends on the CDR input jitter, causing the loop gain of the CDR to change if the jitter magnitude or spectrum varies. This problem is illustrated in Fig. 6.7.1 where small jitter leads to excessive loop gain and hence to an underdamped behaviour in the CDR jitter tolerance (JTOL), while large jitter leads to insufficient loop gain and hence to low overall JTOL. To prevent this, we propose a CDR with an adaptive loop gain, KG, as shown in Fig. 6.7.1.
Evaluation of the Effect of Cultural Characteristics on Use of Health Care Services Using the "Giger and Davidhizar's Transcultural Assessment Model: A Sample from a Village in Eastern Turkey

Knowing the cultural factors existing behind health behaviors is important for improving the acceptance of services and for elevating the quality of service.  This study was conducted for the purpose of evaluating the effect of cultural characteristics on use of health care services using the “Giger and Davidhizar’s Transcultural Assessment Model”.  The research is qualitative. The study group was 31 individuals who volunteered to participate in the study and living in a rural area. The snowball method was used. Data were collected in 2005.  Limitations/obstacles to the use of health care services were the widespread gender, use of traditional treatment methods, a high level of environmental control, and a fatalistic attitude about health.  According to the results the most important limitation/obstacle to using health care services was being a woman.
Determining Future Success of College Students

The years that students spend in college are perhaps the most influential years on the rest of their lives. College students face many different decisions day in and day out that may determine how successful they will be in the future. They will choose majors, whether or not to play a sport, which clubs to join, whether they should join a fraternity or sorority, which classes to take, and how much time to spend studying. It is unclear what aspects of college will benefit a person the most down the road. Are some majors better than others? Is earning a high GPA important? Or will simply getting a degree be enough to make a good living? These are a few of the many questions that college students have.