nils-herrmann/clean_scirepeval huggingface dataset:
Exploring Ways To Mitigate Sensor-Based Smartphone Fingerprinting

Modern smartphones contain motion sensors, such as accelerometers and gyroscopes. These sensors have many useful applications; however, they can also be used to uniquely identify a phone by measuring anomalies in the signals, which are a result from manufacturing imperfections. Such measurements can be conducted surreptitiously in the browser and can be used to track users across applications, websites, and visits. We analyze techniques to mitigate such device fingerprinting either by calibrating the sensors to eliminate the signal anomalies, or by adding noise that obfuscates the anomalies. To do this, we first develop a highly accurate fingerprinting mechanism that combines multiple motion sensors and makes use of (inaudible) audio stimulation to improve detection. We then collect measurements from a large collection of smartphones and evaluate the impact of calibration and obfuscation techniques on the classifier accuracy.
Principles of micro-RNA production and maturation

Micro-RNAs (miRNAs) are a class of approximately 22-nucleotide non-coding RNAs expressed in multicellular organisms. They are first transcribed in a similar manner to pre-mRNAs. The transcripts then go through a series of processing steps, including endonucleolytic cleavage, nuclear export and a strand selection procedure, to yield the single-stranded mature miRNA products. The transcription and processing of miRNAs determines the abundance and the sequence of mature miRNAs and has important implications for the function of miRNAs.
A low-Cost, Open-Source, BCI- VR Game Control Development Environment Prototype for Game Based Neurorehabilitation

In this paper we present a low-cost and open-source brain-computer interface (BCI) virtual-reality (VR) Game Control Development Environment prototype, which we demonstrate using real-time signal processing of Electroencephalography (EEG) event-related desynchronization and synchronization changes (ERD/ERS) within the Precentral Gyrus (Motor Cortex), allowing a user to control a 3D object within a Virtual Reality Environment. This BCI-VR system prototype was functionally tested on multiple participants and demonstrated live before an audience during the 2017 ‘Hack the Brain’ at the Dublin Science Gallery. The availability of such an open-source, effective, BCI-VR Game Control Development Environment, at a level acceptable for industry experimentation, has the potential to open up this field to a much wider range of researchers and games developers and to assist the investigation of gaming experiences which both incorporate the specific control features available through BCI as a core element of the game play and the potential for its use in neurorehabilitation.
WIR: Warp Instruction Reuse to Minimize Repeated Computations in GPUs

Warp instructions with an identical arithmetic operation on same input values produce the identical computation results. This paper proposes warp instruction reuse to allow such repeated warp instructions to reuse previous computation results instead of actually executing the instructions. Bypassing register reading, functional unit, and register writing operations improves energy efficiency. This reuse technique is especially beneficial for GPUs since a GPU warp register is usually as wide as thousands of bits. In addition, we propose warp register reuse which allows identical warp register values to share a single physical register through register renaming. The register reuse technique enables to see if different logical warp registers have an identical value by only looking at their physical warp register IDs. Based on this observation, warp register reuse helps to perform all necessary operations for warp instruction reuse with register IDs, which is substantially more efficient than directly manipulating register values. Performance evaluation shows that 20.5% SM energy and 10.7% GPU energy can be saved by allowing 18.7% of warp instructions to reuse prior results.
Multi-modal and multi-brain-computer interfaces: A review

In this short paper we survey recent research views in non-traditional brain-computer interfaces. That is, interfaces that can process brain activity input, but that are designed for non-clinical purposes, that are meant to be used by `healthy' users, that process other user input as well, and that even allow input, voluntarily or involuntarily, from multiple users. This is a rather new research and BCI application area. Control of applications can be made more robust by fusing brain activity information with other information, either explicitly provided by a user (such as commands) or extracted from the user by interpreting his or her behavior (movements, posture, gaze, facial expression, nonverbal speech) or sensing (neuro-)physiological characteristics (skin conductivity, heart rate, brain activity). We see also research where brain activity of multiple users is fused to get a (sometimes critical) control task done. We see also the emergence of applications where BCI measurements help to improve group performance, for example, during meetings, or where information obtained from these measurements helps to learn about audience preferences. Multi-brain BCI has also become a paradigm in artistic and game applications. Artistic interactive BCI applications may require audience participation. In game environments brain activity of various players can be used to control or adapt the game. Both competition and collaboration in serious games, entertainment games and artistic installations requires fusion of EEG measurements from different subjects.
Face recognition/detection by probabilistic decision-based neural network

This paper proposes a face recognition system, based on probabilistic decision-based neural networks (PDBNN). With technological advance on microelectronic and vision system, high performance automatic techniques on biometric recognition are now becoming economically feasible. Among all the biometric identification methods, face recognition has attracted much attention in recent years because it has potential to be most nonintrusive and user-friendly. The PDBNN face recognition system consists of three modules: First, a face detector finds the location of a human face in an image. Then an eye localizer determines the positions of both eyes in order to generate meaningful feature vectors. The facial region proposed contains eyebrows, eyes, and nose, but excluding mouth (eye-glasses will be allowed). Lastly, the third module is a face recognizer. The PDBNN can be effectively applied to all the three modules. It adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. The paper demonstrates a successful application of PDBNN to face recognition applications on two public (FERET and ORL) and one in-house (SCR) databases. Regarding the performance, experimental results on three different databases such as recognition accuracies as well as false rejection and false acceptance rates are elaborated. As to the processing speed, the whole recognition process (including PDBNN processing for eye localization, feature extraction, and classification) consumes approximately one second on Sparc10, without using hardware accelerator or co-processor.
The Anatomy of Motivation: An Evolutionary-Ecological Approach

There have been few attempts to bring evolutionary theory to the study of human motivation. From this perspective motives can be considered psychological mechanisms to produce behavior that solves evolutionarily important tasks in the human niche. From the dimensions of the human niche we deduce eight human needs: optimize the number and survival of gene copies; maintain bodily integrity; avoid external threats; optimize sexual, environmental, and social capital; and acquire reproductive and survival skills. These needs then serve as the foundation for a necessary and sufficient list of 15 human motives, which we label: lust, hunger, comfort, fear, disgust, attract, love, nurture, create, hoard, affiliate, status, justice, curiosity, and play. We show that these motives are consistent with evidence from the current literature. This approach provides us with a precise vocabulary for talking about motivation, the lack of which has hampered progress in behavioral science. Developing testable theories about the structure and function of motives is essential to the project of understanding the organization of animal cognition and learning, as well as for the applied behavioral sciences.
A memetic algorithm based extreme learning machine for classification

Extreme Learning Machine (ELM) is an elegant technique for training Single-hidden Layer Feedforward Networks (SLFNs) with extremely fast speed that attracts significant interest recently. One potential weakness of ELM is the random generation of the input weights and hidden biases, which may deteriorate the classification accuracy. In this paper, we propose a new Memetic Algorithm (MA) based Extreme Learning Machine (M-ELM) for classification problems. M-ELM uses Memetic Algorithm which is a combination of population-based global optimization technique and individual-based local heuristic search method to find optimal network parameters for ELM. The optimized network parameters will enhance the classification accuracy and generalization performance of ELM. Experiments and comparisons on 22 benchmark data sets demonstrate that M-ELM is able to provide highly competitive results compared with other state-of-the-art varieties of ELM algorithms.
MoveIt!: Autonomous Underwater Free-Floating Manipulation

Today, autonomous underwater vehicles (AUVs) are mostly used for survey missions, but many existing applications require manipulation capabilities, such as the maintenance of permanent observatories, submerged oil wells, cabled sensor networks, and pipes; the deployment and recovery of benthic stations; or the search and recovery of black boxes. Currently, these tasks require the use of work-class remotely operated vehicles (ROVs) deployed from vessels equipped with dynamic positioning, leaving such solutions expensive to adopt. To face these challenges during the last 25 years, scientists have researched the idea of increasing the autonomy of underwater intervention systems.
Microstrip Rectangular 4x1 Patch Array Antenna at 2.5GHz for WiMax Application

This paper presents the design of microstrip rectangular patch antenna with center frequency at 2.5GHz for WiMAX application. The array of four by one (4x1) patch array microstrip rectangular antenna with microstrip line feeding based on quarter wave impedance matching technique was designed and simulated using Computer Simulation Tool (CST) Microwave Environment software. The performance of the designed antenna was than compared with the single patch rectangle antenna in term of return loss, Voltage Standing Wave Ratio (VSWR), bandwidth, directivity, radiation pattern and gain. The array antenna was then fabricated on the substrate type FR-4 with dielectric constant of 4.9 and thickness of 1.6mm respectively. The array antenna was measured in the laboratory using Vector Network Analyzer (VNA) and the results show good agreement with the array antenna simulated performances.
Collaborative data mining for clinical trial analytics

This paper proposes a collaborative data mining technique to provide multi-level analysis from clinical trials data. Clinical trials for clinical research and drug development generate large amount of data. Due to dispersed nature of clinical trial data, it remains a challenge to harness this data for analytics. In this paper, we propose a novel method using master data management (MDM) for analyzing clinical trial data, scattered across multiple databases, through collaborative data mining. Our aim is to validate findings by collaboratively utilizing multiple data mining techniques such as classification, clustering, and association rule mining. We complement our results with the help of interactive visualizations. The paper also demonstrates use of data stratification for identifying disparities between various subgroups of clinical trial participants. Overall, our approach aims at extracting useful knowledge from clinical trial data in order to improve design of clinical trials by gaining confidence in the outcomes using multi-level analysis. We provide experimental results in drug abuse clinical trial data.
SDR-Based Resilient Wireless Communications

As the use of wireless technologies increases significantly due to ease of deployment, cost-effectiveness and the increase in bandwidth, there is a critical need to make the wireless communications secure, and resilient to attacks or faults (malicious or natural). Wireless communications are inherently prone to cyberattacks due to the open access to the medium. While current wireless protocols have addressed the privacy issues, they have failed to provide effective solutions against denial of service attacks, session hijacking and jamming attacks.In this paper, we present a resilient wireless communication architecture based on Moving Target Defense, and Software Defined Radios (SDRs). The approach achieves its resilient operations by randomly changing the runtime characteristics of the wireless communications channels between different wireless nodes to make it extremely difficult to succeed in launching attacks. The runtime characteristics that can be changed include packet size, network address, modulation type, and the operating frequency of the channel. In addition, the lifespan for each configuration will be random. To reduce the overhead in switching between two consecutive configurations, we use two radio channels that are selected at random from a finite set of potential channels, one will be designated as an active channel while the second acts as a standby channel. This will harden the wireless communications attacks because the attackers have no clue on what channels are currently being used to exploit existing vulnerability and launch an attack. The experimental results and evaluation show that our approach can tolerate a wide range of attacks (Jamming, DOS and session attacks) against wireless networks.
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this two part treatise, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct classes of algorithms, namely continuous time and discrete time models. The resulting neural networks form a new class of data-efficient universal function approximators that naturally encode any underlying physical laws as prior information. In this first part, we demonstrate how these networks can be used to infer solutions to partial differential equations, and obtain physics-informed surrogate models that are fully differentiable with respect to all input coordinates and free parameters.
A frequency-division MIMO FMCW radar system using delta-sigma-based transmitters

In this paper, we present a system that allows to realize frequency-division multiplexing multiple-input multiple-output (MIMO) radars which are based on the frequency-modulated continuous-wave (FMCW) principle. Multiple frequency-shifted FMCW signals, which can be separated in the receiver, are used as transmit (TX) signals. The frequency shifting is implemented using digitally generated sinusoids in conjunction with delta-sigma-based transmitters. The proposed principle requires only little adaptions compared to conventional FMCW-setups. Furthermore a fast Fourier-transform stage, being present in many conventional FMCW radars, can be used to separate the TX signals leading to an efficient usage of existing building blocks. Measurements carried out using a prototype system with 48 MIMO channels demonstrate the feasibility of the proposed approach.
Language-based multimodal displays for the handover of control in autonomous cars

This paper presents a first evaluation of multimodal language-based warnings for handovers of control in autonomous cars. A set of possible handover situations varying in urgency is described. A set of multimodal, language-based warnings for these situations is then introduced. All combinations of audio, tactile and visual warnings for handovers were evaluated in terms of perceived urgency, annoyance and alerting effectiveness. Results showed clear recognition of the warning urgency in this new context, as well as low perceived annoyance overall, and higher perceived effectiveness for critical warnings. The time of transition from self-driving to manual mode in the presence of the warnings was then evaluated. Results showed quicker transitions for highly urgent warnings and poor driving performance for unimodal visual warnings. These results provide a novel set of guidelines for an effective transition of control between car and driver in an autonomous vehicle.
Implementation of a Cognitive Radio Front-End Using Rotatable Controlled Reconfigurable Antennas

This communication presents a new antenna system designed for cognitive radio applications. The antenna structure consists of a UWB antenna and a frequency reconfigurable antenna system. The UWB antenna scans the channel to discover “white space” frequency bands while tuning the reconfigurable section to communicate within these bands. The frequency agility is achieved via a rotational motion of the antenna patch. The rotation is controlled by a stepper motor mounted on the back of the antenna structure. The motor's rotational motion is controlled by LABVIEW on a computer connected to the motor through its parallel port. The computer's parallel port is connected to a NPN Darlington array that is used to drive the stepper motor. The antenna has been simulated with the driving motor being taken into consideration. A good agreement is found between the simulated and the measured antenna radiation properties.
A Bidirectional LSTM Approach with Word Embeddings for Sentence Boundary Detection

Recovering sentence boundaries from speech and its transcripts is essential for readability and downstream speech and language processing tasks. In this paper, we propose to use deep recurrent neural network to detect sentence boundaries in broadcast news by modeling rich prosodic and lexical features extracted at each inter-word position. We introduce an unsupervised word embedding to represent word identity, learned from the Continuous Bag-of-Words (CBOW) model, into sentence boundary detection task as an effective feature. The word embedding contains syntactic information that is essential for this detection task. In addition, we propose another two low-dimensional word embeddings derived from a neural network that includes class and context information to represent words by supervised learning: one is extracted from the projection layer, the other one comes from the last hidden layer. Furthermore, we propose a deep bidirectional Long Short Term Memory (LSTM) based architecture with Viterbi decoding for sentence boundary detection. Under this framework, the longrange dependencies of prosodic and lexical information in temporal sequences are modeled effectively. Compared with Chenglin Xu xuchenglin@ntu.edu.sg Lei Xie lxie@nwpu-aslp.org Xiong Xiao xiaoxiong@ntu.edu.sg 1 School of Computer Science, Northwestern Polytechnical University, Xi’an, China 2 Temasek Laboratories@NTU, Nanyang Technological University, Singapore, Singapore previous state-of-the-art DNN-CRF method, the proposed LSTM approach reduces 24.8% and 9.8% relative NIST SU error in reference and recognition transcripts, respectively.
Improved Option Pricing Using Artificial Neural Networks and Bootstrap Methods

A hybrid neural network is used to predict the difference between the conventional option-pricing model and observed intraday option prices for stock index option futures. Confidence intervals derived with bootstrap methods are used in a trading strategy that only allows trades outside the estimated range of spurious model fits to be executed. Whilst hybrid neural network option pricing models can improve predictions they have bias. The hybrid option-pricing bias can be reduced with bootstrap methods. A modified bootstrap predictor is indexed by a parameter that allows the predictor to range from a pure bootstrap predictor, to a hybrid predictor, and finally the bagging predictor. The modified bootstrap predictor outperforms the hybrid and bagging predictors. Greatly improved performance was observed on the boundary of the training set and where only sparse training data exists. Finally, bootstrap bias estimates were studied.
Greater trochanteric pain syndrome: a review of anatomy, diagnosis and treatment.

Greater trochanteric pain syndrome (GTPS) is a term used to describe chronic pain overlying the lateral aspect of the hip. This regional pain syndrome, once described as trochanteric bursitis, often mimics pain generated from other sources, including, but not limited to myofascial pain, degenerative joint disease, and spinal pathology. The incidence of greater trochanteric pain is reported to be approximately 1.8 patients per 1000 per year with the prevalence being higher in women, and patients with coexisting low back pain, osteoarthritis, iliotibial band tenderness, and obesity. Symptoms of GTPS consist of persistent pain in the lateral hip radiating along the lateral aspect of the thigh to the knee and occasionally below the knee and/or buttock. Physical examination reveals point tenderness in the posterolateral area of the greater trochanter. Most cases of GTPS are self-limited with conservative measures, such as physical therapy, weight loss, nonsteroidal antiinflammatory drugs and behavior modification, providing resolution of symptoms. Other treatment modalities include bursa or lateral hip injections performed with corticosteroid and local anesthetic. More invasive surgical interventions have anecdotally been reported to provide pain relief when conservative treatment modalities fail.
Critical period regulation.

Neuronal circuits are shaped by experience during critical periods of early postnatal life. The ability to control the timing, duration, and closure of these heightened levels of brain plasticity has recently become experimentally accessible, especially in the developing visual system. This review summarizes our current understanding of known critical periods across several systems and species. It delineates a number of emerging principles: functional competition between inputs, role for electrical activity, structural consolidation, regulation by experience (not simply age), special role for inhibition in the CNS, potent influence of attention and motivation, unique timing and duration, as well as use of distinct molecular mechanisms across brain regions and the potential for reactivation in adulthood. A deeper understanding of critical periods will open new avenues to "nurture the brain"-from international efforts to link brain science and education to improving recovery from injury and devising new strategies for therapy and lifelong learning.