nils-herrmann/clean_scirepeval huggingface dataset:
VLSI implementations of threshold logic-a comprehensive survey

This paper is an in-depth review on silicon implementations of threshold logic gates that covers several decades. In this paper, we will mention early MOS threshold logic solutions and detail numerous very-large-scale integration (VLSI) implementations including capacitive (switched capacitor and floating gate with their variations), conductance/current (pseudo-nMOS and output-wired-inverters, including a plethora of solutions evolved from them), as well as many differential solutions. At the end, we will briefly mention other implementations, e.g., based on negative resistance devices and on single electron technologies.
Face classification using electronic synapses

Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system.
Dissociating contributions of ACC and vmPFC in reward prediction, outcome, and choice

Acting in an uncertain environment requires estimating the probability and the value of potential outcomes. These computations are typically ascribed to various parts of the medial prefrontal cortex (mPFC), but the functional architecture of this region remains debated. The anterior cingulate cortex (ACC) encodes reward prediction and outcome (i.e. win vs lose, Silvetti, Seurinck, & Verguts, 2013. Cortex, 49(6), 1627-35. doi:10.1016/j.cortex.2012.05.008). An outcome-related value signal has also been reported in the ventromedial Prefrontal Cortex (vmPFC, Rangel & Hare, 2010. Current Opinion in Neurobiology, 20(2), 262-70. doi:10.1016/j.conb.2010.03.001). Whether a functional dissociation can be traced in these regions with respect to reward prediction and outcome has been suggested but not rigorously tested. Hence an fMRI study was designed to systematically examine the contribution of ACC and vmPFC to reward prediction and outcome. A striking dissociation was identified, with ACC coding for positive prediction errors and vmPFC responding to outcome, irrespective of probability. Moreover, ACC has been assigned a crucial role in the selection of intentional actions (decision-making) and computing the value associated to these actions (action-based value). Conversely, vmPFC seems to implement stimulus-based value processing (Rudebeck et al., 2008. ), 13775-85. doi:10.1523/JNEUROSCI.3541-08.2008; Rushworth, Behrens, Rudebeck, & Walton, 2007. Trends in Cognitive Sciences, 11(4), 168-76. doi:10.1016/j.tics.2007.01.004). Therefore, a decision-making factor (choice vs. no choice condition) was also implemented in the present paradigm to distinguish stimulus-based versus action-based value coding in the mPFC during both decision and outcome phase. We found that vmPFC was more activated during the outcome phase in the no-choice than in the choice condition, potentially confirming the role of this area in stimulus-based (more than action-based) value processing.
Question Classification using Semantic, Syntactic and Lexical features

Question classification is very important for question answering. This paper present our research work on question classification through machine learning approach. In order to train the learning model, we designed a rich set of features that are predictive of question categories. An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. Question classification is typically done using machine learning techniques. Different lexical, syntactical and semantic features can be extracted from a question. In this work we combined lexical, syntactic and semantic features which improve the accuracy of classification. Furthermore, we adopted three different classifiers: Nearest Neighbors (NN), Naïve Bayes (NB), and Support Vector Machines (SVM) using two kinds of features: bag-of-words and bag-of n grams. Furthermore, we discovered that when we take SVM classifier and combine the semantic, syntactic, lexical feature we found that it will improve the accuracy of classification. We tested our proposed approaches on the well-known UIUC dataset and succeeded to achieve a new record on the accuracy of classification on this dataset.
Soft Robotics : Review of Fluid-Driven Intrinsically Soft Devices ; Manufacturing , Sensing , Control , and Applications in Human-Robot Interaction

The emerging field of soft robotics makes use of many classes of materials including metals, low glass transition temperature (Tg) plastics, andhighTgelastomers.Dependent on the specific design, all of these materials may result in extrinsically soft robots. Organic elastomers, however, have elastic moduli ranging from tens ofmegapascals down to kilopascals; robots composed of suchmaterials are intrinsically soft they are always compliant independent of their shape. This class of soft machines has been used to reduce control complexity and manufacturing cost of robots, while enabling sophisticated and novel functionalities often in direct contact with humans. This review focuses on a particular type of intrinsically soft, elastomeric robot those powered via fluidic pressurization.
An Unsupervised Approach to Product Attribute Extraction

Product Attribute Extraction is the task of automatically discovering attributes of products from text descriptions. In this paper, we propose a new approach which is both unsupervised and domain independent to extract the attributes. With our approach, we are able to achieve 92% precision and 62% recall in our experiments. Our experiments with varying dataset sizes show the robustness of our algorithm. We also show that even a minimum of 5 descriptions provide enough information to identify attributes.
Stability of networked control systems

Control Systems Magazine February 2001 Feedback control systems wherein the control loops are closed through a real-time network are called networked control systems (NCSs) [1]-[4]. The defining feature of an NCS is that information (reference input, plant output, control input, etc.) is exchanged using a network among control system components (sensors, controller, actuators, etc.). Fig. 1 illustrates a typical setup and the information flows of an NCS. The primary advantages of an NCS are reduced system wiring, ease of system diagnosis and maintenance, and increased system agility. The insertion of the communication network in the feedback control loop makes the analysis and design of an NCS complex. Conventional control theories with many ideal assumptions, such as synchronized control and nondelayed sensing and actuation, must be reevaluated before they can be applied to NCSs. Specifically, the following issues need to be addressed. The first issue is the network-induced delay (sensor-to-controller delay and controller-to-actuator delay) that occurs while exchanging data among devices connected to the shared medium. This delay, either constant (up to jitter) or time varying, can degrade the performance of control systems designed without considering the delay and can even destabilize the system. Next, the network can be viewed as a web of unreliable transmission paths. Some packets not only suffer transmission delay but, even worse, can be lost during transmission. Thus, how such packet Stability of Networked Control Systems By Wei Zhang, Michael S. Branicky, and Stephen M. Phillips
Coil Design and Measurements of Automotive Magnetic Resonant Wireless Charging System for High-Efficiency and Low Magnetic Field Leakage

For wireless charging of electric vehicle (EV) batteries, high-frequency magnetic fields are generated from magnetically coupled coils. The large air-gap between two coils may cause high leakage of magnetic fields and it may also lower the power transfer efficiency (PTE). For the first time, in this paper, we propose a new set of coil design formulas for high-efficiency and low harmonic currents and a new design procedure for low leakage of magnetic fields for high-power wireless power transfer (WPT) system. Based on the proposed design procedure, a pair of magnetically coupled coils with magnetic field shielding for a 1-kW-class golf-cart WPT system is optimized via finite-element simulation and the proposed design formulas. We built a 1-kW-class wireless EV charging system for practical measurements of the PTE, the magnetic field strength around the golf cart, and voltage/current spectrums. The fabricated system has achieved a PTE of 96% at the operating frequency of 20.15 kHz with a 156-mm air gap between the coils. At the same time, the highest magnetic field strength measured around the golf cart is 19.8 mG, which is far below the relevant electromagnetic field safety guidelines (ICNIRP 1998/2010). In addition, the third harmonic component of the measured magnetic field is 39 dB lower than the fundamental component. These practical measurement results prove the effectiveness of the proposed coil design formulas and procedure of a WPT system for high-efficiency and low magnetic field leakage.
Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods

Plagiarism can be of many different natures, ranging from copying texts to adopting ideas, without giving credit to its originator. This paper presents a new taxonomy of plagiarism that highlights differences between literal plagiarism and intelligent plagiarism, from the plagiarist's behavioral point of view. The taxonomy supports deep understanding of different linguistic patterns in committing plagiarism, for example, changing texts into semantically equivalent but with different words and organization, shortening texts with concept generalization and specification, and adopting ideas and important contributions of others. Different textual features that characterize different plagiarism types are discussed. Systematic frameworks and methods of monolingual, extrinsic, intrinsic, and cross-lingual plagiarism detection are surveyed and correlated with plagiarism types, which are listed in the taxonomy. We conduct extensive study of state-of-the-art techniques for plagiarism detection, including character n-gram-based (CNG), vector-based (VEC), syntax-based (SYN), semantic-based (SEM), fuzzy-based (FUZZY), structural-based (STRUC), stylometric-based (STYLE), and cross-lingual techniques (CROSS). Our study corroborates that existing systems for plagiarism detection focus on copying text but fail to detect intelligent plagiarism when ideas are presented in different words.
Low-Profile, Multi-Element, Miniaturized Monopole Antenna

A low-profile, electrically small antenna with omnidirectional vertically polarized radiation similar to a short monopole antenna is presented. The antenna features less than lambda/40 dimension in height and lambda/10 or smaller in lateral dimension. The antenna is matched to a 50 Omega coaxial line without the need for external matching. The geometry of the antenna is derived from a quarter-wave transmission line resonator fed at an appropriate location to maximize current through the short-circuited end. To improve radiation from the vertical short-circuited pin, the geometry is further modified through superposition of additional resonators placed in a parallel arrangement. The lateral dimension of the antenna is miniaturized by meandering and turning the microstrip lines into form of a multi-arm spiral. The meandering between the short-circuited end and the feed point also facilitates the impedance matching. Through this technique, spurious horizontally polarized radiation is also minimized and a radiation pattern similar to a short dipole is achieved. The antenna is designed, fabricated and measured. Parametric studies are performed to explore further size reduction and performance improvements. Based on the studies, a dual-band antenna with enhanced gain is realized. The measurements verify that the proposed fabricated antennas feature excellent impedance match, omnidirectional radiation in the horizontal plane and low levels of cross-polarization.
Independent learning of internal models for kinematic and dynamic control of reaching

Psychophysical studies of reaching movements suggest that hand kinematics are learned from errors in extent and direction in an extrinsic coordinate system, whereas dynamics are learned from proprioceptive errors in an intrinsic coordinate system. We examined consolidation and interference to determine if these two forms of learning were independent. Learning and consolidation of two novel transformations, a rotated spatial reference frame and altered intersegmental dynamics, did not interfere with each other and consolidated in parallel. Thus separate kinematic and dynamic models were constructed simultaneously based on errors computed in different coordinate frames, and possibly, in different sensory modalities, using separate working-memory systems. These results suggest that computational approaches to motor learning should include two separate performance errors rather than one.
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning

Let H := (S0, A0, R0, S1, . . . , St−1, At−1, Rt−1, St) be the first t transitions in the episode H . We call H a partial trajectory of length t. Notice that we use subscripts on trajectories to denote the trajectory’s index in D and superscripts to denote partial trajectories—H i is the first t transitions of the ith trajectory in D. Let H be the set of all possible partial trajectories of length t.
Performance Evaluation of DES and Blowfish Algorithms

With the rapid application growing of internet and wireless network, information security becomes significant to protect commerce secret and privacy. Encryption algorithm plays an important role for information security guarantee. In this paper, we evaluate the performance of two symmetric key encryption algorithms: DES and Blowfish which commonly used for network data encryption. In this paper, we analyzed encryption security, evaluated encryption speed and power consumption for both algorithms. Experimental results show that Blowfish algorithm runs faster than DES, while the power consumption is almost the same. It is proved that the Blowfish encryption algorithm maybe more suitable for wireless network application security.
Smartphone-based portable ultrasound imaging system: Prototype implementation and evaluation

In this paper, we present a smart US imaging system (SMUS) based on an android-OS smartphone, which can provide maximally optimized efficacy in terms of weight and size in point-of-care diagnostic applications. The proposed SMUS consists of the smartphone (Galaxy S5 LTE-A, Samsung., Korea) and a 16-channel probe system. The probe system contains analog and digital front-ends, which conducts beamforming and mid-processing procedures. Otherwise, the smartphone performs the back-end processing including envelope detection, log compression, 2D image filtering, digital scan conversion, and image display with custom-made graphical user interface (GUI). Note that the probe system and smartphone are interconnected by the USB 3.0 protocol. As a result, the developed SMUS can provide real-time B-mode image with the sufficient frame rate (i.e., 58 fps), battery run-time for point-of-care diagnosis (i.e., 54 min), and 35.0°C of transducer surface temperature during B-mode imaging, which satisfies the temperature standards for the safety and effectiveness of medical electrical equipment, IEC 60601-1 (i.e., 43°C).
Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including realtime monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate the more that $45 Billion market value of UAV usage. In this survey, we present UAV civil applications and their challenges. We also discuss current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.
Smart Nest Thermostat : A Smart Spy in Your Home

The Nest Thermostat is a smart home automation device that aims to learn a user’s heating and cooling habits to help optimize scheduling and power usage. With its debut in 2011, Nest has proven to be such a success that Google spent $3.2B to acquire the company. However, the complexity of the infrastructure in the Nest Thermostat provides a breeding ground for security vulnerabilities similar to those found in other computer systems. To mitigate this issue, Nest signs firmware updates sent to the device, but the hardware infrastructure lacks proper protection, allowing attackers to install malicious software into the unit. Through a USB connection, we demonstrate how the firmware verification done by the Nest software stack can be bypassed, providing the means to completely alter the behavior of the unit. The compromised Nest Thermostat will then act as a beachhead to attack other nodes within the local network. Also, any information stored within the unit is now available to the attacker, who no longer has to have physical access to the device. Finally, we present a solution to smart device architects and manufacturers aiding the development and deployment of a secure hardware platform.
Love & Loans The Effect of Beauty and Personal Characteristics in Credit Markets∗

I examine whether easily observable variables such as beauty, race, and the way a loan applicant presents himself affect lenders’ decisions, once hard financial information about credit scores, employment history, homeownership, and other financial information are taken into account. I use data from Prosper.com, a 150 million dollars online lending market in which borrowers post loan requests that include verifiable financial information, photos, an offered interest rate, and related context. Borrowers whose beauty is rated above average are 1.41 percentage points more likely to get a loan and, given a loan, pay 81 basis points less than an average-looking borrower with the same credentials. Black borrowers pay between 139 and 146 basis points more than otherwise similar White borrowers, although they are not more likely to become delinquent. Similarity between borrowers and lenders has also a powerful impact on lenders’ decisions. In my sample personal characteristics are not, all else equal, significantly related to subsequent delinquency rates with the exception of beauty, which is associated with substantially higher delinquency probability. The findings are consistent with personal characteristics affecting loan supply through lenders’ preferences (taste-based discrimination a la Becker) and perception, rather than statistical discrimination based on inferences from their previous experience.
Using Scalable Data Mining for Predicting Flight Delays

Flight delays are frequent all over the world (about 20% of airline flights arrive more than 15min late) and they are estimated to have an annual cost of billions of dollars. This scenario makes the prediction of flight delays a primary issue for airlines and travelers. The main goal of this work is to implement a predictor of the arrival delay of a scheduled flight due to weather conditions. The predicted arrival delay takes into consideration both flight information (origin airport, destination airport, scheduled departure and arrival time) and weather conditions at origin airport and destination airport according to the flight timetable. Airline flight and weather observation datasets have been analyzed and mined using parallel algorithms implemented as MapReduce programs executed on a Cloud platform. The results show a high accuracy in predicting delays above a given threshold. For instance, with a delay threshold of 15min, we achieve an accuracy of 74.2% and 71.8% recall on delayed flights, while with a threshold of 60min, the accuracy is 85.8% and the delay recall is 86.9%. Furthermore, the experimental results demonstrate the predictor scalability that can be achieved performing data preparation and mining tasks as MapReduce applications on the Cloud.