nils-herrmann/clean_scirepeval huggingface dataset:
High-level feature representation using recurrent neural network for speech emotion recognition

This paper presents a speech emotion recognition system using a recurrent neural network (RNN) model trained by an efficient learning algorithm. The proposed system takes into account the long-range contextual effect and the uncertainty of emotional label expressions. To extract high-level representation of emotional states with regard to its temporal dynamics, a powerful learning method with a bidirectional long short-term memory (BLSTM) structure is adopted. To overcome the uncertainty of emotional labels, such that all frames in the same utterance are mapped to the same emotional label, it is assumed that the label of each frame is regarded as a sequence of random variables. The sequences are then trained by the proposed learning algorithm. The weighted accuracy of the proposed emotion recognition system is improved up to 12% compared to the DNNELM-based emotion recognition system used as a baseline.
Hardiness commitment, gender, and age differentiate university academic performance.

The increasing diversity of students, particularly in age, attending university has seen a concomitant interest in factors predicting academic success.



This 2-year correlational study examined whether age, gender (demographic variables), and hardiness (cognitive/emotional variable) differentiate and predict university final degree grade point average (GPA) and final-year dissertation mark.


SAMPLE
Data are reported from a total of 134 university undergraduate students.



Participants provided baseline data in questionnaires administered during the first week of their second year of undergraduate study and gave consent for their academic progress to be tracked. Final degree GPA and dissertation mark were the academic performance criteria.



Mature-age students achieved higher final degree GPA compared to young undergraduates. Female students significantly outperformed their male counterparts in each measured academic assessment criteria. Female students also reported a significantly higher mean score on hardiness commitment compared to male students. commitment was the most significant positive correlate of academic achievement. Final degree GPA and dissertation mark were significantly predicted by commitment, and commitment and gender, respectively.



The findings have implications for universities targeting academic support services to maximize student scholastic potential. Future research should incorporate hardiness, gender, and age with other variables known to predict academic success.
Ku-Band Low Profile Antennas for Mobile Satcom

This paper presents three different concepts of Ku-band low profile antennas for mobile satcom. First, a low profile fully active phased array aiming to Ku-Band broadcast reception is presented. Two other hybrid phased arrays are presented, one aiming to receive only applications and one for Tx/Rx operations. All the three presented antennas are low profile and suitable for in-vehicle integration.
Human error: models and management.

PERSON APPROACH The long-standing and widespread tradition of the person approach focuses on the unsafe acts—errors and procedural violations—of people on the front line: nurses, physicians, surgeons, anesthetists, pharmacists, and the like. It views these unsafe acts as arising primarily from aberrant mental processes such as forgetfulness, inattention, poor motivation, carelessness, negligence, and recklessness. The associated countermeasures are directed mainly at reducing unwanted variability in human behavior. These methods include poster campaigns that appeal to people’s fear, writing another procedure (or adding to existing ones), disciplinary measures, threat of litigation, retraining, naming, blaming, and shaming. Followers of these approaches tend to treat errors as moral issues, assuming that bad things happen to bad people—what psychologists have called the “just-world hypothesis.”
SUAV:Q - An improved design for a transformable solar-powered UAV

Throughout the wide range of aerial robot related applications, selecting a particular airframe is often a trade-off. Fixed-wing small-scale unmanned aerial vehicles (UAVs) typically have difficulty surveying at low altitudes while quadrotor UAVs, having more maneuverability, suffer from limited flight time. Recent prior work  proposes a solar-powered small-scale aerial vehicle designed to transform between fixed-wing and quad-rotor configurations. Surplus energy collected and stored while in a fixed-wing configuration is utilized while in a quad-rotor configuration. This paper presents an improvement to the robot's design in  by pursuing a modular airframe, an optimization of the hybrid propulsion system, and solar power electronics. Two prototypes of the robot have been fabricated for independent testing of the airframe in fixed-wing and quad-rotor states. Validation of the solar power electronics and hybrid propulsion system designs were demonstrated through a combination of simulation and empirical data from prototype hardware.
Design Methodology of LLC Resonant Converters for Electric Vehicle Battery Chargers

In this paper, an inductor-inductor-capacitor (LLC) resonant dc-dc converter design procedure for an onboard lithium-ion battery charger of a plug-in hybrid electric vehicle (PHEV) is presented. Unlike traditional resistive load applications, the characteristic of a battery load is nonlinear and highly related to the charging profiles. Based on the features of an LLC converter and the characteristics of the charging profiles, the design considerations are studied thoroughly. The worst-case conditions for primary-side zero-voltage switching (ZVS) operation are analytically identified based on fundamental harmonic approximation when a constant maximum power (CMP) charging profile is implemented. Then, the worst-case operating point is used as the design targeted point to ensure soft-switching operation globally. To avoid the inaccuracy of fundamental harmonic approximation approach in the below-resonance region, the design constraints are derived based on a specific operation mode analysis. Finally, a step-by-step design methodology is proposed and validated through experiments on a prototype converting 400 V from the input to an output voltage range of 250-450 V at 3.3 kW with a peak efficiency of 98.2%.
Wireless security through RF fingerprinting

The process of identifying radio transmitters by examining their unique transient characteristics at the beginning of transmission is called RF fingerprinting. The security of wireless networks can be enhanced by challenging a user to prove its identity if the fingerprint of a network device is unidentified or deemed to be a threat. This paper addresses the problem of identifying an individual node in a wireless network by means of its RF fingerprint. A complete identification system is presented, including data acquisition, transient detection, RF fingerprint extraction, and classification subsystems. The classification performance of the proposed system has been evaluated from experimental data. It is demonstrated that the RF fingerprinting technique can be used as an additional tool to enhance the security of wireless networks.
Open data challenges at Facebook

At Facebook, our data systems process huge volumes of data, ranging from hundreds of terabytes in memory to hundreds of petabytes on disk. We categorize our systems as “small data” or “big data” based on the type of queries they run. Small data refers to OLTP-like queries that process and retrieve a small amount of data, for example, the 1000s of objects necessary to render Facebook's personalized News Feed for each person. These objects are requested by their ids; indexes limit the amount of data accessed during a single query, regardless of the total volume of data. Big data refers to queries that process large amounts of data, usually for analysis: trouble-shooting, identifying trends, and making decisions. Big data stores are the workhorses for data analysis at Facebook. They grow by millions of events (inserts) per second and process tens of petabytes and hundreds of thousands of queries per day. In this tutorial, we will describe our data systems and the current challenges we face. We will lead a discussion on these challenges, approaches to solve them, and potential pitfalls. We hope to stimulate interest in solving these problems in the research community.
Interactive analysis of event data using space-time cube

In exploratory data analysis, the choice of tools depends on the data to be analyzed and the analysis tasks, i.e. the questions to be answered. The same applies to design of new analysis tools. In this paper, we consider a particular type of data: data that describe transient events having spatial and temporal references, such as earthquakes, traffic incidents, or observations of rare plants or animals. We focus on the task of detecting spatio-temporal patterns in event occurrences. We demonstrate the insufficiency of the existing techniques and approaches to event exploration and substantiate the need in a new exploratory tool. The technique of space-time cube, which has been earlier proposed for the visualization of movement in geographical space, possesses the required properties. However, it must be implemented so as to allow particular interactive manipulations: changing the viewing perspective, temporal focusing, and dynamic linking with a map display through simultaneous highlighting of corresponding symbols. We describe our implementation of the space-time cube technique and demonstrate by an example how it can be used for detecting spatio-temporal clusters of events.
Towards Transparent AI Systems: Interpreting Visual Question Answering Models

Deep neural networks have shown striking progress and obtained state-of-the-art results in many AI research fields in the recent years. However, it is often unsatisfying to not know why they predict what they do. In this paper, we address the problem of interpreting Visual Question Answering (VQA) models. Specifically, we are interested in finding what part of the input (pixels in images or words in questions) the VQA model focuses on while answering the question. To tackle this problem, we use two visualization techniques – guided backpropagation and occlusion – to find important words in the question and important regions in the image. We then present qualitative and quantitative analyses of these importance maps. We found that even without explicit attention mechanisms, VQA models may sometimes be implicitly attending to relevant regions in the image, and often to appropriate words in the question.
Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions

Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.
18–40-GHz Beam-Shaping/Steering Phased Antenna Array System Using Fermi Antenna

This paper concerns 18-40 GHz 1times 16 beam shaping and 1times 8 beam steering phased antenna arrays (PAAs) realized on a single low-cost printed circuit board substrate. The system consists of a wideband power divider with amplitude taper for sidelobe suppression, wideband microstrip-to-slotline transition, a low-cost true time piezoelectric transducer (PET)-controlled phase shifter, and wideband Fermi antennas with corrugations along the sides. A coplanar stripline is used under a PET-controlled phase shifter, which can generate 50% more phase shift compared to the perturbation on microstrip lines previously published. The systems are fabricated using electro-fine-forming microfabrication technology. Measured return loss is better than 10 dB from 18 to 40 GHz for both the beam-shaping and beam-steering PAAs. The beam-shaping PAA has a 12deg 3-dB beamwidth broadening range. The sidelobe ratios (SLRs) are 27, 23, and 20 dB at 20, 30, and 40 GHz, respectively, without perturbation. The SLRs are 20, 16, and 15 dB at 20, 30, and 40 GHz with maximum perturbation. The beam-steering PAA has a 36deg (-17deg to +19deg ) beam-scanning range measured at 30 GHz.
The impact of full disk encryption on digital forensics

The integration of strong encryption into operating systems is creating challenges for forensic examiners, potentially preventing us from recovering any digital evidence from a computer. Because strong encryption cannot be circumvented without a key or passphrase, forensic examiners may not be able to access data after a computer is shut down, and must decide whether to perform a live forensic acquisition. In addition, with encryption becoming integrated into the operating system, in some cases, virtualization is the most effective approach to performing a forensic examination of a system with FDE. This paper presents the evolution of full disk encryption (FDE) and its impact on digital forensics. Furthermore, by demonstrating how full disk encryption has been dealt with in past investigations, this paper provides forensics examiners with practical techniques for recovering evidence that would otherwise be inaccessible.
Driving with Sharks: Rethinking Connected Vehicles with Vehicle Cybersecurity

In a public service announcement on 17 March 2016, the Federal Bureau of Investigation jointly with the U.S. Department of Transportation and the National Highway Traffic Safety Administration (NHTSA) released a warning regarding the increasing vulnerability of motor vehicles to remote exploits [18]. Engine shutdowns, disabled brakes, and locked doors are a few examples of possible vehicle cybersecurity attacks. Modern cars grow into a new target for cyberattacks as they become increasingly connected. While driving on the road, sharks (i.e., hackers) need only to be within communication range of a vehicle to attack it. However, in some cases, they can hack into it while they are miles away. In this article, we aim to illuminate the latest vehicle cybersecurity threats including malware attacks, on-board diagnostic (OBD) vulnerabilities, and automobile apps threats. We illustrate the in-vehicle network architecture and demonstrate the latest defending mechanisms designed to mitigate such threats.
Improved route planning and scheduling of waste collection and transport

The collection of waste is a highly visible and important municipal service that involves large expenditures. Waste collection problems are, however, one of the most difficult operational problems to solve. This paper describes the optimization of vehicle routes and schedules for collecting municipal solid waste in Eastern Finland. The solutions are generated by a recently developed guided variable neighborhood thresholding metaheuristic that is adapted to solve real-life waste collection problems. Several implementation approaches to speed up the method and cut down the memory usage are discussed. A case study on the waste collection in two regions of Eastern Finland demonstrates that significant cost reductions can be obtained compared with the current practice. q 2005
Recommender systems in e-commerce

Internet is speeding up and modifying the manner in which daily tasks such as online shopping, paying utility bills, watching new movies, communicating, etc., are accomplished. As an example, in older shopping methods, products were mass produced for a single market and audience but that approach is no longer viable. Markets based on long product and development cycles can no longer survive. To stay competitive, markets need to provide different products and services to different customers with different needs. The shift to online shopping has made it incumbent on producers and retailers to customize for customers' needs while providing more options than were possible before. This, however, poses a problem for customers who must now analyze every offering in order to determine what they actually need and will benefit from. To aid customers in this scenario, we discuss about common recommender systems techniques that have been employed and their associated trade-offs.
First Person vs. Third Person Perspective in Digital Games: Do Player Preferences Affect Immersion?

Contemporary digital game developers offer a variety of games for the diverse tastes of their customers. Although the gaming experience often depends on one's preferences, the same may not apply to the level of their immersion. It has been argued whether the player perspective can influence the level of player's involvement with the game. The aim of this study was to research whether interacting with a game in first person perspective is more immersive than playing in the third person point of view (POV). The set up to test the theory involved participants playing a role-playing game in either mode, naming their preferred perspective, and subjectively evaluating their immersive experience. The results showed that people were more immersed in the game play when viewing the game world through the eyes of the character, regardless of their preferred perspectives.
Database Design for NoSQL Systems

We propose a database design methodology for NoSQL systems. The approach is based on NoAM (NoSQL Abstract Model), a novel abstrac t d ta model for NoSQL databases, which exploits the commonalities of various N SQL systems and is used to specify a system-independent representatio n of the application data. This intermediate representation can be then implemented in target NoSQL databases, taking into account their specific features. Ov rall, the methodology aims at supporting scalability, performance, and consisten cy, as needed by next-generation web applications.
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition

The popularity of automatic speech recognition (ASR) systems, like Google Assistant, Cortana, brings in security concerns, as demonstrated by recent attacks. The impacts of such threats, however, are less clear, since they are either less stealthy (producing noise-like voice commands) or requiring the physical presence of an attack device (using ultrasound speakers or transducers). In this paper, we demonstrate that not only are more practical and surreptitious attacks feasible but they can even be automatically constructed. Specifically, we find that the voice commands can be stealthily embedded into songs, which, when played, can effectively control the target system through ASR without being noticed. For this purpose, we developed novel techniques that address a key technical challenge: integrating the commands into a song in a way that can be effectively recognized by ASR through the air, in the presence of background noise, while not being detected by a human listener. Our research shows that this can be done automatically against real world ASR applications1. We also demonstrate that such CommanderSongs can be spread through Internet (e.g., YouTube) and radio, potentially affecting millions of ASR users. Finally we present mitigation techniques that defend existing ASR systems against such threat.
A novel permanent magnet motor with doubly salient structure

A new type of doubly salient machine is presented in which the field excitation is provided by a nonrotating permanent magnet (PM). This doubly salient PM (DSPM) motor is shown to be kindred to square waveform PM brushless DC motors. Linear and nonlinear analysis has been performed to investigate the characteristics of this new type of PM motors. A prototype DSPM motor has been designed, and a comparison has been made between this new type of motor and the induction motor. It is shown that, by fully exploiting modern high-energy PM material and the doubly salient structure, the DSPM motor can offer superior performance over existing motors in terms of efficiency, torque density, torque-to-current ratio, and torque-to-inertia ratio, while retaining a simple structure amenable to automatic manufacture.<<ETX>>
A Low-EMI 3-W Audio Class-D Amplifier Compatible With AM/FM Radio

A 3-W audio class-D amplifier compatible with AM/FM radio while requiring minimal external filtering is presented. The amplifier uses driver slew rate control to reduce electromagnetic interference (EMI) in the FM band and digital modulation to spread the PWM common-mode signal and place a null at the tuned AM channel. Two original feedback techniques are employed to linearize the H-bridge. One cancels signal-dependent propagation delays in the H-bridge to improve harmonic distortion and eliminate high-frequency noise folding into the audio band. The second technique boosts the power supply rejection ratio to simplify and reduce the cost of supply filters. The amplifier system, implemented in a standard 110-nm CMOS process, operates with a supply voltage ranging from 2.7 to 6.6 V and achieves an SNR of 95 dB and 85% efficiency.
WiDeo: Fine-grained Device-free Motion Tracing using RF Backscatter

Could we build a motion tracing camera using wireless communication signals as the light source? This paper shows we can, we present the design and implementation of WiDeo, a novel system that enables accurate, high resolution, device free human motion tracing in indoor environments using WiFi signals and compact WiFi radios. The insight behind WiDeo is to mine the backscatter reflections from the environment that WiFi transmissions naturally produce to trace where reflecting objects are located and how they are moving. We invent novel backscatter measurement techniques that work in spite of the low bandwidth and dynamic range of WiFi radios, new algorithms that separate out the moving backscatter from the clutter that static reflectors produce and then trace the original motion that produced the backscatter in spite of the fact that it could have undergone multiple reflections. We prototype WiDeo using off-the-shelf software radios and show that it accurately traces motion even when there are multiple independent human motions occurring concurrently (up to 5) with a median error in the traced path of less than 7cm.