nils-herrmann/clean_scirepeval huggingface dataset:
Evaluation of pupil center-eye corner vector for gaze estimation using a web cam

Low cost eye tracking is an actual challenging research topic for the eye tracking community. Gaze tracking based on a web cam and without infrared light is a searched goal to broaden the applications of eye tracking systems. Web cam based eye tracking results in new challenges to solve such as a wider field of view and a lower image quality. In addition, no infrared light implies that glints cannot be used anymore as a tracking feature. In this paper, a thorough study has been carried out to evaluate pupil (iris) center-eye corner (PC-EC) vector as feature for gaze estimation based on interpolation methods in low cost eye tracking, as it is considered to be partially equivalent to the pupil center-corneal reflection (PC-CR) vector. The analysis is carried out both based on simulated and real data. The experiments show that eye corner positions in the image move slightly when the user is looking at different points of the screen, even with a static head position. This lowers the possible accuracy of the gaze estimation, significantly reducing the accuracy of the system under standard working conditions to 2--3 degrees.
Internet of things for planting in smart farm hydroponics style

Recently, the farmers gain more profits by producing the quality product. The effects of the global warming make more difficult planning in uncontrolled environment. On the other hand, the yield does not match customers' needs. For these reasons, planting in a greenhouse is easy to maintain and to control important factors such as light, temperature, and humidity. Using of sensors coming in a greenhouse as Wireless Sensor Networks System are one efficiency of technology used in agricultural development by sending data to the cloud and controlling values such as temperature, light, etc. The results of this study will be useful for the farmer and related organizations applying in the farm.
Forensic analysis of ordered data structures on the example of JPEG files

JPEG file format standards define only a limited number of mandatory data structures and leave room for interpretation. Differences between implementations employed in digital cameras, image processing software, and software to edit metadata provide valuable clues for basic authentication of digital images. We show that there exists a realistic chance to fool state-of-the-art image file forensic methods using available software tools and introduce the analysis of ordered data structures on the example of JPEG file formats and the EXIF metadata format as countermeasure. The proposed analysis approach enables basic investigations of image authenticity and documents a much better trustworthiness of EXIF metadata than commonly accepted. Manipulations created with the renowned metadata editor ExifTool and various image processing software can be reliably detected. Analysing the sequence of elements in complex data structures is not limited to JPEG files and might be a general principle applicable to different multimedia formats.
Building a shared world: mapping distributional to model-theoretic semantic spaces

In this paper, we introduce an approach to automatically map a standard distributional semantic space onto a set-theoretic model. We predict that there is a functional relationship between distributional information and vectorial concept representations in which dimensions are predicates and weights are generalised quantifiers. In order to test our prediction, we learn a model of such relationship over a publicly available dataset of feature norms annotated with natural language quantifiers. Our initial experimental results show that, at least for domain-specific data, we can indeed map between formalisms, and generate high-quality vector representations which encapsulate set overlap information. We further investigate the generation of natural language quantifiers from such vectors.
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control

Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. We extend policy distillation methods to the continuous action setting and leverage this technique to combine expert policies, as evaluated in the domain of simulated bipedal locomotion across different classes of terrain. We also introduce an input injection method for augmenting an existing policy network to exploit new input features. Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills. The combination of these methods allows a policy to be incrementally augmented with new skills. We compare our progressive learning and integration via distillation (PLAID) method against three alternative baselines.
Multilingual Open Information Extraction

Open Information Extraction (OIE) is a recent unsupervised strategy to extract great amounts of basic propositions (verb-based triples) from massive text corpora which scales to Web-size document collections. We propose a multilingual rule-based OIE method that takes as input dependency parses in the CoNLL-X format, identifies argument structures within the dependency parses, and extracts a set of basic propositions from each argument structure. Our method requires no training data and, according to experimental studies, obtains higher recall and higher precision than existing approaches relying on training data. Experiments were performed in three languages: English, Portuguese, and Spanish.
Towards the improvement of Cuckoo search algorithm

Cuckoo search algorithm via L'evy flights by Xin-She Yang and Saush Deb  for optimizing a nonlinear function uses generation of random numbers with symmetric L'evy distribution obtained by Mantegna's algorithm. However, instead of using the original algorithm, they have used a simplified version to generate L'evy flights during the Cuckoo search algorithm . Also, apaper by MatteoLeccardi  describes three algorithms to generate such random numbers and claims that McCulloch's algorithm is outperforming the other two, namely, Mantegna's algorithm and rejection algorithm. The idea in this paper is to compare and see if the Cuckoo Search algorithm shows any improvement in the performance in three cases when the simplified version algorithm, Mantegna's algorithm and McCulloch's algorithm each of them is included in Cuckoo Search algorithm to generate L'evy flights.
Efficient Biometric Verification in Encrypted Domain

Biometric authentication over public networks leads to a variety of privacy issues that needs to be addressed before it can become popular. The primary concerns are that the biometrics might reveal more information than the identity itself, as well as provide the ability to track users over an extended period of time. In this paper, we propose an authentication protocol that alleviates these concerns. The protocol takes care of user privacy, template protection and trust issues in biometric authentication systems. The protocol uses asymmetric encryption, and captures the advantages of biometric authentication. The protocol provides non-repudiable identity verification, while not revealing any additional information about the user to the server or vice versa. We show that the protocol is secure under various attacks. Experimental results indicate that the overall method is efficient to be used in practical scenarios.
Getting a Job via Career-Oriented Social Networking Sites: The Weakness of Ties

By asking users of career-oriented social networking sites I investigated their job search behavior. For further IS-theorizing I integrated the number of a user's contacts as an own construct into Venkatesh's et al. UTAUT2 model, which substantially rose its predictive quality from 19.0 percent to 80.5 percent concerning the variance of job search success. Besides other interesting results I found a substantial negative relationship between the number of contacts and job search success, which supports the experience of practitioners but contradicts scholarly findings. The results are useful for scholars and practitioners.
Sequence alignment for masquerade detection

The masquerade attack, where an attacker takes on the identity of a legitimate user to maliciously utilize that user’s privileges, poses a serious threat to the security of information systems. Such attacks completely undermine traditional security mechanisms due to the trust imparted to user accounts once they have been authenticated. Many attempts have been made at detecting these attacks, yet achieving high levels of accuracy remains an open challenge. In this paper, we discuss the use of a specially tuned sequence alignment algorithm, typically used in bioinformatics, to detect instances of masquerading in sequences of computer audit data. By using the alignment algorithm to align sequences of monitored audit data with sequences known to have been produced by the user, the alignment algorithm can discover areas of similarity and derive a metric that indicates the presence or absence of masquerade attacks. Additionally, we present several scoring systems, methods for accommodating variations in user behavior, and heuristics for decreasing the computational requirements of the algorithm. Our technique is evaluated against the standard masquerade detection dataset provided by Schonlau et al. , and the results show that the use of the sequence alignment technique provides, to our knowledge, the best results of all masquerade detection techniques to date.
A Singing Robot Realized by a Collaboration of VOCALOID and Cybernetic Human HRP-4 C

This paper presents a singing robot system realized by collaboration of the singing synthesis technology “VOCALOID” (developed by YAMAHA) and the novel biped humanoid robot HRP-4C named “Miim” (developed by AIST). One of the advantages of the cybernetic human HRP-4C is is found on its capacity to perform a variety of body motions and realistic facial expressions. To achieve a realistic robot-singing performance, facial motions such as lip-sync, eyes blinking and facial gestures are required. We developed a demonstration system for VOCALOID and HRP-4C, mainly consisting of singing data and the corresponding facial motions. We report in this work the technical overview of the system and the results of an exhibition presented at CEATEC JAPAN 2009.
Toward a threat model for storage systems

The growing number of storage security breaches as well as the need to adhere to government regulations is driving the need for greater storage protection. However, there is the lack of a comprehensive process to designing storage protection solutions. Designing protection for storage systems is best done by utilizing proactive system engineering rather than reacting with ad hoc countermeasures to the latest attack du jour. The purpose of threat modeling is to organize system threats and vulnerabilities into general classes to be addressed with known storage protection techniques. Although there has been prior work on threat modeling primarily for software applications, to our knowledge this is the first attempt at domain-specific threat modeling for storage systems. We discuss protection challenges unique to storage systems and propose two different processes to creating a threat model for storage systems: one based on classical security principles Confidentiality, Integrity, Availability, Authentication, or CIAA) and another based on the Data Lifecycle Model. It is our hope that this initial work will start a discussion on how to better design and implement storage protection solutions against storage threats.
Fundamental Considerations of Three-Level DC–DC Converters: Topologies, Analyses, and Control

This paper discusses the basic family of three-level (TL) dc-dc converters. The origin of TL converters and their basic topological variations are described. Systematic procedures leading to improved and simplified circuit topologies are discussed. A feedforward control scheme that ensures the proper functioning of these converters is proposed. Moreover, the virtues and drawbacks of these converters as compared to conventional converters are highlighted. In particular, the advantages of TL converters include reduced voltage stress of the switches, reduced filter size, and improved dynamic response. It is shown that TL converters are highly suitable for high input/output voltage and medium-to-high-power dc-dc power conversions.
Kinetic and kinematic workspaces of the index finger following stroke.

The objective of this study was to explore motor impairment of the index finger following stroke. More specifically, the kinetics and kinematics of the index finger were analysed throughout its workspace. Twenty-four stroke survivors with chronic hemiparesis of the hand participated in the trials, along with six age-matched controls. Hand impairment was classified according to the clinical Chedoke-McMaster Stage of Hand scale. Subjects were instructed to generate fingertip force in six orthogonal directions at five different positions within the workspace. Split-plot analysis of variance revealed that clinical impairment level had a significant effect on measured force (P < 0.001), with the weakness in stroke survivors being directionally dependent (P < 0.01). Electromyographic recordings revealed altered muscle activation patterns in the more impaired subjects. Unlike the control subjects, these subjects exhibited peak muscle excitation of flexor digitorum superficialis, extensor digitorum communis and first dorsal interosseous during the generation of fingertip flexion forces. Subjects also attempted to reach locations scattered throughout the theoretical workspace of the index finger. Quantification of the active kinematic workspace demonstrated a relationship between impairment level and the percentage of the theoretical workspace that could be attained (P < 0.001). The stroke survivors exhibited a high correlation between mean force production and active workspace (R = 0.90). Thus, our data suggest that altered muscle activation patterns contribute to directionally dependent weakness following stroke. Both the modulation of muscle excitation with force direction and the independence of muscle activation seem to be reduced. These alterations translate into a significantly reduced active range of motion for the fingers.
High-performance secure multi-party computation for data mining applications

Secure multi-party computation (MPC) is a technique well suited for privacy-preserving data mining. Even with the recent progress in two-party computation techniques such as fully homomorphic encryption, general MPC remains relevant as it has shown promising performance metrics in real-world benchmarks. Sharemind is a secure multi-party computation framework designed with real-life efficiency in mind. It has been applied in several practical scenarios, and from these experiments, new requirements have been identified. Firstly, large datasets require more efficient protocols for standard operations such as multiplication and comparison. Secondly, the confidential processing of financial data requires the use of more complex primitives, including a secure division operation. This paper describes new protocols in the Sharemind model for secure multiplication, share conversion, equality, bit shift, bit extraction, and division. All the protocols are implemented and benchmarked, showing that the current approach provides remarkable speed improvements over the previous work. This is verified using real-world benchmarks for both operations and algorithms.
Assessment of eating disorders: interview or self-report questionnaire?

A detailed comparison was made of two methods for assessing the features of eating disorders. An investigator-based interview was compared with a self-report questionnaire based directly on that interview. A number of important discrepancies emerged. Although the two measures performed similarly with respect to the assessment of unambiguous behavioral features such as self-induced vomiting and dieting, the self-report questionnaire generated higher scores than the interview when assessing more complex features such as binge eating and concerns about shape. Both methods underestimated body weight.
A 150nW, 5ppm/o C, 100kHz On-Chip clock source for ultra low power SoCs

This paper presents an ultra low power clock source using a 1μW temperature compensated on-chip digitally controlled oscillator (Osc<sub>CMP</sub>) and a 100nW uncompensated oscillator (Osc<sub>UCMP</sub>) with respective temperature stabilities of 5ppm/°C and 1.67%/°C. A fast locking circuit re-locks Osc<sub>UCMP</sub> to Osc<sub>CMP</sub> often enough to achieve a high effective temperature stability. Measurements of a 130nm CMOS chip show that this combination gives a stability of 5ppm/°C from 20°C to 40°C (14ppm/°C from 20°C to 70°C) at 150nW if temperature changes by 1°C or less every second. This result is 7X lower power than typical XTALs and 6X more stable than prior on-chip solutions.
A lexicon model for deep sentiment analysis and opinion mining applications

This paper presents a lexicon model for the description of verbs, nouns and adjectives to be used in applicatons like sentiment analysis and opinion mining. The model aims to describe the detailed subjectivity relations that exist between the actors in a sentence expressing separate attitudes for each actor. Subjectivity relations that exist between the different actors are labeled with information concerning both the identity of the attitude holder and the orientation (positive vs. negative) of the attitude. The model includes a categorization into semantic categories relevant to opinion mining and sentiment analysis and provides means for the identification of the attitude holder and the polarity of the attitude and for the description of the emotions and sentiments of the different actors involved in the text. Special attention is paid to the role of the speaker/writer of the text whose perspective is expressed and whose views on what is happening are conveyed in the text. Finally, validation is provided by an annotation study that shows that these subtle subjectivity relations are reliably identifiable by human annotators.
An Adaptive Learning Rate for Stochastic Variational Inference

Stochastic variational inference finds good posterior approximations of probabilistic models with very large data sets. It optimizes the variational objective with stochastic optimization, following noisy estimates of the natural gradient. Operationally, stochastic inference iteratively subsamples from the data, analyzes the subsample, and updates parameters with a decreasing learning rate. However, the algorithm is sensitive to that rate, which usually requires hand-tuning to each application. We solve this problem by developing an adaptive learning rate for stochastic inference. Our method requires no tuning and is easily implemented with computations already made in the algorithm. We demonstrate our approach with latent Dirichlet allocation applied to three large text corpora. Inference with the adaptive learning rate converges faster and to a better approximation than the best settings of hand-tuned rates.
Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing

Crowdsourcing has gained immense popularity in machine learning applications for obtaining large amounts of labeled data. Crowdsourcing is cheap and fast, but suffers from the problem of low-quality data. To address this fundamental challenge in crowdsourcing, we propose a simple payment mechanism to incentivize workers to answer only the questions that they are sure of and skip the rest. We show that surprisingly, under a mild and natural “no-free-lunch” requirement, this mechanism is the one and only incentive-compatible payment mechanism possible. We also show that among all possible incentive-compatible mechanisms (that may or may not satisfy no-free-lunch), our mechanism makes the smallest possible payment to spammers. Interestingly, this unique mechanism takes a “multiplicative” form. The simplicity of the mechanism is an added benefit. In preliminary experiments involving over several hundred workers, we observe a significant reduction in the error rates under our unique mechanism for the same or lower monetary expenditure.
OPT: a new framework for overlapped and parallel triangulation in large-scale graphs

Graph triangulation, which finds all triangles in a graph, has been actively studied due to its wide range of applications in the network analysis and data mining. With the rapid growth of graph data size, disk-based triangulation methods are in demand but little researched. To handle a large-scale graph which does not fit in memory, we must iteratively load small parts of the graph. In the existing literature, achieving the ideal cost has been considered to be impossible for billion-scale graphs due to the memory size constraint. In this paper, we propose an overlapped and parallel disk-based triangulation framework for billion-scale graphs, OPT, which achieves the ideal cost by (1) full overlap of the CPU and I/O operations and (2) full parallelism of multi-core CPU and FlashSSD I/O. In OPT, triangles in memory are called the internal triangles while triangles constituting vertices in memory and vertices in external memory are called the external triangles. At the macro level, OPT overlaps the internal triangulation and the external triangulation, while it overlaps the CPU and I/O operations at the micro level. Thereby, the cost of OPT is close to the ideal cost. Moreover, OPT instantiates both vertex-iterator and edge-iterator models and benefits from multi-thread parallelism on both types of triangulation. Extensive experiments conducted on large-scale datasets showed that (1) OPT achieved the elapsed time close to that of the ideal method with less than 7% of overhead under the limited memory budget, (2) OPT achieved linear speed-up with an increasing number of CPU cores, (3) OPT outperforms the state-of-the-art parallel method by up to an order of magnitude with 6 CPU cores, and (4) for the first time in the literature, the triangulation results are reported for a billion-vertex scale real-world graph.
A 5-Gb/s 2.67-mW/Gb/s Digital Clock and Data Recovery With Hybrid Dithering Using a Time-Dithered Delta–Sigma Modulator

A digital clock and data recovery (CDR) employing a time-dithered delta-sigma modulator (TDDSM) is presented. By enabling hybrid dithering of a sampling period as well as an output bit of the TDDSM, the proposed CDR enhances the resolution of digitally controlled oscillator, removes a low-pass filter in the integral path, and reduces jitter generation. Fabricated in a 65-nm CMOS process, the proposed CDR operates at 5-Gb/s data rate with BER <; 10-12 for PRBS 31. The CDR consumes 13.32 mW at 5 Gb/s and achieves 2.14 and 29.7 ps of a long-term rms and peak-to-peak jitter, respectively.