nils-herrmann/clean_scirepeval huggingface dataset:
A 0.5 V 1.28-MS/s 4.68-fJ/Conversion-Step SAR ADC With Energy-Efficient DAC and Trilevel Switching Scheme

This paper describes a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with an energy-efficient trilevel alternate switching capacitive digital-to-analog converter (CDAC). The switching scheme of this CDAC preserves the features of the asymmetric-switching CDAC. By narrowing and smoothing the dynamic variation of DAC voltage, the switching scheme diminishes the dynamic offset effect induced by the asymmetric-switching CDAC. The CDAC reduces the capacitor requirement by almost fourfold and improves the average switching energy efficiency by almost 86.5% when compared with the conventional switching CDACs. This SAR ADC was implemented using the 90-nm CMOS technology, and its measured performances were as follows: 1) spurious free dynamic range of 56.98 dB; 2) signal-to-noise-and-distortion ratio of 68.79 dB; and 3) power dissipation of 3.45 μW at an operation of 0.5 V and 1.28 MS/s. The ADC achieves a figure-of-merit of 4.68-fJ/conversion-step.
Sound Localization for Humanoid Robots - Building Audio-Motor Maps based on the HRTF

Being able to locate the origin of a sound is important for our capability to interact with the environment. Humans can locate a sound source in both the horizontal and vertical plane with only two ears, using the head related transfer function HRTF, or more specifically features like interaural time difference ITD, interaural level difference ILD, and notches in the frequency spectra. In robotics notches have been left out since they are considered complex and difficult to use. As they are the main cue for humans' ability to estimate the elevation of the sound source this have to be compensated by adding more microphones or very large and asymmetric ears. In this paper, we present a novel method to extract the notches that makes it possible to accurately estimate the location of a sound source in both the horizontal and vertical plane using only two microphones and human-like ears. We suggest the use of simple spiral-shaped ears that has similar properties to the human ears and make it easy to calculate the position of the notches. Finally we show how the robot can learn its HRTF and build audiomotor maps using supervised learning and how it automatically can update its map using vision and compensate for changes in the HRTF due to changes to the ears or the environment
Code-Pointer Integrity

Systems code is often written in low-level languages like C/C++, which offer many benefits but also delegate memory management to programmers. This invites memory safety bugs that attackers can exploit to divert control flow and compromise the system. Deployed defense mechanisms (e.g., ASLR, DEP) are incomplete, and stronger defense mechanisms (e.g., CFI) often have high overhead and limited guarantees [19, 15, 9]. We introduce code-pointer integrity (CPI), a new design point that guarantees the integrity of all code pointers in a program (e.g., function pointers, saved return addresses) and thereby prevents all control-flow hijack attacks, including return-oriented programming. We also introduce code-pointer separation (CPS), a relaxation of CPI with better performance properties. CPI and CPS offer substantially better security-to-overhead ratios than the state of the art, they are practical (we protect a complete FreeBSD system and over 100 packages like apache and postgresql), effective (prevent all attacks in the RIPE benchmark), and efficient: on SPEC CPU2006, CPS averages 1.2% overhead for C and 1.9% for C/C++, while CPI’s overhead is 2.9% for C and 8.4% for C/C++. A prototype implementation of CPI and CPS can be obtained from http://levee.epfl.ch.
Anger Is More Influential than Joy: Sentiment Correlation in Weibo

Recent years have witnessed the tremendous growth of the online social media. In China, Weibo, a Twitter-like service, has attracted more than 500 million users in less than five years. Connected by online social ties, different users might share similar affective states. We find that the correlation of anger among users is significantly higher than that of joy. While the correlation of sadness is surprisingly low. Moreover, there is a stronger sentiment correlation between a pair of users if they share more interactions. And users with larger number of friends possess more significant sentiment correlation with their neighborhoods. Our findings could provide insights for modeling sentiment influence and propagation in online social networks.
Social Media Analytics and Business Value: A Theoretical Framework and Case Study

Social media analytics (SMA) uses advanced techniques to analyze patterns in social media data to enable informed and insightful decision-making. It provides organizations with new ways to create value and gain competitive advantage. In this paper, we present a theoretical framework that explains how organizations create value with SMA. We use the framework as a lens for a case study involving a large financial institution (Bankco) that used SMA as a critical component of a major and highly successful marketing campaign. Bankco successfully distinguished itself from its competitors with a marketing campaign based on the creative and innovative use of a number of social media channels. SMA provided Bankco with important insights into customer sentiments, engagement and brand awareness. A number of important lessons learned about effective use of SMA are discussed.
Characterizing flash memory: Anomalies, observations, and applications

Despite flash memory's promise, it suffers from many idiosyncrasies such as limited durability, data integrity problems, and asymmetry in operation granularity. As architects, we aim to find ways to overcome these idiosyncrasies while exploiting flash memory's useful characteristics. To be successful, we must understand the trade-offs between the performance, cost (in both power and dollars), and reliability of flash memory. In addition, we must understand how different usage patterns affect these characteristics. Flash manufacturers provide conservative guidelines about these metrics, and this lack of detail makes it difficult to design systems that fully exploit flash memory's capabilities. We have empirically characterized flash memory technology from five manufacturers by directly measuring the performance, power, and reliability. We demonstrate that performance varies significantly between vendors, devices, and from publicly available datasheets. We also demonstrate and quantify some unexpected device characteristics and show how we can use them to improve responsiveness and energy consumption of solid state disks by 44% and 13%, respectively, as well as increase flash device lifetime by 5.2x.
Ocular artifacts in EEG and event-related potentials I: Scalp topography

The ocular artifacts that contaminate the EEG derive from the potential difference between the cornea and the fundus of the eye. This corneofundal or corneoretinal potential can be considered as an equivalent dipole with its positive pole directed toward the cornea. The cornea shows a steady DC potential of approximately +13 mV relative to the forehead. Blink potentials are caused by the eyelids sliding down over the positively charged cornea. The artifacts from eye-movements result from changes in orientation of the corneo-fundal potential. The scalp-distribution of the ocular artifacts can be described in terms of propagation factors — the fraction of the EOG signal at periocular electrodes that is recorded at a particular scalp location. These factors vary with the location of the scalp electrode. Propagation factors for blinks and upward eye-movements are significantly different.
The Study on An Application of Otsu Method in Canny Operator

Canny algorithm can be used in extracting the object’s contour clearly by setting the appropriate parameters. The Otsu algorithm can calculate the high threshold value which is significant to the Canny algorithm, and then this threshold value can be used in the Canny algorithm to detect the object’s edge. From the exprimental result, the Otsu algorithm can be applied in choosing the threshold value which can be used in Canny algorithm, and this method improves the effect of extracting the edge of the Canny algorithm, and achieves the expect result finally.
Genetic diagnosis by whole exome capture and massively parallel DNA sequencing.

Protein coding genes constitute only approximately 1% of the human genome but harbor 85% of the mutations with large effects on disease-related traits. Therefore, efficient strategies for selectively sequencing complete coding regions (i.e., "whole exome") have the potential to contribute to the understanding of rare and common human diseases. Here we report a method for whole-exome sequencing coupling Roche/NimbleGen whole exome arrays to the Illumina DNA sequencing platform. We demonstrate the ability to capture approximately 95% of the targeted coding sequences with high sensitivity and specificity for detection of homozygous and heterozygous variants. We illustrate the utility of this approach by making an unanticipated genetic diagnosis of congenital chloride diarrhea in a patient referred with a suspected diagnosis of Bartter syndrome, a renal salt-wasting disease. The molecular diagnosis was based on the finding of a homozygous missense D652N mutation at a position in SLC26A3 (the known congenital chloride diarrhea locus) that is virtually completely conserved in orthologues and paralogues from invertebrates to humans, and clinical follow-up confirmed the diagnosis. To our knowledge, whole-exome (or genome) sequencing has not previously been used to make a genetic diagnosis. Five additional patients suspected to have Bartter syndrome but who did not have mutations in known genes for this disease had homozygous deleterious mutations in SLC26A3. These results demonstrate the clinical utility of whole-exome sequencing and have implications for disease gene discovery and clinical diagnosis.
Towards Ontology-based Data Quality Inference in Large-Scale Sensor Networks

This paper presents an ontology-based approach for data quality inference on streaming observation data originating from large-scale sensor networks. We evaluate this approach in the context of an existing river basin monitoring program called the Intelligent River®. Our current methods for data quality evaluation are compared with the ontology-based inference methods described in this paper. We present an architecture that incorporates semantic inference into a publish/subscribe messaging middleware, allowing data quality inference to occur on real-time data streams. Our preliminary benchmark results indicate delays of 100ms for basic data quality checks based on an existing semantic web software framework. We demonstrate how these results can be maintained under increasing sensor data traffic rates by allowing inference software agents to work in parallel. These results indicate that data quality inference using the semantic sensor network paradigm is viable solution for data intensive, large-scale sensor networks.
Teaching Deep Convolutional Neural Networks to Play Go

Mastering the game of Go has remained a longstanding challenge to the field of AI. Modern computer Go programs rely on processing millions of possible future positions to play well, but intuitively a stronger and more ‘humanlike’ way to play the game would be to rely on pattern recognition rather than brute force computation. Following this sentiment, we train deep convolutional neural networks to play Go by training them to predict the moves made by expert Go players. To solve this problem we introduce a number of novel techniques, including a method of tying weights in the network to ‘hard code’ symmetries that are expected to exist in the target function, and demonstrate in an ablation study they considerably improve performance. Our final networks are able to achieve move prediction accuracies of 41.1% and 44.4% on two different Go datasets, surpassing previous state of the art on this task by significant margins. Additionally, while previous move prediction systems have not yielded strong Go playing programs, we show that the networks trained in this work acquired high levels of skill. Our convolutional neural networks can consistently defeat the well known Go program GNU Go and win some games against state of the art Go playing program Fuego while using a fraction of the play time.
From Observational Studies to Causal Rule Mining

Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore, observational studies based on passively observed data are widely accepted as an alternative to RCTs. However, in observational studies, prior knowledge is required to generate the hypotheses about the cause-effect relationships to be tested, and hence they can only be applied to problems with available domain knowledge and a handful of variables. In practice, many datasets are of high dimensionality, which leaves observational studies out of the opportunities for causal discovery from such a wealth of data sources. In another direction, many efficient data mining methods have been developed to identify associations among variables in large datasets. The problem is that causal relationships imply associations, but the reverse is not always true. However, we can see the synergy between the two paradigms here. Specifically, association rule mining can be used to deal with the high-dimensionality problem, whereas observational studies can be utilised to eliminate noncausal associations. In this article, we propose the concept of causal rules (CRs) and develop an algorithm for mining CRs in large datasets. We use the idea of retrospective cohort studies to detect CRs based on the results of association rule mining. Experiments with both synthetic and real-world datasets have demonstrated the effectiveness and efficiency of CR mining. In comparison with the commonly used causal discovery methods, the proposed approach generally is faster and has better or competitive performance in finding correct or sensible causes. It is also capable of finding a cause consisting of multiple variables—a feature that other causal discovery methods do not possess.
A Convenient Multicamera Self-Calibration for Virtual Environments

Virtual immersive environments or telepresence setups often consist of multiple cameras that have to be calibrated. We present a convenient method for doing this. The minimum is three cameras, but there is no upper limit. The method is fully automatic and a freely moving bright spot is the only calibration object. A set of virtual 3D points is made by waving the bright spot through the working volume. Its projections are found with subpixel precision and verified by a robust RANSAC analysis. The cameras do not have to see all points; only reasonable overlap between camera subgroups is necessary. Projective structures are computed via rank-4 factorization and the Euclidean stratification is done by imposing geometric constraints. This linear estimate initializes a postprocessing computation of nonlinear distortion, which is also fully automatic. We suggest a trick on how to use a very ordinary laser pointer as the calibration object. We show that it is possible to calibrate an immersive virtual environment with 16 cameras in less than 60 minutes reaching about 1/5 pixel reprojection error. The method has been successfully tested on numerous multicamera environments using varying numbers of cameras of varying quality.
Goal-oriented requirements enginering: a roundtrip from research to practice [enginering read engineering]

The software industry is more than ever facing the challenge of delivering WYGIWYW software (what you get is what you want). A well-structured document specifying adequate, complete, consistent, precise, and measurable requirements is a critical prerequisite for such software. Goals have been recognized to be among the driving forces for requirements elicitation, elaboration, organization, analysis, negotiation, documentation, and evolution. Growing experience with goal-oriented requirements engineering suggests synergistic links between research in this area and good practice. We discuss one journey along this road from influencing ideas and research results to tool developments to good practice in industrial projects. On the way, we discuss some lessons learnt, obstacles to technology transfer, and challenges for better requirements engineering research and practice.
Q-Learning in Continuous State and Action Spaces

Q-learning can be used to learn a control policy that maximises a scalar reward through interaction with the environment. Qlearning is commonly applied to problems with discrete states and actions. We describe a method suitable for control tasks which require continuous actions, in response to continuous states. The system consists of a neural network coupled with a novel interpolator. Simulation results are presented for a non-holonomic control task. Advantage Learning, a variation of Q-learning, is shown enhance learning speed and reliability
Outdoor Visible Light Communication for inter- vehicle communication using Controller Area Network

In this paper, we implemented the outdoor Visible Light Communication (VLC) system based on Controller Area Network (CAN) which is normally used in a car, plane, ship, product line control system, medical device and industrial device. For vehicle to vehicle (V2V) or indoor vehicle communication, we used common use device such as head light and back light. We made an application circuit and explained the principle. Also, we found an outdoor VLC problem which is sun light noise and photo detector (PD) saturation problem. Circuit's pulse features were demonstrated by signal. Total communication distance was 20 m in the daytime outdoor condition and its experimental transmission performance is presented.
Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques

The IoT is a network of interconnected everyday objects called “things” that have been augmented with a small measure of computing capabilities. Lately, the IoT has been affected by a variety of different botnet activities. As botnets have been the cause of serious security risks and financial damage over the years, existing Network forensic techniques cannot identify and track current sophisticated methods of botnets. This is because commercial tools mainly depend on signature-based approaches that cannot discover new forms of botnet. In literature, several studies have conducted the use of Machine Learning (ML) techniques in order to train and validate a model for defining such attacks, but they still produce high false alarm rates with the challenge of investigating the tracks of botnets. This paper investigates the role of ML techniques for developing a Network forensic mechanism based on network flow identifiers that can track suspicious activities of botnets. The experimental results using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets’ attacks and their tracks.
Knowledge expansion over probabilistic knowledge bases

Information extraction and human collaboration techniques are widely applied in the construction of web-scale knowledge bases. However, these knowledge bases are often incomplete or uncertain. In this paper, we present ProbKB, a probabilistic knowledge base designed to infer missing facts in a scalable, probabilistic, and principled manner using a relational DBMS. The novel contributions we make to achieve scalability and high quality are: 1) We present a formal definition and a novel relational model for probabilistic knowledge bases. This model allows an efficient SQL-based inference algorithm for knowledge expansion that applies inference rules in batches; 2) We implement ProbKB on massive parallel processing databases to achieve further scalability; and 3) We combine several quality control methods that identify erroneous rules, facts, and ambiguous entities to improve the precision of inferred facts. Our experiments show that ProbKB system outperforms the state-of-the-art inference engine in terms of both performance and quality.
Assessing computational learning in K-12

As computing curricula continue to make their way into K-12 schools, the issue of assessing student learning of computational concepts remains a thorny one. This paper describes the multiple forms of assessments used in a 6-week middle school curriculum with the goal of capturing a holistic view of student learning. A key aspect of this research is the use of instruments developed and shared in prior research. Included among these were several questions used in an Israeli nationwide exam to test middle school student learning of programming in Scratch. This paper reports on the use of the curriculum in two studies conducted in a public US middle school classroom, and compares performances of these students with those reported by the Israeli Ministry of Education in their large-scale study. It also argues for multiple modes of assessment of computational learning in K-12 settings.
Real Time Mapping and Dynamic Navigation for Mobile Robots

This paper discusses the importance, the complexity and the challenges of mapping mobile robot’s unknown and dynamic environment, besides the role of sensors and the problems inherited in map building. These issues remain largely an open research problems in developing dynamic navigation systems for mobile robots. The paper presenst the state of the art in map building and localization for mobile robots navigating within unknown environment, and then introduces a solution for the complex problem of autonomous map building and maintenance method with focus on developing an incremental grid based mapping technique that is suitable for real-time obstacle detection and avoidance. In this case, the navigation of mobile robots can be treated as a problem of tracking geometric features that occur naturally in the environment of the robot. The robot maps its environment incrementally using the concept of occupancy grids and the fusion of multiple ultrasonic sensory information while wandering in it and stay away from all obstacles. To ensure real-time operation with limited resources, as well as to promote extensibility, the mapping and obstacle avoidance modules are deployed in parallel and distributed framework. Simulation based experiments has been conducted and illustrated to show the validity of the developed mapping and obstacle avoidance approach.