nils-herrmann/clean_scirepeval huggingface dataset:
Ontology-based information extraction for subject-focussed automatic essay evaluation

Automatic essay evaluation (AEE) systems are designed to assist a teacher in the task of classroom assessment in order to alleviate the demands of manual subject evaluation. However, although numerous AEE systems are available, most of these systems do not use elaborate domain knowledge for evaluation, which limits their ability to give informative feedback to students and also their ability to constructively grade a student based on a particular domain of study. This paper is aimed at improving on the achievements of previous studies by providing a subject-focussed evaluation system that considers the domain knowledge while scoring and provides informative feedback to its user. The study employs a combination of techniques such as system design and modelling using Unified Modelling Language (UML), information extraction, ontology development, data management, and semantic matching in order to develop a prototype subject-focussed AEE system. The developed system was evaluated to determine its level of performance and usability. The result of the usability evaluation showed that the system has an overall mean rating of 4.17 out of maximum of 5, which indicates ‘good usability’. In terms of performance, the assessment done by the system was also found to have sufficiently high correlation with those done by domain experts, in addition to providing appropriate feedback to the user.
Plantar fasciitis in athletes: diagnostic and treatment strategies. A systematic review.

Plantar fasciitis (PF) is reported in different sports mainly in running and soccer athletes. Purpose of this study is to conduct a systematic review of published literature concerning the diagnosis and treatment of PF in both recreational and élite athletes. The review was conducted and reported in accordance with the PRISMA statement.



The following electronic databases were searched: PubMed, Cochrane Library and Scopus. As far as PF diagnosis, we investigated the electronic databases from January 2006 to June 2016, whereas in considering treatments all data in literature were investigated.



For both diagnosis and treatment, 17 studies matched inclusion criteria. The results have highlighted that the most frequently used diagnostic techniques were Ultrasonography and Magnetic Resonance Imaging. Conventional, complementary, and alternative treatment approaches were assessed.



In reviewing literature, we were unable to find any specific diagnostic algorithm for PF in athletes, due to the fact that no different diagnostic strategies were used for athletes and non-athletes. As for treatment, a few literature data are available and it makes difficult to suggest practice guidelines. Specific studies are necessary to define the best treatment algorithm for both recreational and élite athletes.
Capsule Networks for Protein Structure Classification and Prediction

Capsule Networks have great potential to tackle problems in structural biology because of their aention to hierarchical relationships. is paper describes the implementation and application of a Capsule Network architecture to the classication of RAS protein family structures on GPU-based computational resources. e proposed Capsule Network trained on 2D and 3D structural encodings can successfully classify HRAS and KRAS structures. e Capsule Network can also classify a protein-based dataset derived from a PSI-BLAST search on sequences of KRAS and HRAS mutations. Our results show an accuracy improvement compared to traditional convolutional networks, while improving interpretability through visualization of activation vectors.
AI and HCI: Two Fields Divided by a Common Focus

48 AI MAGAZINE AI and human-computer interaction (HCI) are converging. “Usable AI” conference events in 2008 and 2009 preceded this special issue, and ACM will launch a widely-supported Transactions on Interactive Intelligent Systems. AI techniques are in the toolset of more and more HCI researchers, and applications of machine learning are increasingly visible in the HCI literature. Other maturing AI technologies seek input from the HCI community. The two fields have met under shared tents for some time, notably within International Journal of Man-Machine Studies (subsequently International Journal of Human-Computer Studies) and at the Intelligent User Interface conferences cosponsored by ACM’s Special Interest Groups on Computer-Human Interaction (SIGCHI) and Artificial Intelligence (SIGART). But little of this research has flowed back to the major AI and HCI conferences and journals. In this article, I describe some research that has bridged the fields, but contact has been sporadic. Logically, they could have been closer. Both explore the nexus of computing and intelligent behavior. Both claim Allen Newell and Herb Simon as founding figures. Working over the years as an HCI person in AI groups at Wang Laboratories, MIT, MCC, and Microsoft, and alongside AI faculty at Aarhus University and the University of California, Irvine, I was puzzled by the separation. The introduction to this special issue notes the different “monocular views” of interaction with intelligent systems. AI focused on devising better algorithms, HCI on how to improve the use of existing algorithms. AI originated in mathematics and engineering, HCI in psychology. But half a century is enough time to spawn a hybrid or synthesis had forces not pushed the two fields apart.
Analysis and research on the images of drivers and passengers wearing seat belt in traffic inspection

It is common to see drivers and passengers not wearing seat belt in China, which is a traffic violation, thereby to strengthen supervision by resorting to advanced technical means is imminent. In the existing research, the detection effect is favorable when there is visible difference between the seat belt and the background image. However, in the opposing situation, it can easily lead to missed detection or mis-detection, and greatly reducing the detection accuracy and making it difficult to meet the demands of intelligent and automatic traffic monitoring. Study on the images of drivers and passengers wearing seat belt in traffic inspection covers three aspects: vehicle target positioning, driver and passenger area positioning and seat belt wearing behavior decision. Vehicle target positioning studies the enhanced vehicle features and looks for a vehicle classification model that is suitable for enhancing the features, and obtains the determined vehicle area from the study detection. Driver and passenger area positioning studies a multi-level joint supervised learning method according to the inclusive relationship between the non-rigid human body and the rigid vehicle body, and gets the optimal driver and passenger area by designing the multi-level learning classifier. Seat belt wearing behavior decision studies from the method of linear target recognition and uses the perceptual grouping mechanism of human visual system for robust seat belt detection. In the experimental phase, we validated the validity and accuracy, and the experiment images were from the practical traffic junctions. The results show that the method proposed in this paper can carry out Seat Belt detection accurately and effectively. Compared with other Seat Belt target detection methods, it has high detection rate and low false positive rate, which can achieve the accurate detection of Seat Belt in intelligent transportation.
Modeling of Stepped-Impedance Slot Antenna

In this study, the resonant analysis and modeling of the microstrip-fed stepped-impedance (SI) slot antenna are presented by utilizing the transmission-line and lumped-element circuit topologies. This study analyzes the SI-slot antenna and systematically summarizes its frequency response characteristics, such as the resonance condition, spurious response, and equivalent circuit. Design formulas with respect to the impedance ratio of the SI slot antenna were analytically derived. The antenna designers can predict the resonant modes of the SI slot antenna without utilizing expensive EM-simulation software.
Business Model of Botnets

Botnets continue to be an active threat against institutions and individuals worldwide. Previous research regarding botnets has unveiled information on how the system and their stakeholders operate, but an insight on the economic structure behind these stakeholders is lacking. The objective of this research is to build the business model and determine the structure of the underground botnet economy. This means determining the botnet life-cycle, revenue streams and overall economic impact on institutions and stakeholders. Compared to other botnet related research, this paper focuses on the financial aspects, breaking down the components of the botnet life-cycle and estimating the money flow to the different actors involved. What can be concluded is that building a full scale cyber army from scratch can only be done by large institutions or governments, as it is too costly. In contrast, by outsourcing different tasks and making use of existing malware packages, costs are reduced to a minimum and reachable for the average person. Applying this method to earlier researched botnets, in every case the botnet resulted in being profitable for the botmaster. Initial setupand monthly costs were minimal compared to total revenue.
A performance evaluation of Apache Kafka in support of big data streaming applications

Stream computing is becoming a more and more popular paradigm as it enables the real-time promise of data analytics. Apache Kafka is currently the most popular framework used to ingest the data streams into the processing platforms. However, how to tune Kafka and how much resources to allocate for it remains a challenge for most users, who now rely mainly on empirical approaches to determine the best parameter settings for their deployments. In this poster, we make a through evaluation of several configurations and performance metrics of Kafka in order to allow users avoid bottlenecks, reach its full potential and avoid bottlenecks and eventually leverage some good practice for efficient stream processing.
Experimental characterization of indoor visible light communication channels

The impulse response and frequency response of indoor visible light communication diffuse channels are characterized experimentally in this paper. Both the short pulse technique and frequency sweep technique are adopted for experimental investigation. The iterative site-based modeling is also carried out to simulate the channel impulse response, and good conformity is observed between the experimental and simulation results. Finally, the impact of receiver pointing angles and field of view on the channel 3dB bandwidth is investigated.
Comparison of passive cell balancing and active cell balancing for automotive batteries

This paper presents a quantitative performance analysis of a conventional passive cell balancing method and a proposed active cell balancing method for automotive batteries. The proposed active cell balancing method was designed to perform continuous cell balancing during charge and discharge with high balancing current. An experimentally validated model was used to simulate the balancing process of both balancing circuits for a high capacity battery module. The results suggest that the proposed method can improve the power loss and extend the discharge time of a battery module. Hence, a higher energy output can be yielded.
Fast Embedded Software Hashing

We present new software speed records for several popular hash functions on low-end 8-bit AVR microcontrollers. Target algorithms include widely deployed hash functions like SHA-1 and SHA-256 as well as the SHA-3 (second round) candidates Blake-32 and Skein-256. A significant aspect of our implementations is that they reduce the overall resource requirements, improving not only execution time but also RAM footprint and sometimes ROM/Flash memory footprint at the same time, providing the best memory/performance trade-offs reported so far. We believe that our results will shed new light on the ongoing SHA-3 competition, and be helpful for the final stage of the competition.
On-line calibration of partial discharge monitoring for power cable by HFCT method

Partial discharge (PD) detection is an effective method for finding insulation defects in HV and EHV power cables. PD apparent charge is typically expressed in picocoulombs (pC) when the calibration procedure defined in IEC 60270 is applied during off-line tests. During on-line PD detection, measured signals are usually denoted in mV or dB without transforming the measured signal into a charge quantity. For AC XLPE power cable systems, on-line PD detection is conducted primarily with the use of high frequency current transformer (HFCT). The HFCT is clamped around the cross-bonding link of the joint or the grounding wire of termination. In on-line occasion, PD calibration is impossible from the termination. A novel on-line calibration method using HFCT is introduced in this paper. To eliminate the influence of cross-bonding links, the interrupted cable sheath at the joint was reconnected via the high-pass C-arm connector. The calibration signal was injected into the cable system via inductive coupling through the cable sheath. The distributed transmission line equivalent circuit of the cable was used in consideration of the signal attenuation. Both the conventional terminal calibration method and the proposed on-line calibration method were performed on the coaxial cable model loop for experimental verification. The amplitude and polarity of signals that propagate in the cable sheath and the conductor were evaluated. The results indicate that the proposed method can calibrate the measured signal during power cable on-line PD detection.
Combining model-based policy search with online model learning for control of physical humanoids

We present an automatic method for interactive control of physical humanoid robots based on high-level tasks that does not require manual specification of motion trajectories or specially-designed control policies. The method is based on the combination of a model-based policy that is trained off-line in simulation and sends high-level commands to a model-free controller that executes these commands on the physical robot. This low-level controller simultaneously learns and adapts a local model of dynamics on-line and computes optimal controls under the learned model. The high-level policy is trained using a combination of trajectory optimization and neural network learning, while considering physical limitations such as limited sensors and communication delays. The entire system runs in real-time on the robot's computer and uses only on-board sensors. We demonstrate successful policy execution on a range of tasks such as leaning, hand reaching, and robust balancing behaviors atop a tilting base on the physical robot and in simulation.
The Manufacturing Knowledge Repository - Consolidating Knowledge to Enable Holistic Process Knowledge Management in Manufacturing

The manufacturing industry is faced with strong competition making the companies’ knowledge resources and their systematic management a critical success factor. Yet, existing concepts for the management of process knowledge in manufacturing are characterized by major shortcomings. Particularly, they are either exclusively based on structured knowledge, e. g., formal rules, or on unstructured knowledge, such as documents, and they focus on isolated aspects of manufacturing processes. To address these issues, we present the Manufacturing Knowledge Repository, a holistic repository that consolidates structured and unstructured process knowledge to facilitate knowledge management and process optimization in manufacturing. First, we define requirements, especially the types of knowledge to be handled, e. g., data mining models and text documents. On this basis, we develop a conceptual repository data model associating knowledge items and process components such as machines and process steps. Furthermore, we discuss implementation issues including storage architecture variants and finally present both an evaluation of the data model and a proof of concept based on a prototypical implementation in a case example.
A unified model for high-power, air-core toroidal PCB inductors

In high-frequency and very-high-frequency power converters, which operate with switching frequencies between 3 and 300 MHz, efficient power inductors cannot be constructed with commercially available core materials. In this regime, air-core inductors are preferred, and the toroidal geometry best constrains the magnetic field. These inductors have been implemented as printed circuit board (PCB) traces. Here, we present the first unified electrical model for high-power, air-core toroidal inductors implemented as PCB traces, which includes closed-form calculations for the lumped capacitance, resistance, and inductance of the structure. In this regime, the self-resonant frequency (SRF) of the inductor may be of the same order of magnitude as the circuit operating frequency, so the capacitance must be carefully modeled and tightly controlled. The lumped capacitance is calculated by assuming that the impedance between two turns is well approximated by the characteristic impedance of a microstrip geometry. We fabricate inductors with inductances ranging from 44 nH to 10 μH and PCB heights of 1 mm, 1.6 mm, 2 mm, 3.2 mm, and 6.4 mm. For all inductors from 44 nH to 2 μH, the SRF is predicted within 10% and the modeled capacitance is within 1 pF of the measured value.
Practical clickjacking with BeEF

A lot of effort has been put into researching client-side attacks, including vulnerabilities like cross-site scripting, cross-site request forgery, and more recently, clickjacking. Similar to other client-side attacks, a clickjacking vulnerability can use the browser to exploit weaknesses in cross domain isolation and the same origin policy. It does this by tricking the user to click on something that is actually not what the user perceives they are clicking on. In the most extreme cases, this vulnerability can cause an unsuspecting user to have their account compromised with a single click. Although there are protections available for clickjacking, the web applications implementing these mitigations are far and in between. Additionally, although the possibility for an attacker to frame a page is easy to detect, it is much more difficult to demonstrate or assess the impact of a clickjacking vulnerability than more traditional client-side vectors. Tools do not currently exist to reliably demonstrate clickjacking exploitation, and the rare demonstrations that are done typically use custom JavaScript and HTML for each individual vulnerability. Worse, many times this esoteric code is never made public, leaving everyone to rewrite their own from scratch. BeEF, known as the Browser Exploitation Framework, is a tool designed to help professional penetration testers easily demonstrate the impact of client-side security vulnerabilities. In this paper, we present a plugin module for BeEF which provides a way for penetration testers to easily demonstrate the impact of clickjacking vulnerabilities.
A Convolutional Neural Network in Legal Question Answering

Our legal question answering system combines legal information retrieval and textual entailment, and we propose a legal question answering system that exploits a deep convolutional neural network. We have evaluated our system using the training data from the competition on legal information extraction/entailment (COLIEE). The competition focuses on the legal information processing related to answering yes/no questions from Japanese legal bar exams, and it consists of two phases: legal ad-hoc information retrieval, and textual entailment. Phase 1 requires the identification of Japan civil law articles relevant to a legal bar exam query. For that phase, we have implemented TF-IDF and Ranking SVM. Phase 2 is to answer “Yes” or “No” to previously unseen queries, by comparing the meanings of queries with relevant articles. Our choice of features used for Phase 2 focuses on word embeddings, syntactic similarities and identification of negation/antonym relations. Phase 2 is a textual entailment problem, and we use a convolutional neural network with dropout regularization and Rectified Linear Units. To our knowledge, our study is the first approach adopting deep learning in the textual entailment field. Experimental evaluation demonstrates the effectiveness of the convolutional neural network and dropout regularization. The results show that our deep learning-based method outperforms the SVM-based supervised model.
Blockade of the ganglion impar (walther), using ultrasound and a loss of resistance technique.

The ganglion impar is an unpaired sympathetic structure located at             the level of the sacrococcygeal joint. Blockade of this structure has been utilised             to treat chronic perineal pain. Methods to achieve this block often involve the             use of fluoroscopy which is associated with radiation exposure of staff involved             in providing these procedures. We report a combined loss of resistance injection             technique in association with ultrasound guidance to achieve the block. Ultrasound             was used to identify the sacrococcygeal joint and a needle was shown to enter this             region. Loss of resistance was then used to demonstrate that the needle tip lies in             a presacral space. The implication being that any injectate would be located in an             adequate position. The potential exception would be a neurodestructive procedure             as radiographic control of needle tip in relation to the rectum should be performed             and recorded. However when aiming for a diagnostic or local anaesthetic based             treatment option we feel that this may become an accepted method.
Lorenzo Magnani: Abductive Cognition: The Epistemological and Eco-Cognitive Dimensions of Hypothetical Reasoning

My aim is to combine philosophical, logical, cognitive, eco-cognitive, neurological, and computational issues, while also discussing some cases of reasoning in everyday settings, in expert inferences, and in science. The main thesis is that abduction is a basic kind of human cognition, not only helpful in delineating the first principles of a new theory of science, but also extremely useful in the unification of interdisciplinary perspectives, which would otherwise remain fragmented and dispersed, and thus devoid of the necessary philosophical analysis.