nils-herrmann/clean_scirepeval huggingface dataset:
Teaching Programming in Secondary Education Through Embodied Computing Platforms: Robotics and Wearables

Pedagogy has emphasized that physical representations and tangible interactive objects benefit learning especially for young students. There are many tangible hardware platforms for introducing computer programming to children, but there is limited comparative evaluation of them in the context of a formal classroom. In this work, we explore the benefits of learning to code for tangible computers, such as robots and wearable computers, in comparison to programming for the desktop computer. For this purpose, 36 students participated in a within-groups study that involved three types of target computer platform tangibility: (1) desktop, (2) wearable, and (3) robotic. We employed similar blocks-based visual programming environments, and we measured emotional engagement, attitudes, and computer programming performance. We found that students were more engaged by and had a higher intention of learning programming with the robotic rather than the desktop computer. Furthermore, tangible computing platforms, either robot or wearable, did not affect the students’ performance in learning basic computational concepts (e.g., sequence, repeat, and decision). Our findings suggest that computer programming should be introduced through multiple target platforms (e.g., robots, smartphones, wearables) to engage children.
HapTip: Displaying Haptic Shear Forces at the Fingertips for Multi-Finger Interaction in Virtual Environments

The fingertips are one of the most important and sensitive parts of our body. They are the first stimulated areas of the hand when we interact with our environment. Providing haptic feedback to the fingertips in virtual reality could, thus, drastically improve perception and interaction with virtual environments. In this paper, we present a modular approach called HapTip to display such haptic sensations at the level of the fingertips. This approach relies on a wearable and compact haptic device able to simulate 2 Degree of Freedom (DoF) shear forces on the fingertip with a displacement range of ±2mm. Several modules can be added and used jointly in order to address multi-finger and/or bimanual scenarios in virtual environments. For that purpose, we introduce several haptic rendering techniques to cover different cases of 3D interaction, such as touching a rough virtual surface, or feeling the inertia or weight of a virtual object. In order to illustrate the possibilities offered by HapTip, we provide four use cases focused on touching or grasping virtual objects. To validate the efficiency of our approach, we also conducted experiments to assess the tactile perception obtained with HapTip. Our results show that participants can successfully discriminate the directions of the 2 DoF stimulation of our haptic device. We found also that participants could well perceive different weights of virtual objects simulated using two HapTip devices. We believe that HapTip could be used in numerous applications in virtual reality for which 3D manipulation and tactile sensations are often crucial, such as in virtual prototyping or virtual training.
Flight PID controller design for a UAV quadrotor

This paper presents the modeling of a four rotor vertical take-off and landing (VTOL) unmanned air vehicle known as the quad rotor aircraft. The paper presents a new model design method for the flight control of an autonomous quad rotor. The paper describes the controller architecture for the quad rotor as well. The dynamic model of the quad-rotor, which is an under actuated aircraft with fixed four pitch angle rotors was described. The Modeling of a quad rotor vehicle is not an easy task because of its complex structure. The aim is to develop a model of the vehicle as realistic as possible. The model is used to design a stable and accurate controller. This paper explains the developments of a PID (proportional-integral-derivative) control method to obtain stability in flying the Quad-rotor flying object. The model has four input forces which are basically the thrust provided by each propeller connected to each rotor with fixed angle. Forward (backward) motion is maintained by increasing (decreasing) speed of front (rear) rotor speed while decreasing (increasing) rear (front) rotor speed simultaneously which means changing the pitch angle. Left and right motion is accomplished by changing roll angle by the same way. The front and rear motors rotate counter-clockwise while other motors rotate clockwise so that the yaw command is derived by increasing (decreasing) counter-clockwise motors speed while decreasing (increasing) clockwise motor speeds.
Feature-based attention influences motion processing gain in macaque visual cortex

Changes in neural responses based on spatial attention have been demonstrated in many areas of visual cortex, indicating that the neural correlate of attention is an enhanced response to stimuli at an attended location and reduced responses to stimuli elsewhere. Here we demonstrate non-spatial, feature-based attentional modulation of visual motion processing, and show that attention increases the gain of direction-selective neurons in visual cortical area MT without narrowing the direction-tuning curves. These findings place important constraints on the neural mechanisms of attention and we propose to unify the effects of spatial location, direction of motion and other features of the attended stimuli in a ‘feature similarity gain model’ of attention.
Designing Next Generation Smart City Initiatives - harnessing Findings and Lessons from a Study of Ten Smart City Programs

The proliferation of “Smart Cities” initiatives around the world is part of the strategic response by governments to the challenges and opportunities of increasing urbanization and the rise of cities as the nexus of societal development. As a framework for urban transformation, Smart City initiatives aim to harness Information and Communication Technologies and Knowledge Infrastructures for economic regeneration, social cohesion, better city administration and infrastructure management. However, experiences from earlier Smart City initiatives have revealed several technical, management and governance challenges arising from the inherent nature of a Smart City as a complex “Sociotechnical System of Systems”. While these early lessons are informing modest objectives for planned Smart Cities programs, no rigorous developed framework based on careful analysis of existing initiatives is available to guide policymakers, practitioners, and other Smart City stakeholders. In response to this need, this paper presents a “Smart City Initiative Design (SCID) Framework” grounded in the findings from the analysis of ten major Smart Cities programs from Netherlands, Sweden, Malta, United Arab Emirates, Portugal, Singapore, Brazil, South Korea, China and Japan. The findings provide a design space for the objectives, implementation options, strategies, and the enabling institutional and governance mechanisms for Smart City initiatives.
splab at the NTCIR-13 STC-2 Task

The splab team participated in the Chinese subtask of the NTCIR-13 on Short Text Conversation(STC-2)[9] Task. A large amount of pairs of post-comments are provided as data repository or training set. Given a new post, we need to retrieve suitable responses from the existing comments(retrievalbased method) or generate appropriate replies with the model trained on the data(generation-based method). We adopt the generation-based method and develop several systems based on the encoder-decoder framework. In our systems, we first implement the basic encoder-decoder architecture enhanced by attention mechanism. However this model tends to generate short and dull responses. And to alleviate this problem, we utilize two ways to enrich the information contained in the generated responses. One way is multiresolution recurrent neural network and the other is sequence generation based on word-level external memory. Both methods focus on some keywords in the post so as to generate targeted and informative responses. And we also use a DSSM to rerank the candidates to select the most reasonable responses. The evaluation of submitted results do not correspond with what we have expected and we believe that it reflects the weakness of human subjective evaluation.
Oral squamous cell carcinoma overview.

Most cancer in the head and neck is squamous cell carcinoma (HNSCC) and the majority is oral squamous cell carcinoma (OSCC). This is an overview of oral squamous cell carcinoma (OSCC) highlighting essential points from the contributors to this issue, to set the scene. It emphasises the crucial importance of prevention and the necessarily multidisciplinary approach to the diagnosis and management of patients with OSCC.
VISIBLE: Video-Assisted Keystroke Inference from Tablet Backside Motion

The deep penetration of tablets in daily life has made them attractive targets for keystroke inference attacks that aim to infer a tablet user’s typed inputs. This paper presents VISIBLE, a novel video-assisted keystroke inference framework to infer a tablet user’s typed inputs from surreptitious video recordings of tablet backside motion. VISIBLE is built upon the observation that the keystrokes on different positions of the tablet’s soft keyboard cause its backside to exhibit different motion patterns. VISIBLE uses complex steerable pyramid decomposition to detect and quantify the subtle motion patterns of the tablet backside induced by a user’s keystrokes, differentiates different motion patterns using a multi-class Support Vector Machine, and refines the inference results using a dictionary and linguistic relationship. Extensive experiments demonstrate the high efficacy of VISIBLE for inferring single keys, words, and sentences. In contrast to previous keystroke inference attacks, VISIBLE does not require the attacker to visually see the tablet user’s input process or install any malware on the tablet.
Squat jump training at maximal power loads vs. heavy loads: effect on sprint ability.

Training at a load maximizing power output (Pmax) is an intuitively appealing strategy for enhancement of performance that has received little research attention. In this study we identified each subject's Pmax for an isoinertial resistance training exercise used for testing and training, and then we related the changes in strength to changes in sprint performance. The subjects were 18 well-trained rugby league players randomized to two equal-volume training groups for a 7-week period of squat jump training with heavy loads (80% 1RM) or with individually determined Pmax loads (20.0-43.5% 1RM). Performance measures were 1RM strength, maximal power at 55% of pretraining 1RM, and sprint times for 10 and 30 m. Percent changes were standardized to make magnitude-based inferences. Relationships between changes in these variables were expressed as correlations. Sprint times for 10 m showed improvements in the 80% 1RM group (-2.9 +/- 3.2%) and Pmax group (-1.3 +/- 2.2%), and there were similar improvements in 30-m sprint time (-1.9 +/- 2.8 and -1.2 +/- 2.0%, respectively). Differences in the improvements in sprint time between groups were unclear, but improvement in 1RM strength in the 80% 1RM group (15 +/- 9%) was possibly substantially greater than in the Pmax group (11 +/- 8%). Small-moderate negative correlations between change in 1RM and change in sprint time (r approximately -0.30) in the combined groups provided the only evidence of adaptive associations between strength and power outputs, and sprint performance. In conclusion, it seems that training at the load that maximizes individual peak power output for this exercise with a sample of professional team sport athletes was no more effective for improving sprint ability than training at heavy loads, and the changes in power output were not usefully related to changes in sprint ability.
A DC hybrid circuit breaker with ultra-fast contact opening and integrated gate-commutated thyristors (IGCTs)

Air-magnetic circuit breakers are widely used for short-circuit protection in dc electric railways or industrial plants. The well-known drawbacks of these breakers are mainly a slow breaking action, a short lifetime, and high maintenance costs due to the destructive effects of the arc. The use of power semiconductors in a full-static circuit breaker configuration allows the elimination of these disadvantages but is limited by excessive conduction losses. The paper will present the study of a so-called hybrid breaker, drawing its name from the combination of a high-speed mechanical switch and a bi-directional integrated gate-controlled thyristor assembly connected in parallel. The paper also includes experimental results from a prototype real scale.
met*: A Method for Discriminating Matonymy and Metaphor by Computer

The met* method distinguishes selected examples of metonymy from metaphor and from literalness and anomaly in short English sentences. In the met* method, literalness is distinguished because it satisfies contextual constraints that the nonliteral others all violate. Metonymy is discriminated from metaphor and anomaly in a way that  supports Lakoff and Johnson's (1980) view that in metonymy one entity stands for another whereas in metaphor one entity is viewed as another,  permits chains of metonymies (Reddy 1979), and [3] allows metonymies to co-occur with instances of either literalness, metaphor, or anomaly. Metaphor is distinguished from anomaly because the former contains a relevant analogy, unlike the latter. The met* method is part of Collative Semantics, a semantics for natural language processing, and has been implemented in a computer program called meta5. Some examples of meta5's analysis of metaphor and metonymy are given. The met* method is compared with approaches from artificial intelligence, linguistics, philosophy, and psychology.
Oscillator-based assistance of cyclical movements: model-based and model-free approaches

In this article, we propose a new method for providing assistance during cyclical movements. This method is trajectory-free, in the sense that it provides user assistance irrespective of the performed movement, and requires no other sensing than the assisting robot’s own encoders. The approach is based on adaptive oscillators, i.e., mathematical tools that are capable of learning the high level features (frequency, envelope, etc.) of a periodic input signal. Here we present two experiments that we recently conducted to validate our approach: a simple sinusoidal movement of the elbow, that we designed as a proof-of-concept, and a walking experiment. In both cases, we collected evidence illustrating that our approach indeed assisted healthy subjects during movement execution. Owing to the intrinsic periodicity of daily life movements involving the lower-limbs, we postulate that our approach holds promise for the design of innovative rehabilitation and assistance protocols for the lower-limb, requiring little to no user-specific calibration.
Social Incentive Optimization in Online Social Networks

Most online social networks provide a mechanism for users to broadcast messages to their personalized network through actions like shares, likes and tweets. Receiving positive feedback from the network such as likes, comments and retweets in response to such actions can provide a strong incentive for users to broadcast more often in the future. We call such feedback by the network, that influences a user to perform certain desirable future actions, social incentives. For example, after a user shares an article to her social network, receiving positive feedback such as a ''like'' from a friend can potentially encourage her to continue sharing more regularly. Typically, for every user's visit to an online social network site, good messages need to be ranked and selected by a recommender system from a large set of candidate messages (broadcasted by the user's network). In this paper, we propose a novel recommendation problem: How should we recommend messages to users to incentivize neighbors in their personal network to perform desirable actions in the future with high likelihood, without significantly hurting overall engagement for the entire system? For instance, messages could be content shared by neighbors. The goal in this case would be to encourage more content shares in the future. We call this problem social incentive optimization and study an instance of it for LinkedIn's news feed. We observe that a user who receives positive social feedback from neighbors has a higher likelihood of broadcasting more frequently. Using this observation, we develop a novel recommendation framework that incentivize users to broadcast more often, without significantly hurting overall feed engagement. We demonstrate the effectiveness of our approach through causal analysis on retrospective data and online A/B experiments.
Beyond engagement analytics: which online mixed-data factors predict student learning outcomes?

This mixed-method study focuses on online learning analytics, a research area of importance. Several important student attributes and their online activities are examined to identify what seems to work best to predict higher grades. The purpose is to explore the relationships between student grade and key learning engagement factors using a large sample from an online undergraduate business course at an accredited American university (n = 228). Recent studies have discounted the ability to predict student learning outcomes from big data analytics but a few significant indicators have been found by some researchers. Current studies tend to use quantitative factors in learning analytics to forecast outcomes. This study extends that work by testing the common quantitative predictors of learning outcome, but qualitative data is also examined to triangulate the evidence. Pre and post testing of information technology understanding is done at the beginning of the course. First quantitative data is collected, and depending on the hypothesis test results, qualitative data is collected and analyzed with text analytics to uncover patterns. Moodle engagement analytics indicators are tested as predictors in the model. Data is also taken from the Moodle system logs. Qualitative data is collected from student reflection essays. The result was a significant General Linear Model with four online interaction predictors that captured 77.5 % of grade variance in an undergraduate business course.
Continuous affect prediction using eye gaze and speech

Affective computing research traditionally focused on labeling a person's emotion as one of a discrete number of classes e.g. happy or sad. In recent times, more attention has been given to continuous affect prediction across dimensions in the emotional space, e.g. arousal and valence. Continuous affect prediction is the task of predicting a numerical value for different emotion dimensions. The application of continuous affect prediction is powerful in domains involving real-time audio-visual communications which could include remote or assistive technologies for psychological assessment of subjects. Modalities used for continuous affect prediction may include speech, facial expressions and physiological responses. As opposed to single modality analysis, the research community have combined multiple modalities to improve the accuracy of continuous affect prediction. In this context, this paper investigates a continuous affect prediction system using the novel combination of speech and eye gaze. A new eye gaze feature set is proposed. This novel approach uses open source software for real-time affect prediction in audio-visual communication environments. A unique advantage of the human-computer interface used here is that it does not require the subject to wear specialized and expensive eye-tracking headsets or intrusive devices. The results indicate that the combination of speech and eye gaze improves arousal prediction by 3.5% and valence prediction by 19.5% compared to using speech alone.
IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems

The Internet of Things (IoT) is entering the daily operation of many industries; applications include but are not limited to smart cities, smart grids, smart homes, physical security, e-health, asset management, and logistics. For example, the concept of smart cities is emerging in multiple continents, where enhanced street lighting controls, infrastructure monitoring, public safety and surveillance, physical security, gunshot detection, meter reading, and transportation analysis and optimization systems are being deployed on a city-wide scale. A related and cost-effective user-level IoT application is the support of IoT-enabled smart buildings. Commercial space has substantial requirements in terms of comfort, usability, security, and energy management. IoT-based systems can support these requirements in an organic manner. In particular, power over Ethernet, as part of an IoT-based solution, offers disruptive opportunities in revolutionizing the in-building connectivity of a large swath of devices. However, a number of deployment-limiting issues currently impact the scope of IoT utilization, including lack of comprehensive end-to-end standards, fragmented cybersecurity solutions, and a relative dearth of fully-developed vertical applications. This paper reviews some of the technical opportunities offered and the technical challenges faced by the IoT in the smart building arena.
Neural networks and principal component analysis: Learning from examples without local minima

We consider the problem of learning from examples in layered linear feed-forward neural networks using optimization methods, such as back propagation, with respect to the usual quadratic error function E of the connection weights. Our main result is a complete description of the landscape attached to E in terms of principal component analysis. We show that E has a unique minimum corresponding to the projection onto the subspace generated by the first principal vectors of a covariance matrix associated with the training patterns. All the additional critical points of E are saddle points (corresponding to projections onto subspaces generated by higher order vectors). The auto-associative case is examined in detail. Extensions and implications for the learning algorithms are discussed.
IT 4 BI Master Thesis Representing ETL Flows with BPMN 2 . 0

Extract, Transform and Load (ETL) processes are widely used in Data Warehousing in order to extract, cleanse and load data into a centralized location for better analysis and decision-making. As users become more demanding for on-line decision making, ETL processes grow large and more complex. Most processes are deployed at the physical level without any abstraction, thus costs of maintenance and efforts for reuse are considerable. Therefore, having logical and conceptual abstractions of ETL processes makes such tasks substantially easier. In this thesis, given a logical ETL representation, we provide an algorithm that automatically translates logical ETL flows into their BPMN representation. To achieve this goal, we create a dictionary that defines simple and composite ETL flow patterns and their corresponding BPMN elements. The pattern dictionary follows a formalized grammar and can be further extended with additional ETL flow patterns. As a result, we can produce conceptual ETL flows in BPMN 2.0 format that can be further edited by the business user. The patterns defined in the dictionary help to move away from technical details and complexity of the ETL flows and make the output model semantics more intuitive and understandable for the business users, as shown during the approach validation.