nils-herrmann/clean_scirepeval huggingface dataset:
The relationship among young adult college students' depression, anxiety, stress, demographics, life satisfaction, and coping styles.

Recent research indicates that young adult college students experience increased levels of depression, anxiety, and stress. It is less clear what strategies college health care providers might use to assist students in decreasing these mental health concerns. In this paper, we examine the relative importance of coping style, life satisfaction, and selected demographics in predicting undergraduates' depression, anxiety, and stress. A total of 508 full-time undergraduate students aged 18-24 years completed the study measures and a short demographics information questionnaire. Coping strategies and life satisfaction were assessed using the Brief COPE Inventory and an adapted version of the Brief Students' Multidimensional Life Satisfaction Scale. Depression, anxiety, and stress were measured using the Depression Anxiety and Stress Scale-21 (DASS-21). Multiple regression analyses were used to examine the relative influence of each of the independent variables on depression, anxiety, and stress. Maladaptive coping was the main predictor of depression, anxiety, and stress. Adaptive coping was not a significant predictor of any of the three outcome variables. Reducing maladaptive coping behaviors may have the most positive impact on reducing depression, anxiety, and stress in this population.
Table-to-Text Generation by Structure-Aware Seq2seq Learning

Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the WIKIBIO dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on https://github.com/tyliupku/wiki2bio.
Union simulation on lower limbs rehabilitation robot based on MATLAB and ADAMS

For people who suffers the lower limbs motor dysfunction, using lower limbs exoskeleton rehabilitation devices is a very effective method to restore the motor ability of lower limbs. However, it works only if the device has a suitable mechanical structure and appropriate design parameters for the specific individual. To get this work completed, we carry out the modeling and simulation of a new proposed lower limbs rehabilitation robot with joint help of two kinds of software, i.e. the MATLAB and the ADAMS. Firstly, the three-dimensional model of the robot is created with the SolidWorks software. After importing the model into ADAMS, some essential kinematic pairs are added into the movable joints. Meanwhile, the control system model is established using the MATLAB/SIMULINK software. Then, the union simulation of the lower limbs rehabilitation robot is carried out by using the interface modular ADAMS/CONTROL. The results show that the proposed rehabilitation robot has a good kinematic performance, which means that the device can be used in the actual rehabilitation process. Besides, the results also indicate that the control method is of good trajectory tracking ability. These simulated results will be significant for the development of physical prototype of lower limbs rehabilitation robot.
Hierarchies of octrees for efficient 3D mapping

In this paper, we present a novel multi-resolution approach to efficiently mapping 3D environments. Our representation models the environment as a hierarchy of probabilistic 3D maps, in which each submap is updated and transformed individually. In addition to the formal description of the approach, we present an implementation for tabletop manipulation tasks and an information-driven exploration algorithm for autonomously building a hierarchical map from sensor data. We evaluate our approach using real-world as well as simulated data. The results demonstrate that our method is able to efficiently represent 3D environments at high levels of detail. Compared to a monolithic approach, our maps can be generated significantly faster while requiring significantly less memory.
Plot Units and Narrative Summarization

In order to summarize a story, i t is necessary to access a high level analysis of the story that highlights its central concepts. A technique of memory representation based on plot units appears to provide a rich foundation for such an analysis. Plot units are conceptual structures that overlap with each other when a narrative is cohesive. When overlapping intersections between plot units are interpreted as arcs in a graph of plot units, the resulting graph encodes the plot of the story. Structural features of the graph then reveal which concepts are central to the story, and which concepts are peripheral. Plot unit analysis is currently being investigated as a processing strategy for narrative summarization by both computer simulotion ond psychological experiments.
A Loop-Free Extended Bellman-Ford Routing Protocol Without Bouncing Effect

Distributed algorithms for shortest-path problems are important in the context of routing in computer communication networks. We present a protocol that maintains the shortest-path routes in a dynamic topology, that is, in an environment where links and nodes can fail and recover at arbitrary times. The novelty of this protocol is that it avoids the bouncing effect and the looping problem that occur in the previous approaches of the distributed implementation of Bellman-Ford algorithm. The bouncing effect refers to the very long duration for convergence when failures happen or weights increase, and the nonterminating exchanges of messages, or counting-to-infinity behavior, in disconnected components of the network resulting from failures. The looping problems cause data packets to circulate and, thus, waste bandwidth. These undesirable effects are avoided without any increase in the overall message complexity of previous approaches required in the connected part of the network. The time complexity is better than the distributed Bellman-Ford algorithm encountering failures. The key idea in the implementation is to maintain only loop-free paths, and search for the shortest path only from this set.
Software-Defined Wireless Networking Opportunities and Challenges for Internet-of-Things: A Review

With the emergence of Internet-of-Things (IoT), there is now growing interest to simplify wireless network controls. This is a very challenging task, comprising information acquisition, information analysis, decision-making, and action implementation on large scale IoT networks. Resulting in research to explore the integration of software-defined networking (SDN) and IoT for a simpler, easier, and strain less network control. SDN is a promising novel paradigm shift which has the capability to enable a simplified and robust programmable wireless network serving an array of physical objects and applications. This paper starts with the emergence of SDN and then highlights recent significant developments in the wireless and optical domains with the aim of integrating SDN and IoT. Challenges in SDN and IoT integration are also discussed from both security and scalability perspectives.
Extensible records in Idris

Extensible records are records structures that can be dynamically extended with new fields. In some languages, extensible records are supported as a primitive, in others they are implemented as a user library, each alternative with its benefits and drawbacks. This paper presents a library to strongly-typed extensible records in Idris, a functional programming language with dependent types. Like HList, a Haskell library for extensible records, we use heterogeneous lists to represent our records, but now exploiting the power of dependent types. We show the benefits of our solution by means of examples.
Distribution Free Prediction Bands

We study distribution free, nonparametric prediction bands with a special focus on their finite sample behavior. First we investigate and develop different notions of finite sample coverage guarantees. Then we give a new prediction band estimator by combining the idea of “conformal prediction” (Vovk et al., 2009) with nonparametric conditional density estimation. The proposed estimator, called COPS (Conformal Optimized Prediction Set), always has finite sample guarantee in a stronger sense than the original conformal prediction estimator. Under regularity conditions the estimator converges to an oracle band at a minimax optimal rate. A fast approximation algorithm and a data driven method for selecting the bandwidth are developed. The method is illustrated first in simulated data. Then, an application shows that the proposed method gives desirable prediction intervals in an automatic way, as compared to the classical linear regression modeling.
Ontology-Driven Conceptual Design of ETL Processes Using Graph Transformations

One of the main tasks during the early steps of a data warehouse project is the identification of the appropriate transformations and the specification of inter-schema mappings from the source to the target data stores. This is a challenging task, requiring firstly the semantic and secondly the structural reconciliation of the information provided by the available sources. This task is a part of the Extract-Transform-Load (ETL) process, which is responsible for the population of the data warehouse. In this paper, we propose a customizable and extensible ontologydriven approach for the conceptual design of ETL processes. A graphbased representation is used as a conceptual model for the source and target data stores. We then present a method for devising flows of ETL operations by means of graph transformations. In particular, the operations comprising the ETL process are derived through graph transformation rules, the choice and applicability of which are determined by the semantics of the data with respect to an attached domain ontology. Finally, we present our experimental findings that demonstrate the applicability of our approach.
A NOVEL WIDEBAND ANTENNA ARRAY WITH TIGHTLY COUPLED OCTAGONAL RING ELEMENTS

A novel phased array antenna with wide bandwidth and wide scan angle is presented. The radiating aperture of the phased array consists of periodically and closely spaced octagonal ring elements. Tight capacitive coupling between adjacent elements is realized by interdigitating the end portions of the ring elements. To improve the impedance matching of the individual antenna elements over wide frequency band, a novel impedance matching layer consists of periodic octagonal ring element is subtly designed and placed over the radiating aperture. Both of the radiating elements and impedance matching layer are printed on a flexible membrane substrate with a thickness of 0.04mm. Measured results of a 16-element linear array demonstrate that good impedance matching over a 4.4 : 1 bandwidth can be obtained for beam scan angles within ±45◦ from broadside. As compared to conventional wideband phased array such as tapered slot antenna array, the proposed phased array has the features such as low cost, low profile, light weight, and ease of fabrication.
An Interview Reflection on “Intelligent Tutoring Goes to School in the Big City”

Our 1997 article in IJAIED reported on a study that showed that a new algebra curriculum with an embedded intelligent tutoring system (the Algebra Cognitive Tutor) dramatically enhanced high-school students’ learning. The main motivation for the study was to demonstrate that intelligent tutors that have cognitive science research embedded in them could have real impact in schools. This study was one of the first large-scale classroom evaluations of the integrated use of an Intelligent Tutoring System (ITS) in high schools. A core challenge was figuring out how to embed this new technology into a curriculum and into the existing social context of schools. A key element of the study design was to include multiple kinds of assessments, including standardized test items and items measuring complex problem solving and use of representations. The results were powerful: “On average the 470 students in experimental classes outperformed students in comparison classes by 15 % on standardized tests and 100 % on tests targeting the [course] objectives.” We suggested that the study was evidence “that laboratory tutoring systems can be scaled up and made to work, both technically and pedagogically, in real and unforgiving settings like urban high schools.” Since this study, many more classroom studies comparing instruction that includes an ITS against business as usual have been conducted, often showing advantages for the ITS-enhanced curricula. More rigorous randomized field trials are now more commonplace, but the approach of using multiple assessments in large-scale randomized field trials has not caught on. Cognitive task analysis will remain fundamental to the success of ITSs. A key remaining question for ITS is to find out how they can be used most effectively to support open-ended problem solving, either online or offline. Given all the recent excitement around Massive Open Online Courses (MOOCs), it is interesting to note that our field of Artificial Intelligence in Education has been making huge, less recognized, progress with impact on millions of students and with the majority of those students finishing the course!
Know Your Body Through Intrinsic Goals

The first "object" that newborn children play with is their own body. This activity allows them to autonomously form a sensorimotor map of their own body and a repertoire of actions supporting future cognitive and motor development. Here we propose the theoretical hypothesis, operationalized as a computational model, that this acquisition of body knowledge is not guided by random motor-babbling, but rather by autonomously generated goals formed on the basis of intrinsic motivations. Motor exploration leads the agent to discover and form representations of the possible sensory events it can cause with its own actions. When the agent realizes the possibility of improving the competence to re-activate those representations, it is intrinsically motivated to select and pursue them as goals. The model is based on four components: (1) a self-organizing neural network, modulated by competence-based intrinsic motivations, that acquires abstract representations of experienced sensory (touch) changes; (2) a selector that selects the goal to pursue, and the motor resources to train to pursue it, on the basis of competence improvement; (3) an echo-state neural network that controls and learns, through goal-accomplishment and competence, the agent's motor skills; (4) a predictor of the accomplishment of the selected goals generating the competence-based intrinsic motivation signals. The model is tested as the controller of a simulated simple planar robot composed of a torso and two kinematic 3-DoF 2D arms. The robot explores its body covered by touch sensors by moving its arms. The results, which might be used to guide future empirical experiments, show how the system converges to goals and motor skills allowing it to touch the different parts of own body and how the morphology of the body affects the formed goals. The convergence is strongly dependent on competence-based intrinsic motivations affecting not only skill learning and the selection of formed goals, but also the formation of the goal representations themselves.
Can virtual reality simulations be used as a research tool to study empathy, problems solving and perspective taking of educators?: theory, method and application

Simulations in virtual environments are becoming an important research tool for educators. These simulations can be used in a variety of areas from the study of emotions and psychological disorders to more effective training. The current study uses animated narrative vignette simulation technology to observe a classroom situation depicting a low level aggressive peer-to-peer victim dyad. Participants were asked to respond to this situation as if they were the teacher, and these responses were then coded and analyzed. Consistent with other literature, the pre-service teachers expressed very little empathic concern, problem-solving or management of the situation with the victim. Future direction and educational implications are presented.
Kinesthetics eXtreme: An External Infrastructure for Monitoring Distributed Legacy Systems

Autonomic computing – self-configuring, self-healing, self-optimizing applications, systems and networks – is widely believed to be a promising solution to everincreasing system complexity and the spiraling costs of human system management as systems scale to global proportions. Most results to date, however, suggest ways to architect new software constructed from the ground up as autonomic systems, whereas in the real world organizations continue to use stovepipe legacy systems and/or build “systems of systems” that draw from a gamut of new and legacy components involving disparate technologies from numerous vendors. Our goal is to retrofit autonomic computing onto such systems, externally, without any need to understand or modify the code, and in many cases even when it is impossible to recompile. We present a meta-architecture implemented as active middleware infrastructure to explicitly add autonomic services via an attached feedback loop that provides continual monitoring and, as needed, reconfiguration and/or repair. Our lightweight design and separation of concerns enables easy adoption of individual components, as well as the full infrastructure, for use with a large variety of legacy, new systems, and systems of systems. We summarize several experiments spanning multiple domains.
Stability of switched neural networks with time-varying delays

This paper investigates the globally exponential stability of switched neural networks with time-varying delays. By virtue of mode-dependent average dwell time, some significant criteria of exponential stability are obtained for delayed switched neural networks with only stable subsystems or with both stable subsystems and unstable subsystems. The proposed theoretical results could be utilized not only to verify the globally exponential stability of switched neural networks, but also to design appropriate switching signal to guarantee the globally exponential stabilization. They can explicitly reflect the effect of mode-dependent average dwell time on the stability of switched neural networks. In contrast to the previous results, the proposed criteria are more straightforward and effective in the real-world application. Three numerical examples are introduced to illustrate the effectiveness of the proposed results.
Interaction-free dressing of virtual humans

We describe an interaction-free method for a geometric pre-positioning of virtual cloth patterns with human 3D scans. Combined with the following physically based sewing simulation, a fully automated dressing of virtual humans becomes possible. This reduces the time needed for dressing virtual humans by eliminating the time needed for the interactive placement of the cloth patterns in previous methods, where the patterns have to be pre-positioned by user interaction. The result of our pre-positioning method is computed in less than 0:3 s CPU time for various examples on current PCs and designed to converge efficiently in the simulation step afterwards.
TinyECC: A Configurable Library for Elliptic Curve Cryptography in Wireless Sensor Networks

Public Key Cryptography (PKC) has been the enabling technology underlying many security services and protocols in traditional networks such as the Internet. In the context of wireless sensor networks, elliptic curve cryptography (ECC), one of the most efficient types of PKC, is being investigated to provide PKC supportin sensor network applications so that the existing PKC-based solutions can be exploited. This paper presents the design, implementation, and evaluation of TinyECC, a configurable library for ECC operations in wireless sensor networks. The primary objective of TinyECC is to provide a ready-to-use, publicly available software package for ECC-based PKC operations that can be flexibly configured and integrated into sensor network applications. TinyECC provides a number of optimization switches, which can turn specific optimizations on or off based on developers' needs. Different combinations of the optimizations have different execution time andresource consumptions, giving developers great flexibility in integrating TinyECC into sensor network applications. This paperalso reports the experimental evaluation of TinyECC on several common sensor platforms, including MICAz, Tmote Sky, and Imote2. The evaluation results show the impacts of individual optimizations on the execution time and resource consumptions, and give the most computationally efficient and the most storage efficient configuration of TinyECC.
Social Cognition and the effect of Product Quality on Online Repurchase Intention

An electronic commerce marketing channel is fully mediated by information technology, creating information asymmetry (i.e., limited information). Such asymmetry may impede consumers’ ability to effectively assess certain types of products, thus creating challenges for online sellers. Signaling theory can aid in the understanding of how extrinsic cues—signals—can be used by sellers to convey product quality information to consumers, reducing uncertainty and facilitating a purchase or exchange. This study proposes a model to investigate website quality as a potential signal of product quality and consider the moderating effects of product information asymmetries and signal credibility. The study also finds that perceived value and cognitive lock-in can predict consumer purchase intentions. Furthermore, personalized product recommendation (PPR) services offered by online retailers are found to influence consumer store loyalty. The results indicate that website quality influences consumers’ perceptions of product quality, and affects online purchase intentions. Website quality is found to have a greater influence on perceived product quality when consumers have higher information asymmetry. Signal credibility is found to strengthen the relationship between website quality and product quality perceptions for a high quality website. The implications of cognitive lock-in and product cues for increasing purchase intentions are discussed. Retailer learning reflected in higher quality PPRs is associated with both lower product screening cost and higher product evaluation cost. We also discuss which PPRs influence consumer repurchase intentions in electronic markets.