Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Network Analysis: Peer Support and Peer Management in Multidisciplinary, Vertically Integrated Teams

In the Vertically Integrated Projects (VIP) Program, undergraduates earn academic credit for their participation in long-term, large-scale projects. Teams are created at the request of faculty and are embedded in their ongoing research/innovation efforts. Students can participate for multiple semesters and up to three years. Two important elements of VIP teams are peer-to-peer support and peer project management. Encouraging students to assist each other (peer support), and to be aware of each other’s work and hold each other accountable (peer management) shifts ownership of key aspects of the project to students, thus decreasing instructor time spent on low and mid-level operational/logistics issues. Through social network analysis of peer evaluations (N=483), this paper quantifies peer support and management between students on VIP teams at the Georgia Institute of Technology, examining patterns within individual teams and across the site. A previous study found that within teams, students interacted more often with students from majors other than their own and more often with students of other races/ethnicities than their own. Another previous study found stronger connections between students within academic ranks (sophomore to sophomore, junior to junior, etc.). To better understand dynamics within VIP teams, this analysis considers how 1) academic rank, 2) student major, and 3) number of semesters in VIP affect student interactions in peer support and peer management. The study looks at team-level interactions as well as program-wide patterns, providing a wide view of VIP student engagement across many different projects and teams. The results and method of analysis would be of interest to current and prospective VIP sites, as well as programs seeking to develop or quantify multidisciplinary student experiences.
A Framework for Information Extraction, Storage and Retrieval

This paper presents a set of tools that were developed in order to facilitate and speed up the process of building information extraction and retrieval systems for documents that exhibit a set of predefined characteristics. Specifically, the work presents a simple framework for extracting information found in publications or documents that are issued in large volumes and which cover similar concepts or issues within a given domain. The paper presents a simple model for defining background knowledge and for using that to automatically augment segments of input documents with metadata in order to assist users in easily locating information within these documents through a structured front end. The model presented makes use of both document structure as well as dynamically acquired background knowledge to achieve its goals.
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach

Given a dynamic graph stream, how can we detect the sudden appearance of anomalous patterns, such as link spam, follower boosting, or denial of service attacks? Additionally, can we categorize the types of anomalies that occur in practice, and theoretically analyze the anomalous signs arising from each type? In this work, we propose AnomRank, an online algorithm for anomaly detection in dynamic graphs. AnomRank uses a two-pronged approach defining two novel metrics for anomalousness. Each metric tracks the derivatives of its own version of a 'node score' (or node importance) function. This allows us to detect sudden changes in the importance of any node. We show theoretically and experimentally that the two-pronged approach successfully detects two common types of anomalies: sudden weight changes along an edge, and sudden structural changes to the graph. AnomRank is (a) Fast and Accurate: up to 49.5x faster or 35% more accurate than state-of-the-art methods, (b) Scalable: linear in the number of edges in the input graph, processing millions of edges within 2 seconds on a stock laptop/desktop, and (c) Theoretically Sound: providing theoretical guarantees of the two-pronged approach.
Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring

Existing document embedding approaches mainly focus on capturing sequences of words in documents. However, some document classification and regression tasks such as essay scoring need to consider discourse structure of documents. Although some prior approaches consider this issue and utilize discourse structure of text for document classification, these approaches are dependent on computationally expensive parsers. In this paper, we propose an unsupervised approach to capture discourse structure in terms of coherence and cohesion for document embedding that does not require any expensive parser or annotation. Extrinsic evaluation results show that the document representation obtained from our approach improves the performance of essay Organization scoring and Argument Strength scoring.
A neural network using oscillators

The authors propose the oscillator neural network, in which the sigmoid nonlinearity of the Hopfield neural network is replaced by the Van der Pol oscillator. The oscillator neural network has the advantage that no simulated annealing is necessary to obtain a reasonable solution. They demonstrate that the 4-b analog to digital (A/D) converter problem, the 10-city traveling salesman problem and the associative memory problem, can be solved by the proposed neural network. <<ETX>>
Learning Sleep Quality from Daily Logs

Precision psychiatry is a new research field that uses advanced data mining over a wide range of neural, behavioral, psychological, and physiological data sources for classification of mental health conditions. This study presents a computational framework for predicting sleep efficiency of insomnia sufferers. A smart band experiment is conducted to collect heterogeneous data, including sleep records, daily activities, and demographics, whose missing values are imputed via Improved Generative Adversarial Imputation Networks (Imp-GAIN). Equipped with the imputed data, we predict sleep efficiency of individual users with a proposed interpretable LSTM-Attention (LA Block) neural network model. We also propose a model, Pairwise Learning-based Ranking Generation (PLRG), to rank users with high insomnia potential in the next day. We discuss implications of our findings from the perspective of a psychiatric practitioner. Our computational framework can be used for other applications that analyze and handle noisy and incomplete time-series human activity data in the domain of precision psychiatry.
Neural News Recommendation with Long- and Short-term User Representations

Personalized news recommendation is important to help users find their interested news and improve reading experience. A key problem in news recommendation is learning accurate user representations to capture their interests. Users usually have both long-term preferences and short-term interests. However, existing news recommendation methods usually learn single representations of users, which may be insufficient. In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations. The core of our approach is a news encoder and a user encoder. In the news encoder, we learn representations of news from their titles and topic categories, and use attention network to select important words. In the user encoder, we propose to learn long-term user representations from the embeddings of their IDs.In addition, we propose to learn short-term user representations from their recently browsed news via GRU network. Besides, we propose two methods to combine long-term and short-term user representations. The first one is using the long-term user representation to initialize the hidden state of the GRU network in short-term user representation. The second one is concatenating both long- and short-term user representations as a unified user vector. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of neural news recommendation.
Designing and Publishing Illegally Parked Bicycle LOD

The illegal parking of bicycles is an urban problem in Japan and other countries. Illegally parked bicycles (IPBs) obstruct vehicles and pedestrian traffic, cause road accidents, encourage thefts, and disfigure streets. To solve the challenge posed by IPBs, we believed that it is necessary to collect and republish the data as reusable format. Therefore, we collected the number of IPBs, location information, time, and factors. Then, we integrated and republished these data as Linked Open Data (LOD) on the Web. In this paper, we described a schema design of illegally parked bicycle LOD (IPBLOD) and a methodology of designing LOD schema. Then, we collected data from SNS and website of municipality, and built the LOD of 98,982 triples. Furthermore, we presented several statistics based on IPBLOD.
Dense Procedure Captioning in Narrated Instructional Videos

Understanding narrated instructional videos is important for both research and real-world web applications. Motivated by video dense captioning, we propose a model to generate procedure captions from narrated instructional videos which are a sequence of step-wise clips with description. Previous works on video dense captioning learn video segments and generate captions without considering transcripts. We argue that transcripts in narrated instructional videos can enhance video representation by providing fine-grained complimentary and semantic textual information. In this paper, we introduce a framework to (1) extract procedures by a cross-modality module, which fuses video content with the entire transcript; and (2) generate captions by encoding video frames as well as a snippet of transcripts within each extracted procedure. Experiments show that our model can achieve state-of-the-art performance in procedure extraction and captioning, and the ablation studies demonstrate that both the video frames and the transcripts are important for the task.
A Methodological Model for Life-Based Design

Technology should exist for bringing added value to pe ple's life, thus improving itsquality. Technology design, therefore,ought toconsider human -technology interaction (HTI) in a much larger context than that of technology use. Compreh ension of people’s lives should be the real starting point of the design. In addition, we need to have well-grounded design methods and tools, which can make good use ofour investigations f life and applythis knowledge to the design work. Life-Based Design (LBD) is a multi-dim ensional and holistic approach, which integratesHTI design issues with the concepts inhum an life sciences and calls attention to a careful analysis of people’s forms of life. This p aper outlines a methodological model forLife-Based Design. Themodel has four main phases : form-of-life analysis, concept design, fit-for-life design and innovation design. Itcovers the main steps in designing concepts, which can be used in technical design.
WebSIS - a web based portal with an integrated e-assessment environment

WebSIS, a web based portal, is being developed at the Technical University of Ilmenau as a central information source and contact point for students, prospective students, interested pupils, and students who have to repeat an examination at the TU Ilmenau. One of the integral parts of WebSIS is the e-assessment environment TestWeb. Dynamic generation of tests based on parameterized tasks with different solution forms (currently: numeric value input, multiple choice, matching tasks, conversion of units), simulation of test conditions, the automated verification and rating of tests, and customized statistical summaries are some specific features of TestWeb .
Novel Bit-Parallel and Digit-Serial Systolic Finite Field Multipliers Over $GF(2^m)$ Based on Reordered Normal Basis

Efficient implementation of finite field multipliers based on a reordered normal basis (RNB) is highly desirable in the current/emerging cryptosystems since it offers almost free realization of squaring operation. Therefore, in this paper, we propose novel bit-parallel and digit-serial finite field multipliers over $GF(2^{m})$ based on RNB. By efficient transformation of the core multiplication algorithm using a unique circular shifting feature, we have derived an efficient algorithm for low-complexity systolic mapping. Both bit-parallel and digit-serial structures of the multipliers are then obtained and optimized to enhance the area–time efficiency. We have also utilized the unique feature of the proposed multiplication algorithm to obtain the systolic multipliers by Karatsubalike decomposition. Detailed analysis and comparison show the superior performance of the proposed implementation. For example, the proposed regular and Karatsuba-based bit-parallel designs involve at least 48.4% less area-delay product (ADP) and 42.2% less power-delay product (PDP) than the best existing ones (37.7% and 55.3% less ADP and PDP on field-programmable gate array platform), respectively. The proposed multipliers, because of their lower area–time complexities, can be used for efficient realization of cryptographic applications.
College Choice Criteria Utilizing Conjoint Analysis Enabled on a SaaS Platform

College enrollments and low to moderate household incomes are stagnating while tuition costs are increasing. The New York State Legislature enacted the nation’s first tuition-free degree program, the Excelsior Scholarship, designed to make a college education more affordable to middle class families. This legislation can impact what institution of higher education students will choose upon high school graduation. In order to understand the choice criteria for selecting an institution of higher education, a research study was conducted among a representative sample of high school upper classmen and parents of this respondent segment. The data collection and analysis were accomplished using an information management technology platform leveraging a statistical technique; “choice based conjoint analysis (CBC)”. This is a SaaS platform called Discover CBC. The platform is developed by Sawtooth Software, a leading technology provider in information management for complex problems, especially when understanding and predicting consumer choice is required. Key themes discovered in the research in terms of choice criteria for a college/university are relatively low cost but also the importance of college/university campus racial and ethnic diversity among parents and college bound high school students.
Detecting Subevents using Discourse and Narrative Features

Recognizing the internal structure of events is a challenging language processing task of great importance for text understanding. We present a supervised model for automatically identifying when one event is a subevent of another. Building on prior work, we introduce several novel features, in particular discourse and narrative features, that significantly improve upon prior state-of-the-art performance. Error analysis further demonstrates the utility of these features. We evaluate our model on the only two annotated corpora with event hierarchies: HiEve and the Intelligence Community corpus. No prior system has been evaluated on both corpora. Our model outperforms previous systems on both corpora, achieving 0.74 BLANC F1 on the Intelligence Community corpus and 0.70 F1 on the HiEve corpus, respectively a 15 and 5 percentage point improvement over previous models.
Contiki IPSN 2009 Tutorial Handouts

Welcome to this Contiki tutorial! The purpose of the tutorial is twofold: to understand Contiki, and to set up an IPv6-based sensor network with Contiki. These notes and the slides presented during the course should be saved for future reference. Contiki is a state-of-the-art, open source operating system for sensor networks and other networked embedded devices [ 3]. Contiki was the first operating system for sensor networks to provide TCP/IP communication (with the uIP stack) [ 1, 6], loadable modules [2], threading on top of its event-driven kernel [ 3], protothreads [ 5], protocol-independent radio networking (with the Rime stack) [ 7], cross-layer network simulation (with Cooja) [10], and software-based power profiling [4]. Recent features include a networked shell and the memory efficient flash-based Coffee file system. In this tutorial, we use the uIPv6 stack to communicate with Tmote Sky motes and the network shell to interact with the network. These notes describe the practical, hands-on session with Tmote Sky motes. The purpose of this session is to get experience with how to use Contiki with actual hardware. We hope that this experience will be of help when later working with Contiki. We use the Instant Contiki development environment. Instant Contiki is a single-file download that contains the Contiki source code and all necessary compilers and tools required for developing software for Contiki. The Instant Contiki environment is a Ubuntu Linux installation that runs within the VMware Player virtual machine execution environment. VMware Player is available for free at the VMware website. In this tutorial, we use Instant Contiki 2.2. 1 and an updated version of Contiki 2.2. 3 that has a number of bugfixes from the current CVS version, as well as a set Figure 1: A Tmote Sky mote.
Asymptotically optimal empirical fusion of bit estimates derived from a bank of receivers

In this paper we derive an asymptotically optimal algorithm for blindly fusing bit estimates from various distributed detectors when the performance of each detector is unknown. Results show that in practical regimes, this new fusion rule outperforms the best individual detector in the bank and outperforms the standard "majority rule" fusion detector.
A comparison of efficient beamforming algorithms

Time domain and frequency domain concepts which aid in the design and efficient implementation of a digital beamformer have been described at various times in the literature. The numerous beam-former structures that result are discussed with an emphasis on hardware requirements and spectral areas of application. Time domain procedures which include delay-sum, partial-sum, interpolation and shifted-sideband beamforming, and frequency domain techniques which include the application of discrete Fourier transforms and phase shift beam-forming are considered. Hardware considerations are primarily in the areas of analog-to-digital conversion, data storage, and computational throughput requirements.
Monitoring Testbed Experiments with MonEx

Almost all testbed experiments deal with different kinds of metrics which are collected from and/or about various kinds of resources. Despite the importance of collecting experiment metrics in the experiment life cycle, this phase is often done via ad hoc, manual, and artisanal actions such as manually combining multiple scripts, or manipulating some missing values. A few tools (e.g. Vendetta, OML) can be used for monitoring experiments. However their work is restricted to communicating metrics towards a central server, and they do not cover different features from user perspective such as drawing and archiving experiments results. In this talk, we will firstly discuss the requirements of experiment monitoring. Having a well-defined set of requirements eliminates the potential ambiguity around what should be targeted by any Experiment Monitoring Framework (EMF). The defined requirements are not testbed dependent nor technology-dependent, so any testbed community can build their own EMF by implementing these requirements, using different software systems. We will then describe our own proposition, MonEx (for long: Monitoring Experiments), which is an EMF that satisfies all the defined requirements. MonEx is built over several off-the-shelf infrastructure monitoring tools, and supports various monitoring approaches such as pull- and push-based monitoring, agent-based and agent-less monitoring. MonEx covers all the required steps of monitoring experiments from collecting metrics to archiving experiments data and producing figures. We will then demonstrate MonEx’s usability through a set of experiments , performed on the Grid’5000 testbed and being monitored by MonEx. Each of those experiments have different requirements, and as a group they show how MonEx meets all defined requirements. We show how MonEx nicely integrates the experimental workflow and how it simplifies the monitoring task, reducing the efforts of users during experimentation and pushing towards the repeatability of experiments’ analysis and metrics comparison.
Genetic search for redundancy optimization in complex systems

Purpose – Conceiving reliable systems is a strategic issue for any industrial society for its economical and technical development. This paper aims to focus on solving highly constrained redundancy optimization problems in complex systems.Design/methodology/approach – Genetic algorithms (GAs), one of the metaheuristic techniques, have been used and a dynamic adaptive penalty strategy is proposed, which makes use of feedback obtained during the search along with a dynamic distance metric and helps the algorithm to search efficiently for final, optimal or near optimal solution.Findings – The effectiveness of the adaptive penalty function is studied and shown graphically on the solution quality as well as the speed of evolution convergence for several highly constrained problems. The investigations show that this approach can be powerful and robust for problems with large search space, even of size 1017, and difficult‐to‐satisfy constraints.Practical implications – The results obtained in this paper would be.
Instructional technique questions in the searching phase of project based learning to increase concept map scores

The research aims to increase the student’s Concept Map (CM) score through the application of instructional technique questions in the stages of searching for the theoretical background of the driving question in Project Based Learning. Research is a Class Action Research with 3 Cycles, procedure of the research are: planning the lesson plan, implementation of actions, observation, reflection, and construct of expert CM. The research subjects consisted of 32 high school students. Percentage of students’s CM score calculated based Novak & Gowin. Validation test uses triangulation method: verification of CM scores suitability and documentation based on CM experts and interviews. Data analyzed as qualitative descriptive by reducing, presenting data and drawing conclusions based on complete CM data. The results shown that the instructional technique of teacher’s questions increased the CM scores of students, but variation presented in component CM.
Edge-based Transcoding for Adaptive Live Video Streaming

User-generated video content is imposing an increasing burden on live video service architectures such as Facebook Live. These services are responsible for ingesting large amounts of video, transcoding that video into different quality levels (i.e., bitrates), and adaptively streaming it to viewers. These tasks are expensive, both computationally and network-wise, often forcing service providers to sacrifice the “liveness” of delivered video. Given the steady increases in smartphone bandwidth and energy resources, coupled with the fact that commodity smartphones now include hardware-accelerated codecs, we propose that live video services augment their existing infrastructure with edge support for transcoding and transmission. We present measurements to motivate the feasibility of incorporating such edge-support into the live video ecosystem, present the design of a peer-to-peer adaptive live video streaming system, and discuss directions for future work to realize this vision in practice.