Miracle-dz/UniFAR-FTUs huggingface dataset:
Aging Effects on Phonological and Semantic Priming in the Tip-of-the-Tongue: Evidence From a Two-Step Approach

The mechanism underlying the age difference in spoken word production remains controversial. We used a two-step approach proposed by Gollan and Brown (2006) to investigate the semantic and phonological retrieval deficits when tip-of-the-tongue occurs in young and older adults. Importantly, we controlled the inhibition ability in both older and young groups. In experiment 1 with a people pictures naming task, older adults produced more TOTs than young adults, and they suffered from phonological retrieval deficit rather than semantic retrieval deficit in speaking. In experiment 2 with a priming paradigm, participants were presented semantically related or phonologically related names before target pictures, which formed semantic or phonological priming conditions for lexical access. Compared with young adults, older adults showed a greater effect of phonological priming on decreasing TOTs occurrence. For semantic retrieval deficit, older adults exhibited a smaller phonological facilitation effect and a larger semantic interference effect than young adults. For phonological retrieval deficit, older adults presented a larger phonological facilitation effect in the first-name related priming condition than the first-syllable related priming condition, whereas young adults showed similar facilitation effects between the two phonological priming conditions. Our findings provide consistent evidence for the transmission deficit hypothesis, and highlight that aging affects bidirectional connections between semantic and phonological nodes in speech production.
Evaluating A Systems Approach to District CS Education Implementation

Computer science education exists within a broader education environment than individual classrooms. Formal, or during the school day, K-12 education requires implementation within schools and local education agencies with leadership structures, budgets, and often competing priorities. In this experience report we share outcomes from a series of district workshops for supporting critical systems planning needed to ensure the integration and sustainability of computer science education within K-12 systems. The paper will briefly discuss the program's theory of change, the actions CSforALL took to deliver the program at scale, and data with short and long term outcomes from program implementation. Discussion will include the intersection of program and curricular and professional development and the critical frame of continuous improvement as a theoretical model for sustainability and rigor in computer science education implementation.
Development and Adoption of an Adaptive Learning System: Reflections and Lessons Learned

Adaptive learning systems (ALSs) aim to provide an efficient, effective and customised learning experience for students by dynamically adapting learning content to suit their individual abilities or preferences. Despite consistent evidence of their effectiveness and success in improving student learning over the past three decades, the actual impact and adoption of ALSs in education remain restricted to mostly research projects. In this paper, we provide a brief overview of reflections and lessons learned from developing and piloting an ALS in a course on relational databases. While our focus has been on adaptive learning, many of the presented lessons are also applicable to the development and adoption of educational tools and technologies in general. Our aim is to provide insight for other instructors, educational researchers and developers that are interested in adopting ALSs or are involved in the implementation of educational tools and technologies.
An Application of Distributed Data Mining to Identify Data Quality Problems

When dealing with huge data sets, during the integration process of distributed data into a single data warehouse, one is not only confronted with time and security factors but with the well known problem of low data quality as well. In order to cope with such issues that the integration of distributed data often is faced with, we present in this paper an approach that applies distributed data mining, to facilitate a data quality analysis of the data in their distributed state. Data quality problems are identified by a classifier, which uses the knowledge gained from the clustering (subspace clustering) process performed on the distributed data. Experiments on real data show that the distributed analysis results are comparable to those conducted on the central data warehouse using classical data mining.
Study of machine learning methods for customer churn prediction in telecommunication company

The paper presents the results of investigation which machine learning techniques are most suited for customer churn prediction. Different approaches were compared, starting from the simple K-means method, through decision trees, ending with the artificial neural network. The authors trained the models with each method and predicted whether a customer is going to leave the current telecommunication company.
GPU initiated OpenSHMEM: correct and efficient intra-kernel networking for dGPUs

Current state-of-the-art in GPU networking utilizes a host-centric, kernel-boundary communication model that reduces performance and increases code complexity. To address these concerns, recent works have explored performing network operations from within a GPU kernel itself. However, these approaches typically involve the CPU in the critical path, which leads to high latency and inefficient utilization of network and/or GPU resources. In this work, we introduce GPU Initiated OpenSHMEM (GIO), a new intra-kernel PGAS programming model and runtime that enables GPUs to communicate directly with a NIC without the intervention of the CPU. We accomplish this by exploring the GPU's coarse-grained memory model and correcting semantic mismatches when GPUs wish to directly interact with the network. GIO also reduces latency by relying on a novel template-based design to minimize the overhead of initiating a network operation. We illustrate that for structured applications like a Jacobi 2D stencil, GIO can improve application performance by up to 40% compared to traditional kernel-boundary networking. Furthermore, we demonstrate that on irregular applications like Sparse Triangular Solve (SpTS), GIO provides up to 44% improvement compared to existing intra-kernel networking schemes.
Analyzing the Effects of Active Learning Classrooms in CS2

Active learning environments have only recently started to be analyzed in the CS discipline, in terms of their effect on student performance. Recent studies in CS1 found contradictory results, in part due to different control on the learning pedagogy used, and issued a call for further investigation. This study evaluates the effects of the learning space on student performance in CS2, as measured by their grades. We use a quasi-experimental setup with 529 participants across five lecture sections over one academic term. All sections employ the same active learning method (inverted classroom), identical lecture materials, and the same number of TAs for in-class support, but differ in terms of classroom type (active learning classroom vs traditional lecture hall), instructor, and lecture time of day. Similarly to a recent study in CS1, we find no significant impact of the learning space in CS2. We also inspect factors not analyzed in previous studies, such as student prior preparation (as measured by prerequisite CS1 grades), course drop rates, and exam failure rates, and find that the CS2 sections are statistically similar. This work also examines student survey responses, to assess student perception differences on properties of the learning space which may impact their learning experience, such as the use of technology, ability to hear the instructor, ability to get help during lectures, and conduciveness of desk types to group work.
Dependency-Driven Trace-Based Network-on-Chip Emulation on FPGAs

FPGA emulation is a promising approach to accelerating Network-on-Chip (NoC) modeling which has traditionally relied on software simulators. In most early studies of FPGA-based NoC emulators, only synthetic workloads like uniform and bit permutations were considered. Although a set of carefully designed synthetic workloads can reveal a relatively thorough coverage of the characteristics of the NoC under evaluation, they alone are insufficient, especially when the NoC needs to be optimized for specific applications. In such cases, trace-driven workloads are effective. However, there is a problem with conventional trace-driven workloads that has been pointed out by some recent studies: the network load and congestion may be distorted because dependencies between packets are not considered. These studies also provide infrastructures for extending existing software simulators to enforce dependencies between packets. Unfortunately, enforcing dependencies between packets is not trivial in the FPGA emulation approach. Therefore, although there are some recent FPGA-based NoC emulators supporting trace-driven workloads, most of them ignore packet dependencies. In this paper, we first clarify the challenges of supporting trace-driven workloads with dependencies between packets taken into account in the FPGA emulation approach. We then propose efficient methods and architectures to tackle these challenges and build an FPGA-based NoC emulator, which we call DNoC, based on the proposals. Our evaluation results show that (1) on a VC707 FPGA board, DNoC achieves an average speed of 10,753K cycles/s when emulating an 8x8 NoC with trace data collected from full-system simulation of the PARSEC benchmark suite, which is 274x higher than the speed reported in a recent related work on dependency-driven trace-based NoC emulation on FPGAs; (2) Compared to BookSim, one of the most popular NoC simulators, DNoC is 395x faster while providing the same results; (3) DNoC can scale to a 4,096-node NoC on a VC707 board, and the size of the largest NoC depends on only the on-chip memory capacity of the target FPGA.
Analysis of multi-drug resistance of Mycobacterium tuberculosis derived from tuberculosis patients in Suzhou city

Objective To investigate the multi-drug resistance of of Mycobacterium tuberculosis derived from tuberculosis patients diagnosed at the affiliated infectious hospital of soochow university in Suzhou.Methods From September 2008 to May 2011,the type of clinical isolates from the hospital were demonstrated and then the multi-drug resistance of Mycobacterium tuberculosis from the patients was analyzed by drug sensitive test.Results The multi-drug resistance rate of 1749 Mycobacterium tuberculosis was 10.5%. The multi-drug resistance rate arising during 2010 and 2011 was 18.2%,which was profoundly higher than that occurred in 2008 and 2009.Conclusion The multi-dug resistance rate of Mycobacterium tuberculosis from the tuberculosis patients in Suzhou city is high and presents increasing.
A Modular, Practical Test for a Programming Course

In order to evaluate students' programming skills during a university course, a practical programming test can be administered, in which students are required to implement a short yet complete program according to a provided specification. However, such tests often suffer from drawbacks that prevent comprehensive and accurate assessment of students' abilities. In this paper we identify these drawbacks and then present a modular, practical test that avoids common testing pitfalls, as well as show how to design such a test based on course learning outcomes. A key aspect of our approach is adoption of modularity, which ensures independent and comprehensive verification of learning outcomes. We have used our method to evaluate object-oriented programming skills of undergraduate students over several years and have found that our testing approach has proven its validity and superiority over approaches employed previously.
Application of Image Data Analytics for Immediate Disaster Response

This study identifies a novel source of data, i.e. images from smart urban infrastructures, that would be helpful in effective disaster management decision-making. For this purpose, we collected images from disaster-hit environments of Central Mexico (2017 earthquake). Also, this study utilizes deep learning convolutional neural network to analyze this novel dataset and evaluates the model effectiveness and technical viability during crisis scenarios. TensorFlow was utilized for the image classification task. The findings have important significance for effective disaster response.
DCAT: A Deep Context-Aware Trust Prediction Approach for Online Social Networks

Customer reviews are now increasingly available on Online Social Networks (OSNs) for a wide range of products and services. Trust in the review's author is a crucial basis for believing in the reliability of reviews generated on such networks. In this context, the main challenge is to predict the unknown trust relationship between two users. Existing trust prediction approaches fail to incorporate textual footprint of users. To address this challenge, we present a deep learning-based graph analytics model to predict trust relations in OSNs. We leverage and extend GraphSAGE, a method for computing node representations in an inductive manner, to develop a deep classifier. We present our experiment with datasets from review websites to train classifiers that predict trust relations between pairs of users, and highlight how our approach significantly improves the quality of predicted trust relations compared to the state-of-the-art approaches.
Smart Sensing, Communication, and Control in Perishable Food Supply Chain

Transportation and distribution (T8D) of fresh food products is a substantial and increasing part of the economic activities throughout the world. Unfortunately, fresh food T8D not only suffers from significant spoilage and waste, but also from dismal efficiency due to tight transit timing constraints between the availability of harvested food until its delivery to the retailer. Fresh food is also easily contaminated, and together with deteriorated fresh food is responsible for much of food-borne illnesses. The logistics operations are undergoing rapid transformation on multiple fronts, including infusion of information technology in the logistics operations, automation in the physical product handling, standardization of labeling, addressing and packaging, and shared logistics operations under 3rd party logistics (3PL) and related models. In this article, we discuss how these developments can be exploited to turn fresh food logistics into an intelligent cyberphysical system driven by online monitoring and associated operational control to enhance food freshness and safety, reduce food waste, and increase T8D efficiency. Some of the issues discussed in this context are fresh food quality deterioration processes, food quality/contamination sensing technologies, communication technologies for transmitting sensed data through the challenging fresh food media, intelligent management of the T8D pipeline, and various other operational issues. The purpose of this article is to stimulate further research in this important emerging area that lies at the intersection of computing and logistics.
Morphius: A Self-configuring modular robot that navigates through cracks and debris for inspection

Modular robots are made up of mechanically constrained smaller robots with different degrees of freedom. These smaller robots can either detach or attach together to get a desired structure or configuration. The existing technology in this field consists of robots with homogeneous modules that lack the decision making capability and are not currently applicable in disaster struck area for inspection. To improve their performance, heterogeneity has been introduced in its structure making it possible to design versatile, application tailored modules having decision making capability. Each module will independently be able to perform a specific task depending on the attributes and given characteristics with self-configuring and reconfiguring capability. At the same time all the modules communicate and share their data with each other to collectively accomplish the goal. We have also introduced wheels for effective and faster locomotion and self-docking capability using a neutral connector. This paper addresses the two main challenges while developing such modular robotic system - intelligence and speed, in the field of rescue operations with adaptive morphology.
Comparing TUIs and GUIs for Primary School Programming

There is considerable interest in using tangible user interfaces (TUIs) to support teaching children programming, but evidence for the benefits is mixed, and their deployment in school environments presents more challenges than graphical user interfaces (GUIs). This study investigates the effect of GUIs and TUIs on learning outcomes, attitudes toward computing, and reported enjoyment in a computer-programming activity with primary-school students aged 6-7 in Saudi Arabia. Forty-two students engaged in a 45-minute learning activity using either a TUI or GUI programming environment. The study used a between-groups design, and quantitative data were collected, including pre-test and post-test results, and ratings on attitudinal and enjoyment surveys. Learning gains were significantly higher for the GUI group than the TUI group. However, post-activity increases in reported attitude toward computing were significantly higher for the TUI group. There was no difference in activity enjoyment scores, which were high for both groups.
The Effects of Compilation Mechanisms and Error Message Presentation on Novice Programmer Behavior

It is generally accepted that learning to program could be easier for many students. One of the most important components of this experience is the programming environment. Novices learn in a variety of environments, from basic command-line interfaces to industry-strength IDEs. These environments can differ substantially in compilation behavior and error message presentation - arguably two of the most important mechanisms through which users interact with the programming language. In this study, we utilize Blackbox data to compare the programming behavior of thousands of users programming in Java, who all used BlueJ versions 3 and 4. These two versions differ drastically in terms of compilation behavior and error message presentation. BlueJ 3 is a click-to-compile editor that delivers text-based error messages from javac to the user, but only presents the first error message, even if the compiler produces several. BlueJ 4 automatically compiles in the background but retains click-to-compile ability. In addition, all error messages (not just the first) may be viewed by the user. We find that the programming experience and behavior of these users can be substantially affected by changes in these mechanisms, causing numbers of manual compilations, successful compilations, and error messages presented in each version to differ, in cases, markedly. Our results provide evidence on how changes in programming environment affect user behavior in conditions that reasonably control for variables other than the programming environment. This can inform the decisions of educators, tool designers, and HCI researchers in their work to make learning more effective for novice programmers.
What Independent Game Developers Expect from Recommender Systems: A Qualitative Study

Digital distribution and electronic recommender systems have transformed the digital games industry. They serve as the enabler of new business models that allow independent game developers to publish their projects without the mediation of third party publishers and decrease costs of game production. This paper aims at analyzing the relevance of recommender systems from the perspective of independent game developers. For this reason, a qualitative study using semi-structured expert interviews has been carried out. The results of this study support (i) independent game developers to improve their business models, and (ii) designers and owners of electronic recommender systems to provide enhanced services for their content creators.
Towards Context-Aware Social Behavioral Analytics

The confluence of technological and societal advances, and more specifically, engagement with Web, social media, and smart devices has the potential to affect the mental behavior of the individuals. Examples include extremist and criminal behaviors such as radicalization and cyber-bullying, which are causing serious issues for humanity. Major barriers to the effective understanding of behavioral disorders on social networks includes the ability to understand the content and context of social documents, as well as the activity of social users. Understanding the patterns of behavioral disorders (e.g., criminal and extremist activities) on social networks, is challenging and requires techniques to contextualize the content of social documents based on the time-aware analysis of personality, behaviour and past activities of social users. In this context, semantic information extraction and enrichment from social documents has the potential to become a vital asset to explore the sign of behavioral disorders and prevent serious issues such as cyber-bullying, suicidal related behavior and radicalization. To address this challenge, in this paper, we present a novel social document analysis pipeline to enable analysts engage with social documents (e.g., a Tweet in Twitter or a post on Facebook) to explore cognitive aspects of behavioral disorders. We implement the pipeline as an extensible and scalable architecture and present the evaluation results.
Neurolytic Lumbar Sympathetic Blockade: Duration of Denervation and Relief of Rest Pain

In an objective evaluation, 386 patients with rest pain due to occlusive vascular disease were studied before and after neurolytic lumbar sympathectomy by either 100% alcohol (n = 35), 6% phenol in water (n = 151) or 10% phenol in Conray 420 contrast medium (n = 200). A trained independent observer made assessments of:— (a) Completeness and duration of sympathetic ablation; (b) Degree and duration of pain relief; (c) Changes in skin temperature and skin plethysmography; (d) Completeness of coverage of antero-lateral aspects of L2, L3, and L4, by contrast medium (in patients receiving 10% phenol). (e) Side effects, including L1 neuralgia and postural hypotension. Mean duration of sweat test modification (6.0 ± 1.0 months) and pain relief (5.9 ± 0.6 months) were similar; onset of pain relief also coincided with onset of sympathetic blockade and increase in skin blood flow. This strongly pointed to sympathetic denervation as the basis for the relief of rest pain rather than a placebo response. Complete pain relief was achieved in 49% of patients, 31% had partial relief and 20% none. In the absence of pre-existing gangrenous changes a higher percentage of patients had complete or partial relief (84%) compared to those with gangrenous changes (56%). Significant postural hypotension was not observed. Frequency of L, neuralgia differed markedly for the three agents: 100% alcohol > 6% phenol in water > 10% phenol in Conray. It is concluded that neurolytic lumbar sympathectomy is an effective method of relief of rest pain with the advantage over surgical sympathectomy of performance without general anaesthesia on an outpatient basis. It is estimated that, in a series of the size reported in this paper, the potential economic benefit could be a saving of the order of $360,000. The technique deserves further controlled evaluation in comparison to surgical sympathectomy.