nils-herrmann/clean_scirepeval huggingface dataset:
Selling points: What cognitive abilities are tapped by casual video games?

The idea that video games or computer-based applications can improve cognitive function has led to a proliferation of programs claiming to "train the brain." However, there is often little scientific basis in the development of commercial training programs, and many research-based programs yield inconsistent or weak results. In this study, we sought to better understand the nature of cognitive abilities tapped by casual video games and thus reflect on their potential as a training tool. A moderately large sample of participants (n=209) played 20 web-based casual games and performed a battery of cognitive tasks. We used cognitive task analysis and multivariate statistical techniques to characterize the relationships between performance metrics. We validated the cognitive abilities measured in the task battery, examined a task analysis-based categorization of the casual games, and then characterized the relationship between game and task performance. We found that games categorized to tap working memory and reasoning were robustly related to performance on working memory and fluid intelligence tasks, with fluid intelligence best predicting scores on working memory and reasoning games. We discuss these results in the context of overlap in cognitive processes engaged by the cognitive tasks and casual games, and within the context of assessing near and far transfer. While this is not a training study, these findings provide a methodology to assess the validity of using certain games as training and assessment devices for specific cognitive abilities, and shed light on the mixed transfer results in the computer-based training literature. Moreover, the results can inform design of a more theoretically-driven and methodologically-sound cognitive training program.
Analytical inverse kinematic solution with self-motion constraint for the 7-DOF restore robot arm

This paper presents an analytical inverse kinematic (IK) solution for the seven degree-of-freedom (7-DOF) “Restore” robot arm with non-zero joint offsets. We provide two closed-form solutions - the shoulder-elbow-wrist (SEW) IK and the θ1-based solution. Both allow self-motion and together they solve the problem of algorithmic singularities within the workspace. Non-zero link offsets require careful selection of shoulder, elbow, and wrist locations. The solution is simple, fast and exact, providing full solution space (i.e. all eight solutions) per pose.
Fine-Grained Video Captioning for Sports Narrative

Despite recent emergence of video caption methods, how to generate fine-grained video descriptions (i.e., long and detailed commentary about individual movements of multiple subjects as well as their frequent interactions) is far from being solved, which however has great applications such as automatic sports narrative. To this end, this work makes the following contributions. First, to facilitate this novel research of fine-grained video caption, we collected a novel dataset called Fine-grained Sports Narrative dataset (FSN) that contains 2K sports videos with ground-truth narratives from YouTube.com. Second, we develop a novel performance evaluation metric named Fine-grained Captioning Evaluation (FCE) to cope with this novel task. Considered as an extension of the widely used METEOR, it measures not only the linguistic performance but also whether the action details and their temporal orders are correctly described. Third, we propose a new framework for fine-grained sports narrative task. This network features three branches: 1) a spatio-temporal entity localization and role discovering sub-network; 2) a fine-grained action modeling sub-network for local skeleton motion description; and 3) a group relationship modeling sub-network to model interactions between players. We further fuse the features and decode them into long narratives by a hierarchically recurrent structure. Extensive experiments on the FSN dataset demonstrates the validity of the proposed framework for fine-grained video caption.
The use of Semantic Web technologies for decision support - a survey

The Semantic Web shares many goals with Decision Support Systems (DSS), e.g., being able to precisely interpret information, in order to deliver relevant, reliable and accurate information to a user when and where it is needed. DSS have in addition more specific goals, since the information need is targeted towards making a particular decision, e.g., making a plan or reacting to a certain situation. When surveying DSS literature, we discover applications ranging from Business Intelligence, via general purpose social networking and collaboration support, Information Retrieval and Knowledge Management, to situation awareness, emergency management, and simulation systems. The unifying element is primarily the purpose of the systems, and their focus on information management and provision, rather than the specific technologies they employ to reach these goals. Semantic Web technologies have been used in DSS during the past decade to solve a number of different tasks, such as information integration and sharing, web service annotation and discovery, and knowledge representation and reasoning. In this survey article, we present the results of a structured literature survey of Semantic Web technologies in DSS, together with the results of interviews with DSS researchers and developers both in industry and research organizations outside the university. The literature survey has been conducted using a structured method, where papers are selected from the publisher databases of some of the most prominent conferences and journals in both fields (Semantic Web and DSS), based on sets of relevant keywords representing the intersection of the two fields. Our main contribution is to analyze the landscape of semantic technologies in DSS, and provide an overview of current research as well as open research areas, trends and new directions. An added value is the conclusions drawn from interviews with DSS practitioners, which give an additional perspective on the potential of Semantic Web technologies in this field; including scenarios for DSS, and requirements for Semantic Web technologies that may attempt to support those scenarios.
Application of a quantification SWOT analytical method

In this paper we present a Quantified SWOT (Strengths, Weaknesses, Opportunities and Threats) analytical method which provides more detailed and quantified data for SWOT analysis. The Quantified SWOT analytical method adopts the concept of Multiple-Attribute Decision Making (MADM), which uses a multi-layer scheme to simplify complicated problems, and thus is able to perform SWOT analysis on several enterprises simultaneously. Container ports in East Asia are taken as a case study in this paper. Quantified SWOT analysis is used to assess the competing strength of each port and then suggest an adoptable competing strategy for each.
Dynamic Compressive Sensing-Based Multi-User Detection for Uplink Grant-Free NOMA

Non-orthogonal multiple access (NOMA) can support more users than OMA techniques using the same wireless resources, which is expected to support massive connectivity for Internet of Things in 5G. Furthermore, in order to reduce the transmission latency and signaling overhead, grant-free transmission is highly expected in the uplink NOMA systems, where user activity has to be detected. In this letter, by exploiting the temporal correlation of active user sets, we propose a dynamic compressive sensing (DCS)-based multi-user detection (MUD) to realize both user activity and data detection in several continuous time slots. In particular, as the temporal correlation of the active user sets between adjacent time slots exists, we can use the estimated active user set in the current time slot as the prior information to estimate the active user set in the next time slot. Simulation results show that the proposed DCS-based MUD can achieve much better performance than that of the conventional CS-based MUD in NOMA systems.
ChainMOB: Mobility Analytics on Blockchain

Mobile devices generate massive amounts of data that is used to get an insight into the user behavior by enterprise systems. Data privacy is a concern in such systems as users have little control over the data that is generated by them. Blockchain systems offer ways to ensure privacy and security of the user data with the implementation of an access control mechanism. In this demonstration, we present ChainMOB, a mobility analytics application that is built on top of blockchain and addresses the fundamental privacy and security concerns in enterprise systems. Further, the extent of data sharing along with the intended audience is also controlled by the user. Another exciting feature is that user is part of the business model and is incentivized for sharing the personal mobility data. The system also supports queries that can be used in a variety of application domains.
BD180LV - 0.18 μm BCD technology with best-in-class LDMOS from 7V to 30V

0.18μm BCD technology with the best-in-class nLDMOS is presented. The drift of nLDMOS is optimized to ensure lowest Rsp by using multi-implants and appropriate thermal recipe. The optimized 24V nLDMOS has BV<inf>DSS</inf>=36V and Rsp=14.5 mΩ-mm<sup>2</sup>. Electrical SOA and long-term hot electron (HE) SOA are also evaluated. The maximum operating voltage less than 10% degradation of on-resistance is 24.4V.
Teachers ’ Attitudes towards the Use of Technology in Education

Present study was done with the purpose of evaluating teachers’ attitude in the city of Babol to implement technology in education. The study method was descriptive survey. Statistical population consists of all 3381 teachers of Babol in the academic year of 2011-12. The samples of 380 teachers were selected based on Morgan table according to stratified sampling method in which each academic term selected as a category related to population size. In this study in order to review teachers’ attitude in implementing technology in education, a questionnaire was used. To analyze data, descriptive statistics methods such as frequency, percentage, mean and standard deviation were used. Also, in order to assess research questions, one sample T test was used. Teachers’ attitude about advantages of implementing technology in education, implementing technology in education, the amount of technology efficacy in education, preconditions of implementing technology in education and effectiveness of technology in education was positive. The results indicated that teacher’s attitude toward the use of technology in education, was positive. As the change made by entering information and communication technology in Education system’s elements, traditional methods can’t help learner’s requirements in today’s society and it’s necessary that the main elements of education system particularly teachers subject to right alteration with global progresses and with positive attitude in this path, use this changes for upgrading their knowledge level and usage of them to improve education.
Cross-platform machine learning characterization for task allocation in IoT ecosystems

With the emergence of the Internet of Things (IoT) and Big Data era, many applications are expected to assimilate a large amount of data collected from environment to extract useful information. However, how heterogeneous computing devices of IoT ecosystems can execute the data processing procedures has not been clearly explored. In this paper, we propose a framework which characterizes energy and performance requirements of the data processing applications across heterogeneous devices, from a server in the cloud and a resource-constrained gateway at edge. We focus on diverse machine learning algorithms which are key procedures for handling the large amount of IoT data. We build analytic models which automatically identify the relationship between requirements and data in a statistical way. The proposed framework also considers network communication cost and increasing processing demand. We evaluate the proposed framework on two heterogenous devices, a Raspberry Pi and a commercial Intel server. We show that the identified models can accurately estimate performance and energy requirements with less than error of 4.8% for both platforms. Based on the models, we also evaluate whether the resource-constrained gateway can process the data more efficiently than the server in the cloud. The results present that the less-powerful device can achieve better energy and performance efficiency for more than 50% of machine learning algorithms.
An Empirical Comparison of Four Text Mining Methods

The amount of textual data that is available for researchers and businesses to analyze is increasing at a dramatic rate. This reality has led IS researchers to investigate various text mining techniques. This essay examines four text mining methods that are frequently used in order to identify their advantages and limitations. The four methods that we examine are (1) latent semantic analysis, (2) probabilistic latent semantic analysis, (3) latent Dirichlet allocation, and (4) the correlated topic model. We compare these four methods and highlight the optimal conditions under which to apply the various methods. Our paper sheds light on the theory that underlies text mining methods and provides guidance for researchers who seek to apply these methods.
Design of human symbiotic robot TWENDY-ONE

In this paper, we propose a sophisticated design of human symbiotic robots that provide physical supports to the elderly such as attendant care with high-power and kitchen supports with dexterity while securing contact safety even if physical contact occurs with them. First of all, we made clear functional requirements for such a new generation robot, amounting to fifteen items to consolidate five significant functions such as “safety”, “friendliness”, “dexterity”, “high-power” and “mobility”. In addition, we set task scenes in daily life where support by robot is useful for old women living alone, in order to deduce specifications for the robot. Based on them, we successfully developed a new generation of human symbiotic robot, TWENDY-ONE that has a head, trunk, dual arms with a compact passive mechanism, anthropomorphic dual hands with mechanical softness in joints and skins and an omni-wheeled vehicle. Evaluation experiments focusing on attendant care and kitchen supports using TWENDY-ONE indicate that this new robot will be extremely useful to enhance quality of life for the elderly in the near future where human and robot co-exist.
Kalman filter based multiple objects detection-tracking algorithm robust to occlusion

Visual target tracking is one of the major fields in computer vision system. Object tracking has many practical applications such as automated surveillance system, military guidance, traffic management system, fault detection system, artificial intelligence and robot vision system. But it is difficult to track objects with image sensor. Especially, multiple objects tracking is harder than single object tracking. This paper proposes multiple objects tracking algorithm based on the Kalman filter. Our algorithm uses the Kalman filter as many as the number of moving objects in the image frame. If many moving objects exist in the image, however, we obtain multiple measurements. Therefore, precise data association is necessary in order to track multiple objects correctly. Another problem of multiple objects tracking is occlusion that causes merge and split. For solving these problems, this paper defines the cost function using some factors. Experiments using Matlab show that the performance of the proposed algorithm is appropriate for multiple objects tracking in real-time.
Pursuits: Spontaneous Eye-Based Interaction for Dynamic Interfaces

Although gaze is an attractive modality for pervasive interaction, real-world implementation of eye-based interfaces poses significant challenges. In particular, user calibration is tedious and time consuming. Pursuits is an innovative interaction technique that enables truly spontaneous interaction with eye-based interfaces. A user can simply walk up to the screen and readily interact with moving targets. Instead of being based on gaze location, Pursuits correlates eye pursuit movements with objects dynamically moving on the interface.
A highly scalable 8-layer Vertical Gate 3D NAND with split-page bit line layout and efficient binary-sum MiLC (Minimal Incremental Layer Cost) staircase contacts

We demonstrate an 8-layer 3D Vertical Gate NAND Flash with WL half pitch =37.5nm, BL half pitch=75nm, 64-WL NAND string with 63% array core efficiency. This is the first time that a 3D NAND Flash can be successfully scaled to below 3Xnm half pitch in one lateral dimension, thus an 8-layer stack device already provides a very cost effective technology with lower cost than the conventional sub-20nm 2D NAND. Our new VG architecture has two key features: (1) To improve the manufacturability a new layout that twists the even/odd BL's (and pages) in the opposite direction (split-page BL) is adopted. This allows the island-gate SSL devices  and metal interconnections be laid out in double pitch, creating much larger process window for BL pitch scaling; (2) A novel staircase BL contact formation method using binary sum of only M lithography and etching steps to achieve 2M contacts. This not only allows precise landing of the tight-pitch staircase contacts, but also minimizes the process steps and cost. We have successfully fabricated an 8-layer array using TFT BE-SONOS charge-trapping device. The array characteristics including reading, programming, inhibit, and block erase are demonstrated.
Tupleware: Redefining Modern Analytics

There is a fundamental discrepancy between the targeted and actual users of current analytics frameworks. Most systems are designed for the data and infrastructure of the Googles and Facebooks of the world—petabytes of data distributed across large cloud deployments consisting of thousands of cheap commodity machines. Yet, the vast majority of users operate clusters ranging from a few to a few dozen nodes, analyze relatively small datasets of up to several terabytes, and perform primarily computeintensive operations. Targeting these users fundamentally changes the way we should build analytics systems. This paper describes the design of Tupleware, a new system specifically aimed at the challenges faced by the typical user. Tupleware’s architecture brings together ideas from the database, compiler, and programming languages communities to create a powerful end-to-end solution for data analysis. We propose novel techniques that consider the data, computations, and hardware together to achieve maximum performance on a case-by-case basis. Our experimental evaluation quantifies the impact of our novel techniques and shows orders of magnitude performance improvement over alternative systems.
Introduction to Visualization for Computer Security

Networked computers are ubiquitous, and are subject to attack, misuse, and abuse. Automated systems to combat this threat are one potential solution, but most automated systems require vigilant human oversight. This automated approach undervalues the strong analytic capabilities of humans. While automation affords opportunities for increased scalability, humans provide the ability to handle exceptions and novel patterns. One method to counteracting the ever increasing cyber threat is to provide the human security analysts with better tools to discover patterns, detect anomalies, identify correlations, and communicate their findings. This is what visualization for computer security (VizSec) researchers and developers are doing. VizSec is about putting robust information visualization tools into the hands of humans to take advantage of the power of the human perceptual and cognitive processes in solving computer security problems. This chapter is an introduction to the VizSec research community and the papers in this volume.
A Multi-task Approach to Predict Likability of Books

We investigate the value of feature engineering and neural network models for predicting successful writing. Similar to previous work, we treat this as a binary classification task and explore new strategies to automatically learn representations from book contents. We evaluate our feature set on two different corpora created from Project Gutenberg books. The first presents a novel approach for generating the gold standard labels for the task and the other is based on prior research. Using a combination of hand-crafted and recurrent neural network learned representations in a dual learning setting, we obtain the best performance of 73.50% weighted F1-score.
Spatially-partitioned environmental representation and planning architecture for on-road autonomous driving

Conventional layered planning architecture temporally partitions the spatiotemporal motion planning by the path and speed, which is not suitable for lane change and overtaking scenarios with moving obstacles. In this paper, we propose to spatially partition the motion planning by longitudinal and lateral motions along the rough reference path in the Frenét Frame, which makes it possible to create linearized safety constraints for each layer in a variety of on-road driving scenarios. A generic environmental representation methodology is proposed with three topological elements and corresponding longitudinal constraints to compose all driving scenarios mentioned in this paper according to the overlap between the potential path of the autonomous vehicle and predicted path of other road users. Planners combining A∗ search and quadratic programming (QP) are designed to plan both rough long-term longitudinal motions and short-term trajectories to exploit the advantages of both search-based and optimization-based methods. Limits of vehicle kinematics and dynamics are considered in the planners to handle extreme cases. Simulation results show that the proposed framework can plan collision-free motions with high driving quality under complicated scenarios and emergency situations.
Facebook in Higher Education : Students ’ Use and Perceptions

Facebook has rapidly become the most popular Social Networking Site (SNS) among faculty and students in higher education institutions in recent years. Due to the various interactive and collaborative features Facebook supports, it offers great opportunities for higher education institutions to support student engagement and improve different aspects of teaching and learning. To understand the social aspects of Facebook use among students and how they perceive using it for academic purposes, an exploratory survey has been distributed to 105 local and international students at a large public technology university in Malaysia. Results reveal consistent patterns of usage compared to what has been reported in literature reviews in relation to the intent of use and the current use for educational purposes. A comparison was conducted of male and female, international and local, postgraduate and undergraduate students respectively, using nonparametric tests. The results indicate that the students’ perception of using Facebook for academic purposes is not significantly related to students’ gender or students’ background; while it is significantly related to study level and students’ experience. Moreover, based on the overall results of the survey and literature reviews, the paper presents recommendations and suggestions for further research of social networking in a higher education context.