nils-herrmann/clean_scirepeval huggingface dataset:
Cloud Automation: Precomputing Roadmaps for Flexible Manipulation

The goal of this article is to highlight the benefits of cloud automation for industrial adopters and some of the research challenges that must be addressed in this process. The focus is on the use of cloud computing for efficiently planning the motion of new robot manipulators designed for flexible manufacturing floors. In particular, different ways that a robot can interact with a computing cloud are considered, where an architecture that splits computation between the remote cloud and the robot appears advantageous. Given this synergistic robot-cloud architecture, this article describes how solutions from the recent literature can be employed on the cloud during a periodically updated preprocessing phase to efficiently answer manipulation queries on the robot given changes in the workspace. In this setup, interesting tradeoffs arise between path quality and computational efficiency, which are evaluated through simulation. These tradeoffs motivate further research on how motion planning should be executed given access to a computing cloud.
Assigning bug reports using a vocabulary-based expertise model of developers

For popular software systems, the number of daily submitted bug reports is high. Triaging these incoming reports is a time consuming task. Part of the bug triage is the assignment of a report to a developer with the appropriate expertise. In this paper, we present an approach to automatically suggest developers who have the appropriate expertise for handling a bug report. We model developer expertise using the vocabulary found in their source code contributions and compare this vocabulary to the vocabulary of bug reports. We evaluate our approach by comparing the suggested experts to the persons who eventually worked on the bug. Using eight years of Eclipse development as a case study, we achieve 33.6% top-1 precision and 71.0% top-10 recall.
CAB: Connectionist Analogy Builder

The ability to make informative comparisons is central to human cognition. Comparison involves aligning two representations and placing their elements into correspondence. Detecting correspondences is a necessary component of analogical inference, recognition, categorization, schema formation, and similarity judgment. Connectionist Analogy Builder (CAB) determines correspondences through a simple iterative computation that matches elements in one representation with elements playing compatible roles in the other representation while simultaneously enforcing structural constraints. CAB shows promise as a process model of comparison as its performance can be related to human performance (e.g., solution trajectory, error patterns, time-on-task). Furthermore, CAB’s bounded working memory allows it to account for the inherent capacity limitations of human processing. CAB’s strengths are its parsimony, transparency of operations, and ability to generate performance predictions. In this paper, CAB is evaluated against benchmark phenomena from the analogy literature.
BSSync: Processing Near Memory for Machine Learning Workloads with Bounded Staleness Consistency Models

Parallel machine learning workloads have become prevalent in numerous application domains. Many of these workloads are iterative convergent, allowing different threads to compute in an asynchronous manner, relaxing certain read-after-write data dependencies to use stale values. While considerable effort has been devoted to reducing the communication latency between nodes by utilizing asynchronous parallelism, inefficient utilization of relaxed consistency models within a single node have caused parallel implementations to have low execution efficiency. The long latency and serialization caused by atomic operations have a significant impact on performance. The data communication is not overlapped with the main computation, which reduces execution efficiency. The inefficiency comes from the data movement between where they are stored and where they are processed. In this work, we propose Bounded Staled Sync (BSSync), a hardware support for the bounded staleness consistency model, which accompanies simple logic layers in the memory hierarchy. BSSync overlaps the long latency atomic operation with the main computation, targeting iterative convergent machine learning workloads. Compared to previous work that allows staleness for read operations, BSSync utilizes staleness for write operations, allowing stale-writes. We demonstrate the benefit of the proposed scheme for representative machine learning workloads. On average, our approach outperforms the baseline asynchronous parallel implementation by 1.33x times.
Very linear ramp-generators for high resolution ADC BIST and calibration

Two very linear ramp-generator designs are presented. The circuits are to be used in high-resolution ADC built-in-self-test (BIST) and on-chip calibration. The first design is to charge a capacitor by a small current, which is linear enough to test 14-bit ADCs. The second design is in a relaxation oscillator architecture. It is linear enough to test up to 12-bit ADCs. The two designs have been fabricated in CMOS 2um and 1.2um processes separately.
The Impact of Information Technology Investment Announcements on the Market Value of the Firm

Determining whether investments in information technology (IT) have an impact on firm performance has been and continues to be a major problem for information systems researchers and practitioners. Financial theory suggests that managers should make investment'decisions that maximize the value of the firm. Using event-study methodology, we provide empirical evidence on the effect of announcements of IT investments on the market value of the firm for a sample of 97 IT investments from the finance and manufacturing industries from 1981 to 1988. Over the announcement period, we find no excess returns for either the full sample or for any one of the industry subsamples. However, cross-sectional analysis reveals that the market reacts differently to announcements of innovative IT investments than to foUowup, or noninnovative investments in IT. Innovative IT investments increase firm value, while noninnovative investments do not. Furthermore, the market's reaction to announcements of innovative and noninnovative IT investments is independent of industry classification. These results indicate that, on average, IT investments are zero net present value (NPV) investments; they are worth as much as they cost. Innovative IT investments, however, increase the value of the firm.
Mode-seeking by Medoidshifts

We present a nonparametric mode-seeking algorithm, called medoidshift, based on approximating the local gradient using a weighted estimate of medoids. Like meanshift, medoidshift clustering automatically computes the number of clusters and the data does not have to be linearly separable. Unlike meanshift, the proposed algorithm does not require the definition of a mean. This property allows medoidshift to find modes even when only a distance measure between samples is defined. In this sense, the relationship between the medoidshift algorithm and the meanshift algorithm is similar to the relationship between the k-medoids and the k-means algorithms. We show that medoidshifts can also be used for incremental clustering of growing datasets by recycling previous computations. We present experimental results using medoidshift for image segmentation, incremental clustering for shot segmentation and clustering on nonlinearly separable data.
Network Slicing in 5G Mobile Communication Architecture, Profit Modeling, and Challenges

Efficient flexibility and higher system scalability call for enhanced network performance, better energy consumption, lower infrastructure cost, and effective resource utilization. To accomplish this, an architectural optimization and reconstruction of existing cellular network is required. Network slicing is considered to be one of the key enablers and an architectural answer of communication system of 2020 and beyond. Traditional mobile operators provide all types of services to various kinds of customers through a single network, however, with the deployment of network slicing operators are now able to divide entire network into different slices each with its own configuration and specific Quality of Service (QoS) requirements. In a slice-based network, each slice will be considered as a separate logical network. In this way, the infrastructure utilization and resource allocation will be much more energy and cost efficient in comparison to traditional network. In this paper, we provided a comprehensive discussion on concept and system architecture of network slicing with particular focus on its business aspect and profit modeling. We throughly discussed two different dimensions of profit modeling, so called Own-Slice Implementation and Resource Leasing for Outsourced Slices. We further addressed open research directions and existing challenges with the purpose of motivating new advances and adding realistic solutions to this emerging technology.
Embedded Assistive Stick for Visually Impaired Persons

In this paper, a smart stick is intended and executed to aid blind persons so that they can walk independently without much difficulty. Firstly, pothole detection and avoidance system are implemented by setting the ultrasonic sensor at 30-degree angle on a suitable blind stick to sense if there is a hole or staircase in front of the blind at about 30 cm distance to avoid a person from falling and as a result may be producing many damages. Secondly, a moisture sensor is placed at the down of stick to measure the degree of water land soil moisture in forward-facing of the user and aware him as soon as that degree exceeds a measured level that may submerge the foot of him. Thirdly, knee above obstacle detection and avoidance system is implemented by using an additional ultrasonic sensor on the top of the stick to turn an alarm and vibration ON when there is a person, obstacle or wall at a distance of 50 cm in front to avoid an accident and thus helping the person to move independently. Fourthly, an ultrasonic sensor is placed down the stick at about 20 cm from the ground level to detect and avoid knee below obstacles and stairs at a distance of 70 cm in front of the user. Fifthly, a wireless remote consisting of RF modules (transmitter and receiver) is implemented, so if a person drops stick or forget it somewhere, he can press a switch of the remote consisting of transmitter part, and as a result alarm with vibrations will turn on, so the user can know the location of the stick. The stick is implemented practically using single wheel leg blinding cane, Arduino microcontroller three ultrasonic sensors RF modules. Also, two buzzers and two vibration motor are used on the stick to fit on when any difficulties occur.
Lessons from the journey: a query log analysis of within-session learning

The Internet is the largest source of information in the world. Search engines help people navigate the huge space of available data in order to acquire new skills and knowledge. In this paper, we present an in-depth analysis of sessions in which people explicitly search for new knowledge on the Web based on the log files of a popular search engine. We investigate within-session and cross-session developments of expertise, focusing on how the language and search behavior of a user on a topic evolves over time. In this way, we identify those sessions and page visits that appear to significantly boost the learning process. Our experiments demonstrate a strong connection between clicks and several metrics related to expertise. Based on models of the user and their specific context, we present a method capable of automatically predicting, with good accuracy, which clicks will lead to enhanced learning. Our findings provide insight into how search engines might better help users learn as they search.
Task-Agnostic Meta-Learning for Few-shot Learning

Meta-learning approaches have been proposed to tackle the few-shot learning problem. Typically, a meta-learner is trained on a variety of tasks in the hopes of being generalizable to new tasks. However, the generalizability on new tasks of a meta-learner could be fragile when it is over-trained on existing tasks during meta-training phase. In other words, the initial model of a meta-learner could be too biased towards existing tasks to adapt to new tasks, especially when only very few examples are available to update the model. To avoid a biased meta-learner and improve its generalizability, we propose a novel paradigm of Task-Agnostic Meta-Learning (TAML) algorithms. Specifically, we present an entropy-based approach that meta-learns an unbiased initial model with the largest uncertainty over the output labels by preventing it from over-performing in classification tasks. Alternatively, a more general inequality-minimization TAML is presented for more ubiquitous scenarios by directly minimizing the inequality of initial losses beyond the classification tasks wherever a suitable loss can be defined. Experiments on benchmarked datasets demonstrate that the proposed approaches outperform compared meta-learning algorithms in both few-shot classification and reinforcement learning tasks.
Implementation of dual-frequency longitudinal slot array antenna on substrate integrated waveguide at X-band

In this paper, a dual-frequency substrate integrated waveguide (SIW) longitudinal slot array antenna has been implemented with the combination of two 1×4 slot array etched on the top metallic surface of the line. The two arrays resonate at two distinctive frequencies (8.6 GHz & 10.8 GHz) within X band (8 - 12 GHz) with good radiation pattern at both frequencies along with a gain of 7.7 dB and 9 dB respectively. The two arrays have been fed by a single SIW line which reduces the complexity of feeding network. The proposed design shows good potential to control radiation pattern and side lobe level independently at both frequencies.
A location-based multiplayer mobile game to encourage pro-environmental behaviours

This paper explores the use of persuasive technology concepts to induce behaviour changes towards a better environmental consciousness, through mobile phones and public displays. More specifically, the paper reports the design, development and evaluation of Gaea, a location-based multiplayer mobile game, which prompts people to recycle virtual objects within a specified spatial context. Initial user tests were conducted to evaluate Gaea's persuasive ability during a small-scale event. Later on, Gaea's usability, and primarily its gameplay, were evaluated more extensively, in the context of an open day event at our University campus attended by 7000 high school students. The results obtained from the user tests were very positive, and may give a considerable contribution to the future development of persuasive applications, regardless the area of study.
Feature Learning with Rank-Based Candidate Selection for Product Search

Nowadays, more and more people buy products via e-commerce websites. We can not only compare prices from different online retailers but also obtain useful review comments from other customers. Especially, people tend to search for visually similar products when they are looking for possible candidates. The need for product search is emerging. To tackle the problem, recent works integrate different additional information (e.g., attributes, image pairs, category) with deep convolutional neural networks (CNNs) for solving cross-domain image retrieval and product search. Based on the state-of-the-art approaches, we propose a rank-based candidate selection for feature learning. Given a query image, we attempt to push hard negative (irrelevant) images away from queries and make ambiguous positive (relevant) images close to queries. We investigate the effects of global and attention-based local features on the proposed method, and achieve 15.8% relative gain for product search.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

We present Spider, a large-scale, complex and cross-domain semantic parsing and textto-SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 unique complex SQL queries on 200 databases with multiple tables, covering 138 different domains. We define a new complex and cross-domain semantic parsing and textto-SQL task where different complex SQL queries and databases appear in train and test sets. In this way, the task requires the model to generalize well to both new SQL queries and new database schemas. Spider is distinct from most of the previous semantic parsing tasks because they all use a single database and the exact same programs in the train set and the test set. We experiment with various state-of-the-art models and the best model achieves only 12.4% exact matching accuracy on a database split setting. This shows that Spider presents a strong challenge for future research. Our dataset and task are publicly available at https://yale-lily. github.io/spider.
Modular converter architecture for medium voltage ultra fast EV charging stations: Global system considerations

Vehicles (EVs) are expected to contribute significantly to the reduction of CO2 emissions and other types of pollution within the next years. However, the concept of their ultra fast charging still poses demanding requirements, both in terms of the EV battery and the impact on the power grid. Towards this direction, emerging power electronics interfaces as well as energy storage technologies will play a significant role into making EV charging stations competitive with the existing gas station infrastructures. This paper focuses on the proposal of a power converter architecture, which aims for the interface between the three-phase medium voltage AC grid and the Electric Vehicle (EV) batteries. The transformerless AC/DC conversion stage motivates the choice of a suitable multilevel topology, namely the Cascaded H-Bridge Converter (CHB), where the medium voltage is split into several dedicated low voltage DC buses. At each of these levels, integrated stationary Battery Energy Storage Systems (BESS) play the role of power buffers, reducing thus the influence of the charging station on the distribution grid. The EV battery is then charged through parallel-connected isolated DC/DC converters, in order to achieve high currents and meet the standards for galvanic isolation.
Learning Structured Natural Language Representations for Semantic Parsing

We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general natural language representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We achieve the state of the art on SPADES and GRAPHQUESTIONS and obtain competitive results on GEOQUERY and WEBQUESTIONS. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated
High-Resolution Remote-Sensing Image Classification via an Approximate Earth Mover's Distance-Based Bag-of-Features Model

High-resolution remote-sensing image classification is a challenging task. In this letter, we first propose a bag-of-features (BOF) model-based classification framework for high-resolution remote-sensing images via Earth mover's distance (EMD) to perform histogram matching. Compared with conventional BOF, EMD-based BOF is insensitive to vector quantization and can explore the relations among visual codes. In addition, such relations can be utilized as a key discriminative feature for image classification task. However, EMD is not practically utilized because of expensive computational cost. Motivated by Pele and Werman, we propose a faster approximate EMD (AEMD), and our AEMD-based BOF can inherit the advantages of EMD. Experimental results on a multicategory remote-sensing image data set demonstrate the effectiveness of our classification framework.
Formal worst-case performance analysis of time-sensitive Ethernet with frame preemption

One of the key challenges in future Ethernet-based automotive and industrial networks is the low-latency transport of time-critical data. To date, Ethernet frames are sent non-preemptively. This introduces a major source of delay, as, in the worst-case, a latency-critical frame might be blocked by a frame of lower priority, which started transmission just before the latency-critical frame. The upcoming  802.3br standard will introduce Ethernet frame preemption to address this problem. While high-priority traffic benefits from preemption, lower-priority (yet still latency-sensitive) traffic experiences a certain overhead, impacting its timing behavior. In this paper, we present a formal timing analysis for Ethernet to derive worst-case latency bounds under preemption. We use a realistic automotive Ethernet setup to analyze the worst-case performance of standard Ethernet and Ethernet TSN under preemption and also compare our results to non-preemptive implementations of these standards.
The Challenge of Optical Music Recognition

This article describes the challenges posed by optical music recognition – a topic in computer science that aims to convert scanned pages of music into an on-line format. First, the problem is described; then a generalised framework for software is presented that emphasises key stages that must be solved: staff line identification, musical object location, musical feature classification, and musical semantics. Next, significant research projects in the area are reviewed, showing how each fits the generalised framework. The article concludes by discussing perhaps the most open question in the field: how to compare the accuracy and success of rival systems, highlighting certain steps that help ease the task.
AR typing interface for mobile devices

We propose a new user interface system for mobile devices. By using augmented reality (AR) technology, the system overlays virtual objects on real images captured by a camera attached to the back of a mobile device, and the user can operate the mobile device by manipulating the virtual objects with his/her hand in the space behind the mobile device. This system allows the user to operate the device in a wide three-dimensional space and to select small objects easily. Also, the AR technology provides the user with a sense of reality in operating the device. We developed a typing application using our system and verified the effectiveness by user studies. The results showed that more than half of the subjects felt that the operation area of the proposed system is larger than that of a smartphone and that both AR and unfixed key-plane are effective for improving typing speed.
Simulation in manufacturing and business: A review

This paper reports the results of a review of simulation applications published within peer-reviewed literature between 1997 and 2006 to provide an up-to-date picture of the role of simulation techniques within manufacturing and business. The review is characterised by three factors: wide coverage, broad scope of the simulation techniques, and a focus on real-world applications. A structured methodology was followed to narrow down the search from around 20,000 papers to 281. Results include interesting trends and patterns. For instance, although discrete event simulation is the most popular technique, it has lower stakeholder engagement than other techniques, such as system dynamics or gaming. This is highly correlated with modelling lead time and purpose. Considering application areas, modelling is mostly used in scheduling. Finally, this review shows an increasing interest in hybrid modelling as an approach to cope with complex enterprise-wide systems.