nils-herrmann/clean_scirepeval huggingface dataset:
Human Parsing with Contextualized Convolutional Neural Network

In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, semantic edge context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network. Given an input human image, Co-CNN produces the pixelwise categorization in an end-to-end way. First, the cross-layer context is captured by our basic local-to-global-to-local structure, which hierarchically combines the global semantic information and the local fine details across different convolutional layers. Second, the global image-level label prediction is used as an auxiliary objective in the intermediate layer of the Co-CNN, and its outputs are further used for guiding the feature learning in subsequent convolutional layers to leverage the global image-level context. Third, semantic edge context is further incorporated into Co-CNN, where the high-level semantic boundaries are leveraged to guide pixel-wise labeling. Finally, to further utilize the local super-pixel contexts, the within-super-pixel smoothing and cross-super-pixel neighbourhood voting are formulated as natural sub-components of the Co-CNN to achieve the local label consistency in both training and testing process. Comprehensive evaluations on two public datasets well demonstrate the significant superiority of our Co-CNN over other state-of-the-arts for human parsing. In particular, the F-1 score on the large dataset  reaches 81.72 percent by Co-CNN, significantly higher than 62.81 percent and 64.38 percent by the state-of-the-art algorithms, M-CNN [2] and ATR , respectively. By utilizing our newly collected large dataset for training, our Co-CNN can achieve 85.36 percent in F-1 score.
Chipless RFID Tag Using Multiple Microstrip Open Stub Resonators

A novel compact RFID tag employing open stubs in a microstrip transmission line is proposed. The prototype of the tag is fabricated on a substrate of dielectric constant 4.4 and loss tangent 0.0018. The tag consists of microstrip open stub resonators and cross polarized transmitting and receiving disc monopole antennas. A prototype of 8 bit data encoded tag is demonstrated in this communication. Method for enhancing the performance of the RFID tag is also proposed. Magnitude or group delay response can be used to decode the tag informations.
"Shall I Be Your Chat Companion?": Towards an Online Human-Computer Conversation System

To establish an automatic conversation system between human and computer is regarded as one of the most hardcore problems in computer science. It requires interdisciplinary techniques in information retrieval, natural language processing, and data management, etc. The challenges lie in how to respond like a human, and to maintain a relevant, meaningful, and continuous conversation. The arrival of big data era reveals the feasibility to create such a system empowered by data-driven approaches. We can now organize the conversational data as a chat companion. In this paper, we introduce a chat companion system, which is a practical conversation system between human and computer as a real application. Given the human utterances as queries, our proposed system will respond with corresponding replies retrieved and highly ranked from a massive conversational data repository. Note that 'practical' here indicates effectiveness and efficiency: both issues are important for a real-time system based on a massive data repository. We have two scenarios of single-turn and multi-turn conversations. In our system, we have a base ranking without conversational context information (for single-turn) and a context-aware ranking (for multi-turn). Both rankings can be conducted either by a shallow learning or deep learning paradigm. We combine these two rankings together in optimization. In the experimental setups, we investigate the performance between effectiveness and efficiency for the proposed methods, and we also compare against a series of baselines to demonstrate the advantage of the proposed framework in terms of p@1, MAP, and nDCG. We present a new angle to launch a practical online conversation system between human and computer.
Robot operating system: A modular software framework for automated driving

Automated vehicles are complex systems with a high degree of interdependencies between its components. This complexity sets increasing demands for the underlying software framework. This paper firstly analyzes the requirements for software frameworks. Afterwards an overview on existing software frameworks, that have been used for automated driving projects, is provided with an in-depth introduction into an emerging open-source software framework, the Robot Operating System (ROS). After discussing the main features, advantages and disadvantages of ROS, the communication overhead of ROS is analyzed quantitatively in various configurations showing its applicability for systems with a high data load.
Printed Slot Loaded Bow-Tie Antenna With Super Wideband Radiation Characteristics for Imaging Applications

A super wideband printed modified bow-tie antenna loaded with rounded-T shaped slots fed through a microstrip balun is proposed for microwave and millimeter-wave band imaging applications. The modified slot-loaded bow-tie pattern increases the electrical length of the bow-tie antenna reducing the lower band to 3.1 GHz. In addition, over the investigated frequency band up to 40 GHz, the proposed modified bow-tie pattern considerably flattens the input impedance response of the bow-tie resulting in a smooth impedance matching performance enhancing the reflection coefficient (S11) characteristics. The introduction of the modified ground plane printed underneath the bow-tie, on the other hand, yields to directional far-field radiation patterns with considerably enhanced gain performance. The S11 and E-plane/H-plane far-field radiation pattern measurements have been carried out and it is demonstrated that the fabricated bow-tie antenna operates across a measured frequency band of 3.1-40 GHz with an average broadband gain of 7.1 dBi.
Risk-Sensitive Inverse Reinforcement Learning via Gradient Methods

We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive. In particular, we model risk-sensitivity in a reinforcement learning framework by making use of models of human decision-making having their origins in behavioral psychology, behavioral economics, and neuroscience. We propose a gradient-based inverse reinforcement learning algorithm that minimizes a loss function defined on the observed behavior. We demonstrate the performance of the proposed technique on two examples, the first of which is the canonical Grid World example and the second of which is a Markov decision process modeling passengers decisions regarding ride-sharing. In the latter, we use pricing and travel time data from a ride-sharing company to construct the transition probabilities and rewards of the Markov decision process.
Does hotel attribute importance differ by hotel? Focusing on hotel star-classifications and customers' overall ratings

Hotel guests’ expectations and actual experiences on hotel service quality often fail to coincide due to guests’ unusually high anticipations, hotels’ complete breakdowns in delivering their standard, or the combination of both. Moreover, this disconfirmation could be augmented contingent upon the level of hotel segment (hotel star-classification) and the overall rating manifested by previous guests. By incorporating a 2 2 matrix design in which a hotel star-classification configures one dimension (2 versus 4 stars) and a customers’ overall rating (lower versus higher overall ratings) configures the other, this explorative multiple case study uses conjoint analyses to examine the differences in the comparative importance of the six hotel attributes (value, location, sleep quality, rooms, cleanliness, and service) among four prominent hotel chain brands located in the United States. Four major and eight minor propositions are suggested for future empirical research based on the results of the four combined studies. Through the analysis of online data, this study may enlighten hotel managers with various ways to accommodate hotel guests’ needs.
Photonic analog-to-digital converters.

This paper reviews over 30 years of work on photonic analog-to-digital converters. The review is limited to systems in which the input is a radio-frequency (RF) signal in the electronic domain and the output is a digital version of that signal also in the electronic domain, and thus the review excludes photonic systems directed towards digitizing images or optical communication signals. The state of the art in electronic ADCs, basic properties of ADCs and properties of analog optical links, which are found in many photonic ADCs, are reviewed as background information for understanding photonic ADCs. Then four classes of photonic ADCs are reviewed: 1) photonic assisted ADC in which a photonic device is added to an electronic ADC to improve performance, 2) photonic sampling and electronic quantizing ADC, 3) electronic sampling and photonic quantizing ADC, and 4) photonic sampling and quantizing ADC. It is noted, however, that all 4 classes of "photonic ADC" require some electronic sampling and quantization. After reviewing all known photonic ADCs in the four classes, the review concludes with a discussion of the potential for photonic ADCs in the future.
Retronasal triangle: a sonographic landmark for the screening of cleft palate in the first trimester.

To describe a new first-trimester sonographic landmark, the retronasal triangle, which may be useful in the early screening for cleft palate.



The retronasal triangle, i.e. the three echogenic lines formed by the two frontal processes of the maxilla and the palate visualized in the coronal view of the fetal face posterior to the nose, was evaluated prospectively in 100 consecutive normal fetuses at the time of routine first-trimester sonographic screening at 11 + 0 to 13 + 6 weeks' gestation. In a separate study of five fetuses confirmed postnatally as having a cleft palate, ultrasound images, including multiplanar three-dimensional views, were analyzed retrospectively to review the retronasal triangle.



None of the fetuses evaluated prospectively was affected by cleft lip and palate. During their first-trimester scan, the retronasal triangle could not be identified in only two fetuses. Reasons for suboptimal visualization of this area included early gestational age at scanning (11 weeks) and persistent posterior position of the fetal face. Of the five cases with postnatal diagnosis of cleft palate, an abnormal configuration of the retronasal triangle was documented in all cases on analysis of digitally stored three-dimensional volumes.



This study demonstrates the feasibility of incorporating evaluation of the retronasal triangle into the routine evaluation of the fetal anatomy at 11 + 0 to 13 + 6 weeks' gestation. Because fetuses with cleft palate have an abnormal configuration of the retronasal triangle, focused examination of the midface, looking for this area at the time of the nuchal translucency scan, may facilitate the early detection of cleft palate in the first trimester.
An Evaluation of the Hibernate Object-Relational Mapping for Processing Interactive Social Networking Actions

With object-oriented programming languages, Object Relational Mapping (ORM) frameworks such as Hibernate have gained popularity due to their ease of use and portability to different relational database management systems. Hibernate implements the Java Persistent API, JPA, and frees a developer from authoring software to address the impedance mismatch between objects and relations. In this paper, we evaluate the performance of Hibernate by comparing it with a native JDBC implementation using a benchmark named BG. BG rates the performance of a system for processing interactive social networking actions such as view profile, extend an invitation from one member to another, and other actions. Our key findings are as follows. First, an object-oriented Hibernate implementation of each action issues more SQL queries than its JDBC counterpart. This enables the JDBC implementation to provide response times that are significantly faster. Second, one may use the Hibernate Query Language (HQL) to refine the object-oriented Hibernate implementation to provide performance that approximates the JDBC implementation.
Dynamic Clustering of Streaming Short Documents

Clustering technology has found numerous applications in mining textual data. It was shown to enhance the performance of retrieval systems in various different ways, such as identifying different query aspects in search result diversification, improving smoothing in the context of language modeling, matching queries with documents in a latent topic space in ad-hoc retrieval, summarizing documents etc. The vast majority of clustering methods have been developed under the assumption of a static corpus of long (and hence textually rich) documents. Little attention has been given to streaming corpora of short text, which is the predominant type of data in Web 2.0 applications, such as social media, forums, and blogs. In this paper, we consider the problem of dynamically clustering a streaming corpus of short documents. The short length of documents makes the inference of the latent topic distribution challenging, while the temporal dynamics of streams allow topic distributions to change over time. To tackle these two challenges we propose a new dynamic clustering topic model - DCT - that enables tracking the time-varying distributions of topics over documents and words over topics. DCT models temporal dynamics by a short-term or long-term dependency model over sequential data, and overcomes the difficulty of handling short text by assigning a single topic to each short document and using the distributions inferred at a certain point in time as priors for the next inference, allowing the aggregation of information. At the same time, taking a Bayesian approach allows evidence obtained from new streaming documents to change the topic distribution. Our experimental results demonstrate that the proposed clustering algorithm outperforms state-of-the-art dynamic and non-dynamic clustering topic models in terms of perplexity and when integrated in a cluster-based query likelihood model it also outperforms state-of-the-art models in terms of retrieval quality.
The Psychology of Security for the Home Computer User

The home computer user is often said to be the weakest link in computer security. They do not always follow security advice, and they take actions, as in phishing, that compromise themselves. In general, we do not understand why users do not always behave safely, which would seem to be in their best interest. This paper reviews the literature of surveys and studies of factors that influence security decisions for home computer users. We organize the review in four sections: understanding of threats, perceptions of risky behavior, efforts to avoid security breaches and attitudes to security interventions. We find that these studies reveal a lot of reasons why current security measures may not match the needs or abilities of home computer users and suggest future work needed to inform how security is delivered to this user group.
Clustrophile: A Tool for Visual Clustering Analysis

While clustering is one of the most popular methods for data mining, analysts lack adequate tools for quick, iterative clustering analysis, which is essential for hypothesis generation and data reasoning. We introduce Clustrophile, an interactive tool for iteratively computing discrete and continuous data clusters, rapidly exploring different choices of clustering parameters, and reasoning about clustering instances in relation to data dimensions. Clustrophile combines three basic visualizations – a table of raw datasets, a scatter plot of planar projections, and a matrix diagram (heatmap) of discrete clusterings – through interaction and intermediate visual encoding. Clustrophile also contributes two spatial interaction techniques, forward projection and backward projection, and a visualization method, prolines, for reasoning about two-dimensional projections obtained through dimensionality reductions.
Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles

Domain ontologies are important information sources for knowledge-based systems. Yet, building domain ontologies from scratch is known to be a very labor-intensive process. In this study, we present our semi-automatic approach to building an ontology for the domain of wind energy which is an important type of renewable energy with a growing share in electricity generation all over the world. Related Wikipedia articles are first processed in an automated manner to determine the basic concepts of the domain together with their properties and next the concepts, properties, and relationships are organized to arrive at the ultimate ontology. We also provide pointers to other engineering ontologies which could be utilized together with the proposed wind energy ontology in addition to its prospective application areas. The current study is significant as, to the best of our knowledge, it proposes the first considerably wide-coverage ontology for the wind energy domain and the ontology is built through a semi-automatic process which makes use of the related Web resources, thereby reducing the overall cost of the ontology building process.
AN IMPROVED ROUND ROBIN CPU SCHEDULING ALGORITHM WITH VARYING TIME QUANTUM

Process management is one of the important tasks performed by the operating system. The performance of the system depends on the CPU scheduling algorithms. The main aim of the CPU scheduling algorithms is to minimize waiting time, turnaround time, response time and context switching and maximizing CPU utilization. First-Come-First-Served (FCFS) Round Robin (RR), Shortest Job First (SJF) and, Priority Scheduling are some popular CPU scheduling algorithms. In time shared systems, Round Robin CPU scheduling is the preferred choice. In Round Robin CPU scheduling, performance of the system depends on the choice of the optimal time quantum. This paper presents an improved Round Robin CPU scheduling algorithm coined enhancing CPU performance using the features of Shortest Job First and Round Robin scheduling with varying time quantum. The proposed algorithm is experimentally proven better than conventional RR. The simulation results show that the waiting time and turnaround time have been reduced in the proposed algorithm compared to traditional RR.
Surveying the Development of Biometric User Authentication on Mobile Phones

Designing reliable user authentication on mobile phones is becoming an increasingly important task to protect users' private information and data. Since biometric approaches can provide many advantages over the traditional authentication methods, they have become a significant topic for both academia and industry. The major goal of biometric user authentication is to authenticate legitimate users and identify impostors based on physiological and behavioral characteristics. In this paper, we survey the development of existing biometric authentication techniques on mobile phones, particularly on touch-enabled devices, with reference to 11 biometric approaches (five physiological and six behavioral). We present a taxonomy of existing efforts regarding biometric authentication on mobile phones and analyze their feasibility of deployment on touch-enabled mobile phones. In addition, we systematically characterize a generic biometric authentication system with eight potential attack points and survey practical attacks and potential countermeasures on mobile phones. Moreover, we propose a framework for establishing a reliable authentication mechanism through implementing a multimodal biometric user authentication in an appropriate way. Experimental results are presented to validate this framework using touch dynamics, and the results show that multimodal biometrics can be deployed on touch-enabled phones to significantly reduce the false rates of a single biometric system. Finally, we identify challenges and open problems in this area and suggest that touch dynamics will become a mainstream aspect in designing future user authentication on mobile phones.
Beyond the Smart Home

Due to progress in wired and wireless home networking, sensor networks, networked appliances, mechanical and control engineering, and computers, we can build smart homes, and many smart home projects are currently proceeding throughout the world. However, we have to be careful not to repeat the same mistake that was made with home automation technologies that were booming in the 1970s. That is, [total?] automation should not be a goal of smart home technologies. I believe the following points are important in construction of smart homes from users¿ viewpoints: development of interface technologies between humans and systems for detection of human intensions, feelings, and situations; improvement of system knowledge; and extension of human activity support outside homes to the scopes of communities, towns, and cities.
SPAD Smart Pixel for Time-of-Flight and Time-Correlated Single-Photon Counting Measurements

We present a smart pixel based on a single-photon avalanche diode (SPAD) for advanced time-of-flight (TOF) and time-correlated single photon counting (TCSPC) applications, fabricated in a cost-effective 0.35- m CMOS technology. The large CMOS detector (30- m active area diameter) shows very low noise (12 counts per second at room temperature at 5-V excess bias) and high efficiency in a wide wavelength range (about 50% at 410 nm and still 5% at 800 nm). The analog front-end electronics promptly senses and quenches the avalanche, thus leading to an almost negligible afterpulsing effect. The in-pixel 10-bit time-to-digital converter (TDC) provides 312-ps resolution and 320-ns full-scale range (FSR), i.e., 10-cm single-shot spatial resolution within 50-m depth range in a TOF system. The in-pixel 10-bit memory and output buffers make this smart pixel the viable building block for advanced single-photon imager arrays for 3-D depth ranging in safety and security applications and for 2-D fluorescence lifetime decays in biomedical imaging.
Enterprise Information Systems as Objects and Carriers of Institutional Forces: The New Iron Cage?

This paper draws upon the institutional theory lens to examine enterprise information systems. We propose that these information systems engender a duality. On one hand, these systems are subject to institutional forces and institutional processes that set the rules of rationality. On the other hand, they are an important embodiment of institutional commitments and serve to preserve these rules by constraining the actions of human agents. The complexity inherent to enterprise technologies renders them an equivoque. This, when combined with the propensity toward lack of mindfulness in organizations, is likely to lead to acquiescence to institutional pressures. Enterprise information systems bind organizations to fundamental choices about how their activities should be organized; unquestioned choices that tend to appear natural. We suggest implications of this view and develop propositions examining: (1) enterprise information systems as objects of institutional forces in the “chartering” and “project” phases, (2) the resolution of institutional misalignments caused by the introduction of new systems, and (3) enterprise information systems as carriers of institutional logics in the “shakeout” and “onward &