nils-herrmann/clean_scirepeval huggingface dataset:
A monolithic current-mode CMOS DC-DC converter with on-chip current-sensing technique

A monolithic current-mode CMOS DC-DC converter with integrated power switches and a novel on-chip current sensor for feedback control is presented in this paper. With the proposed accurate on-chip current sensor, the sensed inductor current, combined with the internal ramp signal, can be used for current-mode DC-DC converter feedback control. In addition, no external components and no extra I/O pins are needed for the current-mode controller. The DC-DC converter has been fabricated with a standard 0.6-/spl mu/m CMOS process. The measured absolute error between the sensed signal and the inductor current is less than 4%. Experimental results show that this converter with on-chip current sensor can operate from 300 kHz to 1 MHz with supply voltage from 3 to 5.2 V, which is suitable for single-cell lithium-ion battery supply applications. The output ripple voltage is about 20 mV with a 10-/spl mu/F off-chip capacitor and 4.7-/spl mu/H off-chip inductor. The power efficiency is over 80% for load current from 50 to 450 mA.
Big Data: Understanding Big Data

Steve Jobs, one of the greatest visionaries of our time was quoted in 1996 saying “a lot of times, people don’t know what they want until you show it to them”[38] indicating he advocated products to be developed based on human intuition rather than research. With the advancements of mobile devices, social networks and the Internet of Things (IoT) enormous amounts of complex data, both structured & unstructured are being captured in hope to allow organizations to make better business decisions as data is now vital for an organizations success. These enormous amounts of data are referred to as Big Data, which enables a competitive advantage over rivals when processed and analyzed appropriately. However Big Data Analytics has a few concerns including Management of Datalifecycle, Privacy & Security, and Data Representation. This paper reviews the fundamental concept of Big Data, the Data Storage domain, the MapReduce programming paradigm used in processing these large datasets, and focuses on two case studies showing the effectiveness of Big Data Analytics and presents how it could be of greater good in the future if handled appropriately. Big Data; Big Data Analytics; Big Data Inconsistencies; Data Storage; MapReduce; Knowledge-Space
Software Defect Reduction Top 10 List

“Avoidable rework” is effort spent fixing difficulties with the software that could have been avoided or discovered earlier and less expensively. This definition implies that there is such thing as “unavoidable rework”. Reducing “avoidable rework” is a major source of software productivity improvement and most effort savings from improving software processes, architectures and risk management are results of reductions in “avoidable rework”.
Learning Analytics at Low Cost: At-risk Student Prediction with Clicker Data and Systematic Proactive Interventions

While learning analytics (LA) practices have been shown to be practical and effective, most of them require a huge amount of data and effort. This paper reports a case study which demonstrates the feasibility of practising LA at a low cost for instructors to identify at-risk students in an undergraduate business quantitative methods course. Instead of using tracking data from a learning management system as predictive variables, this study utilised clicker responses as formative assessments, together with student demographic data and summative assessments. This LA practice makes use of free cloud services, Google Forms and Google Sheets in particular for collecting and analysing clicker data. Despite a small dataset being used, the LA implementation was effective in identifying at-risk students at an early stage. A systematic proactive advising approach is proposed as an intervention strategy based on students’ at-risk probability estimated by a prediction model. The result shows that the intervention success rate increases correspondingly with the number of interventions and the intervention effects on peer groups are far more successful than on individual students. Overall, the students’ pass rate in the study was 7% higher than that for the whole course. Practical recommendations and concerns about using linear regression and logistic regression for classification are also discussed.
Learning using privileged information: SV M+ and weighted SVM

Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information available only at training time-a framework implemented by SVM+. We relate the privileged information to importance weighting and show that the prior knowledge expressible with privileged features can also be encoded by weights associated with every training example. We show that a weighted SVM can always replicate an SVM+ solution, while the converse is not true and we construct a counterexample highlighting the limitations of SVM+. Finally, we touch on the problem of choosing weights for weighted SVMs when privileged features are not available.
Social Media Mining to Understand Public Mental Health

In this paper, we apply text mining and topic modelling to understand public mental health. We focus on identifying common mental health topics across two anonymous social media platforms: Reddit and a mobile journalling/mood-tracking app. Furthermore, we analyze journals from the app to uncover relationships between topics, journal visibility (private vs. visible to other users of the app), and user-labelled sentiment. Our main findings are that 1) anxiety and depression are shared on both platforms; 2) users of the journalling app keep routine topics such as eating private, and these topics rarely appear on Reddit; and 3) sleep was a critical theme on the journalling app and had an unexpectedly negative sentiment.
Ideal patterns of strategic alignment and business performance

Strategic alignment or "fit" is a notion that is deemed crucial in understanding how organizations can translate their deployment of information technology (IT) into actual increases in performance. While previous theoretical and methodological works have provided foundations for identifying the dimensions and performance impacts of the strategic alignment between IT, strategy, and structure, few attempts have been made to test the proposed theory empirically and operationalize fit systemically. Based on a gestalt perspective of fit and theory-based ideal coalignment patterns, an operational model of strategic alignment is proposed and empirically validated through a mail survey of 110 small firms. Using cluster analysis, it was found that low-performance firms exhibited a conflictual coalignment pattern of business strategy, business structure, IT strategy, and IT structure that distinguished them from other firms.
Agile Software Development in Large Organizations

I n recent years, the use of, interest in, and controversies surrounding agile methods have all increased dramatically—as has anecdotal evidence for agile methods’ effectiveness in certain environments and for specific project types. Exactly in which environments and under what conditions agile methods work remains unclear, however. A development team at Motorola, for example, noted that, “a plethora of subjective evidence exists to support the use of agile development methods on non-life-critical software projects.”1 Yet the team found no information about using the approach for its particular development focus: mission-critical software products. This shows the need for more evidence that a new technology works in a certain context before developers promote and deploy it on a larger scale. Although most organizations have similar needs, the need to see compelling evidence before adopting new methods looms greater in large organizations because of their complexity and the need to integrate new technologies and processes with existing ones. To further evaluate agile methods and their underlying software development practices, several Software Experience Center (SEC) member companies initiated a series of activities to discover if agile practices match their organizations’ needs. Although each organization evaluated agile methods according to its specific needs, here we attempt to generalize their findings by analyzing some of their common experiences in the particular context of large organizations with well-established structures and processes. We base this analysis on experience collected and shared among four SEC members—ABB, DaimlerChrysler, Motorola, and Nokia—and focus on the following areas:
Modeling by Tweet Aggregation

Conventional topic modeling schemes, such as Latent Dirichlet Allocation, are known to perform inadequately when applied to tweets, due to the sparsity of short documents. To alleviate these disadvantages, we apply several pooling techniques, aggregating similar tweets into individual documents, and specifically study the aggregation of tweets sharing authors or hashtags. The results show that aggregating similar tweets into individual documents significantly increases topic coherence.
Complex clock gating with integrated clock gating logic cell

Clock gating is an effective technique for minimizing dynamic power in sequential circuits. Applying clock-gating at gate-level not only saves time compared to implementing clock-gating in the RTL code but also saves power and can easily be automated in the synthesis process. This paper presents simulation results on various types of clock-gating at different hierarchical levels on a serial peripheral interface (SPI) design. In general power savings of about 30% and 36% reduction on toggle rate can be seen with different complex clock- gating methods with respect to no clock-gating in the design.
Contextual Slot Carryover for Disparate Schemas

In the slot-filling paradigm, where a user can refer back to slots in the context during a conversation, the goal of the contextual understanding system is to resolve the referring expressions to the appropriate slots in the context. In large-scale multi-domain systems, this presents two challenges scaling to a very large and potentially unbounded set of slot values, and dealing with diverse schemas. We present a neural network architecture that addresses the slot value scalability challenge by reformulating the contextual interpretation as a decision to carryover a slot from a set of possible candidates. To deal with heterogenous schemas, we introduce a simple data-driven method for transforming the candidate slots. Our experiments show that our approach can scale to multiple domains and provides competitive results over a strong baseline.
Large-scale Unit Commitment under uncertainty

The Unit Commitment problem in energy management aims at finding the optimal productions schedule of a set of generation units while meeting various system-wide constraints. It has always been a large-scale, non-convex difficult problem, especially in view of the fact that operational requirements imply that it has to be solved in an unreasonably small time for its size. Recently, the ever increasing capacity for renewable generation has strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex, uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focusing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, also providing entry points to the relevant literature on optimization under uncertainty.
Single Shot Temporal Action Detection

Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from $19.0%$ to $24.6%$ on THUMOS 2014 and from 7.4% to $11.0%$ on MEXaction2.
Deep feature-based face detection on mobile devices

We propose a deep feature-based face detector for mobile devices to detect user's face acquired by the front-facing camera. The proposed method is able to detect faces in images containing extreme pose and illumination variations as well as partial faces. The main challenge in developing deep feature-based algorithms for mobile devices is the constrained nature of the mobile platform and the non-availability of CUDA enabled GPUs on such devices. Our implementation takes into account the special nature of the images captured by the front-facing camera of mobile devices and exploits the GPUs present in mobile devices without CUDA-based frameworks, to meet these challenges.
Research Paper: A General Natural-language Text Processor for Clinical Radiology

Development of a general natural-language processor that identifies clinical information in narrative reports and maps that information into a structured representation containing clinical terms.



The natural-language processor provides three phases of processing, all of which are driven by different knowledge sources. The first phase performs the parsing. It identifies the structure of the text through use of a grammar that defines semantic patterns and a target form. The second phase, regularization, standardizes the terms in the initial target structure via a compositional mapping of multi-word phrases. The third phase, encoding, maps the terms to a controlled vocabulary. Radiology is the test domain for the processor and the target structure is a formal model for representing clinical information in that domain.



The impression sections of 230 radiology reports were encoded by the processor. Results of an automated query of the resultant database for the occurrences of four diseases were compared with the analysis of a panel of three physicians to determine recall and precision.



Without training specific to the four diseases, recall and precision of the system (combined effect of the processor and query generator) were 70% and 87%. Training of the query component increased recall to 85% without changing precision.
Credit scoring with a data mining approach based on support vector machines

The credit card industry has been growing rapidly recently, and thus huge numbers of consumers’ credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer’s credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant’s credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant’s credit score from the applicant’s input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods.
Facebook use and academic performance among college students: A mixed-methods study with a multi-ethnic sample

This paper uses a mixed-methods approach to examine the relation between online academic disclosure and academic performance. A multi-ethnic sample of college students (N = 261; male = 66; female = 195; M age 22 years) responded to open-ended questions about their Facebook use. Thematic analysis revealed that over 14% of the Facebook wall posts/status updates (N = 714) contained academic themes; positive states were more frequent than negative and neutral states and students with lower GPAs expressed negative states more often. A path analysis suggested that academic performance may determine college students’ Facebook use, rather than the reverse. Implications for student support services
A Honeypot Architecture for Detecting and Analyzing Unknown Network Attacks

In this paper, we propose a honeypot architecture for detecting and analyzing unknown network attacks. The main focus of our approach lies in improving the “significance” of recorded events and network traffic that need to be analyzed by a human network security operator in order to identify a new attacking pattern. Our architecture aims to achieve this goal by combining three main components: 1. a packet filter that suppresses all known attacking packets, 2. a proxy host that performs session-individual logging of network traffic, and 3. a honeypot host that executes actual network services to be potentially attacked from the Internet in a carefully supervised environment and that reports back to the proxy host upon the detection of suspicious behavior. Experiences with our first prototype of this concept show that it is relatively easy to specify suspicious behavior and that traffic belonging to an attack can be successfully identified and marked.
How do habits guide behavior ? Perceived and actual triggers of habits in daily life

a r t i c l e i n f o What are the psychological mechanisms that trigger habits in daily life? Two studies reveal that strong habits are influenced by context cues associated with past performance (e.g., locations) but are relatively unaffected by current goals. Specifically, performance contexts—but not goals—automatically triggered strongly habitual behaviors in memory (Experiment 1) and triggered overt habit performance (Experiment 2). Nonetheless, habits sometimes appear to be linked to goals because people self-perceive their habits to be guided by goals. Furthermore, habits of moderate strength are automatically influenced by goals, yielding a curvilinear, U-shaped relation between habit strength and actual goal influence. Thus, research that taps self-perceptions or moderately strong habits may find habits to be linked to goals.  Having cast off the strictures of behaviorism, psychologists are showing renewed interest in the psychological processes that guide This interest is fueled partly by the recognition that automaticity is not a unitary construct. Hence, different kinds of automatic responses may be triggered and controlled in different ways (Bargh, 1994; Moors & De Houwer, 2006). However, the field has not yet converged on a common understanding of the psychological mechanisms that underlie habits. Habits can be defined as psychological dispositions to repeat past behavior. They are acquired gradually as people repeatedly respond in a recurring context (e.g., performance settings, action sequences, Wood & Neal, 2007, 2009). Most researchers agree that habits often originate in goal pursuit, given that people are likely to repeat actions that are rewarding or yield desired outcomes. In addition, habit strength is a continuum, with habits of weak and moderate strength performed with lower frequency and/or in more variable contexts than strong habits This consensus aside, it remains unclear how goals and context cues influence habit automaticity. Goals are motivational states that (a) define a valued outcome that (b) energizes and directs action (e.g., the goal of getting an A in class energizes late night studying; Förster, Liberman, & Friedman, 2007). In contrast, context cues for habits reflect features of the performance environment in which the response typically occurs (e.g., the college library as a setting for late night studying). Some prior research indicates that habits are activated automatically by goals (e.g., Aarts & Dijksterhuis, 2000), whereas others indicate that habits are activated directly by context cues, with minimal influence of goals In the present experiments, we first test the cognitive associations …
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies

We present NEWSROOM, a summarization dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications. Extracted from search and social media metadata between 1998 and 2017, these high-quality summaries demonstrate high diversity of summarization styles. In particular, the summaries combine abstractive and extractive strategies, borrowing words and phrases from articles at varying rates. We analyze the extraction strategies used in NEWSROOM summaries against other datasets to quantify the diversity and difficulty of our new data, and train existing methods on the data to evaluate its utility and challenges. The dataset is available online at summari.es.
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer

In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness. In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as highquality waveform generation. We introduce TimbreTron, a method for musical timbre transfer which applies “image” domain style transfer to a time-frequency representation of the audio signal, and then produces a high-quality waveform using a conditional WaveNet synthesizer. We show that the Constant Q Transform (CQT) representation is particularly well-suited to convolutional architectures due to its approximate pitch equivariance. Based on human perceptual evaluations, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and polyphonic samples. We made an accompanying demo video 1 which we strongly encourage you to watch before reading the paper.