nils-herrmann/clean_scirepeval huggingface dataset:
Low-profile dual-band circularly polarized microstrip antenna for GNSS applications

This paper presents a design of a micro-strip circularly polarized antenna intended for the Global Navigation Satellite Systems (GNSS). The presented device is composed of a micro-strip slotted patch antenna printed on a Rogers RO3006 substrate, a foam layer of 2 mm thick and a wideband commercial 3-dB SMT coupler. The combined fullwave antenna results with the measured S-Parameters of the coupler shows very good performances in terms of antenna matching and axial ratio on larger bandwidths.
Performance Analysis of Enhanced SPECK Algorithm

A secure lightweight block cipher is an effective security solution for applications running on resource-constrained devices. The SPECK family of lightweight block ciphers is designed for low-end devices that need data security. The randomness of the keystream produced by this algorithm is a good indicator of its security strength. Nevertheless, the computed proportion of sequences based on empirical test results falls beyond the range of acceptable interval, an evidence of non-randomness to each algorithm. In this study, the researchers enhanced SPECK (k-SPECK) through the addition of a key derivation function in its processes. Both SPECK and k - SPECK were evaluated with the two sophisticated statistical test suite. The result shows that k - SPECK outperformed SPECK in terms of randomness as its three variations successfully passed all the test of statistical packages NIST and DieHarder, making it more cryptographically secured. Similarly, software implementation was also evaluated showing that k - SPECK 128/192 and 128/256 performed faster in encryption cycle compared to SPECK.
CardBoardiZer: Creatively Customize, Articulate and Fold 3D Mesh Models

Computer-aided design of flat patterns allows designers to prototype foldable 3D objects made of heterogeneous sheets of material. We found origami designs are often characterized by pre-synthesized patterns and automated algorithms. Furthermore, augmenting articulated features to a desired model requires time-consuming synthesis of interconnected joints.  We develop a building platform to allow the designer to 1) import a desired 3D shape, 2) customize articulated partitions into planar or volumetric foldable patterns, and 3) define rotational movements between partitions. The system unfolds the model into 2D crease-cut-slot patterns ready for die-cutting and folding. In this paper, we developed interactive algorithms and validated the usability of CardBoardiZer using various 3D models. Furthermore, comparisons between CardBoardiZer and methods of Autodesk® 123D Make, demonstrated significantly shorter time-to-prototype and ease of fabrication.
Dynamic Online Pricing with Incomplete Information

Consider the pricing decision for a manager at large online retailer, such as Amazon.com, that sells millions of products. The pricing manager must decide on real-time prices for each of these product. Due to the large number of products, the manager must set retail prices without complete demand information. A manager can run price experiments to learn about demand and maximize long run profits. There are two aspects that make the online retail pricing different from traditional brick and mortar settings. First, due to the number of products the manager must be able to automate pricing. Second, an online retailer can make frequent price changes. Pricing differs from other areas of online marketing where experimentation is common, such as online advertising or website design, as firms do not randomize prices to different customers at the same time. In this paper we propose a dynamic price experimentation policy where the firm has incomplete demand information. For this general setting, we derive a pricing algorithm that balances earning profit immediately and learning for future profits. The proposed approach marries statistical machine learning and economic theory. In particular we combine multi-armed bandit (MAB) algorithms with partial identification of consumer demand into a unique pricing policy. Our automated policy solves this problem using a scalable distribution-free algorithm. We show that our method converges to the optimal price faster than standard machine learning MAB solutions to the problem. In a series of Monte Carlo simulations, we show that the proposed approach perform favorably compared to methods in computer science and revenue management. ∗Ross School of Business, University of Michigan. Contact: ericmsch@umich.edu †Rady School of Management, University of California, San Diego. Contact: kamisra@ucsd.edu ‡Department of Electrical Engineering and Computer Science, University of Michigan.
Smartphone physics – a smart approach to practical work in science education ?

In the form of teacher didactical design research, this work addresses a didactical issue encountered during physics teaching in a Swedish upper secondary school. A need for renewed practical laboratory work related to Newtonian mechanics is met by proposing and designing an activity based on highspeed photography using the nowadays omnipresent smartphone, thus bringing new technology into the classroom. The activity – video analysis of the collision physics of football kicks – is designed and evaluated by following a didactical design cycle. The work elaborates on how the proposed laboratory activity relates to the potential and complications of experimental activities in science education, as described in the vast literature on the topic. It is argued that the use of smartphones constitutes an interesting use of new technology for addressing known problems of practical work. Of particular interest is that smartphones offer a way to bridge the gap between the everyday life of students and the world of physics experiments (smartphones are powerful pocket laboratories). The use of smartphones also avoids using unfamiliar laboratory equipment that is known to hinder focus on intended content, while at the same time exploring a powerful tool for data acquisition and analysis. Overall, the use of smartphones (and computers) in this manner can be seen as the result of applying Occam’s razor to didactics: only familiar and readily available instrumentation is used, and skills learned (movie handling and image analysis) are all educationally worthwhile. Although the activity was judged successful, a systematic investigation of learning outcome was out of scope. This means that no strong conclusions can be drawn based on this limited work. Nonetheless, the smartphone activity was well received by the students and should constitute a useful addition to the set of instructional approaches, especially since variation is known to benefit learning. The main failure of the design was an overestimation of student prior knowledge on motion physics (and its application to image data). As a consequence, the activity took required more time and effort than originally anticipated. No severe pitfalls of smartphone usage were identified, but it should be noted that the proposed activity – with its lack of well-defined results due to variations in kick strength – requires that the teacher is capable of efficiently analysing multiple student films (avoiding the feedback process to become overwhelmingly time consuming). If not all student films are evaluated, the feedback to the students may become of low quality, and misconceptions may pass under the radar. On the other hand, given that programming from 2018 will become compulsory, an interesting development of the activity would be to include handling of images and videos using a high-level programming language like Python.
A survey of smoothing techniques for ME models

In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood (ML) training for exponential models, and like other ML methods is prone to overfitting of training data. Several smoothing methods for ME models have been proposed to address this problem, but previous results do not make it clear how these smoothing methods compare with smoothing methods for other types of related models. In this work, we survey previous work in ME smoothing and compare the performance of several of these algorithms with conventional techniques for smoothing -gram language models. Because of the mature body of research in -gram model smoothing and the close connection between ME and conventional -gram models, this domain is well-suited to gauge the performance of ME smoothing methods. Over a large number of data sets, we find that fuzzy ME smoothing performs as well as or better than all other algorithms under consideration. We contrast this method with previous -gram smoothing methods to explain its superior performance.
CatBoost: unbiased boosting with categorical features

This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.
Privacy and Security in Internet of Things and Wearable Devices

Enter the nascent era of Internet of Things (IoT) and wearable devices, where small embedded devices loaded with sensors collect information from its surroundings, process it, and relay it to remote locations for further analysis. Albeit looking harmless, these nascent technologies raise security and privacy concerns. We pose the question of the possibility and effects of compromising such devices. Concentrating on the design flow of IoT and wearable devices, we discuss some common design practices and their implications on security and privacy. Two representatives from each category, the Google Nest Thermostat and the Nike+ Fuelband, are selected as examples on how current industry practices of security as an afterthought or an add-on affect the resulting device and the potential consequences to the user's security and privacy. We then discuss design flow enhancements, through which security mechanisms can efficiently be added into a device, vastly differing from traditional practices.
A Comparative Study on English and Chinese Word Uses with LIWC

This paper compared the linguistic and psychological word uses in English and Chinese languages with LIWC (Linguistic Inquiry and Word Count) programs. A Principal Component Analysis uncovered six linguistic and psychological components, among which five components were significantly correlated. The correlated components were ranked as Negative Valence (r=.92), Embodiment (r=.88), Narrative (r=.68), Achievement (r=.65), and Social Relation (r=.64). However, the results showed the order of the representative features differs in two languages and certain word categories co-occurred with different components in English and Chinese. The differences were interpreted from the perspective of distinctive eastern and western cultures.
Communication Complexity of Distributed Convex Learning and Optimization

We study the fundamental limits to communication-efficient distributed methods for convex learning and optimization, under different assumptions on the information available to individual machines, and the types of functions considered. We identify cases where existing algorithms are already worst-case optimal, as well as cases where room for further improvement is still possible. Among other things, our results indicate that without similarity between the local objective functions (due to statistical data similarity or otherwise) many communication rounds may be required, even if the machines have unbounded computational power.
How is our self related to midline regions and the default-mode network?

The problem of the self has been of increasing interest in recent neuroscience. Brain imaging studies have raised the question of whether neural activity in cortical midline regions is self-specific and whether self-specific activity is related to resting state activity (RSA). A quantitative meta-analysis that included 87 studies, representing 1433 participants, was conducted to discuss these questions. First, the specificity of the self (e.g. hearing one's own name, seeing one's own face) was tested and compared across familiar (using stimuli from personally known people) and other (non-self-non-familiar, i.e. strangers and widely-known figures) conditions. Second, the relationship between the self and resting state activity, as reflected by the default-mode network (DMN), was tested. The results indicated that the perigenual anterior cingulate cortex (PACC) is specifically involved in self-processing when compared to familiarity, other, and task/stimulus effects. On the contrary, other midline regions, i.e., medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) were functionally unspecific as they were recruited during the processing of both self-specific and familiar stimuli. Finally, the PACC was recruited during self-specific stimuli and this activity overlapped with DMN activity during resting state, thus distinguishing the self-related processing from both that of the familiar and other conditions. Taken together, our data suggest that our sense of self may result from a specific kind of interaction between resting state activity and stimulus-induced activity, i.e., rest-stimulus interaction, within the midline regions.
Automating GUI testing for Android applications

Users increasingly rely on mobile applications for computational needs. Google Android is a popular mobile platform, hence the reliability of Android applications is becoming increasingly important. Many Android correctness issues, however, fall outside the scope of traditional verification techniques, as they are due to the novelty of the platform and its GUI-oriented application construction paradigm. In this paper we present an approach for automating the testing process for Android applications, with a focus on GUI bugs. We first conduct a bug mining study to understand the nature and frequency of bugs affecting Android applications; our study finds that GUI bugs are quite numerous. Next, we present techniques for detecting GUI bugs by automatic generation of test cases, feeding the application random events, instrumenting the VM, producing log/trace files and analyzing them post-run. We show how these techniques helped to re-discover existing bugs and find new bugs, and how they could be used to prevent certain bug categories. We believe our study and techniques have the potential to help developers increase the quality of Android applications.
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages

Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models. Machine translation and bilingual word embeddings provide some relief through cross-lingual sentiment approaches. However, they either require large amounts of parallel data or do not sufficiently capture sentiment information. We introduce Bilingual Sentiment Embeddings (BLSE), which jointly represent sentiment information in a source and target language. This model only requires a small bilingual lexicon, a source-language corpus annotated for sentiment, and monolingual word embeddings for each language. We perform experiments on three language combinations (Spanish, Catalan, Basque) for sentencelevel cross-lingual sentiment classification and find that our model significantly outperforms state-of-the-art methods on four out of six experimental setups, as well as capturing complementary information to machine translation. Our analysis of the resulting embedding space provides evidence that it represents sentiment information in the resource-poor target language without any annotated data in that language.
MOBILE LEARNING FOR EDUCATION : BENEFITS AND CHALLENGES

Education and training is the process by which the wisdom, knowledge and skills of one generation are passed on to the next. Today there are two form s f education and training: conventional education and distance education. Mobile learning, or "M-Lear ning", offers modern ways to support learning process through mobile devices, such as handheld an d tablet computers, MP3 players, smart phones and mobile phones. This document introduces the sub ject of mobile learning for education purposes. It examines what impact mobile devices have had on teaching and learning practices and goes on to look at the opportunities presented by the use of d igital media on mobile devices. The main purpose of this paper is to describe the current state of mobi le learning, benefits, challenges, and it’s barrier s to support teaching and learning. Data for this paper w re collected through bibliographic and internet research from January to March 2013. Four key areas will be addressed in this paper: 1. An analysis of Mobile Learning. 2. Differentiating E-Learning from Mobile Learning 3. Value and Benefits of Mobile Learning 4. Challenges and Barriers of M obile Learning: Study showed that M-Learning as a Distance learning brought great benefits to socie ty include : Training when it is needed, Training a t any time; Training at any place; Learner-centred co ntent; Avoidance of re-entry to work problems; Training for taxpayers, and those fully occupied du ring university lectures and sessions at training centres; and The industrialisation of teaching and learning. And also, notebooks, mobile Tablets, iPod touch, and iPads are very popular devices for mobil e learning because of their cost and availability o f apps.
28.7 A 0.7V 12b 160MS/s 12.8fJ/conv-step pipelined-SAR ADC in 28nm CMOS with digital amplifier technique

Wireless standards, e.g. 802.11ac Wave 2 and 802.11ax draft, aim to boost user throughput to cope with growing data traffic. High-speed (fs>100MS/s) and high-resolution (ENOB>9.5b) ADCs are essential for leading-edge wireless SoCs, given the bandwidth and PAPR specifications. Also, low power dissipation (FoM<20fJ/conv) is crucial for mobile applications. A number of pipelined-SAR ADCs have been presented which satisfy these design targets [1–3]. However, in deep submicron CMOS, design of a high DC-gain opamp for the MDAC is a serious obstacle due to reduced intrinsic transistor gain and sub-1V supply voltage. Hence, all designs utilize digital calibration to counter gain error and tolerate the use of a low-gain amplifier. Calibration times of at least several tens of ms are required, resulting in lengthy start-up times and reduced SoC power efficiency. Moreover, such calibration cannot track sudden supply voltage variations and suppressing such fluctuations with bypass capacitors significantly impacts chip cost [1–2]. Furthermore, amplifier non-linearity remains unsolved; with lower supply voltages, the limited amplifier swing tightens SAR noise requirements.
Distributed cognition: toward a new foundation for human-computer interaction research

We are quickly passing through the historical moment when people work in front of a single computer, dominated by a small CRT and focused on tasks involving only local information. Networked computers are becoming ubiquitous and are playing increasingly significant roles in our lives and in the basic infrastructures of science, business, and social interaction. For human-computer interaction to advance in the new millennium we need to better understand the emerging dynamic of interaction in which the focus task is no longer confined to the desktop but reaches into a complex networked world of information and computer-mediated interactions. We think the theory of distributed cognition has a special role to play in understanding interactions between people and technologies, for its focus has always been on whole environments: what we really do in them and how we coordinate our activity in them. Distributed cognition provides a radical reorientation of how to think about designing and supporting human-computer interaction. As a theory it is specifically tailored to understanding interactions among people and technologies. In this article we propose distributed cognition as a new foundation for human-computer interaction, sketch an integrated research framework, and use selections from our earlier work to suggest how this framework can provide new opportunities in the design of digital work materials.
Second language experience modulates functional brain network for the native language production in bimodal bilinguals

The functional brain network of a bilingual's first language (L1) plays a crucial role in shaping that of his or her second language (L2). However, it is less clear how L2 acquisition changes the functional network of L1 processing in bilinguals. In this study, we demonstrate that in bimodal (Chinese spoken-sign) bilinguals, the functional network supporting L1 production (spoken language) has been reorganized to accommodate the network underlying L2 production (sign language). Using functional magnetic resonance imaging (fMRI) and a picture naming task, we find greater recruitment of the right supramarginal gyrus (RSMG), the right temporal gyrus (RSTG), and the right superior occipital gyrus (RSOG) for bilingual speakers versus monolingual speakers during L1 production. In addition, our second experiment reveals that these regions reflect either automatic activation of L2 (RSOG) or extra cognitive coordination (RSMG and RSTG) between both languages during L1 production. The functional connectivity between these regions, as well as between other regions that are L1- or L2-specific, is enhanced during L1 production in bimodal bilinguals as compared to their monolingual peers. These findings suggest that L1 production in bimodal bilinguals involves an interaction between L1 and L2, supporting the claim that learning a second language does, in fact, change the functional brain network of the first language.