nils-herrmann/clean_scirepeval huggingface dataset:
Beyond Bitcoin: The Rise of Blockchain World

Bitcoin—a cryptocurrency built on blockchain technology—was the first currency not controlled by a single  Initially known to a few nerds and criminals,2 bitcoin is now involved in hundreds of thousands of transactions daily. Bitcoin has achieved values of more than US$15,000 per coin (at the end of 2017), and this rising value has attracted attention. For some, bitcoin is digital fool’s gold. For others, its underlying blockchain technology heralds the dawn of a new digital era. Both views could be right. The fortunes of cryptocurrencies don’t define blockchain. Indeed, the biggest effects of blockchain might lie beyond bitcoin, cryptocurrencies, or even the economy. Of course, the technical questions about blockchain have not all been answered. We still struggle to overcome the high levels of processing intensity and energy use. These questions will no doubt be confronted over time. If the technology fails, the future of blockchain will be different. In this article, I’ll assume technical challenges will be solved, and although I’ll cover some technical issues, these aren’t the main focus of this paper. In a 2015 article, “The Trust Machine,” it was argued that the biggest effects of blockchain are on  The article referred to public trust in economic institutions, that is, that such organizations and intermediaries will act as expected. When they don’t, trust deteriorates. Trust in economic institutions hasn’t recovered from the recession of 2008.3 Technology can exacerbate distrust: online trades with distant counterparties can make it hard to settle disputes face to face. Trusted intermediaries can be hard to find, and that’s where blockchain can play a part. Permanent record-keeping that can be sequentially updated but not erased creates visible footprints of all activities conducted on the chain. This reduces the uncertainty of alternative facts or truths, thus creating the “trust machine” The Economist describes. As trust changes, so too does governance.4 Vitalik Buterin of the Ethereum blockchain platform calls blockchain “a magic computer” to which anyone can upload self-executing programs.5 All states of every Beyond Bitcoin: The Rise of Blockchain World
Mean Actor Critic

We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action continuous-state reinforcement learning. MAC is a policy gradient algorithm that uses the agent’s explicit representation of all action values to estimate the gradient of the policy, rather than using only the actions that were actually executed. This significantly reduces variance in the gradient updates and removes the need for a variance reduction baseline. We show empirical results on two control domains where MAC performs as well as or better than other policy gradient approaches, and on five Atari games, where MAC is competitive with state-of-the-art policy search algorithms.
LTCC Multilayered Substrate-Integrated Waveguide Filter With Enhanced Frequency Selectivity for System-in-Package Applications

This paper presents a cross-coupled bandpass filter with stacked substrate-integrated waveguide cavities on low-temperature cofired ceramic substrates. The proposed filter has a local multipoint distribution service band with a novel same-side-feed input/output structure. Conventionally, a cross-coupled structure generates only a single pair of transmission zeros. The proposed filter can generate two pairs of transmission zeros beside the passband, thereby providing an excellent cutoff rate in the stopband and improved frequency selectivity. The additional pair of transmission zeros is created by the same-side-feed structure, which constructs an additional source-load coupling path without increasing the circuit size. A multipath coupling diagram is used to illustrate the conformation of the second pair of transmission zeros and predict its behavior. The experimental filter exhibits responses centered at 27.95 GHz with an insertion loss of -2.8 dB, and a bandwidth of 9%. Two pairs of transmission zeros (at 26.3 and 29.6 GHz, and at 23.2 and 37 GHz) around the passband were obtained, achieving excellent selectivity and a wide stopband.
Predicting Student Academic Performance at Degree Level: A Case Study

Universities gather large volumes of data with reference to their students in electronic form. The advances in the data mining field make it possible to mine these educational data and find information that allow for innovative ways of supporting both teachers and students. This paper presents a case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The data of four academic cohorts comprising 347 undergraduate students have been mined with different classifiers. The results show that it is possible to predict the graduation performance in 4th year at university using only pre-university marks and marks of 1st and 2nd year courses, no socio-economic or demographic features, with a reasonable accuracy. Furthermore courses that are indicators of particularly good or poor performance have been identified.
A New Transformerless Buck – Boost Converter with Positive Output Voltage

A new transformerless buck–boost converterwith simple structure is proposed in this study. Comparedwith the traditional buck–boost converter, the proposedbuck–boost converter’s voltage gain is squared times of theformer’s and its output voltage polarity is positive. Theseadvantages enable it to work in a wider range of positiveoutput. The two power switches of the proposed buck–boost converter operate synchronously. In the continuousconduction mode (CCM), two inductors are magnetized andtwo capacitors are discharged during the switch-on period,while two inductors are demagnetized and two capacitorsare charged during the switch-off period. The power electronicssimulator (PSIM) and the circuit experiments are providedto validate the effectiveness of the proposed buck–boostconverter.
Efficient Unsupervised Recursive Word Segmentation Using Minimum Description Length

Automatic word segmentation is a basic requirement for unsupervised learning in morphological analysis. In this paper, we formulate a novel recursivemethod for minimum description length (MDL) word segmentation, whose basic operation isresegmenting the corpus on a prefix (equivalently, a suffix). We derive a local expression for the change in description length under resegmentation, i.e., one which depends only on properties of the specific prefix (not on the rest of the corpus). Such a formulation permits use of a new and efficient algorithm for greedy morphological segmentation of the corpus in a recursive manner. In particular, our method does not restrict words to be segmented only once, into a stem+affix form, as do many extant techniques. Early results for English and Turkish corpora are promising.
TopicSpam: a Topic-Model based approach for spam detection

Product reviews are now widely used by individuals and organizations for decision making (Litvin et al., 2008; Jansen, 2010). And because of the profits at stake, people have been known to try to game the system by writing fake reviews to promote target products. As a result, the task of deceptive review detection has been gaining increasing attention. In this paper, we propose a generative LDA-based topic modeling approach for fake review detection. Our model can aptly detect the subtle differences between deceptive reviews and truthful ones and achieves about 95% accuracy on review spam datasets, outperforming existing baselines by a large margin.
Hybrid Precoding for mmWave Massive MIMO Systems With Partially Connected Structure

Hybrid precoding is widely studied in millimetre-wave (mmWave) massive MIMO systems due to low cost as well as low power consumption. In general, there are two kinds of hybrid precoding structures: one is fully connected structure (FCS), where each radio frequency (RF) chain is connected to all antennas, and the other is partially connected structure (PCS), where each RF chain is connected to a sub-array. In this paper, we investigate the optimal hybrid precoder design problem for mmWave massive MIMO systems based on PCS, since this kind of structure is more practical for antenna deployment. We first focus on the optimization of analog precoder (AP) and propose two AP design schemes for high signal-to-noise ratio (SNR) condition and low SNR condition, respectively. For each of the schemes, the original optimization problem is reformulated to single-stream optimal transmitter beamforming problem with per-antenna power constraint, which has an optimal solution. Then, the optimal digital precoder is obtained by water-filling algorithm after AP is determined. Moreover, upper bounds of the achievable data rate for the proposed schemes with closed-form expression are derived.
RN / 15 / 07 Causal Impact Analysis Applied to App Releases in Google Play and Windows Phone Store December 16 , 2015

App developers would like to know the characteristics of app releases that achieve high impact. To address this, we mined the most consistently popular Google Play and Windows Phone apps, once per week, over a period of 12 months. In total we collected 3,187 releases, from which we identified 1,547 for which there was adequate prior and posterior time series data to facilitate causal impact assessment, analysing the properties that distinguish impactful and non-impactful releases. We find that 40% of target releases impacted performance in the Google store and 55% of target releases impacted performance in the Windows store. We find evidence that more mentions of features and fewer mentions of bug fixing can increase the chance for a release to be impactful, and to improve rating. UCL DEPARTMENT OF COMPUTER
Computing LTS Regression for Large Data Sets

Data mining aims to extract previously unknown patterns or substructures from large databases. In statistics, this is what methods of robust estimation and outlier detection were constructed for, see e.g. Rousseeuw and Leroy (1987). Here we will focus on least trimmed squares (LTS) regression, which is based on the subset of h cases (out of n) whose least squares fit possesses the smallest sum of squared residuals. The coverage h may be set between n/2 and n. The computation time of existing LTS algorithms grows too much with the size of the data set, precluding their use for data mining. In this paper we develop a new algorithm called FAST-LTS. The basic ideas are an inequality involving order statistics and sums of squared residuals, and techniques which we call ‘selective iteration’ and ‘nested extensions’. We also use an intercept adjustment technique to improve the precision. For small data sets FAST-LTS typically finds the exact LTS, whereas for larger data sets it gives more accurate results than existing algorithms for LTS and is faster by orders of magnitude. This allows us to apply FAST-LTS to large databases.
Comparing Data Streams Using Hamming Norms (How to Zero In)

Massive data streams are now fundamental to many data processing applications. For example, Internet routers produce large scale diagnostic data streams. Such streams are rarely stored in traditional databases, and instead must be processed “on the fly” as they are produced. Similarly, sensor networks produce multiple data streams of observations from their sensors. There is growing focus on manipulating data streams, and hence, there is a need to identify basic operations of interest in managing data streams, and to support them efficiently. We propose computation of the Hamming norm as a basic operation of interest. The Hamming norm formalises ideas that are used throughout data processing. When applied to a single stream, the Hamming norm gives the number of distinct items that are present in that data stream, which is a statistic of great interest in databases. When applied to a pair of streams, the Hamming norm gives an important measure of (dis)similarity: the number of unequal item counts in the two streams. Hamming norms have many uses in comparing data streams. We present a novel approximation technique for estimating the Hamming norm for massive data streams; this relies on what we call the “ l0 sketch” and we prove its accuracy. We test our approximation method on a large quantity of synthetic and real stream data, and show that the estimation is accurate to within a few percentage points. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 28th VLDB Conference, Hong Kong, China, 2002
Nonlinear latent factorization by embedding multiple user interests

Classical matrix factorization approaches to collaborative filtering learn a latent vector for each user and each item, and recommendations are scored via the similarity between two such vectors, which are of the same dimension. In this work, we are motivated by the intuition that a user is a much more complicated entity than any single item, and cannot be well described by the same representation. Hence, the variety of a user's interests could be better captured by a more complex representation. We propose to model the user with a richer set of functions, specifically via a set of latent vectors, where each vector captures one of the user's latent interests or tastes. The overall recommendation model is then nonlinear where the matching score between a user and a given item is the maximum matching score over each of the user's latent interests with respect to the item's latent representation. We describe a simple, general and efficient algorithm for learning such a model, and apply it to large scale, real-world datasets from YouTube and Google Music, where our approach outperforms existing techniques.
Soft error tolerance using Horizontal-Vertical-Double-Bit Diagonal parity method

The likelihood of soft errors increase with system complexity, reduction in operational voltages, exponential growth in transistors per chip, increases in clock frequencies and device shrinking. As the memory bit-cell area is condensed, single event upset that would have formerly despoiled only a single bit-cell are now proficient of upsetting multiple contiguous memory bit-cells per particle strike. While these error types are beyond the error handling capabilities of the frequently used error correction codes (ECCs) for single bit, the overhead associated with moving to more sophisticated codes for multi-bit errors is considered to be too costly. To address this issue, this paper presents a new approach to detect and correct multi-bit soft error by using Horizontal-Vertical-Double-Bit-Diagonal (HVDD) parity bits with a comparatively low overhead.
Surveying instructor and learner attitudes toward e-learning

The trend of using e-learning as a learning and/or teaching tool is now rapidly expanding into education. Although e-learning environments are popular, there is minimal research on instructors’ and learners’ attitudes toward these kinds of learning environments. The purpose of this study is to explore instructors’ and learners’ attitudes toward e-learning usage. Accordingly, 30 instructors and 168 college students are asked to answer two different questionnaires for investigating their perceptions. After statistical analysis, the results demonstrate that instructors have very positive perceptions toward using e-learning as a teaching assisted tool. Furthermore, behavioral intention to use e-learning is influenced by perceived usefulness and self-efficacy. Regarding to learners’ attitudes, self-paced, teacher-led, and multimedia instruction are major factors to affect learners’ attitudes toward e-learning as an effective learning tool. Based on the findings, this research proposes guidelines for developing e-learning environments.
A 57.5-to-90.1GHz magnetically-tuned multi-mode CMOS VCO

A magnetically-tuned multi-mode VCO featuring an ultra-wide frequency tuning range is presented. By changing the magnetic coupling coefficient between the primary and secondary coils in the transformer tank, the frequency tuning range of a dual-band VCO is greatly increased to continuously cover the whole E-band. Fabricated in a 65-nm CMOS process, the presented VCO measures a tuning range of 44.2% from 57.5 to 90.1 GHz while consuming 7mA to 9mA at 1.2V supply. The measured phase noises at 10MHz offset from carrier frequencies of 72.2, 80.5 and 90.1 GHz are -111.8, -108.9 and -105 dBc/Hz, respectively, which corresponds to a FOMT between -192.2 and -184.2dBc/Hz.
B-SCP: A requirements analysis framework for validating strategic alignment of organizational IT based on strategy, context, and process

Ensuring that organizational IT is in alignment with and provides support for an organization’s business strategy is critical to business success. Despite this, business strategy and strategic alignment issues are all but ignored in the requirements engineering research literature. We present B-SCP, a requirements engineering framework for organizational IT that directly addresses an organization’s business strategy and the alignment of IT requirements with that strategy. B-SCP integrates the three themes of strategy, context, and process using a requirements engineering notation for each theme. We demonstrate a means of cross-referencing and integrating the notations with each other, enabling explicit traceability between business processes and business strategy. In addition, we show a means of defining requirements problem scope as a Jackson problem diagram by applying a business modeling framework. Our approach is illustrated via application to an exemplar. The case example demonstrates the feasibility of B-SCP, and we present a comparison with other approaches.
Toward Real-time Indoor Semantic Segmentation Using Depth Information

This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on handcrafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. Using a frame by frame labeling, we obtain nearly state-of-the-art performance on the NYU-v2 depth dataset with an accuracy of 64.5%. We then show that the labeling can be further improved by exploiting the temporal consistency in the video sequence of the scene. To that goal, we present a method producing temporally consistent superpixels from a streaming video. Among the different methods producing superpixel segmentations of an image, the graph-based approach of Felzenszwalb and Huttenlocher is broadly employed. One of its interesting properties is that the regions are computed in a greedy manner in quasi-linear time by using a minimum spanning tree. In a framework exploiting minimum spanning trees all along, we propose an efficient video segmentation approach that computes temporally consistent pixels in a causal manner, filling the need for causal and real-time applications. We illustrate the labeling of indoor scenes in video sequences that could be processed in real-time using appropriate hardware such as an FPGA.
A probabilistic model to resolve diversity–accuracy challenge of recommendation systems

Recommendation systems have wide-spread applications in both academia and industry. Traditionally, performance of recommendation systems has been measured by their precision. By introducing novelty and diversity as key qualities in recommender systems, recently increasing attention has been focused on this topic. Precision and novelty of recommendation are not in the same direction, and practical systems should make a trade-off between these two quantities. Thus, it is an important feature of a recommender system to make it possible to adjust diversity and accuracy of the recommendations by tuning the model. In this paper, we introduce a probabilistic structure to resolve the diversity–accuracy dilemma in recommender systems. We propose a hybrid model with adjustable level of diversity and precision such that one can perform this by tuning a single parameter. The proposed recommendation model consists of two models: one for maximization of the accuracy and the other one for specification of the recommendation list to tastes of users. Our experiments on two real datasets show the functionality of the model in resolving accuracy–diversity dilemma and outperformance of the model over other classic models. The proposed method could be extensively applied to real commercial systems due to its low computational complexity and significant performance.
Promotional Marketing or Word-of-Mouth? Evidence from Online Restaurant Reviews

T value of promotional marketing and word-of-mouth (WOM) is well recognized, but few studies have compared the effects of these two types of information in online settings. This research examines the effect of marketing efforts and online WOM on product sales by measuring the effects of online coupons, sponsored keyword search, and online reviews. It aims to understand the relationship between firms’ promotional marketing and WOM in the context of a third party review platform. Using a three-year panel data set from one of the biggest restaurant review websites in China, the study finds that both online promotional marketing and reviews have a significant impact on product sales, which suggests promotional marketing on third party review platforms is still an effective marketing tool. This research further explores the interaction effects between WOM and promotional marketing when these two types of information coexist. The results demonstrate a substitute relationship between the WOM volume and coupon offerings, but a complementary relationship between WOM volume and keyword advertising.
Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the free energy change upon binding. Interestingly, this method applies a Lamarckian model of genetics, in which environmental adaptations of an individual’s phenotype are reverse transcribed into its genotype and become Ž . heritable traits sic . We consider three search methods, Monte Carlo simulated annealing, a traditional genetic algorithm, and the Lamarckian genetic algorithm, and compare their performance in dockings of seven protein�ligand test systems having known three-dimensional structure. We show that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckian genetic algorithm is the most efficient, reliable, and successful of the three. The empirical free energy function was calibrated using a set of 30 structurally known protein�ligand complexes with experimentally determined binding constants. Linear regression analysis of the observed binding constants in terms of a wide variety of structure-derived molecular properties was performed. The final model had a residual standard �1 Ž �1 . error of 9.11 kJ mol 2.177 kcal mol and was chosen as the new energy Correspondence to: A. J. Olson;  olson@scripps.edu Contract�grant sponsor: National Institutes of Health, contract�grant numbers: GM48870, RR08065 ( ) Journal of Computational Chemistry, Vol. 19, No. 14, 1639�1662 1998