nils-herrmann/clean_scirepeval huggingface dataset:
Learning k for kNN Classification

The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and machine learning due to its simple implementation and distinguished performance. However, setting all test data with the same k value in the previous kNN methods has been proven to make these methods impractical in real applications. This article proposes to learn a correlation matrix to reconstruct test data points by training data to assign different k values to different test data points, referred to as the Correlation Matrix kNN (CM-kNN for short) classification. Specifically, the least-squares loss function is employed to minimize the reconstruction error to reconstruct each test data point by all training data points. Then, a graph Laplacian regularizer is advocated to preserve the local structure of the data in the reconstruction process. Moreover, an ℓ1-norm regularizer and an ℓ2, 1-norm regularizer are applied to learn different k values for different test data and to result in low sparsity to remove the redundant/noisy feature from the reconstruction process, respectively. Besides for classification tasks, the kNN methods (including our proposed CM-kNN method) are further utilized to regression and missing data imputation. We conducted sets of experiments for illustrating the efficiency, and experimental results showed that the proposed method was more accurate and efficient than existing kNN methods in data-mining applications, such as classification, regression, and missing data imputation.
Breaking bad habits by improving executive function in individuals with obesity

Two primary factors that contribute to obesity are unhealthy eating and sedentary behavior. These behaviors are particularly difficult to change in the long-term because they are often enacted habitually. Cognitive Remediation Therapy has been modified and applied to the treatment of obesity (CRT-O) with preliminary results of a randomized controlled trial demonstrating significant weight loss and improvements in executive function. The objective of this study was to conduct a secondary data analysis of the CRT-O trial to evaluate whether CRT-O reduces unhealthy habits that contribute to obesity via improvements in executive function.



Eighty participants with obesity were randomized to CRT-O or control. Measures of executive function (Wisconsin Card Sort Task and Trail Making Task) and unhealthy eating and sedentary behavior habits were administered at baseline, post-intervention and at 3 month follow-up.



Participants receiving CRT-O demonstrated improvements in both measures of executive function and reductions in both unhealthy habit outcomes compared to control. Mediation analyses revealed that change in one element of executive function performance (Wisconsin Card Sort Task perseverance errors) mediated the effect of CRT-O on changes in both habit outcomes.



These results suggest that the effectiveness of CRT-O may result from the disruption of unhealthy habits made possible by improvements in executive function. In particular, it appears that cognitive flexibility, as measured by the Wisconsin Card Sort task, is a key mechanism in this process. Improving cognitive flexibility may enable individuals to capitalise on interruptions in unhealthy habits by adjusting their behavior in line with their weight loss goals rather than persisting with an unhealthy choice.
A fingerprint biometric cryptosystem in FPGA

This paper presents the implementation of a complete fingerprint biometric cryptosystem in a Field Programmable Gate Array (FPGA). This is possible thanks to the use of a novel fingerprint feature, named QFingerMap, which is binary, length-fixed, and ordered. Security of Authentication on FPGA is further improved because information stored is protected due to the design of a cryptosystem based on Fuzzy Commitment. Several samples of fingers as well as passwords can be fused at feature level with codewords of an error correcting code to generate non-sensitive data. System performance is illustrated with experimental results corresponding to 560 fingerprints acquired in live by an optical sensor and processed by the system in a Xilinx Virtex 6 FPGA. Depending on the realization, more or less accuracy is obtained, being possible a perfect authentication (zero Equal Error Rate), with the advantages of real-time operation, low power consumption, and a very small device.
Vertical augmentation with interpositional blocks of anorganic bovine bone vs. 7-mm-long implants in posterior mandibles: 1-year results of a randomized clinical trial.

To evaluate whether 7-mm-long implants could be an alternative to longer implants placed in vertically augmented posterior mandibles.



Sixty patients with posterior mandibular edentulism with 7-8 mm bone height above the mandibular canal were randomized to either vertical augmentation with anorganic bovine bone blocks and delayed 5-month placement of ≥10 mm implants or to receive 7-mm-long implants. Four months after implant placement, provisional prostheses were delivered, replaced after 4 months, by definitive prostheses. The outcome measures were prosthesis and implant failures, any complications and peri-implant marginal bone levels. All patients were followed to 1 year after loading.



One patient dropped out from the short implant group. In two augmented mandibles, there was not sufficient bone to place 10-mm-long implants possibly because the blocks had broken apart during insertion. One prosthesis could not be placed when planned in the 7 mm group vs. three prostheses in the augmented group, because of early failure of one implant in each patient. Four complications (wound dehiscence) occurred during graft healing in the augmented group vs. none in the 7 mm group. No complications occurred after implant placement. These differences were not statistically significant. One year after loading, patients of both groups lost an average of 1 mm of peri-implant bone. There no statistically significant differences in bone loss between groups.



When residual bone height over the mandibular canal is between 7 and 8 mm, 7 mm short implants might be a preferable choice than vertical augmentation, reducing the chair time, expenses and morbidity. These 1-year preliminary results need to be confirmed by follow-up of at least 5 years.
Semantic Relations Between Nominals Vivi Nastase1, Preslav Nakov2, Diarmuid Ó Séaghdha3, and Stan Szpakowicz4 (1FBK, Trento; 2QCRI, Qatar Foundation; 3University of Cambridge; 4University of Ottawa) Morgan & Claypool (Synthesis Lectures on Human Language Technologies, edited by Graeme Hirst, volume 

Understanding noun compounds is the challenge that drew me to study computational linguistics. Think about how just two words, side by side, evoke a whole story: cacao seeds evokes the tree on which the cacao seeds grow, and to understand cacao powder we need to also imagine the seeds of the cacao tree that are crushed to powder. What conjures up these concepts of tree and grow, and seeds and crush, which are not explicitly present in the written word but are essential for our complete understanding of the compounds? The mechanisms by which we make sense of noun compounds can illuminate how we understand language more generally. And because the human mind is so wily as to provide interpretations even when we do not ask it to, I have always found it useful to study these phenomena of language on the computer, because the computer surely does not (yet) have the type of knowledge that must be brought to bear on the problem. If you find these phenomena equally intriguing and puzzling, then you will find this book by Nastase, Nakov, Ó Séaghdga, and Szpakowicz a wonderful summary of past research efforts and a good introduction to the current methods for analyzing semantic relations. To be clear, this book is not only about noun compounds, but explores all types of relations that can hold between what is expressed linguistically as nominal. Such nominals include entities (e.g., Godiva, Belgium) as well as nominals that refer to events (cultivation, roasting) and nominals with complex structure (delicious milk chocolate). In doing so, describing the different semantic relations between chocolate in the 20th century and chocolate in Belgium is within the scope of this book. This is a wise choice as there are then some linguistic cues that will help define and narrow the types of semantic relations (e.g., the prepositions above). Noun compounds are degenerate in the sense that there are few if any overt linguistic cues as to the semantic relations between the nominals.
Recent Advances in Emergency Medical Services Management

This article discusses different modelling approaches considered to address problems related to ambulances' location and relocation as well as dispatching decisions arising in Emergency Medical Services (EMS) management. Over the past 10 years, a considerable amount of research has been devoted to this field and specifically, to the development of strategies that more explicitly take into account the uncertainty and dynamism inherent to EMS. This article thus reviews the most recent advances related to EMS management from an operational level standpoint. It briefly reviews early works on static ambulances' location problems and presents new modelling and solution strategies proposed to address it. However, it intends to concentrate on relocation strategies and dispatching rules, and discuss the interaction between these two types of decisions. Finally, conclusions and perspectives are presented.
Grid-Based Crime Prediction Using Geographical Features

Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design’s baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.
Improving GANs Using Optimal Transport

We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution. This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance defined in an adversarially learned feature space, resulting in a highly discriminative distance function with unbiased mini-batch gradients. Experimentally we show OT-GAN to be highly stable when trained with large mini-batches, and we present state-of-the-art results on several popular benchmark problems for image generation.
A triple active bridge DC-DC converter capable of achieving full-range ZVS

In this paper, a triple active bridge converter is proposed. The topology is capable of achieving ZVS across the full load range with wide input voltage while minimizing heavy load conduction losses to increase overall efficiency. This topology comprises three full bridges coupled by a three-winding transformer. At light load, by adjusting the phase shift between two input bridges, all switching devices can maintain ZVS due to a controlled circulating current. At heavy load, the two input bridges work in parallel to reduce conduction loss. The operation principles of this topology are introduced and the ZVS boundaries are derived. Based on analytical models of power loss, a 200W laboratory prototype has been built to verify theoretical considerations.
Higher Education Business Process Improvement: Achieving BPMM Level 3

The adoption of business process improvement strategies is a challenge to organizations trying to improve the quality and productivity of their services. The quest for the benefits of this improvement on resource optimization and the responsiveness of the organizations has raised several proposals for business process improvement approaches. However, proposals and results of scientific research on process improvement in higher education institutions, extremely complex and unique organizations, are still scarce. This paper presents a method that provides guidance about how practices and knowledge are gathered to contribute for business process improvement based on the communication between different stakeholders.
Value-Aware Quantization for Training and Inference of Neural Networks

We propose a novel value-aware quantization which applies aggressively reduced precision to the majority of data while separately handling a small amount of large data in high precision, which reduces total quantization errors under very low precision. We present new techniques to apply the proposed quantization to training and inference. The experiments show that our method with 3-bit activations (with 2% of large ones) can give the same training accuracy as full-precision one while offering significant (41.6% and 53.7%) reductions in the memory cost of activations in ResNet-152 and Inception-v3 compared with the state-of-the-art method. Our experiments also show that deep networks such as Inception-v3, ResNet-101 and DenseNet-121 can be quantized for inference with 4-bit weights and activations (with 1% 16-bit data) within 1% top-1 accuracy drop.
Persistence in youth unemployment

This paper examines the degree of persistence of youth unemployment (total, male and female) in twenty-four countries by using two alternative measures: the AR coefficient and the fractional differencing parameter, based on shortand long-memory processes respectively. The evidence suggests that persistence is particularly high in Japan and some EU countries such as Spain, Portugal, Ireland and Finland, where appropriate policy actions are of the essence. Specifically, active labour market policies are necessary to prevent short-term unemployment from becoming structural (long-term).
Unsupervised Low-Dimensional Vector Representations for Words, Phrases and Text that are Transparent, Scalable, and produce Similarity Metrics that are Complementary to Neural Embeddings

Neural embeddings are a popular set of methods for representing words, phrases or text as a low dimensional vector (typically 50-500 dimensions). However, it is difficult to interpret these dimensions in a meaningful manner, and creating neural embeddings requires extensive training and tuning of multiple parameters and hyperparameters. We present here a simple unsupervised method for representing words, phrases or text as a low dimensional vector, in which the meaning and relative importance of dimensions is transparent to inspection. We have created a near-comprehensive vector representation of words, and selected bigrams, trigrams and abbreviations, using the set of titles and abstracts in PubMed as a corpus. This vector is used to create several novel implicit word-word and text-text similarity metrics. The implicit word-word similarity metrics correlate well with human judgement of word pair similarity and relatedness, and outperform or equal all other reported methods on a variety of biomedical benchmarks, including several implementations of neural embeddings trained on PubMed corpora. Our implicit word-word metrics capture different aspects of word-word relatedness than word2vecbased metrics and are only partially correlated (rho = ~0.5-0.8 depending on task and corpus). The vector representations of words, bigrams, trigrams, abbreviations, and PubMed title+abstracts are all publicly available from http://arrowsmith.psych.uic.edu for release under CC-BY-NC license. Several public web query interfaces are also available at the same site, including one which allows the user to specify a given word and view its most closely related terms according to direct co-occurrence as well as different implicit similarity metrics.
( a ) Input night image ( b ) Diverse day images sampled by our model ⋯

Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a distribution of possible outputs in a conditional generative modeling setting. The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time. A generator learns to map the given input, combined with this latent code, to the output. We explicitly encourage the connection between output and the latent code to be invertible. This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results. We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code. Our proposed method encourages bijective consistency between the latent encoding and output modes. We present a systematic comparison of our method and other variants on both perceptual realism and diversity.
COBRA: a sensitive and quantitative DNA methylation assay.

We report here on a quantitative technique called COBRA to determine DNA methylation levels at specific gene loci in small amounts of genomic DNA. Restriction enzyme digestion is used to reveal methylation-dependent sequence differences in PCR products of sodium bisulfite-treated DNA as described previously. We show that methylation levels in the original DNA sample are represented by the relative amounts of digested and undigested PCR product in a linearly quantitative fashion across a wide spectrum of DNA methylation levels. In addition, we show that this technique can be reliably applied to DNA obtained from microdissected paraffin-embedded tissue samples. COBRA thus combines the powerful features of ease of use, quantitative accuracy, and compatibility with paraffin sections.
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.
Aeonium: Visual analytics to support collaborative qualitative coding

Qualitative coding offers the potential to obtain deep insights into social media, but the technique can be inconsistent and hard to scale. Researchers using qualitative coding impose structure on unstructured data through “codes” that represent categories for analysis. Our visual analytics interface, Aeonium, supports human insight in collaborative coding through visual overviews of codes assigned by multiple researchers and distributions of important keywords and codes. The underlying machine learning model highlights ambiguity and inconsistency. Our goal was not to reduce qualitative coding to a machine-solvable problem, but rather to bolster human understanding gained from coding and reinterpreting the data collaboratively. We conducted an experimental study with 39 participants who coded tweets using our interface. In addition to increased understanding of the topic, participants reported that Aeonium's collaborative coding functionality helped them reflect on their own interpretations. Feedback from participants demonstrates that visual analytics can help facilitate rich qualitative analysis and suggests design implications for future exploration.
Twitter zombie: architecture for capturing, socially transforming and analyzing the twittersphere

Social computational systems emerge in the wild on popular social networking sites like Facebook and Twitter, but there remains confusion about the relationship between social interactions and the technical traces of interaction left behind through use. Twitter interactions and social experience are particularly challenging to make sense of because of the wide range of tools used to access Twitter (text message, website, iPhone, TweetDeck and others), and the emergent set of practices for annotating message context (hashtags, reply to's and direct messaging). Further, Twitter is used as a back channel of communication in a wide range of contexts, ranging from disaster relief to watching television. Our study examines Twitter as a transport protocol that is used differently in different socio-technical contexts, and presents an analysis of how researchers might begin to approach studies of Twitter interactions with a more reflexive stance toward the application programming interfaces (APIs) Twitter provides. We conduct a careful review of existing literature examining socio-technical phenomena on Twitter, revealing a collective inconsistency in the description of data gathering and analysis methods. In this paper, we present a candidate architecture and methodological approach for examining specific parts of the Twittersphere. Our contribution begins a discussion among social media researchers on the topic of how to systematically and consistently make sense of the social phenomena that emerge through Twitter. This work supports the comparative analysis of Twitter studies and the development of social media theories.
People Counting Based on an IR-UWB Radar Sensor

In this paper, we propose a people counting algorithm using an impulse radio ultra-wideband radar sensor. The proposed algorithm is based on a strategy of understanding the pattern of the received signal according to the number of people, not detecting each of a large number of people in the radar’s received signal. To understand the pattern of the signal, we detect the major clusters from the signal and find the amplitudes of main pulses having the maximum amplitude among the pulses constituting each cluster. We generate a probability density function of the amplitudes of the main pulses from the major clusters according to the number of people and distances. Then, we derive maximum likelihood (ML) equation for people counting. Using the derived ML equation, real-time people counting is possible with a small amount of computation. In addition, since the proposed algorithm does not detect individual clusters for each person but based on the overall cluster behavior of the signals according to the number of people, it enables people counting even in a dense multipath environment, such as a metal-rich environment. In order to prove that the proposed algorithm can be operated in real time in various environments, we performed experiments in an indoor environment and an elevator with a metal structure. Experimental results show that people counting is performed with an mean absolute error of less than one person on average.