nils-herrmann/clean_scirepeval huggingface dataset:
Feature selection by independent component analysis and mutual information maximization in EEG signal classification

Feature selection and dimensionality reduction are important steps in pattern recognition. In this paper, we propose a scheme for feature selection using linear independent component analysis and mutual information maximization method. The method is theoretically motivated by the fact that the classification error rate is related to the mutual information between the feature vectors and the class labels. The feasibility of the principle is illustrated on a synthetic dataset and its performance is demonstrated using EEG signal classification. Experimental results show that this method works well for feature selection.
Evaluating social spammer detection systems

The rising popularity of social network services, such as Twitter, has attracted many spammers and created a large number of fake accounts, overwhelming legitimate users with advertising, malware and unwanted and disruptive information. This not only inconveniences the users' social activities but causes financial loss and privacy issues. Identifying social spammers is challenging because spammers continually change their strategies to fool existing anti-spamming systems. Thus, many researchers have tried to propose new classification systems using various types of features extracted from the content and user's information. However, no comprehensive comparative study has been done to compare the effectiveness and the efficiency of the existing systems. At this stage, it is hard to know what the best anti spamming system is and why. This paper proposes a unified evaluation workbench that allows researchers to access various user and content-based features, implement new features, and evaluate and compare the performance of their systems against existing systems. Through our analysis, we can identify the most effective and efficient social spammer detection features and help develop a faster and more accurate classifier model that has higher true positives and lower false positives.
Safety climate in OHSAS 18001-certified organisations: antecedents and consequences of safety behaviour.

The occupational health and safety standard OHSAS 18001 has gained considerable acceptance worldwide, and firms from diverse sectors and of varying sizes have implemented it. Despite this, very few studies have analysed safety management or the safety climate in OHSAS 18001-certified organisations. The current work aims to analyse the safety climate in these organisations, identify its dimensions, and propose and test a structural equation model that will help determine the antecedents and consequences of employees' safety behaviour. For this purpose, the authors carry out an empirical study using a sample of 131 OHSAS 18001-certified organisations located in Spain. The results show that management's commitment, and particularly communication, have an effect on safety behaviour and on safety performance, employee satisfaction, and firm competitiveness. These findings are particularly important for management since they provide evidence about the factors that should be encouraged to reduce risks and improve performance in this type of organisation.
Speaker Dependent and Independent Isolated Hindi Word Recognizer using Hidden Markov Model (HMM)

Hindi is very complex language with large number of phonemes and being used with various ascents in different regions in India. In this manuscript, speaker dependent and independent isolated Hindi word recognizers using the Hidden Markov Model (HMM) is implemented, under noisy environment. For this study, a set of 10 Hindi names has been chosen as a test set for which the training and testing is performed. The scheme instigated here implements the Mel Frequency Cepstral Coefficients (MFCC) in order to compute the acoustic features of the speech signal. Then, K-means algorithm is used for the codebook generation by performing clustering over the obtained feature space. Baum Welch algorithm is used for re-estimating the parameters, and finally for deciding the recognized Hindi word whose model likelihood is highest, Viterbi algorithm has been implemented; for the given HMM. This work resulted in successful recognition with 98. 6% recognition rate for speaker dependent recognition, for total of 10 speakers (6 male, 4 female) and 97. 5% for speaker independent isolated word recognizer for 10 speakers (male).
Universal robotic gripper based on the jamming of granular material

Gripping and holding of objects are key tasks for robotic manipulators. The development of universal grippers able to pick up unfamiliar objects of widely varying shape and surface properties remains, however, challenging. Most current designs are based on the multifingered hand, but this approach introduces hardware and software complexities. These include large numbers of controllable joints, the need for force sensing if objects are to be handled securely without crushing them, and the computational overhead to decide how much stress each finger should apply and where. Here we demonstrate a completely different approach to a universal gripper. Individual fingers are replaced by a single mass of granular material that, when pressed onto a target object, flows around it and conforms to its shape. Upon application of a vacuum the granular material contracts and hardens quickly to pinch and hold the object without requiring sensory feedback. We find that volume changes of less than 0.5% suffice to grip objects reliably and hold them with forces exceeding many times their weight. We show that the operating principle is the ability of granular materials to transition between an unjammed, deformable state and a jammed state with solid-like rigidity. We delineate three separate mechanisms, friction, suction, and interlocking, that contribute to the gripping force. Using a simple model we relate each of them to the mechanical strength of the jammed state. This advance opens up newpossibilities for the designof simple, yet highly adaptive systems that excel at fast gripping of complex objects.
A Fully Time-domain Neural Model for Subband-based Speech Synthesizer

This paper introduces a deep neural network model for subband-based speech 1 synthesizer. The model benefits from the short bandwidth of the subband signals 2 to reduce the complexity of the time-domain speech generator. We employed 3 the multi-level wavelet analysis/synthesis to decompose/reconstruct the signal to 4 subbands in time domain. Inspired from the WaveNet, a convolutional neural 5 network (CNN) model predicts subband speech signals fully in time domain. Due 6 to the short bandwidth of the subbands, a simple network architecture is enough to 7 train the simple patterns of the subbands accurately. In the ground truth experiments 8 with teacher forcing, the subband synthesizer outperforms the fullband model 9 significantly. In addition, by conditioning the model on the phoneme sequence 10 using a pronunciation dictionary, we have achieved the first fully time-domain 11 neural text-to-speech (TTS) system. The generated speech of the subband TTS 12 shows comparable quality as the fullband one with a slighter network architecture 13 for each subband.
MIStore: a Blockchain-Based Medical Insurance Storage System

Currently, blockchain technology, which is decentralized and may provide tamper-resistance to recorded data, is experiencing exponential growth in industry and research. In this paper, we propose the MIStore, a blockchain-based medical insurance storage system. Due to blockchain’s the property of tamper-resistance, MIStore may provide a high-credibility to users. In a basic instance of the system, there are a hospital, patient, insurance company and n servers. Specifically, the hospital performs a (t, n)-threshold MIStore protocol among the n servers. For the protocol, any node of the blockchain may join the protocol to be a server if the node and the hospital wish. Patient’s spending data is stored by the hospital in the blockchain and is protected by the n servers. Any t servers may help the insurance company to obtain a sum of a part of the patient’s spending data, which servers can perform homomorphic computations on. However, the n servers cannot learn anything from the patient’s spending data, which recorded in the blockchain, forever as long as more than n − t servers are honest. Besides, because most of verifications are performed by record-nodes and all related data is stored at the blockchain, thus the insurance company, servers and the hospital only need small memory and CPU. Finally, we deploy the MIStore on the Ethererum blockchain and give the corresponding performance evaluation.
Towards Synthesizing Complex Programs From Input-Output Examples

In recent years, deep learning techniques have been developed to improve the performance of program synthesis from input-output examples. Albeit its significant progress, the programs that can be synthesized by state-of-the-art approaches are still simple in terms of their complexity. In this work, we move a significant step forward along this direction by proposing a new class of challenging tasks in the domain of program synthesis from input-output examples: learning a context-free parser from pairs of input programs and their parse trees. We show that this class of tasks are much more challenging than previously studied tasks, and the test accuracy of existing approaches is almost 0%. We tackle the challenges by developing three novel techniques inspired by three novel observations, which reveal the key ingredients of using deep learning to synthesize a complex program. First, the use of a non-differentiable machine is the key to effectively restrict the search space. Thus our proposed approach learns a neural program operating a domain-specific non-differentiable machine. Second, recursion is the key to achieve generalizability. Thus, we bake-in the notion of recursion in the design of our non-differentiable machine. Third, reinforcement learning is the key to learn how to operate the non-differentiable machine, but it is also hard to train the model effectively with existing reinforcement learning algorithms from a cold boot. We develop a novel two-phase reinforcement learningbased search algorithm to overcome this issue. In our evaluation, we show that using our novel approach, neural parsing programs can be learned to achieve 100% test accuracy on test inputs that are 500× longer than the training samples.
Energy Management in Autonomous Microgrid Using Stability-Constrained Droop Control of Inverters

This paper presents an energy management system (EMS) for a stand-alone droop-controlled microgrid, which adjusts generators output power to minimize fuel consumption and also ensures stable operation. It has previously been shown that frequency-droop gains have a significant effect on stability in such microgrids. Relationship between these parameters and stability margins are therefore identified, using qualitative analysis and small-signal techniques. This allows them to be selected to ensure stability. Optimized generator outputs are then implemented in real-time by the EMS, through adjustments to droop characteristics within this constraint. Experimental results from a laboratory-sized microgrid confirm the EMS function.
AIR SCORE ASSESSMENT FOR ACUTE APPENDICITIS

Acute appendicitis is the most common cause of acute abdomen. Approximately 7% of the population will be affected by this condition during full life. The development of AIR score may contribute to diagnosis associating easy clinical criteria and two simple laboratory tests.



To evaluate the score AIR (Appendicitis Inflammatory Response score) as a tool for the diagnosis and prediction of severity of acute appendicitis.



Were evaluated all patients undergoing surgical appendectomy. From 273 patients, 126 were excluded due to exclusion criteria. All patients were submitted o AIR score.



The value of the C-reactive protein and the percentage of leukocytes segmented blood count showed a direct relationship with the phase of acute appendicitis.



As for the laboratory criteria, serum C-reactive protein and assessment of the percentage of the polymorphonuclear leukocytes count were important to diagnosis and disease stratification.
Molecular phylogeny of living xenarthrans and the impact of character and taxon sampling on the placental tree rooting.

Extant xenarthrans (armadillos, anteaters and sloths) are among the most derived placental mammals ever evolved. South America was the cradle of their evolutionary history. During the Tertiary, xenarthrans experienced an extraordinary radiation, whereas South America remained isolated from other continents. The 13 living genera are relics of this earlier diversification and represent one of the four major clades of placental mammals. Sequences of the three independent protein-coding nuclear markers alpha2B adrenergic receptor (ADRA2B), breast cancer susceptibility (BRCA1), and von Willebrand Factor (VWF) were determined for 12 of the 13 living xenarthran genera. Comparative evolutionary dynamics of these nuclear exons using a likelihood framework revealed contrasting patterns of molecular evolution. All codon positions of BRCA1 were shown to evolve in a strikingly similar manner, and third codon positions appeared less saturated within placentals than those of ADRA2B and VWF. Maximum likelihood and Bayesian phylogenetic analyses of a 47 placental taxa data set rooted by three marsupial outgroups resolved the phylogeny of Xenarthra with some evidence for two radiation events in armadillos and provided a strongly supported picture of placental interordinal relationships. This topology was fully compatible with recent studies, dividing placentals into the Southern Hemisphere clades Afrotheria and Xenarthra and a monophyletic Northern Hemisphere clade (Boreoeutheria) composed of Laurasiatheria and Euarchontoglires. Partitioned likelihood statistical tests of the position of the root, under different character partition schemes, identified three almost equally likely hypotheses for early placental divergences: a basal Afrotheria, an Afrotheria + Xenarthra clade, or a basal Xenarthra (Epitheria hypothesis). We took advantage of the extensive sampling realized within Xenarthra to assess its impact on the location of the root on the placental tree. By resampling taxa within Xenarthra, the conservative Shimodaira-Hasegawa likelihood-based test of alternative topologies was shown to be sensitive to both character and taxon sampling.
Innovative LDS antenna for 4G applications

An innovative LDS 4G antenna solution operating in the 698-960 MHz band is presented. It is composed of two radiating elements recombined in a broadband single feed antenna system using a multiband matching circuit design. Matching interfaces are synthesized thanks to lumped components placed on the FR4 PCB supporting the LDS antenna. Measurement shows a reflection coefficient better than -6 dB over the 698-960 MHz band, with a 30% peak total efficiency. Measurement using a realistic phone casing showed the same performances. The proposed approach can be extended to additional bands, offering an innovative antenna solution able to address the multi band challenge related to 4G applications.
Suicide Ideation and Attempts among First Nations Peoples Living On-Reserve in Canada: The Intergenerational and Cumulative Effects of Indian Residential Schools.

Suicide rates among Indigenous peoples in Canada are at least twice that of their non-Indigenous counterparts. Although contemporary stressors contribute to this increased risk, historical experiences such as the Indian Residential School (IRS) system may also have continuing links with the risk for suicidal thoughts and behaviors. The current investigation examined the intergenerational and cumulative links between familial IRS attendance in relation to lifetime suicide ideation and attempts among First Nations adults living on-reserve.



Data from the 2008-2010 First Nations Regional Health Survey were analyzed, and participants comprised a representative sample of First Nations adults older than 18 years (weighted N = 127,338; IRS attendees were excluded). Of those who knew their familial IRS history, 38.0% had no history of attendance, 19.3% had a grandparent who attended, 16.2% had a parent who attended, and 26.5% had a parent and grandparent who attended.



Exposure of one previous familial generation to the IRS experience was associated with increased risk for lifetime suicide ideation (odds ratio [OR], 1.46; 95% confidence interval [CI], 1.16 to 1.84; P = 0.001) and attempts (OR, 1.44; 95% CI, 1.07 to 1.94; P < 0.016) compared with those with no IRS history. Having 2 generations of IRS familial history was associated with greater odds of reporting a suicide attempt compared with having one generation (OR, 1.35; 95% CI, 1.05 to 1.75; P = 0.022), which was reduced when current levels of distress and ideation were accounted for.



Findings support the existence of linkages between intergenerational exposure to IRS and risk for suicidal ideation and attempts and for a potential cumulative risk in relation to suicide attempts across generations.
A framework for community identification in dynamic social networks

We propose frameworks and algorithms for identifying communities in social networks that change over time. Communities are intuitively characterized as "unusually densely knit" subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating social networks over time can radically misrepresent the existing and changing community structure. Instead, we propose an optimization-based approach for modeling dynamic community structure. We prove that finding the most explanatory community structure is NP-hard and APX-hard, and propose algorithms based on dynamic programming, exhaustive search, maximum matching, and greedy heuristics. We demonstrate empirically that the heuristics trace developments of community structure accurately for several synthetic and real-world examples.
Accuracy-configurable adder for approximate arithmetic designs

Approximation can increase performance or reduce power consumption with a simplified or inaccurate circuit in application contexts where strict requirements are relaxed. For applications related to human senses, approximate arithmetic can be used to generate sufficient results rather than absolutely accurate results. Approximate design exploits a tradeoff of accuracy in computation versus performance and power. However, required accuracy varies according to applications, and 100% accurate results are still required in some situations. In this paper, we propose an accuracy-configurable approximate (ACA) adder for which the accuracy of results is configurable <u>during runtime.</u> Because of its configurability, the ACA adder can adaptively operate in both approximate (inaccurate) mode and accurate mode. The proposed adder can achieve significant throughput improvement and total power reduction over conventional adder designs. It can be used in accuracy-configurable applications, and improves the achievable tradeoff between performance/power and quality. The ACA adder achieves approximately 30% power reduction versus the conventional pipelined adder at the relaxed accuracy requirement.
Towards a Computational Lexicon for Moroccan Darija: Words, Idioms, and Constructions

We explore the challenges of building a computational lexicon for Moroccan Darija (MD), an Arabic dialect spoken by over 32 million people worldwide that only recently has begun appearing frequently in written form. We raise the question of what belongs in such a lexicon and start by describing our work building traditional word-level lexicon entries with their English translations. We then discuss challenges in translating idiomatic MD phrases and the creation of multi-word expression (MWE) lexicon entries whose meanings could not be fully derived from the individual words. Finally, we describe our preliminary exploration of constructions for inclusion in an MD constructicon, initially eliciting translations of established English constructions, and then shifting to document, when spontaneously offered, variant renderings of native MD counterparts.
Examining user surprise as a symptom of algorithmic filtering

The Facebook News Feed prioritizes posts for display by ranking them more prominently in the News Feed, based on users’ past interactions with the system. This study investigated constraints imposed on social interactions by the algorithm, by triggering participants’ awareness of “missed posts” in their Friends’ Timelines that they did not remember seeing before. If the algorithm prioritizes posts from people that users feel closer to and want to stay in touch with, participants should be less likely to report missed posts from close Friends. However, the results showed that relationship closeness had no effect on the likelihood of noticing a missed post, after controlling for how many Facebook Friends participants had and the accuracy of participants’ memories for their Friends’ Facebook activity. Also, missed posts from close Friends were more surprising, even when participants believed that the actions of the system caused the missed posts, indicating that these instances represent participants’ unmet expectations for the behavior of their News Feeds. Because Facebook posts present opportunities for feedback important for social support and maintaining social ties, this could indicate bias in the way the algorithm promotes content that could affect users’ ability to maintain relationships on Facebook. These findings have implications for approaches to improve user control and increase transparency in systems that use algorithmic filtering.
MRI in the diagnosis of Mayer-Rokitansky-Kuster-Hauser syndrome.

The aim of this study was to establish the role of magnetic resonance imaging (MRI) in patients with Mayer-Rokitansky-Kuster-Hauser syndrome (MRKHS).



Sixteen female MRKHS patients (mean age, 19.4 years; range, 11-39 years) were studied using MRI. Two experienced radiologists evaluated all the images in consensus to assess the presence or absence of the ovaries, uterus, and vagina. Additional urogenital or vertebral pathologies were also noted.



Of the 16 patients, complete aplasia of uterus was seen in five patients (31.3%). Uterine hypoplasia or remnant uterus was detected in 11 patients (68.8%). Ovaries were clearly seen in 10 patients (62.5%), and in two of the 10 patients, no descent of ovaries was detected. In five patients, ovaries could not be detected on MRI. In one patient, agenesis of right ovary was seen, and the left ovary was in normal shape. Of the 16 cases, 11 (68.8%) had no other extragenital abnormalities. Additional abnormalities were detected in six patients (37.5%). Two of the six had renal agenesis, and one patient had horseshoe kidney; renal ectopy was detected in two patients, and one patient had urachal remnant. Vertebral abnormalities were detected in two patients; one had L5 posterior fusion defect, bilateral hemisacralization, and rotoscoliosis, and the other had coccygeal vertebral fusion.



MRI is a useful and noninvasive imaging method in the diagnosis and evaluation of patients with MRKHS.