nils-herrmann/clean_scirepeval huggingface dataset:
Considerations for Incorporating "Well-Being" in Public Policy for Workers and Workplaces.

Action to address workforce functioning and productivity requires a broader approach than the traditional scope of occupational safety and health. Focus on "well-being" may be one way to develop a more encompassing objective. Well-being is widely cited in public policy pronouncements, but often as ". . . and well-being" (e.g., health and well-being). It is generally not defined in policy and rarely operationalized for functional use. Many definitions of well-being exist in the occupational realm. Generally, it is a synonym for health and a summative term to describe a flourishing worker who benefits from a safe, supportive workplace, engages in satisfying work, and enjoys a fulfilling work life. We identified issues for considering well-being in public policy related to workers and the workplace.
Asherman syndrome--one century later.

To provide an update on the current knowledge of Asherman syndrome.



Literature review.



The worldwide reports of this disease.



Patients with Asherman syndrome who presented with amenorrhea or hypomenorrhea, infertility, or recurrent pregnancy loss.



Hysteroscopy and hysteroscopic surgery have been the gold standard of diagnosis and treatment respectively for this condition.



The etiology, pathology, symptomatology, diagnosis, treatment, and reproductive outcomes were analyzed.



This syndrome occurs mainly as a result of trauma to the gravid uterine cavity, which leads to the formation of intrauterine and/or intracervical adhesions. Despite the advances in hysteroscopic surgery, the treatment of moderate to severe Asherman syndrome still presents a challenge. Furthermore, pregnancy after treatment remains high risk with complications including spontaneous abortion, preterm delivery, intrauterine growth restriction, placenta accrete or praevia, or even uterine rupture.


CONCLUSION(S)
The management of moderate to severe disease still poses a challenge, and the prognosis of severe disease remains poor. Close antenatal surveillance and monitoring are necessary for women who conceive after treatment.
Do unbalanced data have a negative effect on LDA?

For two-class discrimination, Ref.  claimed that, when covariance matrices of the two classes were unequal, a (class) unbalanced dataset had a negative effect on the performance of linear discriminant analysis (LDA). Through re-balancing 10 realworld datasets, Ref.  provided empirical evidence to support the claim using AUC (Area Under the receiver operating characteristic Curve) as the performance metric. We suggest that such a claim is vague if not misleading, there is no solid theoretical analysis presented in , and AUC can lead to a quite different conclusion from that led to by misclassification error rate (ER) on the discrimination performance of LDA for unbalanced datasets. Our empirical and simulation studies suggest that, for LDA, the increase of the median of AUC (and thus the improvement of performance of LDA) from re-balancing is relatively small, while, in contrast, the increase of the median of ER (and thus the decline in performance of LDA) from re-balancing is relatively large. Therefore, from our study, there is no reliable empirical evidence to support the claim that a (class) unbalanced data set has a negative effect on the performance of LDA. In addition, re-balancing affects the performance of LDA for datasets with either equal or unequal covariance matrices, indicating that having unequal covariance matrices is not a key reason for the difference in performance between original and re-balanced data.
Cytokine-induced killer cells promote antitumor immunity

The number of immune cells, especially dendritic cells and cytotoxic tumor infiltrating lymphocytes (TIL), particularly Th1 cells, CD8 T cells, and NK cells is associated with increased survival of cancer patients. Such antitumor cellular immune responses can be greatly enhanced by adoptive transfer of activated type 1 lymphocytes. Recently, adoptive cell therapy based on infusion of ex vivo expanded TILs has achieved substantial clinical success. Cytokine-induced killer (CIK) cells are a heterogeneous population of effector CD8 T cells with diverse TCR specificities, possessing non-MHC-restricted cytolytic activities against tumor cells. Preclinical studies of CIK cells in murine tumor models demonstrate significant antitumor effects against a number of hematopoietic and solid tumors. Clinical studies have confirmed benefit and safety of CIK cell-based therapy for patients with comparable malignancies. Enhancing the potency and specificity of CIK therapy via immunological and genetic engineering approaches and identifying robust biomarkers of response will significantly improve this therapy.
A jamming transition from under- to over-parametrization affects loss landscape and generalization

We argue that in fully-connected networks a phase transition delimits the overand under-parametrized regimes where fitting can or cannot be achieved. Under some general conditions, we show that this transition is sharp for the hinge loss. In the whole over-parametrized regime, poor minima of the loss are not encountered during training since the number of constraints to satisfy is too small to hamper minimization. Our findings support a link between this transition and the generalization properties of the network: as we increase the number of parameters of a given model, starting from an under-parametrized network, we observe that the generalization error displays three phases: (i) initial decay, (ii) increase until the transition point — where it displays a cusp — and (iii) slow decay toward a constant for the rest of the over-parametrized regime. Thereby we identify the region where the classical phenomenon of over-fitting takes place, and the region where the model keeps improving, in line with previous empirical observations for modern neural networks.
Design and analysis of a coreless flyback converter with a planar printed-circuit-board transformer

In this paper the development of a 1W DC/DC converter is presented. It is based on a flyback converter for simple applications. The transformer is designed in a form as a spiral coil on a PCB. The special feature of the converter is that the flyback converter is laid out without a core. The area (diameter) of a spiral coil should be bigger as a coil with a core because the air is not a good energy store. The advantage of a coreless transformer is the PCB-Design. The paper describes the theory of the flyback converter and shows a way of the implementation of the coreless planar transformer. An analysis shows some results by different geometry and frequency of the planar transformer.
Collaborative recommender systems: Combining effectiveness and efficiency

Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the ‘‘information overload’’ problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recommend by first developing a model of user ratings. In this paper, we bring to surface factors that affect CF process in order to identify existing false beliefs. In terms of accuracy, by being able to view the ‘‘big picture’’, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% increase in precision in comparison to widely-used CF algorithms. In terms of efficiency, we propose a model-based approach based on latent semantic indexing (LSI), that reduces execution times at least 50% than the classic
Radial flux and axial flux PM machines analysis for More Electric Engine aircraft applications

In this paper the advantages, constraints and drawbacks of the aircraft More Electric Engine (MEE) are presented through a comparison between the radial flux and the axial flux Permanent Magnet (PM) machines for four different positions inside the main gas turbine engine. The electromagnetic preliminary designs are performed on the basis of self-consisting dimensioning equations, starting from the constraints of the available volumes. The actual baseline output generated power is assumed to be 300kVA. Taking into account the technical and reliabilities goals imposed by the specific application, in the paper some technological choices characterizing the projects are discussed too. In order to help the designers in the evaluation of the most promising solutions for this application, the comparison between different proposed design solutions is performed on the basis of several specific design indexes.
A Precedence Graph-Based Approach to Detect Message Injection Attacks in J1939 Based Networks

Vehicles now include Electronic Control Units (ECUs) that communicate with each other via broadcast networks. Cyber-security professionals have shown that such embedded communication networks can be compromised. Very recently, it has been shown that embedded devices connected to commercial vehicle networks can be manipulated to perform unintended actions by injecting spoofed messages. Such attacks can be hard to detect as they can mimic safety critical actions performed by ECUs. We present a precedence graph-based anomaly detection technique to detect malicious message injections. Our approach can detect malicious message injections and is able to distinguish them from safety critical actions like hard braking.
Text mining in a digital library

Digital librarians strive to add value to the collections they create and maintain. One way is through selectivity: a carefully chosen set of authoritative documents in a particular topic area is far more useful to those working in the area than a huge, unfocused collection (like the Web). Another is by augmenting the collection with highquality metadata, which supports activities of searching and browsing in a uniform and useful way. A third way, and our topic here, is to enrich the documents by examining their content, extracting information, and using it to enhance the ways they can be located and presented. Text mining is a burgeoning new field that attempts to glean meaningful information from natural-language text. It may be loosely characterized as the process of analyzing text to extract information that is useful for particular purposes. It most commonly targets text whose function is the communication of factual information or opinions, and the motivation for trying to extract information from such text automatically is compelling – even if success is only partial. “Text mining” (sometimes called “text data mining”; ) defies tight definition but encompasses a wide range of activities: text summarization; document retrieval; document clustering; text categorization; language identification; authorship ascription; identifying phrases, phrase structures, and key phrases; extracting “entities” such as names, dates, and abbreviations; locating acronyms and their definitions; filling predefined templates with extracted information; and even learning rules from such templates . Techniques of text mining have much to offer digital libraries and their users. Here we describe the marriage of a widely used digital library system (Greenstone) with a development environment for text mining (GATE) to enrich the library reader’s experience. The work is in progress: one level of integration has been demonstrated and another is planned. The project has been greatly facilitated by the fact that both systems are publicly available under the GNU public license – and, in addition, this means that the benefits gained by leveraging text mining techniques will accrue to all Greenstone users.
Approximate Greedy Clustering and Distance Selection for Graph Metrics

In this paper, we consider two important problems defined on finite metric spaces, and provide efficient new algorithms and approximation schemes for these problems on inputs given as graph shortest path metrics or high-dimensional Euclidean metrics. The first of these problems is the greedy permutation (or farthest-first traversal) of a finite metric space: a permutation of the points of the space in which each point is as far as possible from all previous points. We describe randomized algorithms to find (1 + ε)-approximate greedy permutations of any graph with n vertices and m edges in expected time O ( ε−1(m + n) log n log(n/ε) ) , and to find (1 + ε)-approximate greedy permutations of points in high-dimensional Euclidean spaces in expected time O(ε−2n1+1/(1+ε) 2+o(1)). Additionally we describe a deterministic algorithm to find exact greedy permutations of any graph with n vertices and treewidth O(1) in worst-case time O(n3/2 log n). The second of the two problems we consider is distance selection: given k ∈ q( n 2 )y , we are interested in computing the kth smallest distance in the given metric space. We show that for planar graph metrics one can approximate this distance, up to a constant factor, in near linear time.
Emotion recognition through wireless signal

Emotion Recognition has expanding significance in helping human-PC collaboration issues. It is a difficult task to understand how other people feel but it becomes even worse to perceive these emotions through a computer. With the advancement in technology and increase in application of artificial intelligence, it has become a necessity to automatically recognize the emotions of the user for the human-computer interactions. The need for emotion recognition keeps increasing and it has become applicable in various fields now days. This paper explores the way to recognize different human emotions from our body through wireless signals.
Comparison of music genre classification using Nearest Centroid Classifier and k-Nearest Neighbours

Music genre is getting complex from time to time. As the size of digital media grows along with amount of data, manual search of digital audio files according to its genre is considered impractical and inefficient; therefore a classification mechanism is needed to improve searching. Zero Crossing Rate (ZCR), Average Energy (E) and Silent Ratio (SR) are a few of features that can be extracted from digital audio files to classify its genre. This research is conducted to classify music from digital audio (songs) into 12 genres: Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop, Electronic, Reggae and Rock using above mentioned features, extracted from WAV audio files. Classification is performed several times using selected 3, 6, 9 and 12 genres respectively. The result shows that classification of 3 music genres (Ballad, Blues, Classic) has the highest accuracy (96.67%), followed by 6 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz) with 70%, and 9 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop) with 53.33% accuracy. Classification of all 12 music genres yields the lowest accuracy of 33.33%. The test results with the k-Nearest Neighbours algorithm to 120 songs for k = 3 accuracy reaches 22.5%, k = 5 accuracy reaches 22.5%, k = 7 accuracy reaching 26.7% and k = 9 accuracy reaches 26.7 %. Results showed that genre classification by matching the shortest distance through the centre of the class, yields better results than using the k-NN algorithm.
A novel side branch protection technique in coronary stent implantation: Jailed Corsair technique.

Side branch occlusion, which was one of the common complications in percutaneous coronary interventions, was closely associated with cardiac death and myocardial infarction. Clinical guidelines also support the importance of preservation of physiologic blood flow in SB during PCI to bifurcation lesions. In order to avoid side branch occlusion during stent implantation, we often performed the jailed wire technique, in which a conventional guide wire was inserted to the side branch before stent implantation to the main vessel. However, the jailed wire technique could not always prevent side branch occlusion. In this case report, we described a case of 72-year-old male suffering from angina pectoris. Coronary angiography revealed the diffuse calcified stenosis in the proximal and middle of left anterior descending coronary artery, and the large diagonal branch originated from the middle of the stenosis. To prevent side branch occlusion, we performed a novel side branch protection technique by using the Corsair microcatheter (Asahi Intecc, Nagoya, Japan). In this case report, we illustrated this "Jailed Corsair technique", and discussed the advantage compared to other side branch protection techniques such as the jailed balloon technique.
A Mutual Authentication and Key Establishment Scheme for M2M Communication in 6LoWPAN Networks

The machine-to-machine (M2M) communication, which plays a vital role in the Internet of Things (IoT), allows wireless and wired systems to monitor environments and exchange the information among various machines automatically without human interventions. In order to promote the development of the IoT and exploit the M2M applications, the Internet Engineering Task Force (IETF) has been developing a standard named Internet Protocol version 6 (IPv6) over low-power wireless personal area networks (6LoWPAN) to enable IP-based M2M devices to connect to the open Internet. Although the 6LoWPAN standard has specified the important issues in the M2M communications, various security issues have not been addressed. In this paper, an enhanced mutual authentication and key establishment scheme is designed for the M2M communications in 6LoWPAN networks. The proposed scheme enables a 6LoWPAN device to securely authenticate with the remote server with a session key established between them. The security proof by the protocol composition logic can prove the logic correctness of the proposed scheme. The formal verification and the simulation show that the proposed scheme in 6LoWPAN could not only enhance the security functionality with the ability to prevent various malicious attacks, but also incur less computational and transmission overhead.
Game Theory Models for the Verification of the Collective Behaviour of Autonomous Cars

The collective of autonomous cars is expected to generate almost optimal traffic. In this position paper we discuss the multi-agent models and the verification results of the collective behaviour of autonomous cars. We argue that non-cooperative autonomous adaptation cannot guarantee optimal behaviour. The conjecture is that intention aware adaptation with a constraint on simultaneous decision making has the potential to avoid unwanted behaviour. The online routing game model is expected to be the basis to formally prove this conjecture.
A Distributed Control Strategy Based on DC Bus Signaling for Modular Photovoltaic Generation Systems With Battery Energy Storage

Modular generation system, which consists of modular power conditioning converters, is an effective solution to integrate renewable energy sources with conventional utility grid to improve reliability and efficiency, especially for photovoltaic generation. A distributed control strategy based on improved dc bus signaling is proposed for a modular photovoltaic (PV) generation system with battery energy storage elements. In this paper, the modular PV generation system is composed of three modular dc/dc converters for PV arrays, two grid-connected dc/ac converters, and one dc/dc converter for battery charging/discharging and local loads, which is available of either grid-connected operation or islanding operation. By using the proposed control strategy, the operations of a modular PV generation system are categorized into four modes: islanding with battery discharging, grid-connected rectification, grid-connected inversion, and islanding with constant voltage (CV) generation. The power balance of the system under extreme conditions such as the islanding operation with a full-charged battery is taken into account in this control strategy. The dc bus voltage level is employed as an information carrier to distinguish different modes and determine mode switching. Control methods of modular dc/dc converters, battery converter, and grid-connected converter are addressed. An autonomous control method for modular dc/dc converters is proposed to realize smooth switching between CV operation and maximum power point tracking operation, which enables the dc bus voltage regulation capability of modular dc/dc converters. Seamless switching of a battery converter between charging and discharging and that of a grid-connected converter between rectification and inversion are ensured by the proposed control methods. Experiments verify the practical feasibility and the effectiveness of the proposed control strategies.
Phylogeny of Egyptian isolates of Cucumber mosaic virus (CMV) and Tomato mosaic virus (ToMV) infecting Solanum lycopersicum

Four Cucumber mosaic virus (CMV) (CMV-HM 1–4) and nine Tomato mosaic virus (ToMV) (ToMV AH 1–9) isolates detected in tomato samples collected from different governorates in Egypt during 2014, were here characterized. According to the coat protein gene sequence and to the complete nucleotide sequence of total genomic RNA1, RNA2 and RNA3 of CMV-HM3 the new Egyptian isolates are related to members of the CMV subgroup IB. The nine ToMV Egyptian isolates were characterized by sequence analysis of the coat protein and the movement protein genes. All isolates were grouped within the same branch and showed high relatedness to all considered isolates (98–99%). Complete nucleotide sequence of total genomic RNA of ToMV AH4 isolate was obtained and its comparison showed a closer degree of relatedness to isolate 99–1 from the USA (99%). To our knowledge, this is the first report of CMV isolates from subgroup IB in Egypt and the first full length sequencing of an ToMV Egyptian isolate.
$1.00 per RT #BostonMarathon #PrayForBoston: Analyzing fake content on Twitter

Online social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information.We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves.