nils-herrmann/clean_scirepeval huggingface dataset:
Relevance of Emoticons in Computer-Mediated Communication Contexts: An Overview

With the constant growth in Information and Communication Technology (ICT) in the last 50 years or so, electronic communication has become part of the present day system of living. Equally, smileys or emoticons were innovated in 1982, and today the genre has attained a substantial patronage in various aspects of computer-mediated communication (CMC). Ever since written forms of electronic communication lack the face-to-face (F2F) situation attributes, emoticons are seen as socio-emotional suppliers to the CMC. This article reviews scholarly research in that field in order to compile variety of investigations on the application of emoticons in some facets of CMC, i.e. Facebook, Instant Messaging (IM), and Short Messaging Service (SMS). Key findings of the review show that emoticons do not just serve as paralanguage elements rather they are compared to word morphemes with distinctive significative functions. In other words, they are morpheme-like units and could be derivational, inflectional, or abbreviations but not unbound. The findings also indicate that emoticons could be conventionalized as well as being paralinguistic elements, therefore, they should be approached as contributory to conversation itself not mere compensatory to language.
"Lupara Bianca" a way to hide cadavers after Mafia homicides. A cemetery of Italian Mafia. A case study.

The Gargano, also known as the 'Spur of Italy', is a sub-region of Italy which is located in North of Puglia, in the Province of Foggia. A ravine located in this area was used as a dumping ground in past. During a clearing operation, a team of speleologists discovered human skeletal remains, which led to an official investigation by the local prosecutor's office. The prosecutor called a team of forensic specialist for a scene investigation to recover and identify the human skeletal remains. Four different human skeletal remains located at four different levels of depth underground were found and were in different conditions. A complete forensic investigation was initiated and comprised of radiological imaging with DNA profiling. Three of the four human skeletal remains that were found were identified as those belonging to men who vanished mysteriously in the nineties. The studies conducted have demonstrated that the victims found were murdered and the murders were attributed to the "Mafia of Gargano". The Mafia of Gargano was officially recognized as a criminal organization dating back to 2009 and their criminal activities included the smuggling of firearms and cigarettes, human trafficking, and smuggling of undocumented immigrants. Murders in which corpses are made to disappear is common practice in criminal activities including that of the Italian Mafia. The "Lupara Bianca" is a colloquial term commonly used in Sicily to refer to concealed murders. To the best of our knowledge, this is the first reported study describing the discovery of one of the locations used extensively by the local Mafia as a "cemetery" for victims of "Lupara Bianca" homicides. Based on evidences collected at the site, an explanation of this homicidal modality will be provided.
Group-sparse Embeddings in Collective Matrix Factorization

CMF is a technique for simultaneously learning low-rank representations based on a collection of matrices with shared entities. A typical example is the joint modeling of useritem, item-property, and user-feature matrices in a recommender system. The key idea in CMF is that the embeddings are shared across the matrices, which enables transferring information between them. The existing solutions, however, break down when the individual matrices have low-rank structure not shared with others. In this work we present a novel CMF solution that allows each of the matrices to have a separate low-rank structure that is independent of the other matrices, as well as structures that are shared only by a subset of them. We compare MAP and variational Bayesian solutions based on alternating optimization algorithms and show that the model automatically infers the nature of each factor using group-wise sparsity. Our approach supports in a principled way continuous, binary and count observations and is efficient for sparse matrices involving missing data. We illustrate the solution on a number of examples, focusing in particular on an interesting use-case of augmented multi-view learning.
The subcutaneous fat compartments in relation to aesthetically important facial folds and rhytides.

The ideal treatment of the nasolabial fold, the tear trough, the labiomandibular fold and the mentolabial sulcus is still discussed controversially. The detailed topographical anatomy of the fat compartments may clarify the anatomy of facial folds and may offer valuable information for choosing the adequate treatment modality. Nine non-fixed cadaver heads in the age range between 72 and 89 years (five female and four male) were investigated. Computed tomographic scans were performed after injection of a radiographic contrast medium directly into the fat compartments surrounding prominent facial folds. The data were analysed after multiplanar image reconstruction. The fat compartments surrounding the facial folds could be defined in each subject. Different arrangement patterns of the fat compartments around the facial rhytides were found. The nasolabial fold, the tear trough and the labiomandibular fold represent an anatomical border between adjacent fat compartments. By contrast, the glabellar fold and the labiomental sulcus have no direct relation to the boundaries of facial fat. Deep fat, underlying a facial rhytide, was identified underneath the nasolabial crease and the labiomental sulcus. In conclusion, an improvement by a compartment-specific volume augmentation of the nasolabial fold, the tear trough and the labiomandibular fold is limited by existing boundaries that extend into the skin. In the area of the nasolabial fold and the mentolabial sulcus, deep fat exists which can be used for augmentation and subsequent elevation of the folds. The treatment of the tear trough deformity appears anatomically the most challenging area since the superficial and deep fat compartments are separated by an osseo-cutaneous barrier, the orbicularis retaining ligament. In severe cases, a surgical treatment should be considered. By contrast, the glabellar fold shows the most simple anatomical architecture. The fold lies above one subcutaneous fat compartment that can be used for augmentation.
Perceptions of social media on students' academic engagement in tertiary education

Social media has been gaining popularity among university students who use social media at higher rates than the general population. Students consequently spend a significant amount of time on social media, which may inevitably have an effect on their academic engagement. Subsequently, scholars have been intrigued to examine the impact of social media on students' academic engagement. Research that has directly explored the use of social media and its impact on students in tertiary institutions has revealed limited and mixed findings, particularly within a South African context; thus leaving a window of opportunity to further investigate the impact that social media has on students' academic engagement. This study therefore aims to investigate the use of social media in tertiary institutions, the impact that the use thereof has on students' academic engagement and to suggest effective ways of using social media in tertiary institutions to improve students' academic engagement from students' perspectives. This study used an interpretivist (inductive) approach in order to determine and comprehend student's perspectives and experiences towards the use of social media and the effects thereof on their academic engagement. A single case study design at Rhodes University was used to determine students' perceptions and data was collected using an online survey. The findings reveal that students use social media for both social and academic purposes. Students further perceived that social media has a positive impact on their academic engagement and suggest that using social media at tertiary level could be advantageous and could enhance students' academic engagement.
Vocsyl: visualizing syllable production for children with ASD and speech delays

Communication disorders occur across the lifespan and encompass a wide range of conditions that interfere with individuals' abilities to hear (e.g., hearing loss), speak (e.g., voice disorders; motor speech disorders), and/or use language (e.g., specific language impairment; aphasia) to meet their communication needs. Such disorders often compromise the social, recreational, emotional, educational, and vocational aspects of an individual's life. This research examines the development and implementation of new software that facilitates multi-syllabic speech production in children with autism and speech delays. The VocSyl software package utilizes a suite of audio visualizations that represent a myriad of audio features in abstract representations. The goal of these visualizations is to provide children with language impairments a new persistent modality in which to experience and practice speech-language skills.
Monitoring and preventing arc-induced wafer damage in 300mm manufacturing

Arcing between the plasma and the wafer, kit, or target in PVD processes can cause significant wafer damage and foreign material contamination which limits wafer yield. Monitoring the plasma and quickly detecting this arcing phenomena is critical to ensuring that today's PVD processes run optimally and maximize product yield. This is particularly true in 300mm semiconductor manufacturing, where energies used are higher and more product is exposed to the plasma with each wafer run than in similar 200mm semiconductor manufacturing processes.
Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose LatPlan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), and a pair of images representing the initial and the goal states (planning inputs), LatPlan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. The contribution of this paper is twofold: (1) State Autoencoder, which finds a propositional state representation of the environment using a Variational Autoencoder. It generates a discrete latent vector from the images, based on which a PDDL model can be constructed and then solved by an off-the-shelf planner. (2) Action Autoencoder / Discriminator, a neural architecture which jointly finds the action symbols and the implicit action models (preconditions/effects), and provides a successor function for the implicit graph search. We evaluate LatPlan using image-based versions of 3 planning domains: 8-puzzle, Towers of Hanoi and LightsOut.  The contents of AAAI-18 submission itself is significantly extended from what has been published in Arxiv, KEPS-17, NeSy-17 or Cognitum-17 workshops. Over half of the paper describing (2) is new. Additionally, this manuscript contains the contents in the supplemental material of AAAI-18 submission. These implementation/experimental details are moved to the Appendix. Note to the ML / deep learning researchers This article combines the Machine Learning systems and the classical, logic-based symbolic systems. Some readers may not be familiar with NNs and related fields like you are, thus we include very basic description of the architectures and the training methods.
Positive Allosteric Modulators of GluN2A-Containing NMDARs with Distinct Modes of Action and Impacts on Circuit Function

To enhance physiological function of NMDA receptors (NMDARs), we identified positive allosteric modulators (PAMs) of NMDARs with selectivity for GluN2A subunit-containing receptors. X-ray crystallography revealed a binding site at the GluN1-GluN2A dimer interface of the extracellular ligand-binding domains (LBDs). Despite the similarity between the LBDs of NMDARs and AMPA receptors (AMPARs), GluN2A PAMs with good selectivity against AMPARs were identified. Potentiation was observed with recombinant triheteromeric GluN1/GluN2A/GluN2B NMDARs and with synaptically activated NMDARs in brain slices from wild-type (WT), but not GluN2A knockout (KO), mice. Individual GluN2A PAMs exhibited variable degrees of glutamate (Glu) dependence, impact on NMDAR Glu EC50, and slowing of channel deactivation. These distinct PAMs also exhibited differential impacts during synaptic plasticity induction. The identification of a new NMDAR modulatory site and characterization of GluN2A-selective PAMs provide powerful molecular tools to dissect NMDAR function and demonstrate the feasibility of a therapeutically desirable type of NMDAR enhancement.
CURA: A Framework for Quality-Retaining Power Saving on Mobile OLED Displays

Organic Light-Emitting Diode (OLED) technology is regarded as a promising alternative to mobile displays. In this article, we introduce the design, algorithm, and implementation of a novel framework called CURA for quality-retaining power saving on mobile OLED displays. First, we link human visual attention to OLED power saving and model the OLED image scaling optimization problem. The objective is to minimize the power required to display an image without adversely impacting the user’s visual experience. Then, we present the algorithm used to solve the modeled problem, and prove its optimality even without an accurate power model. Finally, based on the framework, we implement two practical applications on a commercial OLED mobile tablet. The results of experiments conducted on the tablet with real images demonstrate that CURA can reduce significant OLED power consumption while retaining the visual quality of images.
FPGA modeling of diverse superscalar processors

There is increasing interest in using Field Programmable Gate Arrays (FPGAs) as platforms for computer architecture simulation. This paper is concerned with modeling superscalar processors with FPGAs. To be transformative, the FPGA modeling framework should meet three criteria.  The framework should be able to model diverse superscalar processors, like a software model. In particular, it should be possible to vary superscalar parameters such as fetch, issue, and retire widths, depths of pipeline stages, queue sizes, etc. (2) Automatic: The framework should be able to automatically and efficiently map any one of its superscalar processor configurations to the FPGA. (3) Realistic: The framework should model a modern superscalar microarchitecture in detail, ideally with prototype quality, to enable a new era and depth of microarchitecture research. A framework that meets these three criteria will enjoy the convenience of a software model, the speed of an FPGA model, and the experience of a prototype. This paper describes FPGA-Sim, a configurable, automatically FPGA-synthesizable, and register-transfer-level (RTL) model of an out-of-order superscalar processor. FPGA-Sim enables FPGA modeling of diverse superscalar processors out-of-the-box. Moreover, its direct RTL implementation yields the fidelity of a hardware prototype.
DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules

Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns. We present DiffCoEx, a novel method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression analysis. We demonstrate its usefulness by identifying biologically relevant, differentially coexpressed modules in a rat cancer dataset. DiffCoEx is a simple and sensitive method to identify gene coexpression differences between multiple conditions.
Graphlets versus node2vec and struc2vec in the task of network alignment

Network embedding aims to represent each node in a network as a low-dimensional feature vector that summarizes the given node’s (extended) network neighborhood. The nodes’ feature vectors can then be used in various downstream machine learning tasks. Recently, many embedding methods that automatically learn the features of nodes have emerged, such as node2vec and struc2vec, which have been used in tasks such as node classification, link prediction, and node clustering, mainly in the social network domain. There are also other embedding methods that explicitly look at the connections between nodes, i.e., the nodes’ network neighborhoods, such as graphlets. Graphlets have been used in many tasks such as network comparison, link prediction, and network clustering, mainly in the computational biology domain. Even though the two types of embedding methods (node2vec/struct2vec versus graphlets) have a similar goal – to represent nodes as features vectors, no comparisons have been made between them, possibly because they have originated in the different domains. Therefore, in this study, we compare graphlets to node2vec and struc2vec, and we do so in the task of network alignment. In evaluations on synthetic and real-world biological networks, we find that graphlets are both more accurate and faster than node2vec and struc2vec.
Female genital cosmetic surgery: a review of techniques and outcomes

The aesthetic and functional procedures that comprise female genital cosmetic surgery (FGCS) include traditional vaginal prolapse procedures as well as cosmetic vulvar and labial procedures. The line between cosmetic and medically indicated surgical procedures is blurred, and today many operations are performed for both purposes. The contributions of gynecologists and reconstructive pelvic surgeons are crucial in this debate. Aesthetic vaginal surgeons may unintentionally blur legitimate female pelvic floor disorders with other aesthetic conditions. In the absence of quality outcome data, the value of FGCS in improving sexual function remains uncertain. Women seeking FGCS need to be educated about the range and variation of labia widths and genital appearance, and should be evaluated for true pelvic support disorders such as pelvic organ prolapse and stress urinary incontinence. Women seeking FGCS should also be screened for psychological conditions and should act autonomously without coercion from partners or surgeons with proprietary conflicts of interest.
The role of tonsillectomy in psoriasis treatment.

Psoriasis is a chronic and immune-mediated skin disease with a considerable negative impact on quality of life. The link between psoriasis, especially guttate psoriasis, and streptococcal infections, namely tonsillitis, has been studied for several years. Some authors have also suggested an association with other types of psoriasis, such as plaque psoriasis, which is the most common. The role of tonsillectomy in the treatment of plaque psoriasis is not consensual. This case report aims to discuss this topic. The authors intend to highlight the growing evidence of a relationship between plaque psoriasis and an infectious pathology of the otolaryngology area. A clinical case of severe chronic plaque psoriasis with exacerbations linked to acute tonsillitis is described. The case was recalcitrant to dermatological treatment and tonsillectomy was included in the treatment options. After surgery, the skin disease was evidently controlled and significant improvement on the patient's quality of life was also evident.
Automating weather forecasts based on convolutional networks

Numerical weather models generate a vast amount of information which requires human interpretation to generate local weather forecasts. Convolutional Neural Networks (CNN) can extract features from images showing unprecedented results in many different domains. In this work, we propose the use of CNN models to interpret numerical weather model data which, by capturing the spatial and temporal relationships between the input variables, can produce local forecasts. Different architectures are compared and a methodology to introspect the models is presented.
Implementing a Rule-Based Contract Compliance Checker

The paper describes the design and implementation of an independent, third party contract monitoring service called Contract Compliance Checker (CCC). The CCC is provided with the specification of the contract in force, and is capable of observing and logging the relevant business-to-business (B2B) interaction events, in order to determine whether the actions of the business partners are consistent with the contract. A contract specification language called EROP (for Events, Rights, Obligations and Prohibitions) for the CCC has been developed based on business rules, that provides constructs to specify what rights, obligation and prohibitions become active and inactive after the occurrence of events related to the execution of business operations. The system has been designed to work with B2B industry standards such as ebXML and RosettaNet.
Hawkes processes in finance

In this paper we propose an overview of the recent academic literature devoted to the applications of Hawkes processes in finance. Hawkes processes constitute a particular class of multivariate point processes that has become very popular in empirical high frequency finance this last decade. After a reminder of the main definitions and properties that characterize Hawkes processes, we review their main empirical applications to address many different problems in high frequency finance. Because of their great flexibility and versatility, we show that they have been successfully involved in issues as diverse as estimating the volatility at the level of transaction data, estimating the market stability, accounting for systemic risk contagion, devising optimal execution strategies or capturing the dynamics of the full order book.
Design of a tendon-driven robotic hand with an embedded camera

This paper designs a new five-fingered robotic hand with a camera. Several morphological features of the human hand are integrated to improve the appearance of the hand. The drive system of this hand is under-actuated to eliminate the weight of the hand and to embed all the actuators inside the palm. Despite of this under-actuation, this hand can grasp objects in several different ways. In addition, the two different transmissions are adopted to drive the fingers according to their roles. These transmissions help not only to improve drive efficiency but also to secure the space of the embedded camera.