nils-herrmann/clean_scirepeval huggingface dataset:
REAL-TIME FULLY AUTOMATED RESTAURANT MANAGEMENT AND COMMUNICATION SYSTEM “ RESTO ”

Existing restaurant systems are partially automated. We have developed a fully automated restaurant management and communication system. This paper covers the partial automation that exists and focuses the details regarding how we have implemented full automation in management and communication for restaurants. It also covers technical aspects of web based application and android application and how they are clubbed together with their advantages and disadvantages. It also lights the future work that can be added on regarding android application to make it more user friendly.  REST API, HTTP Client class, jdk, Java, Servlet, IP Address, Android Device, Web based application --------------------------------------------------------------------***------------------------------------------------------------------
Interval-valued fuzzy planar graphs

In this paper, interval-valued fuzzy planar graphs are defined and several properties are studied. The interval-valued fuzzy graphs are more efficient than fuzzy graphs, since the degree of membership of vertices and edges lie within the interval [0, 1] instead at a point in fuzzy graphs. We also use the term ‘degree of planarity’ to measures the nature of planarity of an interval-valued fuzzy graph. The other relevant terms such as strong edges, interval-valued fuzzy faces, strong interval-valued fuzzy faces are defined here. The interval-valued fuzzy dual graph which is closely associated to the interval-valued fuzzy planar graph is defined. Several properties of interval-valued fuzzy dual graph are also studied. An example of interval-valued fuzzy planar graph is given.
Sexual selection and mate choice.

The past two decades have seen extensive growth of sexual selection research. Theoretical and empirical work has clarified many components of pre- and postcopulatory sexual selection, such as aggressive competition, mate choice, sperm utilization and sexual conflict. Genetic mechanisms of mate choice evolution have been less amenable to empirical testing, but molecular genetic analyses can now be used for incisive experimentation. Here, we highlight some of the currently debated areas in pre- and postcopulatory sexual selection. We identify where new techniques can help estimate the relative roles of the various selection mechanisms that might work together in the evolution of mating preferences and attractive traits, and in sperm-egg interactions.
Rejuvenating the Face: An Analysis of 100 Absorbable Suture Suspension Patients.

Absorbable suture suspension (Silhouette InstaLift, Sinclair Pharma, Irvine, CA) is a novel, minimally invasive system that utilizes a specially manufactured synthetic suture to help address the issues of facial aging, while minimizing the risks associated with historic thread lifting modalities.



The purpose of the study was to assess the safety, efficacy, and patient satisfaction of the absorbable suture suspension system in regards to facial rejuvenation and midface volume enhancement.



The first 100 treated patients who underwent absorbable suture suspension, by the senior author, were critically evaluated. Subjects completed anonymous surveys evaluating their experience with the new modality.



Survey results indicate that absorbable suture suspension is a tolerable (96%) and manageable (89%) treatment that improves age related changes (83%), which was found to be in concordance with our critical review.



Absorbable suture suspension generates high patient satisfaction by nonsurgically lifting mid and lower face and neck skin and has the potential to influence numerous facets of aesthetic medicine. The study provides a greater understanding concerning patient selection, suture trajectory, and possible adjuvant therapies.
Image to Image Translation for Domain Adaptation

We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without requiring any training annotations in the target domain. This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network. To this end we propose the novel use of the recently proposed unpaired image-to-image translation framework to constrain the features extracted by the encoder network. Specifically, we require that the features extracted are able to reconstruct the images in both domains. In addition we require that the distribution of features extracted from images in the two domains are indistinguishable. Many recent works can be seen as specific cases of our general framework. We apply our method for domain adaptation between MNIST, USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in classification tasks, and also between GTA5 and Cityscapes datasets for a segmentation task. We demonstrate state of the art performance on each of these datasets.
Platelet-rich plasma efficacy versus corticosteroid injection treatment for chronic severe plantar fasciitis.

Chronic plantar fasciitis is a common orthopedic condition that can prove difficult to successfully treat. In this study, autologous platelet-rich plasma (PRP), a concentrated bioactive blood component rich in cytokines and growth factors, was compared to traditional cortisone injection in the treatment of chronic cases of plantar fasciitis resistant to traditional nonoperative management.



Forty patients (23 females and 17 males) with unilateral chronic plantar fasciitis that did not respond to a minimum of 4 months of standardized traditional nonoperative treatment modalities were prospectively randomized and treated with either a single ultrasound guided injection of 3 cc PRP or 40 mg DepoMedrol cortisone. American Orthopedic Foot and Ankle Society (AOFAS) hindfoot scoring was completed for all patients immediately prior to PRP or cortisone injection (pretreatment = time 0) and at 3, 6, 12, and 24 months following injection treatment. Baseline pretreatment radiographs and MRI studies were obtained in all cases to confirm the diagnosis of plantar fasciitis.



The cortisone group had a pretreatment average AOFAS score of 52, which initially improved to 81 at 3 months posttreatment but decreased to 74 at 6 months, then dropped to near baseline levels of 58 at 12 months, and continued to decline to a final score of 56 at 24 months. In contrast, the PRP group started with an average pretreatment AOFAS score of 37, which increased to 95 at 3 months, remained elevated at 94 at 6 and 12 months, and had a final score of 92 at 24 months.



PRP was more effective and durable than cortisone injection for the treatment of chronic recalcitrant cases of plantar fasciitis.
Leveraging High-Dimensional Side Information for Top-N Recommendation

Top-N recommender systems typically utilize side information to address the problem of data sparsity. As nowadays side information is growing towards high dimensionality, the performances of existing methods deteriorate in terms of both effectiveness and efficiency, which imposes a severe technical challenge. In order to take advantage of high-dimensional side information, we propose in this paper an embedded feature selection method to facilitate top-N recommendation. In particular, we propose to learn feature weights of side information, where zero-valued features are naturally filtered out. We also introduce non-negativity and sparsity to the feature weights, to facilitate feature selection and encourage low-rank structure. Two optimization problems are accordingly put forward, respectively, where the feature selection is tightly or loosely coupled with the learning procedure. Augmented Lagrange Multiplier and Alternating Direction Method are applied to efficiently solve the problems. Experiment results demonstrate the superior recommendation quality of the proposed algorithm to that of the state-of-the-art alternatives.
The Laplacian in RL: Learning Representations with Efficient Approximations

The smallest eigenvectors of the graph Laplacian are well-known to provide a succinct representation of the geometry of a weighted graph. In reinforcement learning (RL), where the weighted graph may be interpreted as the state transition process induced by a behavior policy acting on the environment, approximating the eigenvectors of the Laplacian provides a promising approach to state representation learning. However, existing methods for performing this approximation are ill-suited in general RL settings for two main reasons: First, they are computationally expensive, often requiring operations on large matrices. Second, these methods lack adequate justification beyond simple, tabular, finite-state settings. In this paper, we present a fully general and scalable method for approximating the eigenvectors of the Laplacian in a model-free RL context. We systematically evaluate our approach and empirically show that it generalizes beyond the tabular, finite-state setting. Even in tabular, finite-state settings, its ability to approximate the eigenvectors outperforms previous proposals. Finally, we show the potential benefits of using a Laplacian representation learned using our method in goal-achieving RL tasks, providing evidence that our technique can be used to significantly improve the performance of an RL agent.
The Mesolimbic Dopamine Reward Circuit in Depression

The neural circuitry that mediates mood under normal and abnormal conditions remains incompletely understood. Most attention in the field has focused on hippocampal and frontal cortical regions for their role in depression and antidepressant action. While these regions no doubt play important roles in these phenomena, there is compelling evidence that other brain regions are also involved. Here we focus on the potential role of the nucleus accumbens (NAc; ventral striatum) and its dopaminergic input from the ventral tegmental area (VTA), which form the mesolimbic dopamine system, in depression. The mesolimbic dopamine system is most often associated with the rewarding effects of food, sex, and drugs of abuse. Given the prominence of anhedonia, reduced motivation, and decreased energy level in most individuals with depression, we propose that the NAc and VTA contribute importantly to the pathophysiology and symptomatology of depression and may even be involved in its etiology. We review recent studies showing that manipulations of key proteins (e.g. CREB, dynorphin, BDNF, MCH, or Clock) within the VTA-NAc circuit of rodents produce unique behavioral phenotypes, some of which are directly relevant to depression. Studies of these and other proteins in the mesolimbic dopamine system have established novel approaches to modeling key symptoms of depression in animals, and could enable the development of antidepressant medications with fundamentally new mechanisms of action.
Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How

We show that, during inference with Convolutional Neural Networks (CNNs), more than 2× to 8× ineffectual work can be exposed if instead of targeting those weights and activations that are zero, we target different combinations of value stream properties. We demonstrate a practical application with Bit-Tactical (TCL), a hardware accelerator which exploits weight sparsity, per layer precision variability and dynamic fine-grain precision reduction for activations, and optionally the naturally occurring sparse effectual bit content of activations to improve performance and energy efficiency. TCL benefits both sparse and dense CNNs, natively supports both convolutional and fully-connected layers, and exploits properties of all activations to reduce storage, communication, and computation demands. While TCL does not require changes to the CNN to deliver benefits, it does reward any technique that would amplify any of the aforementioned weight and activation value properties. Compared to an equivalent data-parallel accelerator for dense CNNs, TCLp, a variant of TCL improves performance by 5.05× and is 2.98× more energy efficient while requiring 22% more area.
Ethereum transaction graph analysis

Cryptocurrency platforms such as Bitcoin and Ethereum have become more popular due to decentralized control and the promise of anonymity. Ethereum is particularly powerful due to its support for smart contracts which are implemented through Turing complete scripting languages and digital tokens that represent fungible tradable goods. It is necessary to understand whether de-anonymization is feasible to quantify the promise of anonymity. Cryptocurrencies are increasingly being used in online black markets like Silk Road and ransomware like CryptoLocker and WannaCry. In this paper, we propose a model for persisting transactions from Ethereum into a graph database, Neo4j. We propose leveraging graph compute or analytics against the transactions persisted into a graph database.
Reactive oxygen species (ROS) and response of antioxidants as ROS-scavengers during environmental stress in plants

*Correspondence: Aryadeep Roychoudhury, Post Graduate Department of Biotechnology, St. Xavier’s College (Autonomous), 30, Mother Teresa Sarani, Kolkata 700016, West Bengal, India e-mail: aryadeep.rc@gmail.com Reactive oxygen species (ROS) were initially recognized as toxic by-products of aerobic metabolism. In recent years, it has become apparent that ROS plays an important signaling role in plants, controlling processes such as growth, development and especially response to biotic and abiotic environmental stimuli. The major members of the ROS family include free radicals like O•− 2 , OH • and non-radicals like H2O2 and O2. The ROS production in plants is mainly localized in the chloroplast, mitochondria and peroxisomes. There are secondary sites as well like the endoplasmic reticulum, cell membrane, cell wall and the apoplast. The role of the ROS family is that of a double edged sword; while they act as secondary messengers in various key physiological phenomena, they also induce oxidative damages under several environmental stress conditions like salinity, drought, cold, heavy metals, UV irradiation etc., when the delicate balance between ROS production and elimination, necessary for normal cellular homeostasis, is disturbed. The cellular damages are manifested in the form of degradation of biomolecules like pigments, proteins, lipids, carbohydrates, and DNA, which ultimately amalgamate in plant cellular death. To ensure survival, plants have developed efficient antioxidant machinery having two arms, (i) enzymatic components like superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), guaiacol peroxidase (GPX), glutathione reductase (GR), monodehydroascorbate reductase (MDHAR), and dehydroascorbate reductase (DHAR); (ii) non-enzymatic antioxidants like ascorbic acid (AA), reduced glutathione (GSH), α-tocopherol, carotenoids, flavonoids, and the osmolyte proline. These two components work hand in hand to scavenge ROS. In this review, we emphasize on the different types of ROS, their cellular production sites, their targets, and their scavenging mechanism mediated by both the branches of the antioxidant systems, highlighting the potential role of antioxidants in abiotic stress tolerance and cellular survival. Such a comprehensive knowledge of ROS action and their regulation on antioxidants will enable us to develop strategies to genetically engineer stress-tolerant plants.
Critical Skills of IS Professionals: A Model for Curriculum Development

In the rapidly changing field of Information Systems, educational programs must be continually reevaluated and revised. This can be a daunting task. To make this process more manageable and to create programs that more accurately reflect the demands of the marketplace, a curriculum revision process is presented. As part of the curriculum revision process, a study was conducted to determine the expected skills and knowledge required for Information Systems professionals in three general staffing groups: programmers, analysts, and end-user support. A survey instrument was developed asking respondents to rate the importance of each knowledge/skill area three years from now for each of the staffing groups. The results show that Information Systems knowledge relating to the entire organization and overall business knowledge will be important with less emphasis on advanced IS applications. The so-called ‘soft skills’ such as teamwork and collaboration, planning and leading projects, presentation delivery, and writing skills will be critical for success in the Information Systems profession. More importance will be placed on web-based languages rather than more traditional languages such as COBOL. Based on the analysis, a skills matrix is presented that can be used as a foundation for developing courses. This paper also describes a curriculum development model that can be used as a guide for curriculum revision.
Multi-attribute Decision Analysis in GIS: Weighted Linear Combination and Ordered Weighted Averaging

Decision analysis can be defined as a set of systematic procedures for analysing complex decision problems. Differences between the desired and the actual state of real world geographical system is a spatial decision problem, which can be approached systematically by means of multi-criteria decision making. Many real-world spatially related problems give rise to geographical information system based multi-criteria decision making. Geographical information systems and multi-criteria decision making have developed largely independently, but a trend towards the exploration of their synergies is now emerging. This paper discusses the synergistic role of multi-criteria decisions in geographical information systems and the use of geographical information systems in multi-attribute decision analysis. An example is provided of analysis of land use suitability by use of either weighted linear combination methods or ordered weighting averages.
omputed-aided diagnosis ( CAD ) in the detection of breast cancer

Computer-aided detection (CAD) systems have been developed for interpretation to improve mammographic detection of breast cancer at screening by reducing the number of false-negative interpretation that can be caused by subtle findings, radiologist distraction and complex architecture. They use a digitized mammographic image that can be obtained from both screen-film mammography and full field digital mammography. Its performance in breast cancer detection is dependent on the performance of the CAD itself, the population to which it is applied and the radiologists who use it. There is a clear benefit to the use of CAD in less experienced radiologist and in detecting breast carcinomas presenting as microcalcifications. This review gives a detailed description CAD systems used in mammography and their performance in assistance of reading in screening mammography and as an alternative to double reading. Other CAD systems developed for MRI and ultrasound are also presented and discussed.
The functional logic of corticostriatal connections

Unidirectional connections from the cortex to the matrix of the corpus striatum initiate the cortico-basal ganglia (BG)-thalamocortical loop, thought to be important in momentary action selection and in longer-term fine tuning of behavioural repertoire; a discrete set of striatal compartments, striosomes, has the complementary role of registering or anticipating reward that shapes corticostriatal plasticity. Re-entrant signals traversing the cortico-BG loop impact predominantly frontal cortices, conveyed through topographically ordered output channels; by contrast, striatal input signals originate from a far broader span of cortex, and are far more divergent in their termination. The term ‘disclosed loop’ is introduced to describe this organisation: a closed circuit that is open to outside influence at the initial stage of cortical input. The closed circuit component of corticostriatal afferents is newly dubbed ‘operative’, as it is proposed to establish the bid for action selection on the part of an incipient cortical action plan; the broader set of converging corticostriatal afferents is described as contextual. A corollary of this proposal is that every unit of the striatal volume, including the long, C-shaped tail of the caudate nucleus, should receive a mandatory component of operative input, and hence include at least one area of BG-recipient cortex amongst the sources of its corticostriatal afferents. Individual operative afferents contact twin classes of GABAergic striatal projection neuron (SPN), distinguished by their neurochemical character, and onward circuitry. This is the basis of the classic direct and indirect pathway model of the cortico-BG loop. Each pathway utilises a serial chain of inhibition, with two such links, or three, providing positive and negative feedback, respectively. Operative co-activation of direct and indirect SPNs is, therefore, pictured to simultaneously promote action, and to restrain it. The balance of this rival activity is determined by the contextual inputs, which summarise the external and internal sensory environment, and the state of ongoing behavioural priorities. Notably, the distributed sources of contextual convergence upon a striatal locus mirror the transcortical network harnessed by the origin of the operative input to that locus, thereby capturing a similar set of contingencies relevant to determining action. The disclosed loop formulation of corticostriatal and subsequent BG loop circuitry, as advanced here, refines the operating rationale of the classic model and allows the integration of more recent anatomical and physiological data, some of which can appear at variance with the classic model. Equally, it provides a lucid functional context for continuing cellular studies of SPN biophysics and mechanisms of synaptic plasticity.
Individual differences in child English second language acquisition Comparing child-internal and child-external factors

This study investigated how various child-internal and child-external factors predict English L2 children’s acquisition outcomes for vocabulary size and accuracy with verb morphology. The children who participated (N=169) were between 4;10 and 7;0 years old (mean = 5;10), had between 3 to 62 months of exposure to English (mean = 20 months), and were from newcomer families to Canada. Results showed that factors such as language aptitude (phonological short term memory and analytic reasoning), age, L1 typology, length of exposure to English, and richness of the child’s English environment were significant predictors of variation in children’s L2 outcomes. However, on balance, childinternal factors explained more of the variance in outcomes than child-external factors. Relevance of these findings for Usage-Based theory of language acquisition is discussed.
How can i improve my app? Classifying user reviews for software maintenance and evolution

App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task. In this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify app reviews into the proposed categories. We show that the combined use of these techniques allows to achieve better results (a precision of 75% and a recall of 74%) than results obtained using each technique individually (precision of 70% and a recall of 67%).