nils-herrmann/clean_scirepeval huggingface dataset:
IoT Data Integrity Verification for Cyber-Physical Systems Using Blockchain

Blockchain technologies can enable decentralized and trustful features for the Internet of Things (IoT). Although, existing blockchain based solutions to provide data integrity verification for semi-trusted data storages (e.g. cloud providers) cannot respect the time determinism required by Cyber-Physical Systems (CPS). Additionally, they cannot be applied to resource-constrained IoT devices. We propose an architecture that can take advantage of blockchain features to allow further integrity verification of data produced by IoT devices even in the realm of CPS. Our architecture is divided into three levels, each of them responsible for tasks compatible with their resources capabilities. The first level, composed of sensors, actuators, and gateways, introduces the concept of Proof-of-Trust (PoT), an energy-efficient, time-deterministic and secure communication based on the Trustful Space-Time Protocol (TSTP). Upper levels are responsible for data persistence and integrity verification in the Cloud. The work also comprises a performance evaluation of the critical path of data to demonstrate that the architecture respect time-bounded operations demanded by the sense-decide-actuate cycle of CPSs. The additional delay of 5.894us added by our architecture is negligible for a typical TSTP with IEEE 802.15.4 radios which has communication latencies in the order of hundreds of milisseconds in each hop.
Endodontics: Part 4 Morphology of the root canal system

Unless the practitioner is familiar with the morphology of the roots of all teeth, and the associated intricate root canal anatomy, effective debridement and obturation may be impossible. Recent research has improved knowledge and understanding of this intricate aspect of dental practice. After studying this part you should know in what percentage of each tooth type you may expect unusual numbers of root canals and other anatomical variations.
Deep Reinforcement Learning for Playing 2.5D Fighting Games

Deep reinforcement learning has shown its success in game playing. However, 2.5D fighting games would be a challenging task to handle due to ambiguity in visual appearances like height or depth of the characters. Moreover, actions in such games typically involve particular sequential action orders, which also makes the network design very difficult. Based on the network of Asynchronous Advantage Actor-Critic (A3C), we create an OpenAI-gym-like gaming environment with the game of Little Fighter 2 (LF2), and present a novel A3C+ network for learning RL agents. The introduced model includes a Recurrent Info network, which utilizes game-related info features with recurrent layers to observe combo skills for fighting. In the experiments, we consider LF2 in different settings, which successfully demonstrates the use of our proposed model for learning 2.5D fighting games.
Depth Structure Preserving Scene Image Generation

Key to automatically generate natural scene images is to properly arrange amongst various spatial elements, especially in the depth cue. To this end, we introduce a novel depth structure preserving scene image generation network (DSP-GAN), which favors a hierarchical architecture, for the purpose of depth structure preserving scene image generation. The main trunk of the proposed infrastructure is built upon a Hawkes point process that models high-order spatial dependency between different depth layers. Within each layer generative adversarial sub-networks are trained collaboratively to generate realistic scene components, conditioned on the layer information produced by the point process. We experiment our model on annotated natural scene images collected from SUN dataset and demonstrate that our models are capable of generating depth-realistic natural scene image.
Characterisation and Estimation of the Key Rank Distribution in the Context of Side Channel Evaluations

Quantifying the side channel security of implementations has been a significant research question for several years in academia but also among real world side channel practitioners. As part of security evaluations, efficient key rank estimation algorithms were devised, which in contrast to analyses based on subkey recovery, give a holistic picture of the security level after a side channel attack. However, it has been observed that outcomes of rank estimations show a huge spread in precisely the range of key ranks where enumeration could lead to key recovery. These observations raise the question whether this is because of insufficient rank estimation procedures, or, if this is an inherent property of the key rank. Furthermore, if this was inherent, how could key rank outcomes be translated into practically meaningful figures, suitable to analysing the risk that real world side channel attacks pose? This paper is a direct response to these questions. We experimentally identify the key rank distribution and show that it is independent of different distinguishers and signal-to-noise ratios. Then we offer a theoretical explanation for the observed key rank distribution and determine how many samples thereof are required for a robust estimation of some key parameters. We discuss how this can be naturally integrated into real world side channel evaluation practices. We conclude our research by connecting non-parametric order statistics, in particular percentiles, in a practically meaningful way with business goals.
Visual Aesthetic Quality Assessment with Multi-task Deep Learning

This paper considers the problem of assessing visual aesthetic quality with semantic information. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition offers the key to addressing this problem. Based on convolutional neural networks, we propose a general multi-task framework with four different structures. In each structure, aesthetic quality assessment task and semantic recognition task are leveraged, and different features are explored to improve the quality assessment. Moreover, an effective strategy of keeping a balanced effect between the semantic task and aesthetic task is developed to optimize the parameters of our framework. The correlation analysis among the tasks validates the importance of the semantic recognition in aesthetic quality assessment. Extensive experiments verify the effectiveness of the proposed multi-task framework, and further corroborate the
Simple Black-Box Adversarial Perturbations for Deep Networks

Deep neural networks are powerful and popular learning models that achieve stateof-the-art pattern recognition performance on many computer vision, speech, and language processing tasks. However, these networks have also been shown susceptible to carefully crafted adversarial perturbations which force misclassification of the inputs. Adversarial examples enable adversaries to subvert the expected system behavior leading to undesired consequences and could pose a security risk when these systems are deployed in the real world. In this work, we focus on deep convolutional neural networks and demonstrate that adversaries can easily craft adversarial examples even without any internal knowledge of the target network. Our attacks treat the network as an oracle (blackbox) and only assume that the output of the network can be observed on the probed inputs. Our first attack is based on a simple idea of adding perturbation to a randomly selected single pixel or a small set of them. We then improve the effectiveness of this attack by carefully constructing a small set of pixels to perturb by using the idea of greedy local-search. Our proposed attacks also naturally extend to a stronger notion of misclassification. Our extensive experimental results illustrate that even these elementary attacks can reveal a deep neural network’s vulnerabilities. The simplicity and effectiveness of our proposed schemes mean that they could serve as a litmus test for designing robust networks.
Ontology and semantic matching for diabetic food recommendations

Foods recommendation for diabetes patients is indispensable for controlling blood sugar levels. Currently, the foods preparation is done by a nutrition expert. The patient's dependence on the nutrition experts is very high, thus the selection of foods could not be done independently. The Automation system to determine foods combination for diabetic patients is needed to solve these problems. In this study, the automation system has been designed and implemented. The technologies used in this research are the OWL and SWRL. There are few researches that explore an automation process of foods recommendation for diabetes patients using the technology of OWL and SWRL. Domain knowledge based on Ontology is needed to process foods composition automatically. However, using SWRL and OWL technology is not enough, because the accuracy of the words required. A semantic ontology understanding was added using weighted tree similarity method to specify the composition of foods for diabetic patients. 73% data were able to be correctly predicted by this method.
Students' experiences of blended learning across a range of postgraduate programmes.

The article describes the students' experiences of taking a blended learning postgraduate programme in a school of nursing and midwifery. The indications to date are that blended learning as a pedagogical tool has the potential to contribute and improve nursing and midwifery practice and enhance student learning. Little is reported about the students' experiences to date. Focus groups were conducted with students in the first year of introducing blended learning. The two main themes that were identified from the data were (1) the benefits of blended learning and (2) the challenges to blended learning. The blended learning experience was received positively by the students. A significant finding that was not reported in previous research was that the online component meant little time away from study for the students suggesting that it was more invasive on their everyday life. It is envisaged that the outcomes of the study will assist educators who are considering delivering programmes through blended learning. It should provide guidance for further developments and improvements in using Virtual Learning Environment (VLE) and blended learning in nurse education.
Traceability Techniques: A Critical Study

Continuous and frequently changing requirements require proper requirements change management. Traceability is essential for management of change and analysis of its impact. This research paper presents a survey of existing techniques in the domain of traceability. We identify problems with traceability definition and present criteria which is used to evaluate the existing traceability techniques. Evaluation shows that current traceability techniques are deficient and may cause problems in managing requirements change properly. We categorizing techniques into three classes argue that existing techniques may be combined to address the short comings of each other and to avail maximum benefits from traceability.
On the Origin of Deep Learning On the Origin of Deep Learning

This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep learning like convolutional neural networks, deep belief networks, and recurrent neural networks, and extends to popular recent models like variational autoencoder and generative adversarial nets. In addition to a review of these models, this paper primarily focuses on the precedents of the models above, examining how the initial ideas are assembled to construct the early models and how these preliminary models are developed into their current forms. Many of these evolutionary paths last more than half a century and have a diversity of directions. For example, CNN is built on prior knowledge of biological vision system; DBN is evolved from a trade-off of modeling power and computation complexity of graphical models and many nowadays models are neural counterparts of ancient linear models. This paper reviews these evolutionary paths and offers a concise thought flow of how these models are developed, and aims to provide a thorough background for deep learning. More importantly, along with the path, this paper summarizes the gist behind these milestones and proposes many directions to guide the future research of deep learning. 1 ar X iv :1 70 2. 07 80 0v 2 [ cs .L G ] 1 M ar 2 01 7
The laboratory diagnosis of syphilis.

Syphilis has several clinical manifestations, making laboratory testing a very important aspect of diagnosis. In North America, many unsuspected cases are discovered by laboratory testing. The etiological agent, Treponema pallidum, cannot be cultured, and there is no single optimal alternative test. Serological testing is the most frequently used approach in the laboratory diagnosis of syphilis. The present paper discusses the various serological and alternative tests currently available along with their limitations, and relates their results to the likely corresponding clinical stage of the disease. The need to use multiple tests is discussed, and the importance of quality control is noted. The complexity of syphilis serology means that the services of reference laboratories and clinical experts are often needed.
A Review and Proof of Concept for Phishing Scam Detection and Response using Apoptosis

Phishing scam is a well-known fraudulent activity in which victims are tricked to reveal their confidential information especially those related to financial information. There are various phishing schemes such as deceptive phishing, malware based phishing, DNS-based phishing and many more. Therefore in this paper, a systematic review analysis on existing works related with the phishing detection and response techniques together with apoptosis have been further investigated and evaluated. Furthermore, one case study to show the proof of concept how the phishing works is also discussed in this paper. This paper also discusses the challenges and the potential research for future work related with the integration of phishing detection model and response with apoptosis. This research paper also can be used as a reference and guidance for further study on phishing detection and response. Phishing; apoptosis; phishing detection; phishing
Neurobiology of cocaine addiction: implications for new pharmacotherapy.

The development of pharmacotherapies for cocaine addiction has been disappointingly slow. However, new neurobiological knowledge of how the brain is changed by chronic pharmacological insult with cocaine is revealing novel targets for drug development. Certain drugs currently being tested in clinical trials tap into the underlying cocaine-induced neuroplasticity, including drugs promoting GABA or inhibiting glutamate transmission. Armed with rationales derived from a neurobiological perspective that cocaine addiction is a pharmacologically induced disease of neuroplasticity in brain circuits mediating normal reward learning, one can expect novel pharmacotherapies to emerge that directly target the biological pathology of addiction.
Intimacy of Friendship , Interpersonal Competence , and Adjustment during Preadolescence and Adolescence

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A Research on High-Performance SDN Controller

Software Defined Networking (SDN) is a new programmable network construction technology that enables centrally management and control, which is considered to be the future evolution trend of networks. A modularized carrier-grade SDN controller according to the characteristics of carrier-grade networks is designed and proposed, resolving the problem of controlling large-scale networks of carrier. The modularized architecture offers the system flexibility, scalability and stability. Functional logic of modules and core modules, such as link discovery module and topology module, are designed to meet the carrier's need. Static memory allocation, multi-threads technique and stick-package processing are used to improve the performance of controller, which is C programming language based. Processing logic of the communication mechanism of the controller is introduced, proving that the controller conforms to the OpenFlow specification and has a good interaction with OpenFlow-based switches. A controller cluster management system is used to interact with controllers through the east-west interface in order to manage large-scale networks. Furthermore, the effectiveness and high performance of the work in this paper has been verified by the testing using Cbench testing program. Moreover, the SDN controller we proposed has been running in China Telecom's Cloud Computing Key Laboratory, which showed the good results is achieved.
Supplementary material for “ Masked Autoregressive Flow for Density Estimation ”

Suppose now that we wish to fit the implicit density pu(u) to the base density πu(u) by minimizing the above KL. This corresponds exactly to the objective minimized when employing IAF as a recognition network in stochastic variational inference [7], where πu(u) would be the (typically intractable) posterior. The first step in stochastic variational inference would be to rewrite the expectation in Equation (6) with respect to the base distribution πx(x) used by IAF, which corresponds exactly to Equation (3). This is often referred to as the reparameterization trick [6, 8]. The second step would be to approximate Equation (3) with Monte Carlo, using samples {xn} drawn from πx(x), as follows:
A DISCOURSE-STYLISTIC ANALYSIS OF MOOD STRUCTURES IN SELECTED POEMS OF

From available literature, J.P Clark-Bekederemo's poetry has not been extensively studied from a linguistic perspective. Previous studies on the poet's work have concentrated on the literary and thematic features of the texts. The present study, therefore, examines mood structures (i.e. a grammatical category that pertains to the clause), in the poetry, in order to determine how language is used to express the manner of speaking of interlocutors, and their roles, judgments and attitudes in specific discourse contexts. Through the aid of Halliday's systemic functional Grammar, particularly the tenor aspect of the interpersonal ‘metafunction’ (other metafunctions being ideational and textual), the study highlights the nature of dialogue (i.e. mood structures) between interactants in the poetry, in relation to social contexts. The discourse-stylistic approach adopted for the study, enables us to examine what is communicated (i.e. discourse) and how it is communicated (i.e. stylistics).
New emerging leadership theories and styles

Leadership is one of the most discussed and important topics in the social sciences especially in organizational theory and management. Generally leadership is the process of influencing group activities towards the achievement of goals. A lot of researches have been conducted in this area .some researchers investigated individual characteristics such as demographics, skills and abilities, and personality traits, predict leadership effectiveness. Different theories, leadership styles and models have been propounded to provide explanations on the leadership phenomenon and to help leaders influence their followers towards achieving organizational goals. Today with the change in organizations and business environment the leadership styles and theories have been changed. In this paper, we review the new leadership theories and styles that are new emerging and are according to the need of the organizations. Leadership styles and theories have been investigated to get the deep understanding of the new trends and theories of the leadership to help the managers and organizations choose appropriate style of leadership. key words: new emerging styles, new theories, leadership, organization
Semiconducting layered blue phosphorus: a computational study.

We investigate a previously unknown phase of phosphorus that shares its layered structure and high stability with the black phosphorus allotrope. We find the in-plane hexagonal structure and bulk layer stacking of this structure, which we call "blue phosphorus," to be related to graphite. Unlike graphite and black phosphorus, blue phosphorus displays a wide fundamental band gap. Still, it should exfoliate easily to form quasi-two-dimensional structures suitable for electronic applications. We study a likely transformation pathway from black to blue phosphorus and discuss possible ways to synthesize the new structure.
Cross-modal adaptation for RGB-D detection

In this paper we propose a technique to adapt convolutional neural network (CNN) based object detectors trained on RGB images to effectively leverage depth images at test time to boost detection performance. Given labeled depth images for a handful of categories we adapt an RGB object detector for a new category such that it can now use depth images in addition to RGB images at test time to produce more accurate detections. Our approach is built upon the observation that lower layers of a CNN are largely task and category agnostic and domain specific while higher layers are largely task and category specific while being domain agnostic. We operationalize this observation by proposing a mid-level fusion of RGB and depth CNNs. Experimental evaluation on the challenging NYUD2 dataset shows that our proposed adaptation technique results in an average 21% relative improvement in detection performance over an RGB-only baseline even when no depth training data is available for the particular category evaluated. We believe our proposed technique will extend advances made in computer vision to RGB-D data leading to improvements in performance at little additional annotation effort.