nils-herrmann/clean_scirepeval huggingface dataset:
Hypoxia: a key player in antitumor immune response. A Review in the Theme: Cellular Responses to Hypoxia.

The tumor microenvironment is a complex system, playing an important role in tumor development and progression. Besides cellular stromal components, extracellular matrix fibers, cytokines, and other metabolic mediators are also involved. In this review we outline the potential role of hypoxia, a major feature of most solid tumors, within the tumor microenvironment and how it contributes to immune resistance and immune suppression/tolerance and can be detrimental to antitumor effector cell functions. We also outline how hypoxic stress influences immunosuppressive pathways involving macrophages, myeloid-derived suppressor cells, T regulatory cells, and immune checkpoints and how it may confer tumor resistance. Finally, we discuss how microenvironmental hypoxia poses both obstacles and opportunities for new therapeutic immune interventions.
The palmitoylation of metastasis suppressor KAI1/CD82 is important for its motility- and invasiveness-inhibitory activity.

The cancer metastasis suppressor protein KAI1/CD82 is a member of the tetraspanin superfamily. Recent studies have demonstrated that tetraspanins are palmitoylated and that palmitoylation contributes to the organization of tetraspanin webs or tetraspanin-enriched microdomains. However, the effect of palmitoylation on tetraspanin-mediated cellular functions remains obscure. In this study, we found that tetraspanin KAI1/CD82 was palmitoylated when expressed in PC3 metastatic prostate cancer cells and that palmitoylation involved all of the cytoplasmic cysteine residues proximal to the plasma membrane. Notably, the palmitoylation-deficient KAI1/CD82 mutant largely reversed the wild-type KAI1/CD82's inhibitory effects on migration and invasion of PC3 cells. Also, palmitoylation regulates the subcellular distribution of KAI1/CD82 and its association with other tetraspanins, suggesting that the localized interaction of KAI1/CD82 with tetraspanin webs or tetraspanin-enriched microdomains is important for KAI1/CD82's motility-inhibitory activity. Moreover, we found that KAI1/CD82 palmitoylation affected motility-related subcellular events such as lamellipodia formation and actin cytoskeleton organization and that the alteration of these processes likely contributes to KAI1/CD82's inhibition of motility. Finally, the reversal of cell motility seen in the palmitoylation-deficient KAI1/CD82 mutant correlates with regaining of p130(CAS)-CrkII coupling, a signaling step important for KAI1/CD82's activity. Taken together, our results indicate that palmitoylation is crucial for the functional integrity of tetraspanin KAI1/CD82 during the suppression of cancer cell migration and invasion.
Subspace Clustering by Exploiting a Low-Rank Representation with a Symmetric Constraint

In this paper, we propose a low-rank representation with symmetric constraint (LRRSC) method for robust subspace clustering. Given a collection of data points approximately drawn from multiple subspaces, the proposed technique can simultaneously recover the dimension and members of each subspace. LRRSC extends the original low-rank representation algorithm by integrating a symmetric constraint into the low-rankness property of high-dimensional data representation. The symmetric low-rank representation, which preserves the subspace structures of high-dimensional data, guarantees weight consistency for each pair of data points so that highly correlated data points of subspaces are represented together. Moreover, it can be efficiently calculated by solving a convex optimization problem. We provide a rigorous proof for minimizing the nuclear-norm regularized least square problem with a symmetric constraint. The affinity matrix for spectral clustering can be obtained by further exploiting the angular information of the principal directions of the symmetric low-rank representation. This is a critical step towards evaluating the memberships between data points. Experimental results on benchmark databases demonstrate the effectiveness and robustness of LRRSC compared with several state-of-the-art subspace clustering algorithms.
Remote controlling an autonomous car with an iPhone

This paper describes iDriver, an iPhone software to remote control “Spirit of Berlin”. “Spirit of Berlin” is a completely autonomous car developed by the Free University of Berlin which is capable of unmanned driving in urban areas. iDriver is an iPhone application sending control packets to the car in order to remote control its steering wheel, gas and brake pedal, gear shift and turn signals. Additionally, a video stream from two top-mounted cameras is broadcasted back to the iPhone.
A Deep Reinforced Model for Abstractive Summarization

Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intraattention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit “exposure bias” – they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN/Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN/Daily Mail dataset, an improvement over previous state-of-the-art models. Human evaluation also shows that our model produces higher quality summaries.
Artificial immune systems for the detection of credit card fraud: an architecture, prototype and preliminary results

Some biological phenomena offer clues to solving real-life, complex problems. Researchers have been studying techniques such as neural networks and genetic algorithms for computational intelligence and their applications to such complex problems. The problem of security management is one of the major concerns in the development of eBusiness services and networks. Recent incidents have shown that the perpetrators of cybercrimes are using increasingly sophisticated methods. Hence, it is necessary to investigate non-traditional mechanisms, such as biological techniques, to manage the security of evolving eBusiness networks and services. Towards this end, this paper investigates the use of an Artificial Immune System (AIS). The AIS emulates the mechanism of human immune systems that save human bodies from complex natural biological attacks. The paper discusses the use of AIS on one aspect of security management, viz. the detection of credit card fraud. The solution is illustrated with a case study on the management of frauds in credit card transactions, although this technique may be used in a range of security management applications in eBusiness.
IoT Based Low Cost Smart Irrigation System

One of the essential and basic services to survive on earth is Water. Recent time increasing the scarcity of water due to growing in population. So this is becoming as a universal obstacle. The old irrigation system which demands a lot of water, so it needs smart techniques for reducing the percentage of wasting available water for the irrigation. We have been seeing the increasing of huge demand for Internet of things in every domain from small and simple applications to large and complex applications. Practically implementation of a Smart Irrigation is very complex deal, but association with IoT using Smart wireless sensors it brings a great management system. The Humidity and Temperature Sensor sense the both water vapor content and temperature around the plant. The Soil Moisture Sensor sense the soil moisture of a plant, if water content is below minimum requirement then water will supply from water reservoir using relay and Ultrasonic sensor measures the water level of reservoir after that sends the data to ESP8266 NodeMCU. ESP8266 NodeMCU is a Microcontroller gets the data from smart wireless sensors, process the data and send to destination through A Message Queue Telemetry Transport (MQTT)protocol.
End-to-End Multi-View Networks for Text Classification

We propose a multi-view network for text classification. Our method automatically creates various views of its input text, each taking the form of soft attention weights that distribute the classifier's focus among a set of base features. For a bag-of-words representation, each view focuses on a different subset of the text's words. Ag-gregating many such views results in a more discriminative and robust representation. Through a novel architecture that both stacks and concatenates views, we produce a network that emphasizes both depth and width, allowing training to converge quickly. Using our multi-view architecture , we establish new state-of-the-art accuracies on two benchmark tasks.
Adaptations of skeletal muscle to endurance exercise and their metabolic consequences.

Regularly performed endurance exercise induces major adaptations in skeletal muscle. These include increases in the mitochondrial content and respiratory capacity of the muscle fibers. As a consequence of the increase in mitochondria, exercise of the same intensity results in a disturbance in homeostasis that is smaller in trained than in untrained muscles. The major metabolic consequences of the adaptations of muscle to endurance exercise are a slower utilization of muscle glycogen and blood glucose, a greater reliance on fat oxidation, and less lactate production during exercise of a given intensity. These adaptations play an important role in the large increase in the ability to perform prolonged strenuous exercise that occurs in response to endurance exercise training.
S EQUENCE-TO-SEQUENCE RNN S FOR T EXT S UMMARIZATION

In this work, we cast text summarization as a sequence-to-sequence problem and apply the attentional encoder-decoder RNN that has been shown to be successful for Machine Translation (Bahdanau et al. (2014)). Our experiments show that the proposed architecture significantly outperforms the state-of-the art model of Rush et al. (2015) on the Gigaword dataset without any additional tuning. We also propose additional extensions to the standard architecture, which we show contribute to further improvement in performance.
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue

AutoTutor simulates a human tutor by holding a conversation with the learner in natural language. The dialogue is augmented by an animated conversational agent and three-dimensional (3-D) interactive simulations in order to enhance the learner's engagement and the depth of the learning. Grounded in constructivist learning theories and tutoring research, AutoTutor achieves learning gains of approximately 0.8 sigma (nearly one letter grade), depending on the learning measure and comparison condition. The computational architecture of the system uses the .NET framework and has simplified deployment for classroom trials.
Millennials in the Workplace: A Communication Perspective on Millennials’ Organizational Relationships and Performance

Stereotypes about Millennials, born between 1979 and 1994, depict them as self-centered, unmotivated, disrespectful, and disloyal, contributing to widespread concern about how communication with Millennials will affect organizations and how they will develop relationships with other organizational members. We review these purported characteristics, as well as Millennials' more positive qualities-they work well in teams, are motivated to have an impact on their organizations, favor open and frequent communication with their supervisors, and are at ease with communication technologies. We discuss Millennials' communicated values and expectations and their potential effect on coworkers, as well as how workplace interaction may change Millennials.
A Review of Real-Time Strategy Game AI

This literature review covers AI techniques used for real-time strategy video games, focusing specifically on StarCraft. It finds that the main areas of current academic research are in tactical and strategic decision-making, plan recognition, and learning, and it outlines the research contributions in each of these areas. The paper then contrasts the use of game AI in academia and industry, finding the academic research heavily focused on creating game-winning agents, while the industry aims to maximise player enjoyment. It finds the industry adoption of academic research is low because it is either inapplicable or too time-consuming and risky to implement in a new game, which highlights an area for potential investigation: bridging the gap between academia and industry. Finally, the areas of spatial reasoning, multi-scale AI, and cooperation are found to require future work, and standardised evaluation methods are proposed to produce comparable results between studies.
Kernel-Based Object Tracking

A new approach toward target representation and localization, the central component in visual tracking of non-rigid objects, is proposed. The feature histogram based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples the newmethod successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking.
Deep learning in intrusion detection perspective: Overview and further challenges

Deep learning techniques are famous due to Its capability to cope with large-scale data these days. They have been investigated within various of applications e.g., language, graphical modeling, speech, audio, image recognition, video, natural language and signal processing areas. In addition, extensive researches applying machine-learning methods in Intrusion Detection System (IDS) have been done in both academia and industry. However, huge data and difficulties to obtain data instances are hot challenges to machine-learning-based IDS. We show some limitations of previous IDSs which uses classic machine learners and introduce feature learning including feature construction, extraction and selection to overcome the challenges. We discuss some distinguished deep learning techniques and its application for IDS purposes. Future research directions using deep learning techniques for IDS purposes are briefly summarized.
Screen Dependency Disorders : a new challenge for child neurology

Children’s neurological development is influenced by their experiences. Early experiences and the environments in which they occur can alter gene expression and affect long-term neural development. Today, discretionary screen time, often involving multiple devices, is the single main experience and environment of children. Various screen activities are reported to induce structural and functional brain plasticity in adults. However, childhood is a time of significantly greater changes in brain anatomical structure and connectivity. There is empirical evidence that extensive exposure to videogame playing during childhood may lead to neuroadaptation and structural changes in neural regions associated with addiction. Digital natives exhibit a higher prevalence of screen-related ‘addictive’ behaviour that reflect impaired neurological rewardprocessing and impulse-control mechanisms. Associations are emerging between screen dependency disorders such as Internet Addiction Disorder and specific neurogenetic polymorphisms, abnormal neural tissue and neural function. Although abnormal neural structural and functional characteristics may be a precondition rather than a consequence of addiction, there may also be a bidirectional relationship. As is the case with substance addictions, it is possible that intensive routine exposure to certain screen activities during critical stages of neural development may alter gene expression resulting in structural, synaptic and functional changes in the developing brain leading to screen dependency disorders, particularly in children with predisposing neurogenetic profiles. There may also be compound/secondary effects on neural development. Screen dependency disorders, even at subclinical levels, involve high levels of discretionary screen time, inducing greater child sedentary behaviour thereby reducing vital aerobic fitness, which plays an important role in the neurological health of children, particularly in brain structure and function. Child health policy must therefore adhere to the principle of precaution as a prudent approach to protecting child neurological integrity and well-being. This paper explains the basis of current paediatric neurological concerns surrounding screen dependency disorders and proposes preventive strategies for child neurology and allied professions.
Principled design of the modern Web architecture

The World Wide Web has succeeded in large part because its software architecture has been designed to meet the needs of an Internet-scale distributed hypermedia system. The modern Web architecture emphasizes scalability of component interactions, generality of interfaces, independent deployment of components, and intermediary components to reduce interaction latency, enforce security, and encapsulate legacy systems. In this paper, we introduce the Representational State Transfer (REST) architectural style, developed as an abstract model of the Web architecture to guide our redesign and definition of the Hypertext Transfer Protocol and Uniform Resource Identifiers. We describe the software engineering principles guiding REST and the interaction constraints chosen to retain those principles, contrasting them to the constraints of other architectural styles. We then compare the abstract model to the currently deployed Web architecture in order to elicit mismatches between the existing protocols and the applications they are intended to support.
An Introduction to Intertask Transfer for Reinforcement Learning

environment, are becoming increasingly prevalent. However, if agents are to behave intelligently in complex, dynamic, and noisy environments, we believe that they must be able to learn and adapt. The reinforcement learning (RL) paradigm is a popular way for such agents to learn from experience with minimal feedback. One of the central questions in RL is how best to generalize knowledge to successfully learn and adapt. In reinforcement learning problems, agents sequentially observe their state and execute actions. The goal is to maximize a real-valued reward signal, which may be time delayed. For example, an agent could learn to play a game by being told what the state of the board is, what the legal actions are, and then whether it wins or loses at the end of the game. However, unlike in supervised learning scenarios, the agent is never provided the “correct” action. Instead, the agent can only gather data by interacting with an environment, receiving information about the results, its actions, and the reward signal. RL is often used because of the framework’s flexibility and due to the development of increasingly data-efficient algorithms. RL agents learn by interacting with the environment, gathering data. If the agent is virtual and acts in a simulated environment, training data can be collected at the expense of computer time. However, if the agent is physical, or the agent must act on a “real-world” problem where the online reward is critical, such data can be expensive. For instance, a physical robot will degrade over time and must be replaced, and an agent learning to automate a company’s operations may lose money while training. When RL agents begin learning tabula rasa, mastering difficult tasks may be infeasible, as they require significant amounts of data even when using state-of-the-art RL approaches. There are many contemporary approaches to speed up “vanilla” RL methods. Transfer learning (TL) is one such technique. Transfer learning is an umbrella term used when knowledge is Articles
Optimizing Image Steganography using Particle Swarm Optimization Algorithm

Image Steganography is the computing field of hiding information from a source into a target image in a way that it becomes almost imperceptible from one’s eyes. Despite the high capacity of hiding information, the usual Least Significant Bit (LSB) techniques could be easily discovered. In order to hide information in more significant bits, the target image should be optimized. In this paper, it is proposed an optimization solution based on the Standard Particle Swarm Optimization 2011 (PSO), which has been compared with a previous Genetic Algorithm-based approach showing promising results. Specifically, it is shown an adaptation in the solution in order to keep the essence of PSO while remaining message hosted bits unchanged.
Pricing with a Smile

prices as a function of volatility. If an option price is given by the market we can invert this relationship to get the implied volatility. If the model were perfect, this implied value would be the same for all option market prices, but reality shows this is not the case. Implied Black–Scholes volatilities strongly depend on the maturity and the strike of the European option under scrutiny. If the implied volatilities of at-the-money (ATM) options on the Nikkei 225 index are 20% for a maturity of six months and 18% for a maturity of one year, we are in the uncomfortable position of assuming that the Nikkei oscillates with a constant volatility of 20% for six months but also oscillates with a constant volatility of 18% for one year. It is easy to solve this paradox by allowing volatility to be timedependent, as Merton did (see Merton, 1973). The Nikkei would first exhibit an instantaneous volatility of 20% and subsequently a lower one, computed by a forward relationship to accommodate the one-year volatility. We now have a single process, compatible with the two option prices. From the term structure of implied volatilities we can infer a time-dependent instantaneous volatility, because the former is the quadratic mean of the latter. The spot process S is then governed by the following stochastic differential equation:
Container oriented job scheduling using linear programming model

With Docker gaining widespread popularity in the recent years, the container scheduler becomes a crucial role for the exploding containerized applications and services. In this work, the container host energy conservation, the container image pulling costs from the image registry to the container hosts and the workload network transition costs from the clients to the container hosts are evaluated in combination. By modeling the scheduling problem as an integer linear programming, an effective and adaptive scheduler is proposed. Impressive cost savings were achieved compared to Docker Swarm scheduler. Moreover, it can be easily integrated into the open-source container orchestration frameworks.