nils-herrmann/clean_scirepeval huggingface dataset:
Software-Defined Firewall: Enabling Malware Traffic Detection and Programmable Security Control

Network-based malware has posed serious threats to the security of host machines. When malware adopts a private TCP/IP stack for communications, personal and network firewalls may fail to identify the malicious traffic. Current firewall policies do not have a convenient update mechanism, which makes the malicious traffic detection difficult.
 In this paper, we propose Software-Defined Firewall (SDF), a new security design to protect host machines and enable programmable security policy control by abstracting the firewall architecture into control and data planes. The control plane strengthens the easy security control policy update, as in the SDN (Software-Defined Networking) architecture. The difference is that it further collects host information to provide application-level traffic control and improve the malicious traffic detection accuracy. The data plane accommodates all incoming/outgoing network traffic in a network hardware to avoid malware bypassing it. The design of SDF is easy to be implemented and deployed in today's network. We implement a prototype of SDF and evaluate its performance in real-world experiments. Experimental results show that SDF can successfully monitor all network traffic (i.e., no traffic bypassing) and improves the accuracy of malicious traffic identification. Two examples of use cases indicate that SDF provides easier and more flexible solutions to today's host security problems than current firewalls.
Opportunistic Wireless Networking for Smart Dairy Farming

The integration of precision farming techniques with the Internet of Things, cloud computing, and big data analytics is vital to increase productivity in the challenging dairy industry. At the same time, the energy-efficiency and cost-effectiveness of networking solutions that bring big data from farms to the cloud should be addressed as regards economic and environmental sustainability. This article presents an opportunistic networking paradigm for a pasture-based dairy farm in which cows voluntarily participating in robotic milking are used for mulling delay-tolerant data to a fog computing node at an Internet gateway.
Understanding, avoiding, and managing dermal filler complications.

Dermal fillers are increasingly being utilized for multiple cosmetic dermatology indications. The appeal of these products can be partly attributed to their strong safety profiles. Nevertheless, complications can sometimes occur.  To summarize the complications associated with each available dermal filling agent, strategies to avoid them, and management options if they do arise.  Complications with dermal fillers reported in peer-reviewed publications, prescribing information, and recent presentations at professional meetings were reviewed. Recommendations for avoiding and managing complications are provided, based on the literature review and the author's experience.  Inappropriate placement or superficial placement is one of the most frequent reasons for patient dissatisfaction. Due to the reversibility of hyaluronic acid, complications from these fillers can be easily corrected. Sensitivity to any of the currently approved FDA products is quite rare and can usually be managed with anti-inflammatory agents. Infection is quite uncommon as well and can usually be managed with either antibiotics or antivirals depending on the clinical features. The most concerning complication is cutaneous necrosis, and a protocol to treat the full spectrum of this process is reviewed.  Complications with dermal fillers are infrequent, and strategies to minimize their incidence and impact are easily deployed. Familiarity with each family of soft-tissue augmentation products, potential complications, and their management will optimize the use of these agents.
A novel pantothenate kinase gene (PANK2) is defective in Hallervorden-Spatz syndrome

Hallervorden-Spatz syndrome (HSS) is an autosomal recessive neurodegenerative disorder associated with iron accumulation in the brain. Clinical features include extrapyramidal dysfunction, onset in childhood, and a relentlessly progressive course. Histologic study reveals iron deposits in the basal ganglia. In this respect, HSS may serve as a model for complex neurodegenerative diseases, such as Parkinson disease, Alzheimer disease, Huntington disease and human immunodeficiency virus (HIV) encephalopathy, in which pathologic accumulation of iron in the brain is also observed. Thus, understanding the biochemical defect in HSS may provide key insights into the regulation of iron metabolism and its perturbation in this and other neurodegenerative diseases. Here we show that HSS is caused by a defect in a novel pantothenate kinase gene and propose a mechanism for oxidative stress in the pathophysiology of the disease.
Music-Evoked Emotions—Current Studies

The present study is focused on a review of the current state of investigating music-evoked emotions experimentally, theoretically and with respect to their therapeutic potentials. After a concise historical overview and a schematic of the hearing mechanisms, experimental studies on music listeners and on music performers are discussed, starting with the presentation of characteristic musical stimuli and the basic features of tomographic imaging of emotional activation in the brain, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), which offer high spatial resolution in the millimeter range. The progress in correlating activation imaging in the brain to the psychological understanding of music-evoked emotion is demonstrated and some prospects for future research are outlined. Research in psychoneuroendocrinology and molecular markers is reviewed in the context of music-evoked emotions and the results indicate that the research in this area should be intensified. An assessment of studies involving measuring techniques with high temporal resolution down to the 10 ms range, as, e.g., electroencephalography (EEG), event-related brain potentials (ERP), magnetoencephalography (MEG), skin conductance response (SCR), finger temperature, and goose bump development (piloerection) can yield information on the dynamics and kinetics of emotion. Genetic investigations reviewed suggest the heredity transmission of a predilection for music. Theoretical approaches to musical emotion are directed to a unified model for experimental neurological evidence and aesthetic judgment. Finally, the reports on musical therapy are briefly outlined. The study concludes with an outlook on emerging technologies and future research fields.
[Acute Retinal Pigment Epitheliitis: spectral-domain optical coherence tomography findings].

Through a case presentation of Acute Retinal Pigment Epitheliitis (ARPE) we highlight the role of spectral-domain optical coherence tomography (SD-OCT) in the management of this rare entity.



A 29-year-old woman presented for reduced visual acuity in the right eye occurring one week after a viral episode. Fundus examination showed zones of macular hyperpigmentation surrounded by yellowish hypopigmented haloes. Fluorescein angiography noted early hyperfluorescence of the hypopigmented lesions. ICG angiography revealed central hyperfluorescence surrounded by a hypofluorescent halo. SD-OCT showed a linear disruption between the photoreceptor inner/outer segments (IS/OS) and an accumulation of material in the photoreceptor outer segments and retinal pigment epithelium (RPE). Spontaneous normalization of visual acuity was noted after 10 weeks. SD-OCT revealed restored and continuous inner segment and outer segment layers and some persistent deposits in the photoreceptor layer.



SD-OCT findings suggest that the initial lesion in ARPE is located at the junction between the photoreceptor outer segments and the apical side of the RPE cells. It would correspond to an accumulation of photoreceptor outer segment debris secondary to RPE dysfunction, which can occur as a post-viral reaction.



SD-OCT provides very specific information about the topography of retinal lesions during ARPE, allowing a better understanding of its pathogenesis.
Good ( or bad ) vibrations : clinical intuition in violence risk assessment

Making decisions regarding risk is an integral part of clinical mental health work (Flewett 2010). A plethora of tools and approaches (Quinsey 1998; Otto 2000; Douglas 2010a) now exists to assist with clinical judgement when undertaking this task in the context of risk of violence. The tremendous empirical advances in the field over the past two decades (Douglas 2010a) have seen a welcome increase in emphasis on systematic, structured approaches. In tandem with these developments, however, the role of ‘clinical intuition’ has been marginalised or even denigrated (Quinsey 1998). This article will assert that the intui tive mode of thought has considerable value for clinicians charged with the task of violence risk assessment, provided it is applied in a thoughtful and systematic way. It will outline practical guide­ lines for such application, derived from the work of cognitive psychologist Robin Hogarth.
Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing

Limited research efforts have been made for Mobile CrowdSensing (MCS) to address quality of the recruited crowd, i.e., quality of services/data each individual mobile user and the whole crowd are potentially capable of providing, which is the main focus of the paper. Moreover, to improve flexibility and effectiveness, we consider fine-grained MCS, in which each sensing task is divided into multiple subtasks and a mobile user may make contributions to multiple subtasks. In this paper, we first introduce mathematical models for characterizing the quality of a recruited crowd for different sensing applications. Based on these models, we present a novel auction formulation for quality-aware and fine-grained MCS, which minimizes the expected expenditure subject to the quality requirement of each subtask. Then we discuss how to achieve the optimal expected expenditure, and present a practical incentive mechanism to solve the auction problem, which is shown to have the desirable properties of truthfulness, individual rationality and computational efficiency. We conducted trace-driven simulation using the mobility dataset of San Francisco taxies. Extensive simulation results show the proposed incentive mechanism achieves noticeable expenditure savings compared to two well-designed baseline methods, and moreover, it produces close-to-optimal solutions.
Coexistence in millimeter-wave WBAN: A game theoretic approach

This paper examines and provides the theoretical evidence of the feasibility of 60 GHz mmWave in wireless body area networks (WBANs), by analyzing its properties. It has been shown that 60 GHz based communication could better fit WBANs compared to traditional 2.4 GHz based communication because of its compact network coverage, miniaturized devices, superior frequency reuse, multi-gigabyte transmission rate and the therapeutic merits for human health. Since allowing coexistence among the WBANs can enhance the efficiency of the mmWave based WBANs, we formulated the coexistence problem as a non-cooperative distributed power control game. This paper proves the existence of Nash equilibrium (NE) and derives the best response move as a solution. The efficiency of the NE is also improved by modifying the utility function and introducing a pair of pricing factors. Our simulation results indicate that the proposed pricing policy significantly improves the efficiency in terms of Pareto optimality and social optimality.
PE-PUC: A Graph Based PU-Learning Approach for Text Classification

This paper presents a novel solution for the problem of building text classifier using positive documents (P) and unlabeled documents (U). Here, the unlabeled documents are mixed with positive and negative documents. This problem is also called PU-Learning. The key feature of PU-Learning is that there is no negative document for training. Recently, several approaches have been proposed for solving this problem. Most of them are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. Generally speaking, these existing approaches do not perform well when the size of P is small. In this paper, we propose a new approach aiming at improving the system when the size of P is small. This approach combines the graph-based semi-supervised learning method with the two-step method. Experiments indicate that our proposed method performs well especially when the size of P is small.
The relationship between nature relatedness and anxiety.

This study investigated the relationship between anxiety and feelings of being connected to nature. Two standardised self-report scales, the Nature Relatedness Scale and the State Trait Inventory for Cognitive and Somatic Anxiety, were used in tandem with a qualitative question. Quantitative results indicated that connection to nature was significantly related to lower levels of overall, state cognitive and trait cognitive anxiety. Qualitative results revealed seven themes: relaxation, time out, enjoyment, connection, expanse, sensory engagement and a healthy perspective. Taken together, these results suggest that opportunities that enhance experiences of being connected to nature may reduce unhelpful anxiety.
Ieee Robotics & Automation Magazine Review of Actuators with Passive Adjustable Compliance/controllable Stiffness for Robotic Applications

I n the growing fields of wearable robotics, rehabilitation robotics, prosthetics, and walking robots, variable stiffness actuators (VSAs) or adjustable compliant actuators are being designed and implemented because of their ability to minimize large forces due to shocks, to safely interact with the user, and their ability to store and release energy in passive elastic elements. This review article describes the state of the art in the design of actuators with adaptable passive compliance. This new type of actuator is not preferred for classical position-controlled applications such as pick and place operations but is preferred in novel robots where safe human– robot interaction is required or in applications where energy efficiency must be increased by adapting the actuator’s resonance frequency. The working principles of the different existing designs are explained and compared. The designs are divided into four groups: equilibrium-controlled stiffness, antagonistic-controlled stiffness, structure-controlled stiffness (SCS), and mechanically controlled stiffness. In classical robotic applications, actuators are preferred to be as stiff as possible to make precise position movements or trajectory tracking control easier (faster systems with high bandwidth). The biological counterpart is the muscle that has superior functional performance and a neuromechanical control system that is much more advanced at adapting and tuning its parameters. The superior power-to-weight ratio, force-toweight ratio, compliance, and control of muscle, when compared with traditional robotic actuators, are the main barriers for the development of machines that can match the motion, safety, and energy efficiency of human or other animals. One of the key differences of these systems is the compliance or springlike behavior found in biological systems . Although such compliant
The Throughput of Packet Broadcasting Channels

Packet broadcasting is a form of data communications architecture which can combine the features of packet switching with those of broadcast channels for data communication networks. Much of the basic theory of packet broadcasting has been presented as a byproduct in a sequence of papers with a distinctly practical emphasis. In this paper we provide a unified presentation of packet broadcasting theory. In Section I1 we introduce the theory of packet broadcasting data networks. In Section I11 we provide some theoretical results dealing with the performance of a packet broadcasting network when the users of the network have a variety of data rates. In Section IV we deal with packet broadcasting networks distributed in space, and in Section V we derive some properties of power-limited packet broadcasting channels,showing that the throughput of such channels can approach that of equivalent point-to-point channels.
Application of MUSIC , ESPRIT and ROOT MUSIC in DOA Estimation

Concept of communication systems, which use smart antennas is based on digital signal processing algorithms. Thus, the smart antennas system becomes capable to locate and track signals by the both: users and interferers and dynamically adapts the antenna pattern to enhance the reception in Signal-Of-Interest direction and minimizing interference in Signal-Of-Not-Interest direction. Hence, Space Division Multiple Access system, which uses smart antennas, is being used more often in wireless communications, because it shows improvement in channel capacity and co-channel interference. However, performance of smart antenna system greatly depends on efficiency of digital signal processing algorithms. The algorithm uses the Direction of Arrival (DOA) algorithms to estimate the number of incidents plane waves on the antenna array and their angle of incidence. This paper investigates performance of the DOA algorithms like MUSIC, ESPRIT and ROOT MUSIC on the uniform linear array in the presence of white noise. The simulation results show that MUSIC algorithm is the best. The resolution of the DOA techniques improves as number of snapshots, number of array elements and signalto-noise ratio increases.
Measuring social spam and the effect of bots on information diffusion in social media

Bots have been playing a crucial role in online platform ecosystems, as efficient and automatic tools to generate content and diffuse information to the social media human population. In this chapter, we will discuss the role of social bots in content spreading dynamics in social media. In particular, we will first investigate some differences between diffusion dynamics of content generated by bots, as opposed to humans, in the context of political communication, then study the characteristics of bots behind the diffusion dynamics of social media spam campaigns.
Modeling high-frequency limit order book dynamics with support vector machines

We propose a machine learning framework to capture the dynamics of highfrequency limit order books in financial equity markets and automate real-time prediction of metrics such as mid-price movement and price spread crossing. By characterizing each entry in a limit order book with a vector of attributes such as price and volume at different levels, the proposed framework builds a learning model for each metric with the help of multi-class support vector machines (SVMs). Experiments with real data establish that features selected by the proposed framework are effective for short term price movement forecasts.
Child Safety & Tracking Management System by Using GPS, Geo-Fencing & Android Application: An Analysis

Today, technology is growing rapidly and providingall essential and effective solutions for every requirement. Nowa day's child security is an important area of concern. Thismodel is developed to rectify the worries of parents regardingtheir child security. In this scenario, Our system ensuresmaximum security and ensures live tracking for their kidsbecause parent worries are genuine. This paper proposed amodel for child safety through smart phones that provides theoption to track the location of their children as well as in caseof emergency children is able to send a quick message and itscurrent location via Short Message services. This proposedsystem is validated by testing on the Android platform.
Estimation of the Burned Area in Forest Fires Using Computational Intelligence Techniques

Forest fires have environmental impacts that create economic problems as well as ecological damage. Developing a means to predict the possible size of a fire shortly after it first breaks out has the potential to guide proper resource allocation for improved fire control and was the main motivation of this research. In this study, the burned areas resulting from possible forest fires were estimated using historical forest fire records which contained parameters like geographical conditions of the existing environment, date and time when the fire broke out, meteorological data such as temperature, humidity and wind speed, and the type and number of trees in a unit area. The data was from the Department of Forestry in Turkey and contained 7,920 forest fire records from 2000 and 2009. The output from the estimation methods implemented in this work predicted the size of the area lost due to the fire and the corresponding fire size, i.e. big, medium, or small fire. Some of the estimation methods investigated were Multilayer Perceptron (MLP), Radial Basis Function Networks (RBFN), Support Vector Machines (SVM) and fuzzy logic. The results of these estimates are presented and compared to similar studies in literature.
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning

Intrinsically motivated spontaneous exploration is a key enabler of autonomous lifelong learning in human children. It allows them to discover and acquire large repertoires of skills through self-generation, self-selection, self-ordering and self-experimentation of learning goals. We present the unsupervised multi-goal reinforcement learning formal framework as well as an algorithmic approach called intrinsically motivated goal exploration processes (IMGEP) to enable similar properties of autonomous learning in machines. The IMGEP algorithmic architecture relies on several principles: 1) self-generation of goals as parameterized reinforcement learning problems; 2) selection of goals based on intrinsic rewards; 3) exploration with parameterized time-bounded policies and fast incremental goal-parameterized policy search; 4) systematic reuse of information acquired when targeting a goal for improving other goals. We present a particularly efficient form of IMGEP that uses a modular representation of goal spaces as well as intrinsic rewards based on learning progress. We show how IMGEPs automatically generate a learning curriculum within an experimental setup where a real humanoid robot can explore multiple spaces of goals with several hundred continuous dimensions. While no particular target goal is provided to the system beforehand, this curriculum allows the discovery of skills of increasing complexity, that act as stepping stone for learning more complex skills (like nested tool use). We show that learning several spaces of diverse problems can be more efficient for learning complex skills than only trying to directly learn these complex skills. We illustrate the computational efficiency of IMGEPs as these robotic experiments use a simple memory-based low-level policy representations and search algorithm, enabling the whole system to learn online and incrementally on a Raspberry Pi 3.
Sentiment Analysis on Twitter to Improve Time Series Contextual Anomaly Detection for Detecting Stock Market Manipulation

In this paper, We propose a formalized method to improve the performance of Contextual Anomaly Detection (CAD) for detecting stock market manipulation using Big Data techniques. The method aims to improve the CAD algorithm by capturing the expected behaviour of stocks through sentiment analysis of tweets about stocks. The extracted insights are aggregated per day for each stock and transformed to a time series. The time series is used to eliminate false positives from anomalies that are detected by CAD. We present a case study and explore developing sentiment analysis models to improve anomaly detection in the stock market. The experimental results confirm the proposed method is effective in improving CAD through removing irrelevant anomalies by correctly identifying 28% of false positives.