nils-herrmann/clean_scirepeval huggingface dataset:
Enhancing Feature Selection Using Word Embeddings: The Case of Flu Surveillance

Health surveillance systems based on online user-generated content often rely on the identification of textual markers that are related to a target disease. Given the high volume of available data, these systems benefit from an automatic feature selection process. This is accomplished either by applying statistical learning techniques, which do not consider the semantic relationship between the selected features and the inference task, or by developing labour-intensive text classifiers. In this paper, we use neural word embeddings, trained on social media content from Twitter, to determine, in an unsupervised manner, how strongly textual features are semantically linked to an underlying health concept. We then refine conventional feature selection methods by a priori operating on textual variables that are sufficiently close to a target concept. Our experiments focus on the supervised learning problem of estimating influenza-like illness rates from Google search queries. A “flu infection” concept is formulated and used to reduce spurious —and potentially confounding— features that were selected by previously applied approaches. In this way, we also address forms of scepticism regarding the appropriateness of the feature space, alleviating potential cases of overfitting. Ultimately, the proposed hybrid feature selection method creates a more reliable model that, according to our empirical analysis, improves the inference performance (Mean Absolute Error) of linear and nonlinear regressors by 12% and 28.7%, respectively.
Emotion recognition from speech: a review

Emotion recognition from speech has emerged as an important research area in the recent past. In this regard, review of existing work on emotional speech processing is useful for carrying out further research. In this paper, the recent literature on speech emotion recognition has been presented considering the issues related to emotional speech corpora, different types of speech features and models used for recognition of emotions from speech. Thirty two representative speech databases are reviewed in this work from point of view of their language, number of speakers, number of emotions, and purpose of collection. The issues related to emotional speech databases used in emotional speech recognition are also briefly discussed. Literature on different features used in the task of emotion recognition from speech is presented. The importance of choosing different classification models has been discussed along with the review. The important issues to be considered for further emotion recognition research in general and in specific to the Indian context have been highlighted where ever necessary.
Communities , modules and large-scale structure in networks

Networks, also called graphs by mathematicians, provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and the state spaces of physical systems. In the past decade there have been significant advances in experiments to determine the topological structure of networked systems, but there remain substantial challenges in extracting scientific understanding from the large quantities of data produced by the experiments. A variety of basic measures and metrics are available that can tell us about small-scale structure in networks, such as correlations, connections and recurrent patterns, but it is considerably more difficult to quantify structure on medium and large scales, to understand the ‘big picture’. Important progress has been made, however, within the past few years, a selection of which is reviewed here.
Internet of Things in the 5G Era: Enablers, Architecture, and Business Models

The IoT paradigm holds the promise to revolutionize the way we live and work by means of a wealth of new services, based on seamless interactions between a large amount of heterogeneous devices. After decades of conceptual inception of the IoT, in recent years a large variety of communication technologies has gradually emerged, reflecting a large diversity of application domains and of communication requirements. Such heterogeneity and fragmentation of the connectivity landscape is currently hampering the full realization of the IoT vision, by posing several complex integration challenges. In this context, the advent of 5G cellular systems, with the availability of a connectivity technology, which is at once truly ubiquitous, reliable, scalable, and cost-efficient, is considered as a potentially key driver for the yet-to emerge global IoT. In the present paper, we analyze in detail the potential of 5G technologies for the IoT, by considering both the technological and standardization aspects. We review the present-day IoT connectivity landscape, as well as the main 5G enablers for the IoT. Last but not least, we illustrate the massive business shifts that a tight link between IoT and 5G may cause in the operator and vendors ecosystem.
Deep Convolutional Neural Networks for Multi-instance Multi-task Learning

Multi-instance learning studies problems in which labels are assigned to bags that contain multiple instances. In these settings, the relations between instances and labels are usually ambiguous. In contrast, multi-task learning focuses on the output space in which an input sample is associated with multiple labels. In real world, a sample may be associated with multiple labels that are derived from observing multiple aspects of the problem. Thus many real world applications are naturally formulated as multi-instance multi-task (MIMT) problems. A common approach to MIMT is to solve it task-by-task independently under the multi-instance learning framework. On the other hand, convolutional neural networks (CNN) have demonstrated promising performance in single-instance single-label image classification tasks. However, how CNN deals with multi-instance multi-label tasks still remains an open problem. This is mainly due to the complex multiple-to-multiple relations between the input and output space. In this work, we propose a deep leaning model, known as multi-instance multi-task convolutional neural networks (MIMT-CNN), where a number of images representing a multi-task problem is taken as the inputs. Then a shared sub-CNN is connected with each input image to form instance representations. Those sub-CNN outputs are subsequently aggregated as inputs to additional convolutional layers and full connection layers to produce the ultimate multi-label predictions. This CNN model, through transfer learning from other domains, enables transfer of prior knowledge at image level learned from large single-label single-task data sets. The bag level representations in this model are hierarchically abstracted by multiple layers from instance level representations. Experimental results on mouse brain gene expression pattern annotation data show that the proposed MIMT-CNN model achieves superior performance.
Centralized channel assignment and routing algorithms for multi-channel wireless mesh networks

The IEEE 802.11 Wireless LAN standards allow multiple non-overlapping frequency channels to be used simultaneously to increase the aggregate bandwidth available to end-users. Such bandwidth aggregation capability is routinely used in infrastructure mode operation, where the traffic to and from wireless nodes is distributed among multiple interfaces of an access point or among multiple access points to balance the traffic load. However, bandwidth aggregation is rarely used in the context of multi-hop 802.11-based LANs that operate in the ad hoc mode. Most past research efforts that attempt to exploit multiple radio channels require modifications to the MAC protocol and therefore do not work with commodity 802.11 interface hardware. In this paper, we propose and evaluate one of the first multi-channel multi-hop wireless ad-hoc network architectures that can be built using standard 802.11 hardware by equipping each node with multiple network interface cards (NICs) operating on different channels. We focus our attention on wireless mesh networks that serve as the backbone for relaying end-user traffic from wireless access points to the wired network. The idea of exploiting multiple channels is particularly appealing in wireless mesh networks because of their high capacity requirements to support backbone traffic. To reap the full performance potential of this architecture, we develop a set of centralized channel assignment, bandwidth allocation, and routing algorithms for multi-channel wireless mesh networks. A detailed performance evaluation shows that with intelligent channel and bandwidth assignment, equipping every wireless mesh network node with just 2 NICs operating on different channels can increase the total network goodput by a factor of up to 8 compared with the conventional single-channel ad hoc network architecture.
GENETICS OF LACTASE PERSISTENCE AND LACTOSE INTOLERANCE

■ Abstract The enzyme lactase that is located in the villus enterocytes of the small intestine is responsible for digestion of lactose in milk. Lactase activity is high and vital during infancy, but in most mammals, including most humans, lactase activity declines after the weaning phase. In other healthy humans, lactase activity persists at a high level throughout adult life, enabling them to digest lactose as adults. This dominantly inherited genetic trait is known as lactase persistence. The distribution of these different lactase phenotypes in human populations is highly variable and is controlled by a polymorphic element cis-acting to the lactase gene. A putative causal nucleotide change has been identified and occurs on the background of a very extended haplotype that is frequent in Northern Europeans, where lactase persistence is frequent. This single nucleotide polymorphism is located 14 kb upstream from the start of transcription of lactase in an intron of the adjacent gene MCM6. This change does not, however, explain all the variation in lactase expression.
The arterial Windkessel

Frank’s Windkessel model described the hemodynamics of the arterial system in terms of resistance and compliance. It explained aortic pressure decay in diastole, but fell short in systole. Therefore characteristic impedance was introduced as a third element of the Windkessel model. Characteristic impedance links the lumped Windkessel to transmission phenomena (e.g., wave travel). Windkessels are used as hydraulic load for isolated hearts and in studies of the entire circulation. Furthermore, they are used to estimate total arterial compliance from pressure and flow; several of these methods are reviewed. Windkessels describe the general features of the input impedance, with physiologically interpretable parameters. Since it is a lumped model it is not suitable for the assessment of spatially distributed phenomena and aspects of wave travel, but it is a simple and fairly accurate approximation of ventricular afterload.
Cognition-Based Context-Aware Cloud Computing for Intelligent Robotic Systems in Mobile Education

At present, artificial intelligence (AI) has made considerable progress in recognition of speech, face, and emotion. Potential application to robots could bring significant improvement on intelligent robotic systems. However, limited resource on robots cannot satisfy the large-scale computation and storage that the AI recognition requires. Cloud provides an efficient way for robots, where they off-load the computation too. Therefore, we present a cognition-based context-aware cloud computing framework, which is designed to help robot’s sense environments including user’s emotions. Based on the recognized context information, robots could optimize their responses and improve the user’s experience on interaction. The framework contains a customizable context monitoring system on the mobile end to collect and process the data from the robot’s sensors. Besides, it integrates various AI recognition services in the cloud to extract the context facts by analyzing and understanding the data. Once the context data is extracted, the results are pushed back to mobile end for making a better decision in the next interactions. In this paper, we demonstrate and evaluate the framework by a real case, an educational mobile app for English learning. The results show that the proposed framework could significantly improve the interaction and intelligence of mobile robots.
Potential antibacterial mechanism of silver nanoparticles and the optimization of orthopedic implants by advanced modification technologies

Infection, as a common postoperative complication of orthopedic surgery, is the main reason leading to implant failure. Silver nanoparticles (AgNPs) are considered as a promising antibacterial agent and always used to modify orthopedic implants to prevent infection. To optimize the implants in a reasonable manner, it is critical for us to know the specific antibacterial mechanism, which is still unclear. In this review, we analyzed the potential antibacterial mechanisms of AgNPs, and the influences of AgNPs on osteogenic-related cells, including cellular adhesion, proliferation, and differentiation, were also discussed. In addition, methods to enhance biocompatibility of AgNPs as well as advanced implants modifications technologies were also summarized.
Chapter 2 Bacillus as PGPR in Crop Ecosystem

Plant growth promoting rhizobacteria (PGPR) are beneficial bacteria which have the ability to colonize the roots and either promote plant growth through direct action or via biological control of plant diseases (Kloepper and Schroth 1978). They are associated with many plant species and are commonly present in varied environments. Strains with PGPR activity, belonging to genera Azoarcus, Azospirillum, Azotobacter, Arthrobacter, Bacillus, Clostridium, Enterobacter, Gluconacetobacter, Pseudomonas, and Serratia, have been reported (Hurek and Reinhold-Hurek 2003). Among these, species of Pseudomonas and Bacillus are the most extensively studied. These bacteria competitively colonize the roots of plant and can act as biofertilizers and/or antagonists (biopesticides) or simultaneously both. Diversified populations of aerobic endospore forming bacteria (AEFB), viz., species of Bacillus, occur in agricultural fields and contribute to crop productivity directly or indirectly. Physiological traits, such as multilayered cell wall, stress resistant endospore formation, and secretion of peptide antibiotics, peptide signal molecules, and extracellular enzymes, are ubiquitous to these bacilli and contribute to their survival under adverse environmental conditions for extended periods of time. Multiple species of Bacillus and Paenibacillus are known to promote plant growth. The principal mechanisms of growth promotion include production of growth stimulating phytohormones, solubilization and mobilization of phosphate, siderophore production, antibiosis, i.e., production of antibiotics, inhibition of plant ethylene synthesis, and induction of plant systemic resistance to pathogens (Richardson et al. 2009; Idris et al. 2007; Gutierrez-Manero et al. 2001;
Iterative Method For Approximating a Common Fixed Point of Infinite Family of Strictly Pseudo Contractive Mappings in Real Hilbert Spaces

Let   H K T n n    : 1 be a countable infinite family of k-strictly Pseudo contractive, uniformly weakly closed and inward mappings on a non empty, closed and strictly convex subset K of a real Hilbert space H in to H with     1 ) ( k k T F F is non empty. Let ) 1 , (k   and for each n ,   K hn : be defined by   K x T x x h n n          ) 1 ( : 0 inf ) ( .Then for each , 1 K x    k x h      , ) ( , max 1 1 1 , we define the Krasnoselskii-Mann type algorithm by n n n n n n x T x x    ) 1 ( 1     , where   ,... 2 , 1 , ) ( , max 1 1 1      n x h n n n n   and we prove the weak and strong convergence of the sequence  n x to a common fixed point of the family  1 n n T . Also we prove the weak and strong convergence theorems for the algorithm to the family of nonexpansive mappings in uniformly convex Banach space, which is more general than Hilbert space.
On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization

This paper studies a class of adaptive gradient based momentum algorithms that update the search directions and learning rates simultaneously using past gradients. This class, which we refer to as the “Adam-type”, includes the popular algorithms such as Adam (Kingma & Ba, 2014) , AMSGrad (Reddi et al., 2018) , AdaGrad (Duchi et al., 2011). Despite their popularity in training deep neural networks (DNNs), the convergence of these algorithms for solving non-convex problems remains an open question. In this paper, we develop an analysis framework and a set of mild sufficient conditions that guarantee the convergence of the Adam-type methods, with a convergence rate of order O(log T/ √ T ) for non-convex stochastic optimization. Our convergence analysis applies to a new algorithm called AdaFom (AdaGrad with First Order Momentum). We show that the conditions are essential, by identifying concrete examples in which violating the conditions makes an algorithm diverge. Besides providing one of the first comprehensive analysis for Adam-type methods in the non-convex setting, our results can also help the practitioners to easily monitor the progress of algorithms and determine their convergence behavior.
Evaluating Acute Changes in Joint Range-of-Motion using Self-Myofascial Release , Postural Alignment Exercises , and Static Stretches

. This study was designed to compare the acute effect of self-myofascial release (SMR), postural alignment exercises, and static stretching on joint range-of-motion. Our sample included 27 participants (n = 14 males and n = 13 females) who had below average joint range-of-motion (specifically a sitand-reach score of 13.5 inches [34.3 cm] or less). All were university students 18–27 years randomly assigned to complete two 30–40-minute data collection sessions with each testing session consisting of three sit-and-reach measurements (which involved lumbar spinal flexion, hip flexion, knee extension, and ankle dorsiflexion) interspersed with two treatments. Each treatment included foam-rolling, postural alignment exercises, or static stretching. Participants were assigned to complete session 1 and session 2 on two separate days, 24 hours to 48 hours apart. The data were analyzed so carryover effects could be estimated and showed that no single acute treatment significantly increased posterior mean sit-and-reach scores. However, significant gains (95% posterior probability limits) were realized with both postural alignment exercises and static stretching when used in combination with foam-rolling. For example, the posterior means equaled 1.71 inches (4.34 cm) when postural alignment exercises were followed by foam-rolling; 1.76 inches (4.47 cm) when foam-rolling was followed by static stretching; 1.49 inches (3.78 cm) when static stretching was followed by foam-rolling; and 1.18 inches (2.99 cm) when foam-rolling was followed by postural alignment exercises. Our results demonstrate that an acute treatment of foam-rolling significantly increased joint range-of-motion in participants with below average joint range-of-motion when combined with either postural alignment exercises or static stretching.
Developing a Usability Evaluation Method for e-Learning Applications: Beyond Functional Usability

concepts (principles, formulas, rules, etc.) are illustrated with concrete, specific examples (Horton, 2001) All units/modules in the Web course include an overview and a summary (Chua, 2002; Miller, 2002; Weston et al., 1999) Learners are made aware of learning objective for each unit/module of the Web course.
Societal, Economic, Ethical and Legal Challenges of the Digital Revolution: From Big Data to Deep Learning, Artificial Intelligence, and Manipulative Technologies

In the wake of the digital revolution we will see a dramatic transformation of our economy and societal institutions. While the benefits of this transformation can be massive, there are also tremendous risks. The fundaments of autonomous decision-making, human dignity, and democracy are shaking. After the automation of production processes and vehicle operation, the automation of society is next. This is moving us to a crossroads: we must decide between a society in which the actions are determined in a top-down way and then implemented by coercion or manipulative technologies or a society, in which decisions are taken in a free and participatory way. Modern information and communication systems enable both, but the latter has economic and strategic benefits.
Criminal computer forensics management

This research paper addresses the methodology and approaches to managing criminal computer forensic investigations in a law enforcement environment with management controls, operational controls, and technical controls. Management controls cover policy and standard operating procedures (SOP's), methodology, and guidance. Operational controls cover SOP requirements, seizing evidence, evidence handling, best practices, and education, training and awareness. Technical controls cover acquisition and analysis procedures, data integrity, rules of evidence, presenting findings, proficiency testing, and data archiving.
Determinants of Corporate Social Responsibility Disclosure : An Empirical Study of Polish Listed Companies

In this paper we explore whether a number of elements influence the levels of corporate social responsibility (CSR) disclosure in the annual reports of Polish companies. These elements include the following: company size, profitability, financial leverage, industry environmental sensitivity, board size, women on the board, internationalization, and reputation. We use content analysis to determine the quality of CSR disclosures. We test our hypotheses using a Tobit regression analysis on a sample of 60 reports from Polish companies listed on the Warsaw Stock Exchange. We find industry environmental sensitivity to have significant influence on CSR disclosures. Our research findings also reveal a relationship between company turnover, duration of the stock exchange listing, inclusion in the Respect Index portfolio and foreign capital share, and the level of CSR disclosures. This study extends the scope of previous studies by including non-commonly used independent variables: the company’s internationalization and reputation. To the authors’ knowledge, it is the primary step to investigating CSR reporting practices coupled with the corporate characteristics in a Central and Eastern European country such as Poland. The paper contributes to the understanding of determinants of CSR disclosure and offers findings which are potentially useful for both theory and practice.
Negative Results in Computer Vision: A Perspective

A negative result is when the outcome of an experiment or a model is not what is expected or when a hypothesis does not hold. Despite being often overlooked in the scientific community, negative results are results and they carry value. While this topic has been extensively discussed in other fields such as social sciences and biosciences, less attention has been paid to it in the computer vision community. The unique characteristics of computer vision, particularly its experimental aspect, call for a special treatment of this matter. In this paper, I will address what makes negative results important, how they should be disseminated and incentivized, and what lessons can be learned from cognitive vision research in this regard. Further, I will discuss issues such as computer vision and human vision interaction, experimental design and statistical hypothesis testing, explanatory versus predictive modeling, performance evaluation, model comparison, as well as computer vision research culture.