nils-herrmann/clean_scirepeval huggingface dataset:
Support Vector Machine Soft Margin Classifiers: Error Analysis

The purpose of this paper is to provide a PAC error analysis for the q-norm soft margin classifier, a support vector machine classification algorithm. It consists of two parts: regularization error and sample error. While many techniques are available for treating the sample error, much less is known for the regularization error and the corresponding approximation error for reproducing kernel Hilbert spaces. We are mainly concerned about the regularization error. It is estimated for general distributions by a K-functional in weighted L spaces. For weakly separable distributions (i.e., the margin may be zero) satisfactory convergence rates are provided by means of separating functions. A projection operator is introduced, which leads to better sample error estimates especially for small complexity kernels. The misclassification error is bounded by the V -risk associated with a general class of loss functions V . The difficulty of bounding the offset is overcome. Polynomial kernels and Gaussian kernels are used to demonstrate the main results. The choice of the regularization parameter plays an important role in our analysis.
Problem formulations and solvers in linear SVM: a review

Support vector machine (SVM) is an optimal margin based classification technique in machine learning. SVM is a binary linear classifier which has been extended to non-linear data using Kernels and multi-class data using various techniques like one-versus-one, one-versus-rest, Crammer Singer SVM, Weston Watkins SVM and directed acyclic graph SVM (DAGSVM) etc. SVM with a linear Kernel is called linear SVM and one with a non-linear Kernel is called non-linear SVM. Linear SVM is an efficient technique for high dimensional data applications like document classification, word-sense disambiguation, drug design etc. because under such data applications, test accuracy of linear SVM is closer to non-linear SVM while its training is much faster than non-linear SVM. SVM is continuously evolving since its inception and researchers have proposed many problem formulations, solvers and strategies for solving SVM. Moreover, due to advancements in the technology, data has taken the form of ‘Big Data’ which have posed a challenge for Machine Learning to train a classifier on this large-scale data. In this paper, we have presented a review on evolution of linear support vector machine classification, its solvers, strategies to improve solvers, experimental results, current challenges and research directions.
Sim-to-Real Reinforcement Learning for Deformable Object Manipulation

We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformable objects, solutions using traditional modelling approaches require significant engineering work. Perhaps then, bypassing the need for explicit modelling and instead learning the control in an end-to-end manner serves as a better approach? Despite the growing interest in the use of end-to-end robot learning approaches, only a small amount of work has focused on their applicability to deformable object manipulation. Moreover, due to the large amount of data needed to learn these end-to-end solutions, an emerging trend is to learn control policies in simulation and then transfer them over to the real world. Todate, no work has explored whether it is possible to learn and transfer deformable object policies. We believe that if sim-to-real methods are to be employed further, then it should be possible to learn to interact with a wide variety of objects, and not only rigid objects. In this work, we use a combination of state-of-the-art deep reinforcement learning algorithms to solve the problem of manipulating deformable objects (specifically cloth). We evaluate our approach on three tasks — folding a towel up to a mark, folding a face towel diagonally, and draping a piece of cloth over a hanger. Our agents are fully trained in simulation with domain randomisation, and then successfully deployed in the real world without having seen any real deformable objects.
Rearrange Social Overloaded Posts to Prevent Social Overload

According to the latest investigation, there are 1.7 million active social network users in Taiwan. Previous researches indicated social network posts have a great impact on users, and mostly, the negative impact is from the rising demands of social support, which further lead to heavier social overload. In this study, we propose social overloaded posts detection model (SODM) by deploying the latest text mining and deep learning techniques to detect the social overloaded posts and, then with the developed social overload prevention system (SOS), the social overload posts and non-social overload ones are rearranged with different sorting methods to prevent readers from excessive demands of social support or social overload. The empirical results show that our SOS helps readers to alleviate social overload when reading via social media.
Near Optimal Bayesian Active Learning for Decision Making

How should we gather information to make effective decisions? We address Bayesian active learning and experimental design problems, where we sequentially select tests to reduce uncertainty about a set of hypotheses. Instead ofminimizing uncertainty per se, we consider a set of overlapping decision regions of these hypotheses. Our goal is to drive uncertainty into a single decision region as quickly as possible. We identify necessary and sufficient conditions for correctly identifying a decision region that contains all hypotheses consistent with observations. We develop a novel Hyperedge Cutting (HEC) algorithm for this problem, and prove that is competitive with the intractable optimal policy. Our efficient implementation of the algorithm relies on computing subsets of the complete homogeneous symmetric polynomials. Finally, we demonstrate its effectiveness on two practical applications: approximate comparison-based learning and active localization using a robotmanipulator.
The relationship between passion and the psychological well-being of professional dancers.

The Dualistic Model of Passion defines passion as an intense desire or enthusiasm for a self-defining activity that people love, consider important, and devote significant amounts of time and energy to. The model proposes two distinct types of passion, harmonious (HP) and obsessive (OP). HP occurs when the activity is autonomously internalized into the individual's life and identity, while OP is a result of a controlled internalization of the activity. The aim of this study was to investigate the prevalence and type of passion professional dancers have for dance in relation to their psychological well-being, specifically eating attitudes, self-esteem, and perfectionism. Participants were 92 professional dancers, aged 19 to 35 years (M = 27.03, SD = 3.84), and mostly from the United States, the United Kingdom, and Canada. Results revealed that HP positively predicted self-esteem (SE), while OP positively predicted self-evaluative perfectionism (SEP), conscientious perfectionism (CP), and disordered eating attitudes (EAT-26). Additionally, SEP was found to mediate the relationship between OP and EAT-26, suggesting that OP may lead to SEP, which could in turn motivate disordered eating. Overall, the results of this study have supported and extended previous research suggesting that the two types of passion can have divergent effects on aspects of psychological well-being. Findings indicate that HP should be encouraged and OP discouraged among dancers, for example, via autonomy supportive behaviors of teachers.
Differentiation of the gastric mucosa III. Animal models of oxyntic atrophy and metaplasia.

Gastric cancer in humans arises in the setting of oxyntic atrophy (parietal cell loss) and attendant hyperplastic and metaplastic lineage changes within the gastric mucosa. Helicobacter infection in mice and humans leads to spasmolytic polypeptide-expressing metaplasia (SPEM). In a number of mouse models, SPEM arises after oxyntic atrophy. In mice treated with the parietal cell toxic protonophore DMP-777, SPEM appears to arise from the transdifferentiation of chief cells. These results support the concept that intrinsic mucosal influences regulate and modulate the appearance of gastric metaplasia even in the absence of significant inflammation, whereas chronic inflammation is required for the further neoplastic transition.
A Goal-Based Organizational Perspective on Multi-agent Architectures

A Multi-Agent System (MAS) is an organization of coordinated autonomous agents that interact in order to achieve common goals. Considering real world organizations as an analogy, this paper proposes architectural styles for MAS which adopt concepts from organization theory and strategic alliances literature. The styles are intended to represent a macro-level architecture of a MAS, and they are modeled using the i* framework which offers the notions of actor, goal and actor dependency for modeling multi-agent settings. The styles are also specified as metaconcepts in the Telos modeling language. Moreover, each style is evaluated with respect to a set of software quality attributes, such as predictability and adaptability. The paper also explores the adoption of micro-level patterns proposed elsewhere in order to give a finer-grain description of a MAS architecture. These patterns define how goals assigned to actors participating in an organizational architecture will be fulfilled by agents. An e-business example illustrates both the styles and patterns proposed in this work. The research is being conducted within the context of Tropos, a comprehensive software development methodology for agent-oriented software.
Micromolding of photocrosslinkable chitosan hydrogel for spheroid microarray and co-cultures.

Bioengineering approaches, such as co-cultures of multiple cell types, that aim to mimic the physiological microenvironment may be beneficial for optimizing cell function and for engineering tissues in vitro. This study describes a novel method for preparing a spheroid microarray on microfabricated hydrogels, alone or in co-cultures. Photocrosslinkable chitosan was synthesized and utilized for fabricating hydrogel microstructures through a micromolding process. The chitosan surface was initially cell repellent but became increasingly cell adhesive over time. By using this unique property of chitosan hydrogels, it was possible to generate patterned co-cultures of spheroids and support cells. In this scheme, cells were initially microarrayed within low shear stress regions of microwells. Human hepatoblastoma cells, Hep G2, seeded in these wells formed spheroids with controlled sizes and shapes and stably secreted albumin during the culture period. The change of cell adhesive properties in the chitosan surface facilitated the adhesion and growth of a second cell type, NIH-3T3 fibroblast, and therefore enabled co-cultures of hepatocyte spheroids and fibroblast monolayers. This co-culture system could be a useful platform for studying heterotypic cell-cell interactions, for drug screening, and for developing implantable bioartificial organs.
Cryptographically Enforced Control Flow Integrity

Recent Pwn2Own competitions have demonstrated the continued effectiveness of control hijacking attacks despite deployed countermeasures including stack canaries and ASLR. A powerful defense called Control flow Integrity (CFI) offers a principled approach to preventing such attacks. However, prior CFI implementations use static analysis and must limit protection to remain practical. These limitations have enabled attacks against all known CFI systems, as demonstrated in recent work. This paper presents a cryptographic approach to control flow integrity (CCFI) that is both fine-grain and practical: using message authentication codes (MAC) to protect control flow elements such as return addresses, function pointers, and vtable pointers. MACs on these elements prevent even powerful attackers with random read/ write access to memory from tampering with program control flow. We implemented CCFI in Clang/LLVM, taking advantage of recently available cryptographic CPU instructions. We evaluate our system on several large software packages (including nginx, Apache and memcache) as well as all their dependencies. The cost of protection ranges from a 3–18% decrease in request rate.
In Vitro Evaluation of Antioxidant Activity of Bitter Melon ( Momordica charantia L . )

Find out the scientifically evidence of Bitter Melo n (Momordica charantia L.) for it’s Antioxidant property. Preliminary phytochemical screening and i -vitro antioxidant activity of Bitter Melon ( Momordica charantia L.) extract were investigated but the extraction wa s done at different temperature respectively (35oC,60oC, 100oC) by decoction process. The antiox idant activity was studied in some in-vitro antioxi dant models like DPPH radical scavenging activity, Super oxide radical scavenging activity, Ferric reducing power and Hydrogen peroxide scavenging activity. Total An tioxidant capacity was also determined. The Bitter M lon (Momordica charantia L.) extract showed antioxidant activity by inhibiti ng DPPH, scavenging superoxide and hydrogen peroxide. It also showed reducing power ab ility in ferric reducing model. Total antioxidant c apacity was found to be 19.22 mg/gm expressed as L-Ascorbic acid. Significant antioxidant activity of Water ex tract of Bitter Melon (Momordica charantia L.) was found which might be due to the presence of Acidic compounds, Flavonoids, Phenols, Saponins, Tannins (Phenolic co mpounds) and Triterpenoids etc found in the prelimi nary Phytochemical screening.
Bayesian Optimization with Exponential Convergence

This paper presents a Bayesian optimization method with exponential convergencewithout the need of auxiliary optimization and without the δ-cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex global optimization problem, which can be time-consuming and hard to implement in practice. Also, the existing Bayesian optimization method with exponential convergence  requires access to the δ-cover sampling, which was considered to be impractical [ 1, 2]. Our approach eliminates both requirements and achieves an exponential convergence rate.
Collusion-resistant mechanisms for single-parameter agents

We consider the problem of designing mechanisms with the incentive property that no coalition of agents can engage in a collusive strategy that results in an increase in the combined utility of the coalition. For single parameter agents, we give a characterization that essentially restricts such mechanisms to those that post a "take it or leave it" price to for each agent in advance. We then consider relaxing the incentive property to only hold with high probability. In this relaxed model, we are able to design approximate profit maximizing auctions and approximately efficient auctions. We generalized these results to give a methodology for designing collusion resistant mechanisms for single parameter agents. In addition, we give several results for a weaker incentive property from the literature known as group strategyproofness.
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment

Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of "skewness&#x201D; to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
DStore: A Holistic Key-Value Store Exploring Near-Data Processing and On-Demand Scheduling for Compaction Optimization

Log-structured merge tree (LSM-tree)-based key-value stores are widely deployed in large-scale storage systems. The underlying reason is that the traditional relational databases cannot reach the high performance required by big-data applications. As high-throughput alternatives to relational databases, LSM-tree-based key-value stores can support high-throughput write operations and provide high sequential bandwidth in storage systems. However, the compaction process triggers write amplification and is confronted with the degraded write performance, especially under update-intensive workloads. To address this issue, we design a holistic key-value store to explorer near-data processing (NDP) and on-demand scheduling for compaction optimization in an LSM-tree key-value store, named DStore. DStore makes full use of various computing capacities in the host-side and device-side subsystems. DStore dynamically divides the whole host-side compaction tasks into the above two-side subsystems according to two-side different computing capabilities. Meanwhile, the device must be featured with an NDP model. The divided compaction tasks are performed by the host and the device in parallel. In DStore, the NDP-based devices exhibit low-latency and high-bandwidth performance, thus facilitating key-value stores. DStore not only accomplishes compaction for key-value stores but also improves the system performance. We implement our DStore prototype in a real-world platform, and different kinds of testbeds are employed in our experiment. LevelDB and a static compaction optimization using the NDP model (called Co-KV) are used to compare with the DStore in our evaluation. Results show that DStore achieves about  $3.7 \times $  performance improvement over LevelDB under the db_bench workload. In addition, DStore-enabled key-value stores outperform LevelDB by a factor of about  $3.3 \times $  and 77% in terms of throughput and latency under YCSB benchmark, respectively.
Distinguishing computer graphics from natural images using convolution neural networks

This paper presents a deep-learning method for distinguishing computer generated graphics from real photographic images. The proposed method uses a Convolutional Neural Network (CNN) with a custom pooling layer to optimize current best-performing algorithms feature extraction scheme. Local estimates of class probabilities are computed and aggregated to predict the label of the whole picture. We evaluate our work on recent photo-realistic computer graphics and show that it outperforms state of the art methods for both local and full image classification.
Trends in converted firearms in England & Wales as Identified by the National Firearms Forensic Intelligence Database (NFFID) between September 2003 and September 2008.

The National Firearms Forensic Intelligence Database (NFFID (c) Crown Copyright 2003-2008) was developed by The Forensic Science Service (FSS) as an investigative tool for collating and comparing information from items submitted to the FSS to provide intelligence reports for the police and relevant government agencies. The purpose of these intelligence reports was to highlight current firearm and ammunition trends and their distribution within the country. This study reviews all the trends that have been highlighted by NFFID between September 2003 and September 2008. A total of 8887 guns of all types have been submitted to the FSS over the last 5 years, where an average of 21% of annual submissions are converted weapons. The makes, models, and modes of conversion of these weapons are described in detail. The number of trends identified by NFFID shows that this has been a valuable tool in the analysis of firearms-related crime.
Top-N Recommendations from Implicit Feedback Leveraging Linked Open Data

The advent of the Linked Open Data (LOD) initiative gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper we present SPrank, a novel hybrid recommendation algorithm able to compute top-N item recommendations from implicit feedback exploiting the information available in the so called Web of Data. We leverage DBpedia, a well-known knowledge base in the LOD compass, to extract semantic path-based features and to eventually compute recommendations using a learning to rank algorithm. Experiments with datasets on two different domains show that the proposed approach outperforms in terms of prediction accuracy several state-of-the-art top-N recommendation algorithms for implicit feedback in situations affected by different degrees of data sparsity.
TimeNotes: A Study on Effective Chart Visualization and Interaction Techniques for Time-Series Data

Collecting sensor data results in large temporal data sets which need to be visualized, analyzed, and presented. One-dimensional time-series charts are used, but these present problems when screen resolution is small in comparison to the data. This can result in severe over-plotting, giving rise for the requirement to provide effective rendering and methods to allow interaction with the detailed data. Common solutions can be categorized as multi-scale representations, frequency based, and lens based interaction techniques. In this paper, we comparatively evaluate existing methods, such as Stack Zoom  and ChronoLenses , giving a graphical overview of each and classifying their ability to explore and interact with data. We propose new visualizations and other extensions to the existing approaches. We undertake and report an empirical study and a field study using these techniques.
Influence of a Circuit Parameter for Plasma Water Treatment by an Inductive Energy Storage Circuit Using Semiconductor Opening Switch

In this paper, we investigate a water treatment method that sprays waste water droplets into a pulsed discharge space. For this method, it is important to apply pulsed voltages with a short pulse width and a fast rise to the electrode to realize high energy efficiency. An inductive energy storage (IES) circuit using a semiconductor opening switch (SOS) outputs pulsed voltages similar to the above-mentioned voltage. We report the characteristics of the water treatment using the IES circuit. The capacitance and inductance in the IES circuit are varied. An increase in the inductance in the secondary circuit of the pulse transformer, results in the formation of pulsed voltages with a longer pulse width; thus, the ratio of the thermal loss to the discharge energy becomes high. However, the energy transfer efficiency improves. Additionally, we vary the maximum current of the SOS, keeping the pulse width constant, by adjusting the capacitance and the inductance. As a result, when the current in the SOS increased, pulsed voltages with a higher peak and a shaper rise are obtained. Further, the pulse width of the voltage and the energy transfer efficiency are not affected by the current in the SOS. Hence, the energy density of the discharge is higher. The increase in the secondary inductance and the forward current increased the energy efficiency of the water treatment system owing to the increase in the energy transfer efficiency and the discharge energy density.