nils-herrmann/clean_scirepeval huggingface dataset:
Averaged Collapsed Variational Bayes Inference

This paper presents the Averaged CVB (ACVB) inference and offers convergence-guaranteed and practically useful fast Collapsed Variational Bayes (CVB) inferences. CVB inferences yield more precise inferences of Bayesian probabilistic models than Variational Bayes (VB) inferences. However, their convergence aspect is fairly unknown and has not been scrutinized. To make CVB more useful, we study their convergence behaviors in a empirical and practical approach. We develop a convergence-guaranteed algorithm for any CVB-based inference called ACVB, which enables automatic convergence detection and frees non-expert practitioners from the difficult and costly manual monitoring of inference processes. In experiments, ACVB inferences are comparable to or better than those of existing inference methods and deterministic, fast, and provide easier convergence detection. These features are especially convenient for practitioners who want precise Bayesian inference with assured convergence.
Taint-Enhanced Policy Enforcement: A Practical Approach to Defeat a Wide Range of Attacks

Policy-based confinement, employed in SELinux and specification-based intrusion detection systems, is a popular approach for defending against exploitation of vulnerabilities in benign software. Conventional access control policies employed in these approaches are effective in detecting privilege escalation attacks. However, they are unable to detect attacks that “hijack” legitimate access privileges granted to a program, e.g., an attack that subverts an FTP server to download the password file. (Note that an FTP server would normally need to access the password file for performing user authentication.) Some of the common attack types reported today, such as SQL injection and cross-site scripting, involve such subversion of legitimate access privileges. In this paper, we present a new approach to strengthen policy enforcement by augmenting security policies with information about the trustworthiness of data used in securitysensitive operations. We evaluated this technique using 9 available exploits involving several popular software packages containing the above types of vulnerabilities. Our technique sucessfully defeated these exploits.
Clarithromycin and Tetracycline Binding to Soil Humic Acid in the Absence and Presence of Calcium.

Numerous ionizable organic micropollutants contain positively charged moieties at pH values typical of environmental systems. Describing organic cation and zwitterion interaction with dissolved natural organic matter requires explicit consideration of the pH-dependent speciation of both sorbate and sorbent. We studied the pH-, ionic strength-, and concentration-dependent binding of relatively large, organic cations and zwitterions (viz., the antibiotics clarithromycin and tetracycline) to dissolved humic acid in the absence and presence of Ca(2+) and evaluated the ability of the NICA-Donnan model to describe the data. Clarithromycin interaction with dissolved humic acid was well described by the model including the competitive effect of Ca(2+) on clarithromycin binding over a wide range of solution conditions by considering only the binding of the cationic species to low proton-affinity sites in humic acid. Tetracycline possesses multiple ionizable moieties and forms complexes with Ca(2+). An excellent fit to experimental data was achieved by considering tetracycline cation interaction with both low and high proton-affinity sites of humic acid and zwitterion interaction with high proton-affinity sites. In contrast to clarithromycin, tetracycline binding to humic acid increased in the presence of Ca(2+), especially under alkaline conditions. Model calculations indicate that this increase is due to electrostatic interaction of positively charged tetracycline-Ca complexes with humic acid rather than due to the formation of ternary complexes, except at very low TC concentrations.
RFID Technology for IoT-Based Personal Healthcare in Smart Spaces

The current evolution of the traditional medical model toward the participatory medicine can be boosted by the Internet of Things (IoT) paradigm involving sensors (environmental, wearable, and implanted) spread inside domestic environments with the purpose to monitor the user's health and activate remote assistance. RF identification (RFID) technology is now mature to provide part of the IoT physical layer for the personal healthcare in smart environments through low-cost, energy-autonomous, and disposable sensors. It is here presented a survey on the state-of-the-art of RFID for application to body centric systems and for gathering information (temperature, humidity, and other gases) about the user's living environment. Many available options are described up to the application level with some examples of RFID systems able to collect and process multichannel data about the human behavior in compliance with the power exposure and sanitary regulations. Open challenges and possible new research trends are finally discussed.
High-speed 3D image acquisition using coded structured light projection

In various application fields, high-speed cameras are used to analyze high-speed phenomena. Coded structured light projection methods have been proposed for acquiring three-dimensional images. Most of them are not suitable for measuring high-speed phenomena because the errors are caused when the measured objects move due to light projection of multiple patterns. In this paper, we propose a new coded structured light projection method that can select not only a temporal encoding but also a spatial encoding adaptively for obtaining three-dimensional images at a high-speed frame rate. We also develop an evaluation system that uses a DLP projector and an off-line high-speed video camera, and verify the effectiveness of the proposed method by showing the obtained three-dimensional shapes for moving objects.
On the bipolarity in argumentation frameworks

In this paper, we propose a survey of the use of bipolarity in argumentation frameworks, i.e. the presence of two kinds of entities (a positive entity and a negative entity). An argumentation process follows three steps: building the arguments and the interactions between them, valuating the arguments using or not the interactions and finally defining the acceptability of the arguments. This paper shows on various applications and with some formal definitions that bipolarity appears (in some cases since always) and can be used in each step of this process under different forms.
The mirror-neuron system.

A category of stimuli of great importance for primates, humans in particular, is that formed by actions done by other individuals. If we want to survive, we must understand the actions of others. Furthermore, without action understanding, social organization is impossible. In the case of humans, there is another faculty that depends on the observation of others' actions: imitation learning. Unlike most species, we are able to learn by imitation, and this faculty is at the basis of human culture. In this review we present data on a neurophysiological mechanism--the mirror-neuron mechanism--that appears to play a fundamental role in both action understanding and imitation. We describe first the functional properties of mirror neurons in monkeys. We review next the characteristics of the mirror-neuron system in humans. We stress, in particular, those properties specific to the human mirror-neuron system that might explain the human capacity to learn by imitation. We conclude by discussing the relationship between the mirror-neuron system and language.
Stealing Hyperparameters in Machine Learning

Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentiality of the proprietary algorithms that the learner uses to learn them. In this work, we propose attacks on stealing the hyperparameters that are learned by a learner. We call our attacks hyperparameter stealing attacks. Our attacks are applicable to a variety of popular machine learning algorithms such as ridge regression, logistic regression, support vector machine, and neural network. We evaluate the effectiveness of our attacks both theoretically and empirically. For instance, we evaluate our attacks on Amazon Machine Learning. Our results demonstrate that our attacks can accurately steal hyperparameters. We also study countermeasures. Our results highlight the need for new defenses against our hyperparameter stealing attacks for certain machine learning algorithms.
A grounded theory analysis of modern web applications: knowledge, skills, and abilities for DevOps

Since 2009, DevOps, the combination of development and operation, has been adopted within organizations in industry, such as Netflix, Flickr, and Fotopedia. Configuration management tools have been used to support DevOps. However, in this paper we investigate which Knowledge, Skills, and Abilities (KSA) have been employed in developing and deploying modern web applications and how these KSAs support DevOps. By applying a qualitative analysis approach, namely grounded theory, to three web application development projects, we discover that the KSAs for both Software Development and IT Operator practitioners support the four perspectives of DevOps: collaboration culture, automation, measurement, and sharing.
Building Natural-Language Generation Systems

Reiter and Dale’s book is about natural language generation (NLG), and describes methods for building systems of this kind. It offers a comprehensive overview of the field of NLG, from a perspective that has rarely been described elsewhere. It is very valuable for people with little or even no knowledge about NLG particulars who intend to investigate applications where NLG technology might play a certain role, as well as for people who want to obtain an application-oriented overview of the field. In this sense, all communities intended to be served by the book—students, academics in related areas, and software developers—are well addressed. In addition, some sections provide material that is probably new even to researchers with considerable experience in the field, such as the discussions on corpus analysis and domain modeling. In the introduction, the field of NLG is briefly characterized from researchand application-oriented perspectives and is illustrated by screen shots produced by several systems. Then conditions for effective uses of this technology are elaborated and contrasted with conditions where other techniques are more appropriate, and methods for determining the intended functionality of a system to be built are discussed. The main sections of the book are devoted to the prototypical architecture of applicationoriented NLG systems and their major processing phases: document planning, microplanning, and surface realization. Each of these three phases is illustrated by a number of detailed examples, demonstrating the successive refinements of utterance specifications in the course of processing. In the final section, the embedding of natural language processing technology is discussed, featuring typography, combined uses with graphics and hypertext, and integration with speech. The methods are illustrated by a large number of examples—the book contains more than 120 figures on its 248 pages. At the end of each section, a number of useful references for further reading are related to the section topics. In the appendix, a table summarizing the 35 systems referred to in the book is given. In general, the presentations in the book are beneficial for their high degree of detail in documenting representations. Running examples are illustrated by intermediate representations at various stages of processing. They demonstrate very well the increasing degrees of precision in which utterances are specified and the associated commitments about possible text portions that express these specifications. Particularly valuable are the comparisons of alternative representations, ranging from skeletal propositions over lexicalized case frames to canned text, including a comparison of the consequences for the distribution of work over the system components involved and discussions of the associated pros and cons. These chapters provide a number of valuable hints, given the currently low degree of standardization in the field. Further important aspects addressed, which have been widely ignored in the literature so far,
Evaluating 3D Task Performance for Fish Tank Virtual Worlds

“Fish tank virtual reality” refers to the use of a standard graphics workstation to achieve real-time display of 3D scenes using stereopsis and dynamic head-coupled perspective. Fish tank VR has a number of advantages over head-mounted immersion VR which makes it more practical for many applications. After discussing the characteristics of fish tank VR, we describe a set of three experiments conducted to study the benefits of fish tank VR over a traditional workstation graphics display. These experiments tested user performance under two conditions: (a) whether or not stereoscopic display was used and (b) whether or not the perspective display was coupled dynamically to the positions of a user’s eyes. Subjects using a comparison protocol consistently preferred head coupling without stereo over stereo without head coupling. Error rates in a tree-tracing task similar to one used by Sollenberger and Milgram showed an order of magnitude improvement for head-coupled stereo over a static (nonhead-coupled) display, and the benefits gained by head coupling were more significant than those gained from stereo alone. The final experiment examined two factors that are often associated with human performance in virtual worlds: the lag (or latency) in receiving and processing tracker data arid the rate at which frames are updated. For the tree-tracing task, lag had a larger impact on performance than did frame update rate, with lag having a multiplicative effect on response time. We discuss the relevance of these results for the display of complex 3D data and highlight areas requiring further study,
A refutation of Penrose's Gödelian case against artificial intelligence

Having, as it is generally agreed, failed to destroy the computational conception of mind with the Gödelian attack he articulated in his The Emperor’s New Mind, Penrose has returned, armed with a more elaborate and more fastidious Gödelian case, expressed in Chapters 2 and 3 of his Shadows of the Mind. The core argument in these chapters is enthymematic, and when formalized, a remarkable number of technical glitches come to light. Over and above these defects, the argument, at best, is an instance of either the fallacy of denying the antecedent, the fallacy of petitio principii, or the fallacy of equivocation. More recently, writing in response to his critics in the electronic journal Psyche, Penrose has offered a Gödelian case designed to improve on the version presented in SOTM. But this version is yet again another failure. In falling prey to the errors we uncover, Penrose’s new Gödelian case is unmasked as the same confused refrain J.R. Lucas initiated 35 years ago. ∗We are indebted to Martin Davis, Kelsey Rinella, Marvin Minsky, David Chalmers, Jim Fahey, Michael Zenzen, Ken Ford, Pat Hayes, Bob McNaughton, and Kostas Arkoudas. Selmer would like to express special thanks to Roger Penrose for debate and conversation concerning many of the issues treated herein. Bringsjord and Penrose both believe that the mind is beyond computation; both also believe that Gödelian results can be deployed to demonstrate this. However, as this paper testifies, they differ over how to carry out the demonstration.
Compassionate Mind Training for People with High Shame and Self-Criticism : Overview and Pilot Study of a Group Therapy Approach

Compassionate mind training (CMT) was developed for people with high shame and self-criticism, whose problems tend to be chronic, and who find self-warmth and self-acceptance difficult and/or frightening. This paper offers a short overview of the role of shame and self-criticism in psychological difficulties, the importance of considering different types of affect system (activating versus soothing) and the theory and therapy process of CMT. The paper explores patient acceptability, understanding, abilities to utilize and practice compassion focused processes and the effectiveness of CMT from an uncontrolled trial. Six patients attending a cognitive–behavioural-based day centre for chronic difficulties completed 12 two-hour sessions in compassionate mind training. They were advised that this was part of a research programme to look at the process and effectiveness of CMT and to become active collaborators, advising the researchers on what was helpful and what was not. Results showed significant reductions in depression, anxiety, self-criticism, shame, inferiority and submissive behaviour. There was also a significant increase in the participants’ ability to be self-soothing and focus on feelings of warmth and reassurance for the self. Compassionate mind training may be a useful addition for some patients with chronic difficulties, especially those from traumatic backgrounds, who may lack a sense of inner warmth or abilities to be self-soothing.
Model-Driven Engineering in a Large Industrial Context - Motorola Case Study

In an ongoing effort to reduce development costs in spite of increasing system complexity, Motorola has been a long-time adopter of Model-Driven Engineering (MDE) practices. The foundation of this approach is the creation of rigorous models throughout the development process, thereby enabling the introduction of automation. In this paper we present our experiences within Motorola in deploying a top-down approach to MDE for more than 15 years. We describe some of the key competencies that have been developed and the impact of MDE within the organization. Next we present some of the main issues encountered during MDE deployment, together with some possible resolutions.
Gatekeeper: Monitoring Auto-Start Extensibility Points (ASEPs) for Spyware Management

Spyware is a rapidly spreading problem for PC users causing significant impact on system stability and privacy concerns. It attaches to extensibility points in the system to ensure the spyware will be instantiated when the system starts. Users may willingly install free versions of software containing spyware as an alternative to paying for it. Traditional anti-virus techniques are less effective in this scenario because they lack the context to decide if the spyware should be removed. In this paper, we introduce Auto-Start Extensibility Points (ASEPs) as the key concept for modeling the spyware problem. By monitoring and grouping ‘‘hooking’’ operations made to the ASEPs, our Gatekeeper solution complements the traditional signature-based approach and provides a comprehensive framework for spyware management. We present ASEP hooking statistics for 120 real-world spyware programs. We also describe several techniques for discovering new ASEPs to further enhance the effectiveness of our solution.
"Constant, constant, multi-tasking craziness": managing multiple working spheres

Most current designs of information technology are based on the notion of supporting distinct tasks such as document production, email usage, and voice communication. In this paper we present empirical results that suggest that people organize their work in terms of much larger and thematically connected units of work. We present results of fieldwork observation of information workers in three different roles: analysts, software developers, and managers. We discovered that all of these types of workers experience a high level of discontinuity in the execution of their activities. People average about three minutes on a task and somewhat more than two minutes using any electronic tool or paper document before switching tasks. We introduce the concept of working spheres to explain the inherent way in which individuals conceptualize and organize their basic units of work. People worked in an average of ten different working spheres. Working spheres are also fragmented; people spend about 12 minutes in a working sphere before they switch to another. We argue that design of information technology needs to support people's continual switching between working spheres.
A Traceback Attack on Freenet

Freenet is a popular peer to peer anonymous content-sharing network, with the objective to provide the anonymity of both content publishers and retrievers. Despite more than a decade of active development and deployment and the adoption of well-established cryptographic algorithms in Freenet, it remains unanswered how well the anonymity objective of the initial Freenet design has been met. In this paper we develop a traceback attack on Freenet, and show that the originating machine of a content request message in Freenet can be identified; that is, the anonymity of a content retriever can be broken, even if a single request message has been issued by the retriever. We present the design of the traceback attack, and perform both experimental and simulation studies to confirm the feasibility and effectiveness of the attack. For example, with randomly chosen content requesters (and random contents stored in the Freenet testbed), Emulab-based experiments show that, for <inline-formula><tex-math notation="LaTeX">$24$</tex-math> <alternatives><inline-graphic xlink:href="duan-ieq1-2453983.gif"/></alternatives></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$43$</tex-math><alternatives><inline-graphic xlink:href="duan-ieq2-2453983.gif"/> </alternatives></inline-formula> percent of the content request messages, we can identify their originating machines. We also briefly discuss potential solutions to address the developed traceback attack. Despite being developed specifically on Freenet, the basic principles of the traceback attack and solutions have important security implications for similar anonymous content-sharing systems.
Autoencoders, Minimum Description Length and Helmholtz Free Energy

An autoencoder network uses a set of recognition weights to convert an input vector into a code vector. It then uses a set of generative weights to convert the code vector into an approximate reconstruction of the input vector. We derive an objective function for training autoencoders based on the Minimum Description Length (MDL) principle. The aim is to minimize the information required to describe both the code vector and the reconstruction error. We show that this information is minimized by choosing code vectors stochastically according to a Boltzmann distribution, where the generative weights de ne the energy of each possible code vector given the input vector. Unfortunately, if the code vectors use distributed representations, it is exponentially expensive to compute this Boltzmann distribution because it involves all possible code vectors. We show that the recognition weights of an autoencoder can be used to compute an approximation to the Boltzmann distribution and that this approximation gives an upper bound on the description length. Even when this bound is poor, it can be used as a Lyapunov function for learning both the generative and the recognition weights. We demonstrate that this approach can be used to learn factorial codes.