nils-herrmann/clean_scirepeval huggingface dataset:
Disentangling Disentanglement in Variational Auto-Encoders

We develop a generalisation of disentanglement in variational auto-encoders (VAEs)—decomposition of the latent representation—characterising it as the fulfilment of two factors: a) the latent encodings of the data having an appropriate level of overlap, and b) the aggregate encoding of the data conforming to a desired structure, represented through the prior. Decomposition permits disentanglement, i.e. explicit independence between latents, as a special case, but also allows for a much richer class of properties to be imposed on the learnt representation, such as sparsity, clustering, independent subspaces, or even intricate hierarchical dependency relationships. We show that the β-VAE varies from the standard VAE predominantly in its control of latent overlap and that for the standard choice of an isotropic Gaussian prior, its objective is invariant to rotations of the latent representation. Viewed from the decomposition perspective, breaking this invariance with simple manipulations of the prior can yield better disentanglement with little or no detriment to reconstructions. We further demonstrate how other choices of prior can assist in producing different decompositions and introduce an alternative training objective that allows the control of both decomposition factors in a principled manner.
Predicting The Next App That You Are Going To Use

Given the large number of installed apps and the limited screen size of mobile devices, it is often tedious for users to search for the app they want to use. Although some mobile OSs provide categorization schemes that enhance the visibility of useful apps among those installed, the emerging category of homescreen apps aims to take one step further by automatically organizing the installed apps in a more intelligent and personalized way. In this paper, we study how to improve homescreen apps' usage experience through a prediction mechanism that allows to show to users which app she is going to use in the immediate future. The prediction technique is based on a set of features representing the real-time spatiotemporal contexts sensed by the homescreen app. We model the prediction of the next app as a classification problem and propose an effective personalized method to solve it that takes full advantage of human-engineered features and automatically derived features. Furthermore, we study how to solve the two naturally associated cold-start problems: app cold-start and user cold-start. We conduct large-scale experiments on log data obtained from Yahoo Aviate, showing that our approach can accurately predict the next app that a person is going to use.
Head-mounted display with mid-air tactile feedback

Virtual and physical worlds are merging. Currently users of head-mounted displays cannot have unobtrusive tactile feedback while touching virtual objects. We present a mid-air tactile feedback system for head-mounted displays. Our prototype uses the focus of a modulated ultrasonic phased array for unobtrusive mid-air tactile feedback generation. The array and the hand position sensor are mounted on the front surface of a head-mounted virtual reality display. The presented system can enhance 3D user interfaces and virtual reality in a new way.
 To evaluate the tactile feedback together with visuals on an Oculus Rift VR headset, we had 13 participants do a simple virtual keypad tapping task with and without tactile feedback. The results indicate that while the measured speed and accuracy differed only a little, the subjects were nearly unanimous in that they preferred to use the tactile feedback. The "raw" NASA TLX questionnaires conducted after use revealed that the participants felt slightly less mental, physical and temporal demand with the tactile feedback. The participants' self-assessment of their performance was also higher with the tactile feedback.
Anomaly Detection using Autoencoders in High Performance Computing Systems

Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states). We propose a novel approach for anomaly detection in High Performance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with). We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).
The International Society for Biomechanics ) 5 July 1993 THE USE OF SURFACE ELECTROMYOGRAPHY IN BIOMECHANICS

................................................................................................................... 2 INTRODUCTION ............................................................................................................. 3 FACTORS AFFECTING THE EMG SIGNAL AND FORCE PRODUCED BY A MUSCLE.............. 5 DETECTION AND PROCESSING OF THE EMG SIGNAL.................................................... 10 THE ACTIVATION TIMING OF MUSCLES......................................................................... 12 THE FORCE / EMG SIGNAL RELATIONSHIP .................................................................. 17 THE EMG SIGNAL AS A FATIGUE INDEX ...................................................................... 25 SUMMARY OF RECOMMENDATIONS.............................................................................. 32 SUMMARY OF PROBLEMS FOR RESOLUTION................................................................. 34 ISSUES FOR INTERNATIONAL AGREEMENT.................................................................... 36 ACKNOWLEDGEMENTS ................................................................................................ 37
Embedded Devices Security and Firmware Reverse Engineering BH 13 US Workshop

Embedded devices have become the usual presence in the network of (m)any household(s), SOHO, enterprise or critical infrastructure. The preached Internet of Things promises to gazillionuple their number and heterogeneity in the next few years. However, embedded devices are becoming lately the usual suspects in security breaches and security advisories and thus become the Achilles’ heel of one’s overall infrastructure security. An important aspect is that embedded devices run on what’s commonly known as firmwares. To understand how to secure embedded devices, one needs to understand their firmware and how it works. This workshop aims at presenting a quick-start at how to inspect firmwares and a hands-on presentation with exercises on real firmwares from a security analysis standpoint.
Classification of plant leaf images with complicated background

Image segmentation Plant leaf Complicated background Watershed segmentation Hu geometric moments Zernike moment Moving center hypersphere (MCH) classifier a b s t r a c t Classifying plant leaves has so far been an important and difficult task, especially for leaves with complicated background where some interferents and overlapping phenomena may exist. In this paper, an efficient classification framework for leaf images with complicated background is proposed. First, a so-called automatic marker-controlled watershed segmen-tation method combined with pre-segmentation and morphological operation is introduced to segment leaf images with complicated background based on the prior shape information. Then, seven Hu geometric moments and sixteen Zernike moments are extracted as shape features from segmented binary images after leafstalk removal. In addition , a moving center hypersphere (MCH) classifier which can efficiently compress feature data is designed to address obtained mass high-dimensional shape features. Finally, experimental results on some practical plant leaves show that proposed classification framework works well while classifying leaf images with complicated background. There are twenty classes of practical plant leaves successfully classified and the average correct classification rate is up to 92.6%. Plants play the most important part in the cycle of nature. They are the primary producers that sustain all other life forms including people. This is because plants are the only organisms that can convert light energy from the sun into food. Animals, incapable of making their own food, depend directly or indirectly on plants for their supply of food. Moreover, all of the oxygen available for living organisms comes from plants. Plants are also the primary habitat for thousands of other organisms. In addition, many of the fuel people use today, such as coal, natural gas and gasoline, were made from plants that lived millions of years ago. However, in recent years, people have been seriously destroying the natural environments, so that many plants constantly die and even die out every year. Conversely, the resulting ecological crisis has brought many serious consequences including land desertion, climate anomaly, land flood, and so on, which have menaced the survival of human being and the development of society. Now, people have realized the importance and urgency of protecting plant resource. Besides taking effective measures to protect plants, it is important for ordinary people to know or classify plants which can further enhance the public's consciousness of plant protection. Therefore, besides professional botanists, many non-professional researchers have paid more …
Prioritization of human capital measurement indicators using fuzzy AHP

People in an organization constitute an important and essential asset which tremendously contributes to development and growth of that company by the help of their collective attitudes, skills and abilities. This is why the human capital (HC) can be considered the most important sub-dimension of the intellectual capital. Since you cannot manage what you cannot control, and you cannot control what you do not measure, the measurement of HC is a very important issue. This study aims at defining a methodology to improve the quality of prioritization of HC measurement indicators under fuzziness. To do so, a methodology based on the extent fuzzy analytic hierarchy process (AHP) is proposed. Within the model, five main attributes; talent, strategical integration, cultural relevance, knowledge management, and leadership; their sub-attributes, and 20 indicators are defined. The proposed model can be used for any country. However, the results obtained in the numerical example reflect the situation of HC in Turkey, since the experts are asked to make their evaluations considering the cultural characteristics of Turkey. The results of the study indicate that ‘‘creating results by using knowledge’’, ‘‘employees’ skills index’’, ‘‘sharing and reporting knowledge’’, and ‘‘succession rate of training programs’’ are the four most important measurement indicators for the HC in Turkey.
A Model-Based Method for Computer-Aided Medical Decision-Making

While MYCIN and PIP were under development at Stanford and Tufts! M.I. T., a group of computer scientists at Rutgers University was developing a system to aid in the evaluation and treatment of patients with glaucoma. The group was led by Professor Casimir K ulikou1ski, a researcher with extensive background in mathematical and pattern-recognition approaches to computer-based medical decision making (Nordyke et al., 1971), working within the Rutgers Research Resource on Computers in Biomedicine headed by Professor Saul Amarel. Working collaboratively with Dr. Arin Safir, Professor of Ophthalmology, "who was then based at the Mt. Sinai School of Medicine in New York City, Kulikowski and Sholom Weiss (a graduate student at Rutgers who "went on to become a research scientist there) developed a method of computer-assisted medical decision making that was based on causal-associational network (CASNET) models of disease. Although the work was inspired by the glaucoma domain, the approach had general features that were later refined in the development of the EXPERT system-building tool (see Chapters 18 and 20). A CASNET model consists of three rnain components: observations of a patient, pathophysiological states, and disease classifications. As observations are recorded, they are associated with the appropriate intennediate states. These states, in turn, are typically causally related, thereby forming a network that summarizes the mechanisms of disease. It is these patterns of states in the network that are linked to individual disease classes.
Introducing 'Bones': a parallelizing source-to-source compiler based on algorithmic skeletons

Recent advances in multi-core and many-core processors requires programmers to exploit an increasing amount of parallelism from their applications. Data parallel languages such as CUDA and OpenCL make it possible to take advantage of such processors, but still require a large amount of effort from programmers.
 A number of parallelizing source-to-source compilers have recently been developed to ease programming of multi-core and many-core processors. This work presents and evaluates a number of such tools, focused in particular on C-to-CUDA transformations targeting GPUs. We compare these tools both qualitatively and quantitatively to each other and identify their strengths and weaknesses.
 In this paper, we address the weaknesses by presenting a new classification of algorithms. This classification is used in a new source-to-source compiler, which is based on the algorithmic skeletons technique. The compiler generates target code based on skeletons of parallel structures, which can be seen as parameterisable library implementations for a set of algorithm classes. We furthermore demonstrate that the presented compiler requires little modifications to the original sequential source code, generates readable code for further fine-tuning, and delivers superior performance compared to other tools for a set of 8 image processing kernels.
Business Trend Analysis by Simulation

Business processes are constantly affected by the environment in which they execute. The environment can change due to seasonal and financial trends. For organisations it is crucial to understand their processes and to be able to estimate the effects of these trends on the processes. Business process simulation is a way to investigate the performance of a business process and to analyse the process response to injected trends. However, existing simulation approaches assume a steady state situation. Until now correlations and dependencies in the process have not been considered in simulation models, which can lead to wrong estimations of the performance. In this work we define an adaptive simulation model with a history-dependent mechanism that can be used to propagate changes in the environment through the model. In addition we focus on the detection of dependencies in the process based on the executions of the past. We demonstrate the application of adaptive simulation models by means of an experiment.
Approximation-aware Multi-Level Cells STT-RAM cache architecture

Current manycore processors exhibit large on-chip last-level caches that may reach sizes of 32MB -- 128MB and incur high power/energy consumption. The emerging Multi-Level Cells (MLC) STT-RAM memory technology improves the capacity and energy efficiency issues of large-sized memory banks. However, MLC STT-RAM incurs non-negligible protection overhead to ensure reliable operations when compared to the Single-Level Cells (SLC) STT-RAM. In this paper, we propose an approximation-aware MLC STT-RAM cache architecture, which is partially-protected to restrict the reliability overhead and in turn leverages variable resilience characteristics of different applications for adaptively curtailing the protection overhead under a given error tolerance level. It thereby improves the energy-efficiency of the cache while meeting the reliability requirements. Our cache architecture is equipped with a latency-aware hardware module for double-error correction. To achieve high energy efficiency, approximation-aware read and write policies are proposed that perform approximate storage management while tolerating some errors bounded within the user-provided tolerance level. The architecture also facilitates runtime control on the quality of applications' results. We perform a case study on the next-generation advanced video encoding that exhibit memory-intensive functional blocks with varying resilience properties and support for parallelism. Experimental results demonstrate that our approximation-aware MLC STT-RAM based cache architecture can improve the energy efficiency compared to state-of-the-art fully-protected caches (7%--19%, on average), while incurring minimal quality penalties in the output (-0.219% to -0.426%, on average). Furthermore, our architecture supports complete error protection coverage for all cache data when processing non-resilient application. The hardware overhead to implement our approximation-aware management negligibly affects the energy efficiency (0.15%--1.3% of overhead) and the access latency (only 0.02%--1.56% of overhead).
Analysis and design of HBT Cherry-Hooper amplifiers with emitter-follower feedback for optical communications

In this article, the large-signal, small-signal, and noise performance of the Cherry-Hooper amplifier with emitter-follower feedback are analyzed from a design perspective. A method for choosing the component values to obtain a low group delay distortion or Bessel transfer function is given. The design theory is illustrated with an implementation of the circuit in a 47-GHz SiGe process. The amplifier has 19.7-dB gain, 13.7-GHz bandwidth, and /spl plusmn/10-ps group delay distortion. The amplifier core consumes 34 mW from a -3.3-V supply.
Co-citation in the scientific literature: A new measure of the relationship between two documents

HENRY SfAAU lrwtzt.le /or .%cntijfc [n/.rmatio. Philadelphia, Pemqdvmia 191W A now form of document coupling called co-citation is deffned as tfm frequency with which two documents are cifed togother. Th* co-citation frequency of two tcientiflc papers can b. determined by comporing fists of citing documents in the Science Citafien Index and cwn$ing identical enwics. Networks of co-cited poperi can be generated for speciffc scientific specialties, and an ●xample it drawn from the fiteratum of particle physics. Co-cikttion potferns are found to differ tie. nincantly from bibliographic coupling potterns, but to ogree generally with patterns of direct citation. ChIsiorI of co-cited papers provide a new way to study the wecialty structure of scietico. 7hay moy provida o new approach to indexing and to th. creation of SDI pro film. ●  B1bliographm mtatmns m smentdic pap+rs have bmn used by s mnety of researched to estahbsb relat iomhp among documents, Both direct citat]on-the citing of an earlier document by a new document-and hiblio. gtaphic coupling-the aharmg of one or more references by Iwo documents-have received comidentble attention (f, S, 9, 4) A related measure, which bas been over. looked m earlier dkcu.wions, is co-citation Unhke blbbograpbic coupling wbicb links source documents, co-cit-a. tion hnks cited dmumcnts and IS, therefore, analogous to a measure of descriptor or word asomatlon (5), Tbe purpme of thm paper IS to define th~ new kind of “coupling” and to distingwh it from biblicgm.pbic coupling, using an actual example from the literature of pbys]cs Co+tatwn patternt are found to dfler mgnificantly fmm biblicgrapbic mupling patterns, but to agree generally with patterns of direct citation
Inflammation and α-Synuclein’s Prion-like Behavior in Parkinson's Disease—Is There a Link?

Parkinson’s disease patients exhibit progressive spreading of aggregated α-synuclein in the nervous system. This slow process follows a specific pattern in an inflamed tissue environment. Recent research suggests that prion-like mechanisms contribute to the propagation of α-synuclein pathology. Little is known about factors that might affect the prion-like behavior of misfolded α-synuclein. In this review, we suggest that neuroinflammation plays an important role. We discuss causes of inflammation in the olfactory bulb and gastrointestinal tract and how this may promote the initial misfolding and aggregation of α-synuclein, which might set in motion events that lead to Parkinson's disease neuropathology. We propose that neuroinflammation promotes the prion-like behavior of α-synuclein and that novel anti-inflammatory therapies targeting this mechanism could slow disease progression.
Office Procedure as Practical Action: Models of Work and System Design

The design of office technology relies upon underlying conceptions of human organization and action. The goal of building office information systems requires a representation of office work and its relevant objects. The concern of this paper is that although system designers recognize the centrality of procedural tasks in the office, they tend to ignore the actual work involved in accomplishing those tasks. A perspicuous instance of work in an accounting office is used to recommend a new line of research into the practical problems of office work, and to suggest preliminary implications of that research for office systems design.
Rules for an Ontology-based Approach to Adaptation

Adaptation addresses the complexity and overload caused by an increasing amount of available resources by the identification and presentation of relevant resources. Drawbacks of existing approaches to this end include the limited degree of customization, difficulties in the acquisition of model information and the lack of control and transparency of the system's adaptive behavior. This paper proposes an approach which addresses these drawbacks to perform meaningful and effective adaptation by the application of semantic Web technologies. This comprises the use of an ontology and adaptation rules for knowledge representation and inference engines for reasoning. The focus of this paper lies in the presentation of adaptation rules
All-solid-state dye-sensitized solar cells with high efficiency

Dye-sensitized solar cells based on titanium dioxide (TiO2) are promising low-cost alternatives to conventional solid-state photovoltaic devices based on materials such as Si, CdTe and CuIn1−xGaxSe2 (refs 1, 2). Despite offering relatively high conversion efficiencies for solar energy, typical dye-sensitized solar cells suffer from durability problems that result from their use of organic liquid electrolytes containing the iodide/tri-iodide redox couple, which causes serious problems such as electrode corrosion and electrolyte leakage. Replacements for iodine-based liquid electrolytes have been extensively studied, but the efficiencies of the resulting devices remain low. Here we show that the solution-processable p-type direct bandgap semiconductor CsSnI3 can be used for hole conduction in lieu of a liquid electrolyte. The resulting solid-state dye-sensitized solar cells consist of CsSnI2.95F0.05 doped with SnF2, nanoporous TiO2 and the dye N719, and show conversion efficiencies of up to 10.2 per cent (8.51 per cent with a mask). With a bandgap of 1.3 electronvolts, CsSnI3 enhances visible light absorption on the red side of the spectrum to outperform the typical dye-sensitized solar cells in this spectral region.