nils-herrmann/clean_scirepeval huggingface dataset:
Memory Forensics of a Java Card Dump

Nowadays several papers have shown the ability to dump the EEPROM area of several Java Cards leading to the disclosure of already loaded applet and data structure of the card. Such a reverse engineering process is costly and prone to errors. Currently there are no tools available to help the process. We propose here an approach to find in the raw data obtained after a dump, the area containing the code and the data. Then, once the code area has been identified, we propose to rebuilt the original binary Cap file in order to be able to obtain the source code of the applet stored in the card.
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks

In recent years, much research has been conducted on image super-resolution (SR). To the best of our knowledge, however, few SR methods were concerned with compressed images. The SR of compressed images is a challenging task due to the complicated compression artifacts, while many images suffer from them in practice. The intuitive solution for this difficult task is to decouple it into two sequential but independent subproblems, i.e., compression artifacts reduction (CAR) and SR. Nevertheless, some useful details may be removed in CAR stage, which is contrary to the goal of SR and makes the SR stage more challenging. In this paper, an end-to-end trainable deep convolutional neural network is designed to perform SR on compressed images (CISRDCNN), which reduces compression artifacts and improves image resolution jointly. Experiments on compressed images produced by JPEG (we take the JPEG as an example in this paper) demonstrate that the proposed CISRDCNN yields state-of-the-art SR performance on commonly used test images and imagesets. The results of CISRDCNN on real low quality web images are also very impressive, with obvious quality enhancement. Further, we explore the application of the proposed SR method in low bit-rate image coding, leading to better rate-distortion performance than JPEG.
Human-Computer Interaction Standards

It is often assumed that a standard means a precise specification. Such standards have brought benefits in many fields, eg: bolts which screw into nuts, ATMs which can read credit cards, and compilers which can read programming languages. Some HCI standards are also of this type: many design guides provide a detailed specification of the nature of the user interface. Although standard user interfaces provide the benefit of consistency, they become out of date as technology changes, and are usually only appropriate for limited types of users and tasks (Bevan and Holdaway, 1993). Thus most work on international standards for HCI has not been about precise specification, but instead has concentrated on the principles which need to be applied in order to produce an interface which meets user and task needs. These standards broadly fall into two categories. One is a "top-down" approach which is concerned with usability as a broad quality objective: the ability to use a product for its intended purpose. The other is a product-oriented "bottom-up" view which is concerned with aspects of the interface which make a system easier to use. The broad quality view originates from human factors, and standards of this type are applicable in the broad context of design and quality objectives. The product-oriented view concentrates on the design of specific attributes, and relates more closely to the needs of the interface designer and the role of usability in software engineering (see Bevan, 1995). Section 4 explains how standards can be used to provide a means of meeting the requirements for the operator-computer interface in the European Directive on Display Screen Equipment.
A Survey of Intrusion Detection Systems in Wireless Sensor Networks

Wireless Sensor Networking is one of the most promising technologies that have applications ranging from health care to tactical military. Although Wireless Sensor Networks (WSNs) have appealing features (e.g., low installation cost, unattended network operation), due to the lack of a physical line of defense (i.e., there are no gateways or switches to monitor the information flow), the security of such networks is a big concern, especially for the applications where confidentiality has prime importance. Therefore, in order to operate WSNs in a secure way, any kind of intrusions should be detected before attackers can harm the network (i.e., sensor nodes) and/or information destination (i.e., data sink or base station). In this article, a survey of the state-of-the-art in Intrusion Detection Systems (IDSs) that are proposed for WSNs is presented. Firstly, detailed information about IDSs is provided. Secondly, a brief survey of IDSs proposed for Mobile Ad-Hoc Networks (MANETs) is presented and applicability of those systems to WSNs are discussed. Thirdly, IDSs proposed for WSNs are presented. This is followed by the analysis and comparison of each scheme along with their advantages and disadvantages. Finally, guidelines on IDSs that are potentially applicable to WSNs are provided. Our survey is concluded by highlighting open research issues in the field.
Big data mining with parallel computing: A comparison of distributed and MapReduce methodologies

Mining with big data or big data mining has become an active research area. It is very difficult using current methodologies and data mining software tools for a single personal computer to efficiently deal with very large datasets. The parallel and cloud computing platforms are considered a better solution for big data mining. The concept of parallel computing is based on dividing a large problem into smaller ones and each of them is carried out by one single processor individually. In addition, these processes are performed concurrently in a distributed and parallel manner. There are two common methodologies used to tackle the big data problem. The first one is the distributed procedure based on the data parallelism paradigm, where a given big dataset can be manually divided into n subsets, and n algorithms are respectively executed for the corresponding n subsets. The final result can be obtained from a combination of the outputs produced by the n algorithms. The second one is the MapReduce based procedure under the cloud computing platform. This procedure is composed of the map and reduce processes, in which the former performs filtering and *  Chih-Fong Tsai Department of Information Management, National Central University, Taiwan; Tel: +886-3-422-7151 ; Fax: +886-3-4254604 E-mail address: cftsai@mgt.ncu.edu.tw
Schema Extraction for Tabular Data on the Web

Tabular data is an abundant source of information on the Web, but remains mostly isolated from the latter’s interconnections since tables lack links and computer-accessible descriptions of their structure. In other words, the schemas of these tables — attribute names, values, data types, etc. — are not explicitly stored as table metadata. Consequently, the structure that these tables contain is not accessible to the crawlers that power search engines and thus not accessible to user search queries. We address this lack of structure with a new method for leveraging the principles of table construction in order to extract table schemas. Discovering the schema by which a table is constructed is achieved by harnessing the similarities and differences of nearby table rows through the use of a novel set of features and a feature processing scheme. The schemas of these data tables are determined using a classification technique based on conditional random fields in combination with a novel feature encoding method called logarithmic binning, which is specifically designed for the data table extraction task. Our method provides considerable improvement over the wellknown WebTables schema extraction method. In contrast with previous work that focuses on extracting individual relations, our method excels at correctly interpreting full tables, thereby being capable of handling general tables such as those found in spreadsheets, instead of being restricted to HTML tables as is the case with the WebTables method. We also extract additional schema characteristics, such as row groupings, which are important for supporting information retrieval tasks on tabular data.
The Fano resonance in plasmonic nanostructures and metamaterials.

Since its discovery, the asymmetric Fano resonance has been a characteristic feature of interacting quantum systems. The shape of this resonance is distinctively different from that of conventional symmetric resonance curves. Recently, the Fano resonance has been found in plasmonic nanoparticles, photonic crystals, and electromagnetic metamaterials. The steep dispersion of the Fano resonance profile promises applications in sensors, lasing, switching, and nonlinear and slow-light devices.
Forecasting Non-Stationary Diarrhea, Acute Respiratory Infection, and Malaria Time-Series in Niono, Mali

Much of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with diarrhea, acute respiratory infection, and malaria. With the increasing awareness that the aforementioned infectious diseases impose an enormous burden on developing countries, public health programs therein could benefit from parsimonious general-purpose forecasting methods to enhance infectious disease intervention. Unfortunately, these disease time-series often i) suffer from non-stationarity; ii) exhibit large inter-annual plus seasonal fluctuations; and, iii) require disease-specific tailoring of forecasting methods.



In this longitudinal retrospective (01/1996-06/2004) investigation, diarrhea, acute respiratory infection of the lower tract, and malaria consultation time-series are fitted with a general-purpose econometric method, namely the multiplicative Holt-Winters, to produce contemporaneous on-line forecasts for the district of Niono, Mali. This method accommodates seasonal, as well as inter-annual, fluctuations and produces reasonably accurate median 2- and 3-month horizon forecasts for these non-stationary time-series, i.e., 92% of the 24 time-series forecasts generated (2 forecast horizons, 3 diseases, and 4 age categories = 24 time-series forecasts) have mean absolute percentage errors circa 25%.



The multiplicative Holt-Winters forecasting method: i) performs well across diseases with dramatically distinct transmission modes and hence it is a strong general-purpose forecasting method candidate for non-stationary epidemiological time-series; ii) obliquely captures prior non-linear interactions between climate and the aforementioned disease dynamics thus, obviating the need for more complex disease-specific climate-based parametric forecasting methods in the district of Niono; furthermore, iii) readily decomposes time-series into seasonal components thereby potentially assisting with programming of public health interventions, as well as monitoring of disease dynamics modification. Therefore, these forecasts could improve infectious diseases management in the district of Niono, Mali, and elsewhere in the Sahel.
Theory of Deep Learning III : the non-overfitting puzzle

A main puzzle of deep networks revolves around the apparent absence of overfitting intended as robustness of the expected error against overparametrization, despite the large capacity demonstrated by zero training error on randomly labeled data. In this note, we show that the dynamics associated to gradient descent minimization of nonlinear networks is topologically equivalent, near the asymptotically stable minima of the empirical error, to a gradient system in a quadratic potential with a degenerate (for square loss) or almost degenerate (for logistic or crossentropy loss) Hessian. The proposition depends on the qualitative theory of dynamical systems and is supported by numerical results. The result extends to deep nonlinear networks two key properties of gradient descent for linear networks, that have been recently recognized (1) to provide a form of implicit regularization: 1. For classification, which is the main application of today’s deep networks, there is asymptotic convergence to the maximum margin solution by minimization of loss functions such as the logistic, the cross entropy and the exp-loss . The maximum margin solution guarantees good classification error for “low noise” datasets. Importantly, this property holds independently of the initial conditions. Because of this property, our proposition guarantees a maximum margin solution also for deep nonlinear networks. 2. Gradient descent enforces a form of implicit regularization controlled by the number of iterations, and asymptotically converges to the minimum norm solution for appropriate initial conditions of gradient descent. This implies that there is usually an optimum early stopping that avoids overfitting of the expected risk. This property, valid for the square loss and many other loss functions, is relevant especially for regression. In the case of deep nonlinear networks the solution however is not expected to be strictly minimum norm, unlike the linear case. The robustness to overparametrization has suggestive implications for the robustness of the architecture of deep convolutional networks with respect to the curse of dimensionality.
CREAM: A Concurrent-Refresh-Aware DRAM Memory architecture

As DRAM density keeps increasing, more rows need to be protected in a single refresh with the constant refresh number. Since no memory access is allowed during a refresh, the refresh penalty is no longer trivial and can result in significant performance degradation. To mitigate the refresh penalty, a Concurrent-REfresh-Aware Memory system (CREAM) is proposed in this work so that memory access and refresh can be served in parallel. The proposed CREAM architecture distinguishes itself with the following key contributions: (1) Under a given DRAM power budget, sub-rank-level refresh (SRLR) is developed to reduce refresh power and the saved power is used to enable concurrent memory access; (2) sub-array-level refresh (SALR) is also devised to effectively lower the probability of the conflict between memory access and refresh; (3) In addition, novel sub-array level refresh scheduling schemes, such as sub-array round-robin and dynamic scheduling, are designed to further improve the performance. A quasi-ROR interface protocol is proposed so that CREAM is fully compatible with JEDEC-DDR standard with negligible hardware overhead and no extra pin-out. The experimental results show that CREAM can improve the performance by 12.9% and 7.1% over the conventional DRAM and the Elastic-Refresh DRAM memory, respectively.
Computational principles of synaptic memory consolidation

Memories are stored and retained through complex, coupled processes operating on multiple timescales. To understand the computational principles behind these intricate networks of interactions, we construct a broad class of synaptic models that efficiently harness biological complexity to preserve numerous memories by protecting them against the adverse effects of overwriting. The memory capacity scales almost linearly with the number of synapses, which is a substantial improvement over the square root scaling of previous models. This was achieved by combining multiple dynamical processes that initially store memories in fast variables and then progressively transfer them to slower variables. Notably, the interactions between fast and slow variables are bidirectional. The proposed models are robust to parameter perturbations and can explain several properties of biological memory, including delayed expression of synaptic modifications, metaplasticity, and spacing effects.
Isolated Switched Boost Push Pull DC-DC Step-up Converter

An isolated switched high step up boost DC-DC converter is discussed in this paper. The main objective of this paper is to step up low voltage to very high voltage. This paper mainly initiates at boosting a 30V DC into 240V DC. The discussed converter benefits from the continuous input current. Usually, step-up DC-DC converters are suitable for input whose voltage level is very low. The circuital design comprises of four main stages. Firstly, an impedance network which is used to boost the low input voltage. Secondly a switching network which is used to boost the input voltage then an isolation transformer which is used to provide higher boosting ability and finally a voltage multiplier rectifier which is used to rectify the secondary voltage of the transformer. No switching deadtime is required, which increases the reliability of the converter. Comparing with the existing step-up topologies indicates that this new design is hybrid, portable, higher power density and the size of the whole system is also reduced. The principles as well as operations were analysed and experimentally worked out, which provides a higher efficiency. KeywordImpedance Network, Switching Network, Isolation Transformer, Voltage Multiplier Rectifier, MicroController, DC-DC Boost Converter __________________________________________________________________________________________________
On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors

Recently the problem of determining the best, in the least-squares sense, rank-1 approximation to a higher-order tensor was studied and an iterative method that extends the wellknown power method for matrices was proposed for its solution. This higher-order power method is also proposed for the special but important class of supersymmetric tensors, with no change. A simplified version, adapted to the special structure of the supersymmetric problem, is deemed unreliable, as its convergence is not guaranteed. The aim of this paper is to show that a symmetric version of the above method converges under assumptions of convexity (or concavity) for the functional induced by the tensor in question, assumptions that are very often satisfied in practical applications. The use of this version entails significant savings in computational complexity as compared to the unconstrained higher-order power method. Furthermore, a novel method for initializing the iterative process is developed which has been observed to yield an estimate that lies closer to the global optimum than the initialization suggested before. Moreover, its proximity to the global optimum is a priori quantifiable. In the course of the analysis, some important properties that the supersymmetry of a tensor implies for its square matrix unfolding are also studied.
Sentiment Analysis on Microblogs for Natural Disasters Management: a Study on the 2014 Genoa Floodings

People use social networks for different communication purposes, for example to share their opinion on ongoing events. One way to exploit this common knowledge is by using Sentiment Analysis and Natural Language Processing in order to extract useful information. In this paper we present a SA approach applied to a set of tweets related to a recent natural disaster in Italy; our goal is to identify tweets that may provide useful information from a disaster management perspective.
Learning about natural human-robot interaction styles

If we are to achieve natural human–robot interaction, we may need to complement current vision and speech interfaces. Touch may provide us with an extra tool in this quest. In this paper we demonstrate the role of touch in interaction between a robot and a human. We show how infrared sensors located on robots can be easily used to detect and distinguish human interaction, in this case interaction with individual children. This application of infrared sensors potentially has many uses; for example, in entertainment or service robotics. This system could also benefit therapy or rehabilitation, where the observation and recording of movement and interaction is important. In the long term, this technique might enable robots to adapt to individuals or individual types of user.
Accurate Reactive Power Sharing in an Islanded Microgrid Using Adaptive Virtual Impedances

In this paper, a reactive power sharing strategy that employs communication and the virtual impedance concept is proposed to enhance the accuracy of reactive power sharing in an islanded microgrid. Communication is utilized to facilitate the tuning of adaptive virtual impedances in order to compensate for the mismatch in voltage drops across feeders. Once the virtual impedances are tuned for a given load operating point, the strategy will result in accurate reactive power sharing even if communication is disrupted. If the load changes while communication is unavailable, the sharing accuracy is reduced, but the proposed strategy will still outperform the conventional droop control method. In addition, the reactive power sharing accuracy based on the proposed strategy is immune to the time delay in the communication channel. The sensitivity of the tuned controller parameters to changes in the system operating point is also explored. The control strategy is straightforward to implement and does not require knowledge of the feeder impedances. The feasibility and effectiveness of the proposed strategy are validated using simulation and experimental results from a 2-kVA microgrid.
Applying Deep Learning to ICD-9 Multi-label Classification from Medical Records

Medical records contain detailed notes written by medical care providers about a patient’s physical and mental health, analysis of lab tests and radiology results, treatment courses, and more. This information may be valuable in improving medical care. In this project, we apply deep learning models to the multi-label classification task of assigning ICD-9 labels from these medical notes. Previous works have applied machine learning methods, like logistic regression and hierarchical SVM, using bag-of-words features to this task. On a dataset of around 40,000 critical care unit patients with 10 labels and with 100 labels, we find that a Recurrent Neural Network (RNN) and a RNN with Long Short-term Memory (LSTM) units show an improvement over the Binary Relevance Logistic Regression model.
The invisible addiction: Cell-phone activities and addiction among male and female college students

The primary objective of the present study was to investigate which cell-phone activities are associated with cell-phone addiction. No research to date has studied the full-range of cell-phone activities, and their relationship to cell-phone addiction, across male and female cell-phone users.



College undergraduates (N = 164) participated in an online survey. Participants completed the questionnaire as part of their class requirements. The questionnaire took 10 and 15 minutes to complete and contained a measure of cell-phone addiction and questions that asked how much time participants spent daily on 24 cell-phone activities.



Findings revealed cell-phone activities that are associated significantly with cell-phone addiction (e.g., Instagram, Pinterest), as well as activities that one might logically assume would be associated with this form of addiction but are not (e.g., Internet use and Gaming). Cell-phone activities that drive cell-phone addiction (CPA) were found to vary considerably across male and female cell-phone users. Although a strong social component drove CPA for both males and females, the specific activities associated with CPA differed markedly.



CPA amongst the total sample is largely driven by a desire to connect socially. The activities found to be associated with CPA, however, differed across the sexes. As the functionality of cell-phones continues to expand, addiction to this seemingly indispensable piece of technology becomes an increasingly realistic possibility. Future research must identify the activities that push cell-phone use beyond its “tipping point” where it crosses the line from a helpful tool to one that undermines our personal well-being and that of others.