nils-herrmann/clean_scirepeval huggingface dataset:
Wireless non-contact cardiac and neural monitoring

Ubiquitous physiological monitoring will be a key driving force in the upcoming wireless health revolution. Cardiac and brain signals in the form of ECG and EEG are two critical health indicators that directly benefit from long-term monitoring. Despite advancements in wireless technology and electronics miniaturization, however, the use of wireless home ECG/EEG monitoring is still limited by the inconvenience and discomfort of wet adhesive electrodes.
 We have developed a wireless biopotential instrumentation system using non-contact capacitive electrodes that operate without skin contact. The sensors can be embedded within comfortable layers of fabric for unobtrusive use. All of the issues relating to the design of low noise, high performance capacitive sensors are discussed along with full technical details, circuit schematics and construction techniques.
 The non-contact electrode has been integrated into both a wearable ECG chest harness as well a EEG headband. We have also designed a compact, battery-powered, wireless data acquisition system to interface with multiple electrodes and monitor patient cardiac and neural signals in real time. Experimental data shows that the non-contact capacitive electrode perform comparable to Ag/AgCl electrodes using our special chest harness and head bands to ensure tight, movement-free electrode positioning.
Orthogonal AMP

Approximate message passing (AMP) is a low-cost iterative signal recovery algorithm for linear system models. When the system transform matrix has independent identically distributed (IID) Gaussian entries, the performance of AMP can be asymptotically characterized by a simple scalar recursion called state evolution (SE). However, SE may become unreliable for other matrix ensembles, especially for ill-conditioned ones. This imposes limits on the applications of AMP. In this paper, we propose an orthogonal AMP (OAMP) algorithm based on de-correlated linear estimation (LE) and divergence-free non-linear estimation (NLE). The Onsager term in standard AMP vanishes as a result of the divergence-free constraint on NLE. We develop an SE procedure for OAMP and show numerically that the SE for OAMP is accurate for general unitarily-invariant matrices, including IID Gaussian matrices and partial orthogonal matrices. We further derive optimized options for OAMP and show that the corresponding SE fixed point coincides with the optimal performance obtained via the replica method. Our numerical results demonstrate that OAMP can be advantageous over AMP, especially for ill-conditioned matrices.
MUTARCH: Architectural diversity for FPGA device and IP security

Field Programmable Gate Arrays (FPGAs) are being increasingly deployed in diverse applications including the emerging Internet of Things (IoT), biomedical, and automotive systems. However, security of the FPGA configuration file (i.e. bitstream), especially during in-field reconfiguration, as well as effective safeguards against unauthorized tampering and piracy during operation, are notably lacking. The current practice of bitstreram encryption is only available in high-end FPGAs, incurs unacceptably high overhead for area/energy-constrained devices, and is susceptible to side channel attacks. In this paper, we present a fundamentally different and novel approach to FPGA security that can protect against all major attacks on FPGA, namely, unauthorized in-field reprogramming, piracy of FPGA intellectual property (IP) blocks, and targeted malicious modification of the bitstream. Our approach employs the security through diversity principle to FPGA, which is often used in the software domain. We make each device architecturally different from the others using both physical (static) and logical (time-varying) configuration keys, ensuring that attackers cannot use a priori knowledge about one device to mount an attack on another. It therefore mitigates the economic motivation for attackers to reverse engineering the bitstream and IP. The approach is compatible with modern remote upgrade techniques, and requires only small modifications to existing FPGA tool flows, making it an attractive addition to the FPGA security suite. Our experimental results show that the proposed approach achieves provably high security against tampering and piracy with worst-case 14% latency overhead and 13% area overhead.
Fiat-Shamir with Aborts: Applications to Lattice and Factoring-Based Signatures

We demonstrate how the framework that is used for creating efficient number-theoretic ID and signature schemes can be transferred into the setting of lattices. This results in constructions of the most efficient to-date identification and signature schemes with security based on the worst-case hardness of problems in ideal lattices. In particular, our ID scheme has communication complexity of around 65, 000 bits and the length of the signatures produced by our signature scheme is about 50, 000 bits. All prior lattice-based identification schemes required on the order of millions of bits to be transferred, while all previous lattice-based signature schemes were either stateful, too inefficient, or produced signatures whose lengths were also on the order of millions of bits. The security of our identification scheme is based on the hardness of finding the approximate shortest vector to within a factor of Õ(n) in the standard model, while the security of the signature scheme is based on the same assumption in the random oracle model. Our protocols are very efficient, with all operations requiring Õ(n) time. We also show that the technique for constructing our lattice-based schemes can be used to improve certain number-theoretic schemes. In particular, we are able to shorten the length of the signatures that are produced by Girault’s factoring-based digital signature scheme ([10,11,31]).
A brain-computer interface using electrocorticographic signals in humans.

Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.
Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management

The increasing demand for Android mobile devices and blockchain has motivated malware creators to develop mobile malware to compromise the blockchain. Although the blockchain is secure, attackers have managed to gain access into the blockchain as legal users, thereby comprising important and crucial information. Examples of mobile malware include root exploit, botnets, and Trojans and root exploit is one of the most dangerous malware. It compromises the operating system kernel in order to gain root privileges which are then used by attackers to bypass the security mechanisms, to gain complete control of the operating system, to install other possible types of malware to the devices, and finally, to steal victims’ private keys linked to the blockchain. For the purpose of maximizing the security of the blockchain-based medical data management (BMDM), it is crucial to investigate the novel features and approaches contained in root exploit malware. This study proposes to use the bio-inspired method of practical swarm optimization (PSO) which automatically select the exclusive features that contain the novel android debug bridge (ADB). This study also adopts boosting (adaboost, realadaboost, logitboost, and multiboost) to enhance the machine learning prediction that detects unknown root exploit, and scrutinized three categories of features including (1) system command, (2) directory path and (3) code-based. The evaluation gathered from this study suggests a marked accuracy value of 93% with Logitboost in the simulation. Logitboost also helped to predicted all the root exploit samples in our developed system, the root exploit detection system (RODS).
Deep Enhanced Representation for Implicit Discourse Relation Recognition

Implicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface features from the input sentence pairs. Thus, properly representing the text is very crucial to this task. In this paper, we propose a model augmented with different grained text representations, including character, subword, word, sentence, and sentence pair levels. The proposed deeper model is evaluated on the benchmark treebank and achieves stateof-the-art accuracy with greater than 48% in 11-way and F1 score greater than 50% in 4-way classifications for the first time according to our best knowledge.
The "shoulds" and "should nots" of moral emotions: a self-regulatory perspective on shame and guilt.

A self-regulatory framework for distinguishing between shame and guilt was tested in three studies. Recently, two forms of moral regulation based on approach versus avoidance motivation have been proposed in the literature. Proscriptive regulation is sensitive to negative outcomes, inhibition based, and focused on what we should not do. Prescriptive regulation is sensitive to positive outcomes, activation based, and focused on what we should do. In the current research, consistent support was found for shame's proscriptive and guilt's prescriptive moral underpinnings. Study 1 found a positive association between avoidance orientation and shame proneness and between approach orientation and guilt proneness. In Study 2, priming a proscriptive orientation increased shame and priming a prescriptive orientation increased guilt. In Study 3, transgressions most apt to represent proscriptive and prescriptive violations predicted subsequent judgments of shame and guilt, respectively. This self-regulatory perspective provides a broad interpretive framework for understanding and extending past research findings.
A Framework for Real-time Path Planning in Changing Environments

We present a new method for generating collision-free paths for robots operating in changing environments. Our approach is closely related to recent probabilistic roadmap approaches. These planners use preprocessing and query stages, and are aimed at planning many times in the same environment. In contrast, our preprocessing stage creates a representation of the configuration space that can be easily modified in real time to account for changes in the environment, thus facilitating real-time planning. As with previous approaches, we begin by constructing a graph that represents a roadmap in the configuration space, but we do not construct this graph for a specific workspace. Instead, we construct the graph for an obstacle-free workspace, and encode the mapping from workspace cells to nodes and arcs in the graph. When the environment changes, this mapping is used to make the appropriate modifications to the graph, and plans can be generated by searching the modified graph. In this paper, we first discuss the construction of the roadmap, including how random samples of the configuration space are generated using an importance sampling approach and how these samples are connected to form the roadmap. We then discuss the mapping from the workspace to the configuration space roadmap, explaining how the mapping is generated and how it can be encoded efficiently using compression schemes that exploit redundancy in the mapping. We then introduce quantitative robustness measures and show how these can be used to enhance the robustness of the roadmap to changes in the environment. Finally, we evaluate an implementation of our approach for serial-link manipulators with up to 20 joints. KEY WORDS—probabilistic roadmaps, motion planning
An Algebraic Fault Attack on the LED Block Cipher

In this paper we propose an attack on block ciphers where we combine techniques derived from algebraic and fault based cryptanalysis. The recently introduced block cipher LED serves us as a target for our attack. We show how to construct an algebraic representation of the encryption map and how to cast the side channel information gained from a fault injection into polynomial form. The resulting polynomial system is converted into a logical formula in conjunctive normal form and handed over to a SAT solver for reconstruction of the secret key. Following this approach we were able to mount a new, successful attack on the version of LED that uses a 64-bit secret key, requiring only a single fault injection.
Dual band compact printed monopole antenna for Bluetooth (2.54GHz) and WLAN (5.2GHz) applications

The printed monopole antenna having dual band is proposed which operates at frequency 2.54 GHz (Bluetooth) and 5.2GHz (WLAN). Bluetooth and WLAN have been widely applied in all new trending laptops and smart phones. These two technologies are well known for its effective cost and high-speed data connection. The antenna comprises of two rectangular patches of different sizes for the required dual-band operations. The presented antenna is fed by corporate feed network which improves impedance bandwidth. The prime motto of this project is to make the smallest (compact) possible antenna so that it can be placed in the limited area of handheld devices. Simulated percentage impedance bandwidth of the antenna are 46.25 (1.958 GHz to 3.13 GHz) and 31.30 (4.15 GHz to 5.69 GHz) respectively. Good return loss and VSWR (less than 2) of the designed antenna is obtained by simulating on IE3D software.
iPinYou Global RTB Bidding Algorithm Competition Dataset

RTB (Real Time Bidding) is one of the most exciting developments in computational advertising in recent years. It drives transparency and efficiency in the display advertising ecosystem and facilitates the healthy growth of the display advertising industry. It enables advertisers to deliver the right message to the right person at the right time, publishers to better monetize their content by leveraging their website audience, and consumers to view relevant information through personalized ads. However, researchers in computational advertising area have been suffering from lack of publicly available datasets. iPinYou organizes a three-season global RTB algorithm competition in 2013. For each season, there is offline stage and online stage. On the offline stage, iPinYou releases a dataset for model training and reserves a dataset for testing. The dataset includes logs of ad biddings, impressions, clicks, and final conversions. After the whole competition ends, iPinYou organizes and releases all these three datasets for public use. These datasets can support experiments of some important research problems such as bid optimization and CTR estimation. To the best of our knowledge, this is the first publicly available dataset on RTB display advertising. In this paper, we give descriptions of these datasets to further boost the interests of computational advertising research community using this dataset.
Evaluating Recommender Systems -- An evaluation framework to predict user satisfaction for recommender systems in an electronic programme guide context

Preface It was January 2007 when Dolf Trieschnigg, my supervisor for the course Information Retrieval , first told me about the availability of graduation projects at TNO Information and Communication Technology. This was a bull's eye since I just started to orientate myself on a subject to investigate. I was also looking for a company where I could perform my research since I wanted to get familiar with working in a professional environment. TNO Information and Communication Technology was one of the companies that seemed interesting to me, so I contacted Stephan Raaijmakers for more information. The subject of the proposed research project, evaluating recommender systems, was completely new to me, but seemed fascinating. And it is. In September 2008 I started my research by crafting the goals and research questions for the project. Now, almost nine months later, the research has resulted in the report that you just started reading. It marks the end of my life as a student (at least for now) and the start of my professional career at TNO. At TNO I can continue to work on personalisation and recommender systems. Although I wrote this thesis the research would never have been completed without the support of other people. In the first place, I would like to thank my supervisors, Erik, Djoerd, Stephan and Dolf for supervising me throughout the research project. They were always there for me when I needed their help and/or expertise and provided loads of valuable comments and suggestions. I also like to thank Jan Telman for his help and advice with respect to statistics (and hiking through the Alps). Furthermore I would like to thank all people that participated in the user study. Without their devotion to the demanding study, the research would not have been completed. I would also like to thank my lovely girlfriend Linda, my family and my colleagues at TNO Information and Communication Technology for being all ears when I tried to explain my approach and ideas to them. They helped me to abstract from all the details and forced me to explain things clearly so I better understood it myself. Finally, I owe special thanks to Mark Prins, who provided me with an inexhaustible source of Tiësto's Club Life, A State of Trance and other dance and trance music.
Comparison of Different Grid Abstractions for Pathfinding on Maps

Unsupervised learning of morphology is an important task for human learners and in natural language processing systems. Previous systems focus on segmenting words into substrings (taking ⇒ tak.ing), but sometimes a segmentation-only analysis is insufficient (e.g., taking may be more appropriately analyzed as take+ing, with a spelling rule accounting for the deletion of the stem-final e). In this paper, we develop a Bayesian model for simultaneously inducing both morphology and spelling rules. We show that the addition of spelling rules improves performance over the baseline morphology-only model.
Redistribution layer (RDL) process development and improvement for 3D interposer

RDL process becomes more and more important with through Si interposer (TSI) application in 3D packaging. RDL line/space needs to be shrinking with the increasing of device density. We had been developed low temperature (LT) damascene process for the RDL formation in 3D interposer integration. The sample failed at thermal reliability test. High temperature (HT) RDL was developed and demonstrated after TSV annealing temperature optimization in this paper.
Personal Computer Adventure Games: Their Structure, Principles, and Applicability for Training

Personal computer adventure games, in which the player assumes the role of a fantasy character to pursue an adventure, have enjoyed enormous popularity and commercial success. Beyond their entertainment value, these games also have an educational value, training users to become better problem solvers in the game domain and probably beyond.In order to understand better this type of game and determine its potential use for managerial training, we analyzed adventure games with respect to three issues. First, what makes a computer simulation an adventure game; second, what makes such a game enjoyable and challenging; and finally, how well does such a game facilitate learning? In search of answers, we analyzed a number of adventure games to determine game structures, and completed a small empirical study in which subjects played a game and had to answer both content and preference related questions.Overall, our analysis reconfirmed people's liking of adventure games. Players seem to enjoy the games' story and interface, and are challenged by the game tasks. Players also learn from gaming; yet levels of learning vary widely, based on the type of knowledge to be conveyed. Fact learning proved to be easiest; plan learning and learning of negative knowledge, the hardest.
Generating UML Diagrams from Natural Language Specifications

The process of generating UML Diagrams from natural language specification is a highly challenging task. This paper proposes a method and tool to facilitate the requirements analysis process and extract UML diagrams from textual requirements using natural language processing (NLP) and Domain Ontology techniques. Requirements engineers analyze requirements manually to understand the scope of the system. The time spent on the analysis and the low quality of human analysis justifies the need of a tool for better understanding of the system. “Requirement analysis to Provide Instant Diagrams (RAPID)” is a desktop tool to assist requirements analysts and Software Engineering students to analyze textual requirements, finding core concepts and its relationships, and extraction UML diagrams. The evaluation of RAPID system is in the process and will be conducted through two forms of evaluation, experimental and expert evaluation.
Ontology Evolution: State of the Art and Future Directions

Ontologies evolve continuously throughout their lifecycle to respond to different change requirements. Several problems emanate from ontology evolution: capturing change requirements, change representation, change impact analysis and resolution, change validation, change traceability, change propagation to dependant artifacts, versioning, etc. The purpose of this chapter is to gather research and current developments to manage ontology evolution. The authors highlight ontology evolution issues and present a state-of-the-art of ontology evolution approach by describing issues raised and the ontology model considered (ontology representation language), and also the ontology engineering tools supporting ontology evolution and maintenance. Furthermore, they sum up the state-of-the-art review by a comparative study based on general characteristics, evolution functionalities supported, and specificities of the existing ontology evolution approaches. At the end of the chapter, the authors discuss future and emerging trends.
CATENA: CAusal and TEmporal relation extraction from NAtural language texts

We present CATENA, a sieve-based system to perform temporal and causal relation extraction and classification from English texts, exploiting the interaction between the temporal and the causal model. We evaluate the performance of each sieve, showing that the rule-based, the machinelearned and the reasoning components all contribute to achieving state-of-the-art performance on TempEval-3 and TimeBank-Dense data. Although causal relations are much sparser than temporal ones, the architecture and the selected features are mostly suitable to serve both tasks. The effects of the interaction between the temporal and the causal components, although limited, yield promising results and confirm the tight connection between the temporal and the causal dimension of texts.