nils-herrmann/clean_scirepeval huggingface dataset:
Anomaly Detection in Industrial Software Systems - Using Variational Autoencoders

Industrial software systems are known to be used for performing critical tasks in numerous fields. Faulty conditions in such systems can cause system outages that could lead to losses. In order to prevent potential system faults, it is important that anomalous conditions that lead to these faults are detected effectively. Nevertheless, the high complexity of the system components makes anomaly detection a high dimensional machine learning problem. This paper presents the application of a deep learning neural network known as Variational Autoencoder (VAE), as the solution to this problem. We show that, when used in an unsupervised manner, VAE outperforms the well-known clustering technique DBSCAN. Moreover, this paper shows that higher recall can be achieved using the semi-supervised one class learning of VAE, which uses only the normal data to train the model. Additionally, we show that one class learning of VAE outperforms semi-supervised one class SVM when training data consist of only a very small amount of anomalous samples. When a tree based ensemble technique is adopted for feature selection, the obtained results evidently demonstrate that the performance of the VAE is highly positively correlated with the selected feature set.
An ergonomic evaluation of city police officers: an analysis of perceived discomfort within patrol duties.

The purpose of this study was to assess the perceived discomfort of patrol officers related to equipment and vehicle design and whether there were discomfort differences between day and night shifts. A total of 16 participants were recruited (10 males, 6 females) from a local police force to participate for one full day shift and one full night shift. A series of questionnaires were administered to acquire information regarding comfort with specific car features and occupational gear, body part discomfort and health and lifestyle. The discomfort questionnaires were administered three times during each shift to monitor discomfort progression within a shift. Although there were no significant discomfort differences reported between the day and night shifts, perceived discomfort was identified for specific equipment, vehicle design and vehicle configuration, within each 12-h shift.
Ribosomal Protein s15 Phosphorylation Mediates LRRK2 Neurodegeneration in Parkinson’s Disease

Mutations in leucine-rich repeat kinase 2 (LRRK2) are a common cause of familial and sporadic Parkinson's disease (PD). Elevated LRRK2 kinase activity and neurodegeneration are linked, but the phosphosubstrate that connects LRRK2 kinase activity to neurodegeneration is not known. Here, we show that ribosomal protein s15 is a key pathogenic LRRK2 substrate in Drosophila and human neuron PD models. Phosphodeficient s15 carrying a threonine 136 to alanine substitution rescues dopamine neuron degeneration and age-related locomotor deficits in G2019S LRRK2 transgenic Drosophila and substantially reduces G2019S LRRK2-mediated neurite loss and cell death in human dopamine and cortical neurons. Remarkably, pathogenic LRRK2 stimulates both cap-dependent and cap-independent mRNA translation and induces a bulk increase in protein synthesis in Drosophila, which can be prevented by phosphodeficient T136A s15. These results reveal a novel mechanism of PD pathogenesis linked to elevated LRRK2 kinase activity and aberrant protein synthesis in vivo.
Design , Optimization , and Test Methods for MicroElectrode-Dot-Array Digital Microfluidic Biochips

Digital microfluidic biochips (DMFBs) are revolutionizing many biochemical analysis procedures, e.g., high-throughput DNA sequencing and point-of-care clinical diagnosis. However, today’s DMFBs suffer from several limitations: (1) constraints on droplet size and the inability to vary droplet volume in a fine-grained manner; (2) the lack of integrated sensors for real-time detection; (3) the need for special fabrication processes and the associated reliability/yield concerns. To overcome the above limitations, DMFBs based on a micro-electrode-dot-array (MEDA) architecture have recently been proposed. Unlike conventional digital microfluidics, where electrodes of equal size are arranged in a regular pattern, the MEDA architecture is based on the concept of a sea-ofmicro-electrodes. The MEDA architecture allows microelectrodes to be dynamically grouped to form a micro-component that can perform different microfluidic operations on the chip. Design-automation tools can reduce the difficulty of MEDA biochip design and help to ensure that the manufactured biochips are versatile and reliable. In order to fully exploit MEDA-specific advantages (e.g., real-time droplet sensing), this dissertation research targets new design, optimization, and test problems for MEDA biochips.
Cylindrical Polarimetric Phased Array Radar: Beamforming and Calibration for Weather Applications

Future weather radar systems will need to provide rapid updates within a flexible multifunctional overall radar network. This naturally leads to the use of electronically scanned phased array antennas. However, the traditional multifaced planar antenna approaches suffer from having radiation patterns that are variant in both beam shape and polarization as a function of electronic scan angle; even with practically challenging angle-dependent polarization correction, this places limitations on how accurately weather can be measured. A cylindrical array with commutated beams, on the other hand, can theoretically provide patterns that are invariant with respect to azimuth scanning with very pure polarizations. This paper summarizes recent measurements of the cylindrical polarimetric phased array radar demonstrator, a system designed to explore the benefits and limitations of a cylindrical array approach to these future weather radar applications.
Introduction Headspace GC-MS Detection of Carbon Monoxide in Decomposed Blood and Hepatic Tissues

Here in a systematic, accurate and reliable method, Head-Space Gas Chromatography-Mass Spectrometry (HS-GC/MS) was developed to determine blood carboxyhemoglobin (COHb%), in order to investigate deaths related to CO exposure especially involving blood and hepatic tissues. Using a column packed with molecular sieve, COHb levels were quantified down to 0.2% in small blood samples quickly and showed good reproducibility with RSD of the COHb <1%. COHb% in hepatic samples stored at different temperatures (-20 °C for 12 years, 0 °C, and 18 °C for two months) can be determined even when the samples are decomposed. The 3-min procedure requires only 0.25 mL of blood sample or 1.0 g of hepatic tissue each time. The technique has a clear advantage over other methods such as UV spectrophotometry.
Questions of Reasoning Under Logical Uncertainty

A logically uncertain reasoner would be able to reason as if they know both a programming language and a program, without knowing what the program outputs. Most practical reasoning involves some logical uncertainty, but no satisfactory theory of reasoning under logical uncertainty yet exists. A better theory of reasoning under logical uncertainty is needed in order to develop the tools necessary to construct highly reliable artificial reasoners. This paper introduces the topic, discusses a number of historical results, and describes a number of open prob-
Tumour-initiating cells: challenges and opportunities for anticancer drug discovery

The hypothesis that cancer is driven by tumour-initiating cells (popularly known as cancer stem cells) has recently attracted a great deal of attention, owing to the promise of a novel cellular target for the treatment of haematopoietic and solid malignancies. Furthermore, it seems that tumour-initiating cells might be resistant to many conventional cancer therapies, which might explain the limitations of these agents in curing human malignancies. Although much work is still needed to identify and characterize tumour-initiating cells, efforts are now being directed towards identifying therapeutic strategies that could target these cells. This Review considers recent advances in the cancer stem cell field, focusing on the challenges and opportunities for anticancer drug discovery.
High-gain GaN doherty power amplifier for Ka-band satellite communications

A Doherty power amplifier (DPA) implemented in 100 nm GaN process is presented. It features a peak Power Added Efficiency (PAE) of 37%, an output power of 35 dBm and 25 dB gain. For a noise-to-power ratio (NPR) of 15 dB, an output power of 33 dBm is achieved.
Hybrid LED traffic light detection using high-speed camera

Automatic traffic light detection (TLD) plays an important role for driver-assistance system and autonomous vehicles. State-of-the-art TLD systems showed remarkable results by exploring visual information from static frames. However, traffic lights from different countries, regions, and manufactures are always visually distinct. The existing large intra-class variance makes the pre-trained detectors perform good on one dataset but fail on the others with different origins. One the other hand, LED traffic lights are widely used because of better energy efficiency. Based on the observation LED traffic light flashes in proportion to the input AC power frequency, we propose a hybrid TLD approach which combines the temporally frequency analysis and visual information using high-speed camera. Exploiting temporal information is shown to be very effective in the experiments. It is considered to be more robust than visual information-only methods.
A novel plasticity rule can explain the development of sensorimotor intelligence

Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system-specific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking, which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.
Concept and Context in Legal Information Retrieval

There exist two broad approaches to information retrieval (IR) in the legal domain: those based on manual knowledge engineering (KE) and those based on natural language processing (NLP). The KE approach is grounded in artificial intelligence (AI) and case-based reasoning (CBR), whilst the NLP approach is associated with open domain statistical retrieval. We provide some original arguments regarding the focus on KE-based retrieval in the past and why this is not sustainable in the long term. Legal approaches to questioning (NLP), rather than arguing (CBR), are proposed as the appropriate jurisprudential and cognitive underpinning for legal IR. Recall within the context of precision is proposed as a better fit to law than the ‘total recall’ model of the past, wherein conceptual and contextual search are combined to improve retrieval performance for both parties in a dispute.
The endocannabinoid anandamide inhibits neuronal progenitor cell differentiation through attenuation of the Rap1/B-Raf/ERK pathway.

Endocannabinoids are neuromodulators that act as retrograde synaptic messengers inhibiting the release of different neurotransmitters in cerebral areas such as hippocampus, cortex, and striatum. However, little is known about other roles of the endocannabinoid system in brain. In the present work we provide substantial evidence that the endocannabinoid anandamide (AEA) regulates neuronal differentiation both in culture and in vivo. Thus AEA, through the CB(1) receptor, inhibited cortical neuron progenitor differentiation to mature neuronal phenotype. In addition, human neural stem cell differentiation and nerve growth factor-induced PC12 cell differentiation were also inhibited by cannabinoid challenge. AEA decreased PC12 neuronal-like generation via CB(1)-mediated inhibition of sustained extracellular signal-regulated kinase (ERK) activation, which is responsible for nerve growth factor action. AEA thus inhibited TrkA-induced Rap1/B-Raf/ERK activation. Finally, immunohistochemical analyses by confocal microscopy revealed that adult neurogenesis in dentate gyrus was significantly decreased by the AEA analogue methanandamide and increased by the CB(1) antagonist SR141716. These data indicate that endocannabinoids inhibit neuronal progenitor cell differentiation through attenuation of the ERK pathway and suggest that they constitute a new physiological system involved in the regulation of neurogenesis.
Semantic Resource Management of Federated IoT Testbeds

Testbeds and experimental network facilities accelerate the expansion of disruptive Internet services and support their evolution. The integration of IoT technologies in the context of Unmanned Vehicles (UxVs) and their deployment in federated, real–world testbeds introduce various challenging research issues. This paper presents the Semantic Aggregate Manager (SAM) that exploits semantic technologies for modeling and managing resources of federated IoT Testbeds. SAM introduces new semantics–based features tailored to the needs of IoT enabled UxVs, but on the same time allows the compatibility with existing legacy, “de facto” standardised protocols, currently utilized by multiple federated testbed management systems. The proposed framework is currently being deployed in order to be evaluated in real–world testbeds across several sites in Europe.
Voice analysis as an objective state marker in bipolar disorder

Changes in speech have been suggested as sensitive and valid measures of depression and mania in bipolar disorder. The present study aimed at investigating (1) voice features collected during phone calls as objective markers of affective states in bipolar disorder and (2) if combining voice features with automatically generated objective smartphone data on behavioral activities (for example, number of text messages and phone calls per day) and electronic self-monitored data (mood) on illness activity would increase the accuracy as a marker of affective states. Using smartphones, voice features, automatically generated objective smartphone data on behavioral activities and electronic self-monitored data were collected from 28 outpatients with bipolar disorder in naturalistic settings on a daily basis during a period of 12 weeks. Depressive and manic symptoms were assessed using the Hamilton Depression Rating Scale 17-item and the Young Mania Rating Scale, respectively, by a researcher blinded to smartphone data. Data were analyzed using random forest algorithms. Affective states were classified using voice features extracted during everyday life phone calls. Voice features were found to be more accurate, sensitive and specific in the classification of manic or mixed states with an area under the curve (AUC)=0.89 compared with an AUC=0.78 for the classification of depressive states. Combining voice features with automatically generated objective smartphone data on behavioral activities and electronic self-monitored data increased the accuracy, sensitivity and specificity of classification of affective states slightly. Voice features collected in naturalistic settings using smartphones may be used as objective state markers in patients with bipolar disorder.
Child abuse inflicted by small children

In cases of child abuse, usually, the parents are initial suspects. A common explanation of the parents is that the injuries were caused by a sibling. Child-on-child violence is reported to be very rare in children less than 5 years of age, and thorough investigation by the police, child protective services, and medicolegal examinations are needed to proof or disproof the parents' statement. We report two cases of physical abuse of infants by small children.
2D and 3D face recognition using convolutional neural network

Face recognition remains a challenge today as recognition performance is strongly affected by variability such as illumination, expressions and poses. In this work we apply Convolutional Neural Networks (CNNs) on the challenging task of both 2D and 3D face recognition. We constructed two CNN models, namely CNN-1 (two convolutional layers) and CNN-2 (one convolutional layer) for testing on 2D and 3D dataset. A comprehensive parametric study of two CNN models on face recognition is represented in which different combinations of activation function, learning rate and filter size are investigated. We find that CNN-2 has a better accuracy performance on both 2D and 3D face recognition. Our experimental results show that an accuracy of 85.15% was accomplished using CNN-2 on depth images with FRGCv2.0 dataset (4950 images with 557 objectives). An accuracy of 95% was achieved using CNN-2 on 2D raw image with the AT&T dataset (400 images with 40 objectives). The results indicate that the proposed CNN model is capable to handle complex information from facial images in different dimensions. These results provide valuable insights into further application of CNN on 3D face recognition.
Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content Measurement

Distributional word representations are widely used in NLP tasks. These representations are based on an assumption that words with a similar context tend to have a similar meaning. To improve the quality of the context-based embeddings, many researches have explored how to make full use of existing lexical resources. In this paper, we argue that while we incorporate the prior knowledge with contextbased embeddings, words with different occurrences should be treated differently. Therefore, we propose to rely on the measurement of information content to control the degree of applying prior knowledge into context-based embeddings different words would have different learning rates when adjusting their embeddings. In the result, we demonstrate that our embeddings get significant improvements on two different tasks: Word Similarity and Analogical Reasoning.
Efficient Graph-Oriented Smart Transportation Using Internet of Things Generated Big Data

The rapid growth in the population density in urban cities and the advancement in technology demands real-time provision of services and infrastructure. Citizens, especially travelers, want to be reached within time to the destination. Consequently, they require to be facilitated with smart and real-time traffic information depending on the current traffic scenario. Therefore, in this paper, we proposed a graph-oriented mechanism to achieve the smart transportation system in the city. We proposed to deploy road sensors to get the overall traffic information as well as the vehicular network to obtain location and speed information of the individual vehicle. These Internet of Things (IoT) based networks generate enormous volume of data, termed as Big Data, depicting the traffic information of the city. To process incoming Big Data from IoT devices, then generating big graphs from the data, and processing them, we proposed an efficient architecture that uses the Giraph tool with parallel processing servers to achieve real-time efficiency. Later, various graph algorithms are used to achieve smart transportation by making real-time intelligent decisions to facilitate the citizens as well as the metropolitan authorities. Vehicular Datasets from various reliable resources representing the real city traffic are used for analysis and evaluation purpose. The system is implemented using Giraph and Spark tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.
Compact, high power capacitor charger

We are developing compact, high-power chargers for rapid charging of energy storage capacitors. The main application is presently rapid charging of the capacitors inside of compact Marx generators for reprated operation. Compact Marx generators produce output pulses with amplitudes above 300 kV with ns or subns rise-times. A typical application is the generation of high power microwaves. Initially all energy storage capacitors in a Marx generator are charged in parallel. During the so-called erection cycle, the capacitors are connected in series. The charging voltage in the parallel configuration is around 40-50 kV. The input voltage of our charger is in the range of several hundred volts. Rapid charging of the capacitors in the parallel configuration will enable a high pulse repetition-rate of the compact Marx generator. The high power charger uses state-of-the-art IGBTs (isolated gate bipolar transistors) in an H-bridge topology and a compact, high frequency transformer. The IGBTs and the associated controls are packaged for minimum weight and maximum power density. The packaging and device selection makes use of burst mode operation (thermal inertia) of the charger. The present charger is considerably smaller than the one presented in Giesselmann, M et al., (2001).
The language of RNA: a formal grammar that includes pseudoknots

In a previous paper, we presented a polynomial time dynamic programming algorithm for predicting optimal RNA secondary structure including pseudoknots. However, a formal grammatical representation for RNA secondary structure with pseudoknots was still lacking.



Here we show a one-to-one correspondence between that algorithm and a formal transformational grammar. This grammar class encompasses the context-free grammars and goes beyond to generate pseudoknotted structures. The pseudoknot grammar avoids the use of general context-sensitive rules by introducing a small number of auxiliary symbols used to reorder the strings generated by an otherwise context-free grammar. This formal representation of the residue correlations in RNA structure is important because it means we can build full probabilistic models of RNA secondary structure, including pseudoknots, and use them to optimally parse sequences in polynomial time.
HeMPS - a framework for NoC-based MPSoC generation

Multi-Processor Systems-on-Chip (MPSoCs) are increasingly popular in embedded systems. Due to their complexity and huge design space to explore for such systems, CAD tools and frameworks to customize MPSoCs are mandatory. Some academic and industrial frameworks are available to support bus-based MPSoCs, but few works target NoCs as underlying communication architecture. A framework targeting MPSoC customization must provide abstract models to enable fast design space exploration, flexible application mapping strategies, all coupled to features to evaluate the performance of running applications. This paper proposes a framework to customize NoC-based MPSoCs with support to static and dynamic task mapping and C/SystemC simulation models for processors and memories. A simple, specifically designed microkernel executes in each processor, enabling multitasking at the processor level. Graphical tools enable debug and system verification, individualizing data for each task. Practical results highlight the benefit of using dynamic mapping strategies (total execution time reduction) and abstract models (total simulation time reduction without losing accuracy).