nils-herrmann/clean_scirepeval huggingface dataset:
Kontinuierliche Preßverfahren in der Spanplattenindustrie-Bison Hydro-Dyn-Presse und Siempelkamp Stabteppich-Presse

In den drei vorangegangenen Mitteilungen wurden die Geschichte, verfahrensbedingten Ersparnisse, Platteneigenschaften des kontinuierlichen Pressens und, als aktuelles Verfahren, das Küsters press-Verfahren behandelt und ein Kostenvergleich mit dem Takt-Preßverfahren angestellt. In der vorliegenden, vorläufig abschließenden Mitteilung werden die ebenfalls aktuellen kontinuierlichen Preßverfahren von Bison und Siempelkamp vorgestellt und die Kosten miteinander verglichen, soweit dies derzeit möglich ist. Da jedoch außerhalb dieser verfahrenstragenden Firmen noch keine dieser Anlagen in Betrieb ist, sollen Erfahrungswerte aus der Betriebspraxis, soweit sie mit diesen Verfahren gesammelt werden können, in einer späteren Veröffentlichung vorgelegt werden. In three preceding papers the history, process-related savings, board properties and the socalled Küsters press process were described and the costs of this pressing method compared to those of the step-pressing process. The present paper reports on processes by Messrs. Bison and Siempelkamp, with a comparison of respective costs as far as they were available. As neither plants are working outside the Bison or Siempelkamp factories yet, data accumulating during actual manufacturing with these processes will be published at a later date.
Current perspectives on the dynamics of antibiotic resistance in different reservoirs.

Antibiotic resistance consists of a dynamic web. In this review, we describe the path by which different antibiotic residues and antibiotic resistance genes disseminate among relevant reservoirs (human, animal, and environmental settings), evaluating how these events contribute to the current scenario of antibiotic resistance. The relationship between the spread of resistance and the contribution of different genetic elements and events is revisited, exploring examples of the processes by which successful mobile resistance genes spread across different niches. The importance of classic and next generation molecular approaches, as well as action plans and policies which might aid in the fight against antibiotic resistance, are also reviewed.
Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform

The quest for more efficient real-time detection of anomalies in time series data is critically important in numerous applications and systems ranging from intelligent transportation, structural health monitoring, heart disease, and earthquake prediction. Although the range of application is wide, anomaly detection algorithms are usually domain specific and build on experts’ knowledge. Here a new signal processing algorithm –inspired by the deep learning paradigm – is presented that combines wavelets, neural networks, and Hilbert transform performs robustly and is transferable. The proposed neural network structure facilitates learning short and long-term pattern 2 interdependencies; a task usually hard to accomplish using standard neural network training algorithms. The paper provides guidelines for selecting the neural network's buffer size, training algorithm, and anomaly detection features. The algorithm learns online the system’s normal behavior and does not require the existence of anomalous data, for assessing its statistical significance. This is essential for applications that require customization. The anomalies are detected by analyzing hierarchically the instantaneous frequency and amplitude of the residual signal. Its applicability is demonstrated through detection of anomalies in the Seismic Electric Signal activity, that is potentially important for earthquake prediction; and automated detection of road anomalies (e.g. potholes, bumps, etc.) using smartphone sensors. The evaluation of the anomaly detection algorithm is based on the statistical significance of the Receiver Operating Characteristic curve. Finally, we propose strategies for decision-making that may increase the efficiency of the application of the algorithm, and expedite evaluation of real-time data.
MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs

We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partiallyobservable Markov decision problems (DECPOMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multirobot coordination, network traffic control, or distributed resource allocation. Solving such problems effectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA* has significant advantages. We introduce an anytime variant of MAA* and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems.
The stigma of mental disorders: A millennia-long history of social exclusion and prejudices.

F ar more than any other type of illness, mental disorders are subject to negative judgements and stigmatization. Many patients not only have to cope with the often devastating effects of their illness, but also suffer from social exclusion and prejudices. Stigmatization of the mentally ill has a long tradition, and the word “stigmatization” itself indicates the negative connotations: in ancient Greece, a “stigma” was a brand to mark slaves or criminals. For millennia, society did not treat persons suffering from depression, autism, schizophrenia and other mental illnesses much better than slaves or criminals: they were imprisoned, tortured or killed. During the Middle Ages, mental illness was regarded as a punishment from God: sufferers were thought to be possessed by the devil and were burned at the stake, or thrown in penitentiaries and madhouses where they were chained to the walls or their beds. During the Enlightenment, the mentally ill were finally freed from their chains and institutions were established to help sufferers of mental illness. However, stigmatization and discrimination reached an unfortunate peak during the Nazi reign in Germany when hundreds of thousands of mentally ill people were murdered or sterilized. ...................................................... “Structural discrimination of the mentally ill is still pervasive, whether in legislation or in rehabilitation efforts.” ......................................................
HSP90 inhibitor NVP-AUY922 enhances TRAIL-induced apoptosis by suppressing the JAK2-STAT3-Mcl-1 signal transduction pathway in colorectal cancer cells.

TRAIL has been shown to induce apoptosis in cancer cells, but in some cases, certain cancer cells are resistant to this ligand. In this study, we explored the ability of representative HSP90 (heat shock protein 90) inhibitor NVP-AUY922 to overcome TRAIL resistance by increasing apoptosis in colorectal cancer (CRC) cells. The combination of TRAIL and NVP-AUY922 induced synergistic cytotoxicity and apoptosis, which was mediated through an increase in caspase activation. The treatment of NVP-AUY922 dephosphorylated JAK2 and STAT3 and decreased Mcl-1, which resulted in facilitating cytochrome c release. NVP-AUY922-mediated inhibition of JAK2/STAT3 signaling and down-regulation of their target gene, Mcl-1, occurred in a dose and time-dependent manner. Knock down of Mcl-1, STAT3 inhibitor or JAK2 inhibitor synergistically enhanced TRAIL-induced apoptosis. Taken together, our results suggest the involvement of the JAK2-STAT3-Mcl-1 signal transduction pathway in response to NVP-AUY922 treatment, which may play a key role in NVP-AUY922-mediated sensitization to TRAIL. By contrast, the effect of the combination treatments in non-transformed colon cells was minimal. We provide a clinical rationale that combining HSP90 inhibitor with TRAIL enhances therapeutic efficacy without increasing normal tissue toxicity in CRC patients.
A semantic network-based evolutionary algorithm for modeling memetic evolution and creativity

We introduce a novel evolutionary algorithm (EA) with a semantic network-based representation. For enabling this, we establish new formulations of EA variation operators, crossover and mutation, that we adapt to work on semantic networks. The algorithm employs commonsense reasoning to ensure all operations preserve the meaningfulness of the networks, using ConceptNet and WordNet knowledge bases. The algorithm can be classified as a novel memetic algorithm (MA), given that (1) individuals represent pieces of information that undergo evolution, as in the original sense of memetics as it was introduced by Dawkins; and (2) this is different from existing MA, where the word “memetic” has been used as a synonym for local refinement after global optimization. For evaluating the approach, we introduce an analogical similarity-based fitness measure that is computed through structure mapping. This setup enables the open-ended generation of networks analogous to a given base network.
Ant Colony Optimization: A Swarm Intelligence based Technique

The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Divide and conquer techniques are one way to solve large and difficult problems. Division of large work into smaller parts and combining the solution of small problems to get the solution of large one has been a practice in computer research since long time. Swarm also exhibits the behavior of division of work and cooperation to achieve difficult tasks. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples which show that nature has been an unending source of inspiration. Artificial Swarm/Ant foraging utilizes various forms of indirect communication, involving the implicit transfer of information from agent to agent through modification of the environment. Using this approach, one can design efficient searching methods that can find solution to complex optimization problems. Over times, several algorithms have been designed and used that are inspired by the foraging behavior of real ants colonies to find solutions to difficult problems. In this paper the idea of Ant Colonies is presented with brief introduction to its applications in different areas of problem solving in computer science.
A Potentially Implementable FPGA for Quantum-Dot Cellular Automata

While still relatively “new”, the quantum-dot cellular automata (QCA) appears to be able to provide many of the properties and functionalities that have made CMOS successful over the past several decades. Early experiments have demonstrated and realized most, if not all, of the “fundamentals” needed for a computational circuit – devices, logic gates, wires, etc. This study introduces the beginning of a next step in experimental work: designing a computationally useful – yet simple and fabricatable circuit for QCA. The design target is a QCA Field Programmable Gate
Performance Analysis of Consensus Algorithm in Private Blockchain

Blockchain technology has the characteristics of decentralization, security and non-modifiable. The consensus algorithm is the core of blockchain framework, which solves the problem of mutual trust among nodes in the distributed network. Selecting the appropriate consensus algorithm or not directly affects the performance of blockchain. However, existing private blockchain platform lacks theory and data support for the performance analysis of the consensus algorithm. This paper proposes a method to evaluate the performance of consensus algorithm in private blockchain platforms of Ethereum and Hyperledger Fabric. Through quantitative analysis of latency and throughput, we obtain the performance evaluation results of consensus algorithms with different numbers of transactions. The results show that the consensus mechanism induces performance bottleneck. Besides, Practical Byzantine Fault Tolerance (PBFT) consistently outperforms Proof-of-Work (PoW) in terms of latency and throughput under varying workloads. This paper contributes to providing not only the quantitative data support for the further research of consensus algorithm, but also the guidance to the blockchain practitioners in the selection of consensus mechanism.
Kinematic analysis of a parallel manipulator-based multi-function mobility assistive device for elderly

Recently, more attention has been directed for developing robotic systems that help elderly live independently or rely less on others. This paper describes a novel multi-function mobility assistive device for elderly. The proposed device aims to help patients who don't have enough physical strength on their lower limbs due to aging or diseases. Rather than one function device, the proposed device is designed to interactively assist in different lower limb activities; namely, sit to stand and walking activities as well as transfer paralyzed patients from bed to wheelchair and help them stand in upright position to improve blood circulation. The device is based on a non-conventional structure of 3-RPR planer parallel manipulator which offers besides the high rigidity of the parallel structure some interesting kinematic advantages. This structure provides kinematic decoupling between the position and orientation that required to position shoulder and orient trunk of the user. Also, it provides a suitable free of singularity workspace that required to perform the abovementioned activities. It also has only one dimensionless design parameter that simplifies the design. Additionally, it has high local kinematic and dynamic dexterity indices which achieve high accuracy and dynamic characteristics. Finally, the device is equipped with an active walker for walking activity. Experimental motion data of three healthy subjects that extracted using VICON human motion capturing system is used in a computer simulation to examine the performance of the device.
Insights into deep neural networks for speaker recognition

Traditional i-vector speaker recognition systems use a Gaussian mixture model (GMM) to collect sufficient statistics. Recently, replacing this GMM with a deep neural network (DNN) has shown promising results. In this paper, we study a number of open issues that relate to performance, computational complexity, and applicability of DNNs as part of the full speaker recognition pipeline. The experimental validation is performed on the female part of the SRE12 telephone condition 2, where our DNN-based system produces the best published results. The insights gained by our study indicate that, for the purpose of speaker recognition, not using fMLLR speaker adaptation and early stopping of the DNN training allow significant computational reduction without sacrificing performance. Also, using a full covariance universal background model (UBM) and a large set of senones produces important performance gains. Finally, the DNN-based approach does not exhibit a strong language dependence as a DNN trained on Spanish data outperforms the conventional GMM-based system on our English task.
Towards a consistent terminology for software measurement

Although software measurement plays an increasingly important role in Software Engineering, there is no consensus yet on many of the concepts and terminology used in this field. Even worse, vocabulary conflicts and inconsistencies can be frequently found amongst the many sources and references commonly used by software measurement researchers and practitioners. This article presents an analysis of the current situation, and provides a comparison framework that can be used to identify and address the discrepancies, gaps, and terminology conflicts that current software measurement proposals present. A basic software measurement ontology is introduced, that aims at contributing to the harmonization of the different software measurement proposals and standards, by providing a coherent set of common concepts used in software measurement. The ontology is also aligned with the metrology vocabulary used in other more mature measurement engineering disciplines. q 2005 Elsevier B.V. All rights reserved.
A survey on sentiment analysis of scientific citations

Sentiment analysis of scientific citations has received much attention in recent years because of the increased availability of scientific publications. Scholarly databases are valuable sources for publications and citation information where researchers can publish their ideas and results. Sentiment analysis of scientific citations aims to analyze the authors’ sentiments within scientific citations. During the last decade, some review papers have been published in the field of sentiment analysis. Despite the growth in the size of scholarly databases and researchers’ interests, no one as far as we know has carried out an in-depth survey in a specific area of sentiment analysis in scientific citations. This paper presents a comprehensive survey of sentiment analysis of scientific citations. In this review, the process of scientific citation sentiment analysis is introduced and recently proposed methods with the main challenges are presented, analyzed and discussed. Further, we present related fields such as citation function classification and citation recommendation that have recently gained enormous attention. Our contributions include identifying the most important challenges as well as the analysis and classification of recent methods used in scientific citation sentiment analysis. Moreover, it presents the normal process, and this includes citation context extraction, public data sources, and feature selection. We found that most of the papers use classical machine learning methods. However, due to limitations of performance and manual feature selection in machine learning, we believe that in the future hybrid and deep learning methods can possibly handle the problems of scientific citation sentiment analysis more efficiently and reliably.
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences

We propose a hierarchical model for sequential data that learns a tree on-thefly, i.e. while reading the sequence. In the model, a recurrent network adapts its structure and reuses recurrent weights in a recursive manner. This creates adaptive skip-connections that ease the learning of long-term dependencies. The tree structure can either be inferred without supervision through reinforcement learning, or learned in a supervised manner. We provide preliminary experiments in a novel Math Expression Evaluation (MEE) task, which is explicitly crafted to have a hierarchical tree structure that can be used to study the effectiveness of our model. Additionally, we test our model in a wellknown propositional logic and language modelling tasks. Experimental results show the potential of our approach.
Wireless requirements and challenges in Industry 4.0

The next generation of industrial advancement which is referred as Industry 4.0 aims to inter-connect and computerize the traditional industrys such as manufacturing. The objective in Industry 4.0 is to make the factories smart enough in terms of improved adaptability, resource efficiency as well as the improved integration of supply and demand processes between the factories. Wireless communication will play a key role in enabling the Industry 4.0 systems and technologies. In this paper we focus the discussion on some of the key wireless communication challenges that will need to be met for the Industry 4.0 era. We look at how the 5th generation of communication standard may address these requirements. For machine to machine communication the three main design criterions that can be considered are latency, longevity and the reliability of communication. We take an example of WiFi communication, and benchmark it against the requirements, so as to emphasize the improvements required in wireless protocols.
Cumulative advantage/disadvantage and the life course: cross-fertilizing age and social science theory.

Age and cumulative advantage/disadvantage theory have obvious logical, theoretical, and empirical connections, because both are inherently and irreducibly related to the passage of time. Over the past 15 years, these connections have resulted in the elaboration and application of the cumulative advantage-disadvantage perspective in social gerontology, especially in relation to issues of heterogeneity and inequality. However, its theoretical origins, connections, and implications are not widely understood. This article reviews the genesis of the cumulative advantage/disadvantage perspective in studies of science, its initial articulation with structural-functionalism, and its expanding importance for gerontology. It discusses its intellectual relevance for several other established theoretical paradigms in sociology, psychology, and economics. On the basis of issues deriving from these perspectives and from the accumulating body of work on cumulative advantage and disadvantage, I identify several promising directions for further research in gerontology.
Probing shear-banding transitions of the VCM model for entangled wormlike micellar solutions using large amplitude oscillatory shear ( LAOS ) deformations

We explore the use of Large Amplitude Oscillatory Shear (LAOS) deformation to probe the dynamics of shear-banding in soft entangled materials, primarily wormlike micellar solutions which are prone to breakage and disentanglement under strong deformations. The state of stress in these complex fluids is described by a class of viscoelastic constitutive models which capture the key linear and nonlinear rheological features of wormlike micellar solutions, including the breakage and reforming of an entangled network. At a frequency-dependent critical strain, the imposed deformation field localizes to form a shear band, with a phase response that depends on the frequency and amplitude of the forcing. The different material responses are comPreprint submitted to Journal of Non-Newtonian Fluid Mechanics 19 June 2010 pactly represented in the form of Lissajous (phase plane) orbits and a corresponding strain-rate and frequency-dependent Pipkin diagram. Comparisons between the full network model predictions and those of a simpler, limiting case are presented.
Polyunsaturated Fatty Acid, Riboflavin and Vitamin C: Effect of Different Storage Conditions of Human Milk

This study addresses a matter of importance for: healthy infants; sick infants in the Neonatal Intensive Care Units; infants fed expressed human milk and infants who receive milk from Human Milk Banks. Current storage parameters for freezing of mother’s milk are not well established and are often contradictory. Pooled fresh human milk was stored raw, in nitrogen gas and following Holder pasteurization for 6 days at 4oC and for 6 months at -20oC and at -80oC.
Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance

This report demonstrates our solution for the Open Images 2018 Challenge. Based on our detailed analysis on the Open Images Datasets (OID), it is found that there are four typical features: large-scale, hierarchical tag system, severe annotation incompleteness and data imbalance. Considering these characteristics, an amount of strategies are employed, including SNIPER, soft sampling, class-aware sampling (CAS), hierarchical non-maximumsuppression (HNMS) and so on. In virtue of these effective strategies, and further using the powerful SENet154 armed with feature pyramid module and deformable ROIalign as the backbone, our best single model could achieve a mAP of 56.9%. After a further ensemble with 9 models, the final mAP is boosted to 62.2% in the public leaderboard (ranked the 2nd place) and 58.6% in the private leaderboard (ranked the 3rd place, slightly inferior to the 1st place by only 0.04 point).