nils-herrmann/clean_scirepeval huggingface dataset:
Missing Data Estimation in High-Dimensional Datasets: A Swarm Intelligence-Deep Neural Network Approach

In this paper, we examine the problem of missing data in high-dimensional datasets by taking into consideration the Missing Completely at Random and Missing at Random mechanisms, as well as the Arbitrary missing pattern. Additionally, this paper employs a methodology based on Deep Learning and Swarm Intelligence algorithms in order to provide reliable estimates for missing data. The deep learning technique is used to extract features from the input data via an unsupervised learning approach by modeling the data distribution based on the input. This deep learning technique is then used as part of the objective function for the swarm intelligence technique in order to estimate the missing data after a supervised fine-tuning phase by minimizing an error function based on the interrelationship and correlation between features in the dataset. The investigated methodology in this paper therefore has longer running times, however, the promising potential outcomes justify the trade-off. Also, basic knowledge of statistics is presumed.
Looking at LTE in Practice: A Performance Analysis of the LTE System Based on Field Test Results

This article introduces a practical performance analysis of the long-term evolution (LTE) cellular system based on field test results from a commercially deployed Third-Generation Partnership Project's (3GPP?s) Release 8 LTE network in Band-3 (i.e., 1,800 MHz, sometimes termed LTE1,800 MHz) with 20-MHz channel bandwidth. The presented analysis demonstrates the downlink (DL) and uplink (UL) throughputs in mobility conditions in addition to other aspects of the LTE system such as link budget, cell coverage, spectral efficiency, and handover performance. LTE's key features such as multiple-input, multiple-output (MIMO) and higher-order modulation [i.e., 64 quadrature amplitude modulation (QAM)] are assessed in different radio frequency (RF) conditions. A comparison is presented between LTE and 3GPP Release 8 evolution of high-speed packet access (HSPA+) with a dual-cell high-speed DL packet access (DC-HSDPA) feature based on the universal mobile telecommunications system (UMTS) 2,100-MHz commercial network with 10-MHz bandwidth (i.e., 2x5-MHz adjacent carriers). The presented LTE network performance analysis and the comparison with the HSPA+ network can be used to benchmark the LTE system performance and evaluate the gains and merits of deploying the LTE network. It has been demonstrated that the LTE system outperforms the HSPA+ system in terms of spectrum efficiency, 64-QAM utilization, cell coverage, and handover delay. The LTE system offers a 34% improvement in spectrum efficiency, 40% 64-QAM utilization versus 7.4% for the HSPA+ system, and a 60% reduction in data interruption time during handover.
Myanmar Spell Checker

Natural Language Processing (NLP) is one of the most important research area carried out in the world of Human Language. For every language, spell checker is an essential component of many of Office Automation Systems and Machine Translation Systems. In this paper, we develop a Myanmar Spell Checker System which can handle Typographic errors, Sequence errors, Phonetic errors, and Context errors. A Myanmar Text Corpus is created for developing Myanmar Spell checker. To check Typographic Errors, corpus look up approach is applied. Myanmar3 Unicode is applied in this system so that it can automatically reorder the character sequence. A compound misused word detection algorithm is proposed for Phonetic Errors checking and Bayesian Classifier is applied for Context Errors checking. In this system, Levenshtein Distance Algorithm is applied to improve users’ efficiency by providing a suggestion list for misspelled Myanmar Words. We provide evaluation results of the system and our approach can handle various types of Myanmar spell errors.
Automatic fine tuning of cavity filters Automatisk finjustering av kavitetsfilter

Cavity filters are a necessary component in base stations used for telecommunication. Without these filters it would not be possible for base stations to send and receive signals at the same time. Today these cavity filters require fine tuning by humans before they can be deployed. This thesis have designed and implemented a neural network that can tune cavity filters. Different types of design parameters have been evaluated, such as neural network architecture, data presentation and data preprocessing. While the results was not comparable to human fine tuning, it was shown that there was a relationship between error and number of weights in the neural network. The thesis also presents some rules of thumb for future designs of neural network used for filter tuning.
Tunable VHF Miniaturized Helical Filters

In this paper, a miniaturization technique and a tuning concept that enable the realization of highly miniaturized and frequency reconfigurable helical filters are reported for the first time. The proposed filter design is based on tunable helical resonators with an internal volume of only 8.6 cm 3 and variable capacitive loading. A second-order bandpass filter was built and measured as a proof-of-concept demonstrator. The helical filter is manufactured with a CNC process and tuning is achieved through piezoelectrically actuated tuners, which are bonded over the helix apex. A measured center frequency tuning between 257-365 MHz was realized with an insertion loss between 0.3-3.2 dB including connector losses. The measured filter bandwidth varied between 6%-7.7%. Tunable resonators were also manufactured so as to extract the unloaded quality factor. It was measured between 392-639 for a center frequency tuning of 2.5:1 (156.5-395 MHz).
A systems thinking framework for knowledge management

Myriad frameworks have been developed for knowledge management. However, the field has been slow in formulating a generally accepted, comprehensive framework for knowledge management. This paper reviews the existing knowledge management frameworks and provides suggestions for what a general framework should include. The distinguishing feature of this research is that it emphasizes placing knowledge management in a larger context of systems thinking so that the influencing factors on its success or failure can better be recognized and understood. q 2001 Elsevier Science B.V. All rights
3-D TCAD simulation to optimize the trench termination design for higher and robust BVdss

Optimizing the edge termination design around the periphery of active area is critically important for achieving the highest and stable breakdown voltage (BVDSS) for any power devices. Active cell structures can be assumed as two dimensional (2-D) in the central part of the die, however as the active cells terminate to the termination regions at the periphery of the die, 2-D and 3-D transition regions are formed at different locations of the die layout with respect to the last edge termination trench. Optimization of the 3-D termination region is imperative to ascertain equal or higher BVDSS of the termination region than the active cell region. Synopsys advanced multi-dimensional TCAD device simulation tool - “Sentaurus Device Editor (SDE) ” is adopted for designing and optimizing the 3-D termination transition region for a higher and robust BVDSS, which is validated by the experimental data.
The Spherical Coil as an Inductor, Shield, or Antenna

The spherical coil is an idealized form of inductor having, on a spherical surface, a single-layer winding of constant axial pitch. Its magnetic field inside is uniform and outside is that of a small coil or magnetic dipole. Its properties exemplify exactly some of the rules that are approximately applicable to practical inductors. Simple formulas are given for self-inductance, mutual inductance, coupling coefficient, effect of iron core, and radiation power factor in free space or sea water. This coil is the basis for evaluating the shielding effect of a closed conductive (nonmagnetic) metal shell. A special winding is described which enables simple and exact computation of self-resonance (the length of wire being just 1/2 wavelength in some cases).
A Voltage Efficient PMOS Charge Pump Architecture

In this paper a charge pump circuit is presented: it is based on PMOS pass transistors with dynamic control of the gate and body voltages. By controlling the gate and the bulk of each PMOS pass transistor, the voltage loss due to the device threshold is removed and the charge is pumped from one stage to the other with negligible voltage drop. Furthermore, the overdrive voltage of the pass transistors grows progressively from the first to the last stage. Hence, a larger output voltage can be achieved compared to conventional charge pumps, still retaining a simple two-phase clocking scheme, along with the Dickson basic architecture. The prototype was fabricated using the STMicroelectronics T10 90nm CMOS process. Measurements results are also presented confirming the validity of the design
Constructing elastic distinguishability metrics for location privacy

With the increasing popularity of hand-held devices, location-based applications and services have access to accurate and real-time location information, raising serious privacy concerns for their users. The recently introduced notion of geo-indistinguishability tries to address this problem by adapting the well-known concept of differential privacy to the area of location-based systems. Although geo-indistinguishability presents various appealing aspects, it has the problem of treating space in a uniform way, imposing the addition of the same amount of noise everywhere on the map. In this paper we propose a novel elastic distinguishability metric that warps the geometrical distance, capturing the different degrees of density of each area. As a consequence, the obtained mechanism adapts the level of noise while achieving the same degree of privacy everywhere. We also show how such an elastic metric can easily incorporate the concept of a “geographic fence” that is commonly employed to protect the highly recurrent locations of a user, such as his home or work. We perform an extensive evaluation of our technique by building an elastic metric for Paris’ wide metropolitan area, using semantic information from the OpenStreetMap database. We compare the resulting mechanism against the Planar Laplace mechanism satisfying standard geo-indistinguishability, using two real-world datasets from the Gowalla and Brightkite location-based social networks. The results show that the elastic mechanism adapts well to the semantics of each area, adjusting the noise as we move outside the city center, hence offering better overall privacy.
Fast routing in road networks with transit nodes.

When you drive to somewhere far away, you will leave your current location via one of only a few important traffic junctions. Starting from this informal observation, we developed an algorithmic approach, transit node routing, that allows us to reduce quickest path queries in road networks to a small number of table lookups. For road maps of Western Europe and the United States, our best query times improved over the best previously published figures by two orders of magnitude. This is also more than one million times faster than the best known algorithm for general networks.
TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment

A single-valued neutrosophic set is a special case of neutrosophic set. It has been proposed as a generalization of crisp sets, fuzzy sets, and intuitionistic fuzzy sets in order to deal with incomplete information. In this paper, a new approach for multi-attribute group decision-making problems is proposed by extending the technique for order preference by similarity to ideal solution to single-valued neutrosophic environment. Ratings of alternative with respect to each attribute are considered as single-valued neutrosophic set that reflect the decision makers’ opinion based on the provided information. Neutrosophic set characterized by three independent degrees namely truth-membership degree (T), indeterminacy-membership degree (I), and falsity-membership degree (F) is more capable to catch up incomplete information. Single-valued neutrosophic set-based weighted averaging operator is used to aggregate all the individual decision maker’s opinion into one common opinion for rating the importance of criteria and alternatives. Finally, an illustrative example is provided in order to demonstrate its applicability and effectiveness of the proposed approach.
Measuring and Analyzing Write Amplification Characteristics of Solid State Disks

Write amplification brings endurance challenges to NAND Flash-based solid state disks (SSDs) such as impacts upon their write endurance and lifetime. A large write amplification degrades program/erase cycles (P/Es) of NAND Flashes and reduces the endurance and performance of SSDs. The write amplification problem is mainly triggered by garbage collections, wear-leveling, metadata updates, and mapping table updates. Write amplification is defined as the ratio of data volume written by an SSD controller to data volume written by a host. In this paper, we propose a four-level model of write amplification for SSDs. The four levels considered in our model include the channel level, chip level, die level, and plane level. In light of this model, we design a method of analyzing write amplification of SSDs to trace SSD endurance and performance by incorporating the Ready/Busy (R/B) signal of NAND Flash. Our practical approach aims to measure the value of write amplification for an entire SSD rather than NAND Flashes. To validate our measurement technique and model, we implement a verified SSD (vSSD) system and perform a cross-comparison on a set of SSDs, which are stressed by micro-benchmarks and I/O traces. A new method for SSDs is adopted in our measurements to study the R/B signals of NAND Flashes in an SSD. Experimental results show that our model is accurate and the measurement technique is generally applicable to any SSDs.
Interdependent Wearables (for Play): A Strong Concept for Design

Typically wearable devices are conceived of and constructed as stand-alone, individually based technologies. However, in practice wearables become part of the social context and ecology of overall device use. We present a strong concept for design: Interdependent Wearables (for play): wearables designed to require shared attention and mutual awareness, with interdependent functionality that encourages and rewards collocated interaction. The concept arose through design, development, and public exhibition of Hotaru, a collocated social game that uses wearables as game controllers. Hotaru has been shown in festivals and also formally playtested with 62 individuals. To more fully articulate the Interdependent Wearables strong concept, we compared this system's design with wearable and embodied systems for play and other purposes, and drew upon relevant HCI theory. The work is of benefit to those in the HCI/UX community focused on the design and development of social wearable technologies, especially those interested in supporting collocated interaction.
Design, Characterization, and Implementation of a Two-DOF Fabric-Based Soft Robotic Arm

Traditional rigid robotic arms have been widely used in various industries. They own the merits of high force output, precise control, and high efficiency. However, a rigid robotic arm is seldom used by people in daily life, as the cost is usually high and, most importantly, its rigidity, large weight, and noncompliance can cause safety concerns when it comes to human–robot interaction. Therefore, we want to build a robotic arm that is cheap, lightweight, and provides safe interaction with humans. This soft inflatable robotic arm, which is made from low-cost fabrics and flexible plastics, is a promising solution for a domestic robotic arm. The softness and natural compliance can guarantee safe interaction with people. In this letter, we present our soft robotic arm (SRA) made from nylon fabric. The SRA contains two joints that can bend the arm vertically and horizontally, and a gripper as an end-effector that can pick up objects of different shapes and sizes. The SRA has the advantages of low cost, good portability, and adequate force output. We characterized the bending modality and force output and demonstrated the functionality of the SRA in object manipulation. This fabric-based soft robotic arm is promising for use in assisting people's daily activities, for example, mounting on a wheelchair to enable a wheelchair bound person to reach objects from a distance.
Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition

End-to-end neural network models for named entity recognition (NER) have shown to achieve effective performances on general domain datasets (e.g. newswire), without requiring additional hand-crafted features. However, in biomedical domain, recent studies have shown that handengineered features (e.g. orthographic features) should be used to attain effective performance, due to the complexity of biomedical terminology (e.g. the use of acronyms and complex gene names). In this work, we propose a novel approach that allows a neural network model based on a long short-term memory (LSTM) to automatically learn orthographic features and incorporate them into a model for biomedical NER. Importantly, our bi-directional LSTM model learns and leverages orthographic features on an end-to-end basis. We evaluate our approach by comparing against existing neural network models for NER using three well-established biomedical datasets. Our experimental results show that the proposed approach consistently outperforms these strong baselines across all of the three datasets.
Cloud computing for industrial automation systems — A comprehensive overview

Cloud computing has recently emerged as a new computing paradigm in many application areas comprising office and enterprise systems. It offers various solutions to provide a dynamic and flexible infrastructure to host computing resources and deliver them as a service on-demand. Since industrial automation systems of the future have to be adaptable and agile, cloud computing can be considered as a promising solution for this area. However, the requirements of industrial automation systems differ significantly from the office and enterprise world. This paper provides an overview of the latest concepts of cloud computing technology for industrial automation. Existing works are categorized based on the levels of automation systems and research gaps are discussed based on these findings. A general cloud model for automation is derived from existing proposals by refining them further. This cloud model will offer automation functions as services from a dynamic infrastructure.
PVC removal from mixed plastics by triboelectrostatic separation.

Ever increasing oil price and the constant growth in generation of waste plastics stimulate a research on material separation for recycling of waste plastics. At present, most waste plastics cause serious environmental problems due to the disposal by reclamation and incineration. Particularly, polyvinyl chloride (PVC) materials among waste plastics generates hazardous HCl gas, dioxins containing Cl, and so on, which lead to air pollution and shorten the life of incinerator, and it makes difficultly recycling of other plastics. Therefore, we designed a bench scale triboelectrostatic separator for PVC removal from mixed plastics (polyvinyl chloride/polyethylene terephthalate), and then carried out material separation tests. In triboelectrostatic separation, PVC and PET particles are charged negatively and positively, respectively, due to the difference of the work function of plastics in tribo charger of the fluidized-bed, and are separated by means of splitter through an opposite electric field. In this study, the charge efficiency of PVC and PET was strongly dependent on the tribo charger material (polypropylene), relative humidity (below 30%), air velocity (over 10 m/s), and mixture ratio (PET:PVC=1:1). At the optimum conditions (electrode potential of 20 kV and splitter position of -2 cm), PVC rejection and PET recovery in PET products were 99.60 and 98.10%, respectively, and the reproducibility of optimal test was very good (+/-1%). In addition, as a change of splitter position, we developed the technique to recover high purity PET (over 99.99%) although PET recovery decreases by degrees.
Lexical input as related to children's vocabulary acquisition: effects of sophisticated exposure and support for meaning.

A corpus of nearly 150,000 maternal word-tokens used by 53 low-income mothers in 263 mother-child conversations in 5 settings (e.g., play, mealtime, and book readings) was studied. Ninety-nine percent of maternal lexical input consisted of the 3,000 most frequent words. Children's vocabulary performance in kindergarten and later in 2nd grade related more to the occurrence of sophisticated lexical items than to quantity of lexical input overall. Density of sophisticated words heard and the density with which such words were embedded in helpful or instructive interactions, at age 5 at home, independently predicted over a third of the variance in children's vocabulary performance in both kindergarten and 2nd grade. These two variables, with controls for maternal education, child nonverbal IQ, and amount of child's talk produced during the interactive settings, at age 5, predicted 50% of the variance in children's 2nd-grade vocabulary.
A Randomized Controlled Trial Comparing the Attention Training Technique and Mindful Self-Compassion for Students With Symptoms of Depression and Anxiety

The Attention Training Technique (ATT) and Mindful Self-Compassion (MSC) are two promising psychological interventions. ATT is a 12-min auditory exercise designed to strengthen attentional control and promote external focus of attention, while MSC uses guided meditation and exercises designed to promote self-compassion. In this randomized controlled trial (RCT), a three-session intervention trial was conducted in which university students were randomly assigned to either an ATT-group (n = 40) or a MSC-group (n = 41). The students were not assessed with diagnostic interviews but had self-reported symptoms of depression, anxiety, or stress. Participants listened to audiotapes of ATT or MSC before discussing in groups how to apply these principles for their everyday struggles. Participants also listened to audiotapes of ATT and MSC as homework between sessions. Participants in both groups showed significant reductions in symptoms of anxiety and depression accompanied by significant increases in mindfulness, self-compassion, and attention flexibility post-intervention. These results were maintained at 6-month follow-up. Improvement in attention flexibility was the only significant unique predictor of treatment response. The study supports the use of both ATT and MSC for students with symptoms of depression and anxiety. Further, it suggests that symptom improvement is related to changes in attention flexibility across both theoretical frameworks. Future studies should focus on how to strengthen the ability for attention flexibility to optimize treatment for emotional disorder.
NGMN View on 5G Architecture

The fifth generation of mobile technology (5G) will address the socio-economic demands and business contexts of 2020 and beyond. Leading operators from around the globe have defined their vision for 5G, considering customer and business contexts as well as potential technologies and migration issues in an initiative set up by the NGMN Alliance. While their concrete vision is described in the NGMN 5G whitepaper, this paper describes the key points, such as 5G design principles, 5G components, network slicing, 5G radio access technology and 5G interfacing options that have implications to the 5G architecture design.