nils-herrmann/clean_scirepeval huggingface dataset:
Modeling prosodic feature sequences for speaker recognition

We describe a novel approach to modeling idiosyncra tic prosodic behavior for automatic speaker recognition. The approach computes various duration , pitch, and energy features for each estimated syl lable in speech recognition output, quantizes the featur s, forms N-grams of the quantized values, and mode ls normalized counts for each feature N-gram using sup port vector machines (SVMs). We refer to these features as “SNERF-grams” (N-grams of Syllable-base d Nonuniform Extraction Region Features). Evaluation of SNERF-gram performance is conducted o n two-party spontaneous English conversational telephone data from the Fisher corpus, using one co versation side in both training and testing. Resul ts show that SNERF-grams provide significant performance ga ins when combined with a state-of-the-art baseline system, as well as with two highly successful longrange feature systems that capture word usage and lexically constrained duration patterns. Further ex periments examine the relative contributions of fea tures by quantization resolution, N-gram length, and feature type. Results show that the optimal number of bins depends on both feature type and N-gram length, but is roughly in the range of 5 to 10 bins. We find t hat longer N-grams are better than shorter ones, and th at pitch features are most useful, followed by dura tion and energy features. The most important pitch features are those capturing pitch level, whereas the most important energy features reflect patterns of risin g a d falling. For duration features, nucleus dura tion is more important for speaker recognition than are dur ations from the onset or coda of a syllable. Overal l, we find that SVM modeling of prosodic feature sequence s yields valuable information for automatic speaker recognition. It also offers rich new opportunities for exploring how speakers differ from each other i n voluntary but habitual ways.
Progress and Challenges in VLSI Placement Research Special Session Paper

Given the significance of placement in IC physical design, extensive research studies performed over the last 50 years addressed numerous aspects of global and detailed placement. The objectives and the constraints dominant in placement have been revised many times over, and continue to evolve. Additionally, the increasing scale of placement instances affects the algorithms of choice for high-performance tools. We survey the history of placement research, the progress achieved up to now, and outstanding challenges.
OBSCURATION-DEPENDENT EVOLUTION OF ACTIVE GALACTIC NUCLEI

We aim to constrain the evolution of active galactic nuclei (AGNs) as a function of obscuration using an X-ray-selected sample of ∼2000 AGNs from a multi-tiered survey including the CDFS, AEGIS-XD, COSMOS, and XMM-XXL fields. The spectra of individual X-ray sources are analyzed using a Bayesian methodology with a physically realistic model to infer the posterior distribution of the hydrogen column density and intrinsic X-ray luminosity. We develop a novel non-parametric method that allows us to robustly infer the distribution of the AGN population in X-ray luminosity, redshift, and obscuring column density, relying only on minimal smoothness assumptions. Our analysis properly incorporates uncertainties from low count spectra, photometric redshift measurements, association incompleteness, and the limited sample size. We find that obscured AGNs with NH > 1022 cm−2 account for 77 −5% of the number density and luminosity density of the accretion supermassive black hole population with LX > 1043 erg s−1, averaged over cosmic time. Compton-thick AGNs account for approximately half the number and luminosity density of the obscured population, and 38 −7% of the total. We also find evidence that the evolution is obscuration dependent, with the strongest evolution around NH ≈ 1023 cm−2. We highlight this by measuring the obscured fraction in Compton-thin AGNs, which increases toward z ∼ 3, where it is 25% higher than the local value. In contrast, the fraction of Compton-thick AGNs is consistent with being constant at ≈35%, independent of redshift and accretion luminosity. We discuss our findings in the context of existing models and conclude that the observed evolution is, to first order, a side effect of anti-hierarchical growth.
Multi-task Maximum Entropy Inverse Reinforcement Learning

Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring multiple reward functions from expert demonstrations. Prior work, built on Bayesian IRL, is unable to scale to complex environments due to computational constraints. This paper contributes a formulation of multi-task IRL in the more computationally efficient Maximum Causal Entropy (MCE) IRL framework. Experiments show our approach can perform one-shot imitation learning in a gridworld environment that single-task IRL algorithms need hundreds of demonstrations to solve. We outline preliminary work using meta-learning to extend our method to the function approximator setting of modern MCE IRL algorithms. Evaluating on multi-task variants of common simulated robotics benchmarks, we discover serious limitations of these IRL algorithms, and conclude with suggestions for further work.
Multi-tenancy authorization models for collaborative cloud services

The cloud service model intrinsically caters to multiple tenants, most obviously in public clouds but also in private clouds for large organizations. Currently most cloud service providers (CSPs) isolate user activities and data within a single tenant boundary with no or minimum cross-tenant interaction. It is anticipated that this situation will evolve soon to foster cross-tenant collaboration supported by Authorization as a Service (AaaS). At present there is no widely accepted model for cross-tenant authorization. Recently, Calero et al  informally presented a multi-tenancy authorization system (MTAS) which extends the well-known role-based access control (RBAC) model by building trust relations among collaborating tenants. In this paper we formalize this MTAS model and propose extensions for finer-grained cross-tenant trust. We also develop an administration model for MTAS (AMTAS). We demonstrate the utility and practical feasibility of MTAS by means of an example policy specification in XACML. We anticipate researchers will develop additional multi-tenant authorization models before eventual consolidation and unification.
Telluriding monolayer MoS2 and WS2 via alkali metal scooter

The conversion of chalcogen atoms to other types in transition metal dichalcogenides has significant advantages for tuning bandgaps and constructing in-plane heterojunctions; however, difficulty arises from the conversion of sulfur or selenium to tellurium atoms owing to the low decomposition temperature of tellurides. Here, we propose the use of sodium for converting monolayer molybdenum disulfide (MoS2) to molybdenum ditelluride (MoTe2) under Te-rich vapors. Sodium easily anchors tellurium and reduces the exchange barrier energy by scooting the tellurium to replace sulfur. The conversion was initiated at the edges and grain boundaries of MoS2, followed by complete conversion in the entire region. By controlling sodium concentration and reaction temperature of monolayer MoS2, we tailored various phases such as semiconducting 2H-MoTe2, metallic 1T′-MoTe2, and 2H-MoS2−x Te x alloys. This concept was further extended to WS2. A high valley polarization of ~37% in circularly polarized photoluminescence was obtained in the monolayer WS2−x Te x alloy at room temperature. Two dimensional monolayer transition metal ditellurides and their alloys are interesting but their growth has been difficult. Herein, Yun et al. demonstrate the use of sodium salts to convert transition metal disulfide to ditelluride and alloys in tellurium vapor at low temperature.
Optimal Tap Setting of Voltage Regulation Transformers in Unbalanced Distribution Systems

In this paper, we propose a method to optimally set the tap position of voltage regulation transformers in distribution systems. We cast the problem as a rank-constrained semidefinite program (SDP), in which the transformer tap ratios are captured by 1) introducing a secondary-side “virtual” bus per transformer, and 2) constraining the values that these virtual bus voltages can take according to the limits on the tap positions. Then, by relaxing the non-convex rank-1 constraint in the rank-constrained SDP formulation, one obtains a convex SDP problem. The tap positions are determined as the ratio between the primary-side bus voltage and the secondary-side virtual bus voltage that result from the optimal solution of the relaxed SDP, and then rounded to the nearest discrete tap values. To efficiently solve the relaxed SDP, we propose a distributed algorithm based on the alternating direction method of multipliers (ADMM). We present several case studies with single- and three-phase distribution systems to demonstrate the effectiveness of the distributed ADMM-based algorithm, and compare its results with centralized solution methods.
Centrality and Cluster Analysis of Yelp Mutual Customer Business Graph

This paper proposes a novel approach to understand customer relationships among businesses and the type of information that can be inferred from these relationships. Our approach is grounded in a unique method of constructing a mutual customer business graph, where businesses are represented by nodes and the weight of the edge connecting two businesses reflects the strength of their mutual customer population, which is estimated based on the reviews from the Yelp academic data set. We construct and analyze these mutual customer business graphs for cities of Las Vegas and Phoenix using centrality and spectral analysis techniques. Centrality analysis computes unweighted and weighted versions of degree and PageRank graph measures; the results reveal that businesses with high graph centralities also tend to be geographically central relative to other businesses. Spectral clustering partitions the graph to group businesses that are frequented by the same set of customers. An analysis of the frequency distribution of words from the reviews within each cluster suggests that businesses aggregate around a theme. Taken together, these findings suggest that customers prefer to visit businesses that are geographically proximate and/or offer similar products and services. We discuss how businesses could strategically position themselves by considering the impact of these two factors in attracting clientele.
Caregiver burden: a concept analysis.

The development of the concept of burden for use in research lacks consistent conceptualization and operational definitions. The purpose of this article is to analyze the concept of burden in an effort to promote conceptual clarity. The technique advocated by Walker and Avant is used to analyze this concept. Critical attributes of burden include subjective perception, multidimensional phenomena, dynamic change, and overload. Predisposing factors are caregiver's characteristics, the demands of caregivers, and the involvement in caregiving. The consequences of burden generate problems in care-receiver, caregiver, family, and health care system. Overall, this article enables us to advance this concept, identify the different sources of burden, and provide directions for nursing intervention.
Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.
A Horizontal Fragmentation Algorithm for the Fact Relation in a Distributed Data Warehouse

Data warehousing is one of the major research topics of appliedside database investigators. Most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data are based. Unfortunately, this approach is very expensive and tends to ignore the advantages realized during the past decade in the area of distribution and support for data localization in a geographically dispersed corporate structure. This research investigates building distributed data warehouses with particular emphasis placed on distribution design for the data warehouse environment. The article provides an architectural model for a distributed data warehouse, the formal definition of the relational data model for data warehouse and a methodology for distributed data warehouse design along with a “horizontal” fragmentation algorithm for the fact relation.
Qualitative Spatial Representation and Reasoning with the Region Connection Calculus

This paper surveys the work of the qualitative spatial reasoning group at the University of Leeds. The group has developed a number of logical calculi for representing and reasoning with qualitative spatial relations over regions. We motivate the use of regions as the primary spatial entity and show how a rich language can be built up from surprisingly few primitives. This language can distinguish between convex and a variety of concave shapes and there is also an extension which handles regions with uncertain boundaries. We also present a variety of reasoning techniques, both for static and dynamic situations. A number of possible application areas are briefly mentioned.
6LoWPAN: a study on QoS security threats and countermeasures using intrusion detection system approach

Fuelled to bring the Internet of Things concept to real life, the Internet Engineering Task Force is working on 6LoWPAN, in which the standard allows a vast number of smart objects to be deployed in local wireless sensor networks (WSNs) using the huge address space of IPv6 for data and information harvesting through the Internet. From the security point of view, 6LoWPAN/WSN will be open to security threats from the local network itself and the Internet. Cryptography techniques applied as the front line of defence or deterrent can easily be broken because of the weak secure nature of LoWPAN devices and the wireless environment. Compromised nodes could lead to insider attacks without being detected by any cryptography checking. An intrusion detection system (IDS) is, primarily needed as a second line of defence to monitor the network operations and raise an alarm in case of any anomaly. This paper analyses potential security threats in 6LoWPAN and reviews the current countermeasures, in particular, the IDS-based solutions for countering insider/internal threats. Additionally, it discovers three novel QoS-related security threats, namely rank attack, local repair attack, and resource depleting attack, which are more seriously affecting the routing protocol for low-power and lossy network, the routing protocol used to establish 6LoWPAN network topology. A new two-layer IDS concept is introduced as a countermeasure method for securing the routing protocol for low-power and lossy network-built network topology from the internal QoS attacks. Potential research works are also presented to provide baseline reference to researchers in this field.
Co-training an Improved Recurrent Neural Network with Probability Statistic Models for Named Entity Recognition

Named Entity Recognition (NER) is a subtask of information extraction in Natural Language Processing (NLP) field and thus being wildly studied. Currently Recurrent Neural Network (RNN) has become a popular way to do NER task, but it needs a lot of train data. The lack of labeled train data is one of the hard problems and traditional co-training strategy is a way to alleviate it. In this paper, we consider this situation and focus on doing NER with co-training using RNN and two probability statistic models i.e. Hidden Markov Model (HMM) and Conditional Random Field (CRF). We proposed a modified RNN model by redefining its activation function. Compared to traditional sigmoid function, our new function avoids saturation to some degree and makes its output scope very close to [0, 1], thus improving recognition accuracy. Our experiments are conducted ATIS benchmark. First, supervised learning using those models are compared when using different train data size. The experimental results show that it is not necessary to use whole data, even small part of train data can also get good performance. Then, we compare the results of our modified RNN with original RNN. 0.5% improvement is obtained. Last, we compare the co-training results. HMM and CRF get higher improvement than RNN after co-training. Moreover, using our modified RNN in co-training, their performances are improved further.
Anti-obesity effects of green tea: from bedside to bench.

During the last decade, the traditional notion that green tea consumption benefits health has received significant scientific attention and, particularly, the areas of cardiovascular disease and cancer were subject to numerous studies. Due to the ever-growing obesity pandemic, the anti-obesity effects of green tea are being increasingly investigated in cell, animal, and human studies. Green tea, green tea catechins, and epigallocatechin gallate (EGCG) have been demonstrated in cell culture and animal models of obesity to reduce adipocyte differentiation and proliferation, lipogenesis, fat mass, body weight, fat absorption, plasma levels of triglycerides, free fatty acids, cholesterol, glucose, insulin and leptin, as well as to increase beta-oxidation and thermogenesis. Adipose tissue, liver, intestine, and skeletal muscle are target organs of green tea, mediating its anti-obesity effects. Studies conducted with human subjects report reduced body weight and body fat, as well as increased fat oxidation and thermogenesis and thereby confirm findings in cell culture systems and animal models of obesity. There is still a need for well-designed and controlled clinical studies to validate the existing and encouraging human studies. Since EGCG is regarded as the most active component of green tea, its specific effects on obesity should also be investigated in human trials.
FokI dimerization is required for DNA cleavage.

FokI is a type IIs restriction endonuclease comprised of a DNA recognition domain and a catalytic domain. The structural similarity of the FokI catalytic domain to the type II restriction endonuclease BamHI monomer suggested that the FokI catalytic domains may dimerize. In addition, the FokI structure, presented in an accompanying paper in this issue of Proceedings, reveals a dimerization interface between catalytic domains. We provide evidence here that FokI catalytic domain must dimerize for DNA cleavage to occur. First, we show that the rate of DNA cleavage catalyzed by various concentrations of FokI are not directly proportional to the protein concentration, suggesting a cooperative effect for DNA cleavage. Second, we constructed a FokI variant, FokN13Y, which is unable to bind the FokI recognition sequence but when mixed with wild-type FokI increases the rate of DNA cleavage. Additionally, the FokI catalytic domain that lacks the DNA binding domain was shown to increase the rate of wild-type FokI cleavage of DNA. We also constructed an FokI variant, FokD483A, R487A, which should be defective for dimerization because the altered residues reside at the putative dimerization interface. Consistent with the FokI dimerization model, the variant FokD483A, R487A revealed greatly impaired DNA cleavage. Based on our work and previous reports, we discuss a pathway of DNA binding, dimerization, and cleavage by FokI endonuclease.
A Normative Agent System to Prevent Cyberbullying

Automated approaches to prevent the negative effects of cyber bullying mainly focus on affective agents that provide support for victims. The current paper takes a complementary approach, which attempts to minimise the amount of occurrences of cyber bullying in the first place. The approach consists of a system of normative agents, which are physically present in a virtual society. The agents, which reason based on a BDI-model, use a number of techniques to detect various norm violations, including insulting and following. By using rewards and punishments, they try to reinforce the desired behaviour of the users. The system has been implemented and tested within a virtual environment for children between 6 and 12 years old, called Club Time Machine. In a real world experiment, the behaviour of the users of the virtual environment has been logged and analysed by means of a logic-based checking tool. The results show that the normative agents have the potential to reduce the amount of norm violations on the long term.