nils-herrmann/clean_scirepeval huggingface dataset:
Prediction of LSTM-RNN Full Context States as a Subtask for N-Gram Feedforward Language Models

Long short-term memory (LSTM) recurrent neural network language models compress the full context of variable lengths into a fixed size vector. In this work, we investigate the task of predicting the LSTM hidden representation of the full context from a truncated n-gram context as a subtask for training an n-gram feedforward language model. Since this approach is a form of knowledge distillation, we compare two methods. First, we investigate the standard transfer based on the Kullback-Leibler divergence of the output distribution of the feedforward model from that of the LSTM. Second, we minimize the mean squared error between the hidden state of the LSTM and that of the n-gram feedforward model. We carry out experiments on different subsets of the Switchboard speech recognition dataset for feedforward models with a short (5-gram) and a medium (10-gram) context length. We show that we get improvements in perplexity and word error rate of up to 8% and 4% relative for the medium model, while the improvements are only marginal for the short model.
Syntactic and Lexical Approaches to Reading Comprehension

Teaching reading comprehension in K – 12 faces a number of challenges. Among them are identifying the portions of a text that are difficult for a student, comprehending major critical ideas, and understanding context-dependent polysemous words. We present a simple, unsupervised but robust and accurate syntactic method for achieving the first objective and a modified hierarchical lexical method for the second objective. Focusing on pinpointing troublesome sentences instead of the overall readability and on concepts central to a reading, we believe these methods will greatly facilitate efforts to help students improve reading skills.
Inducing semantic relations from conceptual spaces: A data-driven approach to plausible reasoning

Commonsense reasoning patterns such as interpolation and a fortiori inference have proven useful for dealing with gaps in structured knowledge bases. An important di culty in applying these reasoning patterns in practice is that they rely on fine-grained knowledge of how di↵erent concepts and entities are semantically related. In this paper, we show how the required semantic relations can be learned from a large collection of text documents. To this end, we first induce a conceptual space from the text documents, using multi-dimensional scaling. We then rely on the key insight that the required semantic relations correspond to qualitative spatial relations in this conceptual space. Among others, in an entirely unsupervised way, we identify salient directions in the conceptual space which correspond to interpretable relative properties such as ‘more fruity than’ (in a space of wines), resulting in a symbolic and interpretable representation of the conceptual space. To evaluate the quality of our semantic relations, we show how they can be exploited by a number of commonsense reasoning based classifiers. We experimentally show that these classifiers can outperform standard approaches, while being able to provide intuitive explanations of classification decisions. A number of crowdsourcing experiments provide further insights into the nature of the extracted semantic relations.
SMarTplan: a Task Planner for Smart Factories

Smart factories are on the verge of becoming the new industrial paradigm, wherein optimization permeates all aspects of production, from concept generation to sales. To fully pursue this paradigm, flexibility in the production means as well as in their timely organization is of paramount importance. AI is planning a major role in this transition, but the scenarios encountered in practice might be challenging for current tools. Task planning is one example where AI enables more efficient and flexible operation through an online automated adaptation and rescheduling of the activities to cope with new operational constraints and demands. In this paper we present SMarTplan, a task planner specifically conceived to deal with real-world scenarios in the emerging smart factory paradigm. Including both special-purpose and general-purpose algorithms, SMarTplan is based on current automated reasoning technology and it is designed to tackle complex application domains. In particular, we show its effectiveness on a logistic scenario, by comparing its specialized version with the general purpose one, and extending the comparison to other state-of-the-art task planners.
An Investigation of Performance Analysis of Anomaly Detection Techniques for Big Data in SCADA Systems

Anomaly detection is an important aspect of data mining, where the main objective is to identify anomalous or unusual data from a given dataset. However, there is no formal categorization of application-specific anomaly detection techniques for big data and this ignites a confusion for the data miners. In this paper, we categorise anomaly detection techniques based on nearest neighbours, clustering and statistical approaches and investigate the performance analysis of these techniques in critical infrastructure applications such as SCADA systems. Extensive experimental analysis is conducted to compare representative algorithms from each of the categories using seven benchmark datasets (both real and simulated) in SCADA systems. The effectiveness of the representative algorithms is measured through a number of metrics. We highlighted the set of algorithms that are the best performing for SCADA systems.
Predicting Future Hourly Residential Electrical Consumption : A Machine Learning Case Study

Traditional whole building energy modeling suffers from several factors, including the large number of inputs required to characterize the building, the specificity required to accurately model building materials and components, simplifying assumptions made by underlying simulation algorithms, and the gap between the as-designed and as-built building. Prior work has attempted to mitigate these problems by using sensor-based machine learning approaches to statistically model energy consumption. We refer to this approach as sensor-based energy modeling (sBEM). However, a majority of the prior sBEM work focuses only on commercial buildings. The sBEM work that focuses on modeling residential buildings primarily focuses on monthly electrical consumption, while commercial sensor-based models focus on hourly consumption. This means there is not a clear indicator of which machine learning approach best predicts next hour residential consumption, since these methods are only evaluated using low-resolution data. We address this issue by testing seven different machine learning algorithms on a unique residential data set, which contains 140 different sensors measurements, collected every 15 minutes. In addition, we validate each learner’s correctness on the ASHRAE Great Energy Prediction Shootout, using the original competition metrics. Our validation results confirm existing conclusions that Neural Network-based methods perform best on commercial buildings. However, the results from testing on our residential data set show that Feed Forward Neural Networks (FFNN), Support Vector Regression (SVR), and Linear Regression methods perform poorly, and that Least Squares Support Vector Preprint submitted to Buildings and Energy October 28, 2011 Machines (LS-SVM) perform best – a technique not previously applied to this domain.
Dietary total antioxidant capacity: a novel indicator of diet quality in healthy young adults.

Overall diet quality measurements have been suggested as a useful tool to assess diet-disease relationships. Oxidative stress has been related to the development of obesity and other chronic diseases. Furthermore, antioxidant intake is being considered as protective against cell oxidative damage and related metabolic complications.



To evaluate potential associations between the dietary total antioxidant capacity of foods (TAC), the energy density of the diet, and other relevant nutritional quality indexes in healthy young adults.



Several anthropometric variables from 153 healthy participants (20.8 +/- 2.7 years) included in this study were measured. Dietary intake was assessed by a validated food-frequency questionnaire, which was also used to calculate the dietary TAC and for daily energy intake adjustment.



Positive significant associations were found between dietary TAC and Mediterranean energy density hypothesis-oriented dietary scores (Mediterranean Diet Score, Alternate Mediterranean Diet Score, Modified Mediterranean Diet Score), non-Mediterranean hypothesis-oriented dietary scores (Healthy Eating Index, Alternate Healthy Eating Index, Diet Quality Index-International, Diet Quality Index-Revised), and diversity of food intake indicators (Recommended Food Score, Quantitative Index for Dietary Diversity in terms of total energy intake). The Mediterranean Diet Quality Index and Diet Quality Index scores (a Mediterranean and a non-Mediterranean hypothesis-oriented dietary score, respectively), whose lower values refer to a higher diet quality, decreased with higher values of dietary TAC. Energy density was also inversely associated with dietary TAC.



These data suggest that dietary TAC, as a measure of antioxidant intake, may also be a potential marker of diet quality in healthy subjects, providing a novel approach to assess the role of antioxidant intake on health promotion and diet-based therapies.
Fusobacterium emphysematous pyomyositis with necrotizing fasciitis of the leg presenting as compartment syndrome: a case report

Fusobacterium necrophorum is a common agent of disease in humans, but the occurrence of primary infections outside the head and neck area is extremely rare. While infection with Fusobacterium necrophorum has a rather benign course above the thorax, the organism is capable of producing very severe disease when located in unusual sites, including various forms of septic thrombophlebitis. No infections of the leg have been documented before; thus, antibiotic coverage for Fusobacterium is currently not recommended in this area.



A 50-year-old homeless African-American man presented complaining of severe pain in his right lower extremity. A clinical workup was consistent with emphysematous pyomyositis and compartment syndrome; he received limb-saving surgical intervention. The offending organism was identified as Fusobacterium necrophorum, and the antibiotic coverage was adjusted accordingly.



Bacteria typically involved in necrotizing infections of the lower extremity include Group A ß-hemolytic Streptococcus, Clostridium perfringens, and common anaerobic bacteria (Bacteroides, Peptococcus, and Peptostreptococcus). This case report presents a case of gas gangrene of the leg caused by Fusobacterium necrophorum, the first such case reported. Fusobacterium should now be included in the differential diagnosis of necrotizing fasciitis of the extremities.
Data-driven demand forecasting method for fused magnesium furnaces

The demand of fused magnesium furnaces (FMFs) refers to the average value of the power of the FMFs over a fixed period of time before the current time. The demand is an indicator of the electricity consumption of high energy-consuming FMFs. When the demand exceeds the limit of the Peak Demand (a predetermined maximum demand), the power supply of some FMF will be cut off to ensure that the demand is no more than Peak Demand. But the power cutoff will destroy the heat balance, reduce the quality and yield of the product. The composition change of magnesite in FMFs will cause demand spike occasionally, which a sudden increase in demand exceeds the limit and then drops below the limit. As a result, demand spike cause the power cutoff. In order to avoid the power cutoff at the moment of demand spike, the demand of FMFs needs to be forecasted. This paper analyzes the dynamic model of the demand of FMFs, using the power data, presents a data-driven demand forecasting method. This method consists of the following: PACF based decision module for the number of the input variables of the forecasting model, RBF neural network (RBFNN) based power variation rate forecasting model and demand forecasting model. Simulations based on actual data and industrial experiments at a fused magnesia plant show the effectiveness of the proposed method.
Routing of Queries in Social Information Retrieval Using Latent and Explicit Semantic Cues

Social Information Retrieval can be interpreted as querying the private information spaces of others within one's social network. One of the crucial steps in such a search approach is to identify the set of potential information providers to route the query to. In this experiment, we compare various routing mechanisms based on topic models (Latent Dirichlet Allocation, LDA), Explicit Semantic Analysis (ESA), and traditional metrics like Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) to identify expertise using a publicly available data collection with 1, 400 scientific abstracts including author information, queries, and relevance judgments. The abstracts are interpreted as knowledge profile in a social information retrieval scenario. Our results suggest that both LDA and ESA can solve the routing problem, whereas the LDA-based approach and a new ESA approach considering links between semantic concepts perform best on the tested dataset.
Event Identification in Social Media

Social media sites such as Flickr, YouTube, and Facebook host substantial amounts of user-contributed materials (e.g., photographs, videos, and textual content) for a wide variety of real-world events. These range from widely known events, such as the presidential inauguration, to smaller, community-specific events, such as annual conventions and local gatherings. By identifying these events and their associated user-contributed social media documents, which is the focus of this paper, we can greatly improve local event browsing and search in state-of-the-art search engines. To address our problem of focus, we exploit the rich “context” associated with social media content, including user-provided annotations (e.g., title, tags) and automatically generated information (e.g., content creation time). We form a variety of representations of social media documents using different context dimensions, and combine these dimensions in a principled way into a single clustering solution—where each document cluster ideally corresponds to one event—using a weighted ensemble approach. We evaluate our approach on a large-scale, real-world dataset of event images, and report promising performance with respect to several baseline approaches. Our preliminary experiments suggest that our ensemble approach identifies events, and their associated images, more effectively than the state-of-the-art strategies on which we build.
Wireless Technologies in Home and Building Automation

The use of wireless technologies in automation systems offers attractive benefits, but introduces a number of new technological challenges. The paper discusses these aspects for home and building automation applications. Relevant standards are surveyed. A wireless extension to KNX/EIB based on tunnelling over IEEE 802.15.4 is presented. The design emulates the properties of the KNX/EIB wired medium via wireless communication, allowing a seamless extension. Furthermore, it is geared towards zero-configuration and supports the easy integration of protocol security.
Incremental Segmentation on Private Data without Catastrophic Forgetting

Despite the success of image segmentation, convolutional neural networks are ill-equipped for incremental learning, i.e., adapting the original model trained on a set of classes to additionally segment new classes, without access of the original training data. They suffer from “catastrophic forgetting” — an abrupt degradation of performance on the old classes, when the training objective is adapted to the new classes. We present a method to address this issue, and learn image segmentation incrementally on private data whose annotations for the original classes in the new training set are unavailable. The key of our proposed solution is to balance the interplay between predictions on the new classes and distillation loss, it minimizes the discrepancy between responses for old classes on updated network via knowledge rehearsal. This incremental learning can be performed multiple times, for a new set of classes in each step, with a moderate drop in performance compared to the baseline network trained on the ensemble of data. We present image segmentation results on the PASCAL VOC 2012 and COCO datasets, on the ResNet and DenseNet architecture, along with a detailed empirical analysis of the approach.
Comparative Analysis of Different Types of Full Adder Circuits

The Full Adder circuit is an important component in application such as Digital Signal Processing (DSP) architecture, microprocessor, and microcontroller and data processing units. This paper discusses the evolution of full adder circuits in terms of lesser power consumption, higher speed. Starting with the most conventional 28 transistor full adder and then gradually studied full adders consisting of as less as 8 transistors. We have also included some of the most popular full adder cells like dynamic CMOS , Dual rail domino logic[14], Static Energy Recovery Full Adder (SERF)  , Adder9A, Adder9B, GDI based full adder.
Performance evaluation of network scanning tools with operation of firewall

As the number of devices connected to the Internet has been exponentially increasing, the degree of threats to those devices and networks has been also increasing. Various network scanning tools, which use fingerprinting techniques, have been developed to make the devices and networks secure by providing the information on its status. However, the tools may be used for malicious purposes. Using network scanning tools, attackers can not only obtain the information of devices such as the name of OS, version, and sessions but also find its vulnerabilities which can be used for further cyber-attacks. In this paper, we compare and analyze the performances of widely used network scanning tools such as Nmap and Nessus. The existing researches on the network scanning tools analyzed a specific scanning tools and they assumed there are only small number of network devices. In this paper, we compare and analyze the performances of several tools in practical network environments with the number of devices more than 40. The results of this paper provide the direction to prevent possible attacks when they are utilized as attack tools as well as the practical understanding of the threats by network scanning tools and fingerprinting techniques.
A large multicenter outcome study of female genital plastic surgery.

Female Genital Plastic Surgery, a relatively new entry in the field of Cosmetic and Plastic Surgery, has promised sexual enhancement and functional and cosmetic improvement for women. Are the vulvovaginal aesthetic procedures of Labiaplasty, Vaginoplasty/Perineoplasty ("Vaginal Rejuvenation") and Clitoral Hood Reduction effective, and do they deliver on that promise? For what reason do women seek these procedures? What complications are evident, and what effects are noted regarding sexual function for women and their partners? Who should be performing these procedures, what training should they have, and what are the ethical considerations?



This study was designed to produce objective, utilizable outcome data regarding FGPS.



1) Reasons for considering surgery from both patient's and physician's perspective; 2) Pre-operative sexual functioning per procedure; 3) Overall patient satisfaction per procedure; 4) Effect of procedure on patient's sexual enjoyment, per procedure; 5) Patient's perception of effect on her partner's sexual enjoyment, per procedure; 6) Complications.



This cross-sectional study, including 258 women and encompassing 341 separate procedures, comes from a group of twelve gynecologists, gynecologic urologists and plastic surgeons from ten centers in eight states nationwide. 104 labiaplasties, 24 clitoral hood reductions, 49 combined labiaplasty/clitoral hood reductions, 47 vaginoplasties and/or perineoplasties, and 34 combined labiaplasty and/or reduction of the clitoral hood plus vaginoplasty/perineoplasty procedures were studied retrospectively, analyzing both patient's and physician's perception of surgical rationale, pre-operative sexual function and several outcome criteria.



Combining the three groups, 91.6% of patients were satisfied with the results of their surgery after a 6-42 month follow-up. Significant subjective enhancement in sexual functioning for both women and their sexual partners was noted (p = 0.0078), especially in patients undergoing vaginal tightening/perineal support procedures. Complications were acceptable and not of major consequence.



While emphasizing that these female genital plastic procedures are not performed to correct "abnormalities," as there is a wide range of normality in the external and internal female genitalia, both parous and nulliparous, many women chose to modify their vulvas and vaginas. From the results of this large study pooling data from a diverse group of experienced genital plastic surgeons, outcome in both general and sexual satisfaction appear excellent.
Performance analysis for Vedic multiplier using modified full adders

Area efficient architecture is today's major concern in the field of VLSI, Digital signal processing circuits, cryptographic algorithms, wireless communications and Internet of Things (IOT). Majority of the architectures use multiplication. Realization of multiplication by using repetitive addition and shift and add methods consumes more area, power and delay. Vedic is one of the efficient multipliers. Design of Vedic multiplier using different sutras reduces area and power. From the structure of Vedic multiplier, it is clearly observed that there is scope to design an efficient architecture. In this research, Vedic multiplier is designed using modified full adder which consumes less number of LUT's, slices and delay when compared to normal conventional Vedic multiplier. Simulation and synthesis are carried on XILINX ISE 12.2 software. FPGA results of the proposed multiplier show that number of LUT's is less by 13.8% in the modified Vedic Multiplier (4×4) and less by 7.5% in modified Vedic Multiplier(8×8). Delay is less by 10% in modified Vedic Multiplier (4×4) and 7.2 % in modified Vedic Multiplier (8×8).
Generating Euler Diagrams

This article describes an algorithm for the automated generation of any Euler diagram starting with an abstract description of the diagram. An automated generation mechanism for Euler diagrams forms the foundations of a generation algorithm for notations such as Harel’s higraphs, constraint diagrams and some of the UML notation. An algorithm to generate diagrams is an essential component of a diagram tool for users to generate, edit and reason with diagrams. The work makes use of properties of the dual graph of an abstract diagram to identify which abstract diagrams are “drawable” within given wellformedness rules on concrete diagrams. A Java program has been written to implement the algorithm and sample output is included.