nils-herrmann/clean_scirepeval huggingface dataset:
Evolving Deep LSTM-based Memory Networks using an Information Maximization Objective

Reinforcement Learning agents with memory are constructed in this paper by extending neuroevolutionary algorithm NEAT to incorporate LSTM cells, i.e. special memory units with gating logic. Initial evaluation on POMDP tasks indicated that memory solutions obtained by evolving LSTMs outperform traditional RNNs. Scaling neuroevolution of LSTM to deep memory problems is challenging because: (1) the fitness landscape is deceptive, and (2) a large number of associated parameters need to be optimized. To overcome these challenges, a new secondary optimization objective is introduced that maximizes the information (Info-max) stored in the LSTM network. The network training is split into two phases. In the first phase (unsupervised phase), independent memory modules are evolved by optimizing for the info-max objective. In the second phase, the networks are trained by optimizing the task fitness. Results on two different memory tasks indicate that neuroevolution can discover powerful LSTM-based memory solution that outperform traditional RNNs.
A Generalized Framework for Automatic Scripting Language Parallelization

Computational scientists are typically not expert programmers, and thus work in easy to use dynamic languages. However, they have very high performance requirements, due to their large datasets and experimental setups. Thus, the performance required for computational science must be extracted from dynamic languages in a manner that is transparent to the programmer. Current approaches to optimize and parallelize dynamic languages, such as just-in-time compilation and highly optimized interpreters, require a huge amount of implementation effort and are typically only effective for a single language. However, scientists in different fields use different languages, depending upon their needs.This paper presents techniques to enable automatic extraction of parallelism within scripts that are universally applicable across multiple different dynamic scripting languages. The key insight is that combining a script with its interpreter, through program specialization techniques, will embed any parallelism within the script into the combined program that can then be extracted via automatic parallelization techniques. Additionally, this paper presents several enhancements to existing speculative automatic parallelization techniques to handle the dependence patterns created by the specialization process. A prototype of the proposed technique, called Partial Evaluation with Parallelization (PEP), is evaluated against two open-source script interpreters with 6 input linear algebra kernel scripts each. The resulting geomean speedup of 5.10× on a 24-core machine shows the potential of the generalized approach in automatic extraction of parallelism in dynamic scripting languages.
Enhancing WPA2-PSK four-way handshaking after re-authentication to deal with de-authentication followed by brute-force attack a novel re-authentication protocol

The nature of wireless network transmission and the emerging attacks are continuously creating or exploiting more vulnerabilities. Despite the fact that the security mechanisms and protocols are constantly upgraded and enhanced, the Small Office/Home Office (SOHO) environments that cannot afford a separate authentication system, and generally adopt the IEEE 802.11 Wi-Fi-Protected-Access-2/Pre-Shared-Key (WPA2-PSK) are still exposed to some attack categories such as de-authentication attacks that aim to push wireless client to re-authenticate to the Access Point (AP) and try to capture the keys exchanged during the handshake to compromise the network security. This kind of attack is impossible to detect or prevent in spite of having an Intrusion Detection and Prevention System (IDPS) installed on the client or on the AP, especially when the attack is not repetitive and is targeting only one client. This paper proposes a novel method which can mitigate and eliminate the risk of exposing the PSK to be captured during the re-authentication process by introducing a novel re-authentication protocol relying on an enhanced four-way handshake which does not require any hardware upgrade or heavy-weight cryptography affecting the network flexibility and performances.
Reliability Modelling of Automated Guided Vehicles by the Use of Failure Modes Effects and Criticality Analysis, and Fault Tree Analysis

Automated Guided Vehicles (AGVs) are being increasingly used for intelligent transportation and distribution of materials in warehouses and auto-production lines. In this paper, a preliminary hazard analysis of an AGV’s critical components is conducted by the approach of Failure Modes Effects and Criticality Analysis (FMECA). To implement this research, a particular AGV transport system is modelled as a phased mission. Then, Fault Tree Analysis (FTA) is adopted to model the causes of phase failure, enabling the probability of success in each phase and hence mission success to be determined. Through this research, a promising technical approach is established, which allows the identification of the critical AGV components and crucial mission phases of AGVs at the design stage.
The Mycology as Forensics Tool

Volume 5 • Issue 2 • 1000226  The main focus of the forensic taphonomy is the study of environmental conditions influencing the decomposition process to estimate the postmortem interval and determine the cause and manner of death. The study is part of a specific branch of the forensic science that makes use of a broad aspect of methodologies taken from different areas of expertise such as botany, archeology, soil microbiology and entomology, all used for uncovering and examining clandestine graves allowing to succeed in the investigation process. Therefore, the “Forensic Mycology” emerges as a new science term meaning the study of the coexistence of fungal species nearby human cadavers as well as those fungal groups potentially useful in establishing a time of death [1,2].
Agent-Based Interactions and Economic Encounters in an Intelligent InterCloud

An InterCloud is an interconnected global “cloud of clouds” that enables each cloud to tap into resources of other clouds. This is the earliest work to devise an agent-based InterCloud economic model for analyzing consumer-to-cloud and cloud-to-cloud interactions. While economic encounters between consumers and cloud providers are modeled as a many-to-many negotiation, economic encounters among clouds are modeled as a coalition game. To bolster many-to-many consumer-to-cloud negotiations, this work devises a novel interaction protocol and a novel negotiation strategy that is characterized by both 1) adaptive concession rate (ACR) and 2) minimally sufficient concession (MSC). Mathematical proofs show that agents adopting the ACR-MSC strategy negotiate optimally because they make minimum amounts of concession. By automatically controlling concession rates, empirical results show that the ACR-MSC strategy is efficient because it achieves significantly higher utilities than the fixed-concession-rate time-dependent strategy. To facilitate the formation of InterCloud coalitions, this work devises a novel four-stage cloud-to-cloud interaction protocol and a set of novel strategies for InterCloud agents. Mathematical proofs show that these InterCloud coalition formation strategies 1) converge to a subgame perfect equilibrium and 2) result in every cloud agent in an InterCloud coalition receiving a payoff that is equal to its Shapley value.
Accurate photovoltaic power forecasting models using deep LSTM-RNN

Photovoltaic (PV) is one of the most promising renewable energy sources. To ensure secure operation and economic integration of PV in smart grids, accurate forecasting of PV power is an important issue. In this paper, we propose the use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems. The LSTM networks can model the temporal changes in PV output power because of their recurrent architecture and memory units. The proposed method is evaluated using hourly datasets of different sites for a year. We compare the proposed method with three PV forecasting methods. The use of LSTM offers a further reduction in the forecasting error compared with the other methods. The proposed forecasting method can be a helpful tool for planning and controlling smart grids.
Perceptual evaluation of speech quality (PESQ): The new ITU standard for end-to-end speech quality a

A new model for perceptual evaluation of speech quality (PESQ) was recently standardised by the ITU-T as recommendation P.862. Unlike previous codec assessment models, such as PSQM and MNB (ITU-T P.861), PESQ is able to predict subjective quality with good correlation in a very wide range of conditions, that may include coding distortions, errors, noise, filtering, delay and variable delay. This paper introduces time delay identification techniques, and outlines some causes of variable delay, before describing the processes that are integrated into PESQ and specified in P.862. More information on the structure of PESQ, and performance results, can be found in the accompanying paper on the PESQ psychoacoustic model.
Entity Analysis with Weak Supervision : Typing , Linking , and Attribute Extraction

Entity Analysis with Weak Supervision: Typing, Linking, and Attribute Extraction Xiao Ling Chair of the Supervisory Committee: Professor Daniel S. Weld Computer Science and Engineering With the advent of the Web, textual information has grown at an explosive rate. To digest this enormous amount of data, an automatic solution, Information Extraction (IE), has become necessary. Information extraction is a task of converting unstructured text strings into structured machine-readable data. The first key step of a general IE pipeline is often to analyze entities mentioned in the text before making holistic conclusions. To fully understand each entity, one needs to detect their mentions, categorize them into semantic types, connect them with their knowledge base entries, and identify their attributes as well as the relationships with others. In this dissertation, we first present the problem of fine-grained entity recognition. Unlike most traditional named entity recognition systems using a small set of entity classes, e.g., person, organization, location or miscellaneous, we define a novel set of over one hundred fine-grained entity types. In order to intelligently understand text and extract a wide range of information, it is useful to more precisely determine the semantic classes of entities mentioned in unstructured text. We formulate the recognition problem as multi-class, multi-label classification, describe an unsupervised method for collecting training data, and present the FIGER implementation. Next, we demonstrate that fine-grained entity types are closely connected with other entity analysis tasks. We describe an entity linking system whose prediction heavily relies on these types and present a simple yet effective implementation, called VINCULUM. An extensive evaluation on nine data sets, comparing VINCULUM with two state-of-the-art systems, elucidates key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence. Finally, we describe an approach to acquire commonsense knowledge from a massive amount of text on the Web. In particular, a system called SIZEITALL is developed to extract numerical attribute values for various classes of entities. To resolve the ambiguity from the surface form text, we canonicalize the extractions with respect to WordNet senses and build a knowledge base on physical size for thousands of entity classes. Throughout all three entity analysis tasks, we show the feasibility of building sophisticated IE systems without a significant investment in human effort to create sufficient labeled data.
A Bird’s Eye View of Human Language Evolution

COMPARATIVE STUDIES OF LINGUISTIC FACULTIES IN ANIMALS POSE AN EVOLUTIONARY PARADOX: language involves certain perceptual and motor abilities, but it is not clear that this serves as more than an input-output channel for the externalization of language proper. Strikingly, the capability for auditory-vocal learning is not shared with our closest relatives, the apes, but is present in such remotely related groups as songbirds and marine mammals. There is increasing evidence for behavioral, neural, and genetic similarities between speech acquisition and birdsong learning. At the same time, researchers have applied formal linguistic analysis to the vocalizations of both primates and songbirds. What have all these studies taught us about the evolution of language? Is the comparative study of an apparently species-specific trait like language feasible? We argue that comparative analysis remains an important method for the evolutionary reconstruction and causal analysis of the mechanisms underlying language. On the one hand, common descent has been important in the evolution of the brain, such that avian and mammalian brains may be largely homologous, particularly in the case of brain regions involved in auditory perception, vocalization, and auditory memory. On the other hand, there has been convergent evolution of the capacity for auditory-vocal learning, and possibly for structuring of external vocalizations, such that apes lack the abilities that are shared between songbirds and humans. However, significant limitations to this comparative analysis remain. While all birdsong may be classified in terms of a particularly simple kind of concatenation system, the regular languages, there is no compelling evidence to date that birdsong matches the characteristic syntactic complexity of human language, arising from the composition of smaller forms like words and phrases into larger ones.
Quantifying Information Overload in Social Media and its Impact on Social Contagions

Information overload has become an ubiquitous problem in modern society. Social media users and microbloggers receive an endless flow of information, often at a rate far higher than their cognitive abilities to process the information. In this paper, we conduct a large scale quantitative study of information overload and evaluate its impact on information dissemination in the Twitter social media site. We model social media users as information processing systems that queue incoming information according to some policies, process information from the queue at some unknown rates and decide to forward some of the incoming information to other users. We show how timestamped data about tweets received and forwarded by users can be used to uncover key properties of their queueing policies and estimate their information processing rates and limits. Such an understanding of users’ information processing behaviors allows us to infer whether and to what extent users suffer from information overload. Our analysis provides empirical evidence of information processing limits for social media users and the prevalence of information overloading. The most active and popular social media users are often the ones that are overloaded. Moreover, we find that the rate at which users receive information impacts their processing behavior, including how they prioritize information from different sources, how much information they process, and how quickly they process information. Finally, the susceptibility of a social media user to social contagions depends crucially on the rate at which she receives information. An exposure to a piece of information, be it an idea, a convention or a product, is much less effective for users that receive information at higher rates, meaning they need more exposures to adopt a particular contagion.
Smith-Lemli-Opitz syndrome.

Smith-Lemli-Opitz syndrome (SLOS) is an autosomal recessive, multiple congenital malformation and intellectual disability syndrome, with clinical characteristics that encompass a wide spectrum and great variability. Elucidation of the biochemical and genetic basis for SLOS, specifically understanding SLOS as a cholesterol deficiency syndrome caused by mutation in DHCR7, opened up enormous possibilities for therapeutic intervention. When cholesterol was discovered to be the activator of sonic hedgehog, cholesterol deficiency with inactivation of this developmental patterning gene was thought to be the cause of SLOS malformations, yet this explanation is overly simplistic. Despite these important research breakthroughs, there is no proven treatment for SLOS. Better animal models are needed to allow potential treatment testing and the study of disease pathophysiology, which is incompletely understood. Creation of human cellular models, especially models of brain cells, would be useful, and in vivo human studies are also essential. Biomarker development will be crucial in facilitating clinical trials in this rare condition, because the clinical phenotype can change over many years. Additional research in these and other areas is critical if we are to make headway towards ameliorating the effects of this devastating condition.
A Stacked-FET Linear SOI CMOS Cellular Antenna Switch With an Extremely Low-Power Biasing Strategy

A stacked field-effect transistor (FET) linear cellular antenna switch adopting a transistor layout with odd-symmetrical drain-source metal wiring and an extremely low-power biasing strategy has been implemented in silicon-on-insulator CMOS technology. A multi-fingered switch-FET device with odd-symmetrical drain-source metal wiring is adopted herein to improve the insertion loss (IL) and isolation of the antenna switch by minimizing the product of the on-resistance and off-capacitance. To remove the spurious emission and digital switching noise problems from the antenna switch driver circuits, an extremely low-power biasing scheme driven by only positive bias voltage has been devised. The proposed antenna switch that employs the new biasing scheme shows almost the same power-handling capability and harmonic distortion as a conventional version based on a negative biasing scheme, while greatly reducing long start-up time and wasteful active current consumption in a stand-by mode of the conventional antenna switch driver circuits. The implemented single-pole four-throw antenna switch is perfectly capable of handling a high power signal up to +35 dBm with suitably low IL of less than 1 dB, and shows second- and third-order harmonic distortion of less than -45 dBm when a 1-GHz RF signal with a power of +35 dBm and a 2-GHz RF signal with a power of +33 dBm are applied. The proposed antenna switch consumes almost no static power.
Software cost estimation

The paper gives an overview of the state o f the art o f software cost estimation (SCE). The main questions to be answered in the paper are: (I) What are the reasons for overruns o f budgets and planned durations? (2) What are the prerequisites for estimating? (3) How can software development effort be estimated? (4) What can software project management expect from SCE models, how accurate are estimations which are made using these kind o f models, and what are the pros and cons o f cost estimation models?
A cloud-based intelligent car parking services for smart cities

This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities, as an important application deployed on the Internet of Things (IoT) paradigm. The corresponding IoT sub-system includes sensor layer, communication layer, and application layer. A high-level view of the system architecture is outlined. To demonstrate the provision of car parking services with the proposed platform, a cloud-based intelligent car parking system for use within a University campus is described along with details of its design and implementation.
Smart city digital twins

Driven by the challenges of rapid urbanization, cities are determined to implement advanced socio-technological changes and transform into smarter cities. The success of such transformation, however, greatly relies on a thorough understanding of the city's states of spatiotemporal flux. The ability to understand such fluctuations in context and in terms of interdependencies that exist among various entities across time and space is crucial, if cities are to maintain their smart growth. Here, we introduce a Smart City Digital Twin paradigm that can enable increased visibility into cities' human-infrastructure-technology interactions, in which spatiotemporal fluctuations of the city are integrated into an analytics platform at the real-time intersection of reality-virtuality. Through learning and exchange of spatiotemporal information with the city, enabled through virtualization and the connectivity offered by Internet of Things (IoT), this Digital Twin of the city becomes smarter over time, able to provide predictive insights into the city's smarter performance and growth.
Tooth wear treated with direct composite restorations at an increased vertical dimension: results at 30 months.

Severe tooth wear localized to the anterior maxillary or mandibular teeth with loss of interocclusal space is difficult to manage.



This study evaluated the outcome of composite restorations placed at an increased vertical dimension of occlusion in such patients.



Sixteen patients were restored with 104 restorations in 2 groups. In group A, Durafill composite and Scotchbond Multipurpose dentine adhesive system were used to place direct anterior restorations (N = 52). In group B, Herculite XRV composite and Optibond dentine bonding agent was used (N = 52). The restorations were placed at an increased vertical dimension of occlusion creating a posterior disclusion of 1 to 4 mm.



Clinical follow-up showed that the posterior occlusion remained satisfactorily restored after a mean duration of 4.6 months (range 1 to 11 months). Mean follow-up of 30 months has shown a combined success rate of 89.4% for both groups with 93 of the restorations remaining in service. Maintenance in group A was high with 33 failures, but low in group B with 6 failures. Patient satisfaction was reported as good.



Direct composite restorations may be a treatment option for localized anterior tooth wear.
Design and Implementation of a Rescue System for the Safety of Women by using Arduino Controller

The main contribution of the paper is to develop a wearable arm band for safety and protection of women and girls. This objective is achieved by the analysis of physiological signal in conjunction with body position. The physiological signals that are analyzed are pulse rate sensor, vibration sensor and if there is any fault it additionally uses a fault detection sensor. Acquisition of raw data makes the Arduino controller function by activating the GPS to send alert messages via GSM and the wireless camera captures images and videos and sends images to the pre-decided contacts and also shares video calling to the family contact. The alarm is employed to alert the surroundings by its sound and meanwhile, she can also use a TAZER as a self-defense mechanism.
Collaborative Partitioning for Coreference Resolution

This paper presents a collaborative partitioning algorithm—a novel ensemblebased approach to coreference resolution. Starting from the all-singleton partition, we search for a solution close to the ensemble’s outputs in terms of a task-specific similarity measure. Our approach assumes a loose integration of individual components of the ensemble and can therefore combine arbitrary coreference resolvers, regardless of their models. Our experiments on the CoNLL dataset show that collaborative partitioning yields results superior to those attained by the individual components, for ensembles of both strong and weak systems. Moreover, by applying the collaborative partitioning algorithm on top of three state-of-the-art resolvers, we obtain the second-best coreference performance reported so far in the literature (MELA v08 score of 64.47).