nils-herrmann/clean_scirepeval huggingface dataset:
Learning Awareness Models

We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent’s body come to represent objects in the external world. In spite of being trained with only internally available signals, these dynamic body models come to represent external objects through the necessity of predicting their effects on the agent’s own body. That is, the model learns holistic persistent representations of objects in the world, even though the only training signals are body signals. Our dynamics model is able to successfully predict distributions over 132 sensor readings over 100 steps into the future and we demonstrate that even when the body is no longer in contact with an object, the latent variables of the dynamics model continue to represent its shape. We show that active data collection by maximizing the entropy of predictions about the body— touch sensors, proprioception and vestibular information—leads to learning of dynamic models that show superior performance when used for control. We also collect data from a real robotic hand and show that the same models can be used to answer questions about properties of objects in the real world. Videos with qualitative results of our models are available at https://goo.gl/mZuqAV.
A Systematic Review of Software Development Cost Estimation Studies

This paper aims to provide a basis for the improvement of software-estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A Web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including: 1) increase the breadth of the search for relevant studies, 2) search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) conduct more studies on estimation methods commonly used by the software industry, and 4) increase the awareness of how properties of the data sets impact the results when evaluating estimation methods
Generalization of Series Elastic Actuator Configurations and Dynamic Behavior Comparison

The Series Elastic Actuator (SEA) has recently been developed by many research groups and applied in various fields. As SEA is the combination of motor, spring, gear and load, various types and configurations of mechanism have been developed as SEAs to satisfy many requirements necessary for the applications. This paper provides a theoretical framework to categorize and compare these various configurations of SEAs. The general structure and model of SEA is provided, and SEA configurations are categorized into Force-sensing Series Elastic Actuator, Reaction Force-sensing Series Elastic Actuator and Transmitted Force-sensing Series Elastic Actuator, based on the relative location of the spring. Criteria such as Force sensitivity, Compliance and Transmissibility of SEA are derived and compared using actual SEAs that have been developed previously.
STRONG VANET SECURITY ON A BUDGET

This article proposes a security authentication process that is well-suited for Vehicular Ad-hoc Networks (VANET). As compared to current Public Key Infrastructure (PKI) proposals for VANET authentication, the scheme is significantly more efficient with regard to bandwidth and computation. The scheme uses time as the creator of asymmetric knowledge. A sender creates a long chain of keys. Each key is used for only a short period of time to sign messages. When a key expires, it is publicly revealed, and then never again used. (The sender subsequently uses the next key in its chain to sign future messages.) Upon receiving a revealed key, recipients authenticate previously received messages. The root of a sender’s keychain is given in a certificate signed by an authority. This article describes several possible certificate exchange methods. It also addresses privacy issues in VANET, specifically the tension between anonymity and the ability to revoke certificates.
Modelling Relational Data using Bayesian Clustered Tensor Factorization

We consider the problem of learning probabilistic models fo r c mplex relational structures between various types of objects. A model can hel p us “understand” a dataset of relational facts in at least two ways, by finding in terpretable structure in the data, and by supporting predictions, or inferences ab out whether particular unobserved relations are likely to be true. Often there is a t radeoff between these two aims: cluster-based models yield more easily interpret abl representations, while factorization-based approaches have given better pr edictive performance on large data sets. We introduce the Bayesian Clustered Tensor Factorization (BCTF) model, which embeds a factorized representation of relatio ns in a nonparametric Bayesian clustering framework. Inference is fully Bayesia n but scales well to large data sets. The model simultaneously discovers interp retable clusters and yields predictive performance that matches or beats previo us probabilistic models for relational data.
2'-fucosyllactose: an abundant, genetically determined soluble glycan present in human milk.

Lactose is the preeminent soluble glycan in milk and a significant source of energy for most newborn mammals. Elongation of lactose with additional monosaccharides gives rise to a varied repertoire of free soluble glycans such as 2'-fucosyllactose (2'-FL), which is the most abundant oligosaccharide in human milk. In infants, 2'-FL is resistant to digestion and reaches the colon where it is partially fermented, behaving as soluble prebiotic fiber. Evidence also suggests that portions of small soluble milk glycans, including 2'-FL, are absorbed, thus raising the possibility of systemic biological effects. 2'-FL bears an epitope of the Secretor histo-blood group system; approximately 70-80% of all milk samples contain 2'-FL, since its synthesis depends on a fucosyltransferase that is not uniformly expressed. The fact that some infants are not exposed to 2'-FL has helped researchers to retrospectively probe for biological activities of this glycan. This review summarizes the attributes of 2'-FL in terms of its occurrence in mammalian phylogeny, its postulated biological activities, and its variability in human milk.
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach

Temporal data mining is the application of data mining techniques to data that takes the time dimension into account. This paper studies changes in cluster characteristics of supermarket customers over a 24 week period. Such an analysis can be useful for formulating marketing strategies. Marketing managers may want to focus on specific groups of customers. Therefore they may need to understand the migrations of the customers from one group to another group. The marketing strategies may depend on the desirability of these cluster migrations. The temporal analysis presented here is based on conventional and modified Kohonen self organizing maps (SOM). The modified Kohonen SOM creates interval set representations of clusters using properties of rough sets. A description of an experimental design for temporal cluster migration studies 0020-0255/$ see front matter 2005  Corresponding author.  E-mail address: pawan.lingras@smu.ca (P. Lingras). 216 P. Lingras et al. / Information Sciences 172 (2005) 215–240 including, data cleaning, data abstraction, data segmentation, and data sorting, is provided. The paper compares conventional and non-conventional (interval set) clustering techniques, as well as temporal and non-temporal analysis of customer loyalty. The interval set clustering is shown to provide an interesting dimension to such a temporal analysis.
newsLens: building and visualizing long-ranging news stories

We propose a method to aggregate and organize a large, multi-source dataset of news articles into a collection of major stories, and automatically name and visualize these stories in a working system. The approach is able to run online, as new articles are added, processing 4 million news articles from 20 news sources, and extracting 80000 major stories, some of which span several years. The visual interface consists of lanes of timelines, each annotated with information that is deemed important for the story, including extracted quotations. The working system allows a user to search and navigate 8 years of story information.
Revisiting Edge and Node Parallelism for Dynamic GPU Graph Analytics

Betweenness Centrality is a widely used graph analytic that has applications such as finding influential people in social networks, analyzing power grids, and studying protein interactions. However, its complexity makes its exact computation infeasible for large graphs of interest. Furthermore, networks tend to change over time, invalidating previously calculated results and encouraging new analyses regarding how centrality metrics vary with time. While GPUs have dominated regular, structured application domains, their high memory throughput and massive parallelism has made them a suitable target architecture for irregular, unstructured applications as well. In this paper we compare and contrast two GPU implementations of an algorithm for dynamic betweenness centrality. We show that typical network updates affect the centrality scores of a surprisingly small subset of the total number of vertices in the graph. By efficiently mapping threads to units of work we achieve up to a 110x speedup over a CPU implementation of the algorithm and can update the analytic 45x faster on average than a static recomputation on the GPU.
The colonial context of Filipino American immigrants' psychological experiences.

Because of the long colonial history of Filipinos and the highly Americanized climate of postcolonial Philippines, many scholars from various disciplines have speculated that colonialism and its legacies may play major roles in Filipino emigration to the United States. However, there are no known empirical studies in psychology that specifically investigate whether colonialism and its effects have influenced the psychological experiences of Filipino American immigrants prior to their arrival in the United States. Further, there is no existing empirical study that specifically investigates the extent to which colonialism and its legacies continue to influence Filipino American immigrants' mental health. Thus, using interviews (N = 6) and surveys (N = 219) with Filipino American immigrants, two studies found that colonialism and its consequences are important factors to consider when conceptualizing the psychological experiences of Filipino American immigrants. Specifically, the findings suggest that (a) Filipino American immigrants experienced ethnic and cultural denigration in the Philippines prior to their U.S. arrival, (b) ethnic and cultural denigration in the Philippines and in the United States may lead to the development of colonial mentality (CM), and (c) that CM may have negative mental health consequences among Filipino American immigrants. The two studies' findings suggest that the Filipino American immigration experience cannot be completely captured by the voluntary immigrant narrative, as they provide empirical support to the notion that the Filipino American immigration experience needs to be understood in the context of colonialism and its most insidious psychological legacy- CM.
Novel LTPS-TFT Pixel Circuit with OLED Luminance Compensation for 3D AMOLED Displays

A novel low-temperature polycrystalline-silicon thin-film-transistor pixel circuit for 3D active-matrix organic light-emitting diode (AMOLED) displays is presented in this work. The proposed pixel circuit employs high frame rate (240 Hz) emission driving scheme and only needs 3.5 μs for input data period. Thus, 3D AMOLED displays can be realized under high speed operations. The simulation results demonstrate excellent stability in the proposed pixel circuit. The relative current error rate is only 0.967% under the threshold voltage deviation ( ΔVTH_DTFT = ± 0.33 V) of driving TFT. With an OLED threshold voltage detecting architecture, the OLED current can be increased with the increased OLED threshold voltage to compensate for the OLED luminance degradation. The proposed pixel circuit can therefore effectively compensate for the DTFT threshold voltage shift and OLED electric degradation at the same time.
TTPDrill: Automatic and Accurate Extraction of Threat Actions from Unstructured Text of CTI Sources

With the rapid growth of the cyber attacks, sharing of cyber threat intelligence (CTI) becomes essential to identify and respond to cyber attack in timely and cost-effective manner. However, with the lack of standard languages and automated analytics of cyber threat information, analyzing complex and unstructured text of CTI reports is extremely time- and labor-consuming. Without addressing this challenge, CTI sharing will be highly impractical, and attack uncertainty and time-to-defend will continue to increase.
 Considering the high volume and speed of CTI sharing, our aim in this paper is to develop automated and context-aware analytics of cyber threat intelligence to accurately learn attack pattern (TTPs) from commonly available CTI sources in order to timely implement cyber defense actions. Our paper has three key contributions. First, it presents a novel threat-action ontology that is sufficiently rich to understand the specifications and context of malicious actions. Second, we developed a novel text mining approach that combines enhanced techniques of Natural Language Processing (NLP) and Information retrieval (IR) to extract threat actions based on semantic (rather than syntactic) relationship. Third, our CTI analysis can construct a complete attack pattern by mapping each threat action to the appropriate techniques, tactics and kill chain phases, and translating it any threat sharing standards, such as STIX 2.1. Our CTI analytic techniques were implemented in a tool, called TTPDrill, and evaluated using a randomly selected set of Symantec Threat Reports. Our evaluation tests show that TTPDrill achieves more than 82% of precision and recall in a variety of measures, very reasonable for this problem domain.
Cyber-Physical Security of a Chemical Plant

The increasing number of cyber-attacks on industriesdemands immediate attention for providing more securemechanisms to safeguard industries and minimize risks. Cyberphysical systems (CPS) being used in industries such as oil and gas, chemical process plants and the like are termed Industrial Control Systems (ICS). Control system security is aimed at preventing intentional or unintentional interference with the proper operation of industrial automation and control systems (ICS). This paper proposes a process-aware approach with the use of some invariant equations based on the physical and chemical properties of the process and a Multiple Security Domain Nondeducibility (MSDND) framework to detect when a sensor signal is being maliciously manipulated. We have taken benzene production plant as case study to illustrate our approach and its effectiveness in determining the state of the system.
Criteria Weighting and 4P's Planning in Marketing Using a Fuzzy Metric Distance and AHP Hybrid Method

Production and consumption relationship shows that marketing plays an important role in enterprises. In the competitive market, it is very important to be able to sell rather than produce. Nowadays, marketing is customeroriented and aims to meet the needs and expectations of customers to increase their satisfaction. While creating a marketing strategy, an enterprise must consider many factors. Which is why, the process can and should be considered as a multi-criteria decision making (MCDM) case. In this study, marketing strategies and marketing decisions in the new-product-development process has been analyzed in a macro level. To deal quantitatively with imprecision or uncertainty, fuzzy sets theory has been used throughout the analysis.
Novel object-size measurement using the digital camera

This paper proposes a novel approach to measure the object size using a regular digital camera. Nowadays, the remote object-size measurement is very crucial to many multimedia applications. Our proposed computer-aided automatic object-size measurement technique is based on a new depth-information extraction (range finding) scheme using a regular digital camera. The conventional range finders are often carried out using the passive method such as stereo cameras or the active method such as ultrasonic and infrared equipment. They either require the cumbersome set-up or deal with point targets only. The proposed approach requires only a digital camera with certain image processing techniques and relies on the basic principles of visible light. Experiments are conducted to evaluate the performance of our proposed new object-size measurement mechanism. The average error-percentage of this method is below 2%. It demonstrates the striking effectiveness of our proposed new method.
Biometric Authentication using Human Footprint

The password based authorization scheme for all available security systems can effortlessly be hacked by the hacker or a malicious user. One might not be able to guarantee that the person who is using the password is authentic or not. Only biometric systems are one which make offered automated authentication. There are very exceptional chances of losing the biometric identity, only if the accident of an individual may persists. Footprint based biometric system has been evaluated so far. In this paper a number of approaches of footprint recognition have been deliberated. General Terms Biometric pattern recognition, Image processing.
Randomized prospective comparison of midline and off-midline closure techniques in pilonidal sinus surgery.

Pilonidal sinus is a chronic inflammatory disorder of the intergluteal sulcus. The disorder often negatively affects patients' quality of life, and there are numerous possible methods of operative treatment for pilonidal sinus. The aim of our study was to compare the results of 3 different operative procedures (tension-free primary closure, Limberg flap, and Karydakis technique) used in the treatment of pilonidal disease.



The study was conducted via a prospective randomized design. The patients were randomized into 3 groups via a closed envelope method. Patients were included in the study after admission to our clinic with pilonidal sinus disease and operative treatment already were planned. The 2 main outcomes of the study were early complications from the methods used and later recurrences of the disease.



A total of 150 patients were included in the study, and the groups were similar in terms of age, sex, and American Society of Anesthesiologists scores. The median follow-up time of the study was 24.2 months (range, 18.5-34.27) postsurgery. The recurrence rates were 6% for both the Limberg and Karydakis groups and 4% for the tension-free primary closure group. Therefore, there was no substantial difference in the recurrence rates.



The search for an ideal treatment modality for pilonidal sinus disease is still ongoing. The main conclusion of our study is that a tension-free healing side is much more important than a midline suture line. Also, tension-free primary closure is as effective as a flap procedure, and it is also easier to perform.
A modified TAM for predicting acceptance of digital educational games by teachers

The use of digital games in education is growing. Digital games with their elements of ‘play’ and ‘challenge’ are increasingly viewed as a successful medium for engaging and motivating students, in situations where students may be uninterested or distant. One such situation is mathematics education in Nigeria where young people in schools can be unenthusiastic about the subject. The introduction of digital educational games is being trialed to see if it can address this issue. A key element for ensuring the success of the introduction of new technologies is that the users are prepared and ready to accept the technology. This also applies to the introduction of digital educational games in the classroom. Technology Acceptance Models (TAMs) have been widely employed to explore users' attitudes to technology and to highlight their main concerns and issues. The aim of this study is to investigate if a modified TAM can be successfully developed and deployed to explore teachers' attitudes to the introduction of digital educational games in their classroom. The study employs a mixed methods approach and combines the outcomes from previous research studies with data gathered from interviews with teachers to develop the modified TAM. This approach of combining the results from previous studies together with interviews from the targeted group enabled the key variables/constructs to be identified. Independent evaluation by a group of experts gave further confidence in the model. The results have shown that this modified TAM is a useful instrument for exploring the attitude of teachers to using digital games for learning and teaching, and highlighting the key areas which require support and input to ensure teachers are ready to accept and use this technology in their classroom practice.
ACFIS: a web server for fragment-based drug discovery

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at
Fully Integrated FPGA-Based Controller for Synchronous Motor Drive

The aim of this paper is to present a fully integrated solution for synchronous motor control. The implemented controller is based on Actel Fusion field-programmable gate array (FPGA). The objective of this paper is to evaluate the ability of the proposed fully integrated solution to ensure all the required performances in such applications, particularly in terms of control quality and time/area performances. To this purpose, a current control algorithm of a permanent-magnet synchronous machine has been implemented. This machine is associated with a resolver position sensor. In addition to the current control closed loop, all the necessary motor control tasks are implemented in the same device. The analog-to-digital conversion is ensured by the integrated analog-to-digital converter (ADC), avoiding the use of external converters. The resolver processing unit, which computes the rotor position and speed from the resolver signals, is implemented in the FPGA matrix, avoiding the use of external resolver-to-digital converter (RDC). The sine patterns used for the Park transformation are stored in the integrated flash memory blocks.