nils-herrmann/clean_scirepeval huggingface dataset:
A Framework for Evaluating Intrusion Detection Architectures in Advanced Metering Infrastructures

The scale and complexity of Advanced Metering Infrastructure (AMI) networks requires careful planning for the deployment of security solutions. In particular, the large number of AMI devices and the volume and diversity of communication expected to take place on the various AMI networks make the role of intrusion detection systems (IDSes) critical. Understanding the trade-offs for a scalable and comprehensive IDS is key to investing in the right technology and deploying sensors at optimal locations. This paper reviews the benefits and costs associated with different IDS deployment options, including either centralized or distributed solution. A general cost-model framework is proposed to help utilities (AMI asset owners) make more informed decisions when selecting IDS deployment architectures and managing their security investments. We illustrate how the framework can be applied through case studies, and highlight the interesting cost/benefit trade-offs that emerge.
Probabilistic Similarity Logic

Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled support for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect–the need to reason about similarities–which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domainspecific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.
Progressive Prediction of Student Performance in College Programs

Accurately predicting students’ future performance based on their tracked academic records in college programs is crucial for effectively carrying out necessary pedagogical interventions to ensure students’ on-time graduation. Although there is a rich literature on predicting student performance in solving problems and studying courses using data-driven approaches, predicting student performance in completing college programs is much less studied and faces new challenges, mainly due to the diversity of courses selected by students and the requirement of continuous tracking and incorporation of students’ evolving progresses. In this paper, we develop a novel algorithm that enables progressive prediction of students’ performance by adapting ensemble learning techniques and utilizing education-specific domain knowledge. We prove its prediction performance guarantee and show its performance improvement against benchmark algorithms on a real-world student dataset from UCLA .
Low power and low voltage VT extractor circuit and MOSFET radiation dosimeter

This work discusses two fundamental blocks of an in vivo MOS dosimeter, namely the radiation sensor and the VT-extractor circuit. It is shown that threshold extractor circuits based on an all-region MOSFET model are very appropriate for low power design. The accuracy of the extractor circuits allows using the PMOS transistors of the integrated circuit CD4007 as the radiation sensor in a dosimeter for radiotherapy applications.
A Soldier Health Monitoring System for Military Applications

With recent advances in technology, various wearable sensors have been developed for the monitoring of human physiological parameters. A Body Sensor Network (BSN) consisting of such physiological and biomedical sensor nodes placed on, near or within a human body can be used for real-time health monitoring. In this paper, we describe an on-going effort to develop a system consisting of interconnected BSNs for real-time health monitoring of soldiers. We discuss the background and an application scenario for this project. We describe the preliminary prototype of the system and present a blast source localization application.
Manipulator state estimation with low cost accelerometers and gyroscopes

Robot manipulator designs are increasingly focused on low cost approaches, especially those envisioned for use in unstructured environments such as households, office spaces and hazardous environments. The cost of angular sensors varies based on the precision offered. For tasks in these environments, millimeter order manipulation errors are unlikely to cause drastic reduction in performance. In this paper, estimates the joint angles of a manipulator using low cost triaxial accelerometers by taking the difference between consecutive acceleration vectors. The accelerometer-based angle is compensated with a uniaxial gyroscope using a complementary filter to give robust measurements. Three compensation strategies are compared: complementary filter, time varying complementary filter, and extended Kalman filter. This sensor setup can also accurately track the joint angle even when the joint axis is parallel to gravity and the accelerometer data does not provide useful information. In order to analyze this strategy, accelerometers and gyroscopes were mounted on one arm of a PR2 robot. The arm was manually moved smoothly through different trajectories in its workspace while the joint angle readings from the on-board optical encoders were compared against the joint angle estimates from the accelerometers and gyroscopes. The low cost angle estimation strategy has a mean error 1.3° over the three joints estimated, resulting in mean end effector position errors of 6.1 mm or less. This system provides an effective angular measurement as an alternative to high precision encoders in low cost manipulators and as redundant measurements for safety in other manipulators.
Mycorrhizosphere interactions to improve plant fitness and soil quality

Arbuscular mycorrhizal fungi are key components of soil microbiota and obviously interact with other microorganisms in the rhizosphere, i.e. the zone of influence of plant roots on microbial populations and other soil constituents. Mycorrhiza formation changes several aspects of plant physiology and some nutritional and physical properties of the rhizospheric soil. These effects modify the colonization patterns of the root or mycorrhizas (mycorrhizosphere) by soil microorganisms. The rhizosphere of mycorrhizal plants, in practice a mycorrhizosphere, harbors a great array of microbial activities responsible for several key ecosystem processes. This paper summarizes the main conceptual principles and accepted statements on the microbial interactions between mycorrhizal fungi and other members of rhizosphere microbiota and discusses current developments and future trends concerning the following topics: (i) effect of soil microorganisms on mycorrhiza formation; (ii) mycorrhizosphere establishment; (iii) interactions involved in nutrient cycling and plant growth; (iv) interactions involved in the biological control of plant pathogens; and (v) interactions to improve soil quality. The main conclusion is that microbial interactions in the rhizosphere of mycorrhizal plants improve plant fitness and soil quality, critical issues for a sustainable agricultural development and ecosystem functioning.
Identifying emotional states using keystroke dynamics

The ability to recognize emotions is an important part of building intelligent computers. Emotionally-aware systems would have a rich context from which to make appropriate decisions about how to interact with the user or adapt their system response. There are two main problems with current system approaches for identifying emotions that limit their applicability: they can be invasive and can require costly equipment. Our solution is to determine user emotion by analyzing the rhythm of their typing patterns on a standard keyboard. We conducted a field study where we collected participants' keystrokes and their emotional states via self-reports. From this data, we extracted keystroke features, and created classifiers for 15 emotional states. Our top results include 2-level classifiers for confidence, hesitance, nervousness, relaxation, sadness, and tiredness with accuracies ranging from 77 to 88%. In addition, we show promise for anger and excitement, with accuracies of 84%.
Reinforcement Learning in Finite MDPs: PAC Analysis

We study the problem of learning near-optimal behavior in fin ite Markov Decision Processes (MDPs) with a polynomial number of samples. These “PAC-MDP” algorithms include the wellknown E3 and R-MAX algorithms as well as the more recent Delayed Q-lea rning algorithm. We summarize the current state-of-the-art by presenting boun ds for the problem in a unified theoretical framework. A more refined analysis for upper and lower bounds is presented to yield insight into the differences between the model-free Delayed Q-learning and the model-based R-MAX.
Swarm Robotics: An Extensive Research Review

Swarm robotics is a new approach to the coordination of large numbers of relatively simple physically embodied robots, that are autonomous, not controlled centrally, capable of local communication and operates based on some sense of biological inspiration (Sharkey & Sharkey, 2006a). Swarm robotic systems have become a major research area since 1980’s, as new solution approaches are being developed and validated, it is often possible to realize the advantages of swarm robotic systems. Table 1 shows the key advantages of swarm
Understanding compromised accounts on Twitter

Social media has become a valuable tool for hackers to disseminate misinformation through compromised accounts. A compromised account is an account accessed by a third party without the user's knowledge. Previous studies have found 13% of online adults experienced their social media accounts compromised. Since compromised accounts can have a significant adverse impact on the social media sites, this has led to the growing research on detecting compromised accounts. However, previous works are limited as they either focus on the detection of hacked accounts for spamming and phishing activities or utilize only twitter content information. In this paper, we performed a systematic study on compromised accounts in Twitter by identifying who compromise the accounts; what information they share, and what patterns their tweets present. Our findings suggest that the accounts can be compromised by two different types of hackers and the content they post tend to follow several common themes. We also showed that, in addition to the text content of the tweets, there are other meta-information that can be exploited to help improve the detection of compromised accounts.
Very Small Ultra-Wide-Band MMIC Magic T and Applications to Combiners and Dividers

An FET-sized 1-18 GHz monolithic active magic T (1 W hybrid) is proposed. It unifies two different dividers, electrically isolated from each other, in a novel GaAs FET electrode configuration, viz. the LUFET concept. Its characteristics and experiment results are presented. Applications of the magic T to miniature wide-band RF signal processing. . .
Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies

Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as SPARQL) and APIs (such as OWLAPI) are not as easy-to-use as object programming languages are. Moreover, they provide few solutions to difficulties encountered with biomedical ontologies. Our objective was to design a tool for accessing easily the entities of an OWL ontology, with high-level constructs helping with biomedical ontologies.



From our experience on medical ontologies, we identified two difficulties: (1) many entities are represented by classes (rather than individuals), but the existing tools do not permit manipulating classes as easily as individuals, (2) ontologies rely on the open-world assumption, whereas the medical reasoning must consider only evidence-based medical knowledge as true. We designed a Python module for ontology-oriented programming. It allows access to the entities of an OWL ontology as if they were objects in the programming language. We propose a simple high-level syntax for managing classes and the associated "role-filler" constraints. We also propose an algorithm for performing local closed world reasoning in simple situations.



We developed Owlready, a Python module for a high-level access to OWL ontologies. The paper describes the architecture and the syntax of the module version 2. It details how we integrated the OWL ontology model with the Python object model. The paper provides examples based on Gene Ontology (GO). We also demonstrate the interest of Owlready in a use case focused on the automatic comparison of the contraindications of several drugs. This use case illustrates the use of the specific syntax proposed for manipulating classes and for performing local closed world reasoning.



Owlready has been successfully used in a medical research project. It has been published as Open-Source software and then used by many other researchers. Future developments will focus on the support of vagueness and additional non-monotonic reasoning feature, and automatic dialog box generation.
A Word Embedding Approach to Predicting the Compositionality of Multiword Expressions

This paper presents the first attempt to use word embeddings to predict the compositionality of multiword expressions. We consider both singleand multi-prototype word embeddings. Experimental results show that, in combination with a back-off method based on string similarity, word embeddings outperform a method using count-based distributional similarity. Our best results are competitive with, or superior to, state-of-the-art methods over three standard compositionality datasets, which include two types of multiword expressions and two languages.
Efficient full-wave analysis of mutual coupling between cavity-backed microstrip patch antennas

A full-wave methodology for a broadband analysis of isolated cavity-backed and probe-fed arbitrarily shaped microstrip patch antennas is combined with an analytical method based on the properties of rotation and translation of spherical modes for obtaining an efficient scattering matrix description of the mutual coupling in a two-element array. The proposed procedure is validated and results are given for identical circular cavity-backed patch antennas.
A Novel Design of Circularly Polarized Omni-directional Antenna for Ka Band

This paper presents a novel design of an antenna with an array of slots, radiating circularly polarized conical beam. The structure of the antenna is simple and suitable for mass production. The radiation characteristics such as radiation pattern and directivity are found to be conical beam radiation, which is omni-directional in the circumferential direction. The polarization characteristics are investigated to perform the circularly polarized radiation. At the operation frequency 35 GHz, the antenna gets a circularly polarized omni-directional radiation pattern at thetas=77deg, the gain is about 1.70-5.08 dB, and the axial ratio is less than 3 dB in the azimuthal plane.
Improving www proxies performance with greedy-dual-size-frequency caching policy

Web, HTTP, WWW proxies, caching policies, replacement algorithms, performance Web proxy caches are used to improve performance of the WWW. Since the majority of Web documents are static documents, caching them at WWW proxies reduces both network traffic and response time. One of the keys to better proxy cache performance is an efficient caching policy which keeps in the cache popular documents and replaces rarely used ones. This paper introduces the Greedy-Dual-Size-Frequency caching policy to maximize hit and byte hit rates for WWW proxies. Proposed caching strategy incorporates in a simple way the most important characteristics of the file and its accesses such as file size, file access frequency and recentness of the last access. Greedy-Dual-Size-Frequency is an improvement of Greedy-Dual-Size algorithm – the current champion among the replacement strategies proposed for Web proxy caches.
An Exploratory Review of Design Principles in Constructivist Gaming Learning Environments.

Creating a design theory for Constructivist Gaming Learning Environment necessitates, among other things, the establishment of design principles. These principles have the potential to help designers produce games, where users achieve higher levels of learning. This paper focuses on twelve design principles: Probing, Distributed, Multiple Routes, Practice, Psychosocial Moratorium, Regime of Competence, Self-Knowledge, Collective Knowledge, Engaging, User Interface Ease of Use, On Demand and Just-in-Time Tutorial, and Achievement. We report on two pilot studies of a qualitative nature in which we test our design principles. Game play testing and observations were carried out on five Massively Multiplayer Online Games (MMOGs): RuneScape, GuildWars, Ragnarok, World of WarCraft, and Final Fantasy XI. Two educational games, Carabella Goes to College and Outbreak at WatersEdge were also observed. Our findings indicate that not all of the popular MMOGs and educational games support all of these principles.
Locality-sensitive hashing scheme based on dynamic collision counting

Locality-Sensitive Hashing (LSH) and its variants are well-known methods for solving the c-approximate NN Search problem in high-dimensional space. Traditionally, several LSH functions are concatenated to form a "static" compound hash function for building a hash table. In this paper, we propose to use a base of m single LSH functions to construct "dynamic" compound hash functions, and define a new LSH scheme called Collision Counting LSH (C2LSH). If the number of LSH functions under which a data object o collides with a query object q is greater than a pre-specified collision threhold l, then o can be regarded as a good candidate of c-approximate NN of q. This is the basic idea of C2LSH.
 Our theoretical studies show that, by appropriately choosing the size of LSH function base m and the collision threshold l, C2LSH can have a guarantee on query quality. Notably, the parameter m is not affected by dimensionality of data objects, which makes C2LSH especially good for high dimensional NN search. The experimental studies based on synthetic datasets and four real datasets have shown that C2LSH outperforms the state of the art method LSB-forest in high dimensional space.
Why Have Child Maltreatment and Child Victimization Declined ?

Various forms of child maltreatment and child victimization declined as much as 40–70% from 1993 until 2004, including sexual abuse, physical abuse, sexual assault, homicide, aggravated assault, robbery, and larceny. Other child welfare indicators also improved during the same period, including teen pregnancy, teen suicide, and children living in poverty. This article reviews a wide variety of possible explanations for these changes: demography, fertility and abortion legalization, economic prosperity, increased incarceration of offenders, increased agents of social intervention, changing social norms and practices, the dissipation of the social changes from the 1960s, and psychiatric pharmacology. Multiple factors probably contributed. In particular, economic prosperity, increasing agents of social intervention, and psychiatric pharmacology have advantages over some of the other explanations in accounting for the breadth and timing of the improvements.
Using Visual Speech Information in Masking Methods for Audio Speaker Separation

This paper examines whether visual speech information can be effective within audio-masking-based speaker separation to improve the quality and intelligibility of the target speech. Two visual-only methods of generating an audio mask for speaker separation are first developed. These use a deep neural network to map the visual speech features to an audio feature space from which both visually derived binary masks and visually derived ratio masks are estimated, before application to the speech mixture. Second, an audio ratio masking method forms a baseline approach for speaker separation which is extended to exploit visual speech information to form audio-visual ratio masks. Speech quality and intelligibility tests are carried out on the visual-only, audio-only, and audio-visual masking methods of speaker separation at mixing levels from $-$ 10 to +10 dB. These reveal substantial improvements in the target speech when applying the visual-only and audio-only masks, but with highest performance occurring when combining audio and visual information to create the audio-visual masks.
A New Framework for Computer Science and Engineering

T raditionally, computing studies has two partitions—science and engineering—that are separated by a line roughly at the computer architecture level. Above the line is computer science and below is computer engineering. Each of these two disciplines then breaks into subspecialties, such as the decomposition of computer science into theory, systems, and AI. Such an approach succeeds in dividing the field into affinity groups, but it tends to isolate its respective parts and the systems that come from them. This isolation, in turn, makes it difficult to incorporate either an integrative systems-oriented perspective or the interdisciplinary work that is rapidly becoming critical to computing studies’  A more effective organization for computer science and engineering (CS&E)—and one that helps point the way toward its promising future—requires an intrinsically interdisciplinary framework that combines academic and systems-oriented computing perspectives. My colleagues and I have been developing such a framework, which is already changing key segments of CS&E at the University of Southern California (USC). The framework reaggregates computer science and computer engineering and then repartitions the resulting single field into analysis and synthesis components.2-4 Analysis, the more academic of the two, focuses on the nature of computing and the relationships between computer science and other major scientific domains. The synthesis component builds up computer engineering as a more pragmatic systems-oriented activity through a hierarchy of related layers, each of which covers important interdisciplinary issues. The framework is based on the notion that science is foremost about dissecting and understanding, and engineering is mostly about envisioning and building. True, scientists also envision and build—integrated models, for example—but such activities are directed mostly at dissecting and understanding something. Likewise engineers also dissect and understand things, but primarily to envision and build. Extending these notions to computing studies, computer science is concerned with dissecting and understanding the nature of computing and its relationship to the other sciences, while computer engineering is concerned To realize the compelling future of computing studies, researchers and analysts must rethink the traditional way of partitioning and structuring computer science and engineering. A framework that USC is already partially implementing combines an explicitly interdisciplinary academic focus with a systems-oriented perspective. Paul S. Rosenbloom University of Southern California