Miracle-dz/UniFAR-FTUs huggingface dataset:
InferIP: Extracting actionable information from security discussion forums

How much useful information can we extract from security forums? Many security initiatives and commercial entities are harnessing the readily public information, but they seem to focus on structured sources of information. Our goal here is to extract information from hacker forums, whose information is provided in ad hoc and unstructured ways. Here, we focus on the problem of identifying malicious IPs addresses, when these are being reported in the forums. We develop a method to automate the identification of malicious IPs with the design goal of being independent of external sources. A key novelty is that we use a matrix decomposition method to extract latent features of the behavioral information of the users, which we combine with textual information from the related posts. As key design feature, our technique can be applied to different language forums since it relies on a simple NLP solution in combination with behavioral features. In particular, our solution only needs a small number of keywords in the new language plus the user's behavior captured by specific features. We also develop a tool to automate the data collection from security forums. We collect approximately 600K posts from 3 different forums. Our method exhibits high classification accuracy, while the precision of identifying malicious IP in post is greater than 88% in all three sites. Furthermore, by applying our method, we find up to 3 times more potentially malicious IPs than compared to the reference blacklist VirusTotal. As the cyber-wars are becoming more intense, having early accesses to useful information becomes more imperative to remove the hackers first-move advantage, and our work is a solid step towards this direction.
Park with Ease: Evaluating Usability to Design Intuitive Interfaces for Car Parking Applications

With a smartphone app or web interface, users with different demographic profiles may book and pay for parking spaces, cutting down on the time and stress involved in locating a place. Hence, such parking systems should be usable and intuitive to end users. Therefore, this research aims to evaluate the usability of existing car parking applications and to propose an intuitive and usable car parking application. To attain these objectives, three smart car parking mobile applications (Parking Koi, ParkWhiz, and SpotHero) were selected and evaluated through heuristic evaluation and a user study. The studies revealed several usability problems such as lack of feedback, instructions, flexibility and adaptability. According to the severity of issues, the Park Whiz application encountered the least number of issues in total, while the SpotHero program experienced the highest number of problems. Comparing with Parking Koi and SpotHero, Parkwhiz's average level of severity is substantially higher. The user study revealed that the three car parking applications' interfaces are poorly designed and provided the suggestions for improvement to enhance user attention. Finally, a prototype of a car parking application with an intuitive UIs is proposed to address the revealed usability problems.
Do customer service perceptions generalize? The case of student and chair ratings of faculty effectiveness

We conceptualize faculty classroom behavior as service delivery and show that student (customer) evaluations of service effectiveness generalize to chairs' ratings of faculty effectiveness in other facets of the faculty role. We compared students' ratings of 132 professors in the classroom with their chairs' ratings of them in seven other roles. Findings suggest that students' ratings of faculty classroom behavior cluster with chairs' ratings of faculty colleagueship and service and that this cluster is relatively independent of chairs'ratings of faculty publication record, grant support, and professional activity. Results are interpreted in light of Gouldner's (1958a, 1958b) local-cosmopolitan distinction. Some implications for research on faculty effectiveness and customers' evaluations of service are presented.
Counterions at charge-modulated substrates

We consider counterions in the presence of a single planar surface with a spatially inhomogeneous charge distribution using Monte Carlo simulations and strong-coupling theory. For high surface charges, multivalent counterions, or pronounced substrate charge modulation the counterions are laterally correlated with the surface charges and their density profile deviates strongly from the limit of a smeared-out substrate charge distribution, in particular exhibiting a much increased laterally averaged density at the surface.
The Relative Importance of Situational and Individual Variables in Predicting Leader Behavior

This study assessed the relative influence of personality traits of leaders, job characteristics, expectations of superiors and subordinates, and trust and loyalty upward upon leader behavior. Results indicated that subordinates' levels oftrust and loyalty toward their leaders were most predictive of supportive leader behavior, whereas personality traits of the leader were most predictive of demanding leader behavior. The findings are discussed in terms of new avenues to pursue in attempts to more fully understand the complicated dynamics of leader behavior.
The effect of the timing, quality and quantity of sleep upon the depression (masking) of body temperature on an irregular sleep/wake schedule

SUMMARY Eight subjects were studied on an irregular sleep/wake schedule which was designed so that prior wake time and the time of day when 4‐h sleep periods were taken were both balanced. Rectal temperature and the sleep EEG were measured throughout the 8‐d protocol. A purification method was used to estimate the depression of rectal temperature (masking) caused by the first and second halves of each 4‐h sleep period. On average this was 0.187 ± 0.015°C during the first 2h and 0.262 ± 0.017°C during the second 2h. Masking increases beyond the first 2 h of sleep, but any effects due to the phase of the rectal temperature when sleep is taken are very weak. However, masking did increase (that is, the temperature was depressed more) when the amount of prior wake time was greater than 4 h. When the effects of sleep content variables were considered also, masking was still greater in the second half of sleep and tended to increase with the amount of slow‐wave sleep, but it decreased with increasing amounts of time awake during a sleep period (sleep latency or sleep discontinuity). Some implications of these results for the mechanism of sleep‐induced masking are considered.
Self-Organizing Incremental Associative Memory-Based Robot Navigation

SUMMARY This paper presents a new incremental approach for robot navigation using associative memory. We defined the association as node→action→node where node is the robot position and action is the action of a robot (i.e., orientation, direction). These associations are used for path planning by retrieving a sequence of path fragments (in form of (node→action→node) → (node→action→node) →· ·· ) starting from the start point to the goal. To learn such associations, we applied the associative memory using Self-Organizing Incremental Associative Memory (SOIAM). Our proposed method comprises three layers: input layer, memory layer and associative layer. The input layer is used for collecting input observations. The memory layer clusters the obtained observations into a set of topological nodes incrementally. In the associative layer, the associative memory is used as the topological map where nodes are associated with actions. The advantages of our method are that 1) it does not need prior knowledge, 2) it can process data in continuous space which is very important for real-world robot navigation and 3) it can learn in an incremental unsupervised manner. Experiments are done with a realistic robot simulator: Webots. We divided the experiments into 4 parts to show the ability of creating a map, incremental learning and symbol-based recognition. Results show that our method offers a 90% success rate for reaching
Quantifying the Impact of Input Data Sets on Program Behavior and its Applications

Having a representative workload of the target domain of a microprocessor is extremely important throughout its design. The composition of a workload involves two issues: (i) which benchmarks to select and (ii) which input data sets to select per benchmark. Unfortunately, it is impossible to select a huge number of benchmarks and respective input sets due to the large instruction counts per benchmark and due to limitations on the available simulation time. In this paper, we use statistical data analysis techniques such as principal components analysis (PCA) and cluster analysis to ecien tly explore the workload space. Within this workload space, dieren t input data sets for a given benchmark can be displayed, a distance can be measured between program-input pairs that gives us an idea about their mutual behavioral dierences and representative input data sets can be selected for the given benchmark. This methodology is validated by showing that program-input pairs that are close to each other in this workload space indeed exhibit similar behavior. The nal goal is to select a limited set of representative benchmark-input pairs that span the complete workload space. Next to workload composition, we discuss two other possible applications, namely getting insight in the impact of input data sets on program behavior and evaluating the representativeness of sampled traces.
CALMOS : A Portable Software System for the Automatic and Interactive Layout of MOS/LSI

CALMOS (Computer Aided Layout of MOS) is a computer system for the layout of custom MOS/LSI circuits. Starting with a standard cell library and a simple circuit connectivity description, the program performs various automatic and/or interactive procedures such as initial placement, assignment of equivalent and equipotential pins, optimization of the placement, prerouting, routing, routing compression, fan in fan out and crosstalk verification and circuit verification. The database has reentrant properties such that a designer can step through the system and try out different possibilities. After each step the results are immediately available and can be compared with previous outputs. This on-line optimization avoids multiple rerunning of the task and will also yield a better chip minimization. CALMOS is written in standard FORTRAN and only needs 32K (16 bit) of memory (300 cell version) together with simple overlay facilities. As a result it can easily be installed on almost any existing computersystem. Because of this portability, the algorithmic strength and the modularity of CALMOS is successfully combined with the on-line interactive facilities of existing graphic systems. Such an integration which is actually under development, will allow for the creation of a stand alone integrated minicomputer based LSI design system. It will be based on a common design language and include logic simulation, layout, circuit analysis, design rule checking, logic verification and test pattern generation.
Designing Better Playlists with Monte Carlo Tree Search

In recent years, there has been growing interest in the study of automated playlist generation — music recommender systems that focus on modeling preferences over song sequences rather than on individual songs in isolation. This paper addresses this problem by learning personalized models on the fly of both song and transition preferences, uniquely tailored to each user’s musical tastes. Playlist recommender systems typically include two main components: i) a preferencelearning component, and ii) a planning component for selecting the next song in the playlist sequence. While there has been much work on the former, very little work has been devoted to the latter. This paper bridges this gap by focusing on the planning aspect of playlist generation within the context of DJ-MC, our playlist recommendation application. This paper also introduces a new variant of playlist recommendation, which incorporates the notion of diversity and novelty directly into the reward model. We empirically demonstrate that the proposed planning approach significantly improves performance compared to the DJ-MC baseline in two playlist recommendation settings, increasing the usability of the framework in real world settings.
Differential calreticulin expression affects focal contacts via the calmodulin/CaMK II pathway

Calreticulin is an ER calcium‐storage protein, which influences gene expression and cell adhesion. In this study, we analysed the differences in adhesive properties of calreticulin under‐ and overexpressing fibroblasts in relation to the calmodulin‐ and calcium/calmodulin‐dependent kinase II (CaMK II)‐dependent signalling pathways. Cells stably underexpressing calreticulin had elevated expression of calmodulin, activated CaMK II, activated ERK and activated c‐src. Inhibition of calmodulin by W7, and CaMK II by KN‐62, caused the otherwise weekly adhesive calreticulin underexpressing cells to behave like the overexpressing cells, via induction of increased cell spreading. Increased vinculin, activated paxillin, activated focal adhesion kinase and fibronectin levels were observed upon inhibition of either the calmodulin or the CaMK II signalling pathways, which was accompanied by an increase in cell spreading and focal contact formation. Both KN‐62 and W7 treatment increased cell motility in underexpressing cells, but W7 treatment led to loss of directionality. Thus, both the calmodulin and CaMK II signalling pathways influence cellular spreading and motility, but subtle differences exist in their distal effects on motility effectors. J. Cell. Physiol. 213: 269–277, 2007. © 2007 Wiley‐Liss, Inc.
Towards Data Analytics Based PIM Detection in Wireless Networks

Passive Intermodulation (PIM) is a physical layer Radio Access Network (RAN) problem observed in both 4G and 5G networks. It is caused by internal physical processes such as inferior cabling or rusting, and external factors such as metallic obstacles in the radio propagation path. PIM degrades the user experience and radio resource efficiency while leading to an operation overhead for detecting and mitigating it on the operator side. Nevertheless, current solutions for PIM typically rely on costly hardware and site visit-based investigation by technicians. This work proposes a Machine Learning (ML) based PIM detection scheme for identifying PIM problems in RAN sites. Our approach relies on network KPI data already collected in the infrastructure for various purposes, including network monitoring, performance control, and maintenance. We investigate the performance of our proposed technique using empirical data collected from actual network cells.
A guaranteed compression scheme for repetitive DNA sequences

We present a text compression scheme dedicated to DNA sequences. The exponential growing of the number of sequences creates a real need for analyzing tools. A specific need emerges for methods that perform sequences classification upon various criteria, one of which is the sequence repetitiveness. A good lossless compression scheme is able to distinguish between "random" and "significative" repeats. Theoretical bases for this statement are found in Kolmogorov complexity theory.
Eye Tapping: How to Beat Out an Accurate Rhythm using Eye Movements

Abstract in Undetermined The aim of this study was to investigate how well subjects beat out a rhythm using eye movements and to establish the most accurate method of doing this. Eighteen subjects participated in an experiment were five different methods were evaluated and a fixation based method was found to be the most accurate. All subjects were able to synchronize their eye movements with a given beat but the accuracy was much lower than usually found in finger tapping stud- ies. Many parts of the body are used to make music but so far, with a few exceptions, the eyes have been silent. The re- search presented here provides guidelines for implementing eye controlled musical interfaces. Such interfaces would en- able performers and artists to use eye movement for musical expression and would open up new, exiting possibilities. (Less)
Recursively Adaptive Randomized MultiTree Coding for First Responder Voice Communications

The recent development of a non-block based speech codec that operates at 12.24 kilobits/s and lower provides the opportunity to re-examine some important speech coding applications. The new Recursively Adaptive Randomized MultiTree Coder (RAR-MTC) adapts the parameters of the code generator, that is, the parameters of the pole/zero speech model and the pitch predictor, based only upon the transmitted codec excitation and the reconstructed output speech at the Decoder. For clean, narrowband speech sampled at 8,000 samples/sec, the RAR-MTC achieves performance competitive with the well-established, block-based Adaptive Multirate speech codec for bit rates at 12.2 kbits/s and below. Since the RAR-MTC is more of a time domain waveform following codec than the popular block based parameter adaptation codecs such as AMR-NB, the RAR-MTC can reproduce the voice of emergency first responders and the background noises in their environment, such as sirens, metal saws, pneumatic chisels, and water flow, more naturally than block based codecs. This paper provides a first pass comparison of the RAR-MTC and the AMR-NB codec for emergency first responder environments. Objective performance results show that the reconstructed voice plus background noise performance is close for the two codecs, while informal listening tests indicate that the AMR-NB codec produces cleaner sounding speech but sometimes with subtle spectral distortions while the RAR-MTC reconstructed voice has a very low level hissing sound but both the voice and the background noises are very natural sounding.
Green: a framework for supporting energy-conscious programming using controlled approximation

Energy-efficient computing is important in several systems ranging from embedded devices to large scale data centers. Several application domains offer the opportunity to tradeoff quality of service/solution (QoS) for improvements in performance and reduction in energy consumption. Programmers sometimes take advantage of such opportunities, albeit in an ad-hoc manner and often without providing any QoS guarantees. We propose a system called Green that provides a simple and flexible framework that allows programmers to take advantage of such approximation opportunities in a systematic manner while providing statistical QoS guarantees. Green enables programmers to approximate expensive functions and loops and operates in two phases. In the calibration phase, it builds a model of the QoS loss produced by the approximation. This model is used in the operational phase to make approximation decisions based on the QoS constraints specified by the programmer. The operational phase also includes an adaptation function that occasionally monitors the runtime behavior and changes the approximation decisions and QoS model to provide strong statistical QoS guarantees. To evaluate the effectiveness of Green, we implemented our system and language extensions using the Phoenix compiler framework. Our experiments using benchmarks from domains such as graphics, machine learning, signal processing, and finance, and an in-production, real-world web search engine, indicate that Green can produce significant improvements in performance and energy consumption with small and controlled QoS degradation.
The IT Security Adoption Conundrum: An Initial Step Toward Validation of Applicable Measures

Within information systems research, technology adoption is one of the most widely investigated and accepted research streams. Since its inception nearly two decades ago, conceptual models of the individual adoption decision, such as the Technology Acceptance Model (Davis 1989) and the Unified Theory of Acceptance and Use of Technology (Venkatesh et al. 2003), have been used in a variety of circumstances with sustained success. However, recent findings suggest constructs borrowed from technology adoption literature are less applicable in the case of security adoption (Warkentin et al. 2004). Unfortunately, there has been little empirical evidence available to substantiate or refute this claim. In an attempt to address this void, the authors have undertaken a multi-phase research approach in which a representative sample of current IT adoption constructs are assessed as to their applicability within the IT security context. Consistent with the recent distinction between formative and reflective constructs, the authors followed a prescribed protocol and determined that perceived ease of use (PEOU) and perceived usefulness (PU) are formative in nature – in contrast to previous studies. These findings have implications for the continued development of applicable measures of IT adoption in the security context as well as in other adoption studies.
Microbial degradation of beta-chlorinated four-carbon aliphatic acids

Alcaligenes sp. strain CC1 is able to grow on several alpha-chlorinated aliphatic acids (2-chlorobutyrate, 2-chloropropionate, and chloroacetate), as well as on the beta-chlorinated four-carbon aliphatic acids trans-3-chlorocrotonate, cis-3-chlorocrotonate, and 3-chlorobutyrate as sole carbon and energy sources. Dehalogenation of alpha-chlorinated acids could be measured by using resting cells grown on all the different carbon sources, whereas dehalogenation of beta-chlorinated four-carbon acids could be detected only by using resting cells grown on four-carbon compounds. A constitutive 2-haloacid dehalogenase, which did not show any activity with beta-chlorinated four-carbon acids, was detected in cell extracts. Cell extracts of crotonate-grown cells additionally contained a beta-haloacid dechlorination activity, which acted on trans-3-chlorocrotonate, cis-3-chlorocrotonate, and 3-chlorobutyrate and was strictly dependent on coenzyme A, ATP, and Mg2+. Dechlorination of beta-chlorinated four-carbon acids takes place after activation of the acids to their coenzyme A derivatives and seems to be independent of the constitutive 2-haloacid dehalogenase.
Sequence-to-Sequence Knowledge Graph Completion Based On Gated Attention Unit

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5’s proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment.