Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding and Utilizing the Hierarchy of Abnormal BGP Events

Abnormal events, such as security attacks, misconfigurations, or electricity failures, could have severe consequences toward the normal operation of the Border Gateway Protocol (BGP) that is in charge of the delivery of packets between different autonomous domains, a key operation for the Internet to function. Unfortunately, it has been a difficult task for network security researchers and engineers to classify and detect these events. In our previous work, we have shown that with classification (which relies on the labeling with domain knowledge from BGP experts), it is feasible to effectively detect and distinguish some worms and blackouts from normal BGP behaviors. In this paper, we move one important step forward—we show that we can automatically detect and classify between different abnormal BGP events based on a hierarchy discovered by clustering. As a systematic application of data mining, we devise a clustering method based on normalized BGP data that forms a tree-like hierarchy of abnormal BGP event classes. We then obtain a set of classification rules for each class (node) in the hierarchy, thus able to label unknown BGP data to a closest class. Our method works even as the BGP dynamics evolve over time, as shown in our experiments with seven different abnormal events during a four-year period. Our work, in a more general context, shows it is promising to conduct an interdisciplinary research between network security and data mining in solving real-world problems.
Effect of a nonsurgical treatment program on the gait pattern of idiopathic toe walking: a case report

Background Recent studies have reported many possibilities for the treatment of idiopathic toe walking (ITW); however, none of them have been sufficiently documented. The purpose of this case study was to document the evolution of the gait pattern of a child with severe ITW using the Gillette Gait Index before and after the third and sixth weeks, a nonsurgical treatment program and then every 3 months to 1 year from the start of the treatment. This is significant because the case study shows that a nonsurgical treatment program can be an alternative treatment method for children with severe ITW. Case description The case study involved a 5-year-old boy diagnosed with severe ITW. An orthopedist recommended a surgical treatment, but his parents refused to provide consent. Intervention The subject participated in a 12-week nonsurgical treatment program that used tone-inhibiting casts (TICs) combined with physiotherapy based on neurodevelopmental treatment principles. The treatment protocol included the following: 1) precast preparation; 2) TICs with treatment; and 3) post-cast treatment to improve the gait pattern. Outcomes After treatment with TICs, the range of motion of ankle dorsiflexion during stance had increased, resulting in an almost normalized gait. The patient stopped toe walking for at least 1 year. Discussion This study demonstrates that nonsurgical treatment should be considered first, with surgical options reserved for resistant cases; however, further research is required given the current lack of knowledge about treatment outcomes using TICs and the wide use of this treatment modality in children with ITW.
A Vision of Experimental Process Improvement

Software systems that support Business Process Management are in wide-spread use. They play an important role in facilitating process automation and process improvement. Yet, there is hardly any insight into whether the implementation of a supposedly improved process model leads to an actual improvement in the process. The research problem this paper addresses is: how can we determine if a new variant of a process model is an improvement over a previous variant, with respect to relevant measures? To this end, we suggest to build on recent software engineering concepts from the DevOps movement, and to develop novel techniques that provide the infrastructure for assessing in how far a specific business process change manifests an improvement.
An ultra-wideband body area propagation channel Model-from statistics to implementation

Body worn wireless sensors for monitoring health information is a promising new application. In developing these sensors, a communication channel model is essential. However, there are currently few measurements or models describing propagation around the body. To address this problem, we have measured electromagnetic waves near the torso and derived relevant statistics. We find that components diffracting around the body are well modeled using correlated log normal variables, and a Nakagami-m distribution can be used to incorporate the influence of arm motions. We have implement this model and evaluated it in terms of important communication metrics. This paper describes body area propagation statistics and proposes a suitable computer model implementation.
Beyond CEO Tenure

Prior research on strategic changes has asserted that long-tenured CEOs are less likely to initiate strategic changes. The authors argue that this assertion may exclude CEOs’ prior experiences since it implicitly assumes that all new CEOs have the same inclination toward change. By viewing CEOs as individuals embedded within experiences and relationships throughout their careers, the authors propose that CEO newness—a concept integrating prior board experience, prior heir apparent experience, and length in the current position—can provide a more complete assessment of a new CEO’s tendency toward change. They further argue that the impact of CEO newness on strategic changes will be moderated by the strategic distance between a focal firm and a CEO’s previous firm, as well as by industry similarity between the two firms. The authors’ analyses of U.S. computer firms from 1994 to 2007 support their arguments, suggesting that it is useful to adopt the concept of CEO newness while considering its boundary conditions in order to better understand strategic changes.
Modeling and characterization of electrostatically actuated CMOS-MEMS Resonator for magnetic field sensing

In this paper a new structure of electrostatically actuated clamped-clamped micro beam resonator is designed, modeled and simulated using aluminum as structural material to measure static to low frequency magnetic fields. The device is designed and simulated using CoventorWare simulation software to get mechanical properties using a static and dynamic displacement analysis and to get the optimum values of the beams parameters. The design is based on CMOS technology and surface micromachining. The resonant frequency increases with decreasing length, width and thickness of the micro beam resonator while the quality factor does not change with the change in the dimension of the resonator.
Transmitter Design for LMS-MIMO-MCCDMA Systems with Pilot Channel Estimates and Zero Forcing Equalizer

We propose a downlink multiple-input multipleoutput (MIMO) multi-carrier code division multiple access (MCCDMA) system with adaptive beamforming algorithm for smart antennas. The algorithm used in this paper is based on the Least Mean Square (LMS), with pilot channel estimation (PCE) and the zero forcing equalizer (ZFE) in the receiver, requiring reference signal and no knowledge channel. MC-CDMA is studied in a multiple antenna context in order to efficiently exploit robustness against multipath effects and multi-user flexibility of MC-CDMA and channel diversity offered by MIMO systems for radio mobile channels. Computer simulations, considering multi-path Rayleigh Fading Channel, interference inter symbol and interference are presented to verify the performance. Simulation results show that the scheme achieves good performance in a multi-user system. Keywords—Adaptive Beamforming, LMS Algorithm, MCCDMA, MIMO System, Smart Antenna.
Scene Classification Using Generalized Local Correlation

Feature extraction is an important issue for generic image recognition. In recent years, methods based on the bag-of-keypoints technique have been quite successful and are widely used. However, this technique requires the quantization of local patches to build visual words as a preprocessing step, the computational cost of which is enormous. On the other hand, methods based on global image features have been used for a long time. Because global image features can be extracted rapidly, it is relatively easy to use them in practical large-scale systems. However, the performance of global feature methods is usually poor compared to bag-of-keypoints. Therefore, it is essential to develop a more powerful scheme of global feature extraction for achieving practical applications of generic image recognition. In this paper, we show that we can boost the performance of global image features by considering the correlations of local features in addition to the mean. We experimentally verify the effectiveness of our method using standard scene classification benchmark datasets.
Delta: Scalable Data Dissemination under Capacity Constraints

In content-based publish-subscribe (pub/sub) systems, users express their interests as queries over a stream of publications. Scaling up content-based pub/sub to very large numbers of subscriptions is challenging: users are interested in low latency, that is, getting subscription results fast, while the pub/sub system provider is mostly interested in scaling, i.e., being able to serve large numbers of subscribers, with low computational resources utilization. We present a novel approach for scalable content-based pub/sub in the presence of constraints on the available CPU and network resources, implemented within our pub/sub system Delta. We achieve scalability by off-loading some subscriptions from the pub/sub server, and leveraging view-based query rewriting to feed these subscriptions from the data accumulated in others. Our main contribution is a novel algorithm for organizing views in a multi-level dissemination network, exploiting view-based rewriting and powerful linear programming capabilities to scale to many views, respect capacity constraints, and minimize latency. The efficiency and effectiveness of our algorithm are confirmed through extensive experiments and a large deployment in a WAN.
Cosmography: Extracting the Hubble series from the supernova data

We perform a number of inter-related cosmographic fits to the legacy05 and gold06 supernova datasets. We pay particular attention to the influence of both statistical and systematic uncertainties, and also to the extent to which the choice of distance scale and manner of representing the redshift scale affect the cosmological parameters. While the "preponderance of evidence" certainly suggests an accelerating universe, we would argue that (based on the supernova data) this conclusion is not currently supported "beyond reasonable doubt". As part of the analysis we develop two particularly transparent graphical representations of the redshift-distance relation -- representations in which acceleration versus deceleration reduces to the question of whether the graph slopes up or down. Turning to the details of the cosmographic fits, three issues in particular concern us: First, the fitted value for the deceleration parameter changes significantly depending on whether one performs a chi^2 fit to the luminosity distance, proper motion distance, angular diameter distance, or other suitable distance surrogate. Second, the fitted value for the deceleration parameter changes significantly depending on whether one uses the traditional redshift variable z, or what we shall argue is on theoretical grounds an improved parameterization y=z/(1+z). Third, the published estimates for systematic uncertainties are sufficiently large that they certainly impact on, and to a large extent undermine, the usual purely statistical tests of significance. We conclude that the case for an accelerating universe is considerably less watertight than commonly believed.
Priming of Hippocampal Population Bursts by Individual Perisomatic-Targeting Interneurons

Hippocampal population bursts (“sharp wave–ripples”) occur during rest and slow-wave sleep and are thought to be important for memory consolidation. The cellular mechanisms involved are incompletely understood. Here we investigated the cellular mechanisms underlying the initiation of sharp waves using a hippocampal slice model. To this end, we used a combination of field recordings with planar multielectrode arrays and whole-cell patch-clamp recordings of individual anatomically identified pyramidal neurons and interneurons. We found that GABAA receptor-mediated inhibition is necessary for sharp wave generation. Moreover, the activity of individual perisomatic-targeting interneurons can both suppress, and subsequently enhance, the local generation of sharp waves. Finally, we show that this is achieved by the tight control of local excitation and inhibition by perisomatic-targeting interneurons. These results suggest that perisomatic-targeting interneurons assist in selecting the subset of pyramidal neurons that initiate each hippocampal sharp wave–ripple.
Per‐channel basis normalization methods for flow cytometry data

Between‐sample variation in high‐throughput flow cytometry data poses a significant challenge for analysis of large‐scale data sets, such as those derived from multicenter clinical trials. It is often hard to match biologically relevant cell populations across samples because of technical variation in sample acquisition and instrumentation differences. Thus, normalization of data is a critical step before analysis, particularly in large‐scale data sets from clinical trials, where group‐specific differences may be subtle and patient‐to‐patient variation common. We have developed two normalization methods that remove technical between‐sample variation by aligning prominent features (landmarks) in the raw data on a per‐channel basis. These algorithms were tested on two independent flow cytometry data sets by comparing manually gated data, either individually for each sample or using static gating templates, before and after normalization. Our results show a marked improvement in the overlap between manual and static gating when the data are normalized, thereby facilitating the use of automated analyses on large flow cytometry data sets. Such automated analyses are essential for high‐throughput flow cytometry. © 2009 International Society for Advancement of Cytometry
Lightweight anycast enumeration and geolocation

Several Internet services such as CDNs, DNS name servers, and sinkholes use IP-layer anycast to reduce user response times and increase robustness with respect to network failures and denial of service attacks. However, current geolocation tools fail with anycast IP addresses. In our recent work [1], we remedy to this by developing an anycast detection, enumeration, and geolocation technique based on a set of delay measurements from a handful of geographically distributed vantage points. The technique (i) detects if an IP is anycast, (ii) enumerates replicas by finding the maximum set of non-overlapping disks (i.e., areas centered around vantage points), and (iii) geolocates the replicas by solving a classification problem and assigning the server location to the most likely city. We propose to demo this technique. In particular, we visually show how to detect an anycast IP, enumerate its replicas, and geolocate them on a map. The demo allows to browse previously geolocated services, as well as to explore new targets on demand.
Climate change reduces nectar secretion in two common Mediterranean plants

The negative effect of climate change on plants and plant–pollinator interactions is a matter of concern worldwide. The Mediterranean region is considered particularly susceptible to climate warming and the communities of this region will need to face considerable climatic changes over the 21st century. We studied the effect of temperature on nectar secretion of two Mediterranean species (Ballota acetabulosa and Teucrium divaricatum) to evaluate their potential responses to climate change and a consequent effect on their pollinators. Both species would handle moderate warming relatively well but would be negatively affected by strong warming predicted for the end of this century.
Taskmaster: recasting email as task management

Email has come to play a central role in task management, yet email tool features have remained relatively static in recent years, lagging behind users’ evolving practices. The Taskmaster system narrows this gap by recasting email as task management and embedding task-centric resources directly in the client. During this workshop, we will describe the field research that inspired Taskmaster and the principles behind its design. We will also show how user studies conducted with “live” email data over a two-week period revealed the value of a task-centric approach to email system design and its potential benefits for overloaded users.
Impact of yurt tourism on labor division in nomadic Kazakh families

ABSTRACT Tourism’s impacts on ethnic cultures have received much attention, but few studies have been done on its impacts on nomadic people, especially for Kazakh nomads in China. Many Kazakh households are experiencing a transition from pastoralism to tourism, with yurt tourism being the popular Kazakh tourism products. This study takes Narat in China as a case to understand the impact of yurt tourism on the gendered labor division of Kazakh families, and the status of Kazakh women. Observations and semi-structured interviews were adopted. We find that the division of labor in yurt tourism is an extension of traditional nomad labor division and that the traditional gender statuses have been reinforced. Women are required to not only work but also face additional social pressure since they are considered to be going against customary rules when greeting tourists. Despite this, women’s self-confidence, business skills, and income have improved since the development of tourism. We argue that the form of tourism product, ideology of gender roles, and position of ethnic families in the tourism market account for this change. Diverse results of tourism impacts on the gender relationships of ethnic communities can be derived from the interactions of these three factors.
Separation of texture and shape in images of faces for image coding and synthesis

Human faces differ in shape and texture. Image representations based on this separation of shape and texture information have been reported by several authors [for a review, see Science272, 1905 (1996)]. We investigate such a representation of human faces based on a separation of texture and two-dimensional shape information. Texture and shape were separated by use of pixel-by-pixel correspondence among the various images, which was established through algorithms known from optical flow computation. We demonstrate the improvement of the proposed representation over well-established pixel-based techniques in terms of coding efficiency and in terms of the ability to generalize to new images of faces. The evaluation is performed by calculating different distance measures between the original image and its reconstruction and by measuring the time that human subjects need to discriminate them.
Placement and Routing for Three-Dimensional FPGAs

We explore physical layout for a three-dimensional (3D) FPGA architecture. For placement, we introduce a top-down partitioning technique based on rectilinear Steiner trees; we then employ a one-step router to produce the (cid:12)nal layout. Experimental results indicate that our approach produces e(cid:11)ective 3D layouts, using considerably shorter average interconnect distance than is achievable with conventional 2D FPGA's of comparable size
Comparison of blocking and field encoding techniques for duplicate detection in contact data

Over time databases can gather a lot of duplicate entries. Detecting these duplicates is essential for data cleansing and the data integration process. Comparing all records in a dataset pair-wise becomes quickly unfeasible as the dataset grows larger. One of the techniques to reduce the number of comparisons is to group records using a hash function before comparing. In this paper we evaluate several supervised learning algorithms for generating such hash functions. For each algorithm, we try several variants, including several that re-encode the field values. The variants are evaluated in a K-fold evaluation on two datasets. The results show only small differences between the different algorithms and their variants. The brute force sequential covering algorithm and the genetic sequential covering algorithm provide the best results. The variants with additional re-encodings of the fields values do not show any difference compared to the variants without.
Real-Time Identification of Parallel Texts from BilingualNewsfeed

Parallel texts are documents that present parallel translations. This paper describes a simple method that can be deployed on a real-time news feed to create an infinitely growing source of parallel texts in French and English. Our experiment was lead on the Canada Newswire news feed. Given some of its intrinsic properties, it was possible to deploy a relatively simple text matching techniques that rely on language independent cognates such numbers, capitalized words, punctuation and new lines characters. On three week of press releases, our system correctly identified the vast majority of parallel press release. It committed only minor errors on repeated news items.
The Influence of the Economic Students’ Motivations and Language Learning Strategies towards Their English Achievement in STIE Muhammadiyah Palopo

The aims of this research were: (1) to investigate the students’ motivations, (2) to investigate the language learning strategies (LLSs) that are used by the students, (3) to investigate the level of students’ LLSs usage, and (4) to investigate the influence of the students’ motivations and the level of students’ LLSs usage towards their English competence. This research gave descriptions about the students’ motivations, LLSs that are used by the students, and the level of students’ LLSs usage. The sample of this research was 111 students that were taken from three classes of the first year students from three study programs, i.e. Ilmu Ekonomi, Manajemen, and Akuntansi. This research revealed that the students were categorized into high motivated to learn English where 70 students (63%) had high motivation, 25 students (23%) had very high motivation, 13 students (12%) had medium motivation, and 3 students (3%) had low motivation. This research also found that the students were dominantly used metacognitive strategies (44%), followed by compensation strategies (18%), affective strategies (14%), social strategies (10%), cognitive strategies (8%), and the least used strategies were memory strategies (6%). For the level of the students’ language learning strategies usage, the researcher found that the students level were categorized into medium, where 78 students (70%) mediumly used the LLSs, 30 students (27%) highly used the LLSs, 2 students (2%) lowly used the LLSs, and 1 student (1%) very highly used the LLSs.