Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis in usability evaluations: an exploratory study

While the planning and implementation of usability evaluations are well described in the literature, the analysis of the evaluation data is not. We present interviews with 11 usability professionals on how they conduct analysis, describing the resources, collaboration, creation of recommendations, and prioritization involved. The interviews indicate a lack of structure in the analysis process and suggest activities, such as generating recommendations, that are unsupported by existing methods. We discuss how to better support analysis, and propose four themes for future research on analysis in usability evaluations.
Analysis of sonographic features in the differentiation of fibroadenoma and invasive ductal carcinoma.

OBJECTIVE The purpose of this study was to determine the predictive power of sonographic tumor descriptors in the differentiation of fibroadenoma from invasive ductal carcinoma of the breast. SUBJECTS AND METHODS Three hundred thirty-six tumors (142 fibroadenomas and 194 invasive ductal carcinomas) of the breast diagnosed using sonography were prospectively recorded with respect to the shape, contour, echo texture, echogenicity, sound transmission, and surrounding tissue of the tumors. Evaluation included odds and odds ratios of single sonographic features as well as sensitivity, specificity, and positive and negative predictive values of combinations of features. Tumor descriptors were also evaluated using multiple logistic regression analysis after adjustment for age and clinical examination. RESULTS Irregular shape and contour, extensive hypoechogenicity, shadowing, echogenic halo, and distortion of surrounding tissue were the findings with the highest predictive value of malignancy. A thin echogenic pseudocapsule was the most important sonographic finding predictive of the benign nature of a solid mass. Echo texture was of little value in the differentiation of breast tumors. Age and clinical examination remained important predictors in a clinically referred patient population because a palpable mass in an elderly patient is most likely a carcinoma. We saw considerable overlap of most sonographic features in both benign and malignant tumors. However, using strict sonographic criteria and a combination of descriptors, we found a negative predictive value of 100% in palpable and 96% in impalpable tumors. CONCLUSION A combination of tumor descriptors gave negative predictive values approaching 100%, allowing downgrading of solid breast masses with a high degree of confidence. Extensive sonographic features analysis in patients with indeterminate clinical and mammographic findings has the potential for downgrading a tumor and possibly obviating the need for excision in a subgroup of patients. Further investigations may provide standardization of sonographic descriptor analysis and establishment of the combination of the most predictive features that would be useful in daily practice.
Learning Expected Hitting Time Distance

Most distance metric learning (DML) approaches focus on learning a Mahalanobis metric for measuring distances between examples. However, for particular feature representations, e.g., histogram features like BOW and SPM, Mahalanobis metric could not model the correlations between these features well. In this work, we define a non-Mahalanobis distance for histogram features, via Expected Hitting Time (EHT) of Markov Chain, which implicitly considers the high-order feature relationships between different histogram features. The EHT based distance is parameterized by transition probabilities of Markov Chain, we consequently propose a novel type of distance learning approach (LED, Learning Expected hitting time Distance) to learn appropriate transition probabilities for EHT based distance. We validate the effectiveness of LED on a series of real-world datasets. Moreover, experiments show that the learned transition probabilities are with good comprehensibility.
A STRONG EXCESS IN THE 20–100 keV EMISSION OF NGC 1365

We present a new Suzaku observation of the obscured active galactic nucleus in NGC 1365, revealing an unexpected excess of X-rays above 20 keV of at least a factor ∼2 with respect to the extrapolation of the best-fitting 3–10 keV model. Additional Swift-BAT and Integral-IBIS observations show that the 20–100 keV is concentrated within ∼1.5 arcmin from the center of the galaxy, and is not significantly variable on timescales from days to years. A comparison of this component with the 3–10 keV emission, which is characterized by a rapidly variable absorption, suggests a complex structure of the circumnuclear medium, consisting of at least two distinct components with rather different physical properties, one of which covers >80% of the source with a column density NH ∼ 3–4×1024 cm−2. An alternative explanation is the presence of a double active nucleus in the center of NGC 1365.
Performance Modeling and Cost Analysis of a Pilot-Scale Reverse Osmosis Process for the Final Purification of Olive Mill Wastewater

A secondary treatment for olive mill wastewater coming from factories working with the two-phase olive oil production process (OMW-2) has been set-up on an industrial scale in an olive oil mill in the premises of Jaén (Spain). The secondary treatment comprises Fenton-like oxidation followed by flocculation-sedimentation and filtration through olive stones. In this work, performance modelization and preliminary cost analysis of a final reverse osmosis (RO) process was examined on pilot scale for ulterior purification of OMW-2 with the goal of closing the loop of the industrial production process. Reduction of concentration polarization on the RO membrane equal to 26.3% was provided upon increment of the turbulence over the membrane to values of Reynolds number equal to 2.6 × 104. Medium operating pressure (25 bar) should be chosen to achieve significant steady state permeate flux (21.1 L h−1 m−2) and minimize membrane fouling, ensuring less than 14.7% flux drop and up to 90% feed recovery. Under these conditions, irreversible fouling below 0.08 L h−2 m−2 bar−1 helped increase the longevity of the membrane and reduce the costs of the treatment. For 10 m3 day−1 OMW-2 on average, 47.4 m2 required membrane area and 0.87 € m−3 total costs for the RO process were estimated.
The role of goal relevance in the occurrence of systematic slip errors in routine procedural tasks

Slip errors can have severe consequences but are notoriously difficult to reduce. Training, visual cues and increasing motivation are generally not effective in eliminating these slips. Instead, the approach this work takes is to identify which steps in a routine task are most error prone, so that these can be designed out of device interactions. In particular, device- and task-oriented steps are investigated. Device-oriented steps are "extra" steps imposed by the device that do not directly contribute towards the task goal. Conversely, task-oriented steps directly bring the user closer to their goal. The main hypothesis addressed in this work is that device-oriented steps are more problematic than task-oriented ones. The concepts of device- and task-oriented steps are investigated more closely, by analysing the literature on routine action and mental representations of different steps. The core diff erence between the steps is found to be how relevant a step is to the goal. This is further supported by two qualitative studies. A series of experimental studies investigates the cognitive mechanisms underlying device and task-oriented steps. This is addressed through six experiments that address error rates, step times, proportion of omissions and sensitivity to working memory load. Participants learned one of three routine tasks, with several carefully controlled device- and task-oriented steps. The results show that on device-oriented steps, error rates are higher, step times are longer, the proportion of omissions is greater, and working memory load has an increased effect. These findings support the hypothesis that activation levels are lower on device-oriented steps. The thesis concludes that a step's relevance to the task goal plays an important role in the occurrence of errors. This work has implications for both our understanding of routine procedural action as well as the design of devices.
ESTIMATION OF PEAK HORIZONTAL ACCELERATIONS FOR THE SITE OF MUZAFFARABAD USING DETERMINISTIC APPROACH

The site of Muzaffarabad is situated in the NW Himalayan Fold – and – Thrust Belt Pakistan, which occupies a 250 km wide and about 560 km long, irregularly shaped mountainous region stretching from the Afghan border near Parachinar, up to Kashmir Basin situated in the active zone of convergence thereby recording a large number of seismic events. Quite a large number of seismically active tectonic features like Main Mantle Thrust (MMT), Main Boundary Thrust (MBT), Mansehra Thrust, Jhelum Fault, Kotli Thrust, Raikot Fault, Thakot Fault, Riasi Thrust, Sangargali Thrust, Hissartang Fault and many others are located within this fold and thrust belt. Historical as well as Instrumental seismological data collected in the present study confirms the active nature of all these faults. In the present work, peak horizontal accelerations have been estimated for the site of Muzaffarabad using deterministic approach. The selection of this site has been based primarily due to the fact that this site is representing the location with heavy population, high seismicity and surrounded by active tectonic features. A total of twelve faults have been considered as a critical seismogenic features to the area and their maximum potential magnitudes and seven available attenuation equations, peak horizontal accelerations have been determined. On the basis of these maximum potential magnitudes and the peak horizontal accelerations, Main Boundary Thrust having peak horizontal accelerations of 0.47 g has been designated as the most hazardous for the site of Muzaffarabad.
MalwareVis: entity-based visualization of malware network traces

This paper presents MalwareVis, a utility that provides security researchers a method to browse, filter, view and compare malware network traces as entities. Specifically, we propose a cell-like visualization model to view the network traces of a malware sample's execution. This model is a intuitive representation of the heterogeneous attributes (protocol, host ip, transmission size, packet number, duration) of a list of network streams associated with a malware instance. We encode these features into colors and basic geometric properties of common shapes. The list of streams is organized circularly in a clock-wise fashion to form an entity. Our design takes into account of the sparse and skew nature of these attributes' distributions and proposes mapping and layout strategies to allow a clear global view of a malware sample's behaviors. We demonstrate MalwareVis on a real-world corpus of malware samples and display their individual activity patterns. We show that it is a simple to use utility that provides intriguing visual representations that facilitate user interaction to perform security analysis.
InAlN/GaN HEMTs for Operation in the 1000 $^{\circ} \hbox{C}$ Regime: A First Experiment

GaN-based heterostructures, and here, particularly, the lattice matched InAlN/GaN configuration, possess high chemical and thermal stability. Concentrating on refractory metal contact schemes, HEMT devices have been fabricated allowing high-temperature 1-MHz large-signal operation at 1000°C (in vacuum) for 25 h. Despite slow gate contact degradation, major degradation of the heterostructure could not be observed. Extrapolation of the RF characteristics suggests that operation up to gigahertz frequencies at this temperature may be feasible.
Concerning a General Framework for the Development of Intelligent Systems

There exists on-going debate between Connectionism and Symbolism as to the nature of and approaches to cognition. Many viewpoints exist and various issues seen as important have been raised. This paper suggests that a combination of these methodologies will lead to a better overall model. The paper reviews and assimilates the opinions and viewpoints of these diverse fields and provides a cohesive list of issues thought to be critical to the modeling of intelligence. Further, this list results in a framework for the development of a general, unified theory of cognition.
In-Place Data Sliding Algorithms for Many-Core Architectures

In-place data manipulation is very desirable in many-core architectures with limited on-board memory. This paper deals with the in-place implementation of a class of primitives that perform data movements in one direction. We call these primitives Data Sliding (DS) algorithms. Notable among them are relational algebra primitives (such as select and unique), padding to insert empty elements in a data structure, and stream compaction to reduce memory requirements. Their in-place implementation in a bulk synchronous parallel model, such as GPUs, is specially challenging due to the difficulties in synchronizing threads executing on different compute units. Using a novel adjacent work-group synchronization technique, we propose two algorithmic schemes for regular and irregular DS algorithms. With a set of 5 benchmarks, we validate our approaches and compare them to the state-of-the-art implementations of these benchmarks. Our regular DS algorithms demonstrate up to 9.11x and 73.25x on NVIDIA and AMD GPUs, respectively, the throughput of their competitors. Our irregular DS algorithms outperform NVIDIA Thrust library by up to 3.24x on the three most recent generations of NVIDIA GPUs.
Eurographics/siggraph Symposium on Computer Animation (2003) Vision-based Control of 3d Facial Animation

Controlling and animating the facial expression of a computer-generated 3D character is a difficult problem because the face has many degrees of freedom while most available input devices have few. In this paper, we show that a rich set of lifelike facial actions can be created from a preprocessed motion capture database and that a user can control these actions by acting out the desired motions in front of a video camera. We develop a real-time facial tracking system to extract a small set of animation control parameters from video. Because of the nature of video data, these parameters may be noisy, low-resolution, and contain errors. The system uses the knowledge embedded in motion capture data to translate these low-quality 2D animation control signals into high-quality 3D facial expressions. To adapt the synthesized motion to a new character model, we introduce an efficient expression retargeting technique whose run-time computation is constant independent of the complexity of the character model. We demonstrate the power of this approach through two users who control and animate a wide range of 3D facial expressions of different avatars.
Good Rationalizations of Voting Rules

We explore the relationship between two approaches to rationalizing voting rules: the maximum likelihood estimation (MLE) framework originally suggested by Condorcet and recently studied by Conitzer, Rognlie, and Xia, and the distance rationalizability (DR) framework of Elkind, Faliszewski, and Slinko. The former views voting as an attempt to reconstruct the correct ordering of the candidates given noisy estimates (i.e., votes), while the latter explains voting as search for the nearest consensus outcome. We provide conditions under which an MLE interpretation of a voting rule coincides with its DR interpretation, and classify a number of classic voting rules, such as Kemeny, Plurality, Borda and Single Transferable Vote (STV), according to how well they fit each of these frameworks. The classification we obtain is more precise than the ones that result from using MLE or DR alone: indeed, we show that the MLE approach can be used to guide our search for a more refined notion of distance rationalizability and vice versa.
Delightful identification & persuasion: towards an analytical and applied rhetoric of digital games

Why and how do digital games make us play with them? What are their argumentative strategies of make-believe like, shaped as they are by possibilities and necessities? How do games induce constant cooperation and persuade us to play, and keep playing1? Finally, what signifies the relationship between game design(er), gameplay, and player? Let us sidestep typical answers to these questions and interogate the fundamental reason for playing human-computer based games. Game designers Greg Costikyan and Chris Crawford cite learning; Malone and Malone/Lepper mention motivational captivation through aspects of intrinsic motivation such as confidence, control, challenge, fantasy, or curiosity. But perhaps it would be better for us to combine our introductory queries by asking more precisely: What is the empirically approximated and social-, media-, and neuro-psychologically rooted rhetoric of digital games? Granted: comprehensively responding to this last matter would likely take much longer than one essay. But the attempt is worthwhile, and overdue to commence with: when designing digital games requires thinking about digital games, and thinking about these games requires designing–or at least prototyping–them in the first place, a rhetoric of digital games can ultimately serve the purpose of bridging the worlds of creating games (that is, applying such a rhetoric) and thinking about games (that is, analyzing games along such a rhetoric). This paper shall provide a first attempt to offer such an anastomosis. General rhetoric–as the mother of all media theory–has provided specific rhetoricae with this same goal for other forms of symbolic action, strategic communication, and effective expression. Think of the speeches and public performances analysed and described
A compact CPW-fed metamaterial antenna for high efficiency and wideband applications

In this paper a compact planar CPW-fed metamaterial antenna with closed ring resonator (CRR) has been proposed. It is observed that by employing CRR into the structure an extra band is found at 2.17 GHz (n = -1 mode) and therefore electrical size of the antenna is decreased significantly. CPW- feed is introduced to enhance the bandwidth and radiation efficiency of proposed antenna. This antenna is operated at 2.15 - 2.19 GHz and 3.2 - 5.25 GHz, at the center frequency 4.23 GHz. It achieves peak antenna gain of 2.97 dB and radiation efficiency upto 97%. In addition to that proposed antenna shows consistent radiation patterns throughout the bandwidth.
Quantum transport senses community structure in networks

Quantum time evolution exhibits rich physics, attributable to the interplay between the density and phase of a wave function. However, unlike classical heat diffusion, the wave nature of quantum mechanics has not yet been extensively explored in modern data analysis. We propose that the Laplace transform of quantum transport (QT) can be used to construct an ensemble of maps from a given complex network to a circle S^{1}, such that closely related nodes on the network are grouped into sharply concentrated clusters on S^{1}. The resulting QT clustering (QTC) algorithm is as powerful as the state-of-the-art spectral clustering in discerning complex geometric patterns and more robust when clusters show strong density variations or heterogeneity in size. The observed phenomenon of QTC can be interpreted as a collective behavior of the microscopic nodes that evolve as macroscopic cluster "orbitals" in an effective tight-binding model recapitulating the network. python source code implementing the algorithm and examples are available at https://github.com/jssong-lab/QTC.
Pathological Buying Online as a Specific Form of Internet Addiction: A Model-Based Experimental Investigation

The study aimed to investigate different factors of vulnerability for pathological buying in the online context and to determine whether online pathological buying has parallels to a specific Internet addiction. According to a model of specific Internet addiction by Brand and colleagues, potential vulnerability factors may consist of a predisposing excitability from shopping and as mediating variable, specific Internet use expectancies. Additionally, in line with models on addiction behavior, cue-induced craving should also constitute an important factor for online pathological buying. The theoretical model was tested in this study by investigating 240 female participants with a cue-reactivity paradigm, which was composed of online shopping pictures, to assess excitability from shopping. Craving (before and after the cue-reactivity paradigm) and online shopping expectancies were measured. The tendency for pathological buying and online pathological buying were screened with the Compulsive Buying Scale (CBS) and the Short Internet Addiction Test modified for shopping (s-IATshopping). The results demonstrated that the relationship between individual’s excitability from shopping and online pathological buying tendency was partially mediated by specific Internet use expectancies for online shopping (model’s R² = .742, p < .001). Furthermore, craving and online pathological buying tendencies were correlated (r = .556, p < .001), and an increase in craving after the cue presentation was observed solely in individuals scoring high for online pathological buying (t(28) = 2.98, p < . 01, d = 0.44). Both screening instruments were correlated (r = .517, p < .001), and diagnostic concordances as well as divergences were indicated by applying the proposed cut-off criteria. In line with the model for specific Internet addiction, the study identified potential vulnerability factors for online pathological buying and suggests potential parallels. The presence of craving in individuals with a propensity for online pathological buying emphasizes that this behavior merits potential consideration within the non-substance/behavioral addictions.
FISH Finder: a high-throughput tool for analyzing FISH images

MOTIVATION Fluorescence in situ hybridization (FISH) is used to study the organization and the positioning of specific DNA sequences within the cell nucleus. Analyzing the data from FISH images is a tedious process that invokes an element of subjectivity. Automated FISH image analysis offers savings in time as well as gaining the benefit of objective data analysis. While several FISH image analysis software tools have been developed, they often use a threshold-based segmentation algorithm for nucleus segmentation. As fluorescence signal intensities can vary significantly from experiment to experiment, from cell to cell, and within a cell, threshold-based segmentation is inflexible and often insufficient for automatic image analysis, leading to additional manual segmentation and potential subjective bias. To overcome these problems, we developed a graphical software tool called FISH Finder to automatically analyze FISH images that vary significantly. By posing the nucleus segmentation as a classification problem, compound Bayesian classifier is employed so that contextual information is utilized, resulting in reliable classification and boundary extraction. This makes it possible to analyze FISH images efficiently and objectively without adjustment of input parameters. Additionally, FISH Finder was designed to analyze the distances between differentially stained FISH probes. AVAILABILITY FISH Finder is a standalone MATLAB application and platform independent software. The program is freely available from: http://code.google. com/p/fishfinder/downloads/list.