Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Fault Localization Using Model-domain Synthesized Failing Test Generation

A test suite is indispensable for conducting effective fault localization, and has two classes of tests: passing tests and failing tests. However, in practice, passing tests heavily outnumber failing tests regarding a fault, leading to failing tests being a minority class in contrast to passing tests. Previous work has empirically shown that the lack of failing tests regarding a fault leads to a class-balanced test suite, which tends to hamper fault localization effectiveness.To address this issue, we propose MSGen: a Model-domain Synthesized Failing Test Generation approach. MSGen utilizes the widely used information model of fault localization (i.e., an abstraction of the execution information and test results of a test suite), and uses the minimum variability of the minority feature space to create new synthesized model-domain failing test samples (i.e., synthesized vectors with failing labels defined as the information model) for fault localization. In contrast to traditional test generation directly from the input domain, MSGen seeks to synthesize failing test samples from the model domain. We apply MSGen to 12 state-of-the-art localization approaches and also compare MSGen to 2 representative data optimization approaches. The experimental results show that our synthesized test generation approach significantly improves fault localization effectiveness with up to 51.22%.
Detection and Classification of Subsea Objects in Forward-Looking Sonar and Electro-Optical Sensors for ROV Autonomy

SeeByte and Greensea Systems, Inc. (Greensea) have teamed up with the sponsorship of the Defense Innovation Unit (DIU) and PMS 408 to advance Automatic Target Recognition (ATR) of Mine Like Objects (MLOs) for the purpose of developing an autonomous capability for Remotely Operated Vehicles (ROVs) used in maritime Explosive Ordnance Disposal operations. Advancements in autonomy requires advancements in the sensor processing to detect the surrounding environment and identify objects of interest. In this case the sensors chosen were a dual frequency Forward Looking Sonar and a Electro-Optical (EO) stereo camera system. The objects of interest are MLOs that need to be located, identified, and inspected by an autonomous submersible robot. SeeByte used Deep Learning Neural Network (DNN) on both of these sensor feeds yielding a very robust detection and classification system. The output of that detection and classification is provided to Greensea’s vehicle control and autonomy software as candidate targets for mapping and prosecution.
Affect Recognition in Hand-Object Interaction Using Object-Sensed Tactile and Kinematic Data

We investigate the recognition of the affective states of a person performing an action with an object, by processing the object-sensed data. We focus on sequences of basic actions such as grasping and rotating, which are constituents of daily-life interactions. iCube, a 5 cm cube, was used to collect tactile and kinematics data that consist of tactile maps (without information on the pressure applied to the surface), and rotations. We conduct two studies: classification of i) emotions and ii) the vitality forms. In both, the participants perform a semi-structured task composed of basic actions. For emotion recognition, 237 trials by 11 participants associated with anger, sadness, excitement, and gratitude were used to train models using 10 hand-crafted features. The classifier accuracy reaches up to 82.7%. Interestingly, the same classifier when learned exclusively with the tactile data performs on par with its counterpart modeled with all 10 features. For the second study, 1135 trials by 10 participants were used to classify two vitality forms. The best-performing model differentiated gentle actions from rude ones with an accuracy of 84.85%. The results also confirm that people touch objects differently when performing these basic actions with different affective states and attitudes.
An inexact reasoning technique based on extended fuzzy productions rules

This paper presents a technique for performing inexact reasoning, where extended fuzzy production rules are used for knowledge representation. An extended fuzzy production role is a role that describes a fuzzy relation between two conditions. The certainty factor (CF) and the truth values of the conditions of an extended fuzzy production role are defined as a continuous range of values. An efficient algorithm is also presented for performing inexact reasoning automatically, where given the truth values of some conditions, the algorithm can perform inexact reasoning to evaluate the truth values of other conditions.
Using Depth Image in 3D Model Retrieval System

For content-based 3D model retrieval, an improved depth image-based feature extraction algorithm is proposed. First, a 3-D model is preprocessed. Secondly, six depth images are generated in three principal directions in the normalized coordinate system. Thirdly, the eigenvectors of 3D model are obtained through 2D Fourier Transform of the depth images. Finally a new method is used for low-frequency sampling. Experiments show that the approach performed quite well despite its apparently simple approach. In our large 3D database, our approach is well for variant resolution models and holds satisfied computational costs.
Low-energy plasma ion observations in Saturn's magnetosphere

We summarize observations of positive ions in Saturn's magnetosphere made by the Voyager plasma science experiments during the two Saturn encounters. In the outer magnetosphere, the number density and temperature of the plasma are quite variable. When cold, two ionic species are clearly resolved: the lighter ion is probably H+ and the heavier ion has a mass/charge ratio consistent with it being O+ or N+. In the vicinity of 15 Saturn radii (Rs), there is a region in which the composition or charge state of the ions apparently changes, but it then returns to the typical composition. The two ionic components do not have the same thermal speeds or temperatures. In the outer magnetosphere, the two components partially corotate with the planet; their azimuthal speeds are approximately 20% below that expected for rigid corotation. There is some evidence for inward and outward radial flow in addition to the azimuthal motion in approximately one-third of the Voyager 1 cold ion spectra. The sources of the plasma are still undetermined. The plasma sheet becomes well established within 16 Rs from the planet in the Voyager 1 data and within 10 Rs in the Voyager 2 data. Near the ring plane crossings close to Saturn, O+ is the dominant ion within 1 Rs of the ring plane, whereas H+ dominates outside of that distance from the ring plane. Two scale heights characterize the O+ ion number density distribution in the vicinity of the ring plane: within 0.5 Rs of the plane, the scale height is ∼0.3 Rs; it is almost twice as great outside of 0.5 Rs. The directly observed temperature of the O+ ions is too high to be consistent with such scale heights. A temperature anisotropy of T⊥/T∥ approximately equal to 6 would explain the observations. The cause of the anisotropy is not certain. In general, the plasma and magnetic field appear to be in dynamical equilibrium. In comparing Saturn's magnetosphere to Jupiter's, there appears to be much less acceleration of low-energy plasma to higher energies at Saturn.
Tuning the electronic properties of molybdenum di-sulphide monolayers via doping using first-principles calculations

In 2D semiconductors, doping offers an effective approach for modulating their structural and electronic properties-owing to the creation of newly formed chemical bonds and bond relaxation. By means of density functional theory (DFT), we systematically explored the electronic properties of monolayer MoS2 doped with X-atoms (X comprises of metals Li, Be, Al; metalloids B, Si; non-metals (NMs) C, N, P, O and the NM atoms belonging to halogen group (F, Cl)). The bonding nature of the host structures with the doped elements have been determined using electron localization function (ELF). Phonon spectra calculations are performed to distinguish between the dynamically stable and unstable systems. The band gap of MoS2 stands divided into smaller values in a variety of magnitude depending on the dopant site and the nature of the substituted atom. The results show that halogen non-metals exhibit n-type conduction in both the (Mo- and S-rich) environments. Thus, substitutional doping of impurity atoms belonging to different groups can successfully tune the band gap of MoS2 to the desired level for its useful applications in semiconducting electronic devices in addition to other interesting information on the nature of doping, which could be adopted to dope other 2D-TMDs to tailor their electronic and optical characteristics for more efficient electronic devices.
A four-step laser interference lithography for patterning pixelated micro-polarizer array

Pixelated micro-polarizer array is an attractive polarization imaging device because of its real-time fully detecting capability to Stokes parameters and high integration level. However, this micro-device is commonly fabricated by electron beam lithography, thus is often high-cost. Since periodic unit of this device is a 2×2 array of four types of one-dimensional (1D) gratings with different orientations (0°, 45°, 90°, 135°), a low-cost grating fabrication technique, interference lithography (IL), is possible to be used to fabricate this device. In this research, a four-steps exposure patterning method of micro-polarizer array based on single Lloyd’s mirror interferometer IL is developed. This interferometer is composed by two perpendicular parts: a mirror and a photoresist holder, and a 1D grating parallel with mirror can be fabricated in the exposure region on photoresist through lithography. A Cr-based mask, whose transparent pattern is a square array of square windows with 15×15 μm2 window size and 17 μm window spacing, is introduced and clamped onto the photoresist to cut exposure region. In four exposure steps, each 15×15 μm2 quadrant area of a polarizer array with 2 μm quadrant spacing will be exposed on photoresist, respectively. Between two exposure steps, mask and photoresist substrate need to be simultaneously horizontal rotated, to adjust the orientation of grating fabricated in next exposed quadrant on photoresist. Based on this method, we fabricated the single-state patterned micro-polarizer array, which effectively controls the cost and fabrication cycle. The experimental results show that the photoresist grating within a single window has stable structure, uniform period and good directivity.
Malware Network Traffic Classification on the Edge

Network traffic classification is a part of many cybersecurity applications, such as intrusion detection systems and anomaly detection. Currently, many cybersecurity tasks employ a cloud computing architecture, including network traffic classification. However, this architecture may not be able to meet the latency demands in the future with the ever-increasing number of network data. Therefore, we propose to perform network traffic classification utilizing edge computing and applying tiny machine learning to classify closer to the data source. Our approach uses TensorFlow Lite to convert a traditional convolutional neural network into a tiny machine learning model that runs on an edge device. In order to assess the impact of edge classification, we ran simulations on the edge device and compared these results to a cloud-computing counterpart. We determined that the edge device was faster and had reduced latency compared to a cloud computing alternative, with a negligible reduction in accuracy and decrease in model size throughout all experiments.
Application of a Novel Hybrid CNN-GNN for Peptide Ion Encoding

Almost all state-of-the-art de novo peptide sequencing algorithms now use machine learning models to encode fragment peaks and hence identify amino acids in mass spectrometry (MS) spectra. Previous work has highlighted how the inherent MS challenges of noise and missing peptide peaks detrimentally affect the performance of these models. In the present research we extracted and evaluated the encoding modules from 3 state-of-the-art de novo peptide sequencing algorithms. We also propose a convolutional neural network-graph neural network machine learning model for encoding peptide ions in tandem MS spectra. We compared the proposed encoding module to those used in the state-of-the-art de novo peptide sequencing algorithms by assessing their ability to identify b-ions and y-ions in MS spectra. This included a comprehensive evaluation in both real and artificial data across various levels of noise and missing peptide peaks. The proposed model performed best across all data sets using two different metrics (area under the receiver operating characteristic curve (AUC) and average precision). The work also highlighted the effect of including additional features such as intensity rank in these encoding modules as well as issues with using the AUC as a metric. This work is of significance to those designing future de novo peptide identification algorithms as it is the first step toward a new approach.
Penerapan Data Mining Pada Hasil Tracer Study Alumni Untuk Menemukan Pola Asosiasi Dengan Algoritma Fp-Growth

Abstrak - Tracer study adalah studi terhadap lulusan lembaga pendidikan tinggi, yang juga merupakan proses untuk mendapatkan informasi terkait transformasi alumni dari masa pendidikan ke dunia industri. Hal ini dilakukan untuk menentukan kelayakan pekerjaan lulusan dan juga penilaian retrospektif, seperti apa pelayanan yang diperoleh selama masa studi. Penelitian ini akan melaksanakan tracer study pada tingkat program studi di Teknik Informatika di UIN Suska Riau dan kemudian hasil studi lanjutan ini akan diolah untuk mendapatkan pola korelasi atau hubungan yang bermakna. Pengolahan data akan menggunakan teknologi data mining dan algoritma fp-growth. Berdasarkan hasil analisa pengujian dan interpretasi dari total data bersih 129 alumni teknik informatika UIN Suska Riau yaitu pola yang dihasilkan dari proses mining dengan minimum support 15% dan minimum confidence 40% adalah sebanyak 608 pola. Peneliti mengambil beberapa pola dengan antecedents berupa ipk, masa tunggu, bidang pekerjaan, gaji, pekerjaan pertama, jenis kelamin untuk diinterpretasikan.Kata kunci: Data Mining, Tracer study, Fp-growth Abstract - Tracer study is a study of graduates of higher education institutions, which is also a process to obtain information related to the transformation of alumni from their education period to the industrial world. This is done to determine the employability of graduates as well as a retrospective assessment, such as what services were obtained during the study period. This research will carry out a tracer study at the study program level in Informatics Engineering at UIN Suska Riau and then the results of this follow-up study will be processed to obtain a significant correlation pattern or relationship. Data processing will use data mining technology and the fp-growth algorithm. Based on the results of the analysis of testing and interpretation of the total net data of 129 alumni of informatics engineering UIN Suska Riau, the patterns generated from the mining process with a minimum support of 15% and a minimum confidence of 40% are 608 patterns. Researchers took several patterns with antecedents in the form of GPA, waiting period, field of work, salary, first job, gender to be interpreted.Keywords: Data Mining, Tracer study, Fp-growth
Approximate time‐dependent analysis of tunnel support structure considering progress of tunnel face

In this study, a method which takes into account the three-dimensional effects of the tunnel face progression in two-dimensional plane strain analyses is proposed by introducing the 'equivalent initial stress'. The proposed method can be easily applied to time-dependent analyses of the behavior of tunnel support structures installed in a visco-elastic medium. The tunnels considered here are circular in shape, being driven in homogeneous isotropic linear visco-elastic media having hydrostatic intial stresses. In the first part of this paper, detailed discussions of the proposed equivalent initial stress are given. The second deals with the mathematical formulations for obtaining closed-form solutions for the pressure acting on tunnel support structures. In the third, numerical results and discussions are given, and special attention is paid to the effects of both the time-dependent mechanical properties of the material and the tunnel face progression. Finally, the theoretical results obtained here are utilized for interpretation of field measurements.
A numerical investigation of side‐loads resulting from rigid body motions of an overexpanded engine nozzle

A numerical model for fluid–structure interactions is presented. Its purpose, within the context of 2D overexpanded engine nozzles, is to improve understanding of interactions between side‐loads and rigid body rotations, and more generally of the underlying physics between a shock in motion and nozzle movements. The model is based on three different solvers, for fluid, structure and mesh deformation respectively, which are linked to a coupling scheme in a parallel environment. In particular it is shown that the nozzle has a natural torsional frequency for which the measured side‐loads are the greatest. This phenomenon is associated with a transversal wave in the flow between the two internal walls of the nozzle. For free coupling cases, our calculations go some way to explain how the mechanical energy is dissipated with a transfer of energy to the shock that encounters the largest motions to dissipate it. It has also been observed that the compression shock may adopt a quasi‐steady state response with regard to nozzle rotations at low frequencies, whereas this will no longer be the case at higher frequencies, where a phase shift may occur between side‐loads and rotational position. This study is aimed at enhancing the only current aeroelastic stability model for overexpanded nozzles (AIAA, 29th Joint Propulsion Conference and Exhibit, Monterey, CA, 28–30 June 1993). Copyright © 2010 John Wiley & Sons, Ltd.
Functional Fermented Milk with Fruit Pulp Modulates the In Vitro Intestinal Microbiota

The effect of putative probiotic fermented milk (FM) with buriti pulp (FMB) or passion fruit pulp (FMPF) or without fruit pulp (FMC) on the microbiota of healthy humans was evaluated. FM formulations were administered into a simulator of the human intestinal microbial ecosystem (SHIME®) to evaluate the viability of lactic acid bacteria (LAB), microbiota composition, presence of short-chain fatty acids (SCFA), and ammonium ions. The probiotic LAB viability in FM was affected by the addition of the fruit pulp. Phocaeicola was dominant in the FMPF and FMB samples; Bifidobacterium was related to FM formulations, while Alistipes was associated with FMPF and FMB, and Lactobacillus and Lacticaseibacillus were predominant in FMC. Trabulsiella was the central element in the FMC, while Mediterraneibacter was the central one in the FMPF and FMB networks. The FM formulations increased the acetic acid, and a remarkably high amount of propionic and butyric acids were detected in the FMB treatment. All FM formulations decreased the ammonium ions compared to the control; FMPF samples stood out for having lower amounts of ammonia. The probiotic FM with fruit pulp boosted the beneficial effects on the intestinal microbiota of healthy humans in addition to increasing SCFA in SHIME® and decreasing ammonium ions, which could be related to the presence of bioactive compounds.
Estimating the Coverage Measure and the Area Explored by a Side-Scan Sonar

Full coverage of an area of interest is a common task for a robot in the underwater environment. Estimating the area explored by the robot is indeed essential for determining if path-planning algorithms lead to complete coverage. In this work, we propose a method for estimating the area explored by a Side-Scan Sonar. The proposed method is able to determine how many times each portion of the space has been sensed by the sonar using a novel approach based on the topological properties of the environment that has been scanned, and more precisely on an estimation of certain winding numbers. This property is useful for localization inside homogeneous environments, e.g. the underwater environment, and assessment for potential revisiting missions.
Modulation of Anionic Lipid Bilayers by Specific Interplay of Protons and Calcium Ions

Biomembranes, important building blocks of living organisms, are often exposed to large local fluctuations of pH and ionic strength. To capture changes in the membrane organization under such harsh conditions, we investigated the mobility and hydration of zwitterionic and anionic lipid bilayers upon elevated H3O+ and Ca2+ content by the time-dependent fluorescence shift (TDFS) technique. While the zwitterionic bilayers remain inert to lower pH and increased calcium concentrations, anionic membranes are responsive. Specifically, both bilayers enriched in phosphatidylserine (PS) and phosphatidylglycerol (PG) become dehydrated and rigidified at pH 4.0 compared to at pH 7.0. However, their reaction to the gradual Ca2+ increase in the acidic environment differs. While the PG bilayers exhibit strong rehydration and mild loosening of the carbonyl region, restoring membrane properties to those observed at pH 7.0, the PS bilayers remain dehydrated with minor bilayer stiffening. Molecular dynamics (MD) simulations support the strong binding of H3O+ to both PS and PG. Compared to PS, PG exhibits a weaker binding of Ca2+ also at a low pH.
Frontal Vehicular Crash Energy Management Using Analytical Model in Multiple Conditions

When it comes to frontal vehicular crash development, matching the stiffness of the front-end structures reasonably, i.e., impact energy management, can effectively improve the safety of the vehicle. A multi-condition analytical model for a frontal vehicular crash is constructed by a three-dimensional decomposition theory. In the analytical model, the spring is used to express the equivalent stiffness of the local energy absorption space at the front-end structure. Then based on the analytical model, the dynamic responses and evaluation indexes of the vehicle in MPDB and SOB conditions are derived with the input of the crash pulse decomposition scheme. Comparing the actual vehicle crash data and the calculation results of the proposed solution method, the error is less than 15%, which verifies validity of the modeling and the accuracy of the solution. Finally, based on the solution method in the MPDB and the SOB conditions, the sensitivities of the crash pulse decomposition scheme to evaluation indexes are analyzed to obtain qualitative rules which guide crash energy management. This research reveals the energy absorption principle of the front-end structure during the frontal impact process, and provides an effective optimization method to manage the multiple conditions of the vehicle crash energy such as the FRB (frontal rigid barrier), the MPDB (mobile progressive deformable barrier), and the SOB (small overlap barrier).