Miracle-dz/UniFAR-FTUs huggingface dataset:
LARGE-SCALE MALWARE EXPERIMENTS: WHY, HOW, AND SO WHAT?

One of the most popular research areas in the anti-malware industry (second only to detection) is to document malware characteristics and understand their operations. Most initiatives are based on reverse engineering of malicious binaries so as to understand a threat’s features. In order to fully understand the challenges faced by a malware operator, it is sometimes necessary to reproduce a scenario where researchers have to manage thousands of infected computers in order to reach a set of objectives. In this paper, we fidiscuss the reasons why one would want to replicate a botnet and perform experiments while managing it. In our case, our objective was to emulate the Waledac botnet and assess the performance of a mitigation scheme against its peer-topeer infrastructure. We then present our experimental methodology and explain the technical decisions we took to perform our experiments. Finally, we explain our results, both in terms of the attacks against the Waledac botnet and the challenges we faced while creating our experimental environment.
Deuterium labeling enables non-invasive 3D proton MR imaging of glucose and neurotransmitter metabolism in the human brain

Impaired brain glucose metabolism characterizes most severe brain diseases. Recent studies have proposed deuterium (2H)-Magnetic Resonance Spectroscopic Imaging (MRSI) as a reliable, non-invasive, and safe method to quantify the human metabolism of 2H-labeled substrates such as glucose and their downstream metabolism (e.g., aerobic/anaerobic glucose utilization and neurotransmitter synthesis) and address the major drawbacks of positron emission tomography (PET) or carbon (13C)-MRS. Here, for the first time, we show an indirect dynamic proton (1H)-MRSI technique in humans, which overcomes four critical 2H-MRSI limitations. Our innovative approach provides higher sensitivity with improved spatial/temporal resolution and higher chemical specificity to differentiate glutamate (Glu4), glutamine (Gln4), and gamma-aminobutyric acid (GABA2) deuterated at specific molecular positions while allowing simultaneous mapping of both labeled and unlabeled metabolites without the need for specialized hardware. Our novel method demonstrated significant Glu4, Gln4, and GABA2 decreases, with 18% faster Glu4 reduction in the gray matter than white matter after ingestion of deuterated glucose. Thus, robustly detected downstream glucose metabolism utilizing clinically available MR hardware without the need for radioactive tracers and PET.
QTL Analysis Based on A Rice Short-Wide Grain CSSL-Z414 and Substitution Mapping of qGL11 and qGW5

Background: Most of rice agronomic traits as grain length etc. are complex traits controlled by multiple genes. Chromosome segment substitution lines (CSSLs) are ideal materials for dissecting and studying of these complex traits. Results: We developed a novel rice short-wide grain CSSL, Z414, deriving from progeny of the recipient parent Xihui 18 (an indica restorer line) and the donor parent Huhan 3 (a japonica cultivar). Z414 contained 4 substitution segments (average length was 3.04 Mb). Compared with Xihui 18, Z414 displayed seven significantly different traits as grain length, width and weight, chalkiness degree, brown rice rate etc. Then, 8 quantitative trait loci (QTLs) were found responding these difference traits by F2 population from Xihui 18/Z414. Among them, 6 QTLs (qPL3, qGW5, qGL11, qRLW5, qRLW11, qGWT5) could be verified by novel developed single segment substitution lines (SSSLs, S1-S6). In addition, 4 QTLs (qGL3, qGL5, qCD3 and qCD5) were novel detected by S1 and S5. Thus, the short–wide grain of Z414 was responded by qGL11, qGL3, qGL5, and qGW5. Then, qGL11 and qGW5 were delimited within intervals of 0.405 and 1.14 Mb on chromosomes 11 and 5, respectively, by substitution mapping. Again by sequencing, qRT-PCR and cell morphology analysis, qGW5 should be a novel allele of GS5 and qGL11 is novel QTL encoding CycT1;3, whose specific function of regulating grain length was still unknown. Finally, pyramid of qGL3 (a=0.22) and qGL11 (a=-0.19) displayed qGL11 epistatic to qGL3. In addition, novel S1 and D2 exhibited different grain sizes and lower chalkiness degree. They are potential to be directly used in breeding hybrid rice varieties.Conclusions: We constructed a novel rice short–wide grain CSSL-Z414 with 4 substitution segments based on the genetic backgrounds of Xihui 18. The broad grain of Z414 was controlled by qGW5, which should be a novel allele of GS5. The short grain of Z414 was controlled by qGL11, qGL3, and qGL5, and qGL11 is a novel QTL encoding CycT1;3, whose specific function of regulating grain length was still unknown, and qGL11 is epistatic to qGL3. Novel S1 and D2 are potential in hybrid rice varieties.
GetCM: A Vision of Cloud Manufacturing

Surviving in an increasing globalization, distribution and flexibility environment, modern manufacturing requires an extremely flexible, self-adaptive foundation capable of dynamic provisioning, coordinating and using infinite manufacturing resources available on demand over large-scale computer networks. In contrast to the conventional networked manufacturing approach, the cloud manufacturing vision (GetCM) introduced in this paper promises elasticity, flexibility and adaptability through the on-demand provisioning of manufacturing resources as a utility by reflecting the basic principles of cloud computing. The discussion is made from technological, functional, economic aspects to provide evidence of the benefits from GetCM in the context of networked manufacturing resource access, provision, sharing and coordination. A primary architecture for GetCM is introduced based on the analysis of key criteria in realizing the vision of a function for cloud manufacturing. Focuses of this paper are placed on the vision and the outline of GetCM architecture.
Reduction of pump current mismatch in charge-pump PLL

A charge pump that minimises the mismatch between the charging and discharging currents and keeps the currents constant across a wide output voltage range is described. The improved current matching helps reduce the static phase offset and reference spur of a charge-pump phase-locked loop (PLL) and the constant currents help control the PLL dynamics precisely. The proposed charge pump with dual compensation circuits demonstrates current mismatch of less than 3.2% and pump-current variation of 1.7% over the output voltage ranging from 0.2 to 1.0%V in the 0.13%%m CMOS process with 1.2%V supply.
Random Phase Multiple Access Network for Public Internet of Things in Batam Island

Batam Island is a production city that mainly connects the industrial businesses of three ASEAN countries; Indonesia, Malaysia, and Singapore. As of now, there are 17 types of industrial businesses that operate massively in Batam Island, and most of them require the Public Internet of Things (IoT) technology for their services. Therefore, it is crucial to plan a network in Batam Island that can sustain the requirements of the anticipated numerous IoT devices. This study aims to design one Low Power Wide Area Network (LPWAN) technology, namely, Random Phase Multiple Access (RPMA), to implement Public IoT services in Batam Island based on the number of sites needed on capacity and coverage planning. Furthermore, RPMA is chosen due to its ability to provide higher capacity and broader coverage than the other LPWAN technologies. This study obtained that 23 sites are needed for capacity planning, while only 15 sites are required for coverage planning. The results were then simulated using a network simulator, Forsk Atoll, to determine the best signal level received and the Signal-to-Noise Ratio (SNR) based on the most significant number of sites demanded in Batam Island. The obtained best signal level received was −69.21 dBm and SNR value of −87.87 dB. These results meet the minimum requirements for RPMA planning in Batam Island.
RF Remote Blood Glucose Sensor and a Microfluidic Vascular Phantom for Sensor Validation

Diabetes has become a major health problem in society. Invasive glucometers, although precise, only provide discrete measurements at specific times and are unsuitable for long-term monitoring due to the injuries caused on skin and the prohibitive cost of disposables. Remote, continuous, self-monitoring of blood sugar levels allows for active and better management of diabetics. In this work, we present a radio frequency (RF) sensor based on a stepped impedance resonator for remote blood glucose monitoring. When placed on top of a human hand, this RF interdigital sensor allows detection of variation in blood sugar levels by monitoring the changes in the dielectric constant of the material underneath. The designed stepped impedance resonator operates at 3.528 GHz with a Q factor of 1455. A microfluidic device structure that imitates the blood veins in the human hand was fabricated in PDMS to validate that the sensor can measure changes in glucose concentrations. To test the RF sensor, glucose solutions with concentrations ranging from 0 to 240 mg/dL were injected into the fluidic channels and placed underneath the RF sensor. The shifts in the resonance frequencies of the RF sensor were measured using a network analyzer via its S11 parameters. Based on the change in resonance frequencies, the sensitivity of the biosensor was found to be 264.2 kHz/mg·dL−1 and its LOD was calculated to be 29.89 mg/dL.
Early and Dynamic Socio-Academic Variables Related to Dropout Intention: A Predictive Model Made During Pandemic

Social and academic integration variables have shown to be relevant for the understanding of university dropout. However, there is less evidence regarding the influence of these variables on dropout intention, as well as predictive models that explain their relationships. Improvements in this topic become relevant considering that dropout intention stands as a useful measure to anticipate and intervene on this phenomenon. The objective of the present study was to evaluate a predictive model for the university dropout intention that considers the relationships between social and academic variables, during the first university semester of 2020. The research was carried out using a cross-sectional associative-predictive design, with a convenience sampling (n=711) due the restrictions of pandemic period. The results showed a good fit of the proposed hypothetical model that explains 38.7% of dropout intention. Both social support and perceived social isolation predicted the sense of belonging, and through it, engagement. Previous academic performance predicted early academic performance, and through it, engagement. The set of variables predicted the intention to quit, through engagement. These results are a contribution both to the understanding of the phenomenon and to guide potential interventions in the early stages of the university experience.
Comparison of direct maximum power point tracking algorithms using EN 50530 dynamic test procedure

Three direct maximum power point tracking (MPPT) algorithms with non-adaptive voltage step are evaluated in the light of their performance for photovoltaic systems under dynamic conditions for a resistive load. A microcontroller-based buck–boost DC–DC converter platform is used to implement and compare the algorithms. Dynamic test procedures from a new standard for inverter efficiency determination EN 50530 were implemented to evaluate dynamic MPPT algorithms’ efficiency and their dependence.Experimental results show that MPPT algorithms’ regulating frequency and regulating voltage step play a crucial role in the dynamic performance of direct algorithms. A range of regulating frequencies and voltage steps were examined. If proper parameter values are chosen, all algorithms perform well and close to each other. Detailed evaluation was performed with determining partial MPPT efficiency under different irradiance slopes. Results show that at least 10 Hz should be used to satisfy the 99% tracking efficiency over all slopes in the range from 0.5 to 100 W/m2/s as specified by EN 50530 standard. Further increase of regulating frequency would result in higher slope efficiency only at dynamic conditions.
Treatment of lagooned sewage sludge in situ using electrokinetic geosynthetics

The treatment and disposal of sewage sludge constitute one of the most problematical issues affecting wastewater treatment in the developed world. Water companies have in the past deposited large volumes of digested sewage sludge in lagoons adjacent to water treatment works, and continue to do so. A lagoon has a potential for causing smell nuisance, represents a major hazard, and frequently occupies a potentially valuable site. Currently no technology exists for treating these lagoons that will both reduce the volume of the problem and allow post-treatment redevelopment of the site. Electrokinetic geosynthetics (EKG), polymer-based materials containing conducting elements, formed as wick drains offer one novel solution. The paper describes a field trial on lagooned sewage sludge using EKG, and discusses the results in the context of a full-scale treatment, considering the power consumption, electrode design, electrode array configuration, length of required treatment time and economic implications.
Unified Pretraining Framework for Document Understanding

Document intelligence automates the extraction of information from documents and supports many business applications. Recent self-supervised learning methods on large-scale unlabeled document datasets have opened up promising directions towards reducing annotation efforts by training models with self-supervised objectives. However, most of the existing document pretraining methods are still language-dominated. We present UDoc, a new unified pretraining framework for document understanding. UDoc is designed to support most document understanding tasks, extending the Transformer to take multimodal embeddings as input. Each input element is composed of words and visual features from a semantic region of the input document image. An important feature of UDoc is that it learns a generic representation by making use of three self-supervised losses, encouraging the representation to model sentences, learn similarities, and align modalities. Extensive empirical analysis demonstrates that the pretraining procedure learns better joint representations and leads to improvements in downstream tasks.
Product-line selection and pricing with remanufacturing under availability constraints

Product line selection and pricing are two crucial decisions for the profitability of a manufacturing firm. Remanufacturing, on the other hand, may be a profitable strategy that captures the remaining value in used products. In this paper we develop a mixed-integer nonlinear programming model form the perspective of an original equipment manufacturer (OEM). The objective of the OEM is to select products to manufacture and remanufacture among a set of given alternatives and simultaneously determine their prices so as to maximize its profit. It is assumed that the probability a customer selects a product is proportional to its utility and inversely proportional to its price. The utility of a product is an increasing function of its perceived quality. In our base model, products are discriminated by their unit production costs and utilities. We also analyze a case where remanufacturing is limited by the available quantity of collected remanufacturable products. We show that the resulting problem is decomposed into the pricing and product line selection subproblems. Pricing problem is solved by a variant of the simplex search procedure which can also handle constraints, while complete enumeration and a genetic algorithm are used for the solution of the product line selection problem. A number of experiments are carried out to identify conditions under which it is economically viable for the firm to sell remanufactured products. We also determine the optimal utility and unit production cost values of a remanufactured product, which maximizes the total profit of the OEM.
The Construction of Magnetic MnFe2O4@TpPa-1 Composite Materials and the Adsorption Removal Performance of Organic Pollutants in Solution

MnFe2O4@TpPa-1 adsorbent was developed by co-precipitation and solvothermal method, using β-ketoenamine linked covalent organic frameworks (COFs, TpPa-1) as supporting material to alleviate the aggregation of MnFe2O4. The properties were characterized by XRD, FT-IR, SEM, TEM, VSM, pHpzc, and N2 adsorption-desorption. The experimental results showed that the pseudo-second-order and Langmuir model best described the adsorption process, suggesting that the adsorption process was chemisorption and spontaneous endothermic reaction, and the maximum adsorption capacity of Bisphenol A (BPA) was 926.65 mg.g-1. The main adsorption mechanism of BPA was hydrogen bonding and π-π conjugation between active functional groups in the TpPa-1 skeleton and BPA. Furthermore, the magnetic MnFe2O4@TpPa-1 showed good regeneration ability, indicating that MnFe2O4@TpPa-1 could be used in water treatment.
Privilege and Oppression: Towards a Feminist Speculative Design Privilege and Oppression: Towards a Feminist Speculative Design

Though critical and speculative design have been increasingly relevant in discussing the social and cultural role of design, there has been a distinct lack of both theory and praxis aimed at questioning gender oppression. Departing from an intersectional feminist analysis of the influences and origins of speculative and critical design, this essay questions the underlying privilege that has been hindering the discussion on gender within the discipline and its role in propagating oppression; it then goes on to propose the concept of a “feminist speculative design” as an approach aimed at questioning the complex relationships between gender, technology and social and cultural oppression.
Incorporating Rubrics to Short Answer Grading

Assignments are crucial for assessing students’ knowledge in a pedagogical setting, where students are generally required to answer questions in relatively short texts. Figure 1 shows a typical example where students are asked to explain a sentence appearing in an essay to test their reading comprehension. A reference answer and rubrics are provided to show the rules for grading assignments. Due to the diversity of open-ended questions in answer scoring, educators’ workload can be excruciatingly timeconsuming. The task of Short Answer Grading (SAG) has been proposed to assist educators with grading. SAG is the task of estimating scores of short-text answers written as a response to a given prompt. The task has been studied mainly with machine learning-based approaches, focusing on exploring better representations of answers. The recently proposed neural models have been yielding strong results [1, 2]. SAG is generally a low-resource task, suffering from the scarcity of training data. Thus, it is difficult to train SAG models from answers only, leading to a poor performance. To improve the performance of SAG models especially for low-resource settings when the available training data is limited, it is easy to have the idea to incorporate rubrics to help train SAG models. However, the question is still unclear how to incorporate rubrics into neural SAG models. In this paper, we explore two approaches to introduce rubrics to SAG models. We first augment training data with rubrics. We extract keyphrases that are highly related to the scores of answers from rubrics, and calculate the weights of words in answers based on span-wise alignments between answers and the extracted keyphrases. Highly weighed words are used as pseudo cues for scoring. Another approach to incorporate rubrics is rule-based models, scoring answers following rules defined by rubrics[3]. Thus we also explore some simple rule-based methods for comparison. The experimental results demonstrate that the performance is improved by the augmented data, especially in a low-resource setting.
Exploring the Context of Pioneer Missionary in Northern Taiwan by Analyzing George Leslie Mackay's Diary

George Leslie Mackay (1844-1901), the first Presbyterian missionary to northern Taiwan, is considered to be one of the most important persons who influence the development of education and medicine in Taiwan. Recently, the manuscripts written by Mackay which detailed his work from 1871 to 1901 in Taiwan have been computerized and published as Mackays diaries. Researchers who are interested in historical topics concerning religious, educational and medical development in Taiwan hence concentrate their attention on these materials. However, little research applied computational techniques to analyze the content of the diaries. That motivated us to apply text mining techniques to explore the content of diaries. The content analysis methodology was used in this research to unveil the context of Mackays missionary work in northern Taiwan. Finally, the context was presented by data visualization techniques.
Stochastic Output-Feedback Tracking Control with Second-Order Moment Process

This paper investigates the output-feedback tracking control problem of stochastic high-order nonlinear systems perturbed by second-order moment process. Compared with most of the existing results where only wiener process is considered, a high-gain homogeneous domination approach is used to construct the output-feedback controller. This controller ensures that the expectation of tracking error can be adjusted to be arbitrarily small and all the states of the closedloop system are bounded in probability. Finally, a numerical example is given to demonstrate the feasibility of the control scheme.
Thermal conductivity measurement of infrared optical fibers based on silver halide solid solution crystals

The aim of the study is to measure the thermal conductivity of silver halide light guides based on crystals of the AgCl-AgBr system used in PSD production technologies. The conductivity temperature coefficient of the samples under study were determined by the laser flash method using the LFA 467 (Hyper Flash) installation. We studied mono- and polycrystalline samples of solid solutions with the composition AgCl0,25AgBr0,75 in the temperature range 298–523 K. The thermal conductivity of the investigated materials was then calculated using literature data on density and heat capacity. The thermal conductivity coefficient ranges from 0.80±0,04 to 0.53±0,03 (W/mK), depending on the microstructure of the sample.
Human Pose Tracking For Video Using SURF features

In this paper, a novel method based on SURF features method for tracking human motion in monocular videos is proposed. With a initial human skeleton joint point template, we use the probability density propagation of the particle filers through the model. This algorithm can automatically achieve right human motion figure from tracking failures, such as occlusion and auto-occlusion problem. Experimental results from 20 classes monocular videos show that the new Based on SURF method is robust and the tracking results are good.
Polychromatic Hilbert diagnostics of phase and temperature disturbances, induced by candle flame in air

The work is motivated by the scientific and practical significance of the problem of non-disturbing diagnostics of phase and temperature fields induced in a gas medium by a flame of a torch (candle). The spatial conditions in which the fields are studied satisfy the model of axial symmetry of the torch associated with the vertical orientation of the candle. A method adequate to the problem to be solved has been developed, based on polychromatic Hilbert visualization of phase optical density fields, measurement of the temperature profile in selected sections of the medium under study, registration and selection of RAW images recorded by the photomatrix in RGB channels. The visualized Hilbert structures contain information on the phase optical density perturbations induced by the temperature field. The reliability of the results is confirmed by comparing the experimentally obtained hilbertograms and those reconstructed from phase structures using the Abel transform.