Miracle-dz/UniFAR-FTUs huggingface dataset:
Using a Computer to Score Parsons Problems Answered on Paper

Parsons Problems are code rearrangement problems that can be used to assess students' programming ability. Our work builds on previous studies involving Parsons Problems by showing that it was possible to have students encode the solution to a Parsons Problem on a special purpose bubble sheet. This allowed students to answer Parsons Problems in a traditional paper-based exam environment while also allowing their responses to be scanned and scored automatically. The nature of the special purpose bubble sheet is described, as is the proof-of-concept software that was developed to analyze the bubble sheets, evaluate the student responses, and distribute the graded responses back to the students. Written feedback received from students on end-of-course surveys showed that the vast majority of students considered electronically scorable Parsons Problems to be a fair and reasonable evaluation technique in a large introductory course, and only minimal concerns were raised about the need to record their solutions on a bubble sheet.
Treatment of pig manure liquid digestate in horizontal flow constructed wetlands: Effect of aeration

With the rapid development of scaled anaerobic digestion of pig manure, the generation of liquid anaerobic digestate exceeds the farmland loading capacity, causing serious environmental pollution. Three laboratory‐scale horizontal subsurface flow constructed wetlands (CWs; planted + aeration, planted, and unplanted) were set up to investigate the feasibility of liquid digestate treatment in wetlands. Treatment capacity in different wetlands was evaluated under different influent concentrations (chemical oxygen demand [COD], 5 days biochemical oxygen demand [BOD5], and nitrogen forms). The effect of aeration and effluent recirculation on organic matter and total nitrogen removal was investigated. Results showed that integrating intermittent aeration in CWs significantly improved the oxygen condition (p < 0.01) in the wetland bed and promoted BOD5 removal to 90% in aerated CWs as compared with <15% in the unaerated CWs. Meanwhile, COD removal between these three wetlands did not show any difference and varied from 52 to 72% under influent concentration of 200–820 mg/L because of the high content of hard‐degradable organic matter in the liquid digestate. Intermittent aeration resulted in high ammonium removal (>98%) although the influent loading varied from 65 to 350 mg/L. However, intermittent aeration caused nitrate accumulation of 300 mg/L and limited total nitrogen (TN) removal of 33%. To intensify the TN removal, we verified effluent recirculation to increase the removal efficiency of TN to 78%. These results not only show the potential application of CWs for treatment of high‐strength liquid anaerobic digested slurry, but also indicate the significance of intermittent aeration on the enhanced removal of organic matter and ammonium.
Strain Relaxation and Dislocation Evolution in Epitaxial Perovskite Oxide Thin Films

Strain relaxation behavior of perovskite epitaxial layers such as ferroelectric (Pb,Sr)TiO3 thin films on (110) NdGaO3 substrates, and SrRuO3 thin films on LaAlO3 substrates were investigated using high resolution x-ray diffraction. Lattice distortion, dislocation densities and interfacial strain distribution were systematically studied with samples under different fabrication conditions. Strain relaxation and dislocation evolution are found to be dependent upon the deposition and cooling process. Reciprocal space maps reveal different scenarios of strain relaxation behavior in (Pb,Sr)TiO3 and SrRuO3 thin films.
Common Code Segment Selection: Semi-Automated Approach and Evaluation

When comparing student programs to check for evidence of plagiarism or collusion, the goal is to identify code segments that are common to two or more programs. Yet some code segments are common for reasons other than plagiarism or collusion, and so should not be considered. A few code similarity detection tools automatically remove very common segment, but they are prone to false results as no human validation is involved. This paper proposes a semi-automated approach for excluding common segments, where human validation is introduced before excluding the segments. As existing selection techniques are not detachable from their similarity detection tools, we propose a new tool to independently select the segments (C2S2), along with several adjustable selection constraints to keep the number of suggested segments reasonable for manual observation. In order to independently evaluate automated selection techniques, we propose and apply three metrics. The evaluation shows our selection technique to be more effective and efficient than the basis underlying existing selection techniques, and establishes the benefit of each of its selection features.
Nutrient and carbon balances in organic vegetable production on an irrigated, sandy soil in northern Oman

Our understanding of nutrient and carbon (C) fluxes in irrigated organic cropping systems of subtropical regions is limited. Therefore, leaching of mineral nitrogen (N) and phosphorus (P), gaseous emissions of NH3, N2O, CO2, and CH4, and total matter balances were measured over 24 months comprising a total cropping period of 260 d in an organic-cropping-systems experiment near Sohar (Oman). The experiment on an irrigated sandy soil with four replications comprised two manure types (ORG1 and ORG2) characterized by respective C : N ratios of 19 and 25 and neutral detergent fiber (NDF)-to-soluble carbohydrates (SC) ratios of 17 and 108. A mineral-fertilizer (MIN) treatment with equivalent levels of mineral N, P, and potassium (K) served as a control. The three treatments were factorially combined with a cropping sequence comprising radish (Raphanus sativus L.) followed by cauliflower (Brassica oleracea L. var. botrytis) or carrot (Daucus carota subsp. sativus). Over the 24-months experimental period gaseous N emissions averaged 45 kg ha–1 (59% NH3-N, 41%N2O-N) for MIN, 55 kg N ha–1 (69% NH3-N, 31%N2O-N) for ORG1, and 49 kg N ha–1 (59% NH3-N, 41% N2O-N) for ORG2. Carbon losses were 6.2 t ha–1 (98% CO2-C, 2% CH4-C) for MIN, 9.7 t C ha–1 (99% CO2-C, 1% CH4-C) for ORG1, and 10.6 t ha–1 (98% CO2-C, 2% CH4-C) for ORG2. Exchange resin–based cumulative leaching of mineral N amounted to 30 kg ha–1 for MIN, 10 kg ha–1 for ORG1, and 56 kg ha–1 for ORG2. Apparent surpluses of 361 kg N ha–1 and 196 kg P ha–1 for radish-carrot and 299 kg N ha–1 and 184 kg P ha–1 for radish-cauliflower were accompanied by K deficits of –59 kg ha–1 and –73 kg ha–1, respectively, for both cropping systems. Net C balances for MIN, ORG1, and ORG2 plots were –7.3, –3.1, and 1.5 t C ha–1 for radish-carrot and –5.0, 1.3, and 4.6 t C ha–1 for radish-cauliflower. The results underline the difficulty to maintain soil C levels in intensively cultivated, irrigated subtropical soils.
CS@Mines Successful S-STEM Scholarship Ecosystem for Low-Income and Underrepresented Students

The primary purpose of PATHS (Path Ambassadors to High Success), an NSF-funded S-STEM scholarship program, is to create new pathways and strengthen existing pathways for academically talented, low-income Colorado high school and community college students to study computer science (CS) at Colorado School of Mines (Mines). PATHS has achieved the following major project goals: 1) Increase number of academically talented, low-income students studying CS in Colorado; 4) Increase retention of these students; 3) Establish an active on-campus community to support PATHS scholars and similar students; 4) Engage scholars to perform CS recruitment and outreach at area high schools and community colleges; 5) Evaluate PATHS activities through comparative analysis to provide new insights on best practices for attracting and retaining academically talented, low-income CS students; 6) Broaden participation of historically underrepresented groups in CS; and 7) Develop a new flexible degree program combining other STEM fields and CS. PATHS students are diverse (e.g., 40.8% from underrepresented groups in computing) and academically successful (e.g., mean GPA is 3.4). Thus far, the program has awarded scholarships to 49 students and retained 93.9% of the students. Of the 49 students, six have graduated and three of the six have also pursued a CS Master's degree.
Zero-Rating and Net Neutrality

An objective of network neutrality is to design regulations for the Internet and ensure that it remains a public, open platform where innovations can thrive. While there is broad agreement that preserving the content quality of service falls under the purview of net neutrality, the role of differential pricing, especially the practice of zero-rating remains controversial. In this paper, we model zero-rating between Internet service providers (ISPs) and content providers (CPs) and show if zero-rating is permitted, the competitiveness in the market is reduced, where low-income CPs often lose utility and high-income CPs often gain utility.
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents

Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels from an extremely large label set. This paper develops the DeepXML framework that addresses these challenges by decomposing the deep extreme multi-label task into four simpler sub-tasks each of which can be trained accurately and efficiently. Choosing different components for the four sub-tasks allows DeepXML to generate a family of algorithms with varying trade-offs between accuracy and scalability. In particular, DeepXML yields the Astec algorithm that could be 2-12% more accurate and 5-30x faster to train than leading deep extreme classifiers on publically available short text datasets. Astec could also efficiently train on Bing short text datasets containing up to 62 million labels while making predictions for billions of users and data points per day on commodity hardware. This allowed Astec to be deployed on the Bing search engine for a number of short text applications ranging from matching user queries to advertiser bid phrases to showing personalized ads where it yielded significant gains in click-through-rates, coverage, revenue and other online metrics over state-of-the-art techniques currently in production. DeepXML's code is available at https://github.com/Extreme-classification/deepxml.
Aerial Flight Paths for Communication

This article presents an understanding of naive users’ perception of the communicative nature of unmanned aerial vehicle (UAV) motions refined through an iterative series of studies. This includes both what people believe the UAV is trying to communicate, and how they expect to respond through physical action or emotional response. Previous work in this area prioritized gestures from participants to the vehicle or augmenting the vehicle with additional communication modalities, rather than communicating without clear definitions of the states attempting to be conveyed. In an attempt to elicit more concrete states and better understand specific motion perception, this work includes multiple iterations of state creation, flight path refinement, and label assignment. The lessons learned in this work will be applicable broadly to those interested in defining flight paths, and within the human-robot interaction community as a whole, as it provides a base for those seeking to communicate using non-anthropomorphic robots. We found that the Negative Attitudes towards Robots Scale (NARS) can be an indicator of how a person is likely to react to a UAV, the emotional content they are likely to perceive from a message being conveyed, and it is an indicator for the personality characteristics they are likely to project upon the UAV. We also see that people commonly associate motions from other non-verbal communication situations onto UAVs. Flight specific recommendations are to use a dynamic retreating motion from a person to encourage following, use a perpendicular motion to their field of view for blocking, simple descending motion for landing, and to use either no motion or large altitude changes to encourage watching. Overall, this research explores the communication from the UAV to the bystander through its motion, to see how people respond physically and emotionally.
Calibrating Network Traffic with One-Dimensional Convolutional Neural Network with Autoencoder and Independent Recurrent Neural Network for Mobile Malware Detection

In response to the surging challenge in the number and types of mobile malware targeting smart devices and their sophistication in malicious behavior camouflage, we propose to compose a traffic behavior modeling method based on one-dimensional convolutional neural network with autoencoder and independent recurrent neural network (1DCAE-IndRNN) for mobile malware detection. )e design solves the problem that most existing approaches for mobile malware traffic detection struggle with capturing the network traffic dynamics and the sequential characteristics of anomalies in the traffic. We reconstruct and apply the one-dimensional convolutional neural network to extract local features from multiple network flows. )e autoencoder is applied to digest the principal traffic features from the neural network and is integrated into the independent recurrent neural network construction to highlight the sequential relationship between the highly significant features. In addition, the Softmax function with the LReLU activation function is adjusted and embedded to the neurons of the independent recurrent neural network to effectively alleviate the problem of unstable training.We conduct a series of experiments to evaluate the effectiveness of the proposedmethod and its performance for the 1DCAE-IndRNN-integrated detection procedure. )e detection results of the public Android malware dataset CICAndMal2017 show that the proposed method achieves up to 98% detection accuracy and recall rates with clear advantages over other benchmark methods.
Towards Time-Sensitive and Verifiable Data Aggregation for Mobile Crowdsensing

Mobile crowdsensing systems use the extraction of valuable information from the data aggregation results of large-scale IoT devices to provide users with personalized services. Mobile crowdsensing combined with edge computing can improve service response speed, security, and reliability. However, previous research on data aggregation paid little attention to data verifiability and time sensitivity. In addition, existing edge-assisted data aggregation schemes do not support access control of large-scale devices. In this study, we propose a time-sensitive and verifiable data aggregation scheme (TSVA-CP-ABE) supporting access control for edge-assisted mobile crowdsensing. Specifically, in our scheme, we use attribute-based encryption for access control, where edge nodes can help IoT devices to calculate keys. Moreover, IoT devices can verify outsourced computing, and edge nodes can verify and filter aggregated data. Finally, the security of the proposed scheme is theoretically proved. The experimental results illustrate that our scheme outperforms traditional ones in both effectiveness and scalability under time-sensitive constraints.
Rate and mode of application of the urease inhibitor N‐(n‐butyl) thiophosphoric triamide on ammonia volatilization from surface‐applied urea

A laboratory study evaluated the effect of rate (0, 100, 250, 500, 750 or 1000 mg/kg) and mode of application of the urease inhibitor N‐(n‐butyl) thiophosphoric triamide (nBTPT) (coating the urea granule, adding to the urea melt or adding to urea ammonium nitrate (UAN) solutions) on NH3 volatilization from urea, at three temperatures (5, 15 and 25 °C), with four contrasting soil types. Daily ammonia loss was measured for up to 21 days after surface N application, using ventilated soil enclosures. Ammonia loss from unamended urea varied with soil type and temperature and ranged from 8.2 to 31.9% of the N applied. nBTPT was highly effective in lowering NH3 volatilization from urea and in delaying the time of maximum rate of loss. The average % inhibition over all soils, temperatures and formulations was 61.2, 69.9, 74.2, 79.2 and 79.8% for the 100, 250, 500, 750 or 1000 mg/kg nBTPT concentration, respectively. The % inhibition with nBTPT was lower at 15 °C compared with at 5 or 25 °C and was lower in UAN solutions than in granular products. There was little difference between the melted and coated granular products in lowering NH3 loss or in soil N transformations. The stability of nBTPT in urea products was dependent on its mode of application and on the storage temperature. Incorporating nBTPT in the urea melt produced a more homogeneous product with superior stability than coating the urea granule.
ELABORATED FEEDABCK AND TRANSFER

29 The provision of feedback with complex information beyond the correct answer, i.e., 30 elaborated feedback, can powerfully shape learning outcomes such as transfer. However, 31 an understanding of neurocognitive mechanisms that support elaborated feedback 32 during instructor-learner interactions remains elusive. Here, a two-person interactive 33 design is used during simultaneous recording of functional near-infrared spectroscopy 34 (fNIRS) signals from adult instructor-learner dyads. Instructors either provided 35 elaborated feedback (i.e., correct answer and an example) or simple feedback (i.e., 36 correct answer only) to learners during a concept learning task. Our results showed that 37 elaborated feedback produced comparable levels of retention to simple feedback, 38 however, transfer was significantly enhanced by elaboration. We also noted significant 39 instructor-learner neural synchronization in frontoparietal regions during the provision 40 of elaborated feedback, especially when examples were provided. Further, interpersonal 41 neural synchronization in the parietal cortex successfully predicted the transfer of 42 knowledge to novel contexts. This prediction was retained for both learner-delayed and 43 learner-preceding neural synchronization, supporting the interpretation that deeper44 level representations of knowledge, such as abstract structure and personal 45 interpretation, may promote the transfer of learning. These findings point toward 46 interpersonal neural synchronization as a key neurocognitive mechanism that supports 47 learning transfer effects, and may have important implications for real-world learning 48 and pedagogical efficacy. 49
A New Model of DC Conductivity of Hygroscopic Solids

We investigate the DC conductivity of cotton, viscose, and wood and develop a quantitative theory. We propose that conduction occurs by exchange of protons within a hydrogen-bonded network of water molecules, in a manner analogous to that in liquid water and ice. According to this model, the very strong dependence of conductivity on regain, which is characteristic of these materials, arises from its dependence on the average distance between adjacent water molecules. We derive expressions for DC conductivity as a function of regain, which provide very good agreement with experimental data. With independent experimental evidence, we confirm a prediction by the theory that a similar conduction process occurs in dry cellulose.
MLIR: Scaling Compiler Infrastructure for Domain Specific Computation

This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure. MLIR addresses software fragmentation, compilation for heterogeneous hardware, significantly reducing the cost of building domain specific compilers, and connecting existing compilers together. MLIR facilitates the design and implementation of code generators, translators and optimizers at different levels of abstraction and across application domains, hardware targets and execution environments. The contribution of this work includes (1) discussion of MLIR as a research artifact, built for extension and evolution, while identifying the challenges and opportunities posed by this novel design, semantics, optimization specification, system, and engineering. (2) evaluation of MLIR as a generalized infrastructure that reduces the cost of building compilers-describing diverse use-cases to show research and educational opportunities for future programming languages, compilers, execution environments, and computer architecture. The paper also presents the rationale for MLIR, its original design principles, structures and semantics.
Mesogenic Behaviour of some 1,4,8,11,15,18,22,25-Octa-alkylphthalocyanines

Abstract Discotic columnar mesophase behaviour exhibited by ten metal-free and copper 1,4,8,11,15,18,22,25- octa-alkylphthalocyanines has been characterised by optical microscopy, differential scanning calorimetry, x-ray diffraction and electronic absorption spectroscopy. Compounds with side-chains as short as hexyl exhibit one or more mesophases, and a total of three different mesophases are apparent for the series as a whole. The highest temperature mesophase is assigned as Dhd. At lower temperatures, some compounds develop a second mesophase of hexagonal symmetry, whereas others form a mesophase of rectangular symmetry. One example shows all three mesophases.
MUC1/CD10/ESA Progenitor Cells Immunophenotypes Expression in Breast Cancer and its Relation with Molecular Subtypes

Breast carcinomas can be divided in at least four subtypes according to their molecular profile: Luminal A (ER+/HER2-), luminal B (ER+/HER2+), HER2 overexpressing (ER-/HER2+) and basal like (ER-/HER2-). In breast tissues, progenitor’s cells MUC1+/CD10-/ESA+ can differentiate in luminal cells; progenitor’s cells MUC1-/CD10+/ESAcan differentiate in myoepithelial (basal) cells, and progenitors cells MUC1-/CD10+/ESA+ can originate both lineages. The role of these progenitors’ immunophenotypes in breast carcinoma pathogenesis, and its relation with molecular subtypes, still merits further investigation. An immunohistochemical study was performed in a tissue microarray containing 86 samples of breast cancer. We found that the MUC1+/ CD10-/ESA+ immunophenotype is the more frequent progenitor cells phenotype in breast carcinomas, and it correlates with the luminal subtypes.
Cognate recognition by young multilingual language learners: The role of age and exposure

The present study set out to investigate the recognition of cognates by youngmultilingual learners of English as a foreign language.While there is awealth of research on the role of cognates in vocabulary recognition by bilingual children, much less is known in relation to bilinguals learning a foreign language with limited exposure to that language. The specific research questions that guided the study were: (1) to what extent do young Spanish-Catalan bilingual learners of English recognise English cognate words over non-cognate words in the PPVT test? and (2) what are the respective roles of age (7 vs. 9 years) and amount of exposure to English in cognate word recognition and non-cognate word recognition? Do these factors have the same roles in non-cognate word recognition? To answer these questions, the study examined the extent to which young learners recognised cognates in the Peabody picture vocabulary test (PPVT) (Dunn & Dunn 2007). Participants were 170 children distributed into a group of 7 year-olds and a group of 9 year-olds. They were all Spanish-Catalan bilinguals, so that English was their third language. The results of the analyses indicated that age was the strongest determinant of cognate word recognition, whereas hours of exposure was the stronger predictor of non-cognate word recognition.