Miracle-dz/UniFAR-FTUs huggingface dataset:
FAST: Feature-Aware Student Knowledge Tracing

Various kinds of e-learning systems, such as Massively Open Online Courses and intelligent tutoring systems, are now producing amounts of feature-rich data from students solving items at different levels of proficiency over time. To analyze such data, researchers often use Knowledge Tracing [4], a 20-year old method that has become the de-facto standard for inferring student’s knowledge from performance data. Knowledge Tracing uses Hidden Markov Models (HMM) to estimate the latent cognitive state (student’s knowledge) from the student’s performance answering items. Since the original Knowledge Tracing formulation does not allow to model general features, a considerable amount of research has focused on ad-hoc modifications to the Knowledge Tracing algorithm to enable modeling a specific feature of interest. This has led to a plethora of different Knowledge Tracing reformulations for very specific purposes. For example, Pardos et al. [5] proposed a new model to measure the effect of students’ individual characteristics, Beck et al. [2] modified Knowledge Tracing to assess the effect of help in a tutor system, and Xu and Mostow [7] proposed a new model that allows measuring the effect of subskills. These ad hoc models are successful for their own specific purpose, but they do not generalize to arbitrary features. Other student modeling methods which allow more flexible features have been proposed. For example, Performance Factor Analysis [6] uses logistic regression to model arbitrary features, but unfortunately it does not make inferences of whether the student has learned a skill. We present FAST (Feature-Aware Student knowledge Tracing), a novel method that allows general features into Knowledge Tracing. FAST combines Performance Factor Analysis (logistic regression) with Knowledge Tracing, by leveraging on previous work on unsupervised learning with features [3]. Therefore, FAST is able to infer student’s knowledge, like Knowledge Tracing does, while also allowing for arbitrary features, like Performance Factor Analysis does. FAST allows general features into Knowledge Tracing by replacing the generative emission probabilities (often called guess and slip probabilities) with logistic regression [3], so that these probabilities can change with time to infer student’s knowledge. FAST allows arbitrary features to train the logistic regression model and the HMM jointly. Training the parameters simultaneously enables FAST to learn from the features. This differs from using regression to analyze the slip and guess probabilities [1]. To validate our approach, we use data collected from real students interacting with a tutor. We present experimental results comparing FAST with Knowledge Tracing and Performance Factor Analysis. We conduct experiments with our model using features like item difficulty, prior successes and failures of a student for the skill (or multiple skills) associated with the item, according to the formulation of Performance Factor Analysis.
Is Smartphone Addiction Comparable between Adolescents and Adults? Examination of the Degree of Smartphone Use, Type of Smartphone Activities, and Addiction Levels Among Adolescents and Adults

To identify the patterns of smartphone use in relation to addiction, this study classifies survey respondents into non-addicts, potential addicts, and addicts groups, and analyzes differences in the use of smartphones by the three groups. Adolescents are found to spend more time using smartphones than adults, and the rates of smartphone addiction are higher among adolescents than among adults. Multinominal regression models show that weekend use and average time per use are significant predictors of smartphone addiction. On the other hand, among the addicts groups, adolescents and adults are found to engage in different sets of activities. The adolescent addicts are more likely to use social networking sites (SNS) and mobile games, while the adult addicts engage in a more diverse set of activities such as SNS, gambling, mobile games, videos and pornography.
DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach

This paper contains the description of our participation at task 4 (sub-task A, Message Polarity Classiﬁcation) of SemEval-2016. Our proposed system consists mainly of three steps. Firstly, the preprocessing step includes the tokenization and identiﬁcation of special elements including URLs, hashtags, user mentions and emoticons. The second step aims at selecting and extracting the feature set. Finally, a supervised approach, in particular a Support Vector Machine has been applied to tackle the classiﬁcation problem.
Analysis And Simulation Of Shunt Active Power Filter Under Unbalanced And Distorted Load Conditions

Abstract— The active power filters eliminates the harmonic distortion caused by non-linear loads and improves the filtering efficiency performance when compared with the passive filters. This paper inspects about the analysis and simulation of a three phase four wire shunt active power f ilter under unbalanced and distorted load conditions. Current harmonic compensation is achieved by the implementation of an Instantaneous Reactive Power theory (IRP). Active power filter monitors the load current and injects equal amplitude and opposite phase compensation currents to neutralize load current harmonics. The proposed control technique using Instantaneous Reactive Power theory (IRP) improves the performance of the active filter under unbalanced voltage condition. The shunt active power filter is investigated through Matlab/Simulink simulation under different load conditions and its results are discussed.
QUANTIFYING THE NET ENVIRONMENTAL IMPACT OF TRADE : NAFTA AS AN EXAMPLE FOR ENDOGENOUS REGULATORY CHANGE

Conventionally, the interaction between trade and environment is analyzed in the Heckscher-Ohlin framework. This results environmentally, in a pessimistic view of trade liberalization. This pessimism, however, is based on the assumption of constant emission factors. Insofar that trade systematically strengthens the demand for regulatory institutions, the net environmental result of trade liberalization is likely to be benign. This is formulated here, as the “institutional optimism hypothesis”. This is examined empirically with a CGE model that treats institutional change as an endogenous process dependent on income. Application to NAFTA supports the institutional optimism hypothesis under a broad range of scenarios. The net pollution effect of trade liberalization is beneficial or insignificant, even for the country specializing in polluting industries. The implication is that in many cases environmental interests are served better in general by a focus on institution building in trading partners, than on the process of trade liberalization itself.
Economic performance and institutions: capturing the dependent variable

In the debate about the relationship between institutions and overall economic performance, the dependent variable has received scant attention – in contrast to the independent variable(s). This paper tries to enhance the understanding of the link between institutions and performance by presenting and assessing a substantively grounded conceptualization and operationalization of overall economic performance based on economic growth, employment, and public debt. A fuzzy-set ideal-type analysis of performance of 19 OECD countries between 1975 and 2005 reveals substantial variation across countries and over time that cannot sufficiently be accounted for by two key institutional features: corporatism and consensus democracy. Corporatism and consensus democracy may account for policy formation and implementation, but hardly for economic performance.
Making I-Contact: Fostering Shared, In-the-moment Subjective Experiences

Numerous research studies have offered evidence that I-sharing (perceived subjective similarity) facilitates interpersonal connection (e.g. Huneke & Pinel, 2016; Pinel, Long, Landau, Alexander, & Pyszczynski, 2006; Pinel & Long, 2012). Despite this research, no interventions currently exist to foster I-sharing between individuals, thereby leaving interventionists and others unable to utilize I-sharing to nurture authentic connections. The current dissertation takes an important step in the direction of developing usable interventions based on I-sharing research. Specifically, I examine the effectiveness of a technique designed to foster I-sharing genuinely between individuals. Building on I-sharing theory, which specifies that people most confidently believe that they I-share when they react simultaneously and identically to the same stimulus (Pinel et al., 2006), I randomly assigned participants either to experience novel, emotionallyarousing stimuli that provoke predictable reactions in a context in which participants could also experience each other’s in-the-moment subjective experiences (the Fostered Isharing condition), or in a condition in which they could not fully experience each other’s experiences (the comparison condition). To investigate whether I-sharing also proves effective for people who see themselves as dissimilar on an important self dimension, I also manipulated perceived value similarity of the other participant prior to the I-sharing intervention. Participants either learned of an unshared value, learned of a shared value, or did not receive any value information. Results showed that the I-sharing intervention significantly increased feelings of subjective similarity, but only increased liking and interpersonal behavior when participants also initially learned that they shared a similar value. I discuss potential explanations for results, and means of intervention improvement.
Multiple-layer Quantum-behaved Particle Swarm Optimization and Toy Model for Protein Structure Prediction

Protein structure prediction, known as an NP-complete problem, is one of the basic problems in computational biology. To get an efficiency approach of protein structure prediction with Toy model, a new algorithm structure based on quantum-behaved particle swarm optimization (QPSO) structure is suggested, which is named as multiple-layer QPSO (MLQPSO). In this structure, population of each generation is divided into elite sub-population, exploitation sub-population and exploration sub-population, respectively using different strategies, sequentially leading to improve the ability of local exploitation and global exploration. Subsequently, the algorithm to predict the structure prediction is evaluated by artificial data and real protein. The experiment shows the MLQPSO is a feasible and efficient algorithm.
An Experimental Comparison of Event Driven and Multi-Threaded Sensor Node Operating Systems

Two different operating system types are currently considered for sensor networks: event driven and multi-threaded. This paper compares the two well-known operating systems TinyOS (event driven) and MANTIS (multi-threaded) regarding their memory usage, power consumption and processing capabilities. TinyOS and MANTIS are both ported to the DSYS25 sensor platform. Both operating systems are used to execute the same sensor network application and the aforementioned parameters of interest are measured. The results presented in this paper show for which set of applications each operating system is preferable
Affinity propagation clustering classification method for aircraft in arrival and departure sequencing

With the increase of the fight flow on airport, the category of aircraft has a great influence on the effect of aircraft arrival and departure sequencing. The classification method based on trial whorl is a popular one, which divides all aircrafts into four types (Heavy, Large, Medium, Small). In this paper, a new classification method is presented, in which all aircrafts are classified with considering not only trial whorl but also some economical factors such as the arrival and departure time, flight mission, flight object, and flight priority. The fast and efficient affinity propagation clustering algorithm is applied to aircraft classification, which regards all the aircraft as the exemplars, and thus reduces the computing time for aircraft classification without iterative circulation. Finally, our new aircraft classification is applied to departure sequencing simulation, the results of which demonstrate our method is more rational, and economic benefit can be distinctly improved compared to the trial whorl method.
An Immuno-Genetic Hybrid Algorithm

The construction of artificial systems by drawing inspiration from nat- ural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this re- search, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algo- rithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also in- troduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population in- creases, the hybrid algorithm used for evolving a fuzzy rule system to solve the well- known Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; sec- ond, the system has a simple fuzzy rule system; therefore, it is human interpretable. The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of our algorithm was ver- ified through its application to a binary character recognition problem. The hybrid algorithm overcomes also GAs and AIS and reached the convergence point before
Security-Aware Virtual Machine Allocation in the Cloud: A Game Theoretic Approach

With the growth of cloud computing, many businesses, both small and large, are opting to use cloud services compelled by a great cost savings potential. This is especially true of public cloud computing which allows for quick, dynamic scalability without many overhead or long-term commitments. However, one of the largest dissuasions from using cloud services comes from the inherent and unknown danger of a shared platform such as the hyper visor. An attacker can attack a virtual machine (VM) and then go on to compromise the hyper visor. If successful, then all virtual machines on that hyper visor can become compromised. This is the problem of negative externalities, where the security of one player affects the security of another. This work shows that there are multiple Nash equilibria for the public cloud security game. It also demonstrates that we can allow the players' Nash equilibrium profile to not be dependent on the probability that the hyper visor is compromised, reducing the factor externality plays in calculating the equilibrium. Finally, by using our allocation method, the negative externality imposed onto other players can be brought to a minimum compared to other common VM allocation methods.
Dimethyl Fumarate Induces Glutathione Recycling by Upregulation of Glutathione Reductase

Neuronal degeneration in multiple sclerosis has been linked to oxidative stress. Dimethyl fumarate (DMF) is an effective oral therapeutic option shown to reduce disease activity and progression in patients with relapsing-remitting multiple sclerosis. DMF activates the transcription factor nuclear factor erythroid 2-related factor 2 (NRF2) leading to increased synthesis of the major cellular antioxidant glutathione (GSH) and prominent neuroprotection in vitro. We previously demonstrated that DMF is capable of raising GSH levels even when glutathione synthesis is inhibited, suggesting enhanced GSH recycling. Here, we found that DMF indeed induces glutathione reductase (GSR), a homodimeric flavoprotein that catalyzes GSSG reduction to GSH by using NADPH as a reducing cofactor. Knockdown of GSR using a pool of E. coli RNase III-digested siRNAs or pharmacological inhibition of GSR, however, also induced the antioxidant response rendering it impossible to verify the suspected attenuation of DMF-mediated neuroprotection. However, in cystine-free medium, where GSH synthesis is abolished, pharmacological inhibition of GSR drastically reduced the effect of DMF on glutathione recycling. We conclude that DMF increases glutathione recycling through induction of glutathione reductase.
Structured output layer with auxiliary targets for context-dependent acoustic modelling

In previous work we have introduced a multi-task training technique for neural network acoustic modelling, in which contextdependent and context-independent targets are jointly learned. In this paper, we extend the approach by structuring the output layer such that the context-dependent outputs are dependent on the context-independent outputs, thus using the contextindependent predictions at run-time. We have also investigated the applicability of this idea to unsupervised speaker adaptation as an approach to overcome the data sparsity issues that comes to the fore when estimating systems with a large number of context-dependent states, when data is limited. We have experimented with various amounts of training material (from 10 to 300 hours) and find the proposed techniques are particularly well suited to data-constrained conditions allowing to better utilise large context-dependent state-clustered trees. Experimental results are reported for large vocabulary speech recognition using the Switchboard and TED corpora.
Matching memory access patterns and data placement for NUMA systems

Many recent multicore multiprocessors are based on a nonuniform memory architecture (NUMA). A mismatch between the data access patterns of programs and the mapping of data to memory incurs a high overhead, as remote accesses have higher latency and lower throughput than local accesses. This paper reports on a limit study that shows that many scientific loop-parallel programs include multiple, mutually incompatible data access patterns, therefore these programs encounter a high fraction of costly remote memory accesses. Matching the data distribution of a program to the individual data access patterns is possible, however it is difficult to find a data distribution that matches all access patterns. Directives as included in, e.g., OpenMP provide a way to distribute the computation, but the induced data partitioning does not take into account the placement of data into the processors' memory. To alleviate this problem we describe a small set of language-level primitives for memory allocation and loop scheduling. Using the primitives together with simple program-level transformations eliminates mutually incompatible access patterns from OpenMP-style parallel programs. This result represents an improvement of up to 3.3X over the default setup, and the programs obtain a speedup of up to 33.6X over single-core execution (19X on average) on a 4-processor 32-core machine.
A Fuzzy Decision Tree for Bus Network Management

The planning and managing of bus networks as a urban public transport system is a relevant problem. Several proposals focus the planning of new bus networks. In this paper, we focus the speci c task of reallocating buses from di erent lines of a previously planned bus network in case of restrictions, such as vehicle breakdowns and drivers absences. Our proposal uses a fuzzy decision tree, which combines the graphical representation and high interpretability of decision trees with the use of linguistic variables of fuzzy systems. The fuzzy decision tree is intended to support human experts that have to make decisions regarding reallocating buses from prede ned lines in order to optimize the average time that users have to wait for a bus service in any bus stop. The model was induced by the FUZZYDT algorithm using real data collected from a public bus system with 26 bus lines. 16 variables related to characteristics of the bus lines and users were considered as inputs to the system. The output variable of the system presents an evaluation of bus lines when changes in the lines occur. The induced fuzzy decision tree includes only 7 key-variables forming 15 rules. The nal model is simple, highly interpretable, and has received positive feedback from experts.
Too Small to Form a Galaxy: How the UV Background Determines the Baryon Fraction

Abstract The cosmic ultraviolet background (UVB) heats the intergalactic medium (IGM), as a result the gas in dark matter halos below a certain mass is too hot to cool within a Hubble time. The UVB effectively suppresses the formation of dwarf galaxies. Using high resolution cosmological hydrodynamical simulations we show that photo heating leads to small baryon fractions in halos below ~ 6× 109h−1M⊙, independent of the cosmic environment. The simulations are carried out assuming a homogeneous UVB with flux densities as given by Haardt, &, Madau (1996). A halo may stop to condense gas significantly after the universe is reionised, namely when its mass falls below the characteristic mass scale set by the photo heating. Assuming a spherical halo model we derive this characteristic mass analytically and identify the main mechanisms that prevent the gas from cooling in small halos. The theoretically derived characteristic mass is smaller than the one obtained from observations. Increasing the energy per ionising photon by a factor between four and eight would be sufficient to reconcile both. This is equivalent to an average temperature of the IGM of ~ 104K. In this sense the faint end of the luminosity function may serve as a calorimeter for the IGM.
Novel Approach for Test Methods Automatic Selection in Product Reliability: Improved Method for Acquiring Part-Whole Relation

In the product development process, test planning can be a time-consuming task, in particular, the test methods for product reliability are usually selected manually because many documents must be referred to when selecting suitable test methods. To make this process more efficient, we have been researched methods to support test planning. In this paper a new automatic selection method for test plans is proposed. The proposed method makes use of information pertaining to cases of failure. We describe a method for searching for failure cases among products having similar attributes for a product currently under consideration. The main principle of this method consists in automated acquisition of names describing the product’s constituent components for use in the search process. Experimental results show that the proposed method delivers a greater degree of acquisition performance, precision, and recall in acquiring names of components compared with a previously proposed method.