Miracle-dz/UniFAR-FTUs huggingface dataset:
An informed neural architecture for aspect-level sentiment classification

The aspect-level sentiment classification task is to determine the polarity of a sentence with respect to an aspect, which is typically a noun phrase. Example applications include product recommendation, where the aspect is the name of the product. The task is challenging since a model needs to learn to focus on only the part of the sentence that is related to the aspect. In this paper, we propose an informed neural network architecture, which takes advantage of a general property of comments that contains expressions of opposite sentiment polarities: the positive expressions often precede the negative ones, or vice versa. Specifically, we apply a "coarse-grained" attention to the left and right side contexts of the aspect, respectively. We evaluate our methods on two datasets: SemEval-2014 and a manual annotation dataset from Twitter. Our model achieves the state-of-the-art performance on the laptop dataset of SemEval-2014 and the Twitter dataset.
Mitigate catastrophic forgetting for continuously learning linked open data using modularity

Nowadays, Linked Open Data is spreading year by year, and its further utilization is expected. Since the size of the data is large, Linked Open Data is attempted to learn by using neural networks. Since the data is still scaling in various region, unlike the existing neural network specialized to learn only one region, a neural network which can continuously learn wide region of knowledge is needed. However, neural network is known in its problem, catastrophic forgetting, which is to lose previously acquired skills when learning a new skill. In this research, we approach catastrophic forgetting from modularity, which is said to exist in human's brain functional network. It is true that there are many previous researches in catastrophic forgetting of neural network, however, most of the research need to know the number of learning tasks in advance, so that it is not applicable for continuously learning wide region of knowledge. The basic idea of our design approach is to design a neural network with multiple learning subsets only considering modularity without considering the number of tasks. In the evaluation, we evaluated not only for tasks learned just before, but also for tasks learned a while before. Our results show that, although, as we can expected, a neural network with high modularity can mitigate forgetting for tasks learned just before because of the low interference, a neural network with low modularity is better for the worst case when evaluating for all the tasks it learned in the past.
Effect of Nano Size TiO2 Particles on Mechanical Properties of AWS E 11018M Type Electrode

Addition of nano size particles of TiO2 in the coating of shielded metal arc welding electrode (E 11018M) partially substituting the conventional micro size TiO2 was studied for possible enhanced electrode characteristics. The results show that the nano size particle of TiO2 improved recovery of elements such as Mn, Ni, Mo, Ti etc. as well as increased all-weld-metal tensile and charpy impact properties at –51℃. Furthermore, the charpy impact properties were found to be very sensitive to variations in Ti content of the weld deposit.
Factor Structure and Gender Invariance Testing for the Sport Anxiety Scale-2 (SAS-2)

This study aimed to provide further psychometric validation of the Sport Anxiety Scale-2 (SAS-2) by assessing the factor structure, invariance across gender, and convergent and divergent validity of the SAS-2 by correlating both related (i.e., anxiety sensitivity, brief fear of negative evaluation, intolerance of uncertainty, and negative affect) and unrelated constructs (i.e., positive affect, self-confidence). A total of 542 current and former competitive athletes completed a questionnaire through Amazon’s Mechanical Turk system. All data were collected via online survey. Participants were randomly assigned to an exploratory factor analysis (n = 271) and confirmatory factor analysis group (n = 271). Results indicated that both exploratory and confirmatory factor analyses supported the three-factor model of anxiety involving somatic anxiety, worry, and concentration disruption. Additionally, this study found the SAS-2 to be reliable, gender invariant, and have strong construct validity. Our findings exte.
Building Guitar Strum Models for an Interactive Air Guitar Prototype

In this work-in-progress, we propose the design of an interaction that allows a guitar player to air guitar with the use of forearm Electromyogram (EMG). We integrate results from our previous study where we have used the same medium in training a classifier to recognize standard guitar chords. In this paper, we aim to train a classifier this time to recognize the different types of strums when playing the guitar. We collected data from ten (10) participants using the Myo armband doing strum repetitions for at least fifty (50) times. The strumming EMG data was then pre-processed and fed into a machine learning task to build a model. A k-Nearest Neighbor (k=11) classifier was trained and yielded an accuracy of at least 46% accuracy with a kappa statistic of 0.3712. Model results de- scribe that data size needs to be improved while considering equally the same set of features. Additionally, user insights and feedback on the armband usage as an alternative creative medium was gathered from our target respondents. Different views and insights are stated which opened opportunities for the improvement of the actual air guitar concept as a creativity tool.
Knowledge graph embedding via multiplicative interaction

Knowledge graphs are playing a crucial role in many machine learning applications. Since most of the knowledge graphs are far from complete, many knowledge graph completion models have been proposed. TransE and its extended models all model knowledge graphs with additive interaction. DistMult demonstrates that multiplicative interaction is more effective for modeling knowledge graphs. However, DistMult performs poorly on 1-to-N, N-to-1 and N-to-N relations. Besides, it does not consider the case that an entity could be a head entity or a tail entity which should be modeled separately. We propose a more fine-grained knowledge graph embedding model called MultE, which models knowledge graphs with multiplicative interaction. In MultE, an entity would have one representation when serving as head entity and another representation when serving as tail entity. For improving performance on 1-to-N, N-to-1 and N-to-N relations, MultE considers all valid tail(or head) entities during training and treats the prediction task as a classification problem. Experiment results show that MultE obtains consistent improvements compared with DistMult and achieves state-of-the-art performance.
Investigation of the latent space of stock market patterns with genetic programming

We suggest a use of genetic programming for transformation from a vector space to an understandable graph representation, which is part of a project to inspect the latent space in matrix factorization. Given a relation matrix, we can apply standard techniques such as non-negative matrix factorization to extract low dimensional latent space in vector representation. While the vector representation of the latent space is useful, it is not intuitive and hard to interpret. The transformation with the help of genetic programming allows us to better understand the underlying latent structure. Applying the method in the context of a stock market, we show that it is possible to recover the tree representation of technical patterns from a relation matrix. Leveraging the properties of the vector representations, we are able to find patterns that correspond to cluster centers of technical patterns. We further investigate the geometry of the latent space.
Learning under selective labels in the presence of expert consistency

We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises when historical decision making blinds us to the true outcome for certain instances. Examples of this are common in many applications, ranging from predicting recidivism using pre-trial release data to diagnosing patients. In this paper we discuss why selective labels often cannot be effectively tackled by standard methods for adjusting for sample selection bias, even if there are no unobservables. We propose a data augmentation approach that can be used to either leverage expert consistency to mitigate the partial blindness that results from selective labels, or to empirically validate whether learning under such framework may lead to unreliable models prone to systemic discrimination.
From Operational to Declarative Specifications Using a Genetic Algorithm

In specification-based test generation, sometimes having a formal specification is not sufficient, since the specification may be in a different formalism from that required by the generation approach being used. In this paper, we deal with this problem specifically in the context in which, while having a formal specification in the form of an operational invariant written in a sequential pro- gramming language, one needs, for test generation, a declarative invariant in a logical formalism. We propose a genetic algorithm that given a catalog of common properties of invariants, such as acyclicity, sortedness and balance, attempts to evolve a conjunction of these that most accurately approximates an original operational specification. We present some details of the algorithm, and an experimental evaluation based on a benchmark of data structures, for which we evolve declarative logical invariants from operational ones.
Automatic code conversion for non-volatile memory

Non-Volatile Memories (NVMs), such as Phase Change Memories (PCMs) and Resistive RAMs (ReRAMs), have been recently proposed as a main memory due to their higher capacity and low leakage power consumption compared to traditional DRAMs. In order to support the NVM-based systems, many software platforms are developed and they provide user-level programming interfaces. However, many existing applications are already written based on the conventional DRAM-based systems; thus, programmers have to rewrite or modify the code in order for the code to successfully run on NVM-based systems. In order to solve this problem, we introduce a code-conversion tool named a Code Regenerator that transforms applications that are originally designed for conventional operating systems using DRAM as a main memory into applications that runs on HEAPO which is a non-volatile memory based software platform. The code regenerator consists of code profiler and code generator. Among all dynamic and static memory objects of an application, code profiler profiles the code to find the objects that fit well into the characteristics of NVM. Based on the profiling result, code generator re-writes the target application code to exploit NVM through HEAPO programming interfaces based on the profiling result. In this paper, we demonstrate that applications transformed through code regenerator stably run on NVM platform without manual code modification. By allocating read-intensive memory objects to NVM, the regenerated applications reduce the energy consumption by up to 44% compared to that of the original applications.
Cycle-bounded model checking of PLC software via dynamic large-block encoding

Bounded model checking is a formal verification technique for checking reachability of unwanted behaviour within a bounded number of steps. In the context of PLC software, specifications treat the whole program as a single step, only considering the controller's observable state at the end of an execution-cycle. For a program containing loops, standard approaches will characterise shorter steps, losing the relation to the original bound, or try to unroll the loops statically. We propose a combination of symbolic execution and adjustable-block encoding to achieve an efficient, incremental, monotonic logical characterisation which lifts the step-size to whole cycles. Evaluation of our prototypical implementation on examples from the PLC Safety library shows its applicability to such specifications, compared to leading BMC approaches.
A Replication Study: Just-in-Time Defect Prediction with Ensemble Learning

Just-in-time defect prediction, which is also known as change-level defect prediction, can be used to efficiently allocate resources and manage project schedules in the software testing and debugging process. Just-in-time defect prediction can reduce the amount of code to review and simplify the assignment of developers to bug fixes. This paper reports a replicated experiment and an extension comparing the prediction of defect-prone changes using traditional machine learning techniques and ensemble learning. Using datasets from six open source projects, namely Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL we replicate the original approach to verify the results of the original experiment and use them as a basis for comparison for alternatives in the approach. Our results from the replicated experiment are consistent with the original. The original approach uses a combination of data preprocessing and a two-layer ensemble of decision trees. The first layer uses bagging to form multiple random forests. The second layer stacks the forests together with equal weights. Generalizing the approach to allow the use of any arbitrary set of classifiers in the ensemble, optimizing the weights of the classifiers, and allowing additional layers, we apply a new deep ensemble approach, called deep super learner, to test the depth of the original study. The deep super learner achieves statistically significantly better results than the original approach on five of the six projects in predicting defects as measured by F1 score.
Sketching with a Purpose: Moving from Supporting Modeling to Supporting Software Engineering Activities

With the advent of large interactive displays and the increasing ubiquity of touch-enabled devices, digital sketching tools are becoming viable alternatives to classic whiteboards for the design and engineering of software systems. In this paper, we argue that current sketching tools still focus largely on creating sketches as artifacts (mostly, models), rather than recognizing sketches as byproducts of engineers' thought processes as they tackle a broad variety of complex engineering activities. We therefore advocate a more activity-than artifact-oriented view of sketching and propose research on a number of ways in which digital sketching can support cognitively demanding software engineering activities more directly.
Structure Learning of Markov Random Fields through Grow-Shrink Maximum Pseudolikelihood Estimation

Learning the structure of Markov random fields (MRFs) plays an important role in multivariate analysis. The importance has been increasing with the recent rise of statistical relational models since the MRF serves as a building block of these models such as Markov logic networks. There are two fundamental ways to learn structures of MRFs: methods based on parameter learning and those based on independence test. The former methods more or less assume certain forms of distribution, so they potentially perform poorly when the assumption is not satisfied. The latter can learn an MRF structure without a strong distributional assumption, but sometimes it is unclear what objective function is maximized/minimized in these methods. In this paper, we follow the latter, but we explicitly define the optimization problem of MRF structure learning as maximum pseudolikelihood estimation (MPLE) with respect to the edge set. As a result, the proposed solution successfully deals with the {\em symmetricity} in MRFs, whereas such symmetricity is not taken into account in most existing independence test techniques. The proposed method achieved higher accuracy than previous methods when there were asymmetric dependencies in our experiments.
HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI

In this work we propose HAMLET, a novel tract learning algorithm, which, after training, maps raw diffusion weighted MRI directly onto an image which simultaneously indicates tract direction and tract presence. The automatic learning of fiber tracts based on diffusion MRI data is a rather new idea, which tries to overcome limitations of atlas-based techniques. HAMLET takes a such an approach. Unlike the current trend in machine learning, HAMLET has only a small number of free parameters HAMLET is based on spherical tensor algebra which allows a translation and rotation covariant treatment of the problem. HAMLET is based on a repeated application of convolutions and non-linearities, which all respect the rotation covariance. The intrinsic treatment of such basic image transformations in HAMLET allows the training and generalization of the algorithm without any additional data augmentation. We demonstrate the performance of our approach for twelve prominent bundles, and show that the obtained tract estimates are robust and reliable. It is also shown that the learned models are portable from one sequence to another.
Discovery of key molecular pathways of C1 metabolism and formaldehyde detoxification in maize through a systematic bioinformatics literature review.

Computational systems biology approaches provide insights to understand complex molecular phenomena in living systems. Such understanding demands the need to systematically interrogate and review existing literature to refine and distil key molecular pathways. This paper explores a methodological process to identify key molecular pathways from systematic bioinformatics literature review. This process is used to identify molecular pathways for a ubiquitous molecular process in all plant biological systems: C1 metabolism and formaldehyde detoxification, specific to maize. The C1 metabolism is essential for all organisms to provide one-carbon units for methylation and other types of modifications, as well as for nucleic acid, amino acid, and other biomolecule syntheses. Formaldehyde is a toxic one-carbon molecule which is produced endogenously and found in the environment, and whose detoxification is an important part of C1 metabolism. This systematic review involves a five-part process: 1) framing of the research question; 2) literature collection based on a parallel search strategy; 3) relevant study selection based on search refinement; 4) molecular pathway identification; and 5) integration of key molecular pathway mechanisms to yield a well-defined set molecular systems associated with a particular biochemical function. Findings from this systematic review produced three main molecular systems: a) methionine biosynthesis; b) the methylation cycle; and c) formaldehyde detoxification. Specific insights from the resulting molecular pathways indicate that normal C1 metabolism involves the transfer of a carbon group from serine through a folate-mediated pathway to methionine, and eventually the methylation of a biomolecule. In photosynthetic tissues, C1 metabolism often proceeds in reverse towards serine biosynthesis and formate oxidation. C1 metabolism, in maize, appears to be present in the developing embryo and endosperm indicating that these cells are vulnerable to perturbations in formaldehyde detoxification. These insights demonstrate the value of a systematic bioinformatics literature review process from a broad spectrum of domain literature to specific and relevant molecular pathways.
Generalizable Protein Interface Prediction with End-to-End Learning

Predicting how proteins interact with one another - that is, which surfaces of one protein bind to which surfaces of another protein - is a central problem in biology. Here we present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction. Despite using only spatial coordinates and identities of atoms as inputs, SASNet outperforms state-of-the-art methods that rely on complex, hand-selected features. These results are particularly striking because we train the method entirely on a significantly biased data set that does not account for the fact that proteins deform when binding to one another. Nonetheless, our network maintains high performance, without retraining, when tested on real cases in which proteins do deform. This suggests that it has learned fundamental properties of protein structure and dynamics, which has important implications for a variety of key problems related to biomolecular structure.
THE EFFECT OF AMPHETAMINE ANALOGS ON CLEAVED MAP-TAU FORMATION IN THE RAT BRAIN

The present study quantified the cleaved form of the microtubule associated protein tau (cleaved MAP-tau, C-tau), a previously demonstrated marker of CNS toxicity, following the administration of monoamine-depleting regimens of the psychostimulant drugs amphetamine (AMPH), methamphetamine (METH), ±3,4-methylenedioxymethamphetamine (MDMA), or paramethoxyamphetamine (PMA) in an attempt to further characterize psychostimulant-induced toxicity. A dopamine (DA)-depleting regimen of AMPH produced an increase in C-tau immunoreactivity in the striatum, while a DAand serotonin (5-HT)-depleting regimen of METH produced an increase in the number of C-tau immunoreactive cells in the striatum and CA2/CA3 and dentate gyrus regions of the hippocampus. MDMA and PMA, two psychostimulant drugs that produce selective 5-HT depletion in the striatum, had no effect on C-tau immunoreactivity in the striatum or hippocampus. Furthermore, 5,7-dihydroxytryptamine (5,7-DHT), an established 5-HT selective neurotoxin, did not produce an increase in C-tau immunoreactivity. Dual fluorescent immunocytochemistry with antibodies to GFAP and C-tau indicated that C-tau immunoreactivity was present in astrocytes, not neurons, suggesting that increased C-tau may be an alternative indicator of reactive gliosis. The present results are consistent with previous findings that the DA-depleting psychostimulants AMPH and METH produce reactive gliosis whereas the 5-HT-depleting drugs MDMA and PMA, as well as the known 5-HT selective neurotoxin 5,7-DHT, do not produce an appreciable glial response.