Reviewerly/exHarmony huggingface dataset:
On Krifka’s “Nominal Reference, TemporalConstitutionandQuantification in Event Semantics”

Krifka, in his paper “Nominal reference, temporal constitution and quantification in event semantics”, provides the first formal mereological (algebraic) analysis of the relation between nominal reference and temporal constitution (also based on his 1986 PhD thesis). The focus is on two manifestations of this relation in the grammar of natural languages. First, as many observed, there are direct structural analogies between the following two sets of distinction: namely, mass/count and atelic/telic. They are clearly reflected in their parallel cooccurrence patterns with quantifiers, numerical and measure expressions. Second, nominal reference and temporal constitution interact and mutually constraint each other in the derivation of meaning of complex verbal predicates. One key example is aspectual composition(ality) e.g., eat soup (atelic) versus eat two apples (telic). In order to provide an adequate analysis of the relevant data Krifka’s principal innovation is to assume a single join semi-lattice structure, undetermined with respect to atomicity, relative to which he defines two higher-order, cross-categorial predicates for reference types of natural language predicates: namely, quantized and cumulative. Specifically in the case of aspectual composition, the interactions and mutual constraints between the structure of objects and eventualities stem from the systematic mappings (homomorphisms) whose source is the lexical semantics of verbs. Such mappings are also independently motivated by other phenomena exhibiting systematic interactions objects and eventualities.
Fostering the Learning Process in a Programming Course With a Chatbot

Novice programmers have to focus and learn different programming skills in programming 1 course at the same time. Therefore, they need more support to answer their queries related to the programming domain. This study developed and offered a chatbot in programming 1 course. The chatbot focuses on course details, fundamental programming concepts, and common programming errors. The perception of programming 1 students and instructors regarding the chatbot in programming education were collected through the survey and focus group respectively. The results of the students’ survey revealed that the chatbot supports students in learning programming and common programming errors in the course. The focus group participants agreed that the chatbot provides one-to-one teaching experience to novices. The chatbot serves as a virtual teaching assistant and promotes students-centered learning. The focus group participants also agreed that the chatbot approach provides additional support to students in their learning process of programming domain.
Self-Healing in Web Service-Based Systems Using QoS

Availability of components in online systems cannot be guaranteed due to the unstable nature of the web (updates, changes, etc.). A well-designed system must take this fact into account in order to ensure the availability of services which is a very difficult challenge due to the confidentiality and autonomy of each service component. An interesting solution for this is to tolerate these problems at the composite level by having a mechanism of recovery, called Self-healing. In this work, we proposed a solution that consists of implementing a formal approach, making it possible to model a business process (web service composition) by timed automata of the type daTA, while ensuring the quality of service taking into account the functional and non-functional needs of the system (in this case, the QoS represents the response time). The main objective of this project is to create a system that allows you to compare two web service compositions in pairs, to decide whether they are equivalent or not to ensure a perfect self-healing working system by the end.
Estimation of Generalized Pareto Distribution Parameters in Traffic Accident Loss Data Modeling

The problem of traffic accidents in Indonesia has a high level of risk. In an effort to minimize losses due to traffic accidents, it is necessary to study the data and characteristics of traffic accidents and identify these events as extreme events. This study was conducted to find out how to estimate the shape and scale parameters using Maximum Likelihood Estimation (MLE), and to explore data on traffic accident losses in Indonesia. The method used to analyze the extreme value of traffic accident losses is the Extreme Value Theory. One approach to identify extreme values is Peaks Over Threshold which follows the Generalized Pareto Distribution (GPD). Traffic accident loss data is divided into three types based on the cause, namely driver negligence, vehicle quality, and other external factors in the period (2008-2017). Estimation of shape and scale parameters is obtained through MLE which is then solved by Newton Raphson because it produces equations that are not closed form. This study resulted in an estimate of the shape and scale of the GPD distribution parameter, as well as a confidence interval (1-α) of 100% with of 5%. In addition, it is concluded that the parameters obtained from the estimation have the same characteristics for each type of risk analyzed, but have different parameter values. Based on parameter estimation, GPD distribution is obtained from each risk which is expected to be useful for related parties in analyzing the number of traffic accident losses in the next period to consider steps that can be taken to reduce losses due to traffic accidents.
Model‐based generation of test scripts across product variants: An experience report from the railway industry

Abstract Software product line engineering emerged as an effective approach for the development of families of software‐intensive systems in several industries. Although its use has been widely discussed and researched, there are still several open challenges for its industrial adoption and application. One of these is how to efficiently develop and reuse shared software artifacts, which have dependencies on the underlying electrical and hardware systems of products in a family. In this work, we report on our experience in tackling such a challenge in the railway industry and present a model‐based approach for the automatic generation of test scripts for product variants in software product lines. The proposed approach is the result of an effort leveraging the experiences and results from the technology transfer activities with our industrial partner Alstom SA in Sweden. We applied and evaluated the proposed approach on the Aventra software product line from Alstom SA. The evaluation showed that the proposed approach mitigates the development effort, development time, and consistency drawbacks associated with the traditional, manual creation of test scripts. We performed an online survey involving 37 engineers from Alstom SA for collecting feedback on the approach. The result of the survey further confirms the aforementioned benefits.
Categorical Account of Gradient Acceptability of Word-Initial Polish Onsets

We examine how well categorical and probabilistic phonotactic learning models extract grammars which predict Polish speakers' acceptability judgments of words with varied initial consonant clusters. Polish is an especially interesting language to look at because of its rich inventory of sonority-sequencing defying consonant clusters, often as a result of yer-deletion. In line with results by Gorman (2013) and Durvasula (2020), we find that the categorical baselines considered here generally outperformed the Hayes and Wilson's (2008) maximum-entropy based phonotactic learner. We conclude that gradient acceptability judgments do not provide unambiguous evidence for gradient, probabilistic grammars.
Emotion Recognition and Classification of Film Reviews Based on Deep Learning and Multimodal Fusion

In terms of cross-cultural exchanges, the film is not only an important embodiment of a country’s cultural soft power but also the most direct and favorable way of communication. The advent of the all-around well-off era has propelled people’s demand for spiritual, cultural, and entertainment which promotes the vigorous development of the film culture industry. The expansion and development of China’s film market, domestic films, are a reflection and extension of China’s culture and ideas. It plays an extremely important role in enhancing cultural self-confidence and cultural output. In order to better grasp the emotional tendency of the audience, understand the viewing needs, and put forward suggestions on domestic film production, it is very necessary to analyze the emotion of film reviews and dig deep into semantics. Since the evaluation of film works considers many factors that are complex and changeable, the choice of model plays a significant role in the process of emotion analysis. The deep learning model represented by a deep neural network has high tolerance to sentence noise, has strong information discrimination, and features self-learning ability. It also has great advantages in emotion classification tasks. This study conducts an in-depth study and research on the traditional emotion analysis methods and finally puts forward an effective emotion analysis framework that combines the traditional emotion analysis method and deep learning network. This framework enhances the text vectorization representation and emotion classification model by performing emotion analysis. The effectiveness is verified by corresponding experiments which justify the superiority of the approach.
Adaptive method for image dynamic range adjustment and detail enhancement

Tone mapping operators (TMOs) aim to adjust high dynamic range (HDR) images to low dynamic range (LDR) ones so that they can be displayed on conventional devices with visual information retained. Nonetheless, existing TMOs can successfully tone-map only limited types of HDR images, and the parameters need to be manually adjusted to yield the best subjective-quality tone-mapped outputs. To cope with the aforementioned issues, an adaptive parameter-free and scene-adaptive TMO for dynamic range adjusting and detail enhancing is proposed to yield a high-resolution and high-subjective-quality tone-mapped output. This method is based on detail/base layer decomposition to decompose the input HDR image into coarse detail, fine detail, and base images. After that, we adopt different strategies to process each layer to adjust the overall brightness and contrast and to retain as much scene information. Finally, a new method, to the best of our knowledge, is proposed for visualization to generate a sequence of artificial images to adjust the brightness. Experiments with numerous HDR images and state-of-the-art TMOs are conducted; the results demonstrate that the proposed method consistently produces better quality tone-mapped images than the state-of-the-art methods.
On Kratzer’s “The Notional Category of Modality”

A. Kratzer’s “The Notional Category of Modality” (NCM) develops a unified analysis of modals and conditionals, which has come to be the classic approach to modality in linguistic semantics and beyond. This chapter presents an overview of NCM’s main contributions and clarifies the motivations behind particular analytical choices, concentrating on the role of conversational backgrounds in the theory. It also highlights new research that has enriched the framework or raised outstanding issues.
A Non-Parametric Constrained Regression Algorithm for Dual Norm Computation and its Applications

Dual norms are useful to modelize smooth and oscillating patterns for decomposingan image into a geometrical component and a textured component.As the dual space of Banach space W1,p0 (Ω) defining (1, p) norm, W−1,q(Ω)corresponds to its dual norm which is an effective description for oscillatingpatterns(smaller for oscillating patterns than smooth patterns) with noassumption of noise process and model(i.e. various types of uncorrelatedand correlated noise). Scholars define the dualnorm of total variation(TV ) as G norm which is the limit of (−1, q) normwith q = ∞. Combined with priors for non-oscillating components, G norm and (−1, q) norm canplay an important role in various image decomposition tasks. However traditionalcomputation of G norm uses projection algorithm and dichotomywhile traditional computation for (−1, q) norm uses steepest descent withouttheoretically guaranteed convergence rate. These methods are inefficientand inaccurate. In this paper, we propose a non-parametricconstrained regression algorithm for G norm and (−1, q) norm computationvia adding lq norm linear regression module(q ∈ [2,∞]) and non-parametricregularization module. Compared with the traditional algorithm, the proposedalgorithm has a theoretically guaranteed bound for faster convergenceto a (1 + ϵ)-approximate solution. With the proposed algorithm, we discussits potential application to calibrate the parameter of traditional dual normbased image decomposition. Since TV can be replaced with other traditionaland deep image priors, the proposed algorithm is general and usefulwith potential applications in many areas.
Real-Time Post-Processing for Physical-Layer Secure Key Distribution in Fiber Networks

Physical-layer secure key distribution (PLSKD) provides robust security and full compatibility with current fiber infrastructure. The post-processing is generally required to improve the key consistency, security, and randomness in PLSKD. However, offline digital signal processing was generally adopted in the previous PLSKD schemes, while the real-time post-processing is necessary towards the practical PLSKD applications. In this paper, a real-time pipeline post-processing is designed and demonstrated using field-programmable gate array (FPGA) boards. A complete set of post-processing is applied, including: double-threshold quantization, information reconciliation with Bose-Chaudhuri-Hocquenghem code, and privacy amplification with SM3 hash algorithm. The system architecture is detailed by combining all these processing steps, where the trade-offs for the post-processing scheme are analyzed. We also assess the performance of post-processing implementation on FPGA in terms of throughput and hardware resource utilization. On the FPGA platform, the final key rate is up to 12.2 Mb/s. The proposed real-time post-processing FPGA prototype is a good candidate for the practical application of PLSKD in fiber networks.
Rock Type Classification Based on Petrophysical, Geochemical, and Core Imaging Data Using Machine and Deep Learning Techniques

Highlights• Petrophysical, geochemical and high-resolution images of core samples were used for rock type classification• Machine learning methods were used to predict rock types using P- and S-wave velocity, Leeb hardness and geochemical characteristics data of core samples• A deep learning method was used to predict rock types based on high-resolution images of the core samples• An expert decision procedure was mimicked by concatenating measured rock properties and image features of core samples for rock type prediction• The concatenation of images and rock properties outperformed the rock type classification based on only measured rock characteristics or core images
Multi-Modal Multi-Channel American Sign Language Recognition

In this paper, we propose a 3D Convolutional Neural Network (3DCNN) based multi-stream framework to recognize American Sign Language (ASL) manual signs and non-manual gestures (face and head movements) in real-time from RGB-D videos. Our approach is based on fusing multimodal features including hand gestures, facial expressions, and body poses from multiple channels (RGB, depth, motion, and skeleton joints). To learn the overall temporal dynamics in a video, a proxy video is generated by selecting a subset of frames for each video which are then used to train the proposed 3DCNN model. Our proposed method achieves 92.88 % accuracy for recognizing 100 ASL sign glosses in our newly collected ASL-100-RGBD dataset. The effectiveness of our framework for recognizing hand gestures from RGB-D videos is further demonstrated on a large-scale dataset, Chalearn IsoGD, achieving the state-of-the-art results.
A Systematic Literature Review on the Impact of Formatting Elements on Program Understandability

Software programs can be written in different but functionally equivalent ways. Even though previous research has compared specific formatting elements to find out which alternatives affect code legibility, seeing the bigger picture of what makes code more or less legible is challenging.We aim to find which formatting elements have been investigated in empirical studies and which ones were found to be more legible for human subjects.We conduct a systematic literature review and identify 12 papers that compare alternative formatting elements. We organize these formatting elements using a card sorting method.We identify 29 formatting elements, which are about formatting styles, spacing, block delimiters, long or complex code lines, and word boundary styles. While some formatting elements were found to be statistically better than other equivalent ones, e.g., camel case over underscore, others were not, e.g., pretty-printed vs. disrupted layout. For some alternatives, divergent results were found, e.g., one study found a significant difference in favor of 2-space indentation, but two other studies did not find it.Our results can serve as guidelines for developers in choosing ways to format their code and help researchers in developing, e.g., recommendation systems, to aid developers to make their code more legible.
On Heim’s “On the Projection Problem for Presuppositions”

Irene Heim’s 1983 paper, “On the Projection Problem for Presuppositions” is, first, a paper about presupposition projection. But perhaps its major significance lies in its role in launching the dynamic turn in formal semantics, whose central idea is that the conventional meaning of an expression is given by a description of how that expression updates a context. The fundamental ideas of context and context change that Heim presented in this brief paper are now part of the basic toolkit of semantics. At the same time, the paper established presupposition and presupposition projection as a topic of central concern for the emerging dynamic approach. In this commentary, I briefly describe the paper’s most direct antecedents, review its central theoretical innovations, and describe some alternative approaches to the formal characterization of contexts and to the analysis of presupposition and presupposition projection.
Fitting Facts to Theory in Digital Literacy Implementation: Critical Reflections on Literacy during the COVID-19 Pandemic

A global pandemic of COVID-19 demands efforts of adjustments in the education sector.One that should be of concern to education policymakers is digitalizing literacy.In this study, we problematize the existing policies to digital literacy intended to meet the uncertainty resulting from the COVID-19 outbreak.We begin by reconsidering the policies upon the issues and present problems and challenges of digital literacy identified from empirical evidence thereafter.We also offer solutions for digital literacy implementation efforts.A focus-group discussion with members of the school supervisors of the local Department of Education and Culture of Malang, East Java, Indonesia was arranged to elicit reflections on factors contributing the success of building digital literacy in the pandemic time.The findings of this study recommend that digital literacy requires the penetration of determining policy and collaborative actions to cut down the impact of online risk in the teaching and learning processes.This can be the initial escalier for schools and parents' alertness in guiding and maximizing the appropriate and safe media using technology for the Indonesian young generation.
Development of a New Means to Improve the Performance of Self-Organizing Maps

In machine learning, a Self-Organizing map plays a significant role in finding hidden patterns or intrinsic structures in data. In this study, a new modified expression is arrived at to update the radius of the neighbourhood of BMU in SOM. Further, a new approach is introduced to find the eligible nodes for an update in SOM. We have also incorporated the previous work, such as new initialization algorithms to find the initial weight vectors, a method to place the weight vectors in each node of the grid and a method to identify the number of clusters to improve the performance of the proposed SOM algorithm. The proposed SOM performance in terms of Quantization error (QE), Convergence time (CT) and Modified Semantic Relevance Index (MSRI) are compared with conventional SOM for both class label and non-class label datasets. In addition to the above measure, Classification Accuracy (CA) is also used to evaluate the performance against class label datasets. The proposed SOM algorithm shows better performance in all cases.
PENGEMBANGAN KECERDASAN VERBAL BAHASA INGGRIS DI TK NEGERI GEMOLONG SRAGE

This paper describes the development of verbal intelligence in English at TK Negeri Gemolong Sragen. English is an international language that must be introduced to children to face global competition in the future. Therefore, this study aims to determine the teacher's strategy in stimulating the verbal intelligence of early childhood in Kindergarten Negeri Gemolong. This research method uses qualitative research methods. The results of this study indicate that the teacher's strategy in developing children's English verbal intelligence is carried out by intracurricular and extracurricular activities. Intracurricular activities are activities that are carried out based on scheduled, regular and structured activities carried out by the teacher during the learning process. In intracurricular activities the teacher uses collaborative methods from several existing methods consisting of the habituation method, the playing method, the singing method. The standard of achievement in English for children in Gemolong Kindergarten, namely self-introduction material, body parts, numbers 1-10, colors, animals, and fruit.
Comparison of Random Data Clusters Classification based on three Neural Networks Weight Training Methods

Random data clusters classification is a popular problem in data processing field. However, lots of research towards this problem focus on solving them through two classical ideas: Least square, Gradient descent. But both of them have disadvantages not considered in relative research, they don’t make comparisons on accuracy between different solutions, what’s more they don’t explore further alternative ideas which could make solution better and flexible. Therefore, in this project, we aim to use three different methods: Least square, Gradient descent, Simulated annealing to solve random data clusters classification problem and make comparisons between their accuracy results. What’ more, we explore some other ways that could make Least square and Gradient descent more flexible. And all three methods are good solutions and satisfying requirement on accuracy, after comparison, the accuracy from high to low is Least square, Gradient descent, Simulated annealing. And due to avoiding disadvantages in previous two, Simulated annealing is a valuable method to solve this problem. And in Least square, the sigmoid activation function could be the best alternative to realise the same accuracy, and then the tanh activation function is also suitable for this method.
A Deep Reinforcement Learning Agent for General Video Game AI Framework Games

The General Video Game AI (GVGAI) software framework, which was originally created for General Game Playing (GGP), can be connected via the OpenAI Gym interface for the purpose of testing machine learning agents on a large number of 2-dimensional arcade-type games. In this paper, we present a Proximal Policy optimization (PPO) based deep reinforcement learning game-playing agent. Using the OpenAI Gym interface, we highlight how this agent performs on a number of randomly selected GVGAI games.