Reviewerly/exHarmony huggingface dataset:
Applying Kahoot to Improve the Senior High School Students’ Vocabulary Achievement

This research was a classroom action research which intended to improve the senior high school studentsâ€™ vocabulary achievement by applying Kahoot as learning media in the teaching and learning process. The research was conducted to 40 students from grade X of Islamic Senior High School (Madrasah) in Kota Blitar. The procedure in this research followed some steps based on Kemmis and Taggart model namely; 1) Planning, 2) Acting, 3) Observing and 4) Reflecting. The data collection of this research were taken from tests and questionnaire. The research showed that the use of Kahoot as media in teaching and learning vocabulary particularly in â€œintroducing oneselfâ€ could improve the students' vocabulary achievement. The pre-test showed that the students' score was 59.23). That score was under the minimum criterion mastery set by the school (75.00). After 1 st cycle, there was an improvement on the students' score became 71.23 but it was still under minimum criteria. Therefore, it needed the 2 nd cycle in which the score improved into 84.58. The improvement on the studentsâ€™ achievement was supported by the studentsâ€™ positive attitude toward the use of Kahoot in teaching and learning.
A Plan-Based Personality Model for Story Characters

Believable virtual characters enhance the experience of interactive virtual worlds. BDI-based state-space narrative planners make their characters more compelling by granting them specific goals to pursue and different (possibly wrong) beliefs about the state of the world. However, such systems do not account for character individuality. We extend work on narrative planning by proposing a personality model based on the Big Five. This paper discusses how each factor of the Big Five is represented by narrative planning features. We also present a study to evaluate whether human readers can perceive the modeled personality traits through simple action patterns in short stories.
Evaluation of Semi-Supervised Clustering and Feature Selection for Human Activity Recognition

A lot of concern is shifted nowadays toward human activity recognition for developing powerful systems that assist numerous humans such as patients and elder people.Such smart systems automatically recognize human activities by learning the Activities of Daily Living (ADLs) and then making a suitable decision.Activity recognition systems are currently employed in developing many smart technologies (e.g., smart homes) and their uses have been dramatically increased with availability of Internet of Things (IoT) technology.Researchers have used various machine learning techniques for developing activity recognition systems.Nevertheless, there are some techniques have not been sufficiently exploited in this research direction.In this work, we present a framework to evaluate performance of one of these techniques.The presented technique is based on employing semi-supervised clustering and feature selection for grouping data collected by sensors located in smart homes.Employing semi-supervised clustering technique decreases the need for preparing a huge amount of labelled data that is required for learning activity recognition systems.Additionally, the presented technique improves performance of data clustering by decreasing a risk of grouping data into clusters that do not correspond to targeted activities.Moreover, we take into consideration importance of decreasing computational time complexity for making the presented technique applicable to smart systems located in homes.We conducted various experiments to evaluate performance of employing semi-supervised clustering and feature selection for human activity recognition by using partially labelled data.Experiment results have shown that the presented technique provides remarkable accuracy.
Towards explaining natural language arguments with background knowledge

In this paper, we propose the task of argument explicitation, a task that makes the structure of a natural language argument explicit, as well as the background knowledge the argument is built on, in the form of implicit premises or contextual knowledge. The purpose of argument explicitation is to support the understanding of an argument by providing users with an end-to-end analysis that offers a critical assessment of arguments including identification of argument weaknesses. Besides, the results of the argument explicitation process can be used by machines to retrieve similar arguments as well as counter-arguments. We propose a framework for argumentation mining sub-tasks that by now have mostly been treated separately in the literature. We identify the challenves this task entails, while at the same time highlighting the opportunities brought by the recent development of structured, external knowledge sources.
When NTT Meets Karatsuba: Preprocess-then-NTT Technique Revisited

The Number Theoretic Transform (NTT) technique is widely used to implement cryptographic schemes based on the Ring Learning With Errors problem(RLWE), since it provides efficient algorithm for multiplication of polynomials over the finite field. However, the module in NTT must be big enough such that the finite field has some special root of unity, which makes the corresponding schemes a bit less efficient. At Inscrypt 2018, Zhou et al. proposed a technique called Preprocess-then-NTT to relax the constraint for the modulus while keeping NTT work, at the cost of time complexity. In this paper, we improve the Preprocess-then-NTT technique by mixing it with Karatsuba multiplication such that the time complexity is better than the original NTT algorithm asymptotically. The claim was also verified in our experiments. As a result, our new algorithm not only relaxes the constraint for modulus but also improves efficiency in practice. In addition, we also present some advantages and applications of our new algorithm.
Live Forensics Analysis of Line App on Proprietary Operating System

The development of computer technology is increasing rapidly. This has positive and negative effects. One of the negative effects that occurred was the use of Line applications to conduct online shop fraud. Line is one of the instant messenger applications that can be used on computers, especially on Windows 8.1 operating system computers. Applications that run on the computer leave traces of data on Random Access Memory (RAM). Data left in RAM can be obtained using digital forensic techniques, namely live forensics which is used when the computer is running and connected to the internet. This study aims to find digital evidence regarding cases of online shop fraud using the National Institute of Standards and Technology (NIST) method. Digital evidence can be obtained using forensic tools, namely RamCapturer, FTK Imager and Winhex. RamCapturer is used to acquire data in RAM, FTK Imager is used for imaging and Winhex is used to analyze data that has been taken. The results obtained in this study were conversational recordings consisting of conversation time, conversation content and conversation status which could be digital evidence in uncovering the online shop fraud crime that occurred.
Optimistic Fair Exchange in Cloud-Assisted Cyber-Physical Systems

Recently, optimistic fair exchange in electronic commerce (e-commerce) or mobile commerce (m-commerce) has made great progress. However, new technologies create large amounts of data and it is difficult to handle them. Fortunately, with the assistance of cloud computing and big data, optimistic fair exchange of digital items in cyber-physical systems (CPSes) can be efficiently managed. Optimistic fair exchange in cloud-assisted CPSes mainly focuses on online data exchange in e-commerce or online contracts signing. However, there exist new forms of risks in the uncertain network environment. To solve the above problems, we use a new technique called verifiably encrypted identity-based signature (VEIS) to construct optimistic fair exchange in cloud-assisted CPSes. VEIS is an encrypted signature, and we can check the validity of the underlying signature without decrypting it. We introduce a robust arbitration mechanism to guarantee fairness of the exchange, and even the trusted third party (TTP) cannot get the original signatures of the exchange parties. And the TTP in our protocol is offline, which greatly improves the efficiency. Besides, we show that our protocol is secure, fair, and practical.
Weighted parsing for grammar-based language models

We develop a general framework for weighted parsing which is built on top of grammarbased language models and employs flexible weight algebras.It generalizes previous work in that area (semiring parsing, weighted deductive parsing) and also covers applications outside the classical scope of parsing, e.g., algebraic dynamic programming.We show an algorithm which terminates and is correct for a large class of weighted grammar-based language models.
umami: A Python package for Earth surface dynamics objective function construction

Models of Earth's surface dynamics are typically designed to simulate timescales that range from years to geologic epochs (10 6 + years).They represent and evolve a primary state variable, Earth's surface.Some applications may use other state variables (e.g., soil thickness).A diverse array of physical and chemical processes may be present.For example, the making and moving of water; the creation of soil and sediment from bedrock; the transport of mobile material due to hillslope processes, river erosion, and landslides; and the deposition of that material into the geologic archive.These models are used for applications as diverse as understanding limits on the height of mountain ranges and predicting the erosion, transport, and fate of contaminated material on human timescales.
Offline signature verification using DAG-CNN

This paper presents the implementation of a DAG-CNN which aims to classify and verify the authenticity of the offline signatures of 3 users, using the writer-independent method. In order to develop this work, 2 databases (training / validation and testing) were built manually, i.e. the manual collection of the signatures of the 3 users as well as forged signatures made by people not belonging to the base and altered by the same users were done, and signatures of another 115 people were used to create the category of non-members. Once the network is trained, its validation and subsequent testing is performed, obtaining overall accuracies of 99.4% and 99.3%, respectively, showing the features learned by the network and verifying the ability of this configuration of neural network to be used in applications for identification and verification of offline signatures.
Grid-based Control of Active Cameras for Waterway Ship Surveillance

This paper has proposed a novel surveillance system to adaptively monitor waterway ships by using active cameras. This active surveillance system consists of two parts: 1) ship detection; 2) grid-based control of active camera for ship tracking. In this first part, we propose using Full Convolution Network (FCN) and Gaussian Mixture Model (GMM) for ship detection. Once a target ship is detected, we propose a grid-based control method of active camera to track it in the surveillance area. In the grid-based control, the error measurements are derived from the grid-based images by using the pin-hole camera model. Afterwards, a PID controller is applied for active camera manipulation. The proposed methods and systems have been tested by monitoring a 2km× 1km waterway of Yangtze River, in Wuhan City, China. The results demonstrate that the proposed system can accurately detect waterway ships and actively track them by the proposed grid-based camera control method.
PPSAS: Lightweight Privacy-preserving Spectrum Aggregation and Auction in Cognitive Radio Networks

Cognitive radio network (CRN) enables dynamic spectrum management where spectrum aggregation and sharing have significantly facilitated relieving the supply-demand gap among skyrocketing number of mobile devices. Secondary users (SUs) can collaboratively detect and exploit the available spectrums of primary users (PUs) from different locations. Unfortunately, it has brought a series of security threats leaking user's location privacy to unauthorized entities. The existing work either had privacy breaches or depended on computationally-intensive public key homomorphic encryption loading intolerably high complexity on resource-constrained SUs. To well address these issues, in this paper, a lightweight privacy-preserving spectrum aggregation and auction scheme PPSAS is proposed in CRNs without leveraging public key homomorphic encryption. Firstly, a new primitive of efficient privacy preserving multiparty data aggregation protocol PPMDA is proposed based on any one-way trapdoor permutation. Then based on PPMDA, an efficient privacy preserving spectrum aggregation scheme PPSRA and spectrum auction scheme PPSSA are respectively devised to constitute our proposed PPSAS. Especially, a decentralized PPMDA is extended to resist collusion attack and a secure extended outsourced stable complete marriage matching protocol is given to flexibly realize SU/auctioneer optimization. Finally, formal security proof and extensive simulations demonstrate that our proposed PPSAS well protects the SUs' location privacy against collusion attacks and possesses the advantages over the sate-of-the-art in terms of auctioneer's revenue, SUs' satisfaction, computational and communication overhead.
Efficient Implementation on Low-Cost SoC-FPGAs of TLSv1.2 Protocol with ECC_AES Support for Secure IoT Coordinators

Security management for IoT applications is a critical research field, especially when taking into account the performance variation over the very different IoT devices. In this paper, we present high-performance client/server coordinators on low-cost SoC-FPGA devices for secure IoT data collection. Security is ensured by using the Transport Layer Security (TLS) protocol based on the TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256 cipher suite. The hardware architecture of the proposed coordinators is based on SW/HW co-design, implementing within the hardware accelerator core Elliptic Curve Scalar Multiplication (ECSM), which is the core operation of Elliptic Curve Cryptosystems (ECC). Meanwhile, the control of the overall TLS scheme is performed in software by an ARM Cortex-A9 microprocessor. In fact, the implementation of the ECC accelerator core around an ARM microprocessor allows not only the improvement of ECSM execution but also the performance enhancement of the overall cryptosystem. The integration of the ARM processor enables to exploit the possibility of embedded Linux features for high system flexibility. As a result, the proposed ECC accelerator requires limited area, with only 3395 LUTs on the Zynq device used to perform high-speed, 233-bit ECSMs in 413 µs, with a 50 MHz clock. Moreover, the generation of a 384-bit TLS handshake secret key between client and server coordinators requires 67.5 ms on a low cost Zynq 7Z007S device.
Experimenting with facilitating collaborative travel recommendations

The aim of undertaken work was to experiment with, and compare, various approaches to facilitate a collaborative recommender system, for travelers who would like to visit “tourist places”. For this purpose, different algorithms, including Kohonen-type neural networks (plain and elastic), as well as semantic technologies, have been applied to a dataset collected form the Web. Experiments have been performed for groups of users (measuring quality of recommendations), as well as for selected individuals. Completed comparison points out to main strength and weakness of each approach.
Re-imagining the Pedagogical Paradigm Within a Technology Mediated Learning Environment

Abstract Traditional didactic pedagogies employed within the culture of the Russian higher education system precluded students’ engagement with problems which were described as generating dissonances in learning cognition. Addressing issues of dissonance within the higher education learning sphere requires re-imagining the educational culture. Re-imagining provides an opportunity to promote new approaches to learning through alternative affordances; one such affordance is technology mediated learning. Pedagogical re-design within an alternative learning paradigm requires deep understanding of the problems associated with the previous paradigm. Re-imagined pedagogical scope for exploration of the professional, learning, cultural, institutional and technical aspects expand the knowledge base beyond the didactic towards an engaging student-centered ethos using open education and gamification. To address issues of learning, culture, technology, and institution, a convergent mixed methods design using student questionnaires and academic interviews alongside performance observations was employed. The research study examined the re-imagining of the educational culture to promote new approaches to learning through the affordances of technology mediated learning within a constructivist, critical realism epistemology using thematic analysis. The re-imagined pedagogical design within a technology mediated learning environment demonstrates a cultural shift towards an engaging and supportive educational experience. The lessons learned may be applied in other higher educational contexts.
Security assessment of four open source software systems

&lt;p class="Abstract"&gt;Incorporating Open Source Software (OSS) tools in software development is increasing day by day due to their accessibility on the internet. With the advantages of OSS comes disadvantages in terms of security vulnerabilities. Therefore, in this paper, we analyzed four famous open source software tools (i.e. Moodle, Joomla, Flask and VLC media player) which are used by software developers nowadays. For the analysis of each system, security vulnerabilities and weakness were identified, threat models were modeled,and code inspection was performed. The findings are discussed in more details.&lt;/p&gt;
Risk Management in E-Commerce—A Fraud Study Case Using Acoustic Analysis through Its Complexity

This work aimed to develop business intelligence towards fraud detection using buyer-placed information combined with the sound analysis from a confirmation purchase call. We used a dataset of 789 orders in 2018, provided by different e-commerce websites and calls fulfilled from every Brazilian state. Nine acoustic index features were used, through entropy in sound and vibration, summarizing the audio plus 6 extra features related, added by 12 customer features to compose two different classifiers (Logistic Regression and Random Forest). The acoustic indexes were, in fact, capable of providing better accuracy of the models, showing a probability associated with the voice characteristics, helping decision-making in credit card fraud.
Detecting Clitics Related Orthographic Errors in Turkish

For the spell correction task, vocabulary based methods have been replaced with methods that take morphological and grammar rules into account.However, such tools are fairly immature, and, worse, nonexistent for many low resource languages.Checking only if a word is well-formed with respect to the morphological rules of a language may produce false negatives due to the ambiguity resulting from the presence of numerous homophonic words.In this work, we propose an approach to detect and correct the "de/da" clitic errors in Turkish text.Our model is a neural sequence tagger trained with a synthetically constructed dataset consisting of positive and negative samples.The model's performance with this dataset is presented according to different word embedding configurations.The model achieved an F 1 score of 86.67% on a synthetically constructed dataset.We also compared the model's performance on a manually curated dataset of challenging samples that proved superior to other spelling correctors with 71% accuracy compared to the second best (Google Docs) with 34% accuracy.
Hand features extractor using hand contour – a case study

Hand gesture recognition is an important topic in natural user interfaces (NUI). Hand features extraction is the first step for hand gesture recognition. This work proposes a novel real time method for hand features recognition. In our framework we use three cameras and the hand region is extracted with the background subtraction method. Features like arm angle and fingers positions are calculated using Y variations in the vertical contour image. Wrist detection is obtained by calculating the bigger distance from a base line and the hand contour, giving the main features for the hand gesture recognition. Experiments on our own data-set of about 1800 images show that our method performs well and is highly efficient.
Sparse Victory – A Large Scale Systematic Comparison of Count-Based and Prediction-Based Vectorizers for Text Classification

In this paper we study the performance of several text vectorization algorithms on a diverse collection of 73 publicly available datasets.Traditional sparse vectorizers like Tf-Idf and Feature Hashing have been systematically compared with the latest state of the art neural word embeddings like Word2Vec, GloVe, FastText and character embeddings like ELMo, Flair.We have carried out an extensive analysis of the performance of these vectorizers across different dimensions like classification metrics (.i.e.precision, recall, accuracy), dataset-size, and imbalanced data (in terms of the distribution of the number of class labels).Our experiments reveal that the sparse vectorizers beat the neural word and character embedding models on 61 of the 73 datasets by an average margin of 3-5% (in terms of macro f1 score) and this performance is consistent across the different dimensions of comparison.
The ERG at MRP 2019: Radically Compositional Semantic Dependencies

The English Resource Grammar (ERG) is a broad-coverage computational grammar of English that derives underspecified logicalform representations of meaning.Elementary Dependency Structures (EDS) and DELPH-IN MRS Bi-Lexical Dependencies (DM) are graph-based simplifications of ERG meaning representations.As a point of reference outside the official competition of the 2019 Shared Task on Cross-Framework Meaning Representation Parsing, we evaluate ERGderived EDS and DM graphs.These graphs yield higher accuracy scores than the purely data-driven parsers in the shared task, suggesting that the general-purpose grammatical knowledge encoded in the ERG aids parsing into these meaning representations.
Efficient Adaptive Multiple Importance Sampling

The adaptive multiple importance sampling (AMIS) algorithm is a powerful Monte Carlo tool for Bayesian estimation in intractable models. The uniqueness of this methodology from other adaptive importance sampling (AIS) schemes is in the weighting procedure, where at each iteration of the algorithm, all samples are re-weighted according to the temporal deterministic mixture approach. This re-weighting allows for substantial variance reduction of the AMIS estimator, at the expense of an increased computational cost that grows quadratically with the number of iterations. In this paper, we propose a novel AIS methodology which obtains most of the AMIS variance reduction while improving upon its computational complexity. The proposed method implements an approximate version of the temporal deterministic mixture approach and requires substantially less computation. Advantages are shown empirically through a numerical example, where the novel method is able to attain a desired mean-squared error with much less computation.