Reviewerly/exHarmony huggingface dataset:
Fuzziness Detection in Thai Law Texts Using Deep Learning

Machine understanding research aims to build machine intelligences. To make a machine understand, precise concepts are necessary. Numerous domains contain vague meanings when making decisions, such as a diagnosis or a legal interpretation. Once an artificial intelligence pretends to be human while dealing with imprecise data, a fuzziness in knowledges must be detected before constructing. This paper presents the methodology to detect a fuzziness in Thai law texts using a deep learning method. The experiments are designed to compare the performances of four well-known text classification methods, namely Decision Tree, Random Forest, Support Vector Machine, and Convolutional Neural Network. The fuzziness in this study refers to an imprecise meaning in law texts which may be ambiguous when interpreted by a machine. We built a labelled corpus from four Thai Law codes namely 1) The Criminal Code 2) The Criminal Procedure Code 3) The Civil and Commercial Code and 4) The Civil Procedure Code. We proposed three conditions to identify the fuzziness, i.e. 1) a decision depends on a judge's opinion 2) a decision that requires the production of evidence and 3) a decision which refers to other sections. The results of the experiment show that a Convolutional Neural Network significantly outperforms the others with 97.54% accuracy in comparison of all the dataset.
Construction of the model of recognition operators in the large dimensional feature space

In this paper, the problem of constructing a modified model of recognition operators based on potential functions is considered. The main advantage of the proposed recognition operators is the improvement of accuracy and a significant reduction in the amount of computational operations for the recognition of unknown objects, which makes it possible to apply them in the construction of real-time recognition systems. To test the efficiency of the proposed model, experimental research was carried out to solve the generated model problem and the problem of face recognition.
Implementation of honeywords as a codeigniter library for a solution to password-cracking detection

Stolen files of password hashes is a common threat to web-based authentication. The password hashes can be cracked with new methods that are considered faster and easier. The attacker can just log-in to the system using the original password resulting from cracking without being detected. To answer these problems, researchers has proposed methods to detect password cracking such as honeywords. In this study we will try to implement an existing-user honeywords scheme into the form of a CodeIgniter web-based library. Honeywords is a decoy passwords that are used as feeds. Library of this study, is expected to be used by web application developers as an alternative authentication library.
Re-engineering the ant colony optimization for CMP architectures

The ant colony optimization (ACO) is inspired by the behavior of real ants, and as a bioinspired method, its underlying computation is massively parallel by definition. This paper shows re-engineering strategies to migrate the ACO algorithm applied to the Traveling Salesman Problem to modern Intel-based multi- and many-core architectures in a step-by-step methodology. The paper provides detailed guidelines on how to optimize the algorithm for the intra-node (thread and vector) parallelization, showing the performance scalability along with the number of cores on different Intel architectures, reporting up to 5.5x speedup factor between the Intel Xeon Phi Knights Landing and Intel Xeon v2. Moreover, parallel efficiency is provided for all targeted architectures, finding that core load imbalance, memory bandwidth limitations, and NUMA effects on data placement are some of the key factors limiting performance. Finally, a distributed implementation is also presented, reaching up to 2.96x speedup factor when running the code on 3 nodes over the single-node counterpart version. In the latter case, the parallel efficiency is affected by the synchronization frequency, which also affects the quality of the solution found by the distributed implementation.
Cross-D-vectorizers: a set of feature-spaces for cross-domain sentiment analysis from consumer review

Supervised sentiment classification approaches require labeled training (source) and testing (target) dataset. Generation of such datasets demands substantial time and effort but cross-domain classification minimizes the effort by considering two different domains for source and target datasets. In this paper, we propose Cross-D-Vectorizers i.e., a set of three sentiment n-gram feature-spaces (Lexical-TFIDF, Lex-Delta-TFIDF and SEND) for the purpose of cross-domain analysis. We construct the features by extracting sentiment unigrams combination with intensifiers and negations from the source dataset. By utilizing an existing lexicon the scores of these features are computed in three different procedures. The scores for each feature are computed by multiplying sentiment value with corresponding TFIDF rating, Delta-TFIDF rating and feature-importance-values (FIV) respectively. Importance-value for each SEND (Sentiment wEight ofN-grams inDataset) feature is calculated by multiplying the number of times the feature appears in the review and the logarithm of its inverse frequency in the corpus. We experiment by using Maximum Entropy, Support Vector Machine and K-Nearest Neighbors classifiers on three benchmark datasets and one proposed dataset for cross-domain classification. Proposed approach show improved results in comparison with existing methods. The advantage of our approach is the complexity of system reduces by considering sentiment n-grams as domain independent features instead of any n-grams.
An Improvement of a Checkpoint-based Distributed Testing Technique on a Big Data Environment

The advancement of storage technologies and the fast-growing number of generated data have made the world moved into the Big Data era. In this past, we had many data mining tools but they are inadequate to process Data-Intensive Scalable Computing workloads. The Apache Spark framework is a popular tool designed for Big Data processing. It leverages in-memory processing techniques that make Spark up to 100 times faster than Hadoop. Testing this kind of Big Data program is time consuming. Unfortunately, developers lack a proper testing framework, which cloud help assure quality of their data-intensive processing programs while saving development time and storage usages.We propose Distributed Test Checkpointing (DTC) for Apache Spark. DTC applies unit testing to the Big Data software development life cycle and reduce time spent for each testing loop with checkpoint. By using checkpoint technique, DTC keeps quality of Big Data processing software while keeps an inexpensive testing cost by overriding original Spark mechanism so that developers no pain to learn how to use DTC. Moreover, DTC has no addition abstraction layers. Developers can upgrade to a new version of Spark seamlessly. From the experimental results, we found that in the subsequence rounds of unit testing, DTC dramatically speed the testing time up to 450-500% faster. In case of storage, DTC can cut unnecessary data off and make the storage 19.7 times saver than the original checkpoint of Spark. DTC can be used either in case of JVM termination or testing with random values.
Continuous Marker Association utilizing Potential Function for Motion Capture Systems

Optical motion capture system can measure the position and the orientation of an object equipped with retroreflective markers. When some markers are not observed due to occlusion, the system often fails to localize the object. In our past study, moving horizon estimation (MHE) is applied to improve the estimation robustness, where states are determined so that the sum of error magnitude of dynamics and observation is minimized under constraints. In general, marker association is crucial for high accuracy estimation. In this paper, we propose a marker association method for MHE where the proposed association method exploits a potential function and optimization so that the association is robust against marker occlusion and fake images similar to the marker. In the simulation, for the environment where the previous method fails due to missing markers and misrecognition, we confirm that the proposed method can successfully realize the estimation.
A generic traceability metamodel for enabling unified end-to-end traceability in software product lines

Mature development methodologies like software product line engineering or model-driven engineering are more and more adopted in software development. Accordingly, the resulting development processes combine artifacts from different disciplines and on different abstraction levels. It is crucial that the relationship between these artifacts is explicitly maintained to be able to track the development process and the reasons for design decisions. This problem becomes exacerbated if variability is considered since it is a cross-cutting concern that impacts all disciplines and artifacts. Traceability links support the linking of artifacts across model boundaries in an end-to-end manner. However, existing traceability solutions are either limited to specific development processes, tools, and artifact types, lack in uniformity, or do not consider variability. Thus, this paper introduces a MOF-based generic traceability metamodel for establishing uniform traceability-enabled workflows in a variability-aware and model-based environment. Necessary steps for instantiating the metamodel to specific artifact types of certain development processes are described. We evaluate the proposed solution with an exemplar of a car headlight and demonstrate the benefits of a consistent traceability concept.
Extraction of Character Personas from Novels Using Dependency Trees and POS Tags

Novels are a rich source of data for extracting interesting information. Besides the plot, the characters of a novel are its most important elements that shape the story and its message. An interesting task to consider is extracting these characters from novels in the form of the personas they embody. In this paper, we define and introduce a method to extract such personas of characters in fiction novels, in the form of descriptive phrases. These personas are divided into three types of description—facts, states and feelings. We show that such a model performs satisfactorily returning an extraction precision of 91% and average classification accuracy of 80%. The algorithm uses universal dependency trees, POS tags and WordNet to capture semantically meaningful descriptions of characters portrayed. The results have the potential to serve as input for future NLP tasks on literature fiction like character clustering and classification using techniques such as sentence embeddings.
An Intensive Review of Data Replication Algorithms for Cloud Systems

Cloud computing is a technological paradigm that facilitates a universal platform for hosting and accessing services and data by means of a large number of shared resources by an equally large number of users across the globe. Since a large amount of data is involved, it becomes crucial to make the data available to the users on the go without compromising with the integrity of data. Data replication is used to make copies of the data and files such that they can be accessed without delay and also act as a backup in case of any failure. Here we have discussed crucial data replication algorithms, namely, adaptive data replication strategy (ADRS), high QoS-first replication (HQFR), build time algorithm, cost-effective incremental replication strategy (CIR) and dynamic replica control strategy (DRCS).
Touch-Typing Detection Using Eyewear: Toward Realizing a New Interaction for Typing Applications

Typing skills are important in the digital information society of this generation. As a method to improve typing speed, in this study, we focused on the training of touch typing that enables typing a key without looking at the keyboard. For support of touch-typing training, it is efficient to apply a penalty if a learner looks at the keyboard; however, to realize the penalty method, the computer needs to be able to recognize whether the learner looked at the keyboard. We, therefore, proposed a method to detect a learner's eye gaze, namely, using eyewear to detect whether the learner looked at the keyboard, and then evaluating the detection accuracy of our proposed method. We examined the necessity for our system by analyzing the relationship between a learner's eye gaze and touch-typing skills.
BioFlockVR

In this paper we present our bio-responsive virtual reality (VR) experience that explores visual forms of entrainment through amorphous nature-inspired phenomena that evolves and reacts to a tightly coupled real-virtual immersive space controlled through two immersants' physiological data. Multiple layers of real-time visuals inspired by nature phenomena are generated in 3D the experiential VR space which are controlled through two immersants' EEG, heart rate and gestures in real-time. In this paper, we present our system framework and setup and present our work-in-progress prototype.
Towards a transfer learning approach to food recommendations through food images

User generated text/multimedia content are increasingly shared in online businesses systems and their effective use in user modelling and recommendation strategies is consequently growing too. In restaurant businesses the food menu along with images are a common practice and users also shared the food images they ordered and feel good about. Yelp data set challenge in their round 9 and onward introduced such rich images data for their competition. In this paper, we motivated from this rich images data of food for semantically incorporating image-specific features to the star-rating and recommendation process. We first applied a transfer learning approach with pre-trained CNNs (Convolution Neural Networks) which were used to label the Yelps food images of the restaurants using the Food101 data-set. We defined star-rating for restaurants by capturing a correlation between restaurant images and users shared images. Our proposed strategy works on discovering hidden aspects of food images and labels to be used in recommendation strategy. We performed an extensive set of experiments by creating a baseline using standard rating provided in the Yelp data-set. The proposed approach produced better Root Mean Square Error (RMSE), which is a clear indication of high-quality recommendation strategy
Cryptanalysis for reduced round Salsa and ChaCha: revisited

Maitra et al. (WCC-2015) proposed the characterisation of valid states by reversing the one round of Salsa20. When revisited, a mistake was found in the one bit change of eighth and ninth word while reversing the one round result to a valid initial state. It was mentioned in WCC-2015 that it would be an interesting combinatorial problem to characterise all such states. Thus, nine more values were characterised, leading to valid initial states. Aumasson et al. (FSE-2008) attacked 128-bit key Salsa20/7 with time and ChaCha6 with time. In this study, the attack was improved on 128-bit key Salsa20/7 with time and ChaCha6 with time. Maitra (DAM-2016) improved the attack on 256-bit key Salsa20/8 and ChaCha7 by choosing the proper initialisation vectors. In congruence with this, 128-bit key Salsa20/7 was attacked with time and ChaCha6 with time. Choudhuri and Maitra (FSE 2017) developed theoretical results on the differential-linear cryptanalysis and thus improved the biases on Salsa/ChaCha. Theoretical work had been extended with triple bits from round to one bit m round of Salsa with the linear approximation holding the probability 1. In consideration of the linear approximation which holds the probability <1, linear approximation for three rounds from m to for Salsa and ChaCha was exhibited.
Robot Path Planning Based on Extended MAKLINK Graph and Improved Ant Colony Optimization

In modern robot navigation systems, path planning plays an important role to search the most efficient path throughout the selected environment. In this paper, a novel and effective method is proposed to achieve path planning, combined with extended MAKLINK graph and improved ant colony optimization (IACO). Firstly, the MAKLINK graph is extended to consider not only convex polygonal obstacles but also concave polygonal obstacles. To overcome the disadvantages of traditional ant colony optimization (ACO), an improved ACO is developed by introducing an adaptive updating rule of pheromone. Finally, the simulation results demonstrate that the proposed method is superior to methods based on Dijkstra's algorithm and traditional ACO, planning the shortest path in the complex space environment of concave-convex polygonal obstacles. Compared with traditional ACO, the IACO not only improves the convergence rate and global search capability but also skips the local optimal, proving its effectiveness and feasibility.
An Innovative Model for the Enhancement of IoT Device Using Lightweight Cryptography

The problem statement is that at present there is no stable algorithm which provides security for resource constrained devices because classic cryptography algorithms are too heavy to be implemented. So we will provide a model about the various cryptographic algorithms in this field which can be modified to be implement on constrained devices. The advantages and disadvantages of IOT devices will be taken into consideration to develop a model. Mainly IOT devices works on three layers which are physical layer, application and commutation layer. We have discuss how IOT devices individually works on these layers and how security is compromised. So, we can build a model where minimum intervention of third party is involved i.e. hackers and we can have higher and tight privacy and security system [1].we will discuss about the different ciphers(block and stream) and functions(hash algorithms) through which we can achieve cryptographic algorithms which can be implemented on resource constrained devices. Cost, safety and productivity are the three parameters which determines the ratio for block cipher. Mostly programmers are forced to choose between these two; either cost and safety, safety and productivity, cost and productivity. The main challenge is to optimize or balance between these three factors which is extremely a difficult task to perform. In this paper we will try to build a model which will optimize these three factors and will enhance the security of IOT devices.
Runtime observable and adaptable UML state machines

An embedded system is a self-contained system that incorporates elements of control logic and real-world interaction. UML State Machines constitute a powerful formalism to model the behaviour of these types of systems. In current industrial environments, the software of these embedded systems have to cope with the increasing complexity and robustness requirements at runtime. One way to manage these requirements is having the software component's behaviour model available at runtime ([email protected]). Thus, it is possible to enhance the safety of the software component by enabling verification and adaptation at runtime. In this paper, we present a model-driven approach to generate software components (namely RESCO framework), which are able both to provide their internal information in model terms at runtime and adapt their behaviour automatically when an error or an unexpected situation is detected. The aforementioned runtime introspection and adaptation abilities are added automatically to the software component and it does not require the developer make any extra effort. The solution has been tested in the design and implementation of an industrial Burner controller. Results indicate that the software components generated by the presented solution provides introspection at runtime. Thanks to this introspection ability at runtime, the software components are able to adapt automatically from their normal-mode behaviour to a safe-mode behaviour which was defined to be used in erroneous or unexpected situations at runtime. Therefore, it is possible to enhance the safety of the systems consisting of these software components.
Content Analysis of Engineering College Library Websites in Goa

ICT- led developments have created new opportunities and challenges for libraries in creation, promotion, dissemination and storage of information. Engineering College libraries are nowadays using the web environment to provide high-quality information for their users, mostly in digital format. The current study investigates and presents the content analysis of Goa University affiliated engineering college library websites in Goa. The data was collected from the websites of the concerned engineering college libraries through a checklist prepared for this purpose. The websites of the libraries were thoroughly scanned and analyzed, based on the analysis we have ranked them. The main purpose of this paper is to guide the librarians in improving their library websites. There is a need to design and develop a unique type of library websites containing resources and services of their individual libraries and also providing links to other IIT libraries. This article gives a detailed account of the study and offers suggestions for developing and managing better library websites.
Interactive Presentation of Information for Shopping Assistance in Intelligent Space

Studies to make space intelligent for human activity support are widely done. There are various applications of it. For example, there are applications such as cooking assist and language learning assist. However, at retail shops, even though sensors such as cameras are installed for crime prevention, application of making space intelligent has not been applied yet. Since the economy is revitalized by increasing the sales of retail stores, a system that increases sales is developed in this study. Current presentation methods of information about products at retail stores are implicit, and they are not effective due to spatial restriction. To solve this problem, we developed a system that presents information to customers effectively by making space inside retail store intelligent. By observing and evaluating the positions of customers in a store, the motion to pick up the products on the shelves, gazes and movement information of the customers, information which the customers are interested in is presented at the effective timing. From the results of the evaluation experiments of the developed system, it was shown that our system was possible to present appropriate information to customers in real time.
Community-aware diversification of recommendations

Intent-aware methods for recommendation diversification seek to ensure that the recommended items cover so-called aspects, which are assumed to define the user's tastes and interests. Most typically, aspects are item features such as movie or music genres. In recent work, we presented a novel intent-aware diversification method, called Subprofile-Aware Diversification (SPAD). In SPAD, aspects are subprofiles of the active user's profile, detected using an item-item similarity method. In this paper, we propose Community-Aware Diversification (CAD), in which aspects are again subprofiles but are detected indirectly through users who are similar to the active user. We evaluate CAD's precision and diversity on four different datasets, and compare it with SPAD and an intent-aware diversification method called xQuAD. We show that on two of the datasets SPAD outperforms CAD, but for the other two CAD outperforms SPAD. For all datasets, both CAD and SPAD achieve higher precision than xQuAD. When it comes to diversity, xQuAD sometimes results in more diverse recommendations but it is more prone to paying for this diversity with decreases in precision. Arguably, SPAD and CAD strike a better balance between the two.