Reviewerly/exHarmony huggingface dataset:
Face Recognition with Voice Assistance for the Visually Challenged

Visually impaired people face a lot of challenges in day-to-day life. Having seen the difficulties faced by them, our primary objective is to facilitate confidence and to empower them to lead a life free from threats related to their safety and well-being. The lack of ability to identify known individuals in the absence of auditory or physical interaction cues drastically limits the visually challenged in their social interactions and poses a threat to their security. Over the past few years many prototype models have been developed to aid this population with the task of face recognition. This application will reduce the inherent difficulty for recognition of a person. It will present a facial recognition application with an intuitive user interface that enables the blind to recognise people and interact socially. The carefully designed interface lets the visually challenged to be able to access and use it without any requirement for visual cues as the users are acquainted by a voice assistant to navigate through the application. The entire build is designed to run efficiently on a Raspberry Pi 3 model B module using the Android Things platform. The Open CV library has been used for the detection and recognition of people in this project. This enables the scope for the software to be run on a multitude of devices such as camera embedded glasses to warn users of their surroundings and identify people to interact safely. Since everything in the application is done in real time with no requirement for prior datasets to be hardcoded it drastically improves the versatility of the software. We hope to make the visually impaired feel closer, comfortable and more secure with the world surrounding them through our application.
Generating Anthropomorphism of Subject and Verb by Transformation Matrix

In this paper, given the subject-verb pairs, we propose a computational model to express the difference of the meaning of a verb when the subject has changed. We propose a method to generate metaphorical expressions consist of subject-verb pairs from the model. "Airship swims" is one of the example. It is the expression about the event that an airship flies in the sky gracefully. There are a few reasons why the expression is accepted for people. "airship flie" and the motion of a sea creature, for example "whale swims" represent both "the normal move in a space" and there is a similarity. Given the input ("airship," "fly"), we propose a method to detect a verb "swims" to generate metaphorical expressions considering these similarity. At first, we test which vectorization method is the best as the vectorization of a subject-verb pair. We calculate a transformation matrix to conserve between the meaning of (non-human subject, verb) pairs and the meaning of ("man", verb) pairs. We calcurate the transformation matrix between them using the stable meaning verbs as the anchors. In this paper, we test an hypothesis that we can use these transformation matrices to find an appropriate verb considering the difference of the meaning occured from the subjects. We gather 67 cases of target figurative expressions from Web. We evaluated the proposed method by defining the information retrieval problem of verbs.
CNN-based Super-resolution Reconstruction for Traffic Sign Detection

Automatic identification for traffic signs is an important part of intelligent driving and traffic safety. Deep learning has already made a great achievement in traffic sign detection. However, the camera on a car may capture a low resolution and blurry image in certain environments, which make it inaccurate for traffic sign detection. Therefore, we propose a method based on image super-resolution reconstruction for improving the detection rate of traffic signs. Firstly, a low-resolution image is transformed by CNN-based super-resolution network into a high-resolution one. Then, to meet the requirements of on-line processing, we use the generated super-resolution image as input for the detection network with 16 filters in this layer. At last, we separately trained two CNNs for super-resolution reconstruction and traffic sign detection, which reduce the processing time. Experimental results demonstrate that our model can achieve better performance than the existing methods for traffic sign detection.
Optimisation: Steepest Ascent, Steepest Descent and Gradient Methods

The history of the method for steepest ascent/descent is discussed. The algorithm is described as a series of steps. It is illustrated algebraically, numerically and with a real world example. The main adjustable parameters are described together with their limitations.
Performance Enhancement of Outlier Removal Using Extreme Value Analysis-Based Mahalonobis Distance

Outlier detection is a part of data analytics that helps users to find discrepancies in working machines by applying outlier detection algorithm on the captured data for every fixed interval. An outlier is a data point that exhibits different properties from other points due to some external or internal forces. These outliers can be detected by clustering the data points. To detect outliers, optimal clustering of data points is important. The problem that arises quite frequently in statistics is identification of groups or clusters of data within a population or sample. The most widely used procedure to identify clusters in a set of observations is k-means using Euclidean distance. Euclidean distance is not so efficient for finding anomaly in multivariate space. This chapter uses k-means algorithm with Mahalanobis distance metric to capture the variance structure of the clusters followed by the application of extreme value analysis (EVA) algorithm to detect the outliers for detecting rare items, events, or observations that raise suspicions from the majority of the data.
A game-theoretic analysis to defend against remote operating system fingerprinting

Remote Operating System (OS) Fingerprinting is a precursory step for launching attacks on the Internet. As a precaution against potential attacks, a remote machine can take a proactive counter-strategy to deceive fingerprinters. This is done by normalizing or mystifying the distinguishing behaviors in the packets. However, the unified modification causes a significant performance degradation to benign clients. Using a game-theoretic approach, we propose a selective and dynamic mechanism for counter-fingerprinting. We analyze the interaction between a fingerprinter and a target by modeling the problem as a signaling game and name it Defense-Game. Here, the defender observes the attacker’s actions and takes moves accordingly. We derive the Nash equilibrium strategy profiles based on the information gain analysis and propose a defense mechanism named Strategic-Defender. Our game-theoretic approach appropriately distinguishes a fingerprinter from a benign client and mystifies packets to confuse the fingerprinter, while minimizing the side effects on benign clients. The performance analysis shows that the defense mechanism can reduce the probability of success of the fingerprinter significantly, without deteriorating the overall performance of other clients. Later, we restructure the game as a screening game where the attacker plays as the follower so that it can recognize the type of the target and act accordingly. We name the game Attack-Game and solve it for the equilibrium strategies. We evaluate the performance of Strategic-Defender when the attacker takes actions according to the optimal results of Attack-Game. The evaluation results show that the attacker’s payoff may increase a bit, however, it is still too low to succeed.
Applying FOSSE Approach to Image Data for Evaluating Game Situations

In this paper, we propose to apply a FOrward Simulation for Situation Evaluation (FOSSE) approach to image data. A FOSSE approach was proposed for evaluating situation of a soccer game in our previous work. It consists of two parts: Forward simulation part and evaluate situation part. At first, the FOSSE approach simulates the future situation by a simulation model before evaluating the current situation. Secondly, the approach evaluates the current situation by a evaluation model with simulated future information. In the FOSSE approach, the future game situations are generated by what is called forward simulation. The first FOSSE approach used numerical information such as the position of the ball and the players. Applying the FOSSE approach to image data is a difficult task because forward simulation of image data is not straight forward. Therefore, in this paper, we focus on the forward simulation of the image data and investigate the suitable image simulation model for the FOSSE approach. As the evaluation model, we employ a 3D Convolutional Neural Network. First, we show that the evaluation performance can be increased by using successive frames of field situations through a series of preliminary computational experiments. Especially, the effectiveness of using future frame information rather than using past frame information is shown. Then, we present FOSSE approach with image data where both the current and the future frame information of game situations are used to evaluate the current game situation. Computational experiments are conducted to investigate the effective forward simulation model for our FOSSE approach.
Design, Construction, and Monitoring of a Hybrid Cofferdam

A temporary cofferdam system was required for a spillway rehabilitation project to provide a dry work area while maintaining the upstream lake at the normal operating pool level. A hybrid cofferdam system consisting of two circular cells and one arc cell connected to the dam embankment by two cantilever sheet pile walls, was designed and constructed on highly plastic, over-consolidated clays. Each of the circular cells has a diameter of 19.5 m and a height of 15.2 m with all cells filled with free-draining, granular fill material. An instrumentation system was designed and implemented to monitor the cofferdam throughout spillway construction. The instrumentation system consists of eleven inclinometers, five settlement plates, seven observation wells, and 224 strain gauges enclosed in eight channels each, strategically located around the cofferdam system. This paper summarizes the unique characteristics of the hybrid cofferdam, challenges encountered during design and construction, and lessons learned from the monitoring program.
Shorter Communication-Path Generation Scheme for Robot Swarms

This paper introduces path communication scheme from the self-organized robot swarms. The main purpose is to work swarm robots with minimal requirements, such as reducing the time required for finding the path between swarm robots from its self-organized network without long-lived state information. The important features include self-organization, self-healing, topology adaptable and choosability of the shorter path from its self-organized neighbors.
Using Microcontrollers to Create Portable, Self-Contained Seismic Sensors

Seismic sensors can be used for a wide variety of applications, including vibration monitoring, near-surface imaging, and horizontal to vertical spectral ratio (HVSR) techniques (e.g. sediment layering and seismic zoning). Unfortunately, professionally developed and engineered seismic sensors are typically expensive, which can make them inaccessible to engineers and researchers without financial resources, such as those in developing countries. As an alternative to these professional, but expensive instruments, we have developed a seismic sensor using a microcontroller that is much more affordable and can perform many of the same applications as the more traditional, professional systems. Our system uses a microcontroller paired with a 24-bit analog to digital converter (ADC) to record seismic signals from three orthogonal axes with an effective resolution of 5 μV and sampling rates up to 250 Hz. In addition, a GPS module is used to record the location (GPS coordinates) of the sensor and to correlate the recorded signals with real time so that signals collected by individual sensors may be meaningfully combined to create sensor arrays.
Fusion Methods &amp; Applications – A Survey

Data fusion is the process in which integration of multiple data sources produce more consistent, efficient, and useful information than that provided by any of the individual data source. The fusion methods can be broadly classified with the help of class information. The fusion methods such as serial feature fusion, parallel feature fusion and canonical correlation analysis (CCA) does not contain any class information. That is, they are unsupervised fusion methods. The methods such as cluster CCA, Generalized Multiview Analysis, Linear Discriminant Analysis, Multiview Discriminant Analysis, Discriminative Multiple CCA, Locality Preserving CCA (LPCCA), BGLPCCA, MGLPCCA contain class information. Applications of each fusion methods are also taken into consideration.
Systematizing the Relationship Between Business Processes’ and Web Services’ Non-functional Requirements

We propose in this paper a conceptual framework for the hierarchical decomposition of Non-Functional Requirements (NFRs) from the business process level to the web service level. This framework seeks to reduce the dependence on a particular IT expert’s knowledge by simplifying the dialog between the business and IT areas. The proposed framework relies on a structure of NFRs interdependence. The main reference was the ISO/IEC 25010 Product Quality Model, extended by additional software quality models and particular QoS attributes. This framework is accompanied by an extensive dictionary of non-functional requirements for both business processes and web services that can serve as a reference for researchers and industry practitioners. We assume that orchestrating web services to run business processes requires a rigorous definition of the functional requirements and NFRs of these web services. Web service NFRs are often defined as Quality of Service (QoS) attributes, which is done at the implementation level by IT teams. The definition of QoS attributes should consider the business process NFRs, since misinterpretations of web service NFRs may affect the behavior of the web services and hence achieving the business goals. The approaches proposed so far in the literature are still heavily dependent on an IT expert’s knowledge to identify the appropriate QoS attributes required to meet particular business process NFRs. However, defining appropriate QoS attributes without reference to business process-level NFRs may be a costly, time-consuming task.
Categorization of Korean Double Accusative Constructions

The current study has reviewed Korean double accusative constructions (DACs), which refer to constructions where two NPs marked with -(l)ul appear at the same time in a sentence. It gives rise to questions about which one is a grammatical object and what the other NP is in a sentence. To solve these questions, we have classified DACs into four types according to syntactic and semantic/pragmatic perspectives. There are four types of DACs: NP2 as a grammatical object, NP1 as a focus or topic; NP2 as a grammatical object, NP1 as a topic; NP1 as a grammatical object, NP2 as a modifier; NP1 as a grammatical object, NP2 as a part of the predicate. That is, in the first two types, NP2 is a grammatical object, while in the other two types, NP1 is a grammatical object. Additionally, the NP, which is not a grammatical object in the first three types, can be left out from a sentence, whereas it is obligatory in the last type. (Kyungwoon University · Kyungpook National University)
A Comparative Study of Unsupervised Classification Algorithms in Multi-Sized Data Sets

The ability to mine and extract useful information automatically, from large data sets, is a common concern for organizations, for the last few decades. Over the internet, data is vastly increasing gradually and consequently the capacity to collect and store very large data is significantly increasing.
On the complexity of index sets for finite predicate logic programs which allow function symbols

Abstract We study the recognition problem in the metaprogramming of finite normal predicate logic programs. That is, let $\mathcal{L}$ be a computable first-order predicate language with infinitely many constant symbols and infinitely many $n$-ary predicate symbols and $n$-ary functions symbols for all $n \geq 1$. Then we can effectively list all the finite normal predicate logic programs $Q_0,Q_1,\ldots $ over $\mathcal{L}$. Given some property $\mathcal{P}$ of finite normal predicate logic programs over $\mathcal{L}$, we define the index set $I_{\mathcal{P}}$ to be the set of indices $e$ such that $Q_e$ has property $\mathcal{P}$. We classify the complexity of the index set $I_{\mathcal{P}}$ within the arithmetic hierarchy for various natural properties of finite predicate logic programs. For example, we determine the complexity of the index sets relative to all finite predicate logic programs and relative to certain special classes of finite predicate logic programs of properties such as (i) having no stable models, (ii) having no recursive stable models, (iii) having at least one stable model, (iv) having at least one recursive stable model, (v) having exactly $c$ stable models for any given positive integer $c$, (vi) having exactly $c$ recursive stable models for any given positive integer $c$, (vii) having only finitely many stable models, (viii) having only finitely many recursive stable models, (ix) having infinitely many stable models and (x) having infinitely many recursive stable models.
Thai Handwritten Character Segmentation Based on Deep Learning

Many computer vision applications rely on segmentation task. To achieve a good result on Handwritten text recognition (HTR), character segmentation is significant in terms of extracting each individual character. In this study, we propose a novel algorithm for tackling offline handwritten character segmentation, particularly for the Thai language. Not only are the characteristics of the Thai language described, but also the problems when performing Thai character segmentation are defined. There are two parts of segmentation: horizontal link segmentation and vertical link segmentation. The chosen type of algorithm is convolutional encoder-decoder network. Our models are based on the renowned encoder-decoder models, U-net and SegNet. The best horizontal link segmentation model achieves up to 0.929 F1-score on the real-world test set. For the vertical link segmentation, the best models of topmost, upper, base, and lower characters attains F1-scores of 0.799, 0.873, 0.932, and 0.820, respectively.
An Algebraic Expert System with Neural Network Concepts for Cyber, Big Data and Data Migration

This paper describes a machine assistance approach to grading decisions for values that might be missing or need validation, using a mathematical algebraic form of an Expert System, instead of the traditional textual or logic forms and builds a neural network computational graph structure. This Experts System approach is also structured into a neural network like format of: input, hidden and output layers that provide a structured approach to the knowledge-base organization, this provides a useful abstraction for reuse for data migration applications in big data, Cyber and relational databases. The approach is further enhanced with a Bayesian probability tree approach to grade the confidences of value probabilities, instead of the traditional grading of the rule probabilities, and estimates the most probable value in light of all evidence presented. This is ground work for a Machine Learning (ML) experts system approach in a form that is closer to a Neural Network node structure.
Probabilistic Named Entity Recognition for nonstandard format entities using cooccurrence word embeddings

The use of short text has become widespread in social media like Twitter and Facebook. Typically, users on social media platforms adopt nonstandard format terms when posting. This introduces challenges for Information Retrieval (IR) and Natural Language Processing (NLP) and standard or classical methods tend not to perform well in this domain. In this paper, we have addressed one of the challenges in IR which is Named Entity Recognition (NER). We introduce a novel probabilistic approach which targets entities occurring in an informal (nonstandard) format within short text. The Probabilistic Named Entity Recognition (PNER) model identifies these entities using cooccurrence patterns. These patterns have been detected using the word cooccurrence embeddings of 278.6 million tweets. The results show an enhancement of 7% on two standard methods when used in combination with PNER. The testing dataset has been created using the standard methods in addition to street names and places taken from the Open Street Map (OSM) database.
Hubungan antara Multiple Intelligences dan Motivasi Belajar dengan Hasil Belajar Biologi Peserta Didik Kelas XI MIA SMAN di Kota Parepare

Abstract:  The various types of intelligence that exist do not operate individually, but can be used at the same time and tend to complement each other when one solves a problem, as well as in the learning process. The intended intelligence is multiple intelligences. This intelligence consists of linguistic intelligence, logical-mathematical, spatial-visual, kinaesthetically, music, interpersonal, intrapersonal, naturalist, and existentialist. Many benefits can be gained by knowing the levels of multiple intelligences of students, both for school and for the students themselves. The benefits of multiple intelligences for students are among others, adding confidence and helping to increase the motivation of students in learning. Motivation is a conscious effort to move, direct and maintain one's behavior so that he is compelled to act to do something so that achieving certain results or goals in this case is to achieve satisfying biology learning outcomes. While the benefits of schools, among others, teachers are more focused in the learning process, helping group students, can approach students according to their type of intelligence. In addition to knowing the multiple levels of students intelligences, teachers can develop with optimum potential in students and can choose the right strategy in the learning process to maximize learning outcomes. Everyone has multiple intelligences in different tiers. Therefore every teacher should be able to understand the intelligence and ability of each student well, because the circumstances of children in one class vary and have different degrees of intelligence. This relates to the role of teacher as a facilitator, a teacher is able to create good interactions with students in order to know the ability of students in learning and teachers as motivators are able to motivate participants Students and create a fun learning atmosphere so that students are comfortable while learning because they feel the teacher is mindful of them. Research on the relationship of multiple intelligences to biology learning outcomes has been conducted by Selaras (2013) with the results that the level of multiple intelligences and biology learning outcomes of high school students in Padang has a significant positive relationship. The purpose of this research is to know the relationship between multiple intelligences and learning motivation with biology learning outcomes of students class XI MIA SMAN in the city of Parepare. This research is a correlational ex post facto research. Ex post facto research is a study conducted to examine variables that have occurred without the need to provide treatment of the variables studied. Keywords: multiple intelligences, learning motivation, and learnig outcomes