Reviewerly/exHarmony huggingface dataset:
News Video Indexing and Story Unit Segmentation using Text Cue

News video Indexing and story segmentation are one of the important steps for the creation of well categorized, organized and indexed video storage system. Such a system allows disciplined storage to cope with an increasing amount of data, and easier retrieval in the future for viewing. Keeping this approach in mind we proposed a News video Indexing and Story unit segmentation algorithm based on keywords obtained from the text displayed in the news frame during a news program. First with the help of our shot categorization algorithm we classify the news shots into commercial and news based on detection of spatial features of the particular News Channel. Then with the help of our shot indexing algorithm, we detect the presence of words in each frame of a shot and index each shot with a keyword. With the help of our Shot Combination algorithm, we combine the shots having similar keywords to form individual stories. The results obtained in this works were satisfactory and in accordance with the results depicted using manual visual observation.
Personal insights for altering decisions of tree-based ensembles over time

Machine Learning models are prevalent in critical human-related decision making, such as resume filtering and loan applications. Refused individuals naturally ask what could change the decision, should they reapply. This question is hard for the model owner to answer: first, the model is typically complex and not easily interpretable; second, models may be updated periodically; and last, attributes of the individual seeking approval are apt to change in time. While each of these challenges have been extensively studied in isolation, their conjunction has not. To this end, we propose a novel framework that allows users to devise a plan of action to individuals in presence of Machine Learning classification, where both the ML model and the user properties are expected to change over time. Our technical solution is currently confined to a particular yet important class of models, namely those of tree-based ensembles (Random Forests, Gradient Boosted trees). In this setting it uniquely combines state-of-the-art solutions for single model interpretation, domain adaptation techniques for predicting future models, and constraint databases to represent and query the space of possible actions. We devise efficient algorithms that leverage these foundations in a novel solution, and experimentally show that they are effective in proposing useful and actionable steps leading to the desired classification.
A Framework for Model Search Across Multiple Machine Learning Implementations

Several recently devised machine learning (ML) algorithms have shown improved accuracy for various predictive problems. Model searches, which explore to find an optimal ML algorithm and hyperparameter values for the target problem, play a critical role in such improvements. During a model search, data scientists typically use multiple ML implementations to construct several predictive models; however, it takes significant time and effort to employ multiple ML implementations due to the need to learn how to use them, prepare input data in several different formats, and compare their outputs. Our proposed framework addresses these issues by providing simple and unified coding method. It has been designed with the following two attractive features: i) new machine learning implementations can be added easily via common interfaces between the framework and ML implementations and ii) it can be scaled to handle large model configuration search spaces via profile-based scheduling. The results of our evaluation indicate that, with our framework, implementers need only write 55-144 lines of code to add a new ML implementation. They also show that ours was the fastest framework for the HIGGS dataset, and the second-fastest for the SECOM dataset.
Rater Effects on L2 Oral Assessment: Focusing on Accent Familiarity of L2 Teachers

In the present study, I examined the effects of rater characteristics, in particular, raters' familiarity with a foreign accent, on the assessment of second language (L2) pronunciation. Forty-three native English-speaking teachers were divided into three groups according to their reported types of familiarity with Korean accents: heritage, familiar, and unfamiliar. The teachers assessed global proficiency and accentedness of speech samples provided by 40 Koreans speaking L2 English. The ratings made on 1–9 numerical scales were analyzed for interrater reliability and rater severity. While the three teacher groups overall exhibited high interrater reliability, the findings suggest that prior exposure to, or regular contact with, foreign-accented speech may affect teachers' consistency in ratings. A comparison of the three groups' severity measures revealed that the heritage teachers were more lenient than the non-heritage (i.e., familiar and unfamiliar) teachers in evaluating global proficiency and accentedness. The difference in the level of severity between the familiar and unfamiliar teacher groups was not significant.
Control of machines and robots: creation of mivar decision-making systems for controlling autonomous tractors and special vehicles of the ministry of emergencies

Abstract To control machines and robots, it is proposed, in addition to well-known control systems of the reflex level, to use a logical level Mivar decision-making systems. In this report, the scientific background on the application of the Mivar technologies for controlling autonomous tractors and special vehicles of the Ministry of Emergencies is shown. The decision-making systems “ROBO!RAZUM” can, on conventional computers and in real time, control autonomous multilevel heterogeneous robotic systems to perform complex actions under conditions of heterogeneous dynamic obstacles and adaptively solve tasks even with malfunctioning technical vision. The main difficulties of the complex of tasks of the “tractor of the Ministry of Emergencies” are analyzed, which are successfully solved by the Mivar decision-making systems for controlling machines and robots.
Computer Assisted Analysis of the Hepatic Spheroid Formation

Computer assisted techniques could enable the use of morphological characteristics of hepatic spheroids as surrogate for their response to various stimuli. The aim of this work is to develop an automatic analysis procedure able to correctly acquire all the important morphological parameters of the hepatic spheroids under static and after stimulation conditions. Within the datasets, several issues can occur related to the non-uniform illumination background and inherent limitation in counting the exact object number when two or more are adjacent. Some of the images include patterns with intensity comparable to the spheroid intensity, such as extended grooves, and they are filtered out based on their eccentricity values. Traditional methods such as Otsu threshold does not segment the spheroid images truthfully due to their energy minimization based approach. To circumvent this limitation initially background removal is applied as a preprocessing step. Filters applied for this depend on the relative size of the spheroids in order not to diminish the image quality. Therefore, we propose a guided automatic threshold value that can discriminate between the background and the spheroids more accurately by finding the critical peak on the pixel intensity histogram. Pixel intensity histograms are composed of three modes and the local minimum after the peak at the lowest values is the threshold value. After applying the new guided thresholding technique, watershed algorithm is used in order to determine the separating nodes between objects that are contiguous to each-other. These two techniques are compared with Gabor filters based methods that are shape based filters. Employing the three methods spheroid parameters including the number, area and the perimeter were determined and their performance and robustness are discussed.
Rehabilitation Using Neighbor-Cluster Based Matching Inducing Artificial Bee Colony Optimization

Muscle sprain has become a common reason for doctor consultation nowadays because of our sedentary lifestyle. Yoga helps to increase one's all-around fitness of body and strength of mind. Without or with very less involvement of a trainer, the ability of one to do the exercises himself/herself will motivate the subject more and flexibility of performing those movements will increase the regularity also. The novelty of our proposed work lies in the automatic measurement of posture accuracy. The skeletal forms of postures are detected by Kinect sensor initially, then after extracting the required features, those are matched with the already stored features of the training dataset. During the performance, the patterns of movements are recognized using neighbor-cluster approach. For the feature selection process, the wrapper method using artificial bee colony optimization is implemented. The proposed work is suitable for any type of patient as the system can be accessed at any time of the day in any environment and it outperforms the already present works in this field also.
Natural Language to Structured Query Language using Elasticsearch for descriptive columns

In the present day, data is captured in large amounts in the form of numbers, text, images etc. The data captured is either very simple to extract any useful information or it is very complex such that it takes a lot of time to generate information. In addition to this, data is being stored either in relational databases like MySQL, PostgreSQL or in document-oriented databases like MongoDB, Cassandra. The extraction of data from these databases requires some special knowledge of writing queries that are designed for that particular database. Hence there is a need for a system which extracts information from an input natural language query and convert it into a query which can be understood by the database in a fast and an efficient way. This paper proposes a way to create such a system by making use of Elasticsearch which is used for extracting data from descriptive columns. This paper is mainly focused on creating optimized SQL queries from natural language inputs using Elasticsearch where the extraction of data happens by means of searching through keywords present in input query throughout all the descriptive columns.
Multilevel Security and Dual OTP System for Online Transaction Against Attacks

In the current internet technology, most of the transactions to banking system are effective through online transaction. Predominantly all these e-transactions are done through e-commerce web sites with the help of credit/debit cards, net banking and lot of other payable apps. So, every online transaction is prone to vulnerable attacks by the fraudulent websites and intruders in the network. As there are many security measures incorporated against security vulnerabilities, network thieves are smart enough to retrieve the passwords and break other security mechanisms. At present situation of digital world, we need to design a secured online transaction system for banking using multilevel encryption of blowfish and AES algorithms incorporated with dual OTP technique. The performance of the proposed methodology is analyzed with respect to number of bytes encrypted per unit time and we conclude that the multilevel encryption provides better security system with faster encryption standards than the ones that are currently in use.
Designing an Accurate and Efficient Algorithm for Matching Arabic Names

A great deal of research has been done to find out an accurate algorithm for name matching that would play major role in the application process. Researchers have developed several algorithms to measure the similarity of string, but most of them are designed mainly to deal with Latin-based languages. However, dealing with the Arabic context is a challenging task, owing to the nature and unique features of the Arabic language. This can explain why the name matching algorithms in the Arabic context are rare. Therefore, this paper aims at designing an accurate and efficient algorithm for matching Arabic names. In this paper, a framework for matching Arabic names has been designed to provide a platform for the current and future investigations, involving matching Arabic names. This framework deals with specific characteristics of Arabic language and the various levels of similarities for Arabic letters, mainly keyboard similarities, letter forms and phonetic similarities. Moreover, the proposed algorithm accounts for the operation of transposition and the enhanced states of substitution, deletion and insertion operations. Therefore, the proposed algorithm reduces the storage space of the process, saves the time of processing time and reduces the time complexity from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) to O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Besides, the experiments show that the proposed algorithm is more efficient and more accurate than the other algorithms. Keywords: Matching Arabic names, String matching, Character N-gram, Levenshtein distance.
NLP methods for automatic candidate’s CV segmentation

The problem of CV (or resume) segmentation and automatic extraction becomes increasingly relevant nowadays as long as it could simplify candidate selection process. The paper proposes a new method of automatic CV segmentation and parsing. The described algorithm is based on Natural Language Processing and Machine Learning methods. The proposed procedure allows to extract information related to the candidates' work experience and education from their CVs which come in pdf or docx format. In particular, CV segmentation into 3 blocks (Basic Information, Education and Work Experience) is performed.
Balancing Theory and Practice in Software Engineering Education – A PBL, toolset based approach

This paper presents an interdisciplinary approach designed to join theoretical foundations required in software engineering with practice required for professional performance in software industry. Although this is a recurring issue on software engineering education, students still have difficulties in dealing with software development environments that are usually more complex than any software projects they have worked at university courses. We developed an approach aligned with market demands and theoretical requirements in software engineering education that incorporates principles of Project Based Learning (PBL) and Agile Methods. It aims to support development of student technical and soft skills in three different, but related, disciplines: Software Engineering Practice, Database Practice and Software Project Management. We describe a set of seven recommendations on how we used largely adopted tools in software development industry in order to develop student technical and soft skills. We describe two case studies: (1) an exploratory case study with three professors and 24 students involving multiple projects; and (2) a confirmatory case study with three professors and 15 students involving a large-scale project. As a result, we point seven recommendations for tool adoption on capstone courses based on lessons learned.
Low-Rank Tensor Tracking

Visual object tracking is an important step for many computer vision applications. Visual tracking becomes more challenging when the target object observes severe occlusion, lighting variations, background clutter, and deformation difficulties to name a few. In the literature, low-rank matrix decomposition methods have shown to be a potential solution for visual tracking in many complex scenarios. These methods first arrange the particles of the target object in a 2-D data matrix and then perform convex optimization to solve the low-rank objective function. However, these methods show performance degradation in the presence of the aforementioned challenges. Because these methods do not consider the intrinsic structure of the target particles, therefore, the object loses its spatial appearance or consistency. To address these challenges, we propose a new low-rank tensor decomposition model for robust object tracking. Our proposed low-rank tensor tracker considers the multi-dimensional data of target particles. We employ the recently proposed tensor-tensor product-based singular value decomposition and a new tensor nuclear norm to promote the intrinsic structure correlation among the target particles. Experimental evaluations on 20 challenging tracking sequences demonstrate the excellent performance of the proposed tracker as compared with state-of-the-art trackers.
A Graph-Based Recommender System for Food Products

In this paper we present a graph-based recommender system that is not relying on explicit item ratings to generate recommendations. Instead, it employs neighborhood-based and graph mining techniques to generate item profiles using their reviews' text. The proposed algorithm uses user review feedback to find products related to each other and tries to find a balance between similar products and highly popular products. It achieves this balance by ranking the products based on the similarity to the target product as well as its connectivity among similar products. The algorithm breaks the entire dataset into subgroups of similar products, which makes the proposed algorithm scalable as well. We present a proof-of-concept implementation of the proposed algorithm in the food product domain and present some preliminary results.
Use of a decision-making laboratory to support student’s visual analysis for the solution of a transportation problem in Mexico City

This Innovative Practice Work in Progress presents a proposal to promote the use of information technologies for students through decision center visualizations in order to facilitate their analysis process of information related to real life problems. To improve the students' learning process, Tecnologico de Monterrey University designed and implemented a set of challenges and curriculum within the Tec21 educational model. It focuses on the creation and development of classes that allow students face real world challenges with the use of the different information technology tools available. As part of this, is the creation of the decision-making laboratory that seeks to support the analysis, visualization and interaction of information in a decision-making process. The decision-making laboratory, together with the department of computational sciences, designed a challenge that aims to carry out the analysis and visualization of open data concerning the urban mobility problem in the financial and corporate center of Mexico City. It was designed for students enrolled in the last third of the Computer Science major. This paper presents the implementation process and the encouraging results through the use of a decision-making laboratory to create and use specialized visualizations regarding an urban mobility problem in Mexico City.
Role-Value Maps and General Concept Inclusions in the Minimal Description Logic with Value Restrictions or Revisiting Old Skeletons in the DL Cupboard

Abstract We investigate the impact that general concept inclusions and role-value maps have on the complexity and decidability of reasoning in the description logic $$\mathcal{FL}_0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>FL</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:math> . On the one hand, we give a more direct proof for ExpTime-hardness of subsumption w.r.t. general concept inclusions in $$\mathcal{FL}_0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>FL</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:math> . On the other hand, we determine restrictions on role-value maps that ensure decidability of subsumption, but we also show undecidability for the cases where these restrictions are not satisfied.
Below the Radar: Spotting DNS Tunnels in Newly Observed Hostnames in the Wild

The domain name system (DNS) is a crucial backbone of the Internet and millions of new domains are created on a daily basis. While the vast majority of these domains are legitimate, adversaries also register new hostnames to carry out nefarious purposes, such as scams, phishing, data stealing via DNS tunneling or other types of attacks in context of e-crime. In this paper, we present insights on the global utilization of DNS through a measurement study examining exclusively newly observed hostnames via passive DNS data analysis. We analyzed more than two billion such hostnames collected over a period of two months. Surprisingly, we find that only three second-level domains are responsible for more than half of all newly observed hostnames every day. More specifically, we found that Google's Accelerated Mobile Pages (AMP) project, the music streaming service Spotify, and a DNS tunnel provider generate the majority of new domains on the Internet. DNS tunneling is a covert channel technique to transfer arbitrary information over DNS via DNS queries and answers. This technique is often (ab)used by attackers to transfer data in a stealthy way, bypassing traditional network security systems. We find that potential DNS tunnels cause a significant fraction of the global DNS requests for new hostnames: our analysis reveals that nearly all resource record type NULL requests and more than a third of all TXT requests can be attributed to DNS tunnels. Motivated by these empirical measurement results, we propose and implement a method to identify DNS tunnels via a step-wise filtering approach that relies on general characteristics of such tunnels (e.g., number of subdomains or resource record type). Using our approach on empirical data, we successfully identified 273 suspicious domains related to DNS tunnels, including two known APT campaigns (Wekby and APT32).
Participatory Heuristic Evaluations of Jeliot Mobile : End-users evaluating usability of their mlearning application

The usability evaluations (testing and inspection) are utmost important in the development of learner-centered educational technology. Poorly designed learning tools put an additional interface learning load on learners' memory and cognition, which may result in significantly decreased attention to the main content learning goal. Nonetheless, discounted inspection (by usability experts) are commonly reported for such tools but expensive user-based testing is a rarity, mostly due to limited budgets and constrict timeframes. In this work, the authors propose to run discounted inspections with special end-user (domain expert) evaluators having a prior experience in usability. The authors postulate that putting discounted inspections into a participatory context shall bring the benefits of both type of usability evaluations to one. It shall also reduce costs typically associated with expensive user-based testing, thus removing hindrances. The argument is put to test via participatory heuristic evaluation experiments on a case tool with a large number of redundant evaluating groups to cross-validate results. Over 164 end-users with some prior usability experience, divided into 45 evaluating groups evaluate the case tool. The outcomes are promising. The dual-personae evaluators find significant amount of domain related problems (missed by usability experts in discounted methods) as well as interface related problems (missed by domain experts in testing), suggesting their usefulness in mixed method. The authors thus advise to evaluate the usability of low-budget educational technology projects with the proposed method.