Reviewerly/exHarmony huggingface dataset:
FACTORS INFLUENCING STUDENTS’ ACADEMIC PERFOMANCE IN KENYA CERTIFICATE OF SECONDARY EDUCATION IN KIRINYAGA CENTRAL SUB-COUNTY, KIRINYAGA COUNTY, KENYA

This paper is on factors students’ academic performance in Kenya Certificate of Secondary Education in Kirinyaga Central Sub-county of Kirinyaga County, Kenya. Literature review dwelt on global examination of academic performance, Africa review and regional including Kenya case. The study was guided by five objectives including but not limited to: factors that influence KCSE performance that include students-teachers ratio, peer pressure, school resources, student’s motivation and family background. The study used descriptive survey research design. The sample comprised of 136 respondents of whom 68 were Heads of department, 34 deans of studies and 34 school captains from all the 34 schools in Kirinyaga Central Sub-county. Results on teacher-students ratio was seen to be highly influencing students’ performance. All the respondents (100%) agreed that high teacher-student ratio influence KCSE performance by either leading to low physical contact between teachers and students hence poor understanding of concepts and poor individual attention to every students. Peer influence influences students’ academic performance. 67.6% of the respondents agreed that some learners might not be interested in academic excellence hence dragging others behind. Peers also influence each other on drug and substance abuse, premarital indecency, dressing indecently, coupling and laziness. It was observed that 94.1% of the respondents agreed that school resources influence students’ performance. These include enough teachers, textbooks, buildings, revision materials etc. on students motivation it was found that attitude of students influence academic performance by 73.3%. It was also noted that 86.7% of the respondents agreed that family background had influence on academic performance.
A Comparative Study of Clustering Algorithms for Mixed Datasets

Clustering groups, a set of elements in a manner that elements in the same categoryhave more common characteristics (based on a given set of attributes) among themthan to elements in other categories. Each group is called a cluster. Clustering is used in many areas: sensor networks, social networks, health, business and other applications. There are many different clustering algorithms with different parameters. The appropriate clustering algorithm and parameter settings depend on data set and the problem being solved. Some work only on numerical data and other on mixed data. Our aim is to do a comparative study of these algorithms.
BalBoa

Balance is an essential physical skill to master, but a challenging one given that it requires a heightened body awareness to control, maintain and develop. In HCI physical training research, the design space of technology support for developing such body awareness remains narrow. Here, we introduce BalBoa, a balancing board to support balance training during handstands. We describe key highlights of the design process behind the Balboa, and present a work-in-progress prototype, which we tested with handstand beginners and experts. We discuss feedback from our users, preliminary insights, and sketch the future steps towards a fully developed prototype.
LOSGSR: Learned Opinion Score Guided Shoeprint Retrieval

As a kind of forensic evidence, a shoeprint conveys many important human characteristics, and it plays a vital role in forensic investigations. Millions of shoeprints are acquired from crime scenes, and it is a challenging task to retrieve the most similar shoeprints for a query shoeprint. Most shoeprint retrieval methods sort shoeprint images by feature similarities with respect to the query shoeprint; however, the results are not always what the investigator expects because the retrieval algorithm cannot determine what the investigator prefers based on only the content of the query shoeprint. This paper proposes a method to guide the shoeprint retrieval process to approximate what the user wants by applying learned opinion scores. Additionally, this paper improves shoeprint retrieval effectiveness by implementing the following four perspectives: 1) using the opinion scores of multiple examples to guide the results to meet the forensic experts' expectations; 2) proposing a learning-based method to refine opinion scores, which corrects the labeled opinion scores of multiple examples and their neighbors; 3) using a manifold ranking method to propagate the opinion scores to other dataset shoeprints; and 4) introducing a coefficient matrix to prevent the tendency of the ranking scores to become low values. The experiments show that the cumulative match scores of the proposed method are more than 96.6% in the top 2% of the dataset composed of 10,096 crime scene shoeprints.
Inferential equivalence, normal forms, and isomorphisms of knowledge bases in institutions of conditional logics

Conditionals of the form "If A, then usually B" play an important role in formal approaches to knowledge representation and reasoning. Sets of such conditionals, called a knowledge base, may represent the explicitly given knowledge of an intelligent agent. For many operations involving kowledge bases, it is advantageous to have a compact and standardized form for them. In this paper, we show how to obtain a unique minimal normal form for every conditional knowledge base. The normal form is independent of a specific notion of semantic models and their particular notion of satisfaction between models and conditionals. This abstraction is achieved by employing Goguen and Burstall's framework of institutions and introducing the general notion of an institution of conditional logic. It covers previously proposed formalizations of conditional logics as institutions with specific semantics, like total preorders on worlds, possibility distributions, ranking functions, or big-stepped probabilities. The concepts of model equivalence and elementwise equivalence of knowledge bases are generalized first to arbitrary institutions of conditional logic and then further by taking also isomorphisms into account. We introduce the notion of inferential equivalence of knowledge bases for two different nonmonotonic inference relations induced by a knowledge base. For inferential equivalences with respect to these nonmonotonic inference relations, we prove how they are preserved under normalization and isomorphisms.
K-mixed prototypes

Many real-life applications involve data with mixed numeric and categorical values. While the notion of similarity/distance measure is well defined for numeric values, defining the distance between categorical values is not as straightforward, mainly because categorical values have no order. The situation is even less clear for clustering data with mixed numeric and categorical values. Among the categorical types of attributes, nominal versus binary types can further be distinguished. Also, for a categorical binary attribute, the contributions of the two types of values may not be of equal importance. Whereas existing similarity measures for categorical values do not recognize these differences, we show that recognizing them can yield improved clustering results. To do this, we first extend an existing clustering method proposed originally for pure categorical data and then we develop k-mixed prototypes for clustering data with mixed numeric and categorical values. Our technique requires only the number of clusters as the input parameter, and it recognizes the differences mentioned. The results of our numerous experiments, using real-life benchmark data, indicate significant increase in accuracy and efficiency of the proposed k-mixed prototype, compared to existing clustering methods.
The Missing Interface

Augmenting arbitrary physical objects with digital content leads to the missing interface problem, because those objects were never designed to incorporate such digital content and so they lack a user interface. A review of related work reveals that current approaches fail due to limited detection fidelity and spatial resolution. Our proposal, based on Google Soli's radar sensing technology, is designed to detect micro-gestures on objects with sub-millimeter precision. Preliminary results with a custom gesture set show that Soli's core features and traditional machine learning models (Random Forest and Support Vector Machine) do not lead to robust recognition accuracy, and so more advanced techniques should be used instead, possibly incorporating additional sensor features.
Designing for Wellbeing-as-Interaction

This paper introduces the concept of wellbeing-as-interaction. Instead of designing and evaluating technologies that locate wellbeing in the individual, this paper presents early-stage work on designing technologies for people to collaboratively express, interpret, discuss and enact wellbeing. To explore this concept, we examined the wellbeing of six pairs of university students through a 7-day deployment of a technology probe 'MoodCloud'. MoodCloud consisted of a mobile app and an ambient display to share wellbeing updates through colour. We observed three patterns of wellbeing interactions: updates, follow-ups, and message chains. Wellbeing interactions benefitted from the ambiguity of colour and a clearly defined target audience, but students also communicated through other channels to make sense of updates and to enact support. The concept of wellbeing-as-interaction seeks to offer an analytic lens for the CHI community as well as inspiration for novel wellbeing technologies that emphasise meaningful interactions with friends.
A Comparative Analysis of Lightweight Cryptographic Protocols for Smart Home

Smart home is an application of Internet of Things. A smart home is a network of smart resource-constraint devices which require secure and confidential transmission of information. Lightweight cryptographic protocols are suitable for IOT applications like smart home. Lightweight cryptographic protocols are characterized by small key size, small block size, and less number of iterations. This paper presents a comparative analysis of lightweight cryptographic protocols in terms of the encryption type, advantages, and resistance to attacks.
<i>Bid</i>-<i>Fish</i>: An android application for online fish auction based on case study from Muara Angke, Indonesia

In utilizing natural resources from the sea, the Indonesian government has obliged fishermen to distribute all the fish they catch at a fish auction institution that has been provided at each landing port, namely the Fish Auction Place or known as Tempat Pelelangan Ikan (TPI) in Bahasa. In this study, we made a sample case at the TPI Muara Angke, North Jakarta which has a function to improve the welfare of fishermen through an auction which is expected to achieve optimal prices. However, in practice, it turns out that there are often price discrepancies expected by fishermen because there are no bidders who bid for more of the fish they have made fishermen prefer to sell their fish catches into cold storage or even directly abroad due to a bad auction system. This is very detrimental to Indonesia because it has lost a lot of its national income. Therefore, it is necessary to digitize the embodied fish auction process in the form of an online mobile platform-based fish auction application to be able to introduce fish training more widely so that many auction participants want to bid higher fish auction prices without limited space and time for bidders participate in the auction so that the potential value obtained by fishermen can be greater and in accordance with the expectations of the fishermen. In this study, we built a mobile application for online fish auctions called "BidFish" which consists of two applications for admin (TPI) and users who have the auction feature (auction) in real time within the time specified by the admin who will automatically determines compiling a price quote from the highest price other than that there is a deposit storage feature (BidPay) which is a requirement for the user to be able to attend an auction. This application is built with the Android platform considering the level of Android smartphone usage is very large in Indonesia so it is expected to be a solution to the price mismatch problem that occurs in an auction by getting more bidders.
Algorithmic Greenlining

In contexts such as college admissions, hiring, and image search, decision-makers often aspire to formulate selection criteria that yield both high-quality and diverse results. However, simultaneously optimizing for quality and diversity can be challenging, especially when the decision-maker does not know the true quality of any criterion and instead must rely on heuristics and intuition. We introduce an algorithmic framework that takes as input a user's selection criterion, which may yield high-quality but homogeneous results. Using an application-specific notion of substitutability, our algorithms suggest similar criteria with more diverse results, in the spirit of statistical or demographic parity. For instance, given the image search query "chairman", it suggests alternative queries which are similar but more gender-diverse, such as "chairperson". In the context of college admissions, we apply our algorithm to a dataset of students' applications and rediscover Texas's "top 10% rule": the input criterion is an ACT score cutoff, and the output is a class rank cutoff, automatically accepting the students in the top decile of their graduating class. Historically, this policy has been effective in admitting students who perform well in college and come from diverse backgrounds. We complement our empirical analysis with learning-theoretic guarantees for estimating the true diversity of any criterion based on historical data.
Usability evaluation of VR products in industry

VR development practices have a diverse set of practices compared to traditional software development. Tasks like scene design, acoustic design, vergence manipulation, image depth, etc. are specific to VR apps and hence require evaluation processes that may be different from the traditional means. Usability Evaluation is one such process which is being executed in an unconventional way by Industrial Practitioners today. In this paper, the researchers detail a Systematic Literature Review of the Usability Evaluation Methods practised by Industrial researchers while building VR Products. The researchers found that VR Product teams follow unique methods to improve usability in their products. Further, the researchers consolidate these methods and provide insights into choosing the best to build a real-world VR Product based on the defined product constraints.
Robust MR image super‐resolution reconstruction with cross‐modal edge‐preserving regularization

Abstract In this article, we propose a novel image super‐resolution (SR) reconstruction method in the field of magnetic resonance imaging, which is based on a cross‐modal edge‐preserving regularization integrating the internal gradient prior from the target‐modal image itself and the external gradient prior from the reference‐modal image obtained by pre‐scan in many medical imaging scenes. The reference‐modal image is a high‐resolution guidance image that has much shareable information such as gradient orientation on edge regions, which can be used to improve the image resolution of the target modal. In addition, to be robust against the misalignment between the target‐modal image and reference‐modal image, a multimodal registration is incorporated in the SR reconstruction process. In this work, the proposed SR method can be formulated as an alternating optimization problem, that is, the target‐modal and reference‐modal images are alternately updated through iterations. Experimental results on simulated and realistic images show the superior performance of the proposed approach over several state‐of‐the‐art SR techniques.
Quantifying the Effectiveness of Patterning, Test Conditions, and DIC Parameters for Characterization of Plastic Strain Localization

Digital image correlation, commonly referred to as DIC, enables full-field measurements of displacements and strains from a surface of interest. While DIC offers major advantages over commonly established methods, such as strain gauges or extensometers, some questions remain in guaranteeing data precision of the DIC measurements. This paper reports on improvements in the precision of DIC measurements of large deformations in copper tensile specimens by optimized patterning and lighting conditions, as well as quantifying the improvements in the data. In the context of detection of strain localization at welds in copper canisters for spent nuclear fuel disposal, the trade-off between spatial resolution and strain resolution is critical. To quantify the improvements in measurement conditions, we propose a method for estimation of spatial resolution and noise level of a full-field strain measurement, which does not require imposing a displacement field with variations in strain or displacement of well-defined length scale.
Reduced-Complexity Depth Filtering and Occlusion Suppression using Modulated-Sparse Light Fields

Application of depth-selective filtering to modulated-sparse light fields towards reducing the DSP and memory complexities in real-time light field processing is investigated. A modulated-sparse light field is obtained by spatially windowing an original 4-D light-field signal, which is subsequently processed by 4-D depth-selective filters to achieve planar focus and occlusion suppression. Multidimensional spectral properties of modulated-sparse light fields are explored and demonstrative examples with real light fields are provided to show that almost similar depth filtering and occlusion suppression performance can be achieved by processing such modulated-sparse light fields, leading to significant reduction in DSP hardware and memory complexities.
Client-side Computational Optimization

Mobile platforms have matured to a point where they can provide the infrastructure required to support sophisticated optimization codes. This opens the possibility to envisage new interest for distributed application codes and the opportunity to intensify research on optimization algorithms requiring limited computational resources, as provided by mobile platforms. In this article, we report on some exploratory experience in this area. We illustrate some practical, real-world cases where running optimization programs on mobile or embedded devices can be useful, with particular emphasis on matheuristics approaches. Then, we discuss a practical use case involving the feasibility version of the generalized assignment problem (GAP). We present a JavaScript implementation of a GAP solver that can be executed inside an ordinary browser supporting ECMAScript. We tested the code on different smartphones of varying age and power, as well as on desktop PCs and other embedded devices. Our experiments confirm the viability of mobile devices for computational intensive tasks.
Older Adults as Makers of Custom Electronics

Researchers have designed technologies for and with older adults to help them age in place, but there is an opportunity to support older adults in creating customized smart devices for themselves through electronic toolkits. We developed a plan for iterating on Craftec - one of the first electronic toolkits designed for older adults - informed by the results of a participatory design workshop and user evaluation. We focused on supporting older adults to create exemplar artifacts, such as medication adherence systems. We contribute the exemplars and the current plan for components of the Craftec system as a way to support older adults to design technology for themselves.
Pupil Localization Using Self-organizing Migrating Algorithm

In this paper, we propose a new method for pupil localization in images. The main contribution of the proposed method is twofold. Firstly, the method is based on the proposed eye model that takes into account physiological properties of eyes (i.e. reflects the properties of pupil, iris, and sclera). Secondly, the correct shape and the position of the model are determined using an evolutionary algorithm called Self-Organizing Migrating Algorithm (SOMA). Thanks to these ideas, the proposed method is faster than the state-of-the-art methods without reduction of accuracy. We evaluated the algorithms on two publicly available data sets in remote tracking scenarios (namely BioID [7] and GI4E [11]).
Data-driven environment modeling for adaptive system-of-systems

Since a System-of-Systems (SoS) is constructed and managed under a complex and dynamic environment, self-adaptability has become one of the key capabilities that SoSs should have. To design an adaptive SoS, analyzing and modeling the environment are important. Studies on self-adaptive systems (SAS) have proposed various analysis and design approaches to deal with dynamic environment and operating conditions. However, most existing approaches require a considerable amount of domain experts' knowledge about the operating environment without specific and practical guidelines, so there still remain many challenges for engineers to analyze and design an adaptive SoS. In this study, we propose a data-driven method of generating environment models for adaptive SoS. To guide the analysis and understanding of the environment, we propose a metamodel that encompasses characteristics of the dynamic environment. Based on the metamodel, an environment model is generated from historical data for effective analysis of the SoS's complex environment. As a case study, we apply our method to a traffic environment modeling with real data. We show that our proposed method can practically help engineers generate environment models with concrete features that are necessary for adaptive SoS modeling by considering the environment as a major entity for SAS analysis and design.
Handling Real-Word Errors of Hindi Language using N-gram and Confusion Set

The two major typographic errors of any language are non-word errors and real-word errors. The researchers have worked rigorously for the former error but the latter has not been given the much attention all this while. In this paper, a technique to identify the real-word error of Hindi language has been proposed which combines the bigram, trigram and confusion set (CS) methods. Left bigrams, right bigrams and trigrams are calculated by taking in to account the immediate left, immediate right word with the Hindi test word and the immediate left, the Hindi test word and the immediate right word respectively. A group of most confusable words is created using Levenstein edit distance method. After that, a composite score is calculated for all the members of the CS using bigram and trigram probabilities. The calculated composite score is used to prepare the suggestion list for the erroneous word. A Hindi text file of 2000 words has been used to evaluate and verify the proposed method which offers considerably good results. It gives the precision, recall and F-score as .70-.75, .80-.85 and .70-.80 respectively. In future, the research can be done to improve the results by considering the whole sentence at once.