Reviewerly/exHarmony huggingface dataset:
Terminology-based Text Embedding for Computing Document Similarities on Technical Content

We propose in this paper a new, hybrid document embedding approach in order to address the problem of document similarities with respect to the technical content. To do so, we employ a state-of-the-art graph techniques to first extract the keyphrases (composite keywords) of documents and, then, use them to score the sentences. Using the ranked sentences, we propose two approaches to embed documents and show their performances with respect to two baselines. With domain expert annotations, we illustrate that the proposed methods can find more relevant documents and outperform the baselines up to 27% in terms of NDCG.
Introducing the structural bases of typicality effects in deep learning

In this paper, we hypothesize that the effects of the degree of typicality in natural semantic categories can be generated based on the structure of artificial categories learned with deep learning models. Motivated by the human approach to representing natural semantic categories and based on the Prototype Theory foundations, we propose a novel Computational Prototype Model (CPM) to represent the internal structure of semantic categories. Unlike other prototype learning approaches, our mathematical framework proposes a first approach to provide deep neural networks with the ability to model abstract semantic concepts such as category central semantic meaning, typicality degree of an object's image, and family resemblance relationship. We proposed several methodologies based on the typicality's concept to evaluate our CPM-model in image semantic processing tasks such as image classification, a global semantic description, and transfer learning. Our experiments on different image datasets, such as ImageNet and Coco, showed that our approach might be an admissible proposition in the effort to endow machines with greater power of abstraction for the semantic representation of objects' categories.
Detecting Clues for Skill Levels and Machine Operation Difficulty from Egocentric Vision

With respect to machine operation tasks, the experiences from different skill level operators, especially novices, can provide worthy understanding about the manner in which they perceive the operational environment and formulate knowledge to deal with various operation situations. In this study, we describe the operator's behaviors by utilizing the relations among their head, hand, and operation location (hotspot) during the operation. A total of 40 experiences associated with a sewing machine operation task performed by amateur operators was recorded via a head-mounted RGB-D camera. We examined important features of operational behaviors in different skill level operators and confirmed their correlation to the difficulties of the operation steps. The result shows that the pure-gazing behavior is significantly reduced when the operator's skill improved. Moreover, the hand-approaching duration and the frequency of attention movement before operation are strongly correlated to the operational difficulty in such machine operating environments.
LibGuides: 6th grade social studies unit on the Stone Age: Paleolithic Vs. Neolithic: Home

The following guide will be used in a 6th grade social studies class to help students learn about the the Stone Age. This LibGuide will focus on the Paleolithic and Neolithic time periods and how they compared.
HHS: Nearly 700,000 affected by data breaches in April

Forty-five businesses and organizations covered by HIPAA reported breaches of protected health information in April, affecting more than 690,000 individuals, according to the U.S.
The Effects of the Introduction of Bitcoin Futures on the Volatility of Bitcoin Returns

This paper investigates the effects of the launch of Bitcoin futures on the intraday volatility of Bitcoin. Based on one-minute price data collected from four cryptocurrency exchanges, we first examine the change in realized volatility after the introduction of Bitcoin futures to investigate their aggregate effects on the intraday volatility of Bitcoin. We then analyze the effects in more detail utilizing the discrete Fourier transform. We show that although the Bitcoin market became more volatile immediately after the introduction of Bitcoin futures, over time it has become more stable than it was before the introduction.
PizzaBox: Studying Internet Connected Physical Object Manipulation based Food Ordering

This paper presents the designing and testing of PizzaBox, a 3D printed, interactive food ordering system that aims to differ from conventional food ordering systems and provide an entertaining and unique experience when ordering a pizza by incorporating underlying technologies that support ubiquitous computing. The PizzaBox has gone through both low and medium fidelity testing while working collaboratively with participants to co-design and refine a product that is approachable to all age groups while maintaining a simple process for ordering food from start to finish. Final testing was conducted at an independent pizzeria where interviews with participants lead us to develop four discussion themes 1) usability and end user engagement, 2) towards connected real-time products and services, 3) healthy eating, 4) evolution of food ordering systems. Our interviews show that in general, PizzaBox would have a greater appeal to a younger audience by providing a fantasy of helping in the creation and baking of the pizza but also has a novelty value that all ages would enjoy. We investigate the effect that the PizzaBox has in encouraging new healthy habits or promoting a healthier lifestyle as well as how we can improve PizzaBox to better encourage these lifestyle changes.
Perception of Young University Students about Acts of Corruption

This article presents and analyses the results of a study carried out at the Autonomous University of Chiapas, Faculty of Law, with the objective of knowing the perception of young university students regarding the acts of corruption.Also, explore common corruption practices.The findings are relevant considering that future professionals identify in their immediate environment acts and actors with corrupt practices that have become naturalized and that locate Mexico in one of the first places in international ranking.The results also indicate the institutionalization of corruption and the lack of control of these acts.Another important fact is that within the social actors identified as corrupt are the politicians, police officers and lawyers who occupy the first three sites (executors of laws) and not far, fourthly, to religious representatives.
NodeDrop: A Condition for Reducing Network Size without Effect on Output

Determining an appropriate number of features for each layer in a neural network is an important and difficult task. This task is especially important in applications on systems with limited memory or processing power. Many current approaches to reduce network size either utilize iterative procedures, which can extend training time significantly, or require very careful tuning of algorithm parameters to achieve reasonable results. In this paper we propose NodeDrop, a new method for eliminating features in a network. With NodeDrop, we define a condition to identify and guarantee which nodes carry no information, and then use regularization to encourage nodes to meet this condition. We find that NodeDrop drastically reduces the number of features in a network while maintaining high performance, reducing the number of parameters by a factor of 114x for a VGG like network on CIFAR10 without a drop in accuracy.
DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets

Smartphones have become the ultimate 'personal' computer, yet despite this, general-purpose data-mining and knowledge discovery tools for mobile devices are surprisingly rare. DataLearner is a new data-mining application designed specifically for Android devices that imports the Weka data-mining engine and augments it with algorithms developed by Charles Sturt University. Moreover, DataLearner can be expanded with additional algorithms. Combined, DataLearner delivers 40 classification, clustering and association rule mining algorithms for model training and evaluation without need for cloud computing resources or network connectivity. It provides the same classification accuracy as PCs and laptops, while doing so with acceptable processing speed and consuming negligible battery life. With its ability to provide easy-to-use data-mining on a phone-size screen, DataLearner is a new portable, self-contained data-mining tool for remote, personalised and learning applications alike. DataLearner features four elements - this paper, the app available on Google Play, the GPL3-licensed source code on GitHub and a short video on YouTube.
A Differentially Private Incentive Design for Traffic Offload to Public Transportationx

Increasingly large trip demands have strained urban transportation capacity, which consequently leads to traffic congestion and rapid growth of greenhouse gas emissions. In this work, we focus on achieving sustainable transportation by incentivizing passengers to switch from private cars to public transport. We address the following challenges. First, the passengers incur inconvenience costs when changing their transit behaviors due to delay and discomfort, and thus need to be reimbursed. Second, the inconvenience cost, however, is unknown to the government when choosing the incentives. Furthermore, changing transit behaviors raises privacy concerns from passengers. An adversary could infer personal information, (e.g., daily routine, region of interest, and wealth), by observing the decisions made by the government, which are known to the public. We adopt the concept of differential privacy and propose privacy-preserving incentive designs under two settings, denoted as two-way communication and one-way communication. Under two-way communication, passengers submit bids and then the government determines the incentives, whereas in one-way communication the government simply sets a price without acquiring information from the passengers. Under one-way communication, we focus on how the government should design the incentives without revealing passengers' inconvenience costs while still preserving differential privacy. We formulate the problem as a convex program, and propose a differentially private and near-optimal solution algorithm. A numerical case study using Caltrans Performance Measurement System (PeMS) data source is presented as evaluation. The results show that the proposed approaches achieve a win-win situation in which both the government and passengers obtain non-negative utilities.
From QBFs to MALL and back via focussing: fragments of multiplicative additive linear logic for each level of the polynomial hierarchy

In this work we investigate how to extract alternating time bounds from 'focussed' proof systems. Our main result is the obtention of fragments of MALLw (MALL with weakening) complete for each level of the polynomial hierarchy. In one direction we encode QBF satisfiability and in the other we encode focussed proof search, and we show that the composition of the two encodings preserves quantifier alternation, yielding the required result. By carefully composing with well-known embeddings of MALLw into MALL, we obtain a similar delineation of MALL formulas, again carving out fragments complete for each level of the polynomial hierarchy. This refines the well-known results that both MALLw and MALL are PSPACE-complete. A key insight is that we have to refine the usual presentation of focussing to account for deterministic computations in proof search, which correspond to invertible rules that do not branch. This is so that we may more faithfully associate phases of focussed proof search to their alternating time complexity. This presentation seems to uncover further dualities at the level of proof search than usual presentations, so could be of further proof theoretic interest in its own right.
Neural Learning of Online Consumer Credit Risk

This paper takes a deep learning approach to understand consumer credit risk when e-commerce platforms issue unsecured credit to finance customers' purchase. The "NeuCredit" model can capture both serial dependences in multi-dimensional time series data when event frequencies in each dimension differ. It also captures nonlinear cross-sectional interactions among different time-evolving features. Also, the predicted default probability is designed to be interpretable such that risks can be decomposed into three components: the subjective risk indicating the consumers' willingness to repay, the objective risk indicating their ability to repay, and the behavioral risk indicating consumers' behavioral differences. Using a unique dataset from one of the largest global e-commerce platforms, we show that the inclusion of shopping behavioral data, besides conventional payment records, requires a deep learning approach to extract the information content of these data, which turns out significantly enhancing forecasting performance than the traditional machine learning methods.
Convex fuzzy \(k\)-medoids clustering

Abstract K-medoids clustering is among the most popular methods for cluster analysis despite its use requiring several assumptions about the nature of the latent clusters. In this paper, we introduce the Convex Fuzzy k-Medoids (CFKM) model, which not only relaxes the assumption that objects must be assigned entirely to one and only one medoid, but also that medoids must be assigned entirely to one and only one cluster. The resulting model is convex, thus its resolution is completely robust to initialization. To illustrate the usefulness of the CFKM model, we compare it with two fuzzy k-medoids clustering models: the Fuzzy k-Medoids (FKM) and the Fuzzy Clustering with Multi-Medoids (FMMdd), both solved approximately by heuristics because of their hard computational complexity. Our experiments with both synthetic and real-world data as well as a user survey reveal that the model is not only more robust to the choice hyperparameters of the fuzzy clustering task, but also that it can uniquely discover important aspects of data inherently fuzzy in nature.
Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies

In Semantic Dependency Parsing (SDP), semantic relations form directed acyclic graphs, rather than trees. We propose a new iterative predicate selection (IPS) algorithm for SDP. Our IPS algorithm combines the graph-based and transition-based parsing approaches in order to handle multiple semantic head words. We train the IPS model using a combination of multi-task learning and task-specific policy gradient training. Trained this way, IPS achieves a new state of the art on the SemEval 2015 Task 18 datasets. Furthermore, we observe that policy gradient training learns an easy-first strategy.
LOD Construction Through SupervisedWeb Relation Extractionand Crowd Validation

Free, unstructured text is the dominant format in which information is stored and published. To interpret such vast amount of data one must employ a programmatic approach. In this paper, we describe a novel approach – a pipeline in which interesting relations are extracted from web portals news texts, stored as RDF triplets, and finally validated by end user via browser extension. In the process, different machine learning algorithms were tested on relation extraction, enhanced with our own set of features and thoroughly evaluated, with excellent precision and recall results compared to models used for semantic knowledge expansion. Building on those results, we implement and describe the component to resolve discovered entities to existing semantic entities from three major online repositories. Finally, we implement and describe the validation process in which RDF triplets are presented to the web portal reader for validation via Chrome extension.
What and Where to Translate: Local Mask-based Image-to-Image Translation

Recently, image-to-image translation has obtained significant attention. Among many, those approaches based on an exemplar image that contains the target style information has been actively studied, due to its capability to handle multimodality as well as its applicability in practical use. However, two intrinsic problems exist in the existing methods: what and where to transfer. First, those methods extract style from an entire exemplar which includes noisy information, which impedes a translation model from properly extracting the intended style of the exemplar. That is, we need to carefully determine what to transfer from the exemplar. Second, the extracted style is applied to the entire input image, which causes unnecessary distortion in irrelevant image regions. In response, we need to decide where to transfer the extracted style. In this paper, we propose a novel approach that extracts out a local mask from the exemplar that determines what style to transfer, and another local mask from the input image that determines where to transfer the extracted style. The main novelty of this paper lies in (1) the highway adaptive instance normalization technique and (2) an end-to-end translation framework which achieves an outstanding performance in reflecting a style of an exemplar. We demonstrate the quantitative and qualitative evaluation results to confirm the advantages of our proposed approach.
Weaving the Dark Web: Legitimacy on Freenet, Tor, and I2P

Few books approach the dark web without leaving the reader with a mystified understanding of it. Gehl not only defines and demystifies the dark web, but through well-organized and scaffolding chapt...
Supervised and Semi-Supervised Learning for MIMO Blind Detection with Low-Resolution ADCs

The use of low-resolution analog-to-digital converters (ADCs) is considered to be an effective technique to reduce the power consumption and hardware complexity of wireless transceivers. However, in systems with low-resolution ADCs, obtaining channel state information (CSI) is difficult due to significant distortions in the received signals. The primary motivation of this paper is to show that learning techniques can mitigate the impact of CSI unavailability. We study the blind detection problem in multiple-input-multiple-output (MIMO) systems with low-resolution ADCs using learning approaches. Two methods, which employ a sequence of pilot symbol vectors as the initial training data, are proposed. The first method exploits the use of a cyclic redundancy check (CRC) to obtain more training data, which helps improve the detection accuracy. The second method is based on the perspective that the to-be-decoded data can itself assist the learning process, so no further training information is required except the pilot sequence. For the case of 1-bit ADCs, we provide a performance analysis of the vector error rate for the proposed methods. Based on the analytical results, a criterion for designing transmitted signals is also presented. Simulation results show that the proposed methods outperform existing techniques and are also more robust.
Pendidikan Lingkungan pada Mata Kuliah Pendukung Pemanfaatan Kebun Percobaan di Jurusan Biologi

This study aims: (1). To identify the main support system in the design of environment-based learning in the Department of Biology, Faculty of Mathematics and Natural Sciences, Makassar State University. (2). Knowing the use of environment-based learning resources in the Department of Biology, Faculty of Mathematics and Natural Sciences, Makassar State University. Through the Needs Assessment instrument in the form of field observations, questionnaire surveys, and interviews with lecturers and students identified problems to determine the right action for the next stage, so the design of learning to be carried out is the best solution. The results of the study are that (1). The Biology Department of UNM has two main locations or areas that strongly support environment-based learning with their respective characteristics. The two areas are the Biology Experiment Garden and the Field of Library Discussion of the Biology Department of FMIPA UNM. (2) Some courses have made use of environment-based learning resources in the Biology Department of FMIPA UNM. Solutions from the identification results obtained are among others; (1). The Department of Biology can be used as a place of learning, a source of learning, and as a learning media in the design of environment-based learning. (2). The Biology Department can be used as a comfortable and pleasant learning environment for the entire academic community. (3). The design of Environmental Based Learning in the Biology Department of UNM can be done in almost all subjects by increasing the role of the lecturer as a facilitator.
SAMPLES DISTINCTION BY PARAMETRIC AND NONPARAMETRIC STATISTICS IN SPSS

Testing samples distinction is necessary in a wide range of practical tasks. Medicine, sociology, psychology, marketing - this is a short list of industries where it is required to conduct tests that establish effectiveness or inefficiency of a certain technology.Diversity of situations and techniques applied to sample distinction create a problem for compliance of testing procedures. The problem rises for tests including large and small samples (dependent or independent) with various distributions. The article proposes a list of problems created by testing differences between two samples. Limits of applicability of parametric and non-parametric tests are established based on selected distribution. Informative examples are included based on simulated data.SPSS software was used for sample distinction tests. It is important to double-check the operation of the machine computing procedure "manually” to understand the nature of tests and in educational purposes. The article provides mathematical illustration for the algorithms used, which can be considered as supplementary information for SPSS help.
DEVELOPMENT OF THE CREATIVITY POTENTIAL OF THE STUDENT’S PERSONALITY

The purpose of this article is to show the increasing of the individual creativity potential on the base of the students’ integrative collaboration during the acquisition of the Building Constructions subject in professional university. The article deals with the issue of demand for highly-qualified, skillful and competitive specialists able to work in interdisciplinary team and organize collective work. Quantitative data analysis method was applied for analyzing the change in students’ creative abilities after the pedagogical experiment – adapted tests of creative abilities, which were developed by E. Torrens. Increase of the students’ creativity in the experimental class was deducted from the obtained data.
Detaching and Reproducting of Data in a Cloud for Excellent Performance and Security

Cloud computing is third party administrative control so our data is outsourced it gives rise to security concerns.security is one of the important aspect of any technology. Therefore high security measures are required to protect our data. In this paper, we propose Detaching and Reproducting of Data in a cloud for excellent performance and security that collectively approaches security issues and data sharing protectively. In this methodology, we divide the file into fragments and replicated over cloud nodes. Files are fragmented and shuffled like (1-2,2-3,3-4.4-1)in sequential order and stored in multiple servers. Moreover nodes sharing fragments are separated with certain distance by using T-colouring graph to prohibit an attacker by guessing the location of fragments. This methodology does not rely on traditional cryptography techniques.
LibGuides: Online Academic Reputation Management: Grad Student Workshop: ORCID

Part 3 in the OGS Becoming a Strategic Scholar Series. This class invites graduate students to map, connect and leverage the various platforms that can document, shape, and convey their respective academic reputations.