Reviewerly/exHarmony huggingface dataset:
Evaluation of Domain Adaptation Approaches for Robust Classification of Heterogeneous Biological Data Sets

Most machine learning algorithms require that training data are identically distributed to ensure effective learning. In biological studies, however, even small variations in the experimental setup can lead to substantial deviations. Domain adaptation offers tools to deal with this problem. It is particularly useful for cases where only a small amount of training data is available in the domain of interest, while a large amount of training data is available in a different, but relevant domain. We investigated to what extent domain adaptation was able to improve prediction accuracy for complex biological data. To that end, we used simulated data and time-lapse movies of differentiating blood stem cells in different cell cycle stages from multiple experiments and compared three commonly used domain adaptation approaches. EasyAdapt, a simple technique of structured pooling of related data sets, was able to improve accuracy when classifying the simulated data and cell cycle stages from microscopic images. Meanwhile, the technique proved robust to the potential negative impact on the classification accuracy that is common in other techniques that build models with heterogeneous data. Despite its implementation simplicity, EasyAdapt consistently produced more accurate predictions compared to conventional techniques. Domain adaptation is therefore able to substantially reduce the amount of work required to create a large amount of annotated training data in the domain of interest necessary whenever the domain changes even a little, which is common not only in biological experiments, but universally exists in almost all data collection routines.
On Error Exponents Under A Privacy-Preserving Voting Regime

Standard method of types and large deviations techniques are utilized to analyze the accuracy of an electronic voting mechanism, where to protect the privacy of the voters, votes are randomly changed by the system before being transmitted. Special attention is given to referendum elections with only YES and NO votes are on the ballot, and a scheme where votes are flipped with a fixed probability. It is shown that the exponent of the probability of incorrectly calling the election can be well-approximated to be proportional to the square of how far the vote is from 50%-50%. This result is then leveraged to understand the interplay between number of voters, the allowed probability of incorrectly calling the election, and the level of privacy.
Attending Over Triads for Learning Signed Network Embedding

Network embedding, which aims at learning distributed representations for nodes in networks, is a critical task with wide downstream applications. Most existing studies focus on networks with a single type of edges, whereas in many cases, the edges of networks can be derived from two opposite relationships, yielding signed networks. This paper studies network embedding for the signed network, and a novel approach called \textbf{TEA} is proposed. Similar to existing methods, \textbf{TEA} learns node representations by predicting the sign of each edge in the network. However, many existing methods only consider the local structural information (i.e., the representations of nodes in an edge) for prediction, which can be biased especially for sparse networks. By contrast, \textbf{TEA} seeks to leverage the high-order structures by drawing inspirations from the Social Balance Theory. More specifically, for an edge linking two nodes, \textbf{TEA} predicts the edge sign by treating the paths connecting the two nodes as features. Meanwhile, an attention mechanism is proposed, which assigns different weights to the paths and further weighted combines them for more precise prediction. We conduct experiments on several real-world signed networks, and the results prove the effectiveness of \textbf{TEA} over many strong baseline approaches.
FutureWare: Designing a Middleware for Anticipatory Mobile Computing

Ubiquitous computing is moving from context-awareness to context-prediction. In order to build truly anticipatory systems developers have to deal with many challenges, from multimodal sensing to modeling context from sensed data, and, when necessary, coordinating multiple predictive models across devices. Novel expressive programming interfaces and paradigms are needed for this new class of mobile and ubiquitous applications. In this paper we present FutureWare, a middleware for seamless development of mobile applications that rely on context prediction. FutureWare exposes an expressive API to lift the burden of mobile sensing, individual and group behavior modeling, and future context querying, from an application developer. We implement FutureWare as an Android library, and through a scenario-based testing and a demo app we show that it represents an efficient way of supporting anticipatory applications, reducing the necessary coding effort by two orders of magnitude.
Malaysian Budget Visualization Using Circle Packing

Data visualization is used to analyze the patterns and trends of data, including budget distributions, business analytics and so forth in a form of diagrams, charts, graphs and so on. In Malaysia, the budget is still in the form of a speech text and infographics. In the previous study, a treemap visualization technique is used to visualize the Malaysia budget, but it has resulted in data congestion as there are too many ministries and its programs in Malaysia. Besides, the visualization failed to compare previous and current budgets. Therefore, this study uses the circle packing technique to visualize Malaysia’s budget where it provides a more presentable and interactive way to explore the budget that can compare the current and previous budget on a single website. In order to construct this data visualization, it starts with the preparation of a JSON file to reorganize the budgets data and imported as an input file. Then, circle packing algorithm is used, which includes creating a new pack layout, followed by packing the root node by assigning coordinate x, y, and radius. Besides, it needs to pack the radius, and then set the size using two elements of an array. It follows by setting the padding and packing the array of siblings circles. Lastly, it encloses the circles to the packing. This algorithm is then integrated with JSON file and HTML to visualize it interactively on the web. This dynamic budget visualization webpage is a better option of exploration and it is beneficial to Malaysians as it helps the citizen in seeing a clearer picture of the distributions of their money. Furthermore, it is handy to understand the government future financial planning for Malaysia in an interactive way. This algorithm can be reusable to visualize any other financial data in a hierarchy structure.
Complementing Course Contents Between IT/CS

Information Technology (IT) and Computer Science (CS) are two well regarded computing programs that produce hundreds of thousands of graduates in each year to meet the diverse needs in the IT industry. Despite being treated as two distinct disciplines, IT and CS do share commonalities as with other computing disciplines (e.g., CE, SE, and IS). In this paper, we provide a detailed analysis on the commonalities and differences between IT and CS along with their respective teaching methodologies, aiming to identify opportunities to complement these two computing disciplines so that they can benefit from each other's unique advantages to better train future computing professionals. A case study on the database courses from both IT and CS helps reveal deeper insights on both the common and different aspects of these two disciplines. These insights are then leveraged to reach important recommendations for future computing curriculum development.
A GIS Open Source Software application for mapping solar energy resources in urban areas

This paper presents the development of an open source geographical information system (GIS) software module for mapping solar energy resources for urban areas. The main goal of this work is to demonstrate the potential use of the r.sun module, a component of GRASS software, in calculating real solar radiation for urban areas. Modelling of the spatial distribution of solar radiation is one of the program functions. The r.sun module is dedicated for that purpose; however, it can only generate the spatial distribution of potential solar radiation. To get the real solar radiation maps it is advisable to use meteorological data, that describe diminution of solar radiation caused by cloud cover. In order to facilitate the generation of maps a GRASS source code modification was made. As a result, the r.sun module used in this work generates the real spatial distribution of solar radiation. The results are shown to be comparable with solar radiation satellite data obtained from the HelioClim project.
Progressive Module Minimization for Re-encoding Transformed Soft Decoding of RS Codes

The interpolation based algebraic decoding for Reed-Solomon (RS) codes can correct errors beyond half of the code's minimum Hamming distance through constructing a minimum polynomial Q(x,y) and finding its y-roots. The progressive algebraic soft decoding (PASD) constructs Q(x, y) with a progressively enlarged y-degree and terminates once the message is decoded, adapting the decoding capability and computation to the channel. This paper proposes the re-encoding transformed PASD algorithm, in which Q(x, y) is progressively constructed by the low-complexity module minimization (MM) technique. Re-encoding transform (ReT) results in a common divisor for polynomials of the image of the submodule basis. It can be removed, leading to a simpler image expansion and reduction. Consequently, Q(x,y) is constructed through the isomorphic image of the progressively enlarged submodule basis. Our complexity analysis characterizes the complexity reduction brought by the transform and shows high rate codes benefit a greater complexity reduction.
Multi-criteria Informativeness Evaluation of Digital Images In Monitoring Systems

In this paper an analysis of known methods of image informativeness is carried out. It was found that in many cases existing methods are not working correctly, in particular, give an inflated evaluation, which is not consistent with human visual perception. A new method that allows us to evaluate the image informativeness more adequately the image informativeness is proposed. The adequacy of the results, obtained with the help of the proposed method was confirmed by a sociological survey of a group of 30 respondents who gave an expert evaluation of the informativeness of test images. As a result, it was proved experimentally that the proposed method gives a more correct evaluate of the image informativeness.
Bone Spect Image Segmentation Based on the Hermite Transform

Nuclear medicine image technology is currently an important modality for medical diagnosis whose applications have been extended for evaluation of a high number of human diseases. In this sense, SPECT (Single Photon Emission Computed Tomography) is one of the most used types of medical images in the field of nuclear medicine. In this work, we present a method for the segmentation of bone SPECT images. The proposed method is based on a region growing scheme implemented in the domain defined by the Hermite transform (HT) which consists of a mathematical tool that decomposes an image into a set of image coefficients representing a set of features. The method uses the coefficients obtained through the HT with the objective of designing a growing criterion that allows the segmentation of the needed region. The designed approach is focused on segmenting the bone structures in SPECT images. We evaluated the proposed scheme using several bone SPECT images. Different metrics have been also used for performance assessment.
Research on Talent Cultivation Mode of Computer Between China and America

In the context of rapid development of science and technology today, cultivation of innovative computer talent is particularly important in today’s information age. The talent cultivation mode of computer between China and America were researched by the international education method of “going out and bringing in”. In the three aspects of teaching materials, operating system and experimental teaching, this paper expounds the differences of the talent cultivation of computer between China and America, discusses and researches these differences.
“Saksham Model” Performance Improvisation Using Node Capability Evaluation in Apache Hadoop

Big Data processing and analysis requires tremendous processing capability. Distributed computing brings many commodity systems under the common platform to answer the need for Big Data processing and analysis. Apache Hadoop is the most suitable set of tools for Big Data storage, processing, and analysis. But Hadoop found to be inefficient when it comes to heterogeneous set computers which have different processing capabilities. In this research, we propose the Saksham model which optimizes the processing time by efficient use of node processing capability and file management. The proposed model shows the performance improvement for Big Data processing. To achieve better performance, Saksham model uses two vital aspects of heterogeneous distributed computing: Effective block rearrangement policy and use of node processing capability. The results demonstrate that the proposed model successfully achieves better job execution time and improves data locality.
A Recursive Information Flow Gated Model for RST-Style Text-Level Discourse Parsing

Text-level discourse parsing is notoriously difficult due to the long-distance dependency over the document and the deep hierarchical structure of the discourse. In this paper, we attempt to model the representation of a document recursively via shift-reduce operations. Intuitively, humans tend to understand macro and micro texts from different perspectives, so we propose a recursive model to fuse multiple information flows and strengthen the representation of text spans. During parsing, the proposed model can synthetically grade each information flow according to the granularity of the text. Experimentation on the RST-DT corpus shows that our parser can outperform the state-of-the-art in nuclearity detection under stringent discourse parsing evaluations.
Gamma-ray log correlation and interpretation of Fitton’s and Casey’s stratigraphy with offshore borehole 99/16-1

The Chale Bay type section of the Lower Greensand Group (LGS) Atherfield Clay, Ferruginous Sands and Sandrock formations is characterised by members, bounded by hiatuses, formerly recognised (if not named as such) by Fitton as his ‘groups’ and Casey in defining ammonite zone and sub-zone boundaries. Subsequent work has shown that some of the formations of the LGS are recognised elsewhere on the Isle of Wight, in southern England and in northern France. Likewise, some members of the Atherfield Clay and Ferruginous Sands formations can be recognised beyond the Chale Bay type section. In this work, we have measured outcrop gamma-ray emission from the type section: gamma-ray peaks recognised on all datasets are used to attempt a correlation of the Lower Greensand Group between the Isle of Wight and an offshore borehole (99/16-1). This is partially successful, showing that Fitton and Casey’s stratigraphy maybe recognised basin-wide.
SMDP-Based Resource Allocation for Slice Requests with Long-term Reward Maximization

Network slicing has emerged as a key technology to support the coexistence of multi-service in the the 5G/B5G networks. However, due to the resource scarcity and the diversity of slice requests, how to allocate resources efficiently to maximize the long-term reward of the infrastructure network is a challenging issue. In this paper, we model the resource allocation problem as a semi-Markov decision process (SMDP), which is defined by state space, action space, reward and transition probability distribution. The reward function jointly considers the total income, the cost of available resource and the utilization of the total resource for the infrastructure network. Not focusing on one step decisions reward, we apply the Bellman equation to obtain long-term reward by accumulating. Then we exploit value iteration algorithm to determine the resource allocation scheme according to a certain state in such a way that the long-term reward can be maximized. Extensive simulation results show that the proposed SMDP can achieve a superior performance compared with the existing heuristic methods.
“I Think It Might Help If We Multiply, and Not Add”: Detecting Indirectness in Conversation

The strategic use of indirect language is crucial in business negotiations, doctor-patient interactions, instructional discourse, and multiple other contextss. Goel, Pranav Being indirect allows interlocutors to diminish the potential threats to their interlocutors’ desired self-image—or, face threat—that may arise by being overtly direct. Handling indirectness well is important for spoken dialogue systems, as being either too indirect or too direct at the wrong time could harm the agent-user relationship. Matsuyama, Yoichi We take the first step towards handling users’ indirection by exploring different supervised machine learning approaches for the task of automatically detecting indirectness in conversations. Madaio, Michael Accurate automated detection of indirectness may help conversational agents better understand their users’ intents, gauge the current relationship with the user in order to appropriately plan a response, and inform the strategic use of indirectness to manage the task goals and social goals of the interaction. Cassell, Justine To our knowledge we are the first to use a multi-modal approach to detecting indirect language: we rely on both verbal and nonverbal features of the interaction. Our best model acheives a 62% F1 score on our dataset, outperforming non-neural baselines including approaches used by past work for related tasks such as uncertainty detection.
Automatic Monitoring System of Blasting Vibration and Its Application Based on TC6850

To meet the current needs of urban blasting engineering automatic monitoring, after studying and analyzing various existing blasting automatic-monitoring instruments and systems, we designed an Automatic Monitoring System of Blasting Vibration (AMSBV) based on a TC6850 network blasting-vibration measuring instrument. The system is composed mainly of a data acquisition and transmission system, data center (server), client, risk management platform, and other modules. The core instrument, a TC6850 network blasting-vibration meter, has the characteristics of a highly integrated and free network. At present, it has been applied successfully to Qingdao Metro Line 4. We achieved the functions of real-time transmission, automatic calculation, intelligent analysis of blasting-vibration monitoring data, blasting-related parameters, automatic alarm, etc. This system has automatic alarm and alarm analysis and processing functions, which provides convenience for automation, informatization, intelligence, and scientific guidance construction of blasting engineering monitoring.
Diffusion and adoption of research data management services

Purpose This paper aims to identify the diffusion patterns, especially the communication channels, in the diffusion and adoption of research data management services (RDMS) among libraries. Design/methodology/approach Literature about the RDMS in individual libraries was gathered and analyzed. Data relevant to the research questions were extracted and analyzed. Findings Early adopters conduct much original research to create RDMS and they often serve as change agents in diffusing their RDMS and related innovations to other libraries. In contrast, late adopters usually learn from early adopters and use their innovations for establishing their own RDMS. Communication channels used in diffusing RDMS deviate slightly from those reported in general diffusion of innovations (DOI) theories. Research limitations/implications Gathered literature provides incomplete and uneven information for RDMS adopters. This makes it difficult to identify adopter categories and test many generalizations in DOI theories. To overcome these limitations, surveys and interviews will be conducted in the future. Originality/value Findings from this project contribute to general DOI theories because RDMS is unique compared with many other innovations. The diffusion of RDMS is a decentralized process that involves a high-degree of reinvention and it involves the generation and diffusion of many relevant innovations. The project also identified scholarly communication and inter-organization networks as new types of communication channels that are not well accounted for in existing DOI theories.
AR Chemistry: An Undergraduate, Technology-Based Research and Development Initiative To Incorporate AR Molecular Models in the Chemistry Curriculum

Chemistry is much less abstract and much easier to learn thanks to molecular models. The Augmented Reality (AR) Chemistry Project is an interdisciplinary, undergraduate research experience aimed at developing technology that integrates AR-based molecular models into the chemistry curriculum. A summary of the technology developed as a result of this research experience, and the impact the research experience has had on undergraduate researchers, is presented.
The Riddle of Togelby

At the 2017 Artificial and Computational Intelligence in Games meeting at Dagstuhl, Julian Togelius asked how to make spaces where every way of filling in the details yielded a good game. This study examines the possibility of enriching search spaces so that they contain very high rates of interesting objects, specifically game elements. While we do not answer the full challenge of finding good games throughout the space, this study highlights a number of potential avenues. These include naturally rich spaces, a simple technique for modifying a representation to search only rich parts of a larger search space, and representations that are highly expressive and so exhibit highly restricted and consequently enriched search spaces. We treat the creation of plausible road systems, useful graphics, highly expressive room placement for maps, generation of cavern-like maps, and combinatorial puzzle spaces.
A Case Study on the Establishment of a National Quality Infrastructure in Korea

Standards are involved in the series of actions of individuals and groups such as governments and markets with different interests and purposes. Standards are created through social and scientific coordination and are used at the national and international level. This study examines the standard governance of Korea, which is structured and implemented around three important elements of national quality infrastructure: standardization, conformity assessment, and metrology, from the legal and organizational perspectives. Article 127 (2) of the Constitution of the Republic of Korea states “the State shall establish a system of national standards” and the Framework Act on National Standards and the Enforcement Decree enacted in 1999 for the establishment and implementation of national quality infrastructure. As three pillars of national quality infrastructure, standardization, conformity assessment, and metrology are interrelated and indispensable for each other to provide a consistent and reliable social as well as scientific and technical grounds for development and implementation of national standards. Drawing on the Korean case of national standards in respect to earthquake risk management, this study examines the problems of imbalance and disconnection of these quality management elements. In conclusion, future plans for national quality assurance are suggested from the perspective of a network governance.