Reviewerly/exHarmony huggingface dataset:
Measuring affective states from technical debt: A psychoempirical software engineering experiment

Software engineering is a human activity. Despite this, human aspects are under-represented in technical debt research, perhaps because they are challenging to valorize. This study's objective was to investigate the relationship between technical debt and affective states (feelings, emotions, and moods) from software practitioners. Forty participants from twelve companies took part in a mixed-methods design, consisting of a repeated-measures experiment, a survey employing a questionnaire, and semi-structured interviews. The statistical analysis shows that different design smells negatively or positively impact affective states. From the qualitative data, it is clear that technical debt activates a substantial portion of the emotional room and is psychologically taxing. Further, reactions to technical debt appear to fall in different levels of maturity. We argue that human aspects in software engineering are an essential factor to consider, as it can result in, e.g., procrastination, apprehension, and burnout.
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems

Dialogue policy learning for task-oriented dialogue systems has enjoyed great progress recently mostly through employing reinforcement learning methods. However, these approaches have become very sophisticated. It is time to re-evaluate it. Are we really making progress developing dialogue agents only based on reinforcement learning? We demonstrate how (1)~traditional supervised learning together with (2)~a simulator-free adversarial learning method can be used to achieve performance comparable to state-of-the-art RL-based methods. First, we introduce a simple dialogue action decoder to predict the appropriate actions. Then, the traditional multi-label classification solution for dialogue policy learning is extended by adding dense layers to improve the dialogue agent performance. Finally, we employ the Gumbel-Softmax estimator to alternatively train the dialogue agent and the dialogue reward model without using reinforcement learning. Based on our extensive experimentation, we can conclude the proposed methods can achieve more stable and higher performance with fewer efforts, such as the domain knowledge required to design a user simulator and the intractable parameter tuning in reinforcement learning. Our main goal is not to beat reinforcement learning with supervised learning, but to demonstrate the value of rethinking the role of reinforcement learning and supervised learning in optimizing task-oriented dialogue systems.
Pemanfaatan Quantum GIS Cloud Untuk Pemetaan Polygon Area Kandang Peternakan di Wilayah Kabupaten Probolinggo

Department of Husbandry and Animal Health (Disnakkeswan) Probolinggo, is a government agency that has data on livestock, one of which is the location mapping data for the stables. This data is needed by Disnakkeswan and the general public to find information on the location of the livestock sheds in the Probolinggo district. Currently, the data on the location of the cage which is owned by the agency is still in the form of excel table data. This creates difficulties for the Animal Husbandry and Animal Health Service (Disnakkeswan) and the general public when looking for information on the location of the livestock pen. Among other things, the difficulty made them have to waste time finding the location of the farm stables. Given the roles and responsibilities of the Department of Animal Husbandry and Animal Health (Disnakkeswan) Probolinggo Regency above, an application is needed that will support or facilitate this task. The information presented in the Geographical Information System mapping the location of livestock stalls is the location of the co-ordinates for beef cattle, dairy and ducks. which can provide information about the location (stables) of the farm in the form of a map using a geographic information system with QGis Cloud.
TEKNIK DEKLAMASI: SEBUAH STRATEGI PENGAJARAN SASTRA BERBASIS AKTIVITAS

Child literature instruction is an effort to develop child creativity toward values contained in lietarature; poetry. One of child creativities is reading poetry through deklamasi. Deklamasi is reading poetry with a rythem meaning that reading with rythem based on appreciation toward poetry read. Declamasi teachnique is one of activity-based instruction literature strategies. Through dekalmasi, a child can show self-expression presented in string of word by word, so the child creativity can develop earlier. Key Words : Declamasi, The teaching of Literature-based activities.
Clustering-based Unsupervised Generative Relation Extraction

This paper focuses on the problem of unsupervised relation extraction. Existing probabilistic generative model-based relation extraction methods work by extracting sentence features and using these features as inputs to train a generative model. This model is then used to cluster similar relations. However, these methods do not consider correlations between sentences with the same entity pair during training, which can negatively impact model performance. To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an "Encoder-Decoder" architecture to perform self-supervised learning so the encoder can extract relation information. Given multiple sentences with the same entity pair as inputs, self-supervised learning is deployed by predicting the shortest path between entity pairs on the dependency graph of one of the sentences. After that, we extract the relation information using the well-trained encoder. Then, entity pairs that share the same relation are clustered based on their corresponding relation information. Each cluster is labeled with a few words based on the words in the shortest paths corresponding to the entity pairs in each cluster. These cluster labels also describe the meaning of these relation clusters. We compare the triplets extracted by our proposed framework (CURE) and baseline methods with a ground-truth Knowledge Base. Experimental results show that our model performs better than state-of-the-art models on both New York Times (NYT) and United Nations Parallel Corpus (UNPC) standard datasets.
A scalable and secure model for surveillance cameras in resource constrained IoT systems

In wireless multimedia surveillance networks (WMSNs), the sensors generate continuous data streams which are saved on cloud servers for processing and future use. Large-scale security and monitoring decisions are based on the video data fed back to the cloud by the visual sensors. Internet of Things (IoT) networks are deployed in certain resource constrained scenarios where edge computing is not available for data analytics and security considering the IoT devices have also limited resources such as memory, power and processing capabilities. In this paper, the problems of limited resources and data security are addressed by the proposal of a secure model for video surveillance systems working in resource constrained IoT assisted scenarios. The suggested approach comprises of the following four main stages: i) save only those video frames in which any activity is detected, ii) encrypt the saved frames for secure transmission, iii) synchronize the encrypted frames between cloud and sensor node and iv) remove the transmitted frames from sensor and decrypt the stored data on cloud. The performance of the encryption process for resource constrained devices is analyzed on different types of sensor nodes during experimentation. The results prove that the proposed method automates and speeds up the process of live video data extraction and occupies less space on cloud when compared to the conventional approach for saving surveillance videos. Furthermore, adding encryption to video frames ensures integrity of video data during their journey from sensor to the cloud.
LibGuides: ARTHIST 480 Winslow Homer in Context: Resources for Remote Research

Explore the art and life of Winslow Homer (1836–1910) and gain expertise in research, documentation, analysis, criticism, expository writing, and citation.
Hands on geochemistry as a means to promote equality, diversity, and inclusion in geoscience

&amp;lt;p&amp;gt;Geochemistry has become central to geoscience research; yet, many students have no access to geochemical laboratories because such labs are expensive to build, maintain, and run. Sophisticated geochemical and isotopic techniques require specialized training and technical support. As a result, most of these lab tend to exist only at well-funded, large, graduate-focused institutions. All of these factors cause isotope techniques to be available to only a small subset of the student and faculty population &amp;amp;#8211; often white, well off and male.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Since 2018, our cosmogenic nuclide clean laboratory at the University of Vermont has been supported by the US National Science Foundation as a community facility for cosmogenic nuclide sample preparation; our primary goal is to increase access to such specialized techniques isotopic techniques. During our first year under NSF funding, we hosted 36 individual users and several group tours. Visitors came for weeks to months at a time to process their own samples and learn laboratory methods. Our visitors included 12 faculty members, 3 professionals, 13 graduate students, and 8 undergraduate students; they represented 27 different institutions across 16 American states and 4 other countries. We have sought to optimize safety and laboratory training procedures, enabling us to host visitors regardless of their previous experience working in a laboratory setting. We work with visitors collaboratively and are involved in their projects from inception to publication, thereby including researchers who have had no experience with isotopes, laboratory science, or geochronology. The diversity of our visitors far exceeds that of US Geoscience as a whole and includes many woman and demographics currently under-represented in Geoscience faculty ranks.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;However, challenges to facilitating a diverse community still exist. Although all training and mentoring costs are covered by the US NSF, visitors to the Community Cosmogenic Facility pay a per-sample fee to cover consumables, pay for AMS analyses, and cover their own travel, all of which restrict access. Certain interested users come from countries for which obtaining a US visa is challenging or impossible; similarly, users may not be able to travel to Vermont for financial or personal reasons. We are exploring additional possibilities for awarding our own internal grants in order to be more inclusive, and also seek to develop robust online content so that collaborators can learn from afar. Increasing diversity, inclusivity, and access in isotopic techniques is a work in progress, which we will continue address in the coming years.&amp;lt;/p&amp;gt;&amp;lt;div&amp;gt; &amp;lt;div&amp;gt; &amp;lt;div&amp;gt;&amp;amp;#160;&amp;lt;/div&amp;gt; &amp;lt;/div&amp;gt; &amp;lt;/div&amp;gt;
THE CORRELATION BETWEEN STUDENTS’ INTERPERSONAL AND INTRAPERSONAL INTELLIGENCES AND THEIR ENGLISH ACHIEVEMENT AT SMAN 2 KENDARI

The purpose of this study was to investigate the correlation between students’ interpersonal and intrapersonal intelligences and their English achievement. The design of this study was correlation design, and the sample of this study was 90 students which is consists of 140 populations. The sampling was taken by simple random sampling. Moreover, the instruments of this study were interpersonal and intrapersonal intelligences. Based on the result of data computation, it showed that coefficient correlation of students’ interpersonal with (r= -.214).), indicates a low correlation, while students intrapersonal has the high correlation with (r=0.071). The result also shown that the significant value of each students’ interpersonal and intrapersonal intelligences had a significant correlation with their English achievement because its probability value (sig.2 tailed) 0.040 and 0.000 were higher than 0.05. so H1 is definitely accepted and H0 is totally rejected.
LibGuides: Carl Joseph Collection & Reading Commons: Home

This guide provides an overview of the the Carl Joseph Collection and Reading Commons on the fifth floor of the Carlson Library.
A Fuzzy Logic Controller for Tasks Scheduling Using Unreliable Cloud Resources

The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you-go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the quality of solutions delivered by scheduling algorithms. In this paper, we present a dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS algorithm) for the management of scientific workflows in Cloud. Our model minimizes both the makespan and the monetary cost by dynamically selecting reliable or unreliable virtualized resources. For covering the uncertainty in decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing model of the resources that will host every task. We evaluate the performance of the proposed algorithm using real workflow applications being tested under the assumption of different probabilities regarding the revocation of unreliable resources. Numerical results depict the performance of the proposed approach and a comparative assessment reveals the position of the paper in the relevant literature.
An Anti-collision Early Warning System for Mine Trucks Based on RBF Network and WIFI

Abstract Heavy-duty trucks in open-pit mines are of huge size and blind areas, so it is difficult for drivers to see other vehicles around the blind areas. In addition, due to the dullness of transportation in mines, drivers are prone to distraction and other phenomena, so collisions between vehicles occur from time to time. In existing technologies, such as radar and infrared ranging, it is difficult to detect vehicles on the other side of the bend at the bend, and it is vulnerable to dust and weather, resulting in false alarm. Aiming at the above problems, a collision prevention and warning scheme for heavy truck in open pit mine based on RBF network and WIFI is proposed. That is to say, a WIFI ranging module is installed in the middle of each mining truck. When the distance between the two trucks is less than the defined range, an early warning signal will be sent, indicating that there are other vehicles near the driver and paying attention to driving safety. The measurement error of WIFI ranging is easy to fluctuate in a long distance, but WIFI ranging has the advantages of long measurement distance, not easily affected by weather and dust, low cost and so on. For the system modeling, the author went to Deerni Copper Mine to collect data, such as the main test parameters: ranging, signal intensity and so on. The model between WIFI signal intensity and distance is constructed. The results show that the system has better measurement accuracy, and thus realizes the early warning function. It realizes the automatic collection and identification of wireless information when vehicles approach, which is of great significance for reducing and eliminating the collision accidents of open-pit transport vehicles and improving the level of safety management.
What Key Aspects Do ICOs Reveal About Their Businesses?

Abstract Blockchain technologies disrupt industries by enabling decentralized and transactional data sharing across a network of untrusted participants, among others. Initial Coin Offerings (ICOs) are a novel form of crowdfunding through which hundreds of blockchain-enabled businesses manage to raise billions of dollars in total only in United States. However, there is a lack of understanding of the ICO phenomenon especially related to the business aspects. In this paper, we describe the results of an exploratory study of 91 ICOs and identify the key business model elements that ICOs reveal in their websites and whitepapers. Furthermore, we also note the immaturity and lack of transparency of the business aspects of businesses behind the ICO campaigns.
Subject Guides: Submitting Electronic Theses and Dissertations (ETDs): Copyright and Your Dissertation

This guide provides guidance for American University students who are required to submit their theses or dissertations electronically. IMPORTANT NOTE: links to AU's official ETD Submission and Style Guide and templates are located under the HOME tab below
DATA STEGANOGRAPHY USING EMBEDDED PRIVATE KEY

LSB2 method of data steganography is one of the most popular methods used to hide secret messages in digital color image. This method keeps the quality of the holding image high but it is not secure and it can be easily hacked. In this paper a method of improving the security of LSB2 method will be proposed, tested and implemented. The added security issues are simple and do require extra memory and time for execution. An embedded key will be extracted from the holding image to encrypt the message, this key will be variable and depends on the selected covering image, selected message length and selected position in the image where to extract the embedded key; the selected position and message length will form a private key to enhance LSB2 security.
A Local-Global Aggregate Network for Facial Landmark Localization

Facial landmark localization plays a critical role in many face analysis tasks. In this paper, we present a novel local-global aggregate network (LGA-Net) for robust facial landmark localization of faces in the wild. The network consists of two convolutional neural network levels which aggregate local and global information for better prediction accuracy and robustness. Experimental results show our method overcomes typical problems of cascaded networks and outperforms state-of-the-art methods on the 300-W [1] benchmark.
TRAJECTORY FOLLOWING SYSTEM FOR AUTONOMOUS CAR

Trajectory following algorithm for autonomous car model with feedback by stereo-camera is presented. Main subsystems of autonomous car control system are described.
Causal Discovery as a novel approach for CMIP6 climate model evaluation

&amp;lt;p&amp;gt;Causal discovery algorithms are machine learning methods that estimate the dependencies between different variables. One of these algorithms, the recently developed PCMCI algorithm (Runge et al., 2019) estimates the time-lagged causal dependency structures from multiple time series and is adapted to common properties of Earth System time series data. The PCMCI algorithm has already been successfully applied in climate science to reveal known interaction pathways between Earth regions commonly referred to as teleconnections, and to explore new teleconnections (Kretschmer et al., 2017). One recent study used this method to evaluate models participating in the Coupled Model Intercomparison Project Phase 5&amp;amp;#160; (CMIP5) (Nowack et al., 2019).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we build on the Nowack et al. study and use PCMCI on dimension-reduced meteorological reanalysis data and the CMIP6 ensembles data. The resulting causal networks represent teleconnections (causal links) in each of the CMIP6 climate models. The models&amp;amp;#8217; performance in representing realistic teleconnections is then assessed by comparing the causal networks of the individual CMIP6 models to the one obtained from meteorological reanalysis. We show that causal discovery is a promising and novel approach that complements existing model evaluation approaches.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;References:&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Runge, J., P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996, 2019.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Kretschmer, M., J. Runge, and D. Coumou, Early prediction of extreme stratospheric polar vortex states based on causal precursors, Geophysical Research Letters, doi:10.1002/2017GL074696, 2017.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Nowack, P. J., J. Runge, V. Eyring, and J. D. Haigh, Causal networks for climate model evaluation and constrained projections, in review, 2019.&amp;lt;/p&amp;gt;
FACTORS OF EDUCATION QUALITY ENSURING

The growth of information flows in different spheres of life, as well as the decay of some professions and emerging of new ones create new, rapid and, therefore, not always successful transformational processes in education. The problem of ensuring the quality of education has always been and remains topical. This has determined the purpose of the present study. The article analyzes the factors of education quality ensuring, the potential for activity components development of scientific-pedagogical employees at higher education institutions (HEI). The result of interaction between the teacher and the degree applicant in the HEI depends not only on the lecturer and their professional competence, but also on the applicant and formation of their key educational competences. The factors of effective interaction between the parties to educational process through the prism of their educational activity have been determined. The problems in studying the quality of educational activities have been stated. The factors determining the interaction nature of the parties to educational activity have been described.
Modified triple assessment in the diagnosis of breast lump in Saurashtra region of Gujarat

Background: Breast lump is a common complaint of women presenting to surgeons. Most of them are benign, careful evaluation, exact diagnosis and definitive treatment is mandatory to rule out cancer. The diagnosis of breast cancer is suggested on clinical examination. Currently a combination of three tests, i.e. clinical examination, radiological imaging (mammography, ultrasonography) and pathology called as triple assessment test is used to accurately diagnose all palpable breast lumps. Together they give sensitivity of 100%. The triple assessment is taken as positive if any of the three components, two are positive or positive report of FNAC and negative only if all of its components are negative for malignancy.Methods: This study was conducted in the Department of Surgery, P.D.U Medical College, Rajkot, Gujarat over a period of 2 years. A total of 100 patients with a breast lump were selected more than 15 years of age. A detailed history, focused clinical examination, radiological imaging and FNAC were used as diagnostic tools for screening of the patients. The aim of this study was to evaluate accuracy of modified triple assessment in the preoperative diagnosis of patients with breast carcinoma.Results: Results shows sensitivity and specificity of all the modalities used in triple assessment when combined together was 100% and 99.3%, respectively. Positive predictive value was 93.3%, negative predictive value was 100%, p-value was highly significant (p&lt;0.0001). We conclude that modified triple assessment is a very useful diagnostic tool to evaluate patients with breast lumps and an overall accuracy of 98%.Conclusions: Thus, triple assessment is an easily available, cost effective, least invasive, rapid and patient compliant diagnostic tool for diagnosis of breast lump.