Reviewerly/exHarmony huggingface dataset:
A Neural Network Model for a Tutoring Companion Supporting Students in a Programming with Java Course

With large class sizes and instructors who may not be equipped to assist struggling students, many students abandon the field, deeming it to be too difficult and not for them. Consistent, constructive, supportive feedback through a Tutoring Companion can scaffold the learning process for students. This poster describes a reasoning model, using neural networks techniques, for a tutoring companion embedded into the Eclipse IDE. The companion provides support for students in a first-year university Java programming course. The companion collects data from students' events and programming assignments, analyzes it for relevant trends, and estimates each student's situation. The input data for the neural network comes from areas with which beginning computer science students often struggle, such as the presence of important keywords and the amount of time spent in a state with errors. Then, it determines the feedback to be provided for students to overcome a detected challenging situation, providing both hints on how to fix the problem with the code, as well as encouragement to help keep students motivated and learning. The effectiveness of the approach is examined among first-year computer science students through the completion of recursion and control flow programming assignments. The students complete surveys regarding their learning experience to assist in evaluating the companion's pedagogical effectiveness, which is discussed with an emphasis on the value of feedback provided.
A Study of Observation Scales Based on Felzenswalb-Huttenlocher Dissimilarity Measure for Hierarchical Segmentation

Hierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method.
Design and Fabrication of Lever Operated Wheelchair for Disabled Person with No Legs

The design and fabrication of lever operated wheelchair is can be renovated.There are many numbers of people who are suffering from disabilities caused by an accidental injury.Various methods are used for developing manual wheelchair in order to help those people.HMI, HCL and Accelerometer in the wheelchair are some of them used by researchers.These techniques modify the existing design but there are many individuals who cannot manage to pay for advanced model.So, in this deign we are replacing the chain drive mechanism by slider crank mechanism for conveying the force to give motion to wheelchair.The wheelchair is designed in such a way that it requires a less effort by an operator to operate and it is economically affordable as compared to other advanced wheelchair.
Modeling and deploying hybrid tenant requests with shared networklets

In recent years, IaaS providers are trying to cater to application layer development, PaaS providers are exposing more infrastructure APIs to customers, and SaaS providers start to allow tenants to build their apps within their software. All of these indicate that, the popular wisdom that cloud computing comes in three flavors—IaaS, PaaS, and SaaS—no longer describes reality. The lines between IaaS, PaaS, and SaaS are becoming blurred as datacenter providers seek to create cloud platforms that can satisfy the needs of enterprises and widen their appeal to developers. In hybrid datacenters, resource requests from tenants are increasingly transforming into hybrid requests that may simultaneously demand IaaS, PaaS, and SaaS resources. This paper tackles the challenge of modeling and deploying hybrid tenant resource requests in datacenter networks, for which we coin “networklet” to represent a set of VMs that collaboratively provide some PaaS or SaaS service. Through extracting networklets from tenant requests and thus sharing them between multiple tenants, we can achieve a win-win situation for datacenter providers and tenants. Extensive evaluations show that, the proposed model and deployment algorithm indeed reduce the completion time of tenant requests while maintaining performance guarantee.
Reply to Lucas &amp; Henneberg: Are human faces unique?

This paper offers a response to the 2015 article of Lucas and Henneberg “Are human faces unique?” (Forensic Sci. Int. 257, 514.e1–514.e6), as well as to the subsequent popularization thereof. In the first part, we assess the probabilistic claims made by the authors and find crucial parts to be unsupported. In particular, the authors offer no probabilistic model on which to base their conclusions. Even if we disregard the errors identified in the first part, we find a troubling discrepancy between the published findings and the popular summary.
Relating Legal Entities via Open Information Extraction

Concepts and relations within existing ontologies usually represent limited subjective and application-oriented views of a domain of interest. However, reusing resources and fine-grained conceptualizations is often challenging and requires significant manual efforts of adaptation to fit with unprecedented usages. In this paper, we present a system that makes use of recent Open Information Extraction technologies to unravel and explore corpus-centered unknown relations in the legal domain.
Сomparative Analysis of Modern Teacher’s Clothing Design on the Example of Foreign Countries’ Teachers

Addresses the problem of modern trends in the creation of the teacher's clothes as a means of achieving professional and personal success. The importance of the appearance and clothes for the pedagogical profession is substantiated. The issue of the development of modern clothing designer's design is also discussed. The comparative analysis of the image teacher modern with teachers from foreign countries is given.
Digital Education: from hype and disappointment to change

Powerful and sophisticated ICT is part of everyday life and the world of learning is not an exception. E-learning, open and distance education have been important fields of intellectual excitement and innovative development. The demand for people with new, enhanced skills is growing. Pressure is on all players of the online education community to keep up with new learning, and supply the skills demanded by growing economies.The challenges posed by the new technologies are permanent whilst the role of human, socio-cultural-economic themes are more and more at the cutting edge. Technology is with us everywhere which validates the horizontal-holistic approach for imperative questions of the period. For the transforming education landscape, challenges come increasingly from the structural and policy fields. Social and economic tensions raise the issues of scalability and micro-credentialing. Practitioners are seeking right approaches to provide learning opportunities. Micro, meso and macro aspects open new lenses for considering the problems. Digital credentials and open badges are the new currencies, starting to transform business models in education.The educational framing, from policy level down to the actual learning scenario, allows for various types and variations of of ICT enhanced, collaborative, open, distance education and e-learning. Finding and applying the right mix of information, knowledge and creativity is of primary importance for the educational experience, to bring together the strengths of the past with the challenges of the present and opportunities for the future.The European Union initiatives emphasize solutions to emerging needs, seek to improve competitiveness and professional development; enhance cross-sectoral skills and fuel the engines of social innovation – creativity, entrepreneurship, critical thinking and problem solving.
Rancang Bangun Aplikasi E-register International Conference Berbasis Online Pada Universitas Lancang Kuning

Computer-based information systems are very influential in the world of work, as well as web-based information systems are used as a means of information enhancement. Utilization will facilitate a job as well as faster data processing, the decision to be taken more precisely, saving time and cost. Because the needs of the yellowish university to realize the vision and mission must be in line with the activities undertaken, one of them by producing a paper or a scientific paper lecturer who indexed such as scopus and thompson, so the yellowish university held an international Conference held in cooperation with other universities, both domestic and foreign, this requires a media or information that can be accessed by lecturers in the country and abroad in terms of registration of scientific work to be on international Conference activities with online-based. In the international Conference that will be held university of lancang kuning no media that can be used as a means to promote, because this is considered very important for the achievement of the purpose of international Conference activities to be implemented. The method used by collecting existing data and analyzing problems in the collection and adjustment of Paper International Conference. The general purpose of this research is to build an E-register paper application to make it easier for users to register papers for seminars at youthful university.
ANALISIS PENERAPAN AKUNTANSI KEPERILAKUAN TERHADAP MAHASISWA AKUNTANSI STIE SAKTI ALAM KERINCI

&lt;p&gt;&lt;em&gt;This research was conducted to population of this researchis the college student study program Accounting in Sakti Alam Kerinci School of Economic. In this study the researchers took a sample of 98 students Accounting as respondents in this study analysis tool used is Descriptive Analysis intends to describe the characteristics of each research variable that is the achievement of respondents' answers (TCR) and also use Utility Theory and Contingency Theory to see the application of Behavior Accounting in Accounting Student Sakti Alam Kerinci School of Economic.&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;Based on the result of Descriptive Analysis, there are achievement of respondent's answer (TCR) that is on Utility theory of decision making variables with highest percentage of 78%, Knowledge Mastery with 77.25%, Information Of All Unconscious Events 76%, while contingency theory on Framework Waterhaouse and Tiessen75.25% work, Gordon and Miller77.25% Framework, Macintosh and Daft72.75% Framework, 80.5% Macintosh Framework, and Ewusi Framework Mensah79.75%,. It can be explained that the application of behavioral accounting in view of Utility Theory and Contingency Theory at value is good to apply with an average TCR of 75,58%.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;Penelitian ini dilakukan terhadap mahasiswa program studi mahasiswa Akuntansi di STIE Sakti Alam Kerinci dimana sampel penelitian ini sebanyak 98. Penelitian ini menggunakan Analisis Deskriptif yang bermaksud untuk mendeskripsikan karakteristik masing-masing variabel penelitian yaitu pencapaian jawaban responden (TCR) dan juga menggunakan Teori Utilitas dan Teori Kontijensi untuk melihat bagaiman penerapan Perilaku Akuntansi pada Mahasiswa Akuntansi STIE Sakti Alam Kerinci.Berdasarkan hasil Analisis Deskriptif, terdapat jawaban responden (TCR) yaitu pada Teori Utilitas variabel pengambilan keputusan dengan persentase tertinggi 78%, Penguasaan Atas Pengetahuan 77,25%, Informasi Dari Semua Peristiwa Yang Tidak Diamati 76%, sedangkan teori kontingensi pada Kerangka kerja Waterhaouse dan Tiessen75.25%, Kerangka Gordon dan Miller 77.25%, Kerangka Macintosh dan Daft 72.75%, Kerangka Macintosh 80.5%, dan Kerangka Ewusi Mensah 79.75%. Dapat dijelaskan bahwa penerapan akuntansi perilaku dalam pandangan Teori Utilitas dan Teori Kontingensi berada pada nilai Baik dengan rata-rata TCR sebesar 75,58%.&lt;/p&gt;
Current Dilemma and Countermeasure of College-Enterprise Cooperation in Higher Vocational Education

Higher vocational education is an important part of China's education system. Higher vocational colleges are the carrier of Higher Vocational education and college-enterprise cooperation is an important way to improve the quality of higher vocational education and provides an important platform for the realization of the goal of training technical applied talents in Higher Vocational education. The depth and breadth of school-enterprise cooperation have an important impact on the effectiveness of Higher Vocational education. Therefore, educators should carefully analyze the current dilemma of college-enterprise cooperation, and put forward effective countermeasures to help improve the effectiveness of Higher Vocational education. General Secretary Xi Jinping emphasized in the report of the Nineteenth National Congress of the Communist Party of China: "Perfecting the vocational education and training system, deepening the integration of industry and education, college-enterprise cooperation." In March 2018, the Ministry of Education, the National Development and Reform Commission, the Ministry of Finance and other six departments jointly issued the Promotion Measures of College-Enterprise Cooperation in Vocational Schools, which clearly pointed out: "The integration of industry and education, College-enterprise cooperation is the basic mode of Vocational education and the key to running vocational education well." It can be seen that the relevant departments of the state attach great importance to promoting the integration of industry and education and promoting college-enterprise cooperation, which provides policy and theoretical support for the extensive development of college-enterprise cooperation in Higher Vocational education.
Is Bitcoin a Currency or an Investment? An IFRS View

The rise of cryptocurrencies raises also question of how to account for them. The paper focuses on two common situations when firms deal with cryptocurrencies—receiving cryptocurrencies as a means of payment or acquiring cryptocurrencies for investment purposes. Using empirical evidence from economic literature, the paper discusses the properties of cryptocurrencies as cash and as an investment asset. Based on this analysis, potential accounting models under IFRS are developed and discussed regarding the motivation with which an entity acquires cryptocurrencies. Arguments are presented to show that a fair value model is the most relevant for investment purposes and a foreign currency approach is appropriate for payment transactions.
Learning-based predictive models: A new approach to integrating large-scale simulations and experiments (Conference Presentation)

We will describe a large research effort at Lawrence Livermore National Laboratory (LLNL) aimed at using recent advances in deep learning, computational workflows, and computer architectures to develop an improved predictive model – the learned predictive model. Our goal is to first train these new models, typically cyclic generative adversarial networks, on simulation data to capture the theory implemented in advanced simulation codes. Later, we improve, or elevate, the trained models by incorporating experimental data. The training and elevation process both improves our predictive accuracy and provides a quantitative measure of uncertainty in such predictions. We will present work using inertial confinement fusion research and experiments at the National Ignition Facility (NIF) as a testbed for development. We will describe advances in machine learning architectures and methods necessary to handle the challenges of ICF science, including rich, multimodal data (images, scalars, time series) and strong nonlinearities. We will also cover state-of-the-art tools that we developed to manage our computational workflow. The tools manage a wide range of tasks, including developing enormous (PB-class) simulated training data sets, driving the training of learned models on simulation data, and elevating learned models through exposure to experiment. We will end by drawing ties to a broad range of scientific applications.
Counter-APT Red Teaming

The concept of counter-APT red teaming (CAPTR teaming) is a method of reverse red teaming I theorized, designed, and evaluated during my doctoral research and in my dissertation. As mentioned in earlier chapters, a red team is at a huge disadvantage when it comes to emulating appropriately, and thus aptly mitigating, an advanced persistent threat. When it comes to red team engagements, APTs specifically represent a replication challenge for even the most talented offensive security professionals. Even when the skills of an ethical hacker and a malicious one are on an even playing field, the modern state of offensive security tilts almost every aspect in favor of the actual attacker verse the emulated one. The end result of attempting to address this issue was an offensive security assessment methodology that, although motivated by the APT challenge, was beneficial in many ways compared to traditional red teaming.
A NEW OPTIMIZATION METHOD FOR SECURITY-CONSTRAINED WORKFLOW SCHEDULING

Broadly speaking, scheduling is a process of developing bunches of security policies and to control the certain available tasks which have to be taken care of using a computer system.Care should be exercised that a scheduler in a particular fields should be quite cautious in adapting scheduling strategy as the environment and the version of task change.In many cases, security is proven to be able to encourage relevance between two entities for a long time.This does imply that a secure scheduling algorithm, of course, can completely alleviate the failure probability during task execution in a security environment.Therefore, to move in a direction of a systematic approach, this paper, very natural, aims at introducing a brand new reliable algorithm using different quality of service (QoS) parameters to manage the scheduling large number of workflows.To do this, it combines two metaheuristic algorithms, CPSO and GA.It should be noted, however, that three different QoS such as execution cost, loadbalancing and security are employed as the most immediate performance measure to handle the scheduling process.Conclusion reached through this algorithm mention that its total execution cost will be minimized while meeting deadline and risk rate constraints.Verification of the proposed algorithm with other algorithms was taken into consideration.The accumulation of results through exercising this algorithm reveals that optimal solution to the problems is promised.Therefore, it is highly desirable to claim that obtained results from this effective approach are better than with the approach used with other algorithms such as CPSO and GA.
From Cost-Sensitive to Tight F-measure Bounds

The F-measure is a classification performance measure, especially suited when dealing with imbalanced datasets, which provides a compromise between the precision and the recall of a classifier. As this measure is non convex and non linear, it is often indirectly optimized using cost-sensitive learning (that affects different costs to false positives and false negatives). In this article, we derive theoretical guarantees that give tight bounds on the best F-measure that can be obtained from cost-sensitive learning. We also give an original geometric interpretation of the bounds that serves as an inspiration for CONE, a new algorithm to optimize for the F-measure. Using 10 datasets exhibiting varied class imbalance, we illustrate that our bounds are much tighter than previous work and show that CONE learns models with either superior F-measures than existing methods or comparable but in fewer iterations.
Why Human Hackers?

Although the previous chapter certainly cemented the purpose for cyber red teaming, it did not make the case satisfactorily for human hackers. As seen, fulfilling the red team mission is a challenging and often expensive endeavor. Both academia and industry have sought to automate or replace the human hacker with various tools and technologies. There are also new services with differing personnel requirements intent on being a substitute for the type of benefits red teaming can provide. After reading this chapter, there should be no doubt regarding why human hackers are the penultimate red team solution over automated replacement, and why hunting for different kinds of security personnel will not provide the proactive mitigation that red teaming can.
The Production of Korean Stops by Vietnamese Beginner Learners

This study aims to understand how Vietnamese beginner learners produce Korean stops through production experiment and accuracy assessment by native Korean. The native Koreans heard stimuli recorded by the Vietnamese subjects and selected corresponding Korean phonemes and measured the degree of the accuracy. The results showed 672(46.1%) error from 1,458 token and significant differences were found in the phonation type (p .01). The frequency of errors was lenis fortis aspirated stops in order and the pattern is lenis perceived as fortis, fortis perceived as aslpirated stops, and aspirated stops perceived as fortis. The accuracy scores of lenis are significantly lower than that of fortis and aspirated stops. Assuming the reason, Vietnamese learners are easy to acquire aspirated stops due to the remarkable phonetic feature, the aspiration. On the other hand, they are difficult to categorize the Korean lenis and fortis because of the L1’s negative transition or the Korean lenis phonetic differences between word-initial and word-medial environment.
A New Lower Bound for Semigroup Orthogonal Range Searching

We report the first improvement in the space-time trade-off of lower bounds for the orthogonal range searching problem in the semigroup model, since Chazelle's result from 1990. This is one of the very fundamental problems in range searching with a long history. Previously, Andrew Yao's influential result had shown that the problem is already non-trivial in one dimension [Yao, 1982]: using m units of space, the query time Q(n) must be Omega(alpha(m,n) + n/(m-n+1)) where alpha(*,*) is the inverse Ackermann's function, a very slowly growing function. In d dimensions, Bernard Chazelle [Chazelle, 1990] proved that the query time must be Q(n) = Omega((log_beta n)^{d-1}) where beta = 2m/n. Chazelle's lower bound is known to be tight for when space consumption is high i.e., m = Omega(n log^{d+epsilon}n). We have two main results. The first is a lower bound that shows Chazelle's lower bound was not tight for low space: we prove that we must have m Q(n) = Omega(n (log n log log n)^{d-1}). Our lower bound does not close the gap to the existing data structures, however, our second result is that our analysis is tight. Thus, we believe the gap is in fact natural since lower bounds are proven for idempotent semigroups while the data structures are built for general semigroups and thus they cannot assume (and use) the properties of an idempotent semigroup. As a result, we believe to close the gap one must study lower bounds for non-idempotent semigroups or building data structures for idempotent semigroups. We develope significantly new ideas for both of our results that could be useful in pursuing either of these directions.