Reviewerly/exHarmony huggingface dataset:
AN ARGUMENTATIVE ANALYSIS OF SIR KEN ROBINSON IN TED TALK

This research used argumentative discourse which is aimed to find out components of argument and structures of argument that is used by Sir Ken Robinson in TED Talk. Toulmin’s model and Cohen’s theory were used to recognize the components and structures of arguments. The data used for analysis were videos and the transcripts. There are two videos which are being analyzed; “Bring on the Learning Revolution” and “Do School Kill Creativiy?”. It presented five data analysis procedures: compiling (choosing data), disassembling (compling data into smaller potion and coding), reassembling (indicating to categories), interpreting( analyzing the data and explaining the data), and concluding (drawing conclusion) The result revealed that from both videos “Bring on the Learning Revolution” and “Do School Kill Creativiy?”, there were several components components that are used; claim, data, and warrant were 100% used in all arguments, qualifier was 55, 56%, rebuttal was 27,80%, and backing was 88,90%. For the structure of arguments that is used by Sir Ken Robinson in both his videos the percentage coherent structure of pre-order was 66,7%, for post-order was 22,2%, and for hybrid was 11,1%. Then, for linguistic clue showed that re- direction clue had 16,7% then connectives had 83,3%. and evidence relationship had 100%.
A Study on Relative Importance and Priority of the Competency of B2B Salesperson Using AHP : Gap in Perception between Suppliers and Buyers

This study aims to present the differentiating factors of B2B salesperson competency through comparing the suppliers and buyers in Korean steel industry in their perception on the importance and priority of B2B salesperson competency. Based on previous studies, analysis on B2B salesperson competency has been analyzed using the B2B salesperson performance competency measure factors and appropriately reorganizing them for better application to the steel industry. The required performance competencies of B2B salesperson can be categorized into 3 different types, namely social exchange competency, advisory sales competency, and skill & knowledge competency. AHP analysis was performed for analyzing the relative importance of B2B salesperson competency based on the factors of previous studies, in which categorization of the aforementioned types had been done. As the result, first, it has been confirmed that there is a difference in 1st layer main factors between the supplier group and buyer group. The supplier group valued the advisory sales competency, while the buyer group valued skill & knowledge competency. Second, it has been proved that there is same result of relative importance in 2nd detailed factors between the supplier group and buyer group. Both group confirmed that customer member, identify customer needs and communication skill are very important factors. Third, as the result of analysis on the gap between B2B salesperson competency of the suppliers and buyers, the gap in the product knowledge and sales team member need improvement for buyer’s satisfaction according to overall results of relative importance and priority. The steel supplier was able to develop B2B salesperson competency according to the buyers’ needs based on the result of this study, and furthermore it is expected that this study will be able to contribute to increase in buyer competitiveness through differentiation in B2B salesperson competency.
Effects of Some Monetary Variables on Fixed Investment in Selected Sub-Saharan African Countries

Monetary variables are not only important for the attainment of stable inflation but also for exercising influences in various ways on the behavior of the real economy, including the level of investment activity. Investment is very crucial in improving a country’s productivity and growth and increasing its competitiveness in the long run. The study aims to investigate how monetary variables such as lending rates, exchange rate, and money supply affect investment actions in some selected Sub-Saharan African countries in the period 1980–2018. Using the panel autoregressive distributive lag method in the long run, a negative and significant relationship between lending rates and investment was discovered. Also, investment is positively related to both money supply and exchange rate in the long run. It is recommended that when central banks take contractionary measures, they must always consider the resulting change in investment as it is an essential part of aggregate demand. In a sluggish economy, interest rates should not be raised to the point where investment is discouraged and assets are suppressed.
Library Services: Principles of Communication: Getting Started

This guide will assist students in locating information for the Principles of Communication Patient Report Project
It's not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT

Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages. We study the word-level translation information embedded in mBERT and present two simple methods that expose remarkable translation capabilities with no fine-tuning. The results suggest that most of this information is encoded in a non-linear way, while some of it can also be recovered with purely linear tools. As part of our analysis, we test the hypothesis that mBERT learns representations which contain both a language-encoding component and an abstract, cross-lingual component, and explicitly identify an empirical language-identity subspace within mBERT representations.
Qsparse-local-SGD: Communication Efficient Distributed SGD with Quantization, Sparsification, and Local Computations

Author(s): Basu, Debraj | Advisor(s): Diggavi, Suhas N | Abstract: Large scale distributed optimization has become increasingly important with the emergence of edge computation architectures such as in the federated learning setup, where large amounts of data, possibly of a secure nature and generated in an online manner can be massively distributed across personal devices. A key bottleneck for many such large-scale problems is in the communication overhead of exchanging information between devices over bandwidth limited networks as well as in the unreliability of communication for distributed optimization. The existing approaches propose to mitigate these bottlenecks either by using different forms of compression or by computing local models and mixing them iteratively. In this thesis we first propose a novel class of highly communication efficient operators that employ stochastic and deterministic quantization with aggressive sparsification such as Top-k in the form of a composed operator. Furthermore, in federated learning one can use local computations to reduce communication. Using such a framework, we incorporate local iterations into our algorithm which allows the communication to be infrequent and possibly asynchronous thereby enabling significantly reduced communication. Putting them together we have distributed Qsparse-local-SGD for federated learning for which our analysis demonstrates convergence rates matching vanilla distributed SGD where we observe that quantization and sparsification are almost for free for smooth functions, both non-convex and convex. We characterize the asymptotic allowable limits of local iterations for synchronous and asynchronous implementations of Qsparse-local-SGD, so as to harness both the distributed processing gains as well as the benefits of quantization, sparsification and local computations. Our numerics demonstrate that Qsparse-local-SGD combines the bit savings of our composed operators, as well as local computations, thereby outperforming the cases where these techniques are individually used. We use it to train ResNet-50 on ImageNet, as well as a softmax multi-class classifier on MNIST, resulting in significant savings over the state-of-the-art, in the number of bits transmitted to reach target accuracy.
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards

In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues reinforcement learning algorithms face when task rewards are sparse, this paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm and can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards. Through integrating and balancing empowerment and curiosity, this approach shows superior performance compared to other state-of-the-art intrinsic exploration approaches during extensive empirical testing. Qualitative analysis also shows that when combined with diversity-driven intrinsic motivations, this approach can help manipulators learn a set of diverse skills which could potentially be applied to other more complicated manipulation tasks and accelerate their learning process.
Dissipative Stabilization of Linear Systems with Time-Varying General Distributed Delays (Complete Version)

New methods are developed for the stabilization of a linear system with general time-varying distributed delays existing at the system's states, inputs and outputs. In contrast to most existing literature where the function of time-varying delay is continuous and bounded, we assume it to be bounded and measurable. Furthermore, the distributed delay kernels can be any square-integrable function over a bounded interval, where the kernels are handled directly by using a decomposition scenario without using approximations. By constructing a Krasovskiĭ functional via the application of a novel integral inequality, sufficient conditions for the existence of a dissipative state feedback controller are derived in terms of matrix inequalities without utilizing the existing reciprocally convex combination lemmas. The proposed synthesis (stability) conditions, which take dissipativity into account, can be either solved directly by a standard numerical solver of semidefinite programming if they are convex, or reshaped into linear matrix inequalities, or solved via a proposed iterative algorithm. To the best of our knowledge, no existing methods can handle the synthesis problem investigated in this paper. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed methodologies.
IDENTIFIKASI MORFOLOGI MIKROBA PADA RUANGAN WATER CLOSET JURUSAN BIOLOGI UNIVERSITAS NEGERI MAKASSAR

ABSTRACT A Water closet is a public place that has the potential for the growth of various types of microbes. The purpose of this study was to isolate and identify the morphology of microbial colonies in one of the water closet rooms of the Biology Department. The method used was microbial isolation using TEA (Tauge Ekstrak Agar) media with the composition for each material being converted to 100. Then placed in a water closet for 15 minutes and incubated at 37oC for 2 days. Observation and identification of microbial colony morphology on the media were carried out with the aid of light. The results obtained were the growth of 2 microbial colonies M1 and M2. M1 has morphological characteristics of white color, moderate size, rhizoid form, filamentous margins, elevation convex. M2 has the characteristics of white morphology, small size, circular form, margin entire, elevation convex. The use of TEA media that has been converted to 100 can grow two types of microbial colonies. Keywords: Morphological Identification, Microbial Isolation, TEA (Tauge Ekstrak Agar)
Pelaksanaan Keterbukaan Informasi Publik di Universitas Jambi: Suatu Telaah Normatif

Openness of public information is one of the pillars of freedom of expression and the pillars of democracy, transparency and good governance. To encourage public participation, Law Number 14 of 2008 concerning Openness of Public Information was born. The provision regulates social norms to obtain data access and guarantees that each individual fulfills his basic rights. This research is focused on the implications of these regulations at the University of Jambi. As a public body, the University of Jambi is required to apply all the provisions that have been set. Aside from being a controlling system, this policy must be addressed as part of bureaucratic reform, especially regarding good governance so that the fundamental changes that occur are directly proportional to the area of change desired. The research methodology uses the normative type of jurisprudence, namely by examining the legal literature through the study of documents with the nature of analytic description so as to produce systematic factual basis.
A Customer Relationship Management (CRM) Aprroach with the Spiral Model (Case Study: Information System at Optik Sejahtera)

Optik Sejahtera is a company engaged in the sale of eye sight/ vision aids or often called glasses. In addition to glasses made of stainless and fiber, there are also many manufacturers in Indonesia that produce glasses from wood. The marketing of wood eyewear products still has problems, among which is still many people do not know about this product. This condition led to stagnant sales transactions in Optik Sejahtera. CRM (Customer Relationship Management) approach method is a marketing activity built on four pillars namely identifying, attracting, maintaining and strengthening brand loyalty, or strengthening relationships to achieve mutually beneficial goal. Therefore, information systems are required with the CRM approach method on Optik Sejahtera to increase sales transactions i.e. with spiral models. Spiral model steps are communication with customers, planning, risk analysis, engineering, construction and launch, and customer evaluation. The systems in this study use the MySQL database.  The results of this study based on analysis system of hypothesis test showed before using the system it took 8,6840 minutes and after using the system it only took 4,3197 minutes. It means, there are changes in sales transactions before and after using the information system in Optik Sejahtera. In other words, the information system on Optik Sejahtera could resulted in an increase of sales transactions. In addition, the results of the study in benefit testing obtained a percentage for utilization of 89.98%, effectiveness of 90%, and efficiency of 92.23%. This means that information systems in Optik Sejahtera using CRM approach with spiral models can increase efficiency in sales transactions.
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing

Backdoor data poisoning is an emerging form of adversarial attack usually against deep neural network image classifiers. The attacker poisons the training set with a relatively small set of images from one (or several) source class(es), embedded with a backdoor pattern and labeled to a target class. For a successful attack, during operation, the trained classifier will: 1) misclassify a test image from the source class(es) to the target class whenever the same backdoor pattern is present; 2) maintain a high classification accuracy for backdoor-free test images. In this paper, we make a break-through in defending backdoor attacks with imperceptible backdoor patterns (e.g. watermarks) before/during the training phase. This is a challenging problem because it is a priori unknown which subset (if any) of the training set has been poisoned. We propose an optimization-based reverse-engineering defense, that jointly: 1) detects whether the training set is poisoned; 2) if so, identifies the target class and the training images with the backdoor pattern embedded; and 3) additionally, reversely engineers an estimate of the backdoor pattern used by the attacker. In benchmark experiments on CIFAR-10, for a large variety of attacks, our defense achieves a new state-of-the-art by reducing the attack success rate to no more than 4.9% after removing detected suspicious training images.
anomaly : Detection of Anomalous Structure in Time Series Data

One of the contemporary challenges in anomaly detection is the ability to detect, and differentiate between, both point and collective anomalies within a data sequence or time series. The anomaly package has been developed to provide users with a choice of anomaly detection methods and, in particular, provides an implementation of the recently proposed Collective And Point Anomaly family of anomaly detection algorithms. This article describes the methods implemented whilst also highlighting their application to simulated data as well as real data examples contained in the package.
Applying Graph-based Deep Learning To Realistic Network Scenarios.

Recent advances in Machine Learning (ML) have shown a great potential to build data-driven solutions for a plethora of network-related problems. In this context, building fast and accurate network models is essential to achieve functional optimization tools for networking. However, state-of-the-art ML-based techniques for network modelling are not able to provide accurate estimates of important performance metrics such as delay or jitter in realistic network scenarios with sophisticated queue scheduling configurations. This paper presents a new Graph-based deep learning model able to estimate accurately the per-path mean delay in networks. The proposed model can generalize successfully over topologies, routing configurations, queue scheduling policies and traffic matrices unseen during the training phase.
Linking average- and worst-case perturbation robustness via class selectivity and dimensionality

Representational sparsity is known to affect robustness to input perturbations in deep neural networks (DNNs), but less is known about how the semantic content of representations affects robustness. Class selectivity-the variability of a unit's responses across data classes or dimensions-is one way of quantifying the sparsity of semantic representations. Given recent evidence that class selectivity may not be necessary for, and in some cases can impair generalization, we investigate whether it also confers robustness (or vulnerability) to perturbations of input data. We found that networks regularized to have lower levels of class selectivity were more robust to average-case (naturalistic) perturbations, while networks with higher class selectivity are more vulnerable. In contrast, class selectivity increases robustness to multiple types of worst-case (i.e. white box adversarial) perturbations, suggesting that while decreasing class selectivity is helpful for average-case perturbations, it is harmful for worst-case perturbations. To explain this difference, we studied the dimensionality of the networks' representations: we found that the dimensionality of early-layer representations is inversely proportional to a network's class selectivity, and that adversarial samples cause a larger increase in early-layer dimensionality than corrupted samples. Furthermore, the input-unit gradient is more variable across samples and units in high-selectivity networks compared to low-selectivity networks. These results lead to the conclusion that units participate more consistently in low-selectivity regimes compared to high-selectivity regimes, effectively creating a larger attack surface and hence vulnerability to worst-case perturbations.
Mad Science is Provably Hard: Puzzles in Hearthstone's Boomsday Lab are NP-hard

We consider the computational complexity of winning this turn (mate-in-1 or “finding lethal”) in Hearthstone as well as several other single turn puzzle types introduced in the Boomsday Lab expansion. We consider three natural generalizations of Hearthstone (in which hand size, board size, and deck size scale) and prove the various puzzle types in each generalization NP-hard.
ON THE ISSUE OF QUANTITATIVE ASSESSMENT OF THE EFFECTIVENESS OF REGIONAL ORGANIZATIONS OF SECONDARY ACADEMICAL EDUCATION

The article is devoted to the problems of evaluating the effectiveness of regional state budgetary secondary vocational institutions in the Altai Territory. The author analyzing the methods used in practice for assessing the activities of professional educational organizations, determines the degree of development of existing assessment models, and considers modern interpretations of the concepts of “criterion” and “indicator”.&#x0D; The current practice of monitoring the activities of professional educational organizations allows to compare the content and structural elements of all existing systems, the results of which revealed duplication of criteria and a methodological error in the form of substitution of effectiveness indicators with performance indicators. To eliminate the identified violation, as well as the justification of decisions on the development of the vocational secondary education, the the author proposes the ways, improving the assessment system when conducting regional and federal monitoring of this area of activity through the development and application, also effectiveness and performance criteria.
The Effects of Forensic Accounting Technology on Fraud Detection in Nigeria

The study explored and explained forensic accounting technology and how it influences fraud detection in Nigeria. The objective of the study was to examine forensic accounting technological influence on fraud detection in Nigeria. A literature review was conducted to set up the conceptual and theoretical framework for the study. A qualitative approach was used by administering questionnaires with both structured and semi structured questions. A total of 38 Investigators from the Economic and Financial Crimes Commission (EFCC) were used as sample for the study. It revealed a significant relationship between forensic accounting technological tools and fraud detection in Nigeria. The study concluded that despite all fraudulent activities, forensic accounting is solving fraud issues and using technology to aid fraud detection.  It was recommended for practitioners to engage in further training on forensic accounting technological tools and invest in research within this area.
Toward Cross-Lingual Definition Generation for Language Learners

Generating dictionary definitions automatically can prove useful for language learners. However, it's still a challenging task of cross-lingual definition generation. In this work, we propose to generate definitions in English for words in various languages. To achieve this, we present a simple yet effective approach based on publicly available pretrained language models. In this approach, models can be directly applied to other languages after trained on the English dataset. We demonstrate the effectiveness of this approach on zero-shot definition generation. Experiments and manual analyses on newly constructed datasets show that our models have a strong cross-lingual transfer ability and can generate fluent English definitions for Chinese words. We further measure the lexical complexity of generated and reference definitions. The results show that the generated definitions are much simpler, which is more suitable for language learners.