Reviewerly/exHarmony huggingface dataset:
Peran Pojok Baca Anggrek Bulan dalam Menjadikan Padang Panjang sebagai Kota Literasi

Reading corner is a means of education related to lifelong learning. It's aimed at improving the quality of education in the community. Reading Corner Bank Sampah Anggrek Bulan is one of the TBMs that is active in developing the capabilities of the community by processing garbage into useful objects. This research aims to describe the contribution of the Anggrek Bulan Reading Corner to improving public to support the mission of Padang Panjang City as a City of Literature. The research method used is qualitative, the type of field research, whereas the analysis approach used is descriptive-qualitative. Sampling technique using purposive sampling. As for the informants in this study, they are the founder of the reading corner, the administrator, and the visitor to the reading corner. Data collection through direct field observations, semi-structured interviews, and documentation. Data analysis is done by means of data collection, data reduction, data presentation, and conclusion drawings. The results of this study show that Anggrek Bulan Reading Corner develops the literacy of the community with a variety of routine activities such as tahfiz, disposing of wastes, and whistling. Its efforts to preserve and promote local wisdom culture are carried out by providing a collection of fiction related to the culture of independence, organizing local cultural activities and inviting experts in the process of transferring information for the young generation to know the culture, in particular Minangkabau culture. These activities have had a significant influence in supporting the Padang Panjang as a City of Literature.
The Impact of HRM Practices and Leadership Styles on Administrative-Office Staff Performance in Palestinian Universities

This study investigates the impact of HRM practices and leadership styles on administrative-office staff performance in traditional universities and university colleges in Palestine. A pilot study was conducted before data collection to check content validity and reliability, which was successfully achieved. The primary data were collected through questionnaires distributed to a purposive sample of administrative-office employees from nine Palestinian universities and university colleges across the West Bank area. A total of 256 questionnaires out of 270 were eligible for data analysis. The response rate was 0.94. The data analysis was carried out using SPSS version 24 utilizing the data analysis program: PLS-SEM version 3. The findings of this research study have reported that HRM practices (training and development, compensation and rewards, employee relations, and performance appraisal) are positively related to administrative-office staff performance. In contrast, leadership styles (transformational and transactional) were found to have no significant impact on administrative-office staff performance. The study's contribution is threefold: the "population gap" contribution, HRM Practices' theoretical-practical gap contribution, and leadership styles' theoretical-practical gap contribution, besides its practical significance. The study has reached several recommendations, the most important of which are the examined HRM practices should be reinforced and emphasized in order to increase administrative-office staff performance and boost their effectiveness and in case of future examination of the relationship between leadership styles and employee performance, the researchers may employ a mediator or focus on investigating different styles of leadership other than transformational and transactional.
Intelligent Support for Construction and Exploration of Advanced Technological Information Space

In this paper, we describe a practical method of extracting, structuring, summarizing and integrating technological information from technical papers in metallurgy. The heart of the method is a packet of domain specific knowledge called KPs (Knowledge Pieces) in which procedures for extracting and structuring technological information are embedded. We studied information structure of ten technical papers in metallurgy and constructed about a hundred KPs. We implemented a system called METIS (METallurgy papers Intelligent Surveyor) which interprets technical papers in metallurgy written in a mark-up language and produces a variety of summaries and surveys such as structured technical summary, visualization of similarities, differences of relevant papers, and cause-effect relations. We have undertaken qualitative and quantitative evaluation of METIS against 186 technical papers so far. The evaluation demonstrates reliability and robustness of our method.
Lexicon acquisition in an interdisciplinary learning context

This paper presents the questions that led to the field research, the theoretical frame, the subsequent methodology, and some of the results and conclusions. One of these conclu-sions is the need of specific tasks aimed at lexicon acquisition and of interdisciplinary work within that acquisition. Some strategies of minimizing failure and e learning enhancement will also be discussed, as well as the learning results achieved in the strict area of lexicon acquisition.
X-ray fluorescence mapping as a first-hand tool in disseminated ore assessment: sandstone-hosted U–Zr mineralization

Analytical capabilities of X-ray fluorescence for 2-dimensional elemental analysis were tested on worldwide-unique sandstone-hosted U–Zr mineralization (Bohemian Cretaceous Basin, Czech Republic), and combined with measured spectral data. The applied approach was aimed at the detection of uranium and zirconium, visualization of their distribution across mineralized lithological units and description of their relationships with associated elements (As, Fe, Nb, Pb, Sr, Ti). The studied samples were low-grade uranium-bearing sediments. Experimental work employed laboratory 2D scanning X-ray fluorescence analyzer with the gold target Mini-X X-ray tube. Maps of U–Zr and other identified elements demonstrated their patchy distribution and a frequent association of the elements with mineral fillings (clay matrix, organic matter or Fe sulfides). A correlation-based analysis was used to verify geochemical coherence between the measured elements. This showed an extremely strong linear dependence (R ≥ 0.90) between U and Zr. In addition, a strong correlation (R ≥ 0.85) between U–Zr and some associated elements (As, Nb ± Fe, Pb, Sr) was confirmed. The efficiency and the limits of X-ray fluorescence mapping were tested by comparison with high-resolution techniques. Non-destructive mapping of large natural samples provided relevant data for further mineralogical study with respect to rapid pre-concentration of the ore material.
Outlier detection in graphs: A study on the impact of multiple graph models

Several previous works proposed techniques to detect outliers in graph data. Usually, some complex dataset is modeled as a graph and a technique for detecting outliers in graphs is applied. The impact of the graph model on the outlier detection capabilities of any method has been ignored. Here we assess the impact of the graph model on the outlier detection performance and the gains that may be achieved by using multiple graph models and combining the results obtained by these models. We show that assessing the similarity between graphs may be a guidance to determine effective combinations, as less similar graphs are complementary with respect to outlier information they provide and lead to better outlier detection.
Data Replica Placement Policy Based on Load Balance in Cloud Storage System

Data replication technology has been widely used in cloud storage system. How to select the appropriate data center to place data replication to effectively improve the access performance of cloud storage system became a problem which is worthy of study. To solve this problem we presented a replica placement algorithm based on load balance in this paper. According to the capital value of the data center, the algorithm selects a new replica node and can ensure load balance between the data centers. Related simulation experiments show that the algorithm can quickly and effectively respond to user requests and it is reliable and efficient.
Wide-Coverage Neural A* Parsing for Minimalist Grammars

Minimalist Grammars (Stabler, 1997) are a computationally oriented, and rigorous formalisation of many aspects of Chomsky's (1995) Minimalist Program.This paper presents the first ever application of this formalism to the task of realistic wide-coverage parsing.The parser uses a linguistically expressive yet highly constrained grammar, together with an adaptation of the A* search algorithm currently used in CCG parsing (Lewis and Steedman, 2014;Lewis et al., 2016), with supertag probabilities provided by a bi-LSTM neural network supertagger trained on MGbank, a corpus of MG derivation trees.We report on some promising initial experimental results for overall dependency recovery as well as on the recovery of certain unbounded long distance dependencies.Finally, although like other MG parsers, ours has a high order polynomial worst case time complexity, we show that in practice its expected time complexity is O(n 3 ).The parser is publicly available.
Doing Math Modelling Outdoors- A Special Math Class Activity designed with MathCityMap

The use of smartphones in classrooms is unfortunately due to the restriction legislated by administrations not very popular. With the MathCityMap project (MCM) we show one possibility to use the own mobile device in a substancial and authentic learning environment. MathCityMap combine the well known math trail idea with the current technological possibilities of mobile devices. The MathCityMap is a two component system. The first component is a webportal (www.mathcitymap.eu) which served as a open access database for authentic math problems in the environment. The other component, the MCM-App, shows on a map where in the environment the problems are hidden. Additional it provides hints, feedback and a sample solution. To solve such an authentic MCM problem you need mathematical modelling competencies. We show with the help of one example in which way the students work with the tasks and how the system deal with different solution which occurs by solving a modelling task.
KDEHatEval at SemEval-2019 Task 5: A Neural Network Model for Detecting Hate Speech in Twitter

In the age of emerging volume of microblog platforms, especially twitter, hate speech propagation is now of great concern.However, due to the brevity of tweets and informal user generated contents, detecting and analyzing hate speech on twitter is a formidable task.In this paper, we present our approach for detecting hate speech in tweets defined in the SemEval-2019 Task 5. Our team KDEHatEval employs different neural network models including multi-kernel convolution (MKC), nested LSTMs (NLSTMs), and multi-layer perceptron (MLP) in a unified architecture.Moreover, we utilize the state-of-the-art pre-trained sentence embedding models including Deep-Moji, InferSent, and BERT for effective tweet representation.We analyze the performance of our method and demonstrate the contribution of each component of our architecture.
$$\delta $$ -subgaussian Random Variables in Cryptography

In the Ring-LWE literature, there are several works that use a statistical framework based on $$\delta $$ -subgaussian random variables. These were introduced by Miccancio and Peikert (Eurocrypt 2012) as a relaxation of subgaussian random variables. In this paper, we completely characterise $$\delta $$ -subgaussian random variables. In particular, we show that this relaxation from a subgaussian random variable corresponds only to the shifting of the mean. Next, we give an alternative noncentral formulation for a $$\delta $$ -subgaussian random variable, which we argue is more statistically natural. This formulation enables us to extend prior results on sums of $$\delta $$ -subgaussian random variables, and on their discretisation.
The Marco Cappato and Fabiano Antoniani (dj Fabo) Case Paves the Way for New Assisted Suicide Legislation in Italy: An Overview of Statutes from Several European Countries

Abstract The article looks into the case involving Fabiano Antoniani, who, following a major road accident, was left tetraplegic. Marco Cappato drove him to a Swiss clinic where Mr. Antoniani took his own life by self-administration of lethal pentobarbital sodium. Cappato was put on trial, but the Italian Constitutional Court urged the Parliament to decriminalise assisted suicide in extremely serious cases. From a comparison with other European countries, approaches range from restrictive (banning both active euthanasia and assisted suicide), to entirely permissive. An intermediate approach only entails a ban on active euthanasia. It would be desirable to uniformise the diverse national statutes on a European level, which would make it possible for everyone to receive assistance towards ending their suffering, with limitations to incurable cases to be medically verified, and at the end of a path designed to ensure that patient freedom of choice is upheld at all time.
EmoSense at SemEval-2019 Task 3: Bidirectional LSTM Network for Contextual Emotion Detection in Textual Conversations

In this paper, we describe a deep-learning system for emotion detection in textual conversations that participated in SemEval-2019 Task 3 "EmoContext".We designed a specific architecture of bidirectional LSTM which allows not only to learn semantic and sentiment feature representation, but also to capture userspecific conversation features.To fine-tune word embeddings using distant supervision we additionally collected a significant amount of emotional texts.The system achieved 72.59% micro-average F 1 score for emotion classes on the test dataset, thereby significantly outperforming the officially-released baseline.Word embeddings and the source code were released for the research community.
Multilayer feature descriptors fusion CNN models for fine‐grained visual recognition

Abstract Fine‐grained image classification is a challenging topic in the field of computer vision. General models based on first‐order local features cannot achieve acceptable performance because the features are not so efficient in capturing fine‐grained difference. A bilinear convolutional neural network (CNN) model exhibits that a second‐order statistical feature is more efficient in capturing fine‐grained difference than a first‐order local feature. However, this framework only considers the extraction of a second‐order feature descriptor, using a single convolutional layer. The potential effective classification features of other convolutional layers are ignored, resulting in loss of recognition accuracy. In this paper, a multilayer feature descriptors fusion CNN model is proposed. It fully considers the second‐order feature descriptors and the first‐order local feature descriptor generated by different layers. Experimental verification was carried out on fine‐grained classification benchmark data sets, CUB‐200‐2011, Stanford Cars, and FGVC‐aircraft. Compared with the bilinear CNN model, the proposed method has improved accuracy by 0.8%, 1.1%, and 5.5%. Compared with the compact bilinear pooling model, there is an accuracy increase of 0.64%, 1.63%, and 1.45%, respectively. In addition, the proposed model effectively uses multiple 1×1 convolution kernels to reduce dimension. The experimental results show that the multilayer low‐dimensional second‐order feature descriptors fusion model has comparable recognition accuracy of the original model.
Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting

Yichao Lu, Manisha Srivastava, Jared Kramer, Heba Elfardy, Andrea Kahn, Song Wang, Vikas Bhardwaj. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
The quantum Zeno and anti-Zeno effects: from weak to strong system-environment coupling

By repeatedly measuring a quantum system, the evolution of the system can be slowed down (the quantum Zeno effect) or sped up (the quantum anti-Zeno effect). We study these effects for a single two-level system coupled to a collection of harmonic oscillators. Previously, such systems have been studied in both the weak and the strong system-environment coupling regimes. In this paper, we apply a polaron transformation in a manner that allows us to study the quantum Zeno and anti-Zeno effects for a large variety of system-environment parameters. Using this approach, we reproduce previous results for the weak and strong system-environment coupling regimes. Moreover, as long as the environment is super-Ohmic, we show how our approach can be used to explore regimes such as the moderate system-environment coupling regime that could not be investigated before in a straightforward manner.
Characterization and Correction of the Geometric Errors in Using Confocal Microscope for Extended Topography Measurement. Part I: Models, Algorithms Development and Validation

This work presents a method for characterizing and correcting the geometric errors of the movement of the lateral stage of Imaging Confocal Microscope (CM) in extended topography measurement. For an extended topography measurement, a defined number of 2D images are taken and stitched by correlation methods. Inaccuracies due to linear displacement, vertical and horizontal straightness errors, angular errors, and squareness errors based on the assumption of the rigid body kinematics are described. A mathematical model for the scale calibration of the X- and Y- coordinates is derived according to the system kinematics, the axis chain vector of CM, and the geometric error functions and their approximations by Legendre polynomials. The correction coefficients of the kinematic modelling are determined by the measured and certified data of a dot grid target standard artefact. To process the measurement data, algorithms for data partitions, fittings of cylinder centers, and determinations of coefficients are developed and validated. During which methods such as form removal, K-means clustering, linear and non-linear Least Squares are implemented. Results of the correction coefficients are presented in Part II based on the experimental studies. The mean residual reduces 29.6% after the correction of the lateral stage errors.
Impact of English Reading Comprehension Abilities on Processing Magazine Style Narrative Visualizations and Implications for Personalization

In this paper, we present research to uncover how the level of reading comprehension abilities impacts how users process textual documents in English with embedded visualizations (i.e., Magazine Style Narrative Visualizations or MSNVs). We analyze performance and gaze data of users processing MSNVs from two user studies, one run in Canada and one in a non-English speaking European country. Our findings provide important insights toward developing automatic, real-time support to MSNV processing personalized according to users' English reading comprehension abilities.
Generative Information Hiding Method Based on Adversarial Networks

Traditional Steganography need to modify the carrier image to hide information, which will leave traces of rewriting, then eventually be perceived by the enemy. In this paper, an information hiding scheme based on Auxiliary Classifier Generative Adversarial Networks (AC-GANs) model is proposed for Steganography. This method designs and trains the networks model based on AC-GANs by constructing a dedicated dictionary and image database. The sender can map the secret information into the category labels through the dictionary, and then use the labels generate the real looking images to be sent through the model. On the contrary, the receiver can identify the image label through the model and obtain the secret information. Through experiments, the feasibility of this method is verified and the reliability of the algorithm is analyzed. This method transmits secret messages by generating images without overwriting the carrier images. It can effectively solve the problem of modification of carrier images in traditional information hiding.
Resolution Lower Bounds for Refutation Statements

For any unsatisfiable CNF formula we give an exponential lower bound on the size of resolution refutations of a propositional statement that the formula has a resolution refutation. We describe three applications. (1) An open question in [Atserias and Muller, 2019] asks whether a certain natural propositional encoding of the above statement is hard for Resolution. We answer by giving an exponential size lower bound. (2) We show exponential resolution size lower bounds for reflection principles, thereby improving a result in [Albert Atserias and Maria Luisa Bonet, 2004]. (3) We provide new examples of CNFs that exponentially separate Res(2) from Resolution (an exponential separation of these two proof systems was originally proved in [Nathan Segerlind et al., 2004]).
Designing a Mobile Social and Vocational Reintegration Assistant for Burn-out Outpatient Treatment

Using Social Agents as health-care assistants or trainers is one focus area of IVA research. This paper presents a concept of our mobile Social Agent EmmA in the role of a vocational reintegration assistant for burn-out outpatient treatment. We follow a typical par- ticipatory design approach including experts and patients in order to address requirements from both sides. Since the success of such treatments is related to a patients emotion regulation capabilities, we employ a real-time social signal interpretation together with a computational simulation of emotion regulation that influences the agent's social behavior as well as the situational selection of verbal treatment strategies. Overall, our interdisciplinary approach sketches a novel integrative concept for Social Agents as assistants for burn-out patients.
The Development of Android-Based Mobile Learning Media (MLM) for Elementary School Students of Kendal Indonesia

Learning Media is one of the essential aspects in creating a meaningful and good quality learning process. The use of mobile device in the learning process is called mobile learning (m-learning). It is deﬁned as a learning in which the learners are demanded to be active or moving to places by utilizing a mobile technology device. The existence of m-learning is indeed unable to substitute e-learning (electronic learning) let alone replacing face-to-face meeting in a class. M-learning is intended to complement the learning took place in a class. It also helps the students to re-learn the material they have not understood anywhere and anytime they want. This surely provides novel learning experience for the students. Developing Mobile Learning Media (MLM) is the alternatives in providing the most suitable and effective media in this era. The type of research used in this study is the developing method of SDLC (System Development Life Cycle) of Waterfall. According to the FGD conducted with elementaryteachersinKaliwunguDistrictKendal,thediscussionleadtotheneedupon android-based MLM was in mathematics subject. The researcher then developed the media with the materials that the students needed. This media had been validated by the experts of material, media, and children psychology. Based on the test conducted for the media, it presented that the media is feasible to be used. The try out for the Android-based MLM-media was limitedly administered to ten students of Fifth Grade and one teacher, and try out was conducted successfully and showed positive results.