nils-herrmann/clean_scirepeval huggingface dataset:
Improving the grid power quality using virtual synchronous machines

This paper presents the improvement of power quality and grid stability for distributed generation using the virtual synchronous machine (VISMA) which embodies a hysteresis controlled three phase inverter with a synchronous machine model on an embedded control computer to calculate the reference currents. Currently the conventional grid-connected inverters are predominantly designed to transmit electrical energy to the grid discounting the maintenance of frequency and voltage and also its transient stability. However, the VISMA is able to regulate both the active and reactive power separately and bidirectionally by setting the virtual torque and virtual excitation to meet the power system requirements. Furthermore, a virtual rotating mass is implemented in the VISMA in order to increase the inertia in the grid and improve the transient frequency stability in analogy to the conventional synchronous generator. Additionally, the virtual damping of the VISMA can reduce the frequency and power oscillation in the grid. All these properties mentioned above have been verified in simulations and measurements in an experimental micro grid.
I Lead, You Help but Only with Enough Details: Understanding User Experience of Co-Creation with Artificial Intelligence

Recent advances in artificial intelligence (AI) have increased the opportunities for users to interact with the technology. Now, users can even collaborate with AI in creative activities such as art. To understand the user experience in this new user--AI collaboration, we designed a prototype, DuetDraw, an AI interface that allows users and the AI agent to draw pictures collaboratively. We conducted a user study employing both quantitative and qualitative methods. Thirty participants performed a series of drawing tasks with the think-aloud method, followed by post-hoc surveys and interviews. Our findings are as follows: (1) Users were significantly more content with DuetDraw when the tool gave detailed instructions. (2) While users always wanted to lead the task, they also wanted the AI to explain its intentions but only when the users wanted it to do so. (3) Although users rated the AI relatively low in predictability, controllability, and comprehensibility, they enjoyed their interactions with it during the task. Based on these findings, we discuss implications for user interfaces where users can collaborate with AI in creative works.
A Tool for the Analysis of DMN Decision Tables

The Decision Model and Notation (DMN) is a standard notation to specify decision logic in business applications. A central construct in DMN is a decision table. The rising use of DMN decision tables to capture and to automate everyday business decisions fuels the need to support analysis tasks on decision tables. This paper presents an opensource DMN editor to tackle three analysis tasks: detection of overlapping rules, detection of missing rules and simplification of decision tables via rule merging. The tool has been tested on large decision tables derived from a credit lending data-set.
Static analysis with paragraph vector for malware detection

Malware damages computers and the threat is a serious problem. Malware can be detected by pattern matching method or dynamic heuristic method. However, it is difficult to detect all new malware subspecies perfectly by existing methods. In this paper, we propose a new method which automatically detects new malware subspecies by static analysis of execution files and machine learning. The method can distinguish malware from benignware and it can also classify malware subspecies into malware families. We combine static analysis of execution files with machine learning classifier and natural language processing by machine learning. Information of DLL Import, assembly code and hexdump are acquired by static analysis of execution files of malware and benignware to create feature vectors. Paragraph vectors of information by static analysis of execution files are created by machine learning of PV-DBOW model for natural language processing. Support vector machine and classifier of k-nearest neighbor algorithm are used in our method, and the classifier learns paragraph vectors of information by static analysis. Unknown execution files are classified into malware or benignware by pre-learned SVM. Moreover, malware subspecies are also classified into malware families by pre-learned k-nearest. We evaluate the accuracy of the classification by experiments. We think that new malware subspecies can be effectively detected by our method without existing methods for malware analysis such as generic method and dynamic heuristic method.
Application of axiomatic design and design structure matrix to the decomposition of engineering systems

A design decomposition-integration model, named COPE, is proposed in which Axiomatic Design Matrices (DM) map Functional Requirements to Design Parameters while Design Structure Matrices (DSM) provide structured representation of the system development context. In COPE, the DM and the DSM co-evolve. Traversing between the two types of matrices allows for some control in the application of the system knowledge which surrounds the decision making process and the definition of the system architecture. It is argued that this approach describes better the design process of complex products which is constrained by the need to utilise existing manufacturing processes, to apply discrete technological innovations and to accommodate work-share and supply chain agreements. Presented is an industrial case study which demonstrated the feasibility of the model.
Regulation of water in agriculture field using Internet Of Things

Indian agriculture is diverse; ranging from impoverished farm villages to developed farms utilizing modern agricultural technologies. Facility agriculture area in China is expanding, and is leading the world. However, its ecosystem control technology and system is still immature, with low level of intelligence. Promoting application of modern information technology in agriculture will solve a series of problems facing by farmers. Lack of exact information and communication leadsto the loss in production. Our paper is designed to over come these problems. This regulator provides an intelligent monitoring platform framework and system structure for facility agriculture ecosystem based on IOT[3]. This will be a catalyst for the transition from traditional farming to modern farming. This also provides opportunity for creating new technology and service development in IOT (internet of things) farming application. The Internet Of Things makes everything connected. Over 50 years since independence, India has made immense progress towards food productivity. The Indian population has tripled, but food grain production more than quadrupled[1]: there has thus been a substantial increase in available food grain per ca-pita. Modern agriculture practices have a great promise for the economic development of a nation. So we have brought-in an innovative project for the welfare of farmers and also for the farms. There are no day or night restrictions. This is helpful at any time.
SIMILARITIES AND DIFFERENCES BETWEEN MANAGEMENT AND LEADERSHIP

Management is the process of setting and achieving organizational goals through its functions: forecasting, organization, coordination, training and monitoring-evaluation.Leadership is: the ability to influence, to make others follow you, the ability to guide, the human side of business for "teacher". Interest in leadership increased during the early part of the twentieth century. Early leadership theories focused on what qualities distinguished between leaders and followers, while subsequent theories looked at other variables such as situational factors and skill levels. Other considerations emphasize aspects that separate management of leadership, calling them two completely different processes.The words manager and lider are very often used to designate the same person who leads, however, they represent different realities and the main difference arises form the way in which people around are motivated. The difference between being a manager and being a leader is simple. Management is a career. Leadership is a calling. A leader is someone who people naturally follow through their own choice, whereas a manager must be obeyed. A manager may only have obtained his position of authority through time and loyalty given to the company, not as a result of his leadership qualities. A leader may have no organisational skills, but his vision unites people behind him. Leadership and management are two notions that are often used interchangeably. However, these words actually describe two different concepts. Leadership is the main component of change, providing vision, and dedication necessary for its realization. Leadership is a skill that is formed by education, experiences, interaction with people and inspiring, of course, practice. Effective leadership depends largely on how their leaders define, follow and share the vision to followers. Leadership is just one important component of the directing function. A manager cannot just be a leader, he also needs formal authority to be effective.
A REVIEW ON IoT PROTOCOLS FOR LONG DISTANCE AND LOW POWER

The evolution of IoT network is basically the effect of requirement of a system that is having better capabilities compared to existing ones. One main feature of the system that we need to satisfy is long range and low power. The protocols IEEE 802.15.4, 6LoWPAN, IPv6 and CoAP are used for achieving low rate, low power, large address space and reducing the data space respectively. In this paper we discuss other protocols which are intended mainly for providing long distance transmission with the less amount of power consumption. It mainly says about LoRa (Long Range protocol), IEEE 802.22 and weightless.
Multidimensional Assessment of Emotion Regulation and Dysregulation : Development , Factor Structure , and Initial Validation of the Difficulties in Emotion Regulation Scale 1

Given recent attention to emotion regulation as a potentially unifying function of diverse symptom presentations, there is a need for comprehensive measures that adequately assess difficulties in emotion regulation among adults. This paper (a) proposes an integrative conceptualization of emotion regulation as involving not just the modulation of emotional arousal, but also the awareness, understanding, and acceptance of emotions, and the ability to act in desired ways regardless of emotional state; and (b) begins to explore the factor structure and psychometric properties of a new measure, the Difficulties in Emotion Regulation Scale (DERS). Two samples of undergraduate students completed questionnaire packets. Preliminary findings suggest that the DERS has high internal consistency, good test–retest reliability, and adequate construct and predictive validity.
An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems

In this paper, we introduce an enhancement for speech recognition systems using an unsupervised speaker clustering technique. The proposed technique is mainly based on I-vectors and Self-Organizing Map Neural Network (SOM). The input to the proposed algorithm is a set of speech utterances. For each utterance, we extract 100-dimensional I-vector and then SOM is used to group the utterances to different speakers. In our experiments, we compared our technique with Normalized Cross Likelihood ratio Clustering (NCLR). Results show that the proposed technique reduces the speaker error rate in comparison with NCLR. Finally, we have experimented the effect of speaker clustering on Speaker Adaptive Training (SAT) in a speech recognition system implemented to test the performance of the proposed technique. It was noted that the proposed technique reduced the WER over clustering speakers with NCLR.
A comparison between SVM and multilayer perceptron in predicting an emerging financial market: Colombian stock market

Achieving accurate stock market forecast impacts strongly to investors, like retirement funds and private investors, giving them tools for making better data based decisions. This article studies the applicability of two soft computing methods, Artificial Neural Networks and Support Vector Machines, to forecast Colombian stock market. Technical indicators were selected as inputs of the machine learning techniques, and up/down movement was selected as output. Cross-validation was employed to improve generalization, and automatic parameter tuning was performed to improve model performance. The results showed that Support Vector Machines performance was better than Artificial Neural Networks, and the results are similar to those found in other studies.
The Fiscal Multiplier ∗

We measure the size of the fiscal multiplier using a model with incomplete markets and rigid prices and wages. Allowing for incomplete markets instead of complete markets—the prevalent assumption in the literature—comes with two advantages. First, the incomplete markets model delivers a realistic distribution of the marginal propensity to consume across the population, whereas all households counterfactually behave according to the permanent income hypothesis if markets are complete. Second, in our model the equilibrium response of prices, output, consumption and employment to fiscal stimulus is uniquely determined for any monetary policy including the zero-lower bound. We find that market incompleteness plays the key role in determining the size of the fiscal multiplier, which is slightly above or below 1 depending on whether spending is tax or deficit financed. The size of fiscal multiplier remains similar in a liquidity trap.
Public versus Private Blockchains Part 1 : Permissioned Blockchains

Blockchain-based solutions are one of the major areas of research for institutions, particularly in the financial and the government sectors. There is little disagreement that backbone technologies currently used in these sectors are outdated and need an overhaul to conform to the needs of the times. Distributed or decentralized ledgers in the form of blockchains are one of themost discussed potential solutions to the stated problem. We provide a description of permissioned blockchain systems that could be used in creating secure ledgers or timestamped registries. We contend that the blockchain protocol and data should be accessible to end users to provide a higher level of decentralization and transparency and argue that proof ofwork could be effectively used in permissioned blockchains as a means of providing and diversifying security.
Discovering Event Evolution Chain in Microblog

Microblog, such as Weibo and Twitter, has become an important platform where people share their opinions. Much research has been done to detect topics and events in microblogs. Due to the dynamic nature of events, it is more crucial to monitor the evolution and trace the development of the events. People pay more attention to the whole evolution chain of the events rather than a single event. In this paper, we propose a method to automatically discover event evolution chain in microblogs based on multiple similarity measures including contents, locations and participants. We build a 5-tuple event description model specifically for events detected from microblogs and analyze their relationships. Inverted index and locality-sensitive hashing are used to improve the efficiency of the algorithm. Experiment shows that our method gain a 143.33% speed up against method without locality-sensitive hashing. In comparison with the ground truth and a baseline method, the result illustrates that it effectively covers ground truth and outperforms the baseline method especially in dealing with the long-term spanning events.
Migrant mothers, left-behind fathers: the negotiation of gender subjectivities in Indonesia and the Philippines

The distinct feminization of labour migration in Southeast Asia - particularly in the migration of breadwinning mothers as domestic and care workers in gender-segmented global labour markets - has altered care arrangements, gender roles and practices, as well as family relationships within the household significantly. Such changes were experienced by both the migrating women and other left-behind members of the family, particularly 'substitute' carers such as left-behind husbands. During the women's absence from the home, householding strategies have to be reformulated when migrant women-as-mothers rewrite their roles (but often not their identities) through labour migration as productive workers who contribute to the well-being of their children via financial remittances and 'long-distance mothering', while left-behind fathers and/or other family members step up to assume some of the tasks vacated by the mother. Using both quantitative and qualitative interview material with returned migrants and left-behind household members in source communities in Indonesia and the Philippines experiencing considerable pressures from labour migration, this article explores how carework is redistributed in the migrant mother's absence, and the ensuing implications on the gender roles of remaining family members, specifically left-behind fathers. It further examines how affected members of the household negotiate and respond to any changing gender ideologies brought about by the mother's migration over time.
Learning with Latent Language

The named concepts and compositional operators present in natural language provide a rich source of information about the kinds of abstractions humans use to navigate the world. Can this linguistic background knowledge improve the generality and efficiency of learned classifiers and control policies? This paper aims to show that using the space of natural language strings as a parameter space is an effective way to capture natural task structure. In a pretraining phase, we learn a language interpretation model that transforms inputs (e.g. images) into outputs (e.g. labels) given natural language descriptions. To learn a new concept (e.g. a classifier), we search directly in the space of descriptions to minimize the interpreter’s loss on training examples. Crucially, our models do not require language data to learn these concepts: language is used only in pretraining to impose structure on subsequent learning. Results on image classification, text editing, and reinforcement learning show that, in all settings, models with a linguistic parameterization outperform those
Mobile Payment: Towards a Customer-Centric Model

Mobile payment normally occurs as a wireless transaction of monetary value and includes the initiation, authorization and the realization of the payment. Such transactions are facilitated by purpose-built mobile payment systems that are part of the service infrastructure supporting the functioning of mobile business applications. A number of stakeholder groups may be involved in concluding a mobile payment transaction, among them customers, mobile operators, financial institutions, merchants, and intermediaries. In this paper, mobile payment systems are characterised from the point of view of the stakeholder groups. Building on existing work, a supply and demand model for the investigation of mPayment services is presented, and applied to a case study.
Weak greedy algorithms

Theoretical greedy type algorithms are studied: a Weak Greedy Algorithm, a Weak Orthogonal Greedy Algorithm, and a Weak Relaxed Greedy Algorithm. These algorithms are defined by weaker assumptions than their analogs the Pure Greedy Algorithm, an Orthogonal Greedy Algorithm, and a Relaxed Greedy Algorithm. The weaker assumptions make these new algorithms more ready for practical implementation. We prove the convergence theorems and also give estimates for the rate of approximation by means of these algorithms. The convergence and the estimates apply to approximation from an arbitrary dictionary in a Hilbert space.
Population validity for educational data mining models: A case study in affect detection

ICT-enhanced research methods such as educational data mining (EDM) have allowed researchers to effectively model a broad range of constructs pertaining to the student, moving from traditional assessments of knowledge to assessment of engagement, meta-cognition, strategy, and affect. The automated detection of these constructs allows EDM researchers to develop intervention strategies that can be implemented either by the software or the teacher. It also allows for secondary analyses of the construct, where the detectors are applied to a data set that is much larger than one that could be analyzed by more traditional methods. However, in many cases, the data used to develop EDM models is collected from students who may not be representative of the broader populations who are likely to use ICT. In order to use EDM models (automated detectors) with new populations, their generalizability must be verified. In this study, we examine whether detectors of affect remain valid when applied to new populations. Models of four educationally relevant affective states were constructed based on data from urban, suburban, and rural students using ASSISTments software for middle school mathematics in the Northeastern United States. We find that affect detectors trained on a population drawn primarily from one demographic grouping do not generalize to populations drawn primarily from the other demographic groupings, even though those populations might be considered part of the same national or regional culture. Models constructed using data from all three sub-populations are more applicable to students in those populations than those trained on a single group, but still do not achieve ideal population validity—the ability to generalize across all sub-groups. In particular, models generalize better across urban and suburban students than rural students. These findings have important implications for data collection efforts, validation techniques, and the design of interventions that are intended to be applied at scale.
Multimodal learning using convolution neural network and Sparse Autoencoder

In the last decade, pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease (AD) have been the subject of extensive research. Deep learning has recently been a great interest in AD classification. Previous works had done almost on single modality dataset, such as Magnetic Resonance Imaging (MRI) or Positron Emission Tomography (PET), shown high performances. However, identifying the distinctions between Alzheimer's brain data and healthy brain data in older adults (age > 75) is challenging due to highly similar brain patterns and image intensities. The corporation of multimodalities can solve this issue since it discovers and uses the further complementary of hidden biomarkers from other modalities instead of only one, which itself cannot provide. We therefore propose a deep learning method on fusion multimodalities. In details, our approach includes Sparse Autoencoder (SAE) and convolution neural network (CNN) train and test on combined PET-MRI data to diagnose the disease status of a patient. We focus on advantages of multimodalities to help providing complementary information than only one, lead to improve classification accuracy. We conducted experiments in a dataset of 1272 scans from ADNI study, the proposed method can achieve a classification accuracy of 90% between AD patients and healthy controls, demonstrate the improvement than using only one modality.