Reviewerly/exHarmony huggingface dataset:
Social Jetlag and Corporate Innovation: Evidence from Time Zone Discontinuities

This paper investigates the effects of sleep deprivation on corporate innovation, using a discontinuity in sleep behavior across the three US time zone borders. We find that a lack of sleep has a negative effect on corporate innovation in terms of innovation radicalness and impact. The results are robust to a battery of empirical tests including different model specifications, a different causal inference strategy, and alternate variable operationalizations. Our findings highlight the importance of sleep on corporate innovation and introduce time zone borders as an important novel factor for firms to keep in mind when deciding where to locate. In addition, our findings also indicate that geographical factors other than infrastructure, tangible resources, and agglomeration are critical to the firm’s innovation.
Image Registration: Fundamentals and Recent Advances Based on Deep Learning

Abstract Registration is the process of establishing spatial correspondences between images. It allows for the alignment and transfer of key information across subjects and atlases. Registration is thus a central technique in many medical imaging applications. This chapter first introduces the fundamental concepts underlying image registration. It then presents recent developments based on machine learning, specifically deep learning, which have advanced the three core components of traditional image registration methods—the similarity functions, transformation models, and cost optimization. Finally, it describes the key application of these techniques to brain disorders.
Solving Problem with Generalists and Specialists: An Agent-based Simulation Model

Organizations must often solve problems with insufficient knowledge. A solver can be specialized in a narrow area of expertise or be a generalist with a broader (yet shallow) understanding of the problem. Despite this well-recognized difference in knowledge structure between generalists and specialists, little is known about its implication on problem-solving performance. This is due to a lack of theoretical understanding of the distinction between knowledge depth and breadth that recognizes that deepening knowledge implies accumulating outcomes within a narrow set of domains, as opposed to broadening knowledge which implies being able to make connections between more domains. In this paper, we model generalists and specialists using knowledge depth and breadth, which alters their search behaviors on a multi-level NK fitness landscape. We propose that generalists and specialists have structurally different knowledge sets, which affords them to have different problem-solving capabilities and consequently will contribute solutions differently, especially when collaborating. Specialists can perceive finer granularity and provide local refinement, while generalists can perceive more interdependencies and provide broad repositioning of problems. Generalists and specialists also differ in the way they misunderstand the problem space. These insights advance our understanding of how we might best coordinate generalists and specialists within organizations.
A Comparative Study for Prediction of Hematopoietic Stem Cell Transplantation-Related Mortality

Multipotent hematopoietic stem cells are transplanted during hematopoietic stem cell therapy (HSCT) in order to replicate inside of a patient and produce more healthy blood cells. These cells are often taken from bone marrow, peripheral blood, or umbilical cord blood. Patients with particular blood or bone marrow malignancies, like multiple myeloma or leukemia, are those who have the procedure the most frequently. Though a lifesaving procedure, it comes with its risk; hence, to minimize the risk, prediction of survivability and the factors affecting it is crucial. In this study, the authors have done an extensive and rigorous analysis of various works done in predicting the mortality of patients undergoing hematopoietic stem cell transplantation that involves the use of machine learning and data mining techniques and have compared them. This study gives an overview of the available machine learning and data mining techniques in improving risk prediction for patients undergoing HSCT that provides an alternative to traditional risk scores and indexes. Though these techniques already outperform the in-use risk scores and indexes, they can be still improved upon by the use of a specialized and large amount of data.
Aims To Increase Civics Activity and Learning Outcomes Through the Use of Team Game Tournament Methods on Senior High School Student

This research aims to increase student activity and learning outcomes using the Team Game Tournament (TGT) method in Civics subjects in class XII ATPH SMK Negeri 3 Salatiga. The issues of this study amounted to 36 students. Data collection techniques were observation and tests, while data analysis techniques used comparative descriptive techniques. The results obtained from this study indicate that the TGT learning method can increase student activity and learning outcomes in Civics subjects. This is evident from the average increase in student learning activeness in the first cycle of the 1st meeting 47.23% (17 students) in the 2nd meeting 69.44% (25 students), then increased in the second cycle of the 1st meeting 80, 56% (29 students) to 91.66% (33 students) at the 2nd meeting. Student learning outcomes also increased in each cycle. Pre-cycle 13.89% (5 students) who scored KKM ≥ 77 increased to 77.78% (28 students) in cycle I, and rose again to 94.44% (34 students) in cycle II. Based on the results of this study, it can be concluded that using the TGT method can increase the activity and learning outcomes of civics subjects’ students of class XII ATPH SMK Negeri 3 Salatiga.
LEGAL REFLECTIONS ON THE DOWNSIDES OF GLOBAL SYSTEM FOR MOBILE COMMUNICATION AS A BOOST TO CRIME OF KIDNAPPING FOR RANSOM IN NIGERIA

The advent of global system for mobile communication otherwise called GSM is no doubt a mixed blessing to humanity. Just as the relevant security agencies are appropriating the benefits offered by the global system for mobile communication in the policing, detecting and preventing acts of criminality in the society, so also the criminal elements are beneficially misusing the communication device for criminal purposes. It is a known fact that crime is continually evolving and adapting. Kidnapping as a form of crime in Nigeria is not novel as there were reported cases of kidnapping even before the advent of colonialism for either rape, ritual or hostage taking during tribal conflicts among other reasons which then certainly excluded ransom payment. The current spate of crime of kidnapping for ransom is however one of the misfortunes unleashed on the citizenry through the advent of mobile communication device. Kidnapping for ransom in Nigeria would have been a fruitless criminal indulgence but for the advent of global system for mobile communication through which kidnappers are able to establish contact with relatives or family members of their victims to demand, for ransom payment. The diametrically opposing usage of this communication device as a handy tool in the hands of law enforcement agents towards checkmating crimes in the society and as a tool of criminal engagement for committing crime of kidnapping for ransom in Nigeria constitutes the thrust of this paper. This study concluded that despite the efforts of government in curbing this menace, it has remained a thorn in the flesh of victims and their relatives.
Penggunaan Media Benda Konkret Dalama Upaya Meningkatkan Minat Belajar Matematika Siswa SD Negeri 6 Mas

Mathematics is one of the important lessons so that students can later apply it in solving problems in everyday life. Unfortunately mathematics is often regarded as a difficult and frightening subject. However, this is not very visible for students at SD Negeri 6 Mas. This is suspected because the teacher uses concrete media objects in the process of learning mathematics to increase students' interest in learning. Students seem happy and enthusiastic during learning activities. Interest in learning greatly determines the success of the teaching and learning process in the classroom. This study aims to: 1) describe the use of concrete object media in an effort to increase students' interest in learning mathematics, and 2) describe the impact of the use of concrete object media. The theory used to analyze this problem is the theory of classical conditioning and behavioristic theory. Methods of data collection using observation, interviews, and documentation. The collected data were analyzed using triangulation techniques. The results of the study show that: 1) the use of concrete object media in mathematics learning is applied to certain materials, most of the material in the lower class can use concrete object media as teaching media, and only a few materials in the high class can use concrete object media as teaching media; 2) the media of concrete objects has an impact on increasing students' interest in learning as indicated by the attitude of not getting bored easily, feeling happy and excited to take part in learning, enthusiastic and concentrating, so it is easy to remember what they have learned.
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation

The performance of current Scene Graph Generation (SGG) models is severely hampered by hard-to-distinguish predicates, e.g., “woman-on/standing on/walking on-beach”. As general SGG models tend to predict head predicates and re-balancing strategies prefer tail categories, none of them can appropriately handle hard-to-distinguish predicates. To tackle this issue, inspired by fine-grained image classification, which focuses on differentiating hard-to-distinguish objects, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Fine-Grained Predicates Learning (FGPL-A)</i> which aims at differentiating hard-to-distinguish predicates for SGG. First, we introduce an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Predicate Lattice (PL-A)</i> to figure out hard-to-distinguish predicates, which adaptively explores predicate correlations in keeping with model's dynamic learning pace. Practically, PL-A is initialized from SGG dataset, and gets refined by exploring model's predictions of current mini-batch. Utilizing PL-A, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Category Discriminating Loss (CDL-A)</i> and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Entity Discriminating Loss (EDL-A)</i> , which progressively regularize model's discriminating process with fine-grained supervision concerning model's dynamic learning status, ensuring balanced and efficient learning process. Extensive experimental results show that our proposed model-agnostic strategy significantly boosts performance of benchmark models on VG-SGG and GQA-SGG datasets by up to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">175% and 76% on Mean Recall@100</i> , achieving new state-of-the-art performance. Moreover, experiments on Sentence-to-Graph Retrieval and Image Captioning tasks further demonstrate practicability of our method.
OPTIMIZATION OF WATER RESOURCES UTILIZATION DISTRIBUTION FOR AGRICULTURAL BENEFITS IN THE CLAMPIS IRRIGATION AREA SAMPANG DISTRICT

Utilization of water resources and management is an important factor and has a decisive role in order to increase production per plant in general and crop production in particular. In order to maintain self-sufficiency in food, it is necessary to make efforts to continuously increase the intensity of potential food crops. These efforts include optimizing the proper distribution of water utilization. Factors to determine the need for irrigation water include land preparation, consumptive use, percolation and seepage, replacement of water layers, and effective rainfall. While the method used in this study is optimization using a linear program using the Solver facility in Microsoft Excel with the aim of optimizing the availability of irrigation water so as to produce maximum profits and distribution of irrigation utilization that is more effective and efficient. From the optimization results using the Solver and Microsoft Excel tools, it can be seen that the maximum area of ​​land that can be planted with the available inflow at the intake for MTI (Rice I 2086 Ha/Tobacco II 667.54 Ha), MT-II (Rice I 667, 54 Ha/Tobacco I 1391 Ha/PolowijoI 567.35 Ha/Rice II 128.11 Ha), MT III (Polowijo I 323.65 Ha/Rice II 348.89 Ha/Tobacco II 1261.46 Ha/Polowijo II 825, 00) and provides an analysis of the maximum profit of agricultural products of Rp. 178,183,800,000.00,-. Keywords: Linear Program Optimization; Utilization of Water Resources; Agricultural Benefits; Klampis Irrigation.
Guiding Vector Fields for Following Occluded Paths

Accurately following a geometric desired path in a two-dimensional space is a fundamental task for many engineering systems, in particular mobile robots. When the desired path is occluded by obstacles, it is necessary and crucial to temporarily deviate from the path for obstacle/collision avoidance. In this chapter, we develop a composite guiding vector field via the use of smooth bump functions, and provide theoretical guarantees that the integral curves of the vector field can follow an arbitrary sufficiently smooth desired path and avoid collision with static and moving obstacles of arbitrary shapes. These two behaviors are reactive since path (re)-planning and symbolic map construction are not involved. To deal with the common deadlock problem, we introduce a switching vector field, and the Zeno behavior is excluded. We also elaborate on the extensions of our approach to higher-dimensional spaces and nonholonomic robot models. Simulations are conducted to support the theoretical results.
Layanan pelanggan berbasis Natural Language Processing melalui chatbot pada aplikasi pesan

The form of service to customers in an appropriate, fast and easy way is the main thing that every business actor needs to pay attention to. Including how to communicate with customers virtually through messaging applications. With the sophistication of information technology, it can be used to create new opportunities for business actors. One of them is Natural Language Processing or NLP technology which allows machine interaction with human natural language. This study aims to provide messaging services to customers effectively and efficiently. Implementation is done by using a chatbot on the messaging application. Chatbot is designed to be able to respond to customer messages automatically and in real-time by implementing the NLP method. Based on the observations made, it is known that the three categories of questions most frequently asked by customers are location, price list, and pick-up service. The results of the study show that chatbots created using natural language can be easily understood and used by customers. Business actors are also facilitated because they no longer need to reply to customer messages one by one, so that service to customers becomes more responsive and optimal.
Stop Watching Me! Potential Negative Effects of Electronic Monitoring on Employees’ Job Performance

Our study conceptualizes how agency theory depicts a relationship between an employer’s use of electronic monitoring and employee performance by incorporating the effects of employee self-evaluation and self-determination from self-determination theory. Electronic monitoring is a growing practice for most companies to ensure the quality of employees’ work when they work from home. Agency theory suggests that electronic monitoring, as a form of surveillance may prevent employees from behaving opportunistically and impact employee performance positively. However, self-determination theory suggests that an employer’s use of electronic monitoring to deter employees’ unwanted behaviors may reduce employees’ felt trust and employees’ perceived autonomy, thus resulting in lower job performance. To disentangle the conflicting outcomes surrounding employee performance in the context of employer’s electronic monitoring, we posit that the intensity of telework will impact employees’ job performance by affecting the relationship between electronic monitoring and employees’ felt trust and autonomy. More specifically, we posit that the use of electronic monitoring bears a negative relationship with employees’ performance, especially when the intensity of telework is high. We suggest that the opposite holds true with the low-intensity of telework (e.g., hybrid), such that employee performance is not negatively compromised given the information exchange that employees receive when in-person reduces the deleterious effects of information asymmetry. We will conceptualize the relationships underlying these expectations in this study.
Multilayer Perceptron Learning for Decreasing the Resistance of Graphene Oxide Thin Films

The capability of the classifier to differentiate between the similarities and differences in the resistance of thin films of reduced Graphene Oxide (rGO) is put to the test via a process known as multilayer Perceptron optimisation. In particular, for the purpose of this research, we used the learning methods of scaled conjugate gradient, levenberg-marquardt, and robust back propagation. As the dataset for this investigation, the sheet resistance of rGO thin films that was given by MIMOS Bhd was used. In order for us to carry out this test, we had to gather data from several sheets of rGO thin film, each of which included a unique combination of thickness and resistance. Normalisation, randomization, and splitting were the three types of pre-processing that were applied to both the input and the output data. There was a thirty-seven percent split between the data used for training, fifteen percent split between the data used for validation, and fifteen percent split between the data used for testing. Different numbers of hidden neurons, ranging from one to ten, were used in MLP in order to enhance the effectiveness of the learning procedures. Because to their hard work, the most cutting-edge learning algorithms that have ever been built for finding MLP in rGO sheet resistance uniformity have been produced. Measurements were taken of everything from mean squared errors (MSE) to accuracy throughout all of the training, validation, and testing datasets, as well as overall performance. The whole investigation was dependent on using the MATLAB programme, version R2018a, to carry out in a mechanised manner all of the necessary analytical procedures. Throughout the course of constructing a knowledge process in MLP used for rGO sheet confrontation, it was found that the LM had a major influence on the whole process. The MSE for LM has been lowered the greatest, particularly in comparison to SCG and resilient backpropagation (RP).
All the World is on Stage: Social Dynamics in Q&amp;A Situations

We theorize how social dynamics between top executives and constituents in question-and-answer situations (Q&As)—such as earnings calls, press conferences, and U.S. congressional hearings—affect social evaluations of organizations. We define Q&A social dynamics as relational episodes that unfold between executives and questioners or between participating executives while they verbally interact with each other in a Q&A situation. We first provide a typology that distinguishes four generalized types of Q&A social dynamics. Second, we theorize that Q&A social dynamics induce audiences to draw a unique set of inferences regarding the executives’ and the top management team’s (TMT) cognitive and affective dispositions and abilities, specifically: the executives’ responsiveness; their respect for stakeholders; the degree to which the TMT members share mental models; and the TMT’s cohesion. Finally, we formalize nomothetic propositions on how these inferences, and, in turn, different kinds of social evaluations are driven by specific examples of Q&A social dynamics. Our conceptual framework contributes by moving beyond audiences’ evaluative responses to substantive informational aspects of Q&A situations and executives’ isolated (verbal) behavior, focusing instead on audiences’ evaluations associated with the inherently interactive nature of Q&A situations—a ubiquitous, theoretically distinct, but undertheorized setting of external communication.
Pelatihan Pembuatan Alat Permainan Edukatif untuk Meningkatkan Kreativitas Guru PAUD

Educational Game Tools (APE) is a basic need for teachers in providing stimulation for children. This activity aims to overcome the problem of teachers who lack knowledge and skills in making educational game tools (APE) so that it is hoped that after this activity teachers will gain knowledge and skills in making APE in addition to the variety of APE in PAUD units will support the growth and development of students . This activity uses several methods, namely lectures, demonstration discussions, exercises/practices, mentoring, and question and answer. The material in this activity is making aprons, finger puppets using flannel. The results of this activity consist of: (1) increasing the knowledge and skills of teachers in making APE media; (2) the products produced are scientific aprons, finger puppets; (3) publication in mass media; and videos. Based on the observations, it is very necessary for teachers to have skills in making various APE media that are innovative, challenging and fun without requiring large costs. Using educational game tools will overcome children who are bored in carrying out activities in class.&#x0D;
Analisis prediktif perubahan nilai profit berdasarkan klasifikasi pengguna pada usaha jasa logistik

The delivery of logistics services continues to experience a rapid increase in line with the high sales of goods through e-commerce. According to the predetermined standards, each cargo expedition outlet is required to meet the monthly shipment achievement target (tonnage) to avoid fines and an increase in the target rate. This study aims to find out how to anticipate failure to achieve targets, so as to prevent additional operating expenses and profit instability. In this study, quantitative analysis was carried out using two algorithms. The Naïve Bayes algorithm is used to classify service user categories. The results showed that the industrial category contributed greatly to the delivery of cargo expeditions with a total tonnage percentage of 69.33%, while the remaining 30.67% was included in the individual category. Furthermore, the Multiple Linear Regression algorithm is used to predict profit values based on category classes. Predictions were made from October to December which resulted in an increase in profit for the individual category and a decrease in profit for the industrial category. Recommendations that can be made include royalty rewards for customers, brand awareness to attract new customers, as well as business cooperation with MSMEs and other business actors around them.