Reviewerly/exHarmony huggingface dataset:
Concept Drift Detection Methods for Deep Learning Cognitive Radios: A Hardware Perspective

Deep learning models usually assume that training dataset and target data have the same distribution. If this is not the case, model mismatch causes performance degradation when the model is used with the real data. With radio frequency (RF) measurements from real data traffic, the exact distribution of the measurements is unknown in many cases and model mismatch is unavoidable. This is known as concept drift, or model mis- specification in deep learning, which we are interested in for cognitive radio dynamic spectrum access predictions. In this paper, we present three concept drift detection methods and their corresponding very large scale integration (VLSI) circuits. The circuits are mapped on a Xilinx Virtex-7 field-programmable gate array (FPGA) and the resource utilization results are provided.
Location Distribution as a Side Channel Countermeasure

In this paper, location distribution is proposed as a novel cryptographic side channel countermeasure that is scalable and algorithm agnostic. Location distribution utilizes the unallocated resources of an FPGA to instantiate multiple instances of an encryption core. We suspect that this will offer a significant increase in the number of traces required to successfully perform an EM side channel attack. A three-part methodology is also presented that will be used to test the countermeasure in the future. All three tests will be measuring the number of traces required to perform a successful differential power analysis and differential EM analysis attack. The first test compares a traditional single encryption core system vs. a distributed three core system of AES-128 to test the validity of the proposed side channel countermeasure. The second test will be plotting the number of traces required to perform a successful power analysis and EM analysis attack as the number of encryption cores increases from one to five to evaluate how the countermeasure scales. The final test will repeat the first test except using RSA, ECC, and SHA-3 to compare against the regression found in part two to test if the countermeasure is algorithm agnostic.
Unsupervised Semantic Discovery Through Visual Patterns Detection

We propose a new fast fully unsupervised method to discover semantic patterns. Our algorithm is able to hierarchically find visual categories and produce a segmentation mask where previous methods fail. Through the modeling of what is a visual pattern in an image, we introduce the notion of “semantic levels" and devise a conceptual framework along with measures and a dedicated benchmark dataset for future comparisons. Our algorithm is composed by two phases. A filtering phase, which selects semantical hotsposts by means of an accumulator space, then a clustering phase which propagates the semantic properties of the hotspots on a superpixels basis. We provide both qualitative and quantitative experimental validation, achieving optimal results in terms of robustness to noise and semantic consistency. We also made code and dataset publicly available.
Explainable Subgradient Tree Boosting for Prescriptive Analytics in Operations Management

Motivated by the overwhelming success of gradient boosting approaches in machine learning and driven by the need for explainable prescriptive analytics approaches in operations management (OM), we propose an explainable prescriptive analytics approach to solving complex OM problems: subgradient tree boosting (STB). The STB approach combines the well-known method of subgradient descent in function space with sample average approximation, and directly prescribes decisions from a problem-specific loss function, historical demand observations, and prescriptive features. The approach is inherently explainable and allows a decision-maker to derive detailed explanations for the prescribed decisions, such as a breakdown of individual features' impact that clarifies the underlying drivers of the prescriptions, which is increasingly useful in practice. We show how subgradients can be derived for many common OM problems, demonstrate STB's applicability to two real-world, complex capacity planning problems in the service industry, benchmark its performance against those of two prescriptive approaches—weighted sample average approximation and kernelized empirical risk minimization—and show how STB's prescriptions can be explained by estimating the impact of individual features. The results suggest that STB's performance is comparable to those of weighted sample average approximation and kernelized empirical risk minimization but also provides explainable prescriptions.
Desain Pintu Air Berbantu Komputer Untuk Saluran Irigasi Tersier di Daerah Irigasi Cikarawang Bogor

Irrigation water gate was one of the supporting components of agricultural activities, especially the management of rice fields, because it was utilized in the management of water flow. The implementation of water gate has a complex and interdependent set of tasks. So that was the problem which can made undesired effects. So that, the planning can be shorted by computer design . Besides, the changes in a design could be overcome quickly and precisely. The purpose of this research is to the effective rainfall, evapotranspiration, discharge of irrigation water needs in the Cikarawang irrigation area, produce a design of computer-aided water gate, and know RAB for the construction of the water gate. The study was conducted from April to July 2020. The place to do the research was Cikarawang Village, Bogor Regency, West Java. The results of the study were the design of water gate by using data plotting with computer-aided script files (SCR). The effective rainfall of 1806.3 mm, the highest evapotranspiration of 5.5 mm/day and the lowest evapotranspiration of plants at 4 mm/day, the highest irrigation water demand was 2.18 lt/dt/ha, The dimensions of 0.5 x 0.75 x 0.012 m, and water gate opening 0.27 m. These water gate openings were used when the plant's water requirements are highest. The cost of making water gate made from fiberglass is Rp. 838.000,-while for steel costs Rp. 3.500.000,-.
New Data Structure for Many-to-Many Relations to Reduce Data Size, Recording Time, and Search Time

This paper proposes a new data structure that reduces data sizes for data in which two item types have many-to-many relations, with the aim of reducing data recording and search times. The proposed data structure newly introduces container variables related to many values of both items, and these container variables record many-to-many relations between them. The proposed data structure maintains data normalization and integrity and is independent of the indexing method conventionally used for the relational database, so both can be used simultaneously. Simulation results shows that data size, recording time search time using the proposed data are smaller than those using the conventional data structure. Especially, search time is extremely shortened when searching in main memory, which is often used for data search on NoSQL, such as Key-Value systems.
Conceptual Analysis of Product Evaluations Using Deep Learning

The process of classifying a small text piece into positive, negative or neutral is called as Sentiment Analysis. For example, if a message is considered, then the meaning of the sentence is identified and then analysis of the sentence has to be done. After analysis, we decide that the sentence is either positive, either negative or neutral. This is referred to as Sentiment Analysis. Nowadays, most of the people prefer the online products compared to the direct buyers. So, the online marketing is growing very high. In fact, the online marketing is fully satisfying the customer needs so that it occupies a great place in people's heart. They are also satisfying the demands of customers with high quality and low price. These are the primary reasons for the online marketing to be in a successful position. If an e-commerce company has an ability to gather information about the customer thoughts and behavior, then the online marketing will be more effective than now. In this paper, we have taken a particular e-commerce dataset called Flipkart and classify the polarity of the comments by using some of the classifiers namely Support Vector Machine Classifier, Guassian Naïve Bayes Classifier and Random Forest Classifier and Multilayer Perceptron Classifier. The existing approach is that the comments were classified based on the attitude of the customer. But, now the proposed approach has been implemented with the help of Multilayer Perceptron (MLP) Neural Network Classifier which is simulated by the SPYDER tool. The accuracy for all the four algorithms namely, Support Vector Machine, Random Forest, Naïve Bayes and Multilayer Perceptron (MLP) Neural Network algorithm has been computed and the best accuracy has been predicted by comparing them. Here, the Multilayer Perceptron (MLP) shows the best accuracy of all others and the accuracy is 99.94%. Moreover, some performance metrics has been determined here. The performance metrics namely Precision, Recall and F-measure has been evaluated for each algorithm individually and then the comparison is made among them. Then, the ROC curve is measured for the classes designated by us for revealing the recognition of parameters between two diagnostic groups. It is predicted individually for each class. At last, the Confusion Matrix is going to enumerate for each algorithm distinctly. It presents the actual and predicted values in a tabular format thereby measuring the performance of a classifier.
The Visualization of ISO/IEC29110 on SCRUM under EPF Composer

In the midst of an increasingly competitive software industry, very small entities (VSEs) have inevitably faced many challenges. High user expectations, frequent changes of user requirements, and the need for rapid deployment are classic examples of these challenges. Many software companies attempt to implement measures for preventing or solving the aforementioned problems. The use of agile methodologies and the implementation of software development standards are usually perceived to be promising solutions to improve the quality of the software development process. Nevertheless, there are several strong incompatibilities between standards and the Agile approach to software development. For example, the need identified in the standards to create many quality artifacts does not conform to agility philosophies. Since Agile focuses on the working software over the documentation, the use of the Agile with standards can be difficult to implement. Additionally, there has been none guidelines for VSE therefore, an external consultant is usually required. This research analyzes various cases of implementing ISO/IEC29110, a software development standard developed especially for VSEs in Scrum environments. The results of this study provide an Eclipse Process Framework (EPF) for effectively and conveniently implementing this standard in Scrum software development.
A Formal Proof of the Soundness of the Hybrid CPS Clock Theory

In this paper, we presented a formalization to the Clock Theory of He Jifeng in the Isabelle interactive theorem prover, we described the basic concepts of the theory in Isabelle and proved its soundness for programming hybrid systems.
Building Scenarios on Mobile Network Testbed with a Transmission Characteristics Similarity Model

Optimizing the Quality of Experience (QoE) of mobile video streaming service has emerged as a hot research topic. Simulation has been the dominant research methodology of validation. However, it is becoming clear that simulation does not sufficiently capture the network capacity irregularity of a real mobile network, especially with mobility. The researchers tend to evaluate their optimizations using prototype implementations by real world experiments. Instead of directly running time and cost intensive field test, it is more efficient to evaluate the optimizations on the mobile network testbed. Only when the transmission characteristics of the testbed is similar to those of the actual mobile network, will the results be meaningful to guide further works. In this paper, we emulate experimental scenarios on the mobile network testbed by tuning the testbed configuration parameters. In these scenarios, the testbed has similar transmission characteristics to the actual mobile network. We build a transmission characteristics data set from a real world mobile network, and train the similarity model through metric learning to guide building experimental scenarios on the testbed. The results show that the proposed similarity model outperforms state-of-the-art similarity models. We also verify that the experimental scenarios really reflect real world scenarios by evaluating the QoE of HTTP Adaptive Streaming (HAS) on the testbed and the actual mobile network.
Application of ray detection technology in landmark visual interaction

With the popularity of smart phones, augmented reality technology has also developed rapidly in recent years. Based on the Unity 3D game engine, this article uses augmented reality technology to interact and display architectural models. First, briefly introduce the development of augmented reality technology and the design process of this article, then mainly introduce the two key augmented reality technologies used in this article, and finally explain the application of Unity 3D, AR ground recognition and gesture recognition and control to build architectural models The overall process of interaction is shown and tested. The test shows that augmented reality technology can be used to display other characteristic architectural models. Through its unique experience and interaction, it can play a role in displaying local characteristic buildings and promoting local traditional culture.
Promotion Media for Children’s Health: Applicable Study of Management Child Illness

BACKGROUND: Public understanding of immunization problems, pneumonia cases, and nutrition in children is important to improve children’s health status. Provision of attractive and illustrated media is one option to increase public knowledge in supporting the success of government programs in the health sector. AIM: The purpose of this study is as a form of promotion effort to support an increase in the immunization movement in infants, a decrease in cases of malnutrition and malnutrition, and a decrease in pneumonia cases in infants. METHODS: The research method used is qualitative and quantitative study. Researchers analyzed the promoting media and the value of benefits in the community. Qualitative analyze used content analysis to get an assessment of media from cadres and parents of children. Quantitative study used univariable analysis to see understanding of illness prevention effort. Interesting picture books for immunization, pneumonia, and malnutrition are used as the promoting media. The number of samples was 20 cadres and 20 parents of children under five, done by simple random sampling technique. RESULTS: The results showed that the media used for learning nutrition, immunization, and pneumonia material was effective for cadres and the community about easy to understand, completeness, attractive, assistance, easy to use, and practically. This is evidenced by a positive assessment of the media and increased understanding of the material from 50% to 90%. CONCLUSION: Thus, it can be said that it is necessary to provide educational media to increase public knowledge and understanding of cases that often arise in the community as a problem.
Expectation-Maximization for Scheduling Problems in Satellite Communication

In this paper we address unsupervised machine learning for two use cases in satellite communication, which are scheduling problems: (i) Ka-band frequency plan optimization and (ii) dynamic configuration of an active antenna array satellite. We apply approaches based on the Expectation-Maximization (EM) framework to both of them. We compare against baselines of currently deployed solutions, and show that they can be significantly outperformed by the EM-based approach. In addition, the approaches can be applied incrementally, thus supporting fast adaptation to small changes in the input configuration.
Research on the Sharing Model of University Archives Information Resources Based on Cloud Computing

In order to overcome the security and integrity problems of the traditional university file information resource sharing model, the paper proposes a new cloud computing-based university file information resource sharing model. Supported by the cloud computing system architecture, the use of cloud computing search algorithms to fully excavate university archives information resources to realize the comprehensive sharing of university information archives, and calculate the utility parameters of university archives information resource sharing based on the mining results of cloud computing, and build a communication consumption matrix for the sharing of archives information resources in colleges and universities, and complete the construction of the sharing model of archives information resources in colleges. The comparative experiment results show that compared with the traditional sharing model, the intrusion rate and sharing transmission loss of the constructed sharing model are significantly reduced, indicating that the constructed model can effectively realize the sharing of archive information resources in universities and ensure the integrity and safety of archives.
Augmented Abstractive Summarization with Document-Level Semantic Graph

Previous abstractive methods apply sequence-to-sequence structures to generate summary without a module to assist the system to detect vital mentions and relationships within a document. To address this problem, we utilize semantic graph to boost the generation performance. Firstly, we extract important entities from each document and then establish a graph inspired by the idea of distant supervision \citep{mintz-etal-2009-distant}. Then, we combine a Bi-LSTM with a graph encoder to obtain the representation of each graph node. A novel neural decoder is presented to leverage the information of such entity graphs. Automatic and human evaluations show the effectiveness of our technique.
Vector Quantizer with Fuzzy Equivalence Relations clustering

The scalar quantizer is often used in many applications due to its simplicity and ease with which it can be implemented. However, whenever we have some constraint in terms of bit rate or distortion, the vector quantizer is almost always a better choice. This is because for a given bit rate or for a given distortion, we can always design a vector quantizer that outperforms the optimal scalar quantizer. There are several algorithms to design a vector quantizer. But, the most popular algorithm is the Linde-Buzo-Gray algorithm which is based on the k-means clustering. For the LBG algorithm, we need to specify the number of clusters as well as the initial reconstruction vectors, which are then updated in successive iterations. Often, choosing the initial reconstruction vectors is not an easy task, especially when we deal with higher dimensions. A better option would be to naturally obtain the initial partitions from the given dataset. In the present article, we describe a hierarchical clustering based vector quantizer design. With our approach, we no longer need to choose the initial reconstruction vectors, but we naturally obtain the partitions for the given bit rate. Moreover, once we obtain the partitions, we simply place our reconstruction vectors at the centroid of the partitions and hence we avoid performing successive iterations and updating the clusters.
Tendulkar’s Cat and Schrodinger’s Bat—Knowledge-Enhanced Real-Word Error Correction

Real-word errors are those errors in which a word is lexically correct or syntactically correct in the sentence it belongs to, but is contextually incorrect. As a result, they are difficult to identify and arguably even more difficult to correct. In this work, we attempt to create a model based on total word similarity which is able to correct real-word errors in sentences based on the simple assumption that a real-word error will not be semantically cohesive with respect to its context, and that the actual word which was intended, will be. The main aspects of our approach are the usage of semantic context, knowledge context, and syntactic context, all together at once. To this approach, we also retain the concept of edit penalty, which is an integral component of most spelling checkers. We modified the Microsoft Research Sentence Completion task of 2011 for measuring the performance of real-word error correction models and achieve 80% accuracy.
Fact Checking on Knowledge Graphs

Fact checking, which verifies whether a given statement is true, could play a vital role in fake news detection. For example, for a given piece of news, a potential solution could involve a series of steps, including extracting statements from the news via text parsing, checking the validity of the extracted statements (i.e., fact checking), and classifying the news as fake if some statements have been confirmed to be false and performing further fake news detection processes otherwise. Considering that knowledge graphs are a popular way of representing knowledge, which could be used for verifying or counter-verifying statements, several solutions have been proposed that make use of knowledge graphs for fact checking. In this chapter, recent studies on fact checking with the help of knowledge graphs are reviewed, and three representative solutions, namely, Knowledge Linker, PredPath, and Knowledge Stream, are introduced with some details. Specifically, Knowledge Linker utilizes the semantic proximity metrics for mining knowledge graphs, PredPath employs the link prediction method and introduces a newly defined metric, and Knowledge Stream models the fact-checking problem as an optimization problem and uses flow theory for solving the problem.
Politics and Governance in Emerging Market Countries

The rise of emerging market countries is of great significance for improving global governance. This study finds that 30 emerging market countries (E30) 1 , especially those in Africa and Asia, are relatively weak in governance. In the future, improvement in governance would be a critical factor for the economic growth of E30 countries, especially if these countries enjoy political stability. Though most of the E30 countries lack political stability, they have the capability for good governance, and their governments are striving to become more effective, which is particularly true for Asian countries. Unfortunately, for Latin American countries, corruption is a serious problem likely to undermine economic development in the region.
Fuzzy Based Deep Belief Network for Activity Recognition

In the automated machinery world, human-computer interaction plays a major role, in which human actions or activities are major key components for a better performance. Hence, activity recognition becomes an active research area, where a lot of algorithms and methods were evolved quickly. Still there is lot of challenges like view occlusions, clothing and speed of action sequence. This paper proposes a novel classifier with skeleton features, to recognize human activities. This work integrates fuzzy with Dragon Deep Belief Network in order to improve the accuracy in complex activities. From the given input videos, initially key frames are selected to extract sufficient features for classification using SSIM (Structure Similarity Measure) then Scale Invariant Feature Transform (SIFT) has been applied to selected Spatial Invariant features and Spatio-Temporal interest points has also extracted to retain temporal features. Finally, combined spatial and temporal features are used for training and testing the classifier. For implementation, input videos are chosen from two common datasets, namely KTH, Weizienam. Detailed performance analyses were done with various actions like walking, running and boxing. The proposed work improves the performance in accuracy better with the maximum range up to 1.