Reviewerly/exHarmony huggingface dataset:
“Internet + Government Service” Optimization Based on Big Data

Our country’s “Internet + government services” “The construction is also on the agenda and is in full swing. In this regard, it is of great significance to conduct research on “Internet + government services” from the perspective of big data. The purpose of this article is to conduct research based on the optimization of “Internet + government services” under big data. In this paper, from the information platforms such as How Net and Fang weipu, the keywords “Internet +” and “government service” are used as the search targets, and the relevant documents found in the search are studied in depth, and the related concepts of “Internet + government service” are studied. This article takes the Municipal Administrative Examination and Approval Bureau as the research object and mainly applies literature analysis method, questionnaire survey method, and adaptive clustering method to carry out research on “Internet + government service”. Research shows that about 88% of those with a junior high school degree or below are not familiar with “Internet + government services”. It can be seen that the level of education directly affects the acceptance of emerging things. It can be seen that the public is not familiar with “Internet + government services”, and the public prefers traditional offline methods to handle business.
On the Comparison of Static and Dynamic Metrics Toward Fault-Proneness Prediction

The notion of predicting fault-proneness by utilizing software metric data has acquired the attention of many researchers in the past three decades. The fault-proneness prediction can assist in the systematic distribution of the software development resources, as testers need to put efforts and time on only those software classes where the chances of faults are very high. This study investigates the dichotomization capability of thresholds identified through Receiver Operating Characteristic (ROC) curve and F-measure. These methods were utilized to compute the cut-off values of the software measures extracted from the jEdit software system. Besides Chidamber and Kemerer metric suite, we also assessed the prediction capability of a dynamic measure - Dynamic Coupling between Object classes (DCBO). The dynamic metrics are capable of revealing true execution behaviour of the software system as here the values can only be extracted at the run-time, therefore can handle the object oriented features, such as, polymorphism, inheritance, and dynamic binding, better than the static metrics. The experimental results highlighted the good performance of the DCBO measure, indicating this particular metric as a promising candidate for the purpose of fault proneness prediction.
Investigating the Efficiency of a Three-Dimensional Facial Tracker Using Stereo Camera Arrangements

Throughout the history of visual tracking technology, it was generally thought that smooth object movement occurred without abrupt changes. However, significant technological developments have been recently made in this field; algorithms have been developed to tackle issues like drifting, abrupt appearance change, and disappearance from scenes. In visual tracking, a 3D world is projected onto a 2D image. This results in a loss of information and various other problems including cluttered backgrounds, partial or full occlusions, realtime processing requirements, image noise, complex object motion, and illumination changes. Issues that may arise in visual tracking include partial and full occlusions, changing illumination, cluttered backgrounds, and complex object motions. Face tracking systems require basic gestural understanding as defined by the interaction environment and basic facial understanding. A system's “robustness” refers to the property that enables it to keep track of a concerned object even under complicated situations. Obtaining robust facial features that are accurate and easy to find requires evaluating multiple options and techniques. Numerous methods have been proposed and developed to combat these challenges. This paper presents an extensive analysis on the performance of stereo camera arrangement methods in detecting and tracking faces. The methods investigated offer a state-of-the-art performance in terms of improved speed, more effective handling of occlusions, and a higher quality of resulting images.
Multilingual Crawling Strategies for Information Retrieval from BRICS Academic Websites

This paper proposes a web crawler for finding details of Indian origin academicians working in foreign academic institutions. While collecting the data of Indian origin academicians, we came across BRICS nations. In BRICS, except South Africa, all other countries have university websites in native languages. Even if the English version is available, it is with lesser data that can’t make the decision of whether an academician is of Indian origin or not. This paper proposes a translation method of the data from the main website in the native language to English language. It is to be noted that google translation on such website does not give output in the desired manner. We discover the area of translation using various APIs as well as other techniques available for the same like UNL, NER (provides a supportive role for translation), NMT, etc. Also, we will explore Stanford NER and segmenter for these operations.
Voice Quality Change Due to the Amount of Training Data for Multi- and Target-Speaker WaveNet Vocoders

In recent years, with the development of deep learning, speech synthesis and voice conversion technologies using neural networks have been attracting attention. A WaveNet vocoder combines WaveNet and a vocoder, which synthesizes audio waveforms. However, to synthesize the voice of the target speaker, a large amount of voice data from that speaker is required. In previous studies, the voice data of the target speaker were reduced by using the voice data of multiple speakers (people other than the target speaker) for training. However, a WaveNet vocoder that uses the voice data of multiple speakers for training has a problem in that the voice quality deteriorates compared with a WaveNet vocoder that uses only the voice data of the target speaker for training. In this study, we proposed to improve the quality of a multi-speaker WaveNet vocoder by converting the acoustic features extracted from speech into auxiliary features and applying convolutional layers when using it for training WaveNet. Furthermore, we proposed models fine-tune the voice data of the target speaker. Additionally, to confirm the effectiveness of the proposed method, we performed experiments on subjective and objective evaluations. The evaluation experiment results confirmed that the voice quality of the multiple-speaker WaveNet vocoder with the proposed method was better than that of the conventional method.
A Predicament in Securing Blockchain Consensus via Controlling Cryptopuzzle Difficulty

Blockchain systems often employ proof-of-work consensus protocols to validate and add transactions into hashchains. These protocols involve competition among miners in solving cryptopuzzles (e.g. SHA-256 hash computation in Bitcoin) in exchange for a monetary reward. Here, we model mining as an all-pay auction, where miners' computational efforts are interpreted as bids, and the allocation function is the probability of solving the cryptopuzzle in a single attempt with unit (normalized) computational capability. Such an allocation function captures how blockchain systems control the difficulty of the cryptopuzzle as a function of miners' computational abilities (bids). In an attempt to reduce mining costs, we investigate designing a mining auction mechanism which induces a logit equilibrium amongst the miners with choice distributions that are unilaterally decreasing with costs at each miner. We show it is impossible to design a lenient allocation function that does this. Specifically, we show that there exists no allocation function that discourages miners to bid higher costs at logit equilibrium, if the rate of change of difficulty with respect to each miner's cost is bounded by the inverse of the sum of costs of all the miners. Additionally, we also show that it is necessary to have allocation functions that decrease with increasing number of players. As a result, it is difficult to achieve decentralization and accomplish secure blockchain systems with global block difficulty which relies only on total hash rate.
High-speed Hardware Accelerator for Trace Decoding in Real-Time Program Monitoring

Multicore processors are currently the focus of new and future critical-system architectures. However, they introduce new problems in regards to safety and security requirements. Real-time control flow monitoring techniques were proposed as solutions to detect the most common types of program errors and security attacks. We propose a new way to use the latest debug and trace architectures to achieve full and isolated real-time control flow monitoring. We present an online trace decoder FPGA component as a solution in the search for scalable and portable monitoring architectures. Our FPGA accelerator achieves real-time CPU monitoring with only 8% of used resources in a Zynq-7000 FPGA.
On post-truth and correctness over the Web

We propose the extension of previous work done on “the Web as a tool for proving” [2] to suggest a basis for a meta-theory for post-truth. We believe that such a theory cannot be based on the current philosophical theories about truth such as the correspondence theory, the coherence theory, the pragmatic theory or the consensus theory [8]. Neither can be based on the constructivist approach that truths in general are social constructions. “Post-truths” are often considered in the literature as real existing entities [7]; the only aspect that takes place in the real world is the processes of assigning truth-values to assertions. Our view is that the truth-assignment processes involve at least two agents – the claim initiator and the truth interpreter - which can be persons, groups of persons or even machines. The same kind of approach applies for Web-based proving which occurs at a particular time and place and involves particular people, some of them can act as administrators. Underlying the truth-assignment processes and the proving processes over the Web, we have some kind of social event. The nature of the Web allows the participation of people who have not necessarily particular skills as members of a certain group. Web proving has been studied [2] as a particular type of Goguen's proof events [3][4]. Proof events, are social events that involve particular persons that form social groups of experts with particular knowledge and skills. These groups are open and have no internal hierarchical structure but they usually have at least one administrator who acts as an overseer of the correctness of the proving processes. In any case, a proof event apart from involving specialists, involves mediating objects such as spoken words, data, videos, scientific papers, etc. Web-based proof-events have a social component, communication medium, prover-interpreter interaction, interpretation process, understanding and validation, historical component, and styles. By truth-interpretation we understand the determination of the definition or meaning of the signs that are fixed by the language or semiotic code of communication used for the claim initiation or for what is thought as truth- assignment. Interpretation is an active process of interactive nature, as allowed by the open Web architecture [1][2].
Model-theoretic Characterizations of Rule-based Ontologies

An ontology specifies an abstract model of a domain of interest via a formal language that is typically based on logic. Although description logics are popular formalisms for modeling ontologies, tuple-generating dependencies (tgds), originally introduced as a unifying framework for database integrity constraints, and later on used in data exchange and integration, are also well suited for modeling ontologies that are intended for data-intensive tasks. The reason is that, unlike description logics, tgds can easily handle higher-arity relations that naturally occur in relational databases. In recent years, there has been an extensive study of tgd-ontologies and of their applications to several different data-intensive tasks. However, the fundamental question of whether the expressive power of tgd-ontologies can be characterized in terms of model-theoretic properties remains largely unexplored. We establish several characterizations of tgd-ontologies, including characterizations of ontologies specified by such central classes of tgds as full, linear, guarded, and frontier-guarded tgds. Our characterizations use the well-known notions of critical instance and direct product, as well as a novel locality property for tgd-ontologies. We further use this locality property to decide whether an ontology expressed by frontier-guarded (respectively, guarded) tgds can be expressed by tgds in the weaker class of guarded (respectively, linear) tgds, and effectively construct such an equivalent ontology if one exists.
Determining Geo-location of Tweets Using Cognitive Intelligence

The fast growth of geotagged social media text has developed new computational interest for exhaustive study of geographical linguistic variations. This research paper presents a multi-level multi-dimensional generative and adaptive model that reasons jointly about latent topics and geographical regions. High-level topics such as “sports” or “entertainment” or “political views” are discussed differently in each geographic region, revealing topic-specific regional characteristics, preferences and biases. When applied to data consisting of geotagged microblogs, the proposed model recommends related topics and their regional variants, while identifying geographic areas of linguistic consistency. The model also supports prediction of an author’s geographic location from raw text, outperforming both text regression systems and supervised topic models. The proposed work uses twitter dataset that includes features such as geo-location, user identifier, time stamp and message content. The primary goal of the research is to create an author profiling based on written work and understand the core sentiments of similar group of users by using cognitive intelligence.
Ball K-Medoids: Faster and Exacter

Cluster analysis can be viewed as a result of the natural evolution of the vast amount of data from daily life, and can discover invisible feature information to contribute to the analysis. K-means algorithm is one of the wide data clustering methods in a variety of real-world applications thanks to its simpleness. However, the k-means is sensitive to noise and outlier data points because a small number of such data can substantially influence the mean value of the cluster. In light of this, the k-medoids algorithm selects a point as a new center that minimizes the sum of the dissimilarities in the cluster, to diminish such sensitivity to outliers. Nevertheless, the line of the k-medoids algorithm is limited by its amounts of computation and not to handle with data efficiently. To this end, we present a novel k-medoids algorithm motivated by the theory of ball cluster, relationship between clusters and partitioning cluster for assigning samples into their nearest medoids efficiently, called ball k-medoids, which drop the distance calculation of sample-medoid significantly. Moreover, a threshold is inferenced by the rollback method for reducing computation of medoid-medoid distance and accelerating clustering. Experiments finally demonstrate that the performance of ball k-medoids achieves more efficient in comparison with other k-medoids algorithms, and it performs exacter accuracy compared with k-means.
Application and Development of Computer Technology in Network Guiding

Strengthening education has always been an important national policy of our country. Especially in the era of knowledge economy, a country's education level has a direct impact on the comprehensive national strength and the sustainable development of the country. Because of the hierarchical education system implemented in China, only a small number of people can receive high-quality higher education. The development of the Internet has provided better conditions for the expansion of teaching scope, and the country has put forward the plan of network distance education since the late 1990s of last century. The plan calls for universities to use their teaching resources to set up teaching websites on the Internet and provide educational services to the public.
From MT to Computational Linguistics and Natural Language Processing

Automatic syntactic analysis constituted the theoretical basis of “the new linguistics”, as recommended by ALPAC, and ensured the legitimacy of computational linguistics. It also conditioned the emergence of the Chomskyan program, which actually was closely associated with the horizon of retrospection created by MT, so that it can be identified as one of its horizons of projection.The development of syntactic analysis in MT centres was the result of three main syntactic approaches, which interacted with each other at one point or another:(i) The distributionalists’ approach, especially that of Hockett and Harris (see Chap. 4) (ii) Bar-Hillel’s approach, directly inspired by Carnap (iii) Chomsky’s approach
Smart Choices and the Selection Monad

Describing systems in terms of choices and their resulting costs and rewards promises to free algorithm designers and programmers from specifying how to make those choices. In implementations, the choices can be realized by optimization or machine-learning methods.We study this approach from a programming-language perspective. We define a small language that supports decision-making abstraction, rewards, and probabilities. We give a globally optimizing operational semantics, and, using the selection monad for decision-making, three denotational semantics with auxiliary monads for reward and probability; the three model various correlations between returned values and expected rewards. We show the two kinds of semantics coincide by proving adequacy theorems; we show that observational equivalence is characterized by semantic equality (at basic types) by proving full abstraction theorems; and we discuss program equations.
Rectified Multi-class AdaBoost for Noisy Dataset Based on Weight Adjustment Standard

Boosting, as a meta-algorithm for ensemble learning, have been widely applied to variety popular machine learning algorithms. However, noises in training and testing datasets could significantly affect the performance of boosting algorithm. SAMME pays too much attention to samples that are not correctly classified in multiple iterations. These samples could be mislabeled samples that cannot be correctly classified, so the classifier cannot learn the actual distribution of the original data. To solve this problem, in this paper, we proposed a rectified algorithm R.SAMME based on multi-class classification algorithm SAMME by limiting the weight of each sample based on current accuracy. We evaluate our approach on UCI benchmark datasets, experiments show that R.SAMME has better performance in noisy datasets.
Generation of Accessible Sets in the Dynamical Modeling of Quantum Network Systems

In this article, we consider the dynamical modeling of a class of quantum network systems consisting of qubits, where information extraction is allowed by performing measurement on several selected qubits of the system. For a variety of applications, a state space model is a useful approach to modeling the system dynamics. To construct a state space model for a quantum network system, the major task is to find an accessible set containing all of the operators coupled to the measurement operators. This article focuses on the generation of a proper accessible set for a given system and measurement scheme. We provide analytic results on simplifying the process of generating accessible sets for systems with a time-independent Hamiltonian. Since the order of elements in the accessible set determines the form of state space matrices, guidance is provided to effectively arrange the ordering of elements in the state vector. Defining a system state according to the accessible set, one can develop a state space model with a special pattern inherited from the system structure. As a demonstration, we specifically consider a typical 1-D-chain system with several common measurements and employ the proposed method to determine its accessible set.
Model-Free Linear Quadratic Regulator

We review recent results on the convergence and sample complexity of the random search method for the infinite-horizon linear quadratic regulator (LQR) problem with unknown model parameters. This method directly searches over the space of stabilizing feedback gain matrices and, in spite of the lack of convexity, it converges to the globally optimal LQR solution at a linear rate. These results demonstrate that for a model-free method that utilizes two-point gradient estimates, the simulation time and the total number of function evaluations required for achieving $$\epsilon $$ -accuracy are both $$O (\log \, (1/\epsilon ) )$$ .
Julia

Even the most resource-constrained IoT devices need to communicate securely. In order to establish a secure channel, key agreement between the communicating parties is used. Today's key agreement protocols require at least three scalar multiplications in the handshake to achieve mutual authentication, forward and backward secrecy, and protection against key-compromise impersonation. As this is a computationally heavy operation, resource-constrained devices benefit from a lower number of scalar multiplications. In this paper we present Julia Key Agreement (JKA), a protocol that satisfies the aforementioned security properties using two scalar multiplications, and thus saves both time and energy. In addition, we define an optimized JKA that only requires a single scalar multiplication for a particular use case.
A Low-cost Deep Neural Network-based Real Time Hand Gesture Recognition System for Home Appliance Control

With recent advancements in deep learning, the performance of hand gesture recognition (HGR) models has greatly increased, enabling reasonably accurate hand gesture detection with real time speed on modern desktop computer systems. Recently, there has been a growing interest in developing smaller and faster deep neural network (DNN) architectures suited for embedded devices. In this paper, we present a new DNN based end-to-end real-time control for home appliances. In purpose to gain reliable accuracy and execution speed, we proposed a new architecture by replacing the backbone of YOLOv3 and refining its head. The proposed network's performance showed advantages compared to popular object detection models. The model was incorporated into a HGR system for home appliance controls. The system provides a user friendly interface to register the correspondence between infrared signals and gestures. The proposed system takes images from a camera as input, recognizes gestures and produces corresponding infrared signals as output, providing a low cost system that can be used to control various home appliances under a complex environment. The code, trained tensor lite models and other software used in this research are available at https://github.com/appleyuta/RaspberryPi-HGR-System.