Reviewerly/exHarmony huggingface dataset:
An Anatomy of Machine Learning Data Visualization

Nowadays, the data literacy become as vital as the language literacy in general and special conditions. The well-designed visualizations results can guide us from millions of data, help to convey the information, facilitate in connecting an idea, and make better decisions in real time. Thus, data visualization is a method that uses multiple visual instruments, both static and vibrant and interactive, to enable individuals to comprehend the big quantities of data that are transformed into information in some situations. In machine learning which is a method of data analysis with algorithms that can learn from data. The results of machine learning, computers can easily identify patterns with massive data so that the system can make accurate and precise predictions. In this work, we try to explore machine learning and display its benefits when we integrate it with data visualization. Thus, this paper is a literature review which display some results from several experiments in making data visualization based on machine learning algorithms that are implemented specifically in Python language platforms.
Immunization against complete subversion without random oracles

We seek constructions of general-purpose immunizers that take arbitrary cryptographic primitives, and transform them into ones that withstand a powerful "malicious but proud" adversary, who attempts to break security by possibly subverting the implementation of all algorithms (including the immunizer itself!), while trying not to be detected. This question is motivated by the recent evidence of cryptographic schemes being intentionally weakened, or designed together with hidden backdoors, e.g., with the scope of mass surveillance. Our main result is a subversion-secure immunizer in the plain model, that works for a fairly large class of deterministic primitives, i.e. cryptoschemes where a secret (but tamperable) random source is used to generate the keys and the public parameters, whereas all other algorithms are deterministic. The immunizer relies on an additional independent source of public randomness, which is used to sample a public seed. Assuming the public source is untamperable, and that the subversion of the algorithms is chosen independently of the seed, we can instantiate our immunizer from any one-way function. In case the subversion is allowed to depend on the seed, and the public source is still untamperable, we obtain an instantiation from collision-resistant hash functions. In the more challenging scenario where the public source is also tamperable, we additionally need to assume that the initial cryptographic primitive has sub-exponential security. Previous work in the area only obtained subversion-secure immunization for very restricted classes of primitives, often in weaker models of subversion and using random oracles.
Model-Based Methods for Quality Evaluation of Cloud Services

The issue of less-than-100% reliability and trustworthiness of third-party controlled cloud components (e.g., IaaS and SaaS components from different vendors) may lead to laxity in the QoS guarantees offered by a service-support system S to various applications. Our goal is to assess how well the internal mechanisms of S are geared to offer a required level of service to the applications. Our external assessment of QoS provisioning involves the construction of a computational model of the actual system under test. We employ system identification technique that uses the computational model to determine the feasible resource allocation for an externally observed behavior and use it to infer the actual system parameters. Our external system assessment methodology delineates domain-knowledge of the system from the assessment process at a meta-level and hence allows re-use of the technique across different systems. We illustrate our QoS assessment method using a replicated data service case study.
A Level Set Method Combined with Gaussian Mixture Model for Image Segmentation

Chan-Vese (CV) model promotes the evolution of level set curve based on the gray distribution inside and outside the curve. It has a better segmentation effect on images with intensity homogeneity and obvious contrast. However, when the gray distribution of image is uneven, the evolution speed of the curve will be significantly slower, and the curve will be guided to the wrong segmentation result. To solve this problem, a method to improve CV model by using of Gaussian mixture model (GMM) is proposed. We use the parameters of the Gaussian submodels to correct the mean value of grayscale inside and outside the curve in the energy function. The target region can be quickly segmented in the images with complex background gray distribution. Experimental results show that the proposed algorithm can significantly reduce the number of iterations and enhance the robustness to noise. The level set curve can quickly evolve into target region in the images with intensity inhomogeneity.
Spider Chart based Pictographic Image Comparison in Walking Speed Estimation

In this paper, spider chart based pictographic image comparison is suggested for determination of gait cycle time for walking speed estimation. Ground reaction force data and ankle's position information from human gait for different walking speed are collected during level walking on motorized treadmill. Accordingly, spider charts of those collected and digitized gait data are developed for different walking speed and changes in pictographic images are compared for characterization of the different gaits. A fusion technique namely, `k out of n' redundant fusion technique is also applied for generating a fail-safe and robust estimation. The gait cycle time estimation technique is further validated by the time recorded during laboratory trials.
First Year’s Actual Operational Results of Efficient Security Measure System with Automatic Isolation in TUAT

To reinforce a security measure on our campus network, we implemented a brand-new style of secure campus network system in 2017. The variety of the device in campus network is more complicated. The existence of various devices also increases the possibility of security incidents. In Tokyo University of Agriculture and Technology (TUAT), since Bring Your Own Device (BYOD) was started from 2016, the types of devices connected to our campus network is increased. Therefore, as a security measure, we designed and implemented a campus network security system based on automatic isolation. In our system, network traffic is monitored on the campus network side (e.g. core switch, edge switch), and a device considered as high security risk is automatically isolated from the campus network on the edge switch. In our system, it is possible not only to perform shutdown automatically but also to automatically perform recovery processing by end-user, and reduce operational cost. After more than a year since the operation began, we clarified the actual operation conditions of our system. In this paper, we introduce the overview of our system, and summarize the operational situation from the start of operation. This makes it possible to overview the appearance of our actual system and the situation of security incident status at the Japanese National University in the science area. By considering the results of our system, we will also help to think about security measures in university environment.
On Consistency of Graph-based Semi-supervised Learning

Graph-based semi-supervised learning is one of the most popular methods in machine learning. Some of its theoretical properties such as bounds for the generalization error and the convergence of the graph Laplacian regularizer have been studied in computer science and statistics literature. However, a fundamental statistical property - consistency - that is, the prediction by the algorithm can identify the underlying truth with unlimited data. This is not to be confused with the existence of solutions in an equation system, which is a term used in algebra. - has not been proved. In this article, we study the consistency problem under a non-parametric framework. We obtain the following two results: 1) We prove that graph-based semi-supervised learning on the test data is consistent in the case that the estimated scores are enforced to be equal to the observed responses for the labeled data (the hard criterion). The sample size of unlabeled data are allowed to grow at a slower rate than the size of the labeled data in this result. 2) We give a counterexample demonstrating that the estimator can be inconsistent for the case when the estimated scores are not required to be equal to the observed responses (the soft criterion), where a tuning parameter is used to balance the loss function and the graph Laplacian regularizer. These somewhat surprising theoretical findings are supported by numerical studies on both synthetic and real datasets. Moreover, numerical studies show that the hard criterion constantly outperforms the soft criterion even when the sample size of unlabeled data is smaller than the size of labeled data. This suggests that practitioners can safely choose the hard criterion without the burden of selecting the tuning parameter in the soft criterion.
Hardware/Software Co-Design of a High-Speed Othello Solver

This paper presents an improved Othello game solver using the Hardware/Software Co-Design approach. Enhancements in Data Communication, Searching Algorithm, and Evaluation function have been made to beat the prevalent implementations. A novel 64-bit Communication model is proposed which transfers the current board position from the Processing System to the Programmable Logic through a 64-bit change board, instead of the complete 128-bit board. The proposal uses NegaScout searching algorithm along with Transposition Table and Node Ordering; and features like Opening Book, Killer Heuristics, and Bitboards. An Adaptive Evaluation function assigns variable weights to Mobility, Stability, Corner Occupancy, and Disc Differential depending upon the game stage. At a search depth of eight, the proposed communication model improves the communication overhead by 19.5%. Overall, the proposed design demonstrates 75.06% improved performance as compared to the prevalent implementation, and 6.53 times faster than its software implementation on the same platform.
Regression Training using Model Parallelism in a Distributed Cloud

Machine learning requires a relevant amount of computational resources and it is usually executed in high-capacity centralized cloud infrastructures (e.g., data centers). In such infrastructures, resources are shared in a scalable manner through instantiation and orchestration of multiple virtualized services. Emerging trends in machine learning are distribution and parallelization of model training, which allows the execution of model training tasks in multiple distributed computational domains, with the aim of reducing the overall training time. A possible drawback in decentralization of machine learning is that performance latency issues may arise when the computation of training is geographically distributed to nodes with long distance from each other. One way to reduce latency is to utilize edge computing infrastructure, i.e., to distribute computation near the origin of the request. As edge resources can be scarce, it is important to orchestrate the model training in a parallelized manner. To this extent, in order to effectively ease the use of parallelization both in centralized and in distributed scenarios, we propose and implement a concept that we refer to Intelligent Agent (IA). An IA is responsible for instantiating and scheduling of the machine learning tasks (e.g., model training), and deriving inferences. In our solution, model training is distributed to multiple IAs in parallel. Each IA is packaged into a Linux container in order to take advantage of container portability across heterogenous deployments and to reuse existing container orchestration tools. We validate our proposal by deploying and instantiating multiple IAs across a distributed cloud environment, where each IA is accounting for a fixed amount of computational resources.
Real-Time Multi-Material Reflectance Reconstruction for Large-Scale Scenes Under Uncontrolled Illumination from RGB-D Image Sequences

Real-time reflectance reconstruction under uncontrolled illumination conditions is well-known to be a challenging task due to the complex interplay of scene geometry, surface reflectance and illumination. Nonetheless, recent works succeed in recovering both unknown reflectance and illumination in an uncontrolled setting. However, they are either limited regarding the scene complexity (single objects / homogeneous materials) or are not suitable for real-time applications. Our proposed method enables the recovery of heterogeneous surface reflectance (multiple objects and spatially varying materials) in complex scenes at real-time frame rates. We achieve this goal in the following way: First, we perform a 3D scene reconstruction from an input RGB-D stream in real-time. We then use a deep learning based method to estimate Ward BRDF parameters from observations gathered from individual segmented scene objects. Subsequently we refine these reflectance parameters to allow for spatial variations across the object surfaces. We evaluate our method on synthetic scenes and successfully apply it to real-world data.
Path Planning Method for General Aviation under Hazardous Weather Using Heuristic Algorithm

Weather is one of the most important factors threatening the safety of general aviation. It is necessary to avoid the possible hazardous weather regions according to the weather forecast in the planning stage. In this paper, a flight path planning method based on phased altitude changes is proposed. The influence of hazardous weather in different altitudes of the airspace is discretized into gridding polygons. In order to evaluate the influence of hazardous weather on flight paths, flight distance is used to construct cost function and maneuver of the aircraft is modeled as the constraint condition. As the result, a flight path with minimum cost is planned using heuristic algorithm in the gridding system. Numerical simulations show that the flight distance of the path planned by the proposed method is shorter than the planned path that with a constant flight altitude.
Handling Big Datasets in Gaussian Processes for Statistical Wind Vector Prediction

We construct a statistical model predicting wind vector over very complex terrain characterized by low wind speeds and changeable wind directions. These are necessary inputs for atmospheric dispersion modelling of hypothetical radioactive pollution events in short-term or medium-term future to better protect the local population. The statistical model uses predictions of a numerical weather prediction model as some of its inputs, so they together form a hybrid model. The statistical model is realized as a nonlinear autoregressive exogenous model whose dynamics is described with a Gaussian process model. It relies on training data, and there is more training data available than the computing system is able to process. One possibility of avoiding this issue is to use a randomly selected subset of the available historical measurements as the training data. However, a better choice of training data may result in a model that performs better. We develop and test a smart training set selection method that selects the training data points based on Euclidean distances between them. The resulting model improvement is insignificant and inconsistent. We explore the reasons for underperformance of the method. We conclude that our example does not offer much opportunity for training set selection methods to achieve better results than random selection.
Searching Tourism Information by Using Vertical Search Engine Based on Nutch and Solr

Since there exist some issues with traditional search engine in information retrieval, such as huge numbers of results, poor profession, low precision rate and other issues, in this paper, we proposed a Vertical Search Engine based on Nutch and Solr. We used forward iteration most granular segmentation algorithm based on dictionary to achieve Chinese word segmentation, employed Vector Space Model (VSM) based on keywords to implement topic relevance, extended the user search module and the tourism domain word library to collect information, filter information retrieval, and relate word various stages. Experiments were also conducted in order to evaluate the algorithm and the results show that the vertical search engine based on Nutch and Solr which is used for tourism information retrieval can improve the user retrieval precision and meet the professional demand of user retrieval.
An Observational Study on the Use of Comics in education

In this paper we report on the findings of an observational study regarding the use of comics in an educational setting. The study is designed as a first level examination of the field, aimed to produce a set of parameters deserving a closer examination in experiments to follow.
The Transfer of Hidden Information in Data in the AMR-WB Codec

In order to hide communication in a voice channel, this paper employs an Adaptive Mode Rate Wide Band codec (G.722.2). By deliberately damaging selected bits and a combination of them in a voice frame, a steganographic channel was created with a capacity of up to 2500 b/s without damaging the voice quality significantly. A number of tests based on the subjective evaluation of the voice quality were carried out to confirm the proposed method. PRNG was used to damage the bits. The findings showed the simplicity, effectiveness and speed of the proposed model.
Template-driven Best Practice Documentation

Knowledge Management has become a key instrument for identifying, creating and sharing organisational knowledge assets. An attractive means for sharing knowledge is Best Practices (BPs). However, the implementation of BPs raises several challenges, one of which is that the low quality of BP documentation can impede the use of BPs. One way to address this challenge is by supporting designers in structuring BP documents. This support can take the form of a BP Document (BPD) Template that prescribes the structure of BP documents by defining relevant attributes for describing BPs. The design of this template is an example of design science research including requirements elicitation, artefact development, demonstration, and evaluation. This paper proposes a comprehensive and practical BPD template for supporting the creation, use and evaluation of BP documents. It thereby offers an effective tool for BP designers, managers, and users that can support them in the design, evaluation and application of BPs.
Initial Thoughts and Application of AI in Geo-hazards Monitoring and Early Warning of Highroad in Beijing

The highroad geo-hazards in Beijing monitored mainly on human inspections at present. However, human inspection exists some risks and lags when encountering bad weather or geo-hazards. The remote video monitoring has real-time characteristics to deal with the intuitional viewing, information richness and traceability, to compensate the shortage of human inspection to some extent. On the other hand, massive image data acquired by the remote video monitoring system to exceeds the processing capability of humans. And the real-time ability of data processing is higher and time constrained. In this consideration, artificial intelligence (AI) technology can meet the above requirements for fast and accurate automatic judgment capability with high importance. This learning integrates the AI techniques with manual control to realize the automatic identification, forecast and early warning of the falling rock on road along with the sliding of slants. This will necessarily improve the intellective level of disaster identification and management, and can provide people with safer and more convenient travel services.
Big Data Driven Computing Offloading Scheme with Driverless Vehicles Assistance

In the era of big data, edge computing is emerged as a promising paradigm to alleviate the pressure on the backbone network and facilitate vehicular services on the road. As edge nodes deployed for vehicular applications, roadside units (RSUs) need to undertake a large number of local computing tasks. However, due to the uncertainty of the vehicular network topology, static RSU deployments are subject to short-term overload and cannot handle various delay-sensitive computing tasks concurrently. To address the problem, we propose a big data driven computing offloading scheme to dispatch idle driverless vehicles to enhance the capacities of RSUs dynamically. First, we present a trust assessment model to evaluate the credibility of driverless vehicles. Then, a multi-attribute reverse auction is applied to maximize the utilities of RSUs and driverless vehicles. In addition, a secure forwarding method is developed to protect the privacy of computing tasks.
Read-Uncommitted Transactions for Smart Contract Performance

Smart contract transactions demonstrate issues of performance and correctness that application programmers must work around. Although the blockchain consensus mechanism approaches ACID compliance, use cases that rely on frequent state changes are impractical due to the block publishing interval of O(10^1) seconds. The effective isolation level is Read-Committed, only revealing state transitions at the end of the block interval. Values read may be stale and not match program order, causing many transactions to fail when a block is committed. This paper perceives the blockchain as a transactional data structure, using this analogy in the development of a new algorithm, Hash-Mark-Set (HMS), that improves transaction throughput by providing a Read-Uncommitted view of state variables. HMS creates a directed acyclic graph (DAG) from the pending transaction pool. The transaction order derived from the DAG is used to provide a Read-Uncommitted view of the data for new transactions, which enter the DAG as they are received. An implementation of HMS is provided, interoperable with Ethereum and ready for use in smart contracts. Over a wide range of transaction mixes, HMS is demonstrated to improve throughput. A side product of the implementation is a new technique, Runtime Argument Augmentation (RAA), that allows smart contracts to communicate with external data services before submitting a transaction. RAA has use cases beyond HMS and can serve as a lightweight replacement for blockchain oracles.
Enhancing Natural Language Understanding through Cross-Modal Interaction: Meaning Recovery from Acoustically Noisy Speech

Cross-modality between vision and language is a key component for effective and efficient communication, and human language processing mechanism successfully integrates information from various modalities to extract the intended meaning. However, incomplete linguistic input, i.e. due to a noisy environment, is one of the challenges for a successful communication. In that case, an incompleteness in one channel can be compensated by information from another one. In this paper, by conducting visual-world paradigm, we investigated the dynamics between syntactically possible gap fillers and the visual arrangements in incomplete German sentences and their effect on overall sentence interpretation.
A Robust Watermarking Scheme for Copyright Protection

In this paper, a robust image watermarking scheme based on all phase sine biorthogonal transform (APSBT), singular value decomposition (SVD) and dynamic stochastic resonance (DSR) is presented. Firstly, the cover image is transformed by APSBT and then a gray-scale logo is embedded through the singular value decomposition. After the authentication process which essentially resolves the false-positive extraction of SVD in watermarking, a phenomenon based on dynamic stochastic resonance is deployed for the logo extraction from the watermarked image. The simulation results demonstrate that the proposed scheme has better performance in the aspect of robustness and invisibility.