Reviewerly/exHarmony huggingface dataset:
The Homomorphic Encryption in Pipelines Accident Prediction by Using Cloud-based Neural Network

Currently, the task of predicting the accident rate of pipelines is relevant. To solve this problem, it is proposed to use the neural networks. Calculations using neural networks located in cloud services are not secure for the transmitted data. It is proposed to use a model of information security based on homomorphic encryption.
Conversational Agents to Democratize Artificial Intelligence

Artificial Intelligence (AI) technology can be found nearly everywhere. However, developing and controlling this technology is generally limited to large companies or those with extensive training in computer science. It is difficult for many who have application ideas for AI to even know where to start when developing AI technology.
Forward-stagewise clustering: An algorithm for convex clustering

This paper proposes an exceptionally simple algorithm, called forward-stagewise clustering, for convex clustering. Convex clustering has drawn recent attention since it nicely addresses the instability issue of traditional non-convex clustering methods. While existing algorithms can precisely solve convex clustering problems, they are sophisticated and produce (agglomerative) clustering paths that contain splits. This motivates us to propose an algorithm that only produces no-split clustering paths. The approach undertaken here follows the line of research initiated in the area of regression. Specifically, we apply the forward-stagewise technique to clustering problems and prove that the algorithm can only produce no-split clustering paths. We then modify the forward-stagewise clustering algorithm to deal with noise and outliers. We further suggest rules of thumb for the algorithm to be applicable to cases where clusters are non-convex. The performance of the proposed algorithm is evaluated through simulations and a real data application.
Machine Learning-Based Traffic Classification of Wireless Traffic

In tactical scenarios, there is limited knowledge available about an adversary's encrypted traffic. To improve traffic classification performance in these scenarios, a new modified naïve Bayes kernel classifier (MNBK) is proposed based on optimal weight-based kernel bandwidth selection. By generating several traffic types expected in modern tactical edge networks, we demonstrate that the proposed MNBK classifier not only improves classification performance on the existing classes, but also detects unknown traffic with very high accuracy, precision, and recall compared with the traditional classifiers. In addition, a real time learning model is proposed based on MNBK and applied to real time traffic classification.
A Guideline-Based Approach for Assisting with the Reproducibility of Experiments in Recommender Systems Evaluation

Recommender systems’ evaluation is usually based on predictive accuracy and information retrieval metrics, with better scores meaning recommendations are of higher quality. However, new algorithms are constantly developed and the comparison of results of algorithms within an evaluation framework is difficult since different settings are used in the design and implementation of experiments. In this paper, we propose a guidelines-based approach that can be followed to reproduce experiments and results within an evaluation framework. We have evaluated our approach using a real dataset, and well-known recommendation algorithms and metrics; to show that it can be difficult to reproduce results if certain settings are missing, thus resulting in more evaluation cycles required to identify the optimal settings.
A general method of computing mixed Poisson probabilities by Monte Carlo sampling

Mixed Poisson distributions form an important class of distributions in applications. However, the application of many of these mixed Poisson distributions is hampered by the complicated probability distributions. The paper examines Monte Carlo sampling as a general technique for computation of mixed Poisson probabilities which is applicable to any mixed Poisson distribution with arbitrary mixing distribution. The accuracy and computational speed of this method is illustrated with the Poisson–inverse Gaussian distribution. The proposed method is then applied to compute probabilities of the Poisson–lognormal distribution, a popular species abundance model. It is also shown that in the maximum likelihood estimation of Poisson–lognormal parameters by E–M algorithm, the application of the proposed Monte Carlo computation in the algorithm avoids numerical problems.
Green Information Extraction from Family Books

There is rising demand for the retrieval of genealogical information from semi-structured books. Nuggets of personal interest are currently transcribed piecemeal by volunteers. GreenBook is an effective alternative for recovering most of the desired information from any of the hundreds of thousands of books of ancestral records that have already been scanned and digitized. It minimizes human intervention by letting the user benefit from automatically compiled statistics from the book, and lets computer search benefit from user insights. GreenBook combines enhanced template matching with spreadsheet-based interaction for the rapid specification of the text to be extracted. The accuracy and completeness of the extracted information are limited only by the user's stamina. About 3 h of user interaction yielded 99% precision and 97% recall on books of Scottish birth and marriage records, Ohio funeral home records, and a family history spanning 300 years. The system is designed to facilitate transition to new books.
C5.0 Algorithm and Synthetic Minority Oversampling Technique (SMOTE) for Rainfall Forecasting in Bandung Regency

Weather is an essential aspect of life because it can affect human activities. Therefore, it is important for weather prediction to have high accuracy. One of the methods used to predict rainfall is data mining. In this study, a classification model was developed using the C5.0 algorithm to forecast rainfall in Bandung Regency. Then, the SMOTE algorithm was used to overcome imbalanced datasets. Weather data for the model development were obtained from the Meteorological, Climatological, and Geophysical Agency (BMKG) of Bandung for the years 2005 until 2017. Subsequently, the model was validated using a k-fold cross-validation. The results of the C5.0 test produced the highest accuracy of 92% for the imbalance dataset, while the accuracy of the addition of data using the SMOTE technique was 99%.
Base Cube One: A location-addressable service-oriented smart environment framework

This paper presents Base Cube One (BCO), an architecture for smart environments that organizes devices and functionality in a location-addressable service-oriented schema so that the complex interplay between sensors, actuators and intermediary computing modules can be organized and set up in a str uctured and effective manner. We show how this can be used by smart components to adapt their generic behavior in order to control individual smart environments such as smart homes with heterogeneous hard- and software components. Additionally, a common ground between smart components and occupants is addressed by sharing the same spatial organization of smart things within the environment. This acknowledges the fast pace at which smart environments are evolving and our approach aims to provide a solid basis for later iterative refinements as new devices are included or removed, or the inter-connectivity is structurally changed. We will present the new architecture in detail and exemplify it using our Cognitive Service-Robotics Apartment project in the context of which this architecture has been developed. The discussion will touch in particular on the requirements for an architecture to both provide a stable foundation for the smart environment yet offer a plasticity for ongoing iterative refinement and supporting the overall system adaptation to the resulting changes.
A Secure Key Delegation Mechanism for Fog Networking

Fog networking enables applications to execute directly on the edge devices (routers, set-top-boxes etc.) located near Internet of Things (IoT) devices, which reduces the load of cloud server and service latency time. The data captured from various IoTs may be accessible to all nearby fog devices Ciphertext Policy-Attribute Based Encryption (CP-ABE) offers a flexible solution to securely and publicly share data contents. In existing CP-ABE schemes, an entity can delegate its access rights to another which may lead to undesirable situation that infringes access policy. In this work, we propose a secure key delegation mechanism for fog networking which implements checks on delegation as well as instantaneous revocation. It also provides for flexibility for allowing or disallowing further delegation by delegated user. A case study requiring access of shared contents by fog devices has been implemented that supports flexible attribute delegation and revocation. The results demonstrate that our scheme improves the functionality of legacy CP-ABE and performance by providing threaded solution that supports concurrent execution.
Feature Selection Based on Variance Distribution of Power Spectral Density for Driving Behavior Recognition

Abnormal driving detection and recognition is a crucial area of research towards achieving safety in intelligent transportation systems (ITS). In this study, we propose a feature extraction approach and use the extracted features to train a deep learning model that is used for abnormal driving behavior recognition. The proposed approach derives the features based on variances calculated from each frequency bin containing the power spectrum data that is generated using the short time fourier transform. A subset of features is further selected based on variance similarity of the power spectral data. Similarity is realized by finding intersecting variance data of different variance samples obtained from defined data segments of a given driving behavior class. The driving behaviors considered are weaving, sudden braking and normal driving. Experiments were performed using an artificial neural network to test the efficiency of the proposed feature extraction approach. Results show that an accuracy of 91.0% can be achieved with accelerometer data. The accuracy is further improved to 96.1% by combining accelerometer with gyroscope data.
Pure iso-type systems

Abstract Traditional designs for functional languages (such as Haskell or ML) have separate sorts of syntax for terms and types. In contrast, many dependently typed languages use a unified syntax that accounts for both terms and types. Unified syntax has some interesting advantages over separate syntax, including less duplication of concepts, and added expressiveness. However, integrating unrestricted general recursion in calculi with unified syntax is challenging when some level of type-level computation is present, since properties such as decidable type-checking are easily lost. This paper presents a family of calculi called pure iso-type systems (PITSs), which employs unified syntax, supports general recursion and preserves decidable type-checking. PITS is comparable in simplicity to pure type systems (PTSs), and is useful to serve as a foundation for functional languages that stand in-between traditional ML-like languages and fully blown dependently typed languages. In PITS, recursion and recursive types are completely unrestricted and type equality is simply based on alpha-equality, just like traditional ML-style languages. However, like most dependently typed languages, PITS uses unified syntax, naturally supporting many advanced type system features. Instead of implicit type conversion, PITS provides a generalization of iso-recursive types called iso-types . Iso-types replace the conversion rule typically used in dependently typed calculus and make every type-level computation explicit via cast operators. Iso-types avoid the complexity of explicit equality proofs employed in other approaches with casts. We study three variants of PITS that differ on the reduction strategy employed by the cast operators: call-by-name , call-by-value and parallel reduction . One key finding is that while using call-by-value or call-by-name reduction in casts loses some expressive power, it allows those variants of PITS to have simple and direct operational semantics and proofs. In contrast, the variant of PITS with parallel reduction retains the expressive power of PTS conversion, at the cost of a more complex metatheory.
Application Prospect of Knowledge Graph Technology in Knowledge Management of Oil and Gas Exploration and Development

A large number of research reports have been produced during the exploration and development of oil and gas. Traditional relational database-based information management systems and keyword-based information retrieval systems cannot effectively analyze, organize, and utilize the knowledge in these research reports. knowledge graph use machine learning, natural language processing, semantic search and other technologies to extract knowledge from multi-source heterogeneous knowledge carriers and build a graphical knowledge base. Knowledge management systems designed for oil and gas exploration and development, semantic search and knowledge Push, smart question and answer, analogy and intelligent prediction.
Con2KG-A Large-scale Domain-Specific Knowledge Graph

This paper presents Con2KG, a large-scale recruitment domain Knowledge Graph that describes 4 million triples as facts from 250 thousands of unstructured data of job postings. We propose a novel framework for Knowledge Graph construction from unstructured text and an unsupervised, dynamically evolving ontology that helps Con2KG to capture hierarchical links between the entities missed by explicit relational facts in the triples. To enrich our graph, we include entity context and its polarity. Towards this end, we discuss Con2KG applications that may benefit the recruitment domain.
An Improved Image Multi-Dimensional Chaos Encryption Algorithm Based on CUDA

Due to the intrinsic features of digital images such as bulk data capacity and high redundancy, traditional encryption algorithms designed for textual information are not suitable for image encryption. With their mixing ability, and their sensitivity to the initial conditions and parameters of chaotic maps, chaos-based encryption has suggested a new and efficient way to deal with the intractable problem of highly secure image encryption. However, as the number of calculation steps and pixels being encrypted increases, the encryption time will increase too. Therefore, this study proposes multi-dimensional chaotic encryption, implemented by parallel programming on GPUs using the Compute United Device Architecture (CUDA) to reduce the redundancy of time-consuming processes in encryption. A 2D Arnold cat map was used for pixel position shuffling and 3D Liu chaotic system was used for pixel gray-scale encryption. Experimental simulations have been carried out, and the results prove high efficiency of the proposed algorithms and turn out to be applicable to real-time applications.
Predicting Mortality with Applied Machine Learning: Can We Get There?

There is growing interest in using AI-based algorithms to support clinician decision-making. An important consideration is how transparent complex algorithms can be for predictions, particularly with respect to imminent mortality in a hospital environment. Understanding the basis of predictions, the process used to generate models and recommendations, how to generalize models based on one patient population to another, and the role of oversight organizations such as the Food and Drug Administration are important topics. In this paper, we debate opposing positions regarding whether these algorithms are 'ready yet' for use today in clinical settings for physicians, patients and caregivers. We report voting results from participating audience members in attendance at the conference debate for each of these positions obtained real-time from a smartphone-based platform.
On the Structural Disambiguation of Multi-word Terms

Multi-word terms pose many challenges in Natural Language Processing (NLP) because of their structure ambiguity. Although the structural disambiguation of multi-word expressions, also known as bracketing, has been widely studied, no definitive solution has as yet been found. Although linguists, terminologists, and translators must deal with bracketing problems, they generally must resolve problems without using advanced NLP systems. This paper describes a series of manual steps for the bracketing of multi-word terms (MWTs) based on their linguistic properties and recent advances in NLP. After analyzing 100 three- and four-term combinations, a set of criteria for MWT bracketing was devised and arranged in a step-by-step protocol based on frequency and reliability. Also presented is a case study that illustrates the procedure.
Accelerating Compact Convolutional Neural Networks with Multi-threaded Data Streaming

Recent advances in convolutional neural networks (CNNs) reveal the trend towards designing compact structures such as MobileNet, which adopts variations of traditional computing kernels such as pointwise and depthwise convolution. Such modified operations significantly reduce model size with an only slight degradation in inference accuracy. State-of-the-art neural accelerators have not yet fully exploit algorithmic parallelism for such computing kernels in compact CNNs. In this work, we propose a multithreaded data streaming architecture for fast and highly parallel execution of pointwise and depthwise convolution, which can be also dynamically reconfigured to process conventional convolution, pooling, and fully connected network layers. The architecture achieves efficient memory bandwidth utilization by exploiting two modes of data alignment. We profile MobileNet on the proposed architecture and demonstrate a 9:36x speed-up compared to single-threaded architecture.
Design of Arduino-Based In-vehicle Warning Device for Inner Wheel Difference

After research, we find that the inner wheel difference has caused a large number of traffic accidents, and its harm cannot be ignored. In order to reduce traffic accidents, an Arduino-based warning device for inner wheel difference is designed in this paper. We first analyze the calculation model of inner wheel difference, then design and assemble each module separately based on the Arduino. In the visual warning part, the Single Shot MultiBox Detector (SSD) object detection model is used to locate and identify pedestrians and common objects, in order to realize a device for alerting both drivers and pedestrians based on vision and ultrasonic wave. Finally, based on the characteristics of the warning device and the social development, we analyze the advantages, technical feasibility and development prospects of the device.
Soft-Biometric Attributes from Selfie Images

The aim of this chapter is to discuss the soft-biometric attributes that can be extracted from selfie images acquired from mobile devices. Existing literature suggests that various features in demographics, such as gender and age, in physical, such as periocular and eyebrow, and in material, such as eyeglasses and clothing, have been extracted from selfie images for continuous user authentication and performance enhancement of primary biometric traits. Due to the limited hardware resources, low resolution of front-facing cameras, and the usage of the device in different environmental conditions, factors such as robustness to low-quality data, consent-free acquisition, lower computational complexity, and privacy, favor soft-biometric prediction in mobile devices.
An Event-Driven Approach for Time-Domain Recognition of Spoken English Letters

This paper suggest an original approach, based on event-driven processing, for time-domain spoken English letter features extraction and classification. The idea is founded on smartly combining the event-driven signal acquisition and segmentation along with local features extraction and voting based classification for realizing an efficient and high precision solution. The incoming spoken letter is digitized with an event-driven A/D converter (EDADC). An activity selection mechanism is employed to efficiently segment the EDADC output. Later on, features of these segments are mined by performing the time-domain analysis. The recognition is done with a specifically developed voting based classifier. The classification algorithm is described. The system functionality is tested for a case study and results are presented. A 9.8 folds reduction in accumulated count of samples is achieved by the devised approach as compared to the traditional counterparts. It aptitudes a significant processing gain and efficiency increase in terms of utilization of power of the suggested approach in contrast to the counterparts. The proposed system attains an average subject dependent recognition accuracy of 92.2%. It demonstrates the potential of using the suggested solution for the realization of computationally efficient automatic speech recognition applications.
Chaos-based Cryptography for Transmitting Multimedia Data over Public Channels

This paper explores using chaos-based cryptography for transmitting multimedia data, mainly speech and voice messages, over public communication channels, such as the internet. The secret message to be transmitted is first converted into a one-dimensional time series, that can be cast in a digital/binary format. The main feature of the proposed technique is mapping the two levels of every corresponding bit of the time series into different multiple chaotic orbits, using a simple encryption function. This one-to-many mapping robustifies the encryption technique and makes it resilient to crypto-analysis methods that rely on associating the energy level of the signal into two binary levels, using return map attacks. A chaotic nonautonomous Duffing oscillator is chosen to implement the suggested technique, using three different parameters that are assumed unknown at the receiver side. Synchronization between the transmitter and the receiver and reconstructing the secret message, at the receiver side, is done using a Lyapunov-based adaptive technique. Achieving stable operation, tuning the required control gains, as well as effective utilization of the bandwidth of the public communication channel are investigated. Two different case studies are presented; the first one deals with text that can be expressed as 8-bit ASCII code, while the second one corresponds to an analog acoustic signal that corresponds to the voice associated with pronouncing a short sentence. Advantages and limitation of the proposed technique are highlighted, while suggesting extensions to other multimedia signals, along with their required additional computational effort.
Sample-specific repetitive learning for photo aesthetic auto-assessment and highlight elements analysis

Aesthetic assessment is subjective, and the distribution of the aesthetic levels is imbalanced. In order to realize the auto-assessment of photo aesthetics, we focus on retraining the CNN-based aesthetic assessment model by dropping out the unavailable samples in the middle levels from the training data set repetitively to overcome the effect of imbalanced aesthetic data on classification. Further, the method of extracting aesthetics highlight region of the photo image by using the two repetitively trained models is presented. Therefore, the correlation of the extracted region with the aesthetic levels is analyzed to illustrate what aesthetics features influence the aesthetic quality of the photo. Moreover, the testing data set is from the different data source called 500px. Experimental results show that the proposed method is effective.