Reviewerly/exHarmony huggingface dataset:
Anomaly Detection with Pattern of Life Extraction for GMTI Tracking

Ground Moving Target Indicator (GMTI) uses the concept of airborne surveillance of moving ground objects to observe and take actions if necessary. With the improvement of surveillance technology, tracking individual target from the surveillance became possible, which allows the extraction of useful features for advance usage. Such features, known as tracks, are the results of GMTI tracking. The system for this paper will be based on GMTI track data. Though the quality of the tracker plays a crucial role to the system performance of this paper, the development of the tracker will not be discuss in this paper. The system will use simulated ideal GMTI tracks as inputs. This paper presents Pattern of Life (PoL) extraction and Anomaly Detection System (ADS). The results from PoL extraction will be used to improve the performance of ADS. The proposing ADS is a semi-supervised learning detection system, in which the system takes prior information to support and improve detection performance, but will still operate without prior information. The results from ADS will also be evaluated. The ADS will use a combination of various anomaly detection algorithms for different anomaly events including statistical approach using Gaussian Mixture Model Expectation Maximization (GMM-EM), Hidden Markov Model (HMM), graphical approach using Weiler-Atherton Polygon Clipping (WAPC) and various clustering algorithms such as K-mean clustering, Spectral clustering and DBSCAN.
Batch construction and multitask learning in visual relationship recognition

An image can be described by the objects within it, as well as interactions between those objects. A pair of object labels together with an interaction label is known as a visual relationship, and is represented as a triplet of the form (subject, predicate, object). Recognising visual relationships in a given image is a challenging task, owing to the combinatorially large number of possible relationship triplets, which leads to an extreme classification problem, as well as a very long tail found typically in the distribution of those possible triplets. We investigate the effects of three strategies that could potentially address these issues. Firstly, instead of predicting the full triplet we opt to predict each element separately. Secondly, we investigate the use of shared network parameters to perform these separate predictions in a multitask setting. Thirdly, we consider a class-selective batch construction strategy to expose the network to more of the many rare classes during mini-batch training. Our experiments demonstrate that batch construction can improve performance on the long tail, possibly at the expense of accuracy on the small number of dominating classes. We also find that a multitask model neither improves nor impedes performance in any significant way, but that its smaller size may be beneficial.
Speech representation based on tensor factor analysis and its application to speaker recognition and language identification

Ahstract-This paper proposes a novel approach to speech representation for both speaker recognition and language identification by characterizing the entire feature space by a tensor. In conventional studies of both tasks, i-vector is commonly used as the state-of-the-art representation. Here, i-vector extraction can be regarded as projection of utterance-based GMM supervector onto a low-dimensional space. In this paper, for the aim of explicit modeling of the correlation among mean vectors of a GMM, an utterance is not modeled as its GMM-based supervector but as its matrix and the entire set of utterances is modeled as its tensor. By applying tensor factor analysis, we obtain a new representation for an input utterance. Experimental evaluations for speaker recognition and language identification show that our proposed approach has effectiveness especially for the speaker recognition task.
A Study of Myanmar (Burmese) to English Machine Translation Performance with Various Myanmar Translated Styles

This paper contributes the first investigation of machine translation (MT) performance differences between Myanmar and English languages with the use of several possible Myanmar translations for the specific primary educational domain. We also developed both one to one and many Myanmar translations corpora (over 8K and 34K sentences) based on old and new English textbooks (including Grade 1 to 3) which are published by the Ministry of Education. Our developing parallel corpora were used for phrase-based statistical machine translation (PBSMT) which is the de facto standard of statistical machine translation. We measured machine translation performance differences among one-to-many English to Myanmar translation corpora. The differences range between 19.68 and 52.38 BLEU scores for English to Myanmar and between 50.17 and 75.12 BLEU scores for Myanmar to English translation. We expect this study can be applied in Myanmar-to-English automatic speech recognition (ASR) development for primary English textbooks. The main purpose is to translate primary English textbooks data correctly even if the children use in several Myanmar conversation styles.
Analyzing Online Transaction Data using Association Rule Mining

Association Rule Mining is a data mining designed to discover the real connections of data items in transaction data build on associativity. The technique utilizes the Apriori Algorithm to discover association rules. Furthermore, the Apriori Algorithm widely treated to discover frequent itemsets in transaction data. This study aims to enhance the efficiency of the Apriori Algorithm in the mining of association rule as a reference to identifying mixed item deals as a regular promo to offer to customers build on item frequency of buying. The study shows that Association rule mining implementation through enhanced Apriori Algorithm generates results at a higher performance rate or lesser runtime rate compared with the original Apriori Algorithm, and it helps the organization in selecting customer product deals. Anticipated in business flow, the study produced a list of package items for consumers based on strong rules generated by association rule mining at a lesser runtime rate.
Quantum Error Corrections

This chapter briefly discusses error-correction algorithms. In an ideal quantum computer, these are not needed nor are they needed in algorithm development. They are included to make the reader aware of the nature of the problems quantum computer designers face and how some of them can be solved algorithmically.
Adaptive Speech Compression and Decompression for Communications

High digital compression ratios are spectrally efficient; however, excellent fidelity makes the application in radio very useful. LPC (Linear predictive coding) methods have been honed for mobile communication; but a more generic form of compression, which can be applied to ham radio and personal communication services (PCS) in the private sector would extend the usefulness of these applications and services. Using AI and self-training techniques, a high fidelity coding a decoding library can be developed by the layman on a computer and applied to radio voice applications using properly equipped hardware. This paper discusses the coding and decoding algorithm, as well as the recommended hardware and software for implementation.
Statistical Modeling of Longitudinal Data with Non-Ignorable Non-Monotone Missingness with Semiparametric Bayesian and Machine Learning Components

In longitudinal studies, outcomes are measured repeatedly over time and it is common that not all the patients will be measured throughout the study. For example patients can be lost to follow-up (monotone missingness) or miss one or more visits (non-monotone missingness); hence there are missing outcomes. In the longitudinal setting, we often assume the missingness is related to the unobserved data, which is non-ignorable. Pattern-mixture models (PMM) analyze the joint distribution of outcome and patterns of missingness in longitudinal data with non-ignorable non-monotone missingness. Existing methods employ PMM and impute the unobserved outcomes using the distribution of observed outcomes, conditioned on missing patterns. We extend the existing methods using latent class analysis (LCA) and a shared-parameter PMM. The LCA groups patterns of missingness with similar features and the shared-parameter PMM allows a subset of parameters to be different between latent classes when fitting a model. We also propose a method for imputation using distribution of observed data conditioning on latent class. Our model improves existing methods by accommodating data with small sample size. In a simulation study our estimator had smaller mean squared error than existing methods. Our methodology is applied to data from a phase II clinical trial that studies quality of life of patients with prostate cancer receiving radiation therapy.
An Experimental Study on English Majors Weak Form Productions of Prepositions

This study compares the acoustic cues Chinese English learners and American native English speakers used to realize the weak forms of prepositions. Six prepositions with the highest frequency of occurrence in COCA, “at, for, from, in, of and to”, were selected as target words, and each of them was collocated with 3 verb phrases (VP) which were further put into experimental sentences with a Subject+VP+Object+Apstructure. Altogether 6 focus conditions were set for each experimental sentence in this research. The vowel formant, duration, pitch movement of prepositions were analyzed within and across two speaker groups, from which we concluded: learners only use pitch and duration as the phonetic cues to distinguish the reduced and the normal prepositions, and the deviation might be caused by their L1 transfer. Specifically, learners' centralization is not as obvious as the native speakers in the process of vowel reduction, instead, they use the shortened Chinese vowels to realize the weak forms of prepositions. In addition, learners' duration of reduced vowel in preposition is longer than that of the native speakers, as there is no lax vowel in their L1 system, so it is difficult for them to shorten the vowels as largely as the native speakers do. Besides, learners' production of prepositions placed after the sentence nuclear is deviant from that of the native speakers in terms of pitch representation. Their pitch range is larger than the native speakers', and the clear falling trend on their pitch contour is observed within the reduced vowel, which is supposed to be caused by the transference of neutral tone features in their L1 Chinese.
Shape Modeling and Atlas-Based Segmentation for Identification of Lower Leg Tissues in pQCT

In this work, we introduce an atlas-based segmentation method for lower leg tissues at 4%, 38%, and 66% tibial length. Our goal is to model the shape of the lower leg tissue types and to identify hard and soft tissues in an automated way. In our methodology, we implemented B-spline based free form deformation (FFD), and symmetric diffeomorphic demons (SDD) deformable models for nonlinear registration, and compared their performances for atlas-based segmentation accuracy on our pQCT data. Overall, we concluded that atlas-based segmentation is a promising technique, especially in the presence of noise and other types of image degradation. We also observed that the diffeomorphic demons algorithm may produce more accurate deformation fields than FFD. On the other hand, FFD produced smoother deformations than SDD. Quantitative analysis using the Dice similarity coefficient (DSC), showed that FFD was slightly better than SDD in identification of the trabecular bone tissue in 4% tibia. At 38% tibial length, SDD produced consistently higher DSC values than FFD, while at 66% tibia, FFD produced slightly higher segmentation accuracy.
A Bayesian based Model to Credential Learners' Content Skill Assessment

In this paper, we present a Bayesian-based knowledge assessment solution using “natural language” information from ebooks to evaluate student comprehension and skill levels regarding specified concepts and relationships between concepts that comprise a specific knowledge domain. Unlike multiple choice assessment tools, the method examines and assesses students' written response to identify evidence that students fully comprehend the concepts being assessed. Besides, it can deeply estimate the probability that a student has grasped concepts and offer students feedbacks on strengths and weaknesses, as well as advising students and instructors of additional learning activities that may help students to resolve any deficiencies.
Saliency Detection based on spectra Destiny Peaks Clustering

Abstract Saliency detection mapping the whole salient object by simulating the human visual system is one of the fundamental problems in computer vision. In this paper we proposed a novel method to map the spatial vector features of image points to spectra, and decompose an image into large scale perceptually homogeneous elements for efficient salient region detection, using spectra destiny peaks clustering. a multi-layer saliency mapping is built based the size of regions, The final saliency map is intergraded in a hierarchical model. This method can overcome the common problem in saliency detection that the detection accuracy could be adversely affected if salient foreground or background in an image containing small-scale high-contrast regions. The experimental results show that the proposed method outperforms are improved greatly.
Hardware design of new multi - mode meteorological acquisition terminal based on CC2538

It is not difficult to see that the Internet of things is based on the characteristics of fast information and data interaction to realize the exchange of information and data with various different substances in real life.Driven by the continuous demand of the Internet of things market, a kind of operating system - Contiki Internet of things operating system was born for Internet of things or wireless sensor network applications.Contiki is a multitasking operating system suitable for embedded memory, its biggest feature is full open source and support network, and perfect support for the 6LoWPAN protocol stack.Time transmission, user detection, navigation and positioning, and short message communication are the functions of the beidou ii global navigation system, which is completely independently constructed in China. Among them, the short message communication function is unique to beidou satellite in the global navigation system.China's beidou satellite has the characteristics of wide coverage, long transmission distance, high stability and easy installation.Based on the above research, based on the principle of embedded system and the principle of hardware circuit, this paper designed a hardware design scheme of multi-mode meteorological acquisition terminal with ARM architecture cortex-m3 kernel as the core and in line with the requirements of meteorological ground observation field.The MCU of this design adopts the CC2538 processor of TI company, and USES the MCU combined with AD acquisition circuit module and front-end conditioning circuit module to package and make a special “chip” for the meteorological industry.In the near field communication, the data is sent wirelessly through 6LoWPAN in the form of nodes.When the remote wireless communication network can be covered, the data can be sent through 4G network in the form of terminal.In remote mountainous areas where the wireless communication network cannot cover or where the communication network is vulnerable to natural disasters, the terminal can use the unique two-way short message technology of beidou ii satellite for communication and transmission.This can greatly reduce the labor cost and make the meteorological data collection more intelligent and diversified.
Orion: A Generic Model and Tool for Data Mining

This paper focuses on the design of autonomous behaviors based on humans behaviors observation. In this context, the contribution of the Orion model is to gather and to take advantage of two approaches: data mining techniques (to extract knowledge from the human) and behavior models (to control the autonomous behaviors). In this paper, the Orion model is described by UML diagrams. More than a model, Orion is an operational tool allowing to represent, transform, visualize and predict data; it also integrates operational standard behavioral models. Orion is illustrated to control a bot in the game Unreal Tournament. Thanks to Orion, we can collect data of low level behaviors through three scenarios performed by human players: movement, long range aiming and close combat. We can easily transform the data and use some data mining techniques to learn behaviors from human players observation. Orion allows us to build a complete behavior using an extension of a Behavior Tree integrating ad hoc features in order to manage aspects of behavior that we have not been able to learn automatically.
Comparison of in-house HPC calculation with public cloud computing for parallel algorithm containing recursive functions

This work focuses on possibilities of practical using public cloud in comparison with high-performance cluster for difficult calculations. A task dealing with problem of coloring the edges of a large set of cubic graphs and examining relations between these graphs was used as a criterion. In general, we dealt with couple of modifications and levels of difficulty of a given task. Assessing the convenience of usage nowadays, not only computer resources, is significant factor and phenomenon an environmental friendliness. In the world of informational technology and in the field of high-performance computing, is established the idea of green computing. Algorithms for effective usage of available computing resources are used to reach the aim within the high-performance computing that is hidden under the green computing. In the connection with service providers of public cloud computing is solving of securing datacenter power from renewable sources an added factor.
Oblivious transfer with hidden access control and outsourced decryption from deterministic finite automata‐based functional encryption for an in‐vehicle sensor database system

Abstract Vehicle sensors are continuously observing the state of the vehicle, collecting and transmitting data from/to surrounding road users. The sensors data are then processed and stored in an in‐vehicle sensor database in the form of records. These records can be accessed and shared by other vehicles or road side units with the aim to improve road safety. However, these records may contain a very sensitive information, such as vehicles' identifiers and location. This information requires extraordinary protection to ensure privacy and confidentiality of the vehicle users. In this article, we propose a secure oblivious transfer protocol with hidden access control and outsourced decryption from deterministic finite automata (DFA)‐based functional encryption (HACOT‐DFA). To reduce the computational costs for users with limited resources, we securely offload the heavy decryption overhead on the user side to an untrusted cloud server. Then, we experimentally compare our HACOT‐DFA protocol against existing HACOT protocols in three different platform settings: the smartphone, the desktop personal computer, and the embedded device Raspberry Pi. The analyses demonstrate that the proposed HACOT‐DFA protocol is more efficient and computationally stable than previous work. Our security proof holds in the standard model using a dual‐system encryption approach.
Document Representation using Extended Locality Preserving Indexing

The main purpose of robust document representation is to facilitate tasks such as classification or indexing. Documents are represented as high dimensional vectors, making them challenging to process. Thus, low dimensional representation of such documents is highly sought. Latent Semantic Indexing(LSI) preserves global structure. While, Locality Preserving Indexing (LPI) preserves local structure in reduced representation by considering neighbors of document. However, it does not retain information about non-neighbors. In this article, Extended Locality Preserving Indexing (ELPI) is proposed which effectively preserves the topology of document space by modifying the weighing scheme and shows encouraging improvement over LPI.
Plaintext Recovery Attacks and Their Mitigation in an Application-Specific SHE Scheme

In addition to storage and computing power, cloud providers ensure confidentiality of user data through the use of various encryption technologies. The need to decrypt the data before it can be operated upon exposes a possible security hole which could be exploited by untrustworthy system administrators. Homomorphic encryption allows operations on encrypted data without the need to first decrypt it making it attractive for cloud computing. However, it incurs significant overhead of storage and computation and is therefore infeasible in practice. Somewhat homomorphic schemes have been proposed to handle specific applications - one such scheme, the Zhou Wornell Scheme, operates on vectors of integers. We demonstrate that this scheme is vulnerable to plaintext recovery attacks for a range of vector sizes. We explore the trade-offs between plaintext vector length, public key size and security. Increasing vector length increases security but at the cost of greatly increased public key size. We suggest a way of reducing the size of the public key by up to 90%. Finally, we propose a variant of this scheme which is secure against plaintext recovery attacks.
Practical side-channel attack on a security device

This paper demonstrates how to mount a side-channel attack on a security device: a cryptocurrency hardware wallet. Our study led to sensitive user data extraction, and was responsibly disclosed to the vendor who was able to patch its firmware. We explain here what we did and the tool we used to extract a user PIN from the device.
Interacting T-S Fuzzy Model Particle Filter

To the uncertainty modeling of nonlinear non-Gaussian dynamic system, a novel interacting T-S fuzzy model particle filter is proposed. In the proposed algorithm, a general interacting T-S fuzzy model framework is constructed. The target feature information is represented by multiple semantic fuzzy sets, and the probability transition model between fuzzy sets is derived based on the closeness degree between fuzzy sets, which is used to estimate the interacting transition probabilities between the models. Furthermore, to identify the premise parameters of T-S fuzzy model, a fuzzy C- regression clustering method is proposed, and a particle filtering algorithm based on a modified extended forgetting recursive least squares estimation method (EFRLS) is used to identify the consequence parameters. Finally, the experimental results show that the tracking performance of the proposed algorithms is better than that of the traditional interacting multiple model (IMM), interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model particle filter (IMMPF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF).
Path Planning and Obstacle Avoidance of UAV Using Adaptive Differential Evolution

Presently, the path planning and obstacle avoidance of unmanned aerial vehicle (UAV) are attracting research field. A variety of techniques have been introduced by the researchers for obtaining optimal path and avoiding obstacles in the path. This paper presents the implementation of adaptive differential evolution (DE) algorithm for collision avoidance as well as obtaining the optimal path in a static environment whereas former being given more importance. Compared to classical DE algorithm, the proposed adaptive DE allows the UAV to reach the target in an optimal path while avoiding obstacles in a collective manner. The overall performance of the proposed algorithm is verified by simulation results.