Reviewerly/exHarmony huggingface dataset:
Multiobject fusion with minimum information loss

Generalized covariance intersection (GCI) has been effective in fusing multiobject densities from multiple agents for multitarget tracking and mapping purposes. From an information-theoretic viewpoint, it has been shown that GCI fusion essentially minimizes the weighted information gain (WIG) from local densities to the fused one. In this paper, the interest is in the fusion rule that dually minimizes the weighted information loss (WIL) and it turns out that such a fusion rule is consistent with the so-called linear opinion pool (LOP). However, the LOP cannot be directly applied to multiobject fusion since the resulting fused multiobject density (FMD), in general, no longer belongs to the same family of the local ones, thus it cannot be utilized as prior information for the next recursion in the context of Bayesian multiobject filtering. In order to overcome such a difficulty, the principle of minimizing WIL is further exploited in that the optimal FMD in the same family of the local ones is looked for. Implementation issues relative to the proposed minimum WIL (MWIL) fusion rule are discussed. Finally, the performance of the MWIL rule is assessed via simulation experiments concerning distributed multitarget tracking over a wireless sensor network.
The Fourier Spectral Characterization for the Correlation-Immune Functions over Fp

The correlation-immune functions serve as an important metric for measuring resistance of a cryptosystem against correlation attacks. Existing literature emphasize on matrices, orthogonal arrays and Walsh-Hadamard spectra to characterize the correlation-immune functions over $\mathbb{F}_p$ ($p \geq 2$ is a prime). %with prime $p$. Recently, Wang and Gong investigated the Fourier spectral characterization over the complex field for correlation-immune Boolean functions. In this paper, the discrete Fourier transform (DFT) of non-binary functions was studied. It was shown that a function $f$ over $\mathbb{F}_p$ is $m$th-order correlation-immune if and only if its Fourier spectrum vanishes at a specific location under any permutation of variables. Moreover, if $f$ is a symmetric function, $f$ is correlation-immune if and only if its Fourier spectrum vanishes at only one location.
A Hybrid Neural Network and Hidden Markov Model for Time-aware Recommender Systems

In this paper, we propose a hybrid model that combines neural network and hidden Markov model for time-aware recommender systems. We use higher-order hidden Markov model to capture the temporal information of users and items in collaborative filtering systems. Because the computation of the transition matrix of higher-order hidden Markov model is hard, we compute the transition matrix by deep neural networks. We implement the algorithms of the hybrid model for offline batch-learning and online updating respectively. Experiments on real datasets demonstrate that the hybrid model has improvement performances over the existing recommender systems.
PENGARUH MODEL PEMBELAJARAN COOPERATIVE INTEGRATED READING AND COMPOSITION (CIRC) TERHADAP KEMAMPUAN MENENTUKAN IDE POKOK PARAGRAF SISWA KELAS V SD NEGERI 003 PULAU KOPUNG KECAMATAN SENTAJO RAYA KABUPATEN KUANTAN SINGINGI

Reading is one of the important aspects in the communication process. Reading can make someone better understand the contents of the reading. In learning to make it easier for students to understand the contents of the reading it will be easier if it begins with the ability to determine the main ideas of the paragraph. Based on this, the researcher conducted a study by applying the Cooperative Integrated Reading and Composition (CIRC) learning model to the ability to determine the main idea of the paragraph at the fifth grade students of SD 003 Pulau Kopung. This study aims to determine the effect of the CIRC learning model on the ability to determine the main ideas of paragraphs of fifth grade students of SD Negeri 003 Pulau Kopung. This research method is a quasi- experimental Nonequivalent Control Group Design. This research was conducted in two classes, the VA class as the control class and VB class as the experimental class with 22 students in each class. The results of the study showed that the CIRC learning model influenced the ability to determine paragraph main ideas with the results of calculations derived from the gain index, the experimental class using the CIRC learning model got an increase in gain of 0.59 with the middle class and the control class with the normal learning model got an increase of 0.31 with medium class.
Analysis of factors affecting the area of forest and land fires in Indonesia uses spatial regression Geoda and SaTScan

This study discusses the factors that influence the extent of forest and land fires in Indonesia that relate several other factors such as rain, fire events, and wind speed which were the events during 2015. Forest fires are one of the environmental and forest problems that is a local and global concern. Countermeasures have been carried out for a long time but are relatively low. By looking for the best regression model with a significance level of 0.05 or 95% using the Spatial Autoregressive Model (SAR) method, the coefficient of determination of 25.00% is obtained which can be obtained by the research regression model and leaves 75.00% needed by other variables that are variables changed
Robots to the Rescue

Robots are becoming increasingly important responders, joining search and rescue teams in their missions. Besides being able to traverse contaminated and dangerous areas, these robots bring a different set of skills to disaster recover site. A new generation of robots is being developed that could quite literally be the difference between life and death in search and rescue operations. This article discusses land, aerial, and aquatic robots in different stages of their training to assist humans in various calamities.
Hierarchical Routing Mixture of Experts

In regression tasks the distribution of the data is often too complex to be fitted by a single model. In contrast, partition-based models are developed where data is divided and fitted by local models. These models partition the input space and do not leverage the input-output dependency of multimodal-distributed data, and strong local models are needed to make good predictions. Addressing these problems, we propose a binary tree-structured hierarchical routing mixture of experts (HRME) model that has classifiers as non-leaf node experts and simple regression models as leaf node experts. The classifier nodes jointly soft-partition the input-output space based on the natural separateness of multimodal data. This enables simple leaf experts to be effective for prediction. Further, we develop a probabilistic framework for the HRME model, and propose a recursive Expectation-Maximization (EM) based algorithm to learn both the tree structure and the expert models. Experiments on a collection of regression tasks validate the effectiveness of our method compared to a variety of other regression models.
Latest Advances in Solving the All-Pairs Sufﬁx Preﬁx Problem

Finding the overlaps between sequences that are generated by Next Generation Sequencing (NGS) technology is a time- and space-consuming step in building a string graph in genome assembly. The problem is known in computer science as all-pairs suffix prefix (APSP). The problem has been tackled since 1992 and several solutions were presented to solve it. While some of them achieve optimal theoretical time consumption, they have a very high space-consumption in addition to being practically slow due to a raised constant factor. Some other recent solutions practically consume much less space and time to solve APSP despite their adaptations to techniques and data structures which don’t have optimal worst-case asymptotic complexity. Other few researches tackled the approximate version of the overlap problem hoping to avoid error-detecting stages in genome assembly. These solutions used the same data structures which were employed to solve APSP in addition to some advanced techniques in order to address the complexity of approximate matching. In this work, we evaluate these recent algorithms, in terms of time and space, in both exact and approximate formats. Our results show that FastAPSP has the best time-consumption unless the size of the data set is large. The high space demand of such large data sets would favor the usage of SOF and Readjoiner. Our experiments also show that AOF is, in general, faster than FM unless the data set is small and repetitive. In addition, it can handle large data sets that cannot be processed by FM.
Autonomous Curiosity for Real-Time Training Onboard Robotic Agents

Learning requires both study and curiosity. A good learner is not only good at extracting information from the data given to it, but also skilled at finding the right new information to learn from. This is especially true when a human operator is required to provide the ground truth-such a source should only be queried sparingly. In this work, we address the problem of curiosity as it relates to online, real-time, human-in-the-loop training of an object detection algorithm onboard a robotic platform, one where motion produces new views of the subject. We propose a deep reinforcement learning approach that decides when to ask the human user for ground truth, and when to move. Through a series of experiments, we demonstrate that our agent learns a movement and request policy that is at least 3x more effective at using human user interactions to train an object detector than untrained approaches, and is generalizable to a variety of subjects and environments.
Collusion Trap Against GVW’13 ABE

Attribute-based encryption has broad application prospects. GVW'13 attribute-based encryption (ABE) scheme is one of the major candidates for ABE. The scheme has exquisite structure and proven security, and the crucial component is two-to-one recoding (TOR) algorithm. However, it has security risks in practical applications. In this paper, we describe this security risk, the collusion trap against GVW'13 ABE. In such a scenario: Alice and Bob collude to share decryption ability. In other words, when anyone can decrypt the ciphertext, he will tell the other one the corresponding plaintext. If Bob decrypts Alice's fake ciphertext and returns “corresponding plaintext” to Alice, Alice can attempt to gradually calculate Bob's secret key. We use the method of chosen-ciphertext attack of Jaulmes and Joux against NTRU to construct our attack. We show that whether for naked encryption or for calibrated encryption, Alice can obtain the equivalent secret key of Bob in polynomial time, although the size of the attack for calibrated encryption is much larger.
Entangling continuous variables with a qubit array

We show that an array of qubits embedded in a waveguide can emit entangled pairs of microwave photon beams. The quadratures obtained from the homodyne detection of these outputs beams form a pair of correlated continuous variables similar to the Einstein-Podolsky-Rosen experiment. The photon pairs are produced by the decay of plasmonlike collective excitations in the qubit array. The maximum intensity of the resulting beams is bounded by only the number of emitters. We calculate the excitation decay rate both into a continuum of the photon state and into a one-mode cavity. We also determine the frequency of Rabi-like oscillations resulting from a detuning.
Mathematical Model Formulation of Stakeholder Management Strategies in Software Engineering Projects

Stakeholder management is the process of identifying, analyzing, and engaging people who have either positive or negative influence in a project. The people involved in any project are called stakeholders and all projects have stakeholders irrespective of the size. Managing these stakeholders is a major function of project managers especially the most important ones because their action will determine whether the project is successful or not. Literatures have outlined different strategies of managing stakeholders which lies around stakeholder identification. This paper formulated mathematical model to determine the most important variable in managing stakeholders. In conclusion, the carrying capacity of a project should be considered alongside other stakeholder management strategies like active listening to bring the project to a successful completion.
An Optimal Algorithm for Stochastic Three-Composite Optimization

We develop an optimal primal-dual first-order algorithm for a class of stochastic three-composite convex minimization problems. The convergence rate of our method not only improves upon the existing methods, but also matches a lower bound derived for all first-order methods that solve this problem. We extend our proposed algorithm to solve a composite stochastic program with any finite number of nonsmooth functions. In addition, we generalize an optimal stochastic alternating direction method of multipliers (SADMM) algorithm proposed for the two-composite case to solve this problem, and establish its connection to our optimal primal-dual algorithm. We perform extensive numerical experiments on a variety of machine learning applications to demonstrate the superiority of our method via-a-vis the state-of-the-art.
Crowdsourced wireless spectrum anomaly detection

Automated wireless spectrum monitoring across frequency, time and space will be essential for many future applications. Manual and fine-grained spectrum analysis is becoming impossible because of the large number of measurement locations and complexity of the spectrum use landscape. Detecting unexpected behaviors in the wireless spectrum from the collected data is a crucial part of this automated monitoring, and the control of detected anomalies is a key functionality to enable interaction between the automated system and the end user. In this paper we look into the wireless spectrum anomaly detection problem for crowdsourced sensors. We first analyze in detail the nature of these anomalies and design effective algorithms to bring the higher dimensional input data to a common feature space across sensors. Anomalies can then be detected as outliers in this feature space. In addition, we investigate the importance of user feedback in the anomaly detection process to improve the performance of unsupervised anomaly detection. Furthermore, schemes for generalizing user feedback across sensors are also developed to close the anomaly detection loop.
Accelerate Performance for Elliptic Curve Scalar Multiplication based on NAF by Parallel Computing

The aim of Elliptic Curve Cryptosystems (ECC) is to achieve the same security level as RSA but with shorter key size. The basic operation in the ECC is scalar multiplication which is an expensive operation. In this paper, we focus on optimizing ECC scalar multiplication based on Non-Adjacent Form (NAF). A new algorithm is introduced that combines an Add-Subtract Scalar Multiplication Algorithm with NAF representation to accelerate the performance of the ECC calculation. Parallelizing the new algorithm shows an efficient method to calculate ECC. The proposed method has speed up the calculation up to 60% compared with the standard method.
Generalized Boltzmann Machine with Deep Neural Structure

Restricted Boltzmann Machine (RBM) is an essential component in many machine learning applications. As a probabilistic graphical model, RBM posits a shallow structure, which makes it less capable of modeling real-world applications. In this paper, to bridge the gap between RBM and artificial neural network, we propose an energy-based probabilistic model that is more flexible on modeling continuous data. By introducing the pair-wise inverse autoregressive flow into RBM, we propose two generalized continuous RBMs which contain deep neural network structure to more flexibly track the practical data distribution while still keeping the inference tractable. In addition, we extend the generalized RBM structures into sequential setting to better model the stochastic process of time series. Performance improvements on probabilistic modeling and representation learning are demonstrated by the experiments on diverse datasets.
On-line Searching in IUPAC Nucleotide Sequences

We propose a novel pattern matching algorithm for consensus nucleotide sequences over IUPAC alphabet, called BADPM (Byte-Aligned Degenerate Pattern Matching). The consensus nucleotide sequences represent a consensus obtained by sequencing a population of the same species and they are considered as so-called degenerate strings. BADPM works at the level of single bytes and it achieves sublinear search time on average. The algorithm is based on tabulating all possible factors of the searched pattern. It needs O(m + mα2 logm)space data structure and O(mα2) time for preprocessing where m is a length of the pattern and α represents a maximum number of variants implied from a 4-gram over IUPAC alphabet. The worst-case locate time is bounded by O(nm2α4) for BADPM where n is the length of the input text. However, the experiments performed on real genomic data proved the sublinear search time. BADPM can easily cooperate with the block q-gram inverted index and so achieve still better locate time. We implemented two other pattern matching algorithms for IUPAC nucleotide sequences as a baseline: Boyer-Moore-Horspool (BMH) and Parallel Naive Search (PNS). Especially PNS proves its efficiency insensitive to the length of the searched pattern m. BADPM proved its strong superiority for searching middle and long patterns.
Detection of Imaged Objects with Estimated Scales

Dealing with multiple sizes of the object in the image has always been a challenge in object detection. Predefined multi-size anchors are usually adopted to address this issue, but they can only accommodate a limited number of object scales and aspect ratios. To cover a wider multi-size variation, we propose a detection method that utilizes depth information to estimate the size of anchors. To be more specific, a general 3D shape is selected, for each class of objects, that represents different sizes of 2D bounding boxes in the image according to the corresponding object depths. Given these 2D bounding boxes, a neural network is used to classify them into different categories and do the regression to obtain more accurate 2D bounding boxes. The KITTI benchmark dataset is used to validate the proposed approach. Compared with the detection method using pre-defined anchors, the proposed method has achieved a significant improvement in detection accuracy.
Representing ill-known parts of a numerical model using a machine learning approach

In numerical modeling of the Earth System, many processes remain unknown or ill represented (let us quote sub-grid processes, the dependence to unknown latent variables or the non-inclusion of complex dynamics in numerical models) but sometimes can be observed. This paper proposes a methodology to produce a hybrid model combining a physical-based model (forecasting the well-known processes) with a neural-net model trained from observations (forecasting the remaining processes). The approach is applied to a shallow-water model in which the forcing, dissipative and diffusive terms are assumed to be unknown. We show that the hybrid model is able to reproduce with great accuracy the unknown terms (correlation close to 1). For long term simulations it reproduces with no significant difference the mean state, the kinetic energy, the potential energy and the potential vorticity of the system. Lastly it is able to function with new forcings that were not encountered during the training phase of the neural network.
Gesture Recognition System Based on Improved Stacked Hourglass Structure

Image-based human gesture recognition technology has been extensively studied. However, in the actual application of human gesture recognition, when the complex reality environment is encountered, the recognition accuracy and real-time performance of the existing methods are degraded. This paper proposes an improved Stacked Hourglass structure for human body gesture recognition, which further reduces the weight and number of hyperparameters of the Stacked Hourglass structure without reducing the recognition rate. In addition, after each convolutional layer in each Hourglass subnet, part of the weighting parameters is discarded according to a certain probability, further reducing the computational cost. Experimental results based on the human body pose data set show that this improved method has certain advantages in accuracy, and the computational cost is reduced due to its structural design.
On-line learning dynamics of ReLU neural networks using statistical physics techniques

We introduce exact macroscopic on-line learning dynamics of two-layer neural networks with ReLU units in the form of a system of differential equations, using techniques borrowed from statistical physics. For the first experiments, numerical solutions reveal similar behavior compared to sigmoidal activation researched in earlier work. In these experiments the theoretical results show good correspondence with simulations. In ove-rrealizable and unrealizable learning scenarios, the learning behavior of ReLU networks shows distinctive characteristics compared to sigmoidal networks.
Wide and Deep Reinforcement Learning for Grid-based Action Games

For the last decade Deep Reinforcement Learning has undergone exponential development; however, less hasbeen done to integrate linear methods into it. Our Wide and Deep Reinforcement Learning framework providesa tool that combines linear and non-linear methods into one. For practical implementations, our framework canhelp integrate expert knowledge while improving the performance of existing Deep Reinforcement Learningalgorithms. Our research aims to generate a simple practical framework to extend such algorithms. To testthis framework we develop an extension of the popular Deep Q-Networks algorithm, which we name WideDeep Q-Networks. We analyze its performance compared to Deep Q-Networks and Linear Agents, as well ashuman players. We apply our new algorithm to Berkley’s Pac-Man environment. Our algorithm considerablyoutperforms Deep Q-Networks’ both in terms of learning speed and ultimate performance showing its potentialfor boosting existing algorithms.
SEKOLAH “EMAK-EMAK” UNTUK BUTA HURUF DI KECAMATAN LECES KABUPATEN PROBOLINGGO

Illiteracy is the inability of individuals or citizens in terms of reading and writing words of words or sentences. Illiteracy does not only occur in Indonesia but has become a world problem for eradication. Illiteracy is closely related to the level of human resources quality owned by a country. Globalization is accompanied by advances in technology and information that demand the creation of quality and competent human resources. Consequently, developing countries are competing to eradicate illiteracy in their countries including Indonesia. In 2017 Indonesia still had 2.07%, equivalent to 3,387,035 residents experiencing illiteracy spread throughout the province. East Java Province is included in the category of people with illiteracy as many as 54,219 residents who incidentally are women. Therefore, the local government formed an institution for Community Learning Centers which could help efforts to eradicate illiteracy. This institution works with all elements of society so that the program designed can run well. This institution established a non-formal school located at the sub-district and village level to accommodate the illiteracy eradication learning program. The purpose of this study is to describe the efforts made by the PKBM institution in eradicating illiteracy, explaining the reasons for the participation of women in illiteracy eradication programs and the impact felt by women through literacy programs in the Leces sub-district. This study used case study with qualitative approach. The results of this study are the establishment of non-formal which are named emak-emak schools for efforts to eradicate illiteracy. Many benefits felt by women as school participants in eradicating illiteracy.