Reviewerly/exHarmony huggingface dataset:
Finding Plans Subject to Stipulations on What Information They Divulge

Motivated by applications where privacy is important, we study planning problems for robots acting in the presence of an observer. We first formulate and then solve planning problems subject to stipulations on the information divulged during plan execution—the appropriate solution concept being both a plan and an information disclosure policy. We pose this class of problem under a worst-case model within the framework of procrustean graphs, formulating the disclosure policy as a particular type of map on edge labels. We devise algorithms that, given a planning problem supplemented with an information stipulation, can find a plan, associated disclosure policy, or both jointly, if and only if some exists. The pair together, comprising the plan and associated disclosure policy, may depend subtly on additional information available to the observer, such as whether the observer knows the robot’s plan (e.g., leaked via a side-channel). Our implementation finds a plan and a suitable disclosure policy, jointly, when any such pair exists, albeit for small problem instances.
COMPARATIVE ANALYSIS OF SUPERPIXEL SEGMENTATION METHODS

Superpixel segmentation showed to be a useful preprocessing step in many computer vision applications. Superpixel’s purpose is to reduce the redundancy in the image and increase efficiency from the point of view of the next processing task. This led to a variety of algorithms to compute superpixel segmentations, each with individual strengths and weaknesses. Many methods for the computation of superpixels were already presented. A drawback of most of these methods is their high computational complexity and hence high computational time consumption. K mean based SLIC method shows better performance as compare to other while evaluating on the bases of under segmentation error and boundary recall, etc parameters.
Application of Blockchain Technology in Land Administration in Ghana

The administration of lands in Ghana has been a major issue in the past years that has resulted in parties seeking arbitration to determine the rightful owners and others resulting in death because of the land-guard menace. The main issues in land administration in Ghana include modification and falsification of land records, difficulty in authenticating the ownership of land property, sales of land property to more than one customer, and lack of transparency in land transactions. This chapter examines the application of Blockchain in land administration in Ghana to solve the issues of unauthorized modification of land records, difficulties in proven ownership of land properties, and the lack of transparency in land transactions. The proposed solution is based on Ethereum Blockchain technology using a smart contract. The solution used a non-fungible token to represent land properties as a digital asset that can be traded on the proposed solution. The proposed solution provides integrity, immutability, provenance, and transparency in land administration.
Too-Late Warnings by Estimating Mw: Earthquake Early Warning in the Near-Fault Region

ABSTRACT Earthquake early warning (EEW) systems aim to provide advance warnings of impending strong ground shaking. Many EEW systems are based on a strategy in which precise and rapid estimates of source parameters, such as hypocentral location and moment magnitude (Mw), are used in a ground-motion prediction equation (GMPE) to predict the strength of ground motion. For large earthquakes with long rupture duration, the process is repeated, and the prediction is updated in accordance with the growth of Mw during the ongoing rupture. However, in some regions near the causative fault this approach leads to late warnings, because strong ground motions often occur during earthquake ruptures before Mw can be confirmed. Mw increases monotonically with elapsed time and reaches its maximum at the end of rupture, and ground motion predicted by a GMPE similarly reaches its maximum at the end of rupture, but actual generation of strong motion is earlier than the end of rupture. A time gap between maximum Mw and strong-motion generation is the first factor contributing to late warnings. Because this time gap exists at any point of time during the rupture, a late warning is inherently caused even when the growth of Mw can be monitored in real time. In the near-fault region, a weak subevent can be the main contributor to strong ground motion at a site if the distance from the subevent to the site is small. A contribution from a weaker but nearby subevent early in the rupture is the second factor contributing to late warnings. Thus, an EEW strategy based on rapid estimation of Mw is not suitable for near-fault regions where strong shaking is usually recorded. Real-time monitoring of ground motion provides direct information for real-time prediction for these near-fault locations.
Preliminary analysis of Bukik Chinangkiek edupark’s potential as a learning resource for physics in senior high school at X Koto Singkarak Solok, Indonesia

Abstract “ Alam takambang jadi guru ” is a Minangkabau philosophy that is suitable implemented in the learning process. Nature tourism in Indonesia is very supportive for the implementation of contextual physics learning, one of which is Bukik Chinangkiek Edupark. Bukik Chinangkiek has natural conditions and rides related to physics for example rotational dynamics but it has not been utilized in physics learning. This study aimed to analyze the potential of Bukik Chinangkiek as an edupark-based learning resource for physics. Analysis was carried out with the Plomp development model at the preliminary analysis phase. The data of this study were obtained from questionnaires and interviews along with teachers and students in high schools throughout X Koto Singkarak Subdistrict, while concepts relevant to physics subject matter were obtained from direct observation of Bukik Chinangkiek Edupark. The technique of data analysis was descriptive percentages. Analysis shows that learning physics has not been integrated into the surrounding natural environment so students have not understood the concept of contextual physics and most students tend to use IT in learning. The results of this study is intended to be used as the basis for developing digital book of edupark (e-book) for high school.
Nature inspired evolutionary approaches for robot navigation: Survey

Abstract Abstract Autonomous robot navigation (ARN) requires intelligent systems equipped with human capabilities to achieve controlled motion among flats, ridgesor obstacles while avoiding collision in known or partially unknown environment with safety of equipment and manpower involved. Its objective is to generate optimal collision free path from initial to target location within minimal time and energy requirement. There are various navigational approaches (classical, heuristics, hybrid or nature inspired) and its choice depends on type of environment (static or dynamic) and application area. Nature inspired evolutionary (NIE) approaches is an emerging paradigm to develop robust and dynamic problems to address non-linear or multi-objective problemsin dynamic environment. This work is an attempt to review recently developed NIE approaches based on different biological groups for application in field of robot path planning (RPP). Subject Classification: 68U99Keywords: Autonomous Robot Navigation (ARN), Nature Inspired Evolutionary (NIE) ApproachesRobot Path Planning (RPP)
rIoT: Enabling Seamless Context-Aware Automation in the Internet of Things

Advances in mobile computing capabilities and an increasing number of Internet of Things (IoT) devices have enriched the possibilities of the IoT but have also increased the cognitive load required of IoT users. Existing context-aware systems provide various levels of automation in the IoT. Many of these systems adaptively take decisions on how to provide services based on assumptions made a priori. The approaches are difficult to personalize to an individual's dynamic environment, and thus today's smart IoT spaces often demand complex and specialized interactions with the user in order to provide tailored services. We propose rIoT, a framework for seamless and personalized automation of human-device interaction in the IoT. rIoT leverages existing technologies to operate across heterogeneous devices and networks to provide a one-stop solution for device interaction in the IoT. We show how rIoT exploits similarities between contexts and employs a decision-tree like method to adaptively capture a user's preferences from a small number of interactions with the IoT space. We measure the performance of rIoT on two real-world data sets and a real mobile device in terms of accuracy, learning speed, and latency in comparison to two state-of-the-art machine learning algorithms.
RSOD: Efficient Technique for Outlier Detection using Reverse Nearest Neighbors Statistics

Unsupervised learning techniques are popular in detecting outliers in various domains. Many parametric and non-parametric outlier detection approaches have been proposed over the last decades. The existing neighborhood-based non-parametric unsupervised approaches like LOF, symmetric neighborhood, LDOF are proven to be effective when outliers are in a region of variable density. However, these techniques wrongly treat an outlier point as inlier in certain scenarios (outlier located between a dense cluster and close to a sparse cluster). In this work, we address this problem by exploiting the information of k-nearest neighbors and reverse nearest neighbors efficiently. We conducted experiments with synthetic, and four real-world datasets, and our proposed technique outperforms popular symmetric neighborhood, LDOF, LOF techniques, and recently introduced RDOS.
Designing a Bangla Stemmer using rule based approach

Stemming is a preprocessing task for natural language processing that involves normalizing inflected words representing the same concept of the original word. Steaming is a process of text normalization that has many applications. There are many techniques for steaming of inflected words for different languages but very few works for Bangla word steaming. Therefore, stemming Bangla word is a unsolved problem. There are many different situations that can occur in Bangla language for word steaming. In this paper, we present a rule based algorithm to stem Bangla words. We developed the rules for infection detection for verb inflection, number inflection, and others. Using our rules, we developed a system to find the root word of Bangla words and found good performance. Sufficient examples are provided to explain the proposed system.
Compression versus traditional machine learning classifiers to detect code-switching in varieties and dialects: Arabic as a case study

Abstract The occurrence of code-switching in online communication, when a writer switches among multiple languages, presents a challenge for natural language processing tools, since they are designed for texts written in a single language. To answer the challenge, this paper presents detailed research on ways to detect code-switching in Arabic text automatically. We compare the prediction by partial matching (PPM) compression-based classifier, implemented in Tawa, and a traditional machine learning classifier sequential minimal optimization (SMO), implemented in Waikato Environment for Knowledge Analysis, working specifically on Arabic text taken from Facebook. Three experiments were conducted in order to: (1) detect code-switching among the Egyptian dialect and English; (2) detect code-switching among the Egyptian dialect, the Saudi dialect, and English; and (3) detect code-switching among the Egyptian dialect, the Saudi dialect, Modern Standard Arabic (MSA), and English. Our experiments showed that PPM achieved a higher accuracy rate than SMO with 99.8% versus 97.5% in the first experiment and 97.8% versus 80.7% in the second. In the third experiment, PPM achieved a lower accuracy rate than SMO with 53.2% versus 60.2%. Code-switching between Egyptian Arabic and English text is easiest to detect because Arabic and English are generally written in different character sets. It is more difficult to distinguish between Arabic dialects and MSA as these use the same character set, and most users of Arabic, especially Saudis and Egyptians, frequently mix MSA with their dialects. We also note that the MSA corpus used for training the MSA model may not represent MSA Facebook text well, being built from news websites. This paper also describes in detail the new Arabic corpora created for this research and our experiments.
Parallel architecture to accelerate superparamagnetic clustering algorithm

Superparamagnetic clustering (SPC) is an unsupervised classification technique in which clusters are self-organised based on data density and mutual interaction energy. Traditional SPC algorithm uses the Swendsen–Wang Monte Carlo approximation technique to significantly reduce the search space for reasonable clustering. However, Swendsen–Wang approximation is a Markov process which limits the conventional superparamagnetic technique to process data clustering in a sequential manner. Here the authors propose a parallel approach to replace the conventional appropriation to allow the algorithm to perform clustering in parallel. One synthetic and one open-source dataset were used to validate the accuracy of this parallel approach in which comparable clustering results were obtained as compared to the conventional implementation. The parallel method has an increase of clustering speed at least 8.7 times over the conventional approach, and the larger the sample size, the more increase in speed was observed. This can be explained by the higher degree of parallelism utilised for the increased data points. In addition, a hardware architecture was proposed to implement the parallel superparamagnetic algorithm using digital electronic technologies suitable for rapid or real-time neural spike sorting.
Perceptual accessible image encryption scheme conjugating multiple chaotic maps

In the recent decade, chaos-based image encryption algorithms gained attention due to their pros and cons. The authors suggest one such algorithm of image encryption for different colour space using feed forward initial condition to pursue random permutation by combining 1D logistic map with a series of tent maps. The algorithm adds one more novel step to protect and encrypt binary image by concealing it within a cover grey image using bit-plane decomposition methods. Sensitivity towards initial condition is shown using block division of image by applying a stream of random initial conditions to each block for encryption along with XOR operation which leads the key space high . For better performance analysis, the proposed scheme has been tested with many colour space images including a binary image. The proposed technique is more efficient with respect to time complexity and resistance to vulnerability aspects.
“Empty Chairs at Empty Tables”: Disenfranchisement by Association

The rise in research publications over the last few decades about disenfranchised grief has deepened our understanding of this field. Importantly, it raises awareness and validates human experiences of bereavement, which are often socially ignored, muted, and stigmatized. However, while researchers in the field actively engage in the task of "enfranchisement," as they present their work at scientific conferences, they might experience what we term disenfranchisement by association in the academic sphere. Based on our own experience, this article will demonstrate the parallel pathways between disenfranchisement of bereaved individuals and disenfranchisement of disenfranchised grief researchers, as it emerges in three main expressions of disenfranchisement: reluctance to listen, shame and guilt inflicting, and demand for euphemism. A conclusive note on how to address this kind of disenfranchisement is suggested.
Higher-Dimensional Quantum Walk in Terms of Quantum Bernoulli Noises

As a discrete-time quantum walk model on the one-dimensional integer lattice Z , the quantum walk recently constructed by Wang and Ye [Caishi Wang and Xiaojuan Ye, Quantum walk in terms of quantum Bernoulli noises, Quantum Information Processing 15 (2016), 1897–1908] exhibits quite different features. In this paper, we extend this walk to a higher dimensional case. More precisely, for a general positive integer d ≥ 2 , by using quantum Bernoulli noises we introduce a model of discrete-time quantum walk on the d-dimensional integer lattice Z d , which we call the d-dimensional QBN walk. The d-dimensional QBN walk shares the same coin space with the quantum walk constructed by Wang and Ye, although it is a higher dimensional extension of the latter. Moreover we prove that, for a range of choices of its initial state, the d-dimensional QBN walk has a limit probability distribution of d-dimensional standard Gauss type, which is in sharp contrast with the case of the usual higher dimensional quantum walks. Some other results are also obtained.
Parameter selection in a fully adaptive tracking radar

A fully adaptive radar framework has been proposed in recent publications, and this paper will implement the framework for a tracking radar. A set of cost functions are developed to balance the performance cost of the track and the resource cost of the track update. The method and cost functions are illustrated in a simulated example which emulating a simple scenario. The performance of the radar is compared when adapting with the different cost functions, and the performance of the track is compared together with the resources spent. It showed an increased performance in track maintenance at less resources spent, when comparing the method adapting over track update interval, number of pulses in dwell and PRF, over a method only adapting over track update interval.
Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?

Although proven powerful in making predictions and finding patterns, machine learning algorithms often struggle to provide explanations and translational knowledge when applied to many problems, especially in biomedical sciences. This is often resulted by the highly complex structure employed by machine learning algorithms to represent and model the relationship of the predictors and the response. The prediction accuracy is increased at the cost of having a “black-box” model that is not amenable for interpretation. Genetic programming may provide a potential solution to explainable machine learning for bioinformatics where learned knowledge and patterns can be translated to clinical actions. In this study, we employed an LGP algorithm for a bioinformatics classification problem. We developed feature selection analysis methods and aimed at explaining which features are influential in the prediction, and whether such an influence is through individual effects or synergistic effects of combining with other features.
Homomorphic Comparison for Point Numbers with User-Controllable Precision and Its Applications

This work is mainly interested in ensuring users’ privacy in asymmetric computing, such as cloud computing. In particular, because lots of user data are expressed in non-integer data types, privacy-enhanced applications built on fully homomorphic encryption (FHE) must support real-valued comparisons due to the ubiquity of real numbers in real-world applications. However, as FHE schemes operate in specific domains, such as that of congruent integers, most FHE-based solutions focus only on homomorphic comparisons of integers. Attempts to overcome this barrier can be grouped into two classes. Given point numbers in the form of approximate real numbers, one class of solution uses a special-purpose encoding to represent the point numbers, whereas the other class constructs a dedicated FHE scheme to encrypt point numbers directly. The solutions in the former class may provide depth-efficient arithmetic (i.e., logarithmic depth in the size of the data), but not depth-efficient comparisons between FHE-encrypted point numbers. The second class may avoid this problem, but it requires the precision of point numbers to be determined before the FHE setup is run. Thus, the precision of the data cannot be controlled once the setup is complete. Furthermore, because the precision accuracy is closely related to the sizes of the encryption parameters, increasing the precision of point numbers results in increasing the sizes of the FHE parameters, which increases the sizes of the public keys and ciphertexts, incurring more expensive computation and storage. Unfortunately, this problem also occurs in many of the proposals that fall into the first class. In this work, we are interested in depth-efficient comparison over FHE-encrypted point numbers. In particular, we focus on enabling the precision of point numbers to be manipulated after the system parameters of the underlying FHE scheme are determined, and even after the point numbers are encrypted. To this end, we encode point numbers in continued fraction (CF) form. Therefore, our work lies in the first class of solutions, except that our CF-based approach allows depth-efficient homomorphic comparisons (more precisely, the complexity of the comparison is O ( log κ + log n ) for a number of partial quotients n and their bit length κ , which is normally small) while allowing users to determine the precision of the encrypted point numbers when running their applications. We develop several useful applications (e.g., sorting) that leverage our CF-based homomorphic comparisons.
Privacy preserving search services against online attack

Searchable functionality is provided in many online services such as mail services or outsourced data storage. To protect users privacy, data in these services is usually stored after being encrypted using searchable encryption. This enables the data user to securely search encrypted data from a remote server without leaking data and query information. Public key encryption with keyword search is one of the research branches of searchable encryption; this provides privacy-preserving searchable functionality for applications such as encrypted email systems. However, it has an inherent vulnerability in that the information of a query may be leaked using a keyword guessing attack. Most of existing works aim to make the system resistant to offline keyword guessing, but this does not protect against online attacks on real world services. In this paper, we move a step forward to present a generic framework able to resist online keyword guessing attack using a server-assisted model. Specifically, we design a novel primitive C mirrored all-but-one lossy encryption, which can prevent a specific user from generating valid encryptions. This primitive can be seen as an access control on encryption ability. Combining searchable encryption technique with the new primitive makes online keyword guessing attack impossible for the specified user, even if the attack is launched online. We further give formal security analysis for the generic framework, and a concrete implementation with efficiency analysis to show that our design is practical.