Reviewerly/exHarmony huggingface dataset:
C-ASFT: Convolutional Neural Networks-Based Anti-spam Filtering Technique

Although several machine learning algorithms including Naive Bayes classifier have been employed in many well-known spam filters, very few efforts are made to employ deep learning strategies like Convolutional Neural Networks (CNN) for learning text-based spam features automatically for further classification. In this work, we propose a CNN-based server-side solution called C-ASFT (CNN-based Anti-Spam Filtering Technique). C-ASFT employs three staged one-dimensional CNN layer model for effective text-based spam detection and filtering. One-dimensional CNN layers learn the spatial structure or invariant features present in the sequence of words in the input mail text data. E-mail server can mark the e-mail as spam/non-spam so that client can decide either to read/delete it. Experimental results confirm the efficacy of the proposed solution.
Partitioned Binary Search Trees: A Generalization of Red Black Trees

We propose a generalized form for Red Black Trees. The structure, called Partitioned Binary Search Trees, tolerates finite successions of Red nodes provoking a degree of imbalance while reducing the number of maintenance operations and speeding up the updates. The tree is interesting not only because of its simple operations but also because it insures the same level of performance of Red Black Trees O(log(n)) and allows an even higher adaptability and efficiency rate in different fields where rebalancing is costly.
Evasive Maneuvers of Optionally Piloted Air Vehicles For Three-Dimensional Geofencing

The proposed algorithm prevents an air vehicle to leave a pre-defined flight area including an optional number of non-flight zones in three-dimensional airspace. This work is based on previously published articles and, as a result, complements a fully-automatic geofencing module for three-dimensional space. The main contribution of this paper is the activation, planning, and conduction of three-dimensional evasive maneuvers for wing-borne flight when the air vehicle hits the boundary of a pre-flight calculated safety area. Since the safety area can be violated in altitude, aside or both simultaneously, a maneuver trigger logic is proposed, which initiates the appropriate maneuver. The evasive maneuvers are designed such that the air vehicle does not exceed airworthiness limitations.
NMF-based DCG Optimization for Collaborative Ranking on Recommendation Systems

A recommendation system predicts a top-N list of items that a target user might like by considering the user's previous rating history. In this paper, we solve the task of recommendation by developing a method that implements an NMF-based DCG optimization for collaborative ranking. Three main processes are applied to calculate the rating prediction for making the list of top-N item recommendations: constructing the user profile, initialising the latent-factor models using NMF (Non-Negative Matrix Factorization), and further optimising the models based on the DCG (Discounted Cumulative Gain). Extensive evaluations show that our proposed method beats all baseline methods on both the Precision and NDCG metrics. This fact confirms that NMF-based DCG optimization is an effective approach to enhance the recommendation performance and to deal with the sparsity problem.
Efficient Monitoring of Library Call Invocation

The ability to monitor when user code invokes a library function offers numerous advantages. For example, during black-box testing of code, high-level control-flow integrity (CFI) checking, run-time access-control policy enforcement and so on. However, for this technique to be useful it must be efficient and able to function even when the target application is provided only as a statically linked executable. In an earlier paper we demonstrated how library calls may be intercepted using wrappers. But this approach works only with dynamically linked code and requires labour-intensive processing of the function arguments. Under the current scheme, each library - either dynamically or statically linked - constitutes a separate code-region. At any point in time, only pages belonging to that one region are marked as executable, so when code branches to a page outside the `home' region, it will land in a non-executable page, a fault will occur and the kernel will take over. By adding suitable code to the kernel, we can determine (a) whether the call should go ahead, (b) whether the arguments are acceptable and (c) ensure that the kernel will be informed when the code returns from the called function. In this paper we present our technique by analyzing the interception of a known exploit of the NGINX server. We show that our mechanism can detect and contain the attack and discuss the performance overheads of our mechanism.
Vocal Intent Programmability for UAS in Disaster Scenarios

Using Unmanned Aircraft Systems (UAS) for disaster response: Advances in drone technologies has expanded the adoption of drones in disaster response. For example, emergency medical services and first responders have embraced the utility of Small Unmanned Aerial Systems (SUAS) or drones and new applications continue to arise. However, safely controlling and operating a drone to its full capacity requires certain set of skills hence the use of this technology is limited to people who have skills to operate them or to people who can hire someone for the task. During or after natural or man-made disasters such as floods, earthquakes or even nuclear accidents, damage assessment, locating survivors or taking any mandatory actions for damage control can only be performed via aerial reconnaissance. Operators could utilize drones to increase the efficiency of their operations to gain insights and for faster situation awareness recognition. Under such circumstances, it is critical that commands or request for relevant situational awareness (SA) information from the drones are processed without delay or ambiguity. The overarching goal of this effort is to develop a framework for command and control of drones through natural language processing and intent recognition.
Stochastic Optimization Framework for Spacecraft Maneuver Detection

The main focus of the paper is the detection and characterization of unobserved spacecraft maneuvers, by considering a stochastic optimal control approach. Specifically, the propagation of uncertainty associated with state estimation errors for a spacecraft is investigated using a Monte Carlo type nonlinear simulation framework. Gaussian statistics were assumed for the initial spacecraft states and possible maneuvers. The nonlinear simulation framework helps identifying the classes of maneuvers that steers the uncertainties associated with state estimation from an initial probability distribution to a final probability distribution. The initial state sample points are generated based on conjugate unscented transformation, capturing the initial statistics of the state estimates. A linearized optimization framework is also developed for rapid computation of the stochastic optimal control metric that measures the maneuverability of the uncertainties between given boundary probability distributions. Numerical simulation results comparing the linear and nonlinear framework are provided.
THE EFFECT OF RECIPE DEMONSTRATION TECHNIQUE ON STUDENTS’ WRITING COMPETENCE IN PROCEDURAL TEXT

This study was aimed at finding the effect of recipe demonstration technique towards students' writing skill in the procedural text at the food and beverage department. It was found that the students of SMK Negeri 1 Batulayar have problems in implementation writing procedural texts, thus the researcher conducted a quasi-experimental class. This research involved 62 students at grade tenth of SMK Negeri 1 Batulayar. The purpose of this research was to find out the effect of recipe demonstration toward students’ writing skill in procedural text. In collecting the data, the researcher conducted all procedures of data collection, starting from pre-test, treatments, and ended up with post-test. The quantitative data was obtained through the result of post-test of the experimental and control class. The analysis method used was independent sample t-test to find out the effect of using recipe demonstration toward students writing skill in procedural text. While the qualitative data was obtained through observation, the researcher acts as the main instrument in identifying the selected students' worksheet through a desk study within five aspects assessed in a procedural text. In this research, the researcher used the technique of data analysis which is involving three steps: (1) data reduction, (2) data display, and (3) conclusion drawing/verification. The researcher got the result by using SPSS analyses, and the score of t-test indicated that the Sig-2 tailed&lt; 0.05 and the t-test shows &gt; t-table, that is the result of the t-test is 3.562 while t-table is 1.66, and it can be concluded that the hypothesis that the researcher set is accepted. In conclusion, the implementation of recipe demonstration is effective to increase students' writing competence in the procedural text of students' at F &amp; b department SMK Negeri 1 Batulayar.
Maintaining Visibility of a Landmark using Optimal Sampling-based Path Planning

An approach to extend sampling-based path planning algorithms to include visual restrictions is presented. This approach imposes of visual constraints during the sampling and optimization processes. Four visual constraints are imposed during sampling: 1) keep the landmark within the sensor field of view, 2) avoid landmark occlusions, 3) maintaining landmark features near the image center, and 4) limit changes in landmark view orientation. These last two are imposed during path optimization. The robot task is to maintain these constraints, in an environment with obstacles, while the robot changes configurations. The sampling-based motion planning algorithm imposes and maintains both physical and visual restrictions. The process uses a collision checker to detect self-and obstacle-collisions, or landmark occlusions. To infer the landmark visibility, the algorithm dynamically builds a 3D model of camera field visibility as seen from the moving robot. To maintaining the landmark features close to the image center, a distance parameter from the field of view boundary to the landmark is used and optimized. The camera roll angle was included as another element to be optimized, limiting changes in orientation. The algorithm has been implemented, and both results in simulation and experiments using a real robot manipulator are presented.
Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Multi-Modal Conditional Learning

This paper studies the supervised learning of the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different modalities, e.g., the output is an photo image and the input is a sketch image. We solve this problem by cooperative training of a fast thinking initializer and slow thinking solver. The initializer generates the output directly by a non-linear transformation of the input as well as a noise vector that accounts for latent variability in the output. The slow thinking solver learns an objective function in the form of a conditional energy function, so that the output can be generated by optimizing the objective function, or more rigorously by sampling from the conditional energy-based model. We propose to learn the two models jointly, where the fast thinking initializer serves to initialize the sampling of the slow thinking solver, and the solver refines the initial output by an iterative algorithm. The solver learns from the difference between the refined output and the observed output, while the initializer learns from how the solver refines its initial output. We demonstrate the effectiveness of the proposed method on various multi-modal conditional learning tasks, e.g., class-to-image generation, image-to-image translation, and image recovery.
Sentiment analysis of public towards infrastructure development in Indonesia on Twitter media using boosting support vector machine method

Infrastructure is one of the important elements which supports the economic development of a country. Increasing the quality of infrastructure will also increase the level of the economy of a country. Infrastructure development also provides easy access for goods distribution or services throughout Indonesia and facilitates connectivity between communities. Besides, parallel infrastructure development is also needed to avoid economic inequality between regions in Indonesia. The development of social media in Indonesia has made people easier to convey their responses regarding the performance of government which one of them is related to infrastructure development. One of the types of social media that is currently popular among Indonesian people is Twitter. Considering that the popularity of Twitter in Indonesia, sentiment analysis as an application from text mining was conducted. We use support vector machine (SVM) as an analytical method to make a prediction model for classification of data opinion on Twitter. Some opinions which derived from it were classified into two groups, negative and positive opinions. The data was unbalanced data between negative class and positive class. Therefore, to handle this, a boosting algorithm on the SVM method was added. As a result, sentiment analysis of infrastructure development in Indonesia using the boosting support vector machine (BSVM) method gives an accuracy of 95.43%, a specificity of 81.67% and sensitivity of 97.54%. The classification results were made in the form of web framework application with visualization in the form of wordcloud and streamgraph graphics that were displayed interactively with a web-based application R Shiny.
A CNN-Based Multi-scale Super-Resolution Architecture on FPGA for 4K/8K UHD Applications

In this paper, based on our previous work, we present a multi-scale super-resolution (SR) hardware (HW) architecture using a convolutional neural network (CNN), where the up-scaling factors of 2, 3 and 4 are supported. In our dedicated multi-scale CNN-based SR HW, low-resolution (LR) input frames are processed line-by-line, and the number of convolutional filter parameters is significantly reduced by incorporating depth-wise separable convolutions with residual connections. As for 3× and 4× up-scaling, the number of channels for point-wise convolution layer before a pixel-shuffle layer is set to 9 and 16, respectively. Additionally, we propose an integrated timing generator that supports 3× and 4× up-scaling. For efficient HW implementation, we use a simple and effective quantization method with a minimal peak signal-to-noise ratio (PSNR) degradation. Also, we propose a compression method to efficiently store intermediate feature map data to reduce the number of line memories used in HW. Our CNN-based SR HW implementation on the FPGA can generate 4K ultra high-definition (UHD) frames of higher PSNR at 60 fps, which have higher visual quality compared to conventional CNN-based SR methods that were trained and tested in software. The resources in our CNN-based SR HW can be shared for multi-scale upscaling factors of 2, 3 and 4 so that can be implemented to generate 8K UHD frames from 2K FHD input frames.
Efficient Threshold Function Secret Sharing With Information-Theoretic Security

Function secret sharing (FSS) is a cryptographic primitive that is introduced by Boyle et al. (Eurocrypt 2015), motivated by application scenarios involving private access to large distributed data while minimising the overhead of communication, for example, private information retrieval. Informally, an n-party FSS scheme splits a function f into n functions f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,...,f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> such that f = f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> +...+ f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> and every strict subset of the function shares hide f . Most of the known FSS constructions only have computational hiding, namely, the hiding property holds only against a computationally bounded adversary. We consider information-theoretic hiding in this work while allowing f to be recovered from t function shares and correspondingly, any (t - 1) function shares unconditionally hide f . Call it (t, n)-threshold function secret sharing ((t, n)-TFSS for short). Using information-theoretic tools and through a series of optimizations, we show that our (t, n)-TFSS have better performance than FSS in terms of communication complexity, a criterion that measures the efficiency of such protocols. Specifically, a (t, n)-TFSS scheme with communication complexity O(l) is designed in this paper and it is better than the existing FSS schemes with lowest communication complexity O(λl), where λ is the length of pseudo-random generator's seeds. In addition, the (t, n)-TFSS have an extra robustness property in the sense that even if up to (n - t) function shares are not available, the protocol still computes the function value at a given point correctly.
The General Functionalist Concept of Artificial Need as the Basis of Artificial General Intelligence

The article proposes to use a generally functionalist approach to create the basis of the conceptual model of Artificial General Intelligence (AGI). AGI is a field of scientific-theoretical and engineering-technological research, focused on the construction and use of computer simulations, models and reproductions of cognitive phenomena of the widest range of life, mental, personal and social manifestations. The methodological basis of AGI is general computer functionalism as a contemporary interpretation of the psychophysiological theory of machine functionalism of H. Putnam of the 1960s. There are collective, conceptual and observant approaches to the formation of general functionalism. A collective approach is the collection, identification, coordination, formalization, systematization, unification, codification of all kinds of functionalist theories. A conceptual approach is the analysis and identification of the main functionalistic characteristics, relationships, patterns, causalities that are invariant with respect to the content of cognitive phenomena. The observation approach allows to evaluate from the position of a person or a social community, immersed in the communicative “waves” of the virtual and real world, the different statuses of technological realizations of general functionalism: ontological, epistemological, logical, linguistic, axiological, aesthetic, ethical and praxeological features of projects of artificial life, artificial consciousness (brain), artificial personality, artificial society. As a basic personal phenomenon, which is defined by a system of functional relations, we take not the pain phenomenon familiar to functionalism but the more productive phenomenon of need. It has an advantage due to the breadth of the scale of phenomenological evidence, epistemological adequacy, ontological foundation. Based on functionalist modeling of need, the so-called “artificial need” arises, which can form the basis for the development of AGI. The author of the article offers one of the options for formalizing needs within the framework of an artificial system based on the general functionalist principle of formalizing cognitive phenomena. The principles of formalization proposed by D. Levin and T.W. Polger, who developed the approach of H. Putnam, are used. The author concludes that the application of this methodology leads us to understanding of an observer in the system of functional relations and considers the AGI as a complexity system.
STORING RDF DATA: A BRIEF SURVEY

There is an actual need to efficiently store and retrieve RDF data like the number and scale of Semantic Web in real-word applications in practice growth. As datasets are linked together, scalability becomes more critical. Efficient data storage and query processing that can scale to large amounts of possibly schema-less data have become an important research topic. The Study objective is to highlight A classification of RDF storage approach; and illustrations a view of two types of RDF data stores (Traditional database stores, NoSQL database stores) And therefore, this paper gives a brief overview of storing RDF data.
2. From Bots to Jokes: Is There a Place For HIV Prevention on Twitter?

Adolescents and young adults (AYA) account for over 20% of new HIV infections in the US, with the majority of new cases among males. Social media is a dominant force in the lives of young men and represents an important, source of information and discussion regarding HIV risk and prevention. Despite this, little is known about the content of online conversations about HIV among AYA men. To fill this gap, our study investigates online discourse about HIV through a rigorous qualitative analysis of popular HIV-related tweets by young men in the U.S.
Simultaneous Flow and Thermal Conductivity Sensing on a Single Chip Using Artificial Neural Networks

A thin-film CMOS MEMS thermal sensor has been designed, fabricated and tested with the addition of through-membrane isolating holes. These holes have been shown to enhance the discrimination towards gases with differing thermal conductivity in the presence of flow. Using three on-membrane resistors as inputs, linear statistical methods alongside Artificial Neural Network pattern recognition techniques have been investigated for decoupling the two parameters of thermal conductivity and flow rate using a single sensor. In addition to this, the addition of the membrane holes increases the sensitivity towards flow rate by 10 times and the sensitivity towards thermal conductivity by 2 times. This sensor design coupled with well-known post-processing techniques will enable a new generation of multi-parameter sensing solutions.
An Online Retrieval Question Answering System for Featured Snippets Triggering

In current search engines, there are two ways to display the results of Question style User Queries (QUQs), the natural results and Featured Snippet (FS). Actually, a search engine that triggers a FS can better satisfy the user's information seeking need, and the data format of FS is usually formulated as Question Answer Pairs (QAPs) in the dictionary. In this setting, an answer can be retrieved as a FS if and only if the QUQ and the question in QAPs are matched. The traditional retrieval method is basically based on keywords, which failed to bridge the semantic gap. On the other hand, neural matching methods may not be deployed online directly due to the high flexibility requirements in complex real-world scenarios. To this end, this paper combines retrieval model and matching model in a unified system for FS triggering. This system contains two stages: the recall stage and the ranking stage. In the recall stage, for a QUQ, we use the vector-based retrieval model rather than the BOW (bag of words)-based one to ensure accurate and quick recall of possible candidates from QAPs. In the ranking stage, we use the ensemble method on multiple models, including pre-trained network BERT, to boost matching performance. To improve the flexibility and adaptability of the system, two query analysis techniques, i.e. query term weighting and query term fuzzy method are also incorporated in the matching network. We conduct extensive experiments on real-word data. The experimental results demonstrate the superiority of our system.