Reviewerly/exHarmony huggingface dataset:
Impact factor based data sanitization in association rule mining

Data sanitization process is employed to market the sharing of transactional databases among organizations and businesses, and alleviates concerns for people and organizations regarding the disclosure of sensitive patterns sanitization process converts the source database into a released database so that unauthorized person cannot identify the sensitive patterns and so data confidentiality is maintained using association rule mining method. This process strongly relies on the minimizing the impact of knowledge sanitization on the info utility by minimizing the amount of lost patterns within the sort of non– sensitive patterns which are not mined from sanitized database. This study proposes a knowledge sanitization algorithm to cover sensitive patterns within the sort of frequent item sets from the database while controlling the impact of sanitization on the data utility using estimation of impact factor of every modification on non-sensitive item sets. In some applications like market basket analysis, Association Rule Mining (ARM) has recently gained more attention in businesses where the regularities within the customer purchasing behavior are found. On the other hand, these discovered patterns may pose a threat to the privacy of data holder; therefore, these patterns should be hidden before data sharing in such a way that the adversaries cannot discover the regularities in customer purchasing behavior.
Policy Gradients with Memory-Augmented Critic

Deep reinforcement learning has been investigated in high-dimensional continuous control tasks. Deep Deterministic Policy Gradients (DDPG) is known as a highly sample-efficient policy gradients algorithm. However, it is reported that DDPG is unstable during training due to bias and variance problems of learning its action-value function. In this paper, we propose Policy Gradients with Memory Augmented Critic (PGMAC) that builds action-value function with the memory module previously proposed as Differentiable Neural Dictionary (DND). Although the DND is only studied in discrete action-space problems, we propose Action-Concatenated Key, which is a technique to combine DDPG-based policy gradient methods and DND. Furthermore, the remarkable advantage of PGMAC is shown that long-term reward calculation and weighted summation of value estimation at DND has an essential mechanism to solve the bias and variance problem. In experiment, PGMAC significantly outperformed baselines in continuous control tasks. The effects of hyperparameters were also investigated to show that the memory-augmented action-value function reduces the bias and variance in policy optimization.
Co-manifold Matrix Factorization

Matrix factorization plays a fundamental role in collaborative filtering. In collaborative filtering setting, the rating matrix R is very sparse. Thus, infinite number of matrices can fit the observed entries in the rating matrix. Without additional assumption, it is hard to decide which matrix can approximate the rating matrix well. The popular assumption is that the rating matrix is of low-rank. With this assumption, the rating matrix can be factorized into two latent factor matrices: P and Q. To improve the performance of the matrix factorization models, more assumptions can be made. A prevalent assumption is that rows of the latent factor matrix P or Q lie on a nonlinear manifold. Under this assumption, new matrix factorization models were proposed, which incorporate the geometric properties of the latent factor matrix P or Q into matrix factorization. In this paper, we assume instead that columns or rows of the rating matrix lie on a nonlinear manifold. With this assumption, we propose a new matrix factorization model, Co-Manifold Matrix Factorization (CoMMF), which incorporates the geometric properties of the rating matrix into matrix factorization. Experiments on real-world datasets show that our proposed model can outperform six state-of-the-art collaborative filtering methods.
Emerging Security Challenges for Ubiquitous Devices

Abstract In this chapter we focus on two important security challenges that naturally emerge for large scale systems composed of cheap devices implementing only symmetric cryptographic algorithms. First, we consider threats due to poor or malicious implementations of protocols, which enable data to be leaked from the devices to an adversary. We present solutions based on a watchdog concept—a man-in-the-middle device that does not know the secrets of the communicating parties, but aims to destroy covert channels leaking secret information. Second, we deal with the problem of tracing devices by means of information exchanged while establishing a communication session. As solutions such as Diffie-Hellman key exchange are unavailable for such devices, implicit identity information might be transmitted in clear and thereby provide a perfect means for privacy violations. We show how to reduce such risks without retreating to asymmetric algorithms.
CovidEmoVis - An Interactive Visual Analytic Tool for Exploring Emotions from Twitter Data of Covid-19

Due to the Covid-19 pandemic, nowadays people comment a lot in Twitter expressing emotions around this. It can be very difficult to analyze and obtain information from big amounts of unstructured data, the objective of this work is the processing of high volumes of these tweets and to get a temporal visual exploration analysis of emotions through an interactive visual analytic tool from Twitter Data of Covid-19. This useful tool could help to the specialists in Psychology to know more about the emotional profile of users and the tweets posted daywise. Further it helps to see the important words of the tweets through word cloud and a two-dimensional plane graph to see the word of each tweet with its VAD (valence, arousal and dominance) scores and emotions. First we preprocessed the data to delete the stopwords, then we used a RNN (Recurrent Neural Network) to get an emotion related to the tweet, then we calculate VAD scores and fill missing emotions, this data can be visualized across this tool on a timeline interactively.
Graphical User Authentication Using Hybrid Visual Cryptographic Technique

User authentication is the important aspect while offering common and utility services. Most of the web services are authenticates the exact user by using the traditional login model but it has security risks. Graphical user authentication is the replacement to solve the issues in traditional model. This paper proposes a new graphical based user authentication model (VerCube) using a hybrid approach. This model implements the cryptographic technique and visual cryptographic representations.
RETRACTED: Calculation and analysis of multi-chamber corrugated steel web composite beam structure based on internet of things system

Steel bars made of corrugated steel have stronger bearing capacity than ordinary steel bars, and generally speaking, corrugated steel will be mixed with concrete when combining, which can enhance its bearing capacity and stability.Because corrugated steel has such excellent performance and bearing capacity, it has been widely used and concerned by architects in the construction of bridges.In this study, we introduce and describe the structure of corrugated steel and its composite bridge in detail.At present, the bridge design code does not give a detailed description of the special structure of the design materials.When this special structure is used in construction, we need to calculate its structure according to foreign standards and traditional construction methods. Composite Girder Bridge and the multi-cell wavy steel mesh have been recently widely used. In the corresponding documentation and the fine calculation method, there are some drawbacks. Currently, most of the design, the space bar system model, plane beam grid model, use a combination of the solid model's calculation method and analysis method.However, this model has some spatial effects and distribution problems in a short time.Because there are many problems, we will analyze them one by one.
Cloud Computing Failures, Recovery Approaches and Management Tools

Fault tolerance is a major concern in clouds in order to guarantee the reliability and the availability, also the data backup and recovery is one of the most important issue in cloud computing environments, and the need of efficient techniques for the data recovery are increasing by the days. When the server can't provide the data for the users or the data has been lost because of one of the different kinds of failures the data recovery techniques used to retrieve the data from the backup server. This paper illustrated the cloud computing failures, recovery approaches and management tools.
Assessment of Access of Reading Resources in Bolstering Extensive Reading Habits and Composition Writing Skills in Secondary Schools in Kenya

Reading resources in schools can help in inculcating the reading culture and reading proficiency amongst students. This may help learners develop rational skills that are crucial in dealing with the current unpredictable and dynamic world. There has been a concern from educationist and researchers about the declining reading interest among secondary school learners in Kenya and one of the causes could be unavailability of reading resources amongst other determiners. This study was prompted by the lack of adequate reading resources that can bolster and sustain reading interest among secondary schools learners in Laikipia County in Kenya. The study therefore sought to establish whether there existed any relationship between availability and access of extensive reading resources and development of extensive reading habits evidenced in composition writing amongst learners. The study was guided by Piaget's (1952)) Schema Theory. The study used descriptive survey research design. Fifteen secondary schools in Laikipia County were sampled using proportional stratified sampling technique in five sub-counties across the county. Purposive sampling was used to sample 30 out of 80 (37.5%) teachers of English language. A Simple random sampling technique was used to sample 327 students out of appproximately 2670 students (12.24%) in form 2 and 3 in the County. The research instruments used for data collection included: questionnaire for students, interview guides for teachers and composition writing test. A pilot study of the instruments was done to ensure their validity and reliability in a co-educational secondary school in the neighbouring county that was not involved in the study. The reliability of the questionnaire and interview guide was estimated using Cronbach’s alpha coefficient as 0.871 which met the recommended threshold of 0.7 and above. Analysis of data was done using descriptive statistics such as frequency, tables and percentages. T-test was done at 95% confidence level (F(2) = 80.933, P = .001) to establish whether there was statistically significance difference between availability of reading resources and development of extensive reading habits captured in composition writing mean scores of different categories of school using (SPSS) version 17. Qualitative data were organized according to the study themes and presented descriptively on the basis of the study objective. It was established that learners in secondary schools with adequate extensive reading resources performed better in composition writing than those who did not. Therefore, it was established that inadequate supply of reading resources in schools had a negative impact on development of reading interest amongst learners. The study recommended that schools should collaborate with various stakeholders to solicit reading resources so as to promote access of reading materials and reading culture in schools and consequently improve learners’ composition writing skills.
DASSL: Dynamic, AI-assisted, Scalable System for Labelling Used Bottle Images

To ensure sustainable consumption and production, one way is to reduce waste generation by increasing the reuse rate. We have been working with the bottle classification facility to enhance the efficiency and productivity. Many used bottles come in with unimaginable ways of dirty, defective conditions. To manage the sheer volume of used bottles, we create an AI-enabled, bottle classification system. However, it requires many labelled images for training to improve accuracy. Unfortunately, the traditional approach, having human label individual images, is very time consuming. Even worse, it is not effective for our dataset because conditions of used bottles are not well defined and studied. From our experiments, the human experts cannot agree on the same labelling for similar bottle conditions, especially when impurities or defects are not separable objects. For 42%-99% of images in certain subcategories, human experts assign different labels to bottles with similar conditions. With huge inconsistency in data labelling, it deteriorates the accuracy of our classification models. To alleviate this problem, we propose a Dynamic, AI-assisted, Scalable System for Labelling used bottle images, called DASSL. DASSL employs multiple algorithms to extract and/or quantize different features of used bottle images, and cluster the images into groups with the supervision of human. With DASSL, we can achieve labelling consistency and improve scalability by reducing the data labelling time by at least 10x. To enhance agility, we can dynamically adjust DASSL to adapt to changes of cleaning machines' capabilities or bottle demand.
Tron: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions

Trajectory optimization is an important tool for control and planning of complex, underactuated robots, and has shown impressive results in real world robotic tasks. However, in applications where the cost function to be optimized is non-smooth, modern trajectory optimization methods have extremely slow convergence. In this work, we present TRON, an iterative solver that can be used for efficient trajectory optimization in applications with non-smooth cost functions that are composed of smooth components. TRON achieves this by exploiting the structure of the objective to adaptively smooth the cost function, resulting in a sequence of objectives that can be efficiently optimized. TRON is provably guaranteed to converge to the global optimum of the non-smooth convex cost function when the dynamics are linear, and to a stationary point when the dynamics are nonlinear. Empirically, we show that TRON has faster convergence and lower final costs when compared to other trajectory optimization methods on a range of simulated tasks including collision-free motion planning for a mobile robot, sparse optimal control for surgical needle, and a satellite rendezvous problem.
Automated Detection and Removal of Cycles in a Concept Map

Over the years, concept maps have been used by several researchers to construct online learning systems. This is due to their flexibility in organizing knowledge. However, for effective use of concept maps in education, detection, and removal of cycles within them is necessary. Cycles in a concept map may result in ambiguity and confusion as one concept can lead back to itself. This study first gives brief details about concept maps and their applications in the field of education. A popular algorithm of graph theory depth-first search is then used for detection of cycles. If any cycles are found, they are removed from the graph in an iterative fashion until there are no more cycles in the graph. A Java program was written to simulate the proposed algorithm and found to yield desired results on sample graphs. Finally, the future uses of concept maps have been discussed.
Another New Family of Gold-Like Sequences

In this correspondence, for a positive odd integer n , a new family U of binary sequences with 2 n + 1 sequences of length 2 n — 1 taking three valued nontrivial correlations — 1 and — 1 ± 2 &lt;mml:math display="block"&gt;&lt;mml:mo&gt;-&lt;/mml:mo&gt;&lt;mml:mn&gt;1&lt;/mml:mn&gt;&lt;mml:mo&gt;±&lt;/mml:mo&gt;&lt;mml:msup&gt;&lt;mml:mn&gt;2&lt;/mml:mn&gt;&lt;mml:mfrac&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;n&lt;/mml:mi&gt;&lt;mml:mo&gt;+&lt;/mml:mo&gt;&lt;mml:mn&gt;1&lt;/mml:mn&gt;&lt;/mml:mrow&gt;&lt;mml:mn&gt;2&lt;/mml:mn&gt;&lt;/mml:mfrac&gt;&lt;/mml:msup&gt;&lt;/mml:math&gt; is presented. This family U is consturcted using the families introduced by Boztas and Kumar[4], Kim and No [7]. This family has the same correlation distribution as that of the well-known Gold sequences. So this family can be considered as another new class of Gold-like sequences.
Autonomous multi-modal vehicle concept designed to traverse ground quickly, collect and transmit data, fly over hazardous terrain, and navigate without using GPS

As the uses for autonomous rovers and drones has increased with their variability and utility, a fundamental gap has emerged - one which melds multi-modal capabilities into a singular unit. This project aims to fill that gap with the introduction of a multi-modal unmanned vehicle concept that can traverse ground quickly, hop or fly over obstacles and hazardous areas, and operate without the use of GPS or a human in the loop. The 2020 NASA Academy at Langley has developed a first iteration conceptual design of such a vehicle. This design has proven to be feasible through analyses confined to simulated environments.
Design of individual soldier situation generation software based on AR

Abstract According to the requirements of the individual combat command system for real-time emergency command and battlefield situation, the software design based on the AREngine platform for the individual battlefield collaborative situation generation is studied. Using the domestic smart phone to carry out the augmented reality situation of the traditional sandbox, and make full use of the powerful AR function of the smart phone with the core processing equipment of the individual soldier system equipped by the infantry commander and the combat personnel, providing the infantry with infantry command, combat deduction, The realistic deployment of power deployment and battlefield situation can be used for wartime preparation of troops and strategic deployment during simulation training, which has practical significance.
ALGORITHM FOR PERSON TRACKING ON VIDEO SEQUENCES USING FACE IDENTIFICATION FOR INDOOR SURVEILLANCE

This paper discusses the algorithmic framework for tracking people on indoor video. To improve tracking accuracy was used face identification algorithm to reduce errorr rate during complicated trajectory of persons in indoor environment. Object detection was performed with CNN Yolov3 that extract rectangular area as a result. Face detection task was resolved eith Cascade CNN MTCNN with following recognition using CNN MobileFaceNetwork. To form person features we used historgrams in HSV colorspave and CNN that includes 29 convolution layers followed by fully connected layer. The Hungarian algorithm was used as decision maker for allignment problem. Experiments were conducted on five videosequences with the variable number of people in it. The main characteristics of the developed algorithm are obtained which confirmed its effectiveness and the possibility of use for indoor video surveillance.
Dynamic Selection of Classifiers Applied to High-Dimensional Small-Instance Data Sets: Problems and Challenges

Dynamic selection (DS) of classifiers have been explored by researchers due to their overall ability to obtain higher accuracy on low-sample data sets when compared majority voting. Little literature, however, has employed DS to high-dimensional data sets with substantially more features than samples. Since, several studies have reported the benefits of applying feature selection methods to high-dimensional data sets, raised the following open research questions: 1. How DS methods perform for such data sets? 2. Do they perform better than majority voting? and 3. Does feature selection as a pre-processing step improve their performance? The performance of 21 DS methods was statistically compared against the performance of majority voting on 10 high-dimensional data sets and with a filter feature selection method. We found that majority voting is among the best ranked classifiers and none of the DS methods perform statistically better than it with and without feature selection. Moreover, we demonstrated that feature selection does improve the performance of DS methods.
Triple Cyclic Codes Over 𝔽q + u𝔽q

Let [Formula: see text], where [Formula: see text] is a power of a prime number [Formula: see text] and [Formula: see text]. A triple cyclic code of length [Formula: see text] over [Formula: see text] is a set that can be partitioned into three parts that any cyclic shift of the coordinates of the three parts leaves the code invariant. These codes can be viewed as [Formula: see text]-submodules of [Formula: see text]. In this paper, we study the generator polynomials and the minimum generating sets of this kind of codes. Some optimal or almost optimal linear codes are obtained from this family of codes. We present the relationship between the generators of triple cyclic codes and their duals. As a special class of triple cyclic codes, separable codes over [Formula: see text] are discussed briefly in the end.
Towards a Hybrid Data Partitioning Technique for Secure Data Outsourcing

In light of the progress achieved by the technology sector in the areas of internet speed and cloud services development, and in addition to other advantages provided by the cloud such as reliability and easy access from anywhere and anytime, most data owners find an opportunity to take advantage of the cloud to store data. However, data owners find a challenge that was and is still facing them in the field of outsourcing, which is protecting sensitive data from leakage. Researchers found that partitioning data into partitions, based on data sensitivity, can be used to protect data from leakage and to increase performance by storing the partition, which contains sensitive data in an encrypted form. In this paper, we review the methods used in designing partitions and dividing data approaches. A hybrid data partitioning approach is proposed to improve these techniques. We consider the frequency attack types used to guess the sensitive data and the most important properties that must be available in order for the encryption to be strong against frequency attacks.
Job Recommendation: An Approach to Match Job-Seeker’s Interest with Enterprise’s Requirement

Globalization has been raising more and more job position changing demand which leads to rapid increase in job seeking. Finding suitable job and appropriate employee are becoming more complicated due to the huge amount of information. In that context, job recommender system becomes a fabulous means to fill the gap between job-seeker and enterprise. The need to find a potential job that matches the enterprise’s requirements with job-seeker’s interest is one of the core features of job recommender system. In this paper, we propose an approach to match job-seeker’s interest with enterprise’s requirement by applying similarity metrics such as Consine, Mahattan, Jaccard and Levenshtein metrics. Our experiments are examined on real dataset of Binh Duong Career Service Center (BDCSC). The experimental results demonstrate that Levenshtein distance shows the best accuracy among metrics.