Reviewerly/exHarmony huggingface dataset:
Learning Logic Programs from Noisy State Transition Data

Real world data are often noisy and fuzzy. Most traditional logical machine learning methods require the data to be first discretized or pre-processed before being able to produce useful output. Such short-coming often limits their application to real world data. On the other hand, neural networks are generally known to be robust against noisy data. However, a fully trained neural network does not provide easily understandable rules that can be used to understand the underlying model. In this paper, we propose a Differentiable Learning from Interpretation Transition ($$\delta $$-LFIT) algorithm, that can simultaneously output logic programs fully explaining the state transitions, and also learn from data containing noise and error.
Cashless Society: Managing Privacy and Security in the Technological Age

A cashless society is an economic state which handles financial transactions not in the form of traditional mediums of currency, such as cash or coins, but by transferring digital data (usually by electronic means, such as credit cards and mobile data) between participating parties.Participants of a cashless society must Figure out a way to protect their transaction data, acknowledging the risks of organizations collecting mass amounts of said data, which result in a reduction of personal privacy. Balancing individual privacy with data security is vital in the information age, especially considering the increasing risk of data breaches and exploitation.In order to increase privacy in a cashless society, a few courses of action can be combined to produce a lasting and desirable result for users: A new kind of banking service that assigns randomized numbers to credit cards, the use of blockchain to monitor all transactions from individuals, and a campaign to educate and inform key stakeholders about security and privacy risks to provide the necessary tools and background knowledge to safeguard their own information before interaction with a foreign entity or other third parties (i.e. cybersecurity departments, IT technicians, etc). Blockchain and card number randomization are both susceptible to zero-day errors, bugs, and varied levels of social acceptance. This preliminary research draws on a systems analysis of cashless systems to identify and analyze a set of social and technical solutions to support a robust cashless system that protects users’ privacy and maintains the security of the system.The information found and analyzed will be beneficial by exposing weak points in current methods of data integrity and security. Learning about current and future methods of managing privacy and data security in the technological age would be helpful in creating preventative countermeasures. This study provides critical steps to prevent the loss of personal privacy in a cashless system.
Using Eye Tracking to Assess the Temporal Dynamics By Which Drivers Notice Cyclists in Daylight

Bicyclists face the risk of being involved in collisions with motor vehicles on roadways. This experiment used eye tracking technology to better understand how drivers notice bicyclists in daylight. Participants searched for bicyclists while wearing a head-mounted eye tracker, as they were driven along an open-road route that included a test bicyclist during the daytime. Participants pressed buttons when they detected that a bicyclist might be present in or near the roadway and when they were confident that a bicyclist was present. Through the use of eye tracking technology, this experiment provided a better understanding of the differences among the distances from which participants glance at, detect, and recognize bicyclists in daytime roadways.
Digital Topological Properties of an Alignment of Fixed Point Sets

The present paper investigates digital topological properties of an alignment of fixed point sets which can play an important role in fixed point theory from the viewpoints of computational or digital topology. In digital topology-based fixed point theory, for a digital image ( X , k ) , let F ( X ) be the set of cardinalities of the fixed point sets of all k-continuous self-maps of ( X , k ) (see Definition 4). In this paper we call it an alignment of fixed point sets of ( X , k ) . Then we have the following unsolved problem. How many components are there in F ( X ) up to 2-connectedness? In particular, let C k n , l be a simple closed k-curve with l elements in Z n and X : = C k n , l 1 ∨ C k n , l 2 be a digital wedge of C k n , l 1 and C k n , l 2 in Z n . Then we need to explore both the number of components of F ( X ) up to digital 2-connectivity (see Definition 4) and perfectness of F ( X ) (see Definition 5). The present paper addresses these issues and, furthermore, solves several problems related to the main issues. Indeed, it turns out that the three models C 2 n n , 4 , C 3 n − 1 n , 4 , and C k n , 6 play important roles in studying these topics because the digital fundamental groups of them have strong relationships with alignments of fixed point sets of them. Moreover, we correct some errors stated by Boxer et al. in their recent work and improve them (see Remark 3). This approach can facilitate the studies of pure and applied topologies, digital geometry, mathematical morphology, and image processing and image classification in computer science. The present paper only deals with k-connected spaces in DTC. Moreover, we will mainly deal with a set X such that X ♯ ≥ 2 .
Determination of Green Spots (Trees) For Google Satellite Images Using MATLAB

This research article proposes an efficacious and remarkable strategy for image processing. A Google satellite image ascertaining by manipulating image normalized cross-correlation, grayscale image, and image thresholding todetermine the percentage of a green portion (trees) within the total area has been developed in this paper. This exertion exploited 2D image normalized cross-correlation to scrutinize a Google satellite image to trace out the green spots in it. In this proposed work an image of the University of Asia Pacific, Green Road, Dhaka, Bangladesh from Google satellite has been exerted. In this maneuver, a template image has been coincided with its prototypical image. Subsequently, correlate the template image with the main image employing image normalized cross-correlation to recognize if the template image exists in the main image or not. As a result of correlation, it has been discerned that the template image exists amid the main image. Latterly convert the main image into a grayscale image to determine the total area. The consequential step is to exert image thresholding. Then the area under two extents is enumerated along with the percentage of a green portion (trees) within the total area has been detected. Due to defective pixel quality of satellite image, it's arduous to study satellite image hence to overcome this hindrance as well as to trace out any definite spot and its area this endeavor is proposed in this paper. This algorithm is executed in MATLAB R2016.
Accelerating re-pair compression using FPGAs

Re-Pair is a compression algorithm well-suited for applications that require random accesses to compressed data, but has not found widespread use in the data management community due to its prohibitively high compression times. As Re-Pair is a computationally expensive algorithm and FPGAs are becoming more and more common to accelerate such problems in data centers, we devise an FPGA system that performs Re-Pair compression. The system is implemented in OpenCL, aside from a hash table and sorting component realized in RTL for more control over the synthesized hardware. Our experiments demonstrate that an Intel Arria® 10 GX FPGA with our system compresses an order of magnitude faster than a highly-optimized CPU version of Re-Pair. We discuss further optimization opportunities and argue that our system can scale to being deployed on a more resourceful FPGA for even better performance.
R-SA: A Rule-based Expert System for Sentiment Analysis

Sentiment analysis plays a very important role in finding out opinion of the user. Due to the complexity and variety of language grammars and heterogeneous data, sentiment analysis from text is still considered to be the most complex problem till date. For the past few years, classical approaches have been overlooked over newer machine learning techniques which are being used across a myriad of domains. These techniques are typically classified into supervised and unsupervised categories. There are many approaches under supervised categories still under development and completely depending on an unsupervised approach is detrimental to accuracy. Therefore, classical supervised rule-based approaches cannot be ignored. The study focuses on text-based sentiment analysis using supervised classical approach. A novel expert system for sentiment analysis developed in Java is proposed which is a unified framework for rule base and rule engine that is reliable, portable, interoperable and easy to modify for any generic use. The rules are designed based on standards and to enhance the knowledge base, cross-domain inputs are used. The system is compared with existing rule-based engines and results prove that R-SA outperforms existing state-of-the-art classical approaches like Jess, IBM MS-BR and Drools thereby providing a robust and high-performance expert system for textual sentiment analysis and many other applications.
PRobPlan: A Framework of Integrating Probabilistic Planning Into ROS

The ROS (Robot Operating System) is a set of software libraries and tools used to build robotic systems. Recently, some researchers have been working on integrating planning systems into ROS. However, robotics domains are often probabilistically interesting. At present, only a few studies focused on integrating probabilistic planning into ROS. Moreover, they have limits on flexibility or scalability. Therefore, we propose a new framework, called PRobPlan (PRobabilistic Robot Planning), to alleviate these problems. On one hand, our framework uses a series of generation programs to build a problem file instead of the knowledge base. This makes it more modifiable. On the other hand, our framework integrates a state-of-the-art planner, SOGBOFA, and enhances it with a new recommendation function. This makes it more scalable. We instantiate the proposed framework in a warehouse-robot domain where mobile robots are allocated tasks of fetching or packing goods. Both of the domain and problem instances are modeled with RDDL (Relational Dynamic Influence Diagram Language). Experimental results showed the effectiveness of the proposed framework. We also tested two top planners, SOGBOFA and PROST, for comparison. The enhanced version of SOGBOFA mildly outperformed its original version and vastly outperformed PROST. Our work promotes the integration of probabilistic planning with robotic systems.
Algorithmic Content Moderation on Social Media in EU Law: Illusion of Perfect Enforcement

Intermediaries today do much more than passively distribute user content and facilitate user interactions. They now have near-total control of users’ online experience and content moderation. Even though these service providers benefit from the same liability exemption regime as technical intermediaries (E-Commerce Directive, Art. 14), they have unique characteristics that must be addressed. Consequently, many debates are ongoing to decide whether or not platforms should be more strictly regulated.Platforms are required to remove illegal content in the event of notice and take-down procedures built on automated processing and are equally encouraged to take proactive and automated measures to detect and remove it. Algorithmic decision-making helps to scale down the massive task of content moderation. It would, therefore, seem that algorithmic decision-making would be the most effective way to provide perfect enforcement.However, this is an illusion. A first difficulty occurs when deciding what, precisely, is illegal. Platforms manage the removal of illegal content automatically, which makes it particularly challenging to verify that the law is being respected. The automated decision-making systems are opaque and many scholars have shown that the main problem here is the over-removal chilling effect. Moreover, content removal is a task which, in many circumstances, should not be automated, as it depends on an appreciation of both the context and the rule of law.To address this multi-faceted issue, I offer solutions to improve algorithmic accountability and to increase the transparency around automated decision-making. Improvements may be made specifically by providing platform users with new rights, which in turn will provide stronger guarantees for judicial and non-judicial redress in the event of over-removal.
Augmented Reality to Enhance Visitors’ Experience at Archaeological Sites

After archaeological excavations are completed, many of the sites are prepared for visitors by including things such as (1) scientific interpretations of what the uncovered structures might represent, (2) reconstructions of ancient structures and (3) historical items or artifacts found during excavations. In addition to these, technology including augmented reality (AR) can be used to provide additional information on site. We study how currently popular global location-based AR games are supplementing the visitors experience at three archaeological sites in the Levant: Tel Hazor, Tel Megiddo and Tel Gezer by inspecting virtual points of interest (PoIs) in the game Ingress Prime. In the three locations, the virtual PoIs are linked to real world locations, however, they cover only a fraction of the visible archaeological structures. A bias was seen in the PoI names and descriptions towards certain archaeological interpretations. We propose location-based AR games should utilize as rigorous information of archaeological sites as possible, in order to provide players the possibility to learn real history in an accurate way.
Avoiding Phishing Attack on Online Votig System Using Visual Cryptography

The main aim of using visual cryptography in voting system is to give provision for casting vote for internal decisions in an organization. It is flexible enough to cast vote from remote places. Election is a total confidential process and is held with maximum secrecy. Hence, we have proposed an online voting system for Maharashtra Carrom Association where members can cast their vote with their computers, and laptops. To maintain the security we are using CAPTCHA code and Image Share technology. The proposed method offers anonymity of voter identity, while keeping the votes private, and the election transparent and secure.
Discovering “Insider IT Sabotage” based on human behaviour

Purpose Malicious activities conducted by disgruntled employees via an email platform can cause profound damage to an organization such as financial and reputational losses. This threat is known as an “Insider IT Sabotage” threat. This involves employees misusing their access rights to harm the organization. Events leading up to the attack are not technical but rather behavioural. The problem is that owing to the high volume and complexity of emails, the risk of insider IT sabotage cannot be diminished with rule-based approaches. Design/methodology/approach Malicious human behaviours that insiders within the insider IT sabotage category would possess are studied and mapped to phrases that would appear in email communications. A large email data set is classified according to behavioural characteristics of these employees. Machine learning algorithms are used to identify occurrences of this insider threat type. The accuracy of these approaches is measured. Findings It is shown in this paper that suspicious behaviour of disgruntled employees can be discovered, by means of machine intelligence techniques. The output of the machine learning classifier depends mainly on the depth and quality of the phrases and behaviour analysis, cleansing and number of email attributes examined. This process of labelling content in isolation could be improved if other attributes of the email data are included, such that a confidence score can be computed for each user. Originality/value This research presents a novel approach to show that the creation of a prototype that can automate the detection of insider IT sabotage within email systems to mitigate the risk within organizations.
University Centers for Students with Disabilities: A Pilot Study

The vast majority of US universities and colleges have specialized centers to support students with disabilities. These centers have various names (Student Accommodation Centers, Student Access Services, Disability Resources), but the goal of these centers is to support students with disabilities who are obtaining a postsecondary education. The previous research reviewed 30 universities that had listed information about their student center in the public domain (through Web sites). This research will move beyond the review of Web-based pages by surveying those employees who are in charge of these centers at the 30 institutions in addition to targeting public 4-year regional universities who not have a publicly accessible webpage. This paper discusses future research and implications from results of the pilot study and the need to further investigate university accommodation centers.
Machine Learning Techniques for Classification of Spambase Dataset

Email has become a necessity for this new generation for official communication purposes. As the use of Internet is becoming more and more the risk of being caught into its darker side is so common. The major concern is spam, which is growing exponentially, and the users are becoming victim of it on daily basis. This paper proposes a hybrid machine learning classification model for the spam classification on the spambase dataset. This model uses the four classification algorithms namely Ensemble Classification, Decision Tree, Random Forest and Support Vector Machine (SVM). There are two phases; First phase deals with the classification of spambase dataset in two classes i.e. spam and ham with Decision Tree machine learning algorithm and the second phase comprises of classification improvisation of the output produced by phase one with four machine learning algorithms i.e. Decision Tree, Random Forest, Support Vector Machine (SVM) and Ensemble Learning. The experiment shows a very promising result with improvised accuracy in second phase.
CSDA: A novel attention-based LSTM approach for code search

Abstract Previous studies have proposed semantic-based approaches for code search over large-scale codebases, which has bridged the gap in understanding the semantics between natural language and source-code language. However, these studies either failed to determine an effective method for semantic representations or lacked the distinction of semantic features. In this study, we propose a novel attention-based LSTM neural network known as CSDA (Code Search based on Description Attention), which can effectively improve the code search performance. The proposed model can focus on different parts of a semantic feature when numerous aspects of a source code snippet are used as input. As opposed to assigning the same weight to different parts of the semantic vector, CSDA takes the semantics of natural language descriptions into account, so that the subtle differences hidden in the code snippet can be discriminated and associated with the corresponding queries. We compare CSDA with the existing state-of-the-art approach CODEnn, which uses a jointly embedding technique for code search. Our experimental evaluation demonstrates that CSDA outperforms previous methods and achieves superior code search performance to CODEnn, with higher success rates and mean reciprocal ranks. This study provides significant insights into the use of semantic representation methods in deep learning-based code search approaches.
Genome Sequences analysis using HMM in Biological Databases

There are variety of problems exist in molecular biology. The sequenced genomes generally give information about to understand the underlying mechanisms of various biological functions in cells. However, as data is available enormously, it is really difficult to analyze them without the help of computational methods. In order to extract meaningful information from the data, we need computational techniques to biological sequence analysis. Due to digitization, the biological databases are continuously improved and updated. In other hand many Biological Databases are available which are classified as primary, secondary and composite type. While we do sequence analysis, we analyze in terms of functional or structural analysis. The hidden data certain time could not reflect in databases which may have vital role in analyzing biological results. The HMM (Hidden Markov Model) is one of the application area of artificial intelligence technique to model data analysis features. This research work here focuses on modeling DNA sequencing error with HMM by identifying hidden data from various depository of molecular databases which will help in re-sequencing genome data.
AIMCS: An Artificial Intelligence based Method for Compression of Short Strings

In recent years, the use of short texts in data communications has dramatically increased. In order to reduce the bandwidth utilization and cost, new compression methods must be applied to short texts. In this paper, we propose an artificial intelligence-based lossless compression algorithm which aims to reduce the size of messages over the network. Evaluating this algorithm, we applied it on tiny-strings (strings with an average length of 160 characters) and the achieved results show that our proposed approach (AIMCS) reduces the size of data significantly. Comparing our algorithm with LZW and Huffman methods also indicates that in the compression process of strings, our method provides better performance in terms of compression rate. In contrast, the compression time increases, which is insignificant comparing to transmission time when real-time text transmission is required.
Mobile learning as a media training of reading literacy: blended learning

Abstract Increasing the professionalism of teachers mostly done by the government through various training. Teachers must attend training or other personal development but they were leaving the class or class time, so students do not get assistance in the learning process, they asked the student to do the task, so that the class is out of control and tends to be noisy. The purpose of this research is to develop mobile learning as a medium in literacy training so that training becomes more effective. This research is a research and development study. This research method was develop training models according to the needs of the trainees. The samples of this study were 32 teachers in SMA N Semarang. The results of this study indicate that the training model using mobile learning media and the learning system conducted in blended learning can make literacy training more effective. This training model also provides solutions to the training problems experienced by teachers. Based on the results of the feasibility test of the model through expert judgment shows that the model is feasible in terms of benefits, ease of use, the attractiveness of the model and the existence of innovations produced for the implementation of training.