Reviewerly/exHarmony huggingface dataset:
Preliminary Analysis On The Indicators Affecting Islamic Information Credibility In Social Media

The continuous use of social media as a means to acquire information by individuals has brought about an avenue for Islamic scholars to use the platform as a means to disseminate Islamic knowledge. However, not many individuals would evaluate, seek clarification and visit the social media pages of these scholars on a daily basis. Inexperienced social media users which usually lack the clues to assess the credibility of online information can be easily mislead by false information. Some may obtain information shared by their friends on social media that may lack credibility. It would be dangerous if the content of the Islamic posting has misinformation and disinformation that could damage the impression and practices of the religion. As Islamic propagation gains more significance in social media, there is a need to have a valid measurement to evaluate the credibility of Islamic information online. This paper explores the indicators that affect Islamic information credibility in social media. An interview was conducted to panel of experts within the Islamic and social media domain to obtain reviews on the indicators identified to be of importance towards the credibility of Islamic information. The three information credibility dimensions are discussed and triangulated with the results of the interview analysis. Results indicate that the three confirmed dimensions of Islamic information credibility in social media are: information source dimension “Authority, Trustworthy and Writing source”, information content dimension “Relevancy, Legitimacy, Objectivity, Adequacy, Accuracy and Authenticity”, and finally social environmental dimension “Social Identity, Subjective norm and Group norm”.
Selective Subtraction for Handheld Cameras

Background subtraction techniques model the background of the scene using the stationarity property and classify the scene into two classes namely foreground and background. In doing so, most moving objects become foreground indiscriminately, except in dynamic scenes (such as those with some waving tree leaves, water ripples, or a water fountain), which are typically “learned” as part of the background using a large training set of video data. We introduce a novel concept of background as the objects other than the foreground, which may include moving objects in the scene that cannot be learned from a training set because they occur only irregularly and sporadically, e.g. a walking person. We propose a “selective subtraction” method as an alternative to standard background subtraction, and show that a reference plane in a scene viewed by two cameras can be used as the decision boundary between foreground and background. In our definition, the foreground may actually occur behind a moving object. Furthermore, the reference plane can be selected in a very flexible manner, using for example the actual moving objects in the scene, if needed. We extend this idea to allow multiple reference planes resulting in multiple foregrounds or backgrounds. We present diverse set of examples to show that: 1) the technique performs better than standard background subtraction techniques without the need for training, camera calibration, disparity map estimation, or special camera configurations; 2) it is potentially more powerful than standard methods because of its flexibility of making it possible to select in real-time what to filter out as background, regardless of whether the object is moving or not, or whether it is a rare event or a frequent one. Furthermore, we show that this technique is relatively immune to camera motion and performs well for hand-held cameras.
Artificial Intelligence-Based Cybercrime

Advances in the field of artificial intelligence (AI) in recent years have led to exciting capabilities, but they have also raised concerns about dangers. A major concern is about super-intelligent AI agents with the initiative, motivation, and the capabilities to attack humans. Although such super-AI are far out in the future, existing AI tools are already being used by cybercriminals to enhance the efficiency of their attacks. The immediate threat from AI includes three types of activities. First, AI gives attackers the ability to automate and enhance online cybercrime as we know it. Second, using AI opens new channels for cybercrime, including some reaching into physical space. A third type of attack using AI is against other AI systems, either in stealing confidential data or in corrupting the operation of AI algorithms to achieve the goals of the attacker. Information security professionals must be prepared for the immediate danger of malicious uses of current AI technologies.
Chaotic Compilation: A (Statistical) Cloak for a Secret Computer

This paper extends a companion paper on compilation for target platforms with hidden deterministic hardware aliasing to generate aliasing as well as compensate for it, in so-called 'chaotic' compilation. That may be applied in encrypted computing to statistically hide any information inadvertently introduced by a human programmer. A prototype compiler implementing the technology currently covers all of ANSI C except longjmp/setjmp, and this paper sketches a proof that it protects against polynomial complexity attacks on runtime data.
Regressing 3D Face Shapes from Arbitrary Image Sets with Disentanglement in Shape Space

Existing methods for reconstructing 3D faces from multiple unconstrained images mainly focus on generating a canonical identity shape. This paper instead aims to optimize both the identity shape and the deformed shapes unique to individual images. To this end, we disentangle 3D face shapes into identity and residual components and leverage facial landmarks on the 2D images to regress both component shapes in shape space directly. Compared with existing methods, our method reconstructs more personalized and visually appealing 3D face shapes thanks to its ability to effectively explore both common and different shape characteristics among the multiple images and to cope with various shape deformation that is not limited to expression changes. Quantitative evaluation shows that our method achieves lower reconstruction errors than state-of-the-art methods.
Collaborative methodologies for pattern evaluation for web personalization using Semantic Web Mining

The process of semantic web mining is very much applicable in social media and networking sites which will mostly result in overloading of the content. The personalized system is required basically to deal with large information system in order to perform information filteration. The user through internet and web media are considered as content therefore internet and social networking media are the optimal way to express bulk of contents. The collaborative filtering techniques compute the ratings and recommendations which is purely based on information about similar user items and their content. The proposed work is a combined technique which results into hybrid approach, where the feature of the content extracted from open linked dataset, and result in better accuracy in the prediction and analysis. A hybrid prototype is proposed and will be implemented in Weka as extension of the work. The work discusses the role and social media in web mining and advantages of content feature retrieval for semantic web mining methodology.
From Curry to Haskell

We expose some basic elements of a style of programming supported by functional languages like Haskell by relating them to a coherent set of notions and techniques from Curry’s work in combinatory logic and formal systems, and their algebraic and categorical interpretations. Our account takes the form of a commentary to a simple fragment of Haskell code attempting to isolate the conceptual sources of the linguistic abstractions involved.
Developing Chatbot with Deep Learning Techniques for Negotiation Course

This research combines the artificial intelligence technique RNN/LSTM to develop a dialogue system, which mainly used in the bargaining unit of a negotiation course. The chatbot was applied in the dialogue of student drills in bargaining situations and investigates the feasibility of the dialogue system used in the course. The two-hour course has 48 second-year university students with different colleges and departments. The results show that most students have a positive view of instant messaging conducts dialogue exercises, and feel interesting. The groups targeting dialogue robots, students prefer courses. For groups with real people, students think it is more helpful for learning, and they have higher satisfaction with the results of bargaining negotiations.
An Improved Restoration Algorithm for Blurred Images Based on Complete Blind Convolution and Non-local Means Filtering

A method of estimating point spread function is designed in this paper. This PSF is applied in complete blind deconvolution for the restoration of defocus blurred images. The ringing artifact which is produced in the procession of image restoration needs to be suppressed. By using histogram to judge the type of random noise in the image, non local means filtering is chosen to denoise. Considering the long running time of the NLM filtering, only the wavelet low frequency coefficients are denoised with it. MATLAB simulations show that the designed PSF and this method have better effect of restoration and shorter running time.
Plasmodium Candidate Detection on Thin Blood Smear Images with Luminance Noise Reduction

Malaria is one of the deadliest diseases over the world. It leads to a serious vector-borne disease caused by a blood parasite of the Plasmodium genus. WHO declared that microscopy-based has become “the gold standard” in detecting malaria. However, manual procedure conducted by human experts may cause fatigue and leads to human error in assessment. Several computer aided detection (CAD) systems to assist parasitologists in detecting malaria have been published. However, the presence of illuminant noise is still a challenge for researchers. This paper proposes a new malaria detection scheme efficiently abled in microscopy image presented illumination noise. By using GGB normalization and gamma enhancement, the parasites are easier to detect. The proposed scheme achieves the best of sensitivity value compared the previous schemes. This result indicates that the proposed scheme in detecting parasite has a potential contribution in the development of CAD for Plasmodium detection.
Reconfigurable Control Allocation Method Based on the Improved Grey Wolf Optimizer Algorithm for Aircrafts with Multiple Actuators

In this paper, a new reconfigurable control allocation based on the improved grey wolf optimizer (IGWO) algorithm is presented for the attitude control of aircraft with multiple actuators. Firstly, we introduce the ADMIRE model which is an aircraft with multiple actuators. And then, the control allocation method based on the IGWO algorithm is introduced. The method for improving the GWO algorithm is optimizing parameters and changing the position update formula. At last, for proving the useful of the IGWO, two conditions which the actuators are healthy or not healthy are both considered. The results show that the attitude control of aircraft with multiple actuators can be realized by this method.
A Lunar Robot Obstacle Avoidance Planning Method Using Deep Reinforcement Learning for Data Fusion

In future exploration and base construction on the moon, obstacle avoidance planning of lunar robots in an uncertain environment is critical for their autonomous movements and operations, with no precise location information of obstacles. In the present work, an obstacle avoidance planning method using deep reinforcement learning with a double-channel Q network is proposed, by which local surveillance video images and navigating data are merged for action value estimation. Through simulation, our method is turned out to achieve motion planning effectively from raw sensing data, and learn faster than the methods using single type of data.
Software reusability metrics prediction and cost estimation by using machine learning algorithms

In this research, a highly robust and efficient software design optimization model has been proposed for object-oriented programming based software solutions while considering the importance of quality and reliability. Due to a piece of information that software component reusability has allowed co st and time-efficient software design. The software reusability metrics prediction and cost estimation play a vital role in the software industry. Software quality prediction is an important feature that can be achieved a novel machine learning approach. It is a process of gathering and analyzing recurring patterns in software metrics. Machine learning techniques play a crucial role in intelligent decision making and proactive forecasting. This paper focuses on analyzing software reusability and cost estimation metrics by providing the data set. In the present world software, cost estimation and reusability prediction problem has been resolved using various newly developed methods. This paper emphasizes to solve the novel machine learning algorithms as well as improved Output layer self-connection recurrent neural networks (OLSRNN) with kernel fuzzy c-means clustering (KFCM). The investigational results confirmed the competence of the proposed method for solving software reusability and cost estimation.
A brake disc character recognition method based on photometric stereo and surface curvature extraction

In view of the situation that the batch number characters of the break disc casting parts are the same color as the background area, and the contrast is low, and the recognition effect of the traditional method based on gray value is poor, the method combining photometric stereo technology and surface curvature extraction is adopted to reconstruct the surface of the disc casting parts to obtain high quality images for subsequent processing. A method based on subpixel contour to calculate disk centroid is proposed to correct image rotation after reconstruction. In the aspect of denoising, morphological methods are used to further optimize the image and remove the disturbing noises such as holes. At last, the characters are extracted and recognized by SVM classifier. Through experimental verification, the recognition speed of this method is fast, and the recognition accuracy is more than 98%, which can fully meet the actual needs of industrial field.
Exploiting the Use of Similar Past Search Results Through a Dynamic Cache

Search engines are prone to the computation of redundant queries. Without any optimization this can be a costly procedure; limited processing power is used to compute new responses for queries that have already been answered. Despite the latter, little work exists on leveraging similar past queries to reduce the engine's workload while improving its precision. This paper describes a dynamic cache based on an on-line algorithm, which takes advantage of similar past queries to reduce the engine's response times, while simultaneously improving precision. Roughly, the algorithm uses a priority queue to build a dynamic index of past queries, alongside their responses, where each query's priority is linked to its overall popularity (the top-K queries along with their results): similar queries that have appeared multiple times are indexed with a high priority value. This ensures that their results will be easily recoverable from the queue without recomputation. Notably, the algorithm also improves response times for queries that only share a subset of their search terms. Several experiments were performed over a dataset of simulated queries and documents. Empirical results show that the proposed dynamic cache improves on both precision and response times.
An Empirical Investigation on Requirements Change Management Practices in Pakistani Agile Based Industry

Requirements engineering is the key process of software development. Effectively dealing with change in requirements is a challenge. Agile methods are flexible and accept change effectively. The question arises in our mind that which requirements change management practices are the utmost to follow? Through a questionnaire-based survey, we explored the Pakistani software development industry to identify the processes, methods, practices being followed by the agile practitioners for requirements change management, and ranked them based on the perceived importance. We identified 30 practices for agile requirements change management from literature study, and conducted a survey with 140 agile practitioners, countrywide. We used a Multi-criteria Decision Method (MCDM) ranking method i.e. PROMETHEE family of methods for ranking of the relative importance of the practices. The findings reveal the most important and highly ranked agile requirements change management practices. Moreover, in numerous cases, the perceived importance of the change management practices depends on the type of the software project (e.g. methodology, domain and type of application or project).
The Reverse Engineering of a Web Application Struts Based in the ADM Approach

&lt;p&gt;Since web technologies are constantly evolving, the adaptation of legacy web applications to new paradigms such as rich internet applications (RIAs) is become a necessity. In such tendencies, we notice that several web leaders has already migrated their web applications to RIAs. However, it faces many challenges due to the variety of frameworks. Nevertheless, and in order to facilitate the process of migration, it would be ideal to use tools that could help automatically generate or ease the generation of UML (Unified Modeling Language) models from legacy web application. In this context, novel technical frameworks used for information integration and migration processes such as Architecture-Driven Modernization Task Force (ADMTF) were fashioned to describe specifications and promote industry accord on the modernization of existing applications. In this paper, we propose a process for migrating application from Struts to UML model using ADM standards and MoDisco. We then present a case study as an example illustrating the different steps of the proposed process. We then validated the proposition within Eclipse Modelling Framework since a number of its tools and run-time environments are indeed aligned with ADM standards.&lt;/p&gt;
Formation Control of Multi-robot Motion Systems and State Estimation using Extended Kalman Filter

The multi-robot motion system is a dynamical system from a group of robots which moved in an unknown environment. Multi-robot is the development of a single robot that can overcome cooperative motion by utilizing information from its neighbours. We consider the formation of controls imposed on the observed multi-robot motion system. Robots that have different starting points will then be controlled to a meeting point (rendezvous), then controlled to move in a formation at a specified speed. The movements trajectory of these robots observed will be estimated using the Extended Kalman filter method. The purpose of this estimation is to observe the movement of robots when we have minimum measurement data. This method is one example of a data assimilation method which is combining model system and measurement system to get better approximation. The results of the tracking estimation are very good with very small error.
A survey on attack vectors in stack cache memory

Over past few decades, various ways have been conducted through side channel attacks to steal information for a computer system. Unlike conventional hardware-based methods, i.e. power-based side channel, side channel on micro-architecture does not require any physical access to the devices under interest. Instead, only compromised programs need to be co-located on the same machine as the victim. For some other scenarios, malicious users can form side channel information leveraging the timing of program execution. In this survey, we are presenting a comprehensive taxonomy of attack vectors in stack cache memory modules, which mostly are the points of attack by side channel. Due to the significance of side channel attacks, the challenges and overheads of these attack vectors are described through the course of this study. We discuss what side channel attacks are, how they are used, and how they can be prevented through the use of obfuscation techniques. To accomplish this, we introduce the findings of a number of relevant works. These include successful attacks on systems using novel variations of side channel attacks and preventative measures against the attacks.
Adaptive cooperative detection method for unmanned planetary vehicles based on deep reinforcement learning

Unmanned planetary vehicles have important significance for human development and utilization of space resources through the detection of extraterrestrial objects. This paper proposes an adaptive cooperative detection method for unmanned planetary vehicles, which can be adapted to the complex and changeable planetary environment through self- learning methods. First, the detection environment is rasterized; then, the convolutional neural network is used to process the detection area information and the lidar information to extract the environmental features; the reinforcement learning method is used to learn the strategy of detecting the environment. The reward function was designed in detail so that the planetary vehicles can quickly detect a given area without collision. At the same time, the detection information can be shared among planetary vehicles to accelerate the learning process. Finally, simulation experiments were carried out in Gazebo. The experimental results show that the planetary vehicles can detect the given area quickly and effectively.
Gradient-direction-based Rectangles and Triangles Traffic Signs Detection Algorithm in Natural Scenes

In this paper, we propose a novel method to detect rectangular and triangular traffic signs in natural scenes according to the geometric features of the rectangle and triangle traffic signs. It is a Gradient-direction-based method that mainly includes four stages: gradient information classification, filter templates-based line segments with different orientation extraction, structural corners detection and rectangle/triangle detection. The extracted corners are categorized into different sets according to the orientation of the line segments, by which we can quickly extract rectangle and triangle traffic signs in image. And the experimental results demonstrate under different conditions are shown that the proposed method can detect rectangular and triangular traffic signs in natural scenes efficiently and robustness.
Cyber Crime Against Women and Girls on Social Sites in Bangladesh

The advent of the internet has made dramatic changes in human lives. In addition, the internet also opens avenues for various criminal activities. Women are more vulnerable to cyber threats than men on social sites. Cybercrime against women is at an alarming state in Bangladesh. There is a need to inform about the pervasiveness and different forms of cybercrimes and increase awareness of safety precautions. This study seeks to explore the crimes committed against women and girls on social sites in Bangladesh. In addition, the research will suggest some remedial measures to control and combat cybercrime against women and girls.