Reviewerly/exHarmony huggingface dataset:
MEMORY FORENSIC: ACQUISITION AND ANALYSIS OF MEMORY AND ITS TOOLS COMPARISON

The enhancement of technology has led to a considerable amount of growth in number of cases pertaining to cyber-crime and has raised an enormous challenge to tackle it effectively. There are various cyber forensic techniques and tools used to recover data from the devices to tackle cyber-crime. Present research paper focuses on performing memory forensic and analyzes the memory which contains many pieces of information relevant to forensic investigation, such as username, password, cryptographic keys, deleted files, deleted logs, running processes; that can be helpful to investigate the cyber-crime pining down the accused. The three main steps followed in memory forensic are acquiring, analyzing and recovering. Recovery of the evidences of crime from the volatile memory can be possible with the knowledge of different tools and techniques used in memory forensic. However, it is always tough to analyze volatile memory as it stays for a very short period. Not all tools can be used for memory forensic in every situation and therefore, it is important to have the knowledge of tools before applying to solve a particular cyber-crime. It is yet to establish on using a single tool for complete investigation, however, most of the tools used are successful in providing reasonable evidences. The present research paper provides an insight on analyzing the memory that stores relevant data, collection of evidences from the device(s), extraction of essential data using different memory forensic tools, tools useful for various purposes and the best suited tool for a particular situation.&#x0D;
Motion Control of the Autonomous Mobile Robot Considering Personal Spaces of Pedestrians

This paper proposed a motion control method for autonomous mobile robot by considering personal spaces of pedestrians. The behaviors of pedestrians are recognized and their personal spaces, including the spaces that potentially used when doing interactions with other people or objects, are defined. The robots are then controlled by potential field method to move towards its destination while avoiding disturbing others. In this way, the robot achieves moving considerately and its social acceptance can be improved. The effectiveness of the proposed method is proven by simulation and experiments.
A Model for Optimal Human Navigation with Stochastic Effects

We present a method for optimal path planning of human walking paths in mountainous terrain, using a control theoretic formulation and a Hamilton-Jacobi-Bellman equation. Previous models for human navigation were entirely deterministic, assuming perfect knowledge of the ambient elevation data and human walking velocity as a function of local slope of the terrain. Our model includes a stochastic component which can account for uncertainty in the problem, and thus includes a Hamilton-Jacobi-Bellman equation with viscosity. We discuss the model in the presence and absence of stochastic effects, and suggest numerical methods for simulating the model. We discuss two different notions of an optimal path when there is uncertainty in the problem. Finally, we compare the optimal paths suggested by the model at different levels of uncertainty, and observe that as the size of the uncertainty tends to zero (and thus the viscosity in the equation tends to zero), the optimal path tends toward the deterministic optimal path.
FlowRec: Prototyping Session-Based Recommender Systems in Streaming Mode

Despite the increasing interest towards session-based and streaming recommender systems, there is still a lack of publicly available evaluation frameworks supporting both these paradigms. To address the gap, we propose FlowRec — an extension of the streaming framework Scikit-Multiflow, which opens plentiful possibilities for prototyping recommender systems operating on sessionized data streams, thanks to the underlying collection of incremental learners and support for real-time performance tracking. We describe the extended functionalities of the adapted prequential evaluation protocol, and develop a competitive recommendation algorithm on top of Scikit-Multiflow’s implementation of a Hoeffding Tree. We compare our algorithm to other known baselines for the next-item prediction task across three different domains.
Secure Network Release with Link Privacy

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sensitive, we aim to seek effective approaches to release utility-preserved yet privacy-protected structured data. In this paper, we leverage the differential privacy (DP) framework, to formulate and enforce rigorous privacy constraints on deep graph generation models, with a focus on edge-DP to guarantee individual link privacy. In particular, we enforce edge-DP by injecting Gaussian noise to the gradients of a link reconstruction based graph generation model, and ensure data utility by improving structure learning with structure-oriented graph comparison. Extensive experiments on two real-world network datasets show that our proposed DPGGAN model is able to generate networks with effectively preserved global structure and rigorously protected individual link privacy.
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning

DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement learning using deep neural networks. DQNs require a large buffer and batch processing for an experience replay and rely on a backpropagation based iterative optimization, making them difficult to be implemented on resource-limited edge devices. In this paper, we propose a lightweight on-device reinforcement learning approach for low-cost FPGA devices. It exploits a recently proposed neural-network based on-device learning approach that does not rely on the backpropagation method but uses OS-ELM (Online Sequential Extreme Learning Machine) based training algorithm. In addition, we propose a combination of L2 regularization and spectral normalization for the on-device reinforcement learning so that output values of the neural network can be fit into a certain range and the reinforcement learning becomes stable. The proposed reinforcement learning approach is designed for PYNQ-Z1 board as a low-cost FPGA platform. The evaluation results using OpenAI Gym demonstrate that the proposed algorithm and its FPGA implementation complete a CartPole-v0 task 29.77x and 89.40x faster than a conventional DQN-based approach when the number of hidden-layer nodes is 64.
A maneuvering target tracking algorithm based on amplitude characteristics in sea clutter

Aiming at the problem of maneuvering target tracking in the background of sea clutter, this paper adopts a method based on the combination of probabilistic strongest neighbor filter algorithm with m-validated measurements (PSNF-m) and adaptive Interactive Multi-Model (AIMM). First, the PSNF-m algorithm uses echo amplitude characteristics to carry out echo correlation, and then uses adaptive interactive multi-model to filter the data after correlation, so as to realize the tracking of maneuvering targets. In this paper, the sea clutter background is simulated by K distribution. The corresponding measurement likelihood is given, and the corresponding simulation is carried out. Simulation results show that the algorithm proposed in this paper can effectively track maneuvering targets under sea clutter, and has better performance than the traditional amplitude assisted Interactive Multi-Model probabilistic data association with target amplitude feature (IMM-PDA-AI) and Interactive Multi-Model strongest neighbor (IMM-SNF).
A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos

Watching instructional videos are often used to learn about procedures. Video captioning is one way of automatically collecting such knowledge. However, it provides only an indirect, overall evaluation of multimodal models with no finer-grained quantitative measure of what they have learned. We propose instead, a benchmark of structured procedural knowledge extracted from cooking videos. This work is complementary to existing tasks, but requires models to produce interpretable structured knowledge in the form of verb-argument tuples. Our manually annotated open-vocabulary resource includes 356 instructional cooking videos and 15,523 video clip/sentence-level annotations. Our analysis shows that the proposed task is challenging and standard modeling approaches like unsupervised segmentation, semantic role labeling, and visual action detection perform poorly when forced to predict every action of a procedure in a structured form.
LFSpy: A Python Implementation of Local Feature Selection for Data Classification with scikit-learn Compatibility

Dhindsa et al., (2020). LFSpy: A Python Implementation of Local Feature Selection for Data Classification with scikit-learn Compatibility. Journal of Open Source Software, 5(49), 1958, https://doi.org/10.21105/joss.01958
Real time line tracking and obstacle avoidance robot

Obstacle avoidance is a task in robotics which includes detection of obstacles and therefore avoiding the collision path of such obstacles. Obstacle avoidance robotics has application in various fields such as autonomous vehicle sector, military applications include unmanned aerial vehicle etc. The paper presents the prototype type design of real time line tracking and obstacle avoiding robot. The robot is designed around Raspberry pi microcontroller and python is used for coding.
Emotional Meaning of Eyelid Positions on a One-Eyed 2D Avatar

Expressing emotions of virtual and robotic agents require complex animations that mimic human behavior. By abstracting the core features in human faces for the emotions described by Ekman, et al., a single-eyed 2D avatar was designed that only moves the upper and lower eyelids. The relationship between how much each eyelid covers the eye and the perceived emotion was evaluated with a within-subjects study with 31 college students. The results showed that it is possible to convey different emotional meanings by changing the eyelids, and adding a white dot to simulate gloss affected the meaning of some emotions considerably. This research contributes to the development of virtual and robotic agents that can show emotions without increasing the complexity of the system, ultimately leading to more natural interactions with artificial systems.
THE EFFECTIVENESS OF GAME-BASED LEARNING MODEL IN THE JAVANESE LANGUAGE CLASS

Purpose of the study: the objective of this study was twofold. Firstly, it aimed to examine the effectiveness of game-based learning in the Javanese language class for junior high school students. Secondly, it was intended to investigate the responses of the students upon completing the class that incorporated the game-based learning.&#x0D; Methodology: This experimental research employed the post-test only control design. The data were collected from documentation, questionnaires, and evaluation tests. Further, the data were examined using a t-test.&#x0D; Main Findings: The game-based model is effective for the Javanese language class. The average score test of the students taught by game-based learning reached 72.6. On the other hand, those participated in a class with different treatment scored 64.5 on average. Such a difference was significant, given the output of the independent sample T-test, which was from the result of analysis using the SPSS software.&#x0D; Applications of this study: This study was beneficial to change the paradigm of the old-fashioned, boring Javanese teaching and learning. It promoted the idea of fun teaching and learning. On top of that, the implementation of the learning model was effective to enhance students’ The Javanese language skills, specifically in Central Java, East Java, and the Special Region of Yogyakarta.&#x0D; Novelty/Originality of this study: Learning models for local content subjects, such as the Javanese language, were still limited since the topic did not attract the attention of many researchers. Testing the game-based learning was among the alternatives of interesting learning methods to stimulate students to learn their local language, and thus, preserve the language in society.
PEDAGOGICAL EXPERIMENT TO TEST THE EFFECTIVENESS OF PROFESSIONAL AND METHODOLOGICAL SUPPORT FOR THE ACTIVITIES OF A MILITARY SPECIALIST IN THE UNIVERSITY ENVIRONMENT OF ACADEMIC CONSULTING

The article describes the structural components of the system of professional and methodical support for pedagogical activity of instructor-teacher that provides practical classes for military university cadets. The system is built on the basis of contextual modeling of a set of approaches in the realization of factors and mechanisms of cadets’ self-study activity under the direction of the academic consultant. Selected factors (social, functional, motivational and organizational) are correlated with the directions of self-study activity to improve the pedagogical competence of a military specialist.
The Dual Polynomial of Bipartite Perfect Matching

We obtain a description of the Boolean dual function of the Bipartite Perfect Matching decision problem, as a multilinear polynomial over the Reals. We show that in this polynomial, both the number of monomials and the magnitude of their coefficients are at most exponential in $\mathcal{O}(n \log n)$. As an application, we obtain a new upper bound of $\mathcal{O}(n^{1.5} \sqrt{\log n})$ on the approximate degree of the bipartite perfect matching function, improving the previous best known bound of $\mathcal{O}(n^{1.75})$. We deduce that, beyond a $\mathcal{O}(\sqrt{\log n})$ factor, the polynomial method cannot be used to improve the lower bound on the bounded-error quantum query complexity of bipartite perfect matching.
A Novel Human Action Recognition and Behaviour Analysis Technique using SWFHOG

In this paper, a new local feature, called, Salient Wavelet Feature with Histogram of Oriented Gradients (SWFHOG) is introduced for human action recognition and behaviour analysis.In the proposed approach, regions having maximum information are selected based on their entropies.The SWF feature descriptor is formed by using the wavelet sub-bands obtained by applying wavelet decomposition to selected regions.To improve the accuracy further, the SWF feature vector is combined with the Histogram of Oriented Gradient global feature descriptor to form the SWFHOG feature descriptor.The proposed algorithm is evaluated using publicly available KTH, Weizmann, UT Interaction, and UCF Sports datasets for action recognition.The highest accuracy of 98.33% is achieved for the UT interaction dataset.The proposed SWFHOG feature descriptor is tested for behaviour analysis to identify the actions as normal or abnormal.The actions from SBU Kinect and UT Interaction dataset are divided into two sets as Normal Behaviour and Abnormal Behaviour.For the application of behaviour analysis, 95% recognition accuracy is achieved for the SBU Kinect dataset and 97% accuracy is obtained for the UT Interaction dataset.Robustness of the proposed SWFHOG algorithm is tested against Camera view angle change and imperfect actions using Weizmann robustness testing datasets.The proposed SWFHOG method shows promising results as compared to earlier methods.
Group Equivariant Generative Adversarial Networks

Recent improvements in generative adversarial visual synthesis incorporate real and fake image transformation in a self-supervised setting, leading to increased stability and perceptual fidelity. However, these approaches typically involve image augmentations via additional regularizers in the GAN objective and thus spend valuable network capacity towards approximating transformation equivariance instead of their desired task. In this work, we explicitly incorporate inductive symmetry priors into the network architectures via group-equivariant convolutional networks. Group-convolutions have higher expressive power with fewer samples and lead to better gradient feedback between generator and discriminator. We show that group-equivariance integrates seamlessly with recent techniques for GAN training across regularizers, architectures, and loss functions. We demonstrate the utility of our methods for conditional synthesis by improving generation in the limited data regime across symmetric imaging datasets and even find benefits for natural images with preferred orientation.
The Correlation Between Students’ Motivation and Their Speaking Ability

Speaking is one of important skill for all students that every student should master it.In the modern era, some students may be not all of them have a good speaking in English.To help their ability to speak, the students should have a high motivation, fine extrinsic or intrinsic motivation.Motivation is an internal condition that exists in humans which can encourage someone to act ina certain direction and engage in certain activities.The purpose of this research is to find out the relationship between students' motivation in learning speaking and speaking ability at State Islamic University of Sunan Ampel Surabaya.To collect the data, the researchers distributed the questionnaire to 30 students of English Teacher Education Department in the third semester who have taken spoken English Class in 2018 to know the students' motivation and their speaking ability.It was discoverd that the students of English Teacher Education Department have not high motivation.Next, from the Spoken English final score, it was found that the students have a high score in speaking ability.It can conclude that there is no positive correlation between students' motivation in learning speaking and their speaking ability.In other words, there is no significant correlation between English Teacher Education Department students in State Islamic University of Sunan Ampel Surabaya with their speaking ability.
AMI-Net+: A Novel Multi-Instance Neural Network for Medical Diagnosis from Incomplete and Imbalanced Data

In medical real-world study (RWS), how to fully utilize the fragmentary and scarce information in model training to generate the solid diagnosis results is a challenging task. In this work, we introduce a novel multi-instance neural network, AMI-Net+, to train and predict from the incomplete and extremely imbalanced data. It is more effective than the state-of-art method, AMI-Net. First, we also implement embedding, multi-head attention and gated attention-based multi-instance pooling to capture the relations of symptoms themselves and with the given disease. Besides, we propose var-ious improvements to AMI-Net, that the cross-entropy loss is replaced by focal loss and we propose a novel self-adaptive multi-instance pooling method on instance-level to obtain the bag representation. We validate the performance of AMI-Net+ on two real-world datasets, from two different medical domains. Results show that our approach outperforms other base-line models by a considerable margin.
THE APPROACH OF MANAGERS REGARDING INNOVATIVE WORK BEHAVIOUR

Purpose of the study: Although many factors can influence innovative work behaviour, we may not know exactly which practices make a difference in influencing innovative work behaviour. This study aims to determine which positive and negative factors can affect the IWB theory understanding. Methodology: This study used a qualitative approach by a systematic review of the literature, the authors conducted a content analysis of 32 international journals. Main Findings: The analysis results gave evidence that transformational leadership, knowledge sharing, and organizational learning were positive factors in creating innovative work behaviour. Meanwhile, organizational politics, job insecurity, and transactional leadership were negative factors that could decrease innovative work behaviour. Applications of this study: The results of this study provide practical information for professional leaders and managers to develop employees’ innovative work behaviour that might help build an innovative environment. Novelty/Originality of this study: This study has a difference in terms of study approaches by looking at positive and negative factors that could affect innovative work behaviour in a literature review technique.
A Comparison of Evolutionary and Tree-Based Approaches for Game Feature Validation in Real-Time Strategy Games with a Novel Metric

When it comes to game playing, evolutionary and tree-based approaches are the most popular approximate methods for decision making in the artificial intelligence field of game research. The evolutionary domain therefore draws its inspiration for the design of approximate methods from nature, while the tree-based domain builds an approximate representation of the world in a tree-like structure, and then a search is conducted to find the optimal path inside that tree. In this paper, we propose a novel metric for game feature validation in Real-Time Strategy (RTS) games. Firstly, the identification and grouping of Real-Time Strategy game features is carried out, and, secondly, groups are included into weighted classes with regard to their correlation and importance. A novel metric is based on the groups, weighted classes, and how many times the playtesting agent invalidated the game feature in a given game feature scenario. The metric is used in a series of experiments involving recent state-of-the-art evolutionary and tree-based playtesting agents. The experiments revealed that there was no major difference between evolutionary-based and tree-based playtesting agents.
Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior

We present a novel method for generating robust adversarial image examples building upon the recent `deep image prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in image synthesis. Adversarial images are commonly generated by perturbing images to introduce high frequency noise that induces image misclassification, but that is fragile to subsequent digital manipulation of the image. We show that using DIP to reconstruct an image under adversarial constraint induces perturbations that are more robust to affine deformation, whilst remaining visually imperceptible. Furthermore we show that our DIP approach can also be adapted to produce local adversarial patches (`adversarial stickers'). We demonstrate robust adversarial examples over a broad gamut of images and object classes drawn from the ImageNet dataset.
On the intuitionistic fuzzy projective Menelaus and Ceva's conditions

In this work, the intuitionistic fuzzy versions of Menelaus and Ceva's theorems in intuitionistic fuzzy projective plane are de…ned and the conditions to the intuitionistic fuzzy versions of Menelaus and Ceva 6-…gures are determined.