Reviewerly/exHarmony huggingface dataset:
Advanced Recommender Systems by Exploiting Social Networks

Social networks have become an indispensable part of our lives, which serve as communication channels, social interaction platforms as well as ubiquitous entertainment tools; meanwhile, social networks constantly generate big social media data that create decision complexity and information overload to users. As a result, recommender systems are emerged to suggest personalized and possibly preferred media for the users. However, social networks have extensively enriched the inputs for recommender systems, such as users' social relations, data source credibility, and new social media types. Consequently, this paper is aimed at identifying the crucial factors that can be used to advance recommender systems in social networks. For each factor, this paper discusses the state-of-the-art recommender system research in that aspect, and suggests how to integrate the featured data to build and improve recommender systems for social networks. The paper further proposes a model to integrate the crucial factors and indicates possible application domains for social media recommender systems.
RSC-DGS

Color (RGB) images captured under low light condition contain much noise with loss of textures. Since near-infrared (NIR) images are robust to noise with clear textures even in low light condition, they can be used to enhance low light RGB images by image fusion. In this paper, we propose fusion of RGB and NIR images using robust spectral consistency (RSC) and dynamic gradient sparsity (DGS), called RSC-DGS. We build the RSC model based on a robust error function to remove noise and preserve color/spectral consistency. We construct the DGS model based on vectorial total variation minimization that uses the NIR image as the reference image. The DGS model transfers clear textures of the NIR image to the fusion result and successfully preserves cross-channel interdependency of the RGB image. We use alternating direction method of multipliers (ADMM) for efficiency to solve the proposed RSC-DGS fusion. Experimental results confirm that the proposed method effectively preserves color/spectral consistency and textures in fusion results while successfully removing noise with high computational efficiency.
Multi-task Planning with the Consideration of Task Priority

Hierarchical Task Network (HTN) is widely used for intelligent planning problems due to its simplicity and efficiency. As an intelligent planner based on HTN, JSHOP2 can complete task planning in order of precedence or randomly. In real-world tasks planning, such as automatic vehicles dispatch, has to deal with complicated auto planning problems where different tasks must be considered properly at the same time. But JSHOP2 ignores the priority of different tasks and thus fails to deal with multi-task planning problems with priorities. In this paper, we improve JSHOP2 planner by adding priorities to different task lists and task items, and adjusting the order of task execution according to priority. Furthermore, we conduct an experiment using a classic logistics problem, and get the results validate the effectiveness of our method.
Security Analysis Combination Secret Sharing Protocol and Three-Pass Protocol

Protocols Secret Sharing is a method used to divide or break a secret message to 2 (two) or more recipients so that another beneficiary cannot know the results of fractional shares, except each recipient to exchange shares for reconstructing the secret. The problem that occurs is when the process transfer of shares, there are others who know the shares of the recipients and then that party is also able to reconstruct the secret, therefore the secret message required additional security, such as encryption of the message. The protocol used are the Three-Pass, this algorithm will guarantee the exchange of shares between the recipients. The three-pass protocol also provides convenience to the recipient to secure distribution share without having to do the encryption key, and the encrypted share is still safe. This study analyzes the security of secret delivery by combining the Secret Sharing Protocol and the Protocol Three-Pass.
Behavioral Biometric System Based on Network Traffic Analysis

Network traffic can be analyzed with various methods. In this work we present the hybrid method of analyzing network traffic data combining signature-based and inference-based approaches. We show that there is the potential of building the bio-metric system upon network traffic data and also show that the identification results for 3 persons identification problem reach 0.95 precision and 0.91 recall metric values.
Multi-Task Learning With Localized Generalization Error Model

In cases, the same or similar network architecture is used to deal with related but different tasks, where tasks come from different statistical distributions in the sample input space and share some common features. Multi-Task Learning (MTL) combines multiple related tasks for training at the same time, so as to learn some shared feature representation among multiple tasks. However, it is difficult to improve each task when statistical distributions of these related tasks are greatly different. This is caused by the difficulty of extracting an effective generalization of feature representation from multiple tasks. Moreover, it also slows down the convergence rate of MTL. Therefore, we propose a MTL method based on the Localized Generalization Error Model (L-GEM). The L-GEM improves the generalization capability of the trained model by minimizing the upper bound of generalization error of it with respect to unseen samples similar to training samples. It also helps to narrow the gap between different tasks due to different statistical distributions in MTL. Experimental results show that the L-GEM speeds up the training process while significantly improves the final convergence results.
Viewer Arousal Display System Using Eye-Tracking and Skin Conductance Data

We developed a viewer arousal display system that can represent the viewers' physiological responses about emotional arousal in fixation areas on frames of a viewed video by measuring and recording viewers' physiological signals and eye movements. We implemented two prototypes of the system. One of the prototypes superimposes the information in position, hue, and saturation of heat maps on the video. The second prototype represents the information with position, height, and color of bar graphs around the video. The system can solve a problem in previous systems, which were unable to display an important area in a frame of a video and can be useful for video creators or analyzers to estimate and analyze viewers' responses to a video. Additionally, we conducted an experiment to verify the effectiveness of the system. The results demonstrate that the fixation areas that the system calculated from the eye-tracking generally overlapped with the subjective reported attention points. However, the subjective arousal values varied slightly from the estimated arousal values from the SC data.
An Adaptive Dark Region Detail Enhancement Method for Low-light Images

The images captured in low-light conditions are often of poor visual quality as most of details in dark regions buried. Although some advanced low-light image enhancement methods could lighten an image and its dark regions, they still cannot reveal the details in dark regions very well. This paper presents an adaptive dark region detail enhancement method for low-light images. As our method is based on the Retinex theory, we first formulate the Retinex-based low-light image enhancement problem into a Bayesian optimization framework. Then, a dark region prior is proposed and an adaptive gradient amplification strategy is designed to incorporate this prior into the illumination estimation. The dark region prior, together with the widely used spatial smooth and structure priors, leads to a dark region and structure-aware smoothness regularization term for illumination optimization. We provide a solver to this optimization and get final enhanced results after post processing. Experiments demonstrate that our method can obtain good enhancement results with better dark region details compared to several state-of-the-art methods.
An Approach to Human Machine Teaming in Legal Investigations Using Anchored Narrative Visualisation and Machine Learning.

During legal investigations, analysts typically create external representations of an investigated domain as resource for cognitive offloading, reflection and collaboration. For investigations involving very large numbers of documents as evidence, creating such representations can be slow and costly, but essential. We believe that software tools, including interactive visualisation and machine learning, can be transformative in this arena, but that design must be predicated on an understanding of how such tools might support and enhance investigator cognition and team-based collaboration. In this paper, we propose an approach to this problem by: (a) allowing users to visually externalise their evolving mental models of an investigation domain in the form of thematically organized Anchored Narratives; and (b) using such narratives as a (more or less) tacit interface to cooperative, mixed initiative machine learning. We elaborate our approach through a discussion of representational forms significant to legal investigations and discuss the idea of linking such representations to machine learning.
Development of a Remotely Controlled Robotic Hand System for Surgical Application

The Hand-Assistive Laparoscopic Surgery (HALS) has made some improvement to the surgical operation; however, it has some risks and limitations. Therefore, we proposed the idea of developing a robotic hand system that can be used as a replacement for the surgeon's hand during the operation. The system is controlled using a teleoperation system, and is equipped with a haptic feedback system. In order to provide the user with the flexibility and robustness of the control we developed a contactless input interface system for controlling the robotic hand motion. The developed system gives the advantages of making the user's hand free of contacting devices which will give a free space for the haptic feedback sensation system.
Value distribution of elementary symmetric polynomials and its perturbations over finite fields

In this article we establish the asymptotic behavior of generating functions related to the exponential sum over finite fields of elementary symmetric functions and their perturbations. This asymptotic behavior allows us to calculate the probability generating function of the probability that the elementary symmetric polynomial of degree k and its perturbations returns β∈Fq where Fq represents the field of q elements. Our study extends many of the results known for perturbations over the binary field to any finite field. In particular, we establish when a particular perturbation is asymptotically balanced over a prime field and provide a construction to find such perturbations over any finite field.
Designing a Bit-Based Model to Accelerate Query Processing Over Encrypted Databases in Cloud

Database users have started moving toward the use of cloud computing as a service because it provides computation and storage needs at affordable prices. However, for most of the users, the concern of privacy plays a major role as they cannot control data access once their data are outsourced, especially if the cloud provider is curious about their data. Data encryption is an effective way to solve privacy concerns, but executing queries over encrypted data is a problem that needs attention. In this research, we introduce a bit-based model to execute different relational algebra operators over encrypted databases at the cloud without decrypting the data. To encrypt data, we use the randomized encryption algorithm (AES-CBC) to provide the maximum-security level. The idea is based on classifying attributes as sensitive and non-sensitive, where only sensitive attributes are encrypted. For each sensitive attribute, the table’s owner predefines the possible partition domains on which the tuples will be encoded into bit vectors before the encryption. We store the bit vectors in an additional column in the encrypted table in the cloud. We use those bits to retrieve only part of encrypted records that are candidates for a specific query. We implemented and evaluated our model and found that the proposed model is practical and success to minimize the range of the retrieved encrypted records to less than 30% of the whole set of encrypted records in a table.
Revealing the Unknown Parameters of a Microcomputer-Based Random Number Generator

A novel attack system is proposed to reveal the vulnerabilities threats of a microcomputer-based random number generator (RNG). Convergence of the proposed attack system is proved using auto-synchronization. Unknown parameters of the microcomputer-based RNG are revealed where the available information are the design of the RNG and a scalar time series observed from the chaotic system used as the core of the RNG. Numerical results verifying the feasibility of the attack system are given such that, next bit can be predicted while the same output sequence of the RNG can be generated.
What are you doing with that thing: Getting Household Service Robots to Explain Themselves

We describe a knowledge-based approach through which a household robot can explain its actions. The approach uses a model of situations, stereotypical scenarios that the robot and/or user may participate in. Situations are also characterized by the items participating in the situation and their affordances. We describe a natural language generation pipeline including content selection heuristics tailored to the household robotics domain, and illustrate our approach on table-setting tasks.
Interpretable Inference Graphs for Face Recognition

Convolutional Neural Networks with Adaptive Inference Graphs (ConvNet-AIG) use adaptive network topologies through on/off gating on network layers for individual images to achieve improved computational efficiency and classification accuracy. Face recognition is a more difficult task than object classification due to fine-grained differences in facial features. We evaluate the performance of ConvNet-AIG for the task of Face Recognition on the IMDb-Face dataset. We analyse the interpretability of inference graphs for different facial features and show that facial features like gender, skin color and race can be interpreted corresponding to individual images. We propose a novel loss function to force interpretable inference graphs(ConvNet-IIG) and empirically show an improvement in classification accuracy.
HCI Using Gestural Recognition for Symbol-Based Communication Methodologies

Sign language in itself is the only tool of communication for the society which is not able to hear voices and speak words. Using sign language, they can express their emotions and thoughts and can convey what they want to say. But not everyone understands sign language, only the people who require it do. So people with such kinds of handicaps need a translator with them in order to convert their language to a common tongue and that is the main reason of sign language recognition becoming such a crucial task. Since sign language consists of different movements and positions of the hand, therefore, the accuracy of sign language depends on how accurately the machine could recognize the gesture. We are trying to develop such a system what we call translating HCI for sign language. In this system, the user has to place their hand in front of the webcam performing sign gestures and in real time, the system will read your hand gesture and will return the respective character/alphabet on the screen. Utilizing the proposed system normal people can understand sign language and can easily communicate with hearing-impaired people.
Semantic ordering of English machine translation based on fuzzy theory

With the development and popularization of electronic computers and the Internet, the problem of language barriers has once again become prominent in the new era, and people are more in need of machine translation. However, there is currently no suitable method for effective semantic ordering of E nglish machine translation. In order to better perform semantic ordering on English machine translation, the article combines fuzzy theory to construct an algorithm model, and analyzes the experimental results through evaluation indicators. The results show that with the increase of training concentration training examples, the semantic parser can learn more natural language sentence analysis methods from the training examples, and the natural language sentences that can be correctly parsed gradually increase, so with the training examples increased recall rate and F value gradually increased. The experimental results also show that the use of higher precision syntax analyzers can effectively improve the performance of statistical machine translation systems, whether in phrase-based or machine-based translation methods.
An Augmented Reality Shooting Game

In this growing world of technology, there are so many things going on at the same time. Humans are progressing in so many fields at a time for the betterment of human race. A technology that holds its place among these is Augmented Reality (AR). AR refers to displaying virtual objects into the real world. It has so much potential and can change the way you live, but not many people are aware of it. People do not know what it is capable of doing and that it can have a huge impact on our day to day lives. Soon it will be so common to our everyday life that it can help us do a lot of things. So just to give the idea of what AR is, me and my team have created a game based on augmented reality to tell the world what it feels like and what it can do to help our lives in the near future.
Image Mining by Multiple Features

This paper discusses an image mining algorithm for efficient and accurate image retrieval. The proposed method uses multiple image features combination. Color feature in the form of first-order, second-order and third-order moment is calculated on a sub-block. These moments calculate statistics features over a local region. The computed first-order moment is considered as mean. Standard deviation and skewness are considered as second-order and third-order moment, respectively. These features are calculated for images and stored in database. For texture extraction, center symmetric local binary pattern (CSLBP) feature is used. CSLBP is straightforward in computation and it is also rotation consistent. CSLBP generates histogram of 16 bins for one sub-block. In order to capture minute details around local region, Laplacian of Gaussian (LoG) is used as a weight factor during CSLBP histogram construction. This weight factor makes texture feature more powerful and increases its discrimination power. Resultant weighted histogram is quantized and binary result is stored in the database. For test image, its texture and color features are compared with the stored texture and color features. Based on the color and texture feature’s correlation with test image, results are retrieved. Our results clearly show that by incorporating multiple features as well as local weight factor, the proposed image mining gives desirable retrieval results.
Achieving Dynamic Resource Allocation in the Hadoop Cloud System

Cloud computing has been extensively adopted to handle the enormous amount of data from Internet of Things, Big Date, and many other cutting-edge research areas in recent years. As cloud systems serve more and more jobs, it will be getting more difficult for time-critical or urgent jobs with high priority in a busy cloud environment to complete their execution as soon as users would like to have. To facilitate the prompt execution of those jobs, it is imperative for cloud systems to provide schemes expediting their execution. The Apache Hadoop is one of the most popular cloud platforms in cloud computing. Unfortunately, it is not equipped with flexible mechanisms to hasten the course of prioritized jobs. There had been various approaches proposed to accelerate the execution of prioritized jobs from different aspects. However, those approaches not only target at just certain existing Hadoop job schedulers but also require modifications made to those job schedulers. Thus, they cannot be directly applied to other job schedulers without major porting efforts, much less to new job schedulers developed in the future. We designed and implemented a new scheme enabling dynamic resource allocation to jobs selected by job schedulers. As a result, without making changes to job schedulers, our scheme could help some current and future Hadoop job schedulers speed up the execution of jobs with high priority. Experimental results demonstrate that jobs executed with high priority can reduce their execution time by up to 68.28%.
Revealing Frequent Itemsets by Local Power set Transaction Scanning

Finding frequent itemsets have known as the most computational consuming task in association rule mining. There are many effective manners proposed by researchers focused on dataset scanning reduction, but those turn to waste the time for accessing and scanning other structures of data in memory which are generated during processing. This work has proposed an effective algorithm utilizing a hash table to accumulate support count of distinct items appearing in local power sets which are generated during each transaction scanning. The complexity analysis and experiments on two real datasets showed that our proposed can reduce time consuming significantly in computation when compared to the existing algorithm that also apply power set for this task.
A formal system of reduction paths for parallel reduction

We introduce a formal system of reduction paths as a category-like structure induced from a digraph. Our motivation behind this work comes from a quantitative analysis of reduction systems based on the perspective of computational cost and computational orbit. From the perspective, we define a formal system of reduction paths for parallel reduction, wherein reduction paths are generated from a quiver by means of three path-operators. Next, we introduce an equational theory and reduction rules for the reduction paths, and show that the rules on paths are terminating and confluent so that normal paths are obtained. Following the notion of normal paths, a graphical representation of reduction paths is provided. Then we show that the reduction graph is a plane graph, and unique path and universal common-reduct properties are established. Finally, a set of transformation rules from a conversion sequence to a reduction path leading to the universal common-reduct is given under a certain strategy.
An Experiment with Random Walks and GrabCut in One Cut Interactive Image Segmentation Techniques on MRI Images

This research work proposes the Random Walks and GrabCut in One Cut interactive image segmentation techniques using MRI images, particularly those posing segmentation challenges in terms of complexity in texture, indistinct and/or noisy object boundaries, lower contrast, etc. We have computed accuracy measures such as Jaccard Index (JI), Dice Coefficient (DC) and Hausdorff Distance (HD) besides Visual assessment to understand and assess segmentation accuracy of these techniques. Comparison of the ground truth with segmented image reveals that Random Walks can detect edges/boundaries quite well, especially when those are noisy, however, has tendency to latch onto stronger edges nearby the desired object boundary. GrabCut in One Cut on the other hand sometimes needs more scribbles to achieve acceptable segmentation.