Reviewerly/exHarmony huggingface dataset:
Data Augmentation using non-rigid CPD Registration for 3D Facial Expression Recognition

3D Facial Expression Recognition (FER) is an active research topic due to its multi-fields human machine applications. We expose in this paper a new approach for Data Augmentation (DA) in order to improve 3D FER using Deep Neural Networks (DNN). Our main contribution consists in using the Coherent Point Drift (CPD) non-rigid registration to generate additional 3D facial data conveying various expressions mainly the prototypical expressions: Happiness, Sadness, Fear, Surprise, Disgust, and Anger. We start by choosing a set of different references defined by arbitrarily selected neutral faces. We apply then the CPD non-rigid registration between each selected neutral face and each 3D facial model conveying various expressions from the whole BU-3DFE database. Thus, we augment the dataset by a factor equal to the used references. Afterwards, we estimate the 3D elastic deformation between the reference (3D neutral face) and the target (3D face with expression) in order to generate consequently various 3D expressions by switching the reference and the target within the registration process. Afterwards, we gather the produced 3D expressions to increase the size of our dataset. Finally, we exploit a DNN architecture to evaluate our proposed DA method. The used DA is effective and increases our DNN performance. Experimental results operated on the whole BU-3DFE database shows promising results reaching 94.88%.
A novel augmented reality to visualize the hidden organs and internal structure in surgeries

Abstract Background Augmented reality (AR) is still a primarily theoretical concept in areas such as bowel, liver, gallbladder, and jaw surgeries because of the limitation of visualization accuracy of hidden organs and internal structures. This paper aims to improve the cutting accuracy, visualizing accuracy, and processing time of the augmented video. Methodology The proposed system consists of an enhanced block‐matching algorithm (BMA) with ghosting map technique. Results Results proved that proposed system reduced the visualization error, which ranges from 1.48 to 1.83 mm against the existing system visualization error 1.67 to 2.0. Similarly, the processing time also improved 59 to 72 ms/frame over the 50 to 58 ms/frame. Conclusion This study showed the improvement and solved the problem soft tissue reconstruction and visualization on the AR video that used in bowel and gallbladder surgeries.
An Experimental Evaluation of Grouping Definitions for Moving Entities

One important pattern analysis task for trajectory data is to find a group: a set of entities that travel together over a period of time. In this paper, we compare four definitions of groups by conducting extensive experiments using various data sets. The grouping definitions are different by one or more of three different characteristics: whether they use the measured sample points or the continuous movement, how distance is used to decide if entities are in the same group, and whether the duration of the group is measured cumulatively or as one contiguous time interval. We are interested in the differences between the definitions and comparisons to human annotated data, if available. We concentrate on pedestrian data and on different crowd densities. Furthermore, we analyze the robustness of the definitions and their dependence on different sampling rates. We use two different types of trajectory data sets: synthetic trajectories from a crowd simulation model, and real-life trajectories extracted from video surveillance. We present the results of the quantitative evaluations. For experiments with real-life trajectories, we augment them with a qualitative evaluation using videos that show groups in the trajectories with a color coding.
A Framework for Virtual Reality Training to Improve Public Speaking

This paper presents the logic and construction of a prototype virtual reality (VR) tool for public speech training. It reflects upon previous endeavours in this area, using them to make informed design decisions. A dictation recognizer is implemented to perform speech to text conversions. With this training simulator, users are be able to step into a virtual environment resembling a podium in an auditorium, with their speech appearing on a virtual cue card. Also, users are presented with a performance metric at the end of their speech to grade their overall performance. We suggest that the VR immersive prototype using speech-to-text recognition has a potential to be engaging and to serve as a tool for public speaking training.
An Approach using Certainty Factor Rules for Aphasia Diagnosis

Artificial Intelligence methods are frequently applied to medical domains assisting in various tasks, like diagnosis. The implementation of corresponding intelligent systems is based on available datasets and expert knowledge. In this paper, we present a rule-based approach used for aphasia diagnosis. The approach uses certainty factor rules created from a dataset of records involving persons diagnosed with aphasia. The employed certainty factor formalism is an extension to a previous certainty factor formalism. To model each case, nineteen attributes are used. Seven of them are discrete and the rest of them are integer. Experiments were performed testing the performance of the specific rule-based approach, a decision tree method and a feedforward neural network. Experimental results show that the rule-based approach performs well and requires only three of the eighteen input attributes to produce an output. To the best of our knowledge, there is no other published approach using certainty factor rules for aphasia diagnosis.
Proposed System for Criminal Detection and Recognition on CCTV Data Using Cloud and Machine Learning

Finding criminals or hunting for people, in a CCTV video footage, after a crime scene or major attack takes place, is a time consuming task. As informed to us by cyber cell members of Goa branch, they make multiple members of the department sit with laptops and computers literally to search through the CCTV footage to find and trace the guilty, as they don't have the automated system for doing this task with them. This process is both time and labor intensive. In this research paper we have tried to survey the existing technologies as well as we propose a new system for criminal Detection & Recognition using Cloud Computing and Machine Learning, which if used by our Crime Agencies would definitely help them to find criminals from CCTV footage. The proposed system can not only help find criminals but if used properly on different sites such as railway stations etc, can also help find missing children and people from the CCTV footage available from the respective site. Existing solutions use traditional face recognition algorithms which can be troublesome in changing Indian environments especially factors like light, weather and especially orientation. Some CCTV are in a bad place and can get tilted resulting in a wild increase in inaccuracy. This research paper proposes to use Microsoft Azure Cognitive services and Cloud system for implementation of the proposed system [21]. The next phase this research will try to compare this proposed methodology with traditional techniques like HAAR cascade to judge performance of the proposed System, as it is important to have a high accuracy, for a project of this sensitivity.
Service Resilience Framework for Enhanced End-to-End Service Quality

A major risk that cloud computing paradigm encounters in enabling computing as a utility is service disruption. With component failures, performance interferences, load dynamics, etc., increasing with scale, it is challenging to meet user expectations concerning the delivery of cloud services. Prevalent cloud solutions deal with service disruptions as application design choices and mostly adopt to redundant or replicated service instances. This black box approach makes the choice expensive and obviates interesting and smarter choices while dealing with disruptions. More so, as clouds are subjected to dynamic variations in workload, resources and disruptions. This paper details disruptionscenarios where autonomous service resilience features are useful, to motivate the work. Further, to address the observed lacunae in cloud setups, the paper proposes and describes a service resilience framework that brings in autonomous cross-layer consciousness across cloud service layers for delivering improved service resilience. This framework is analyzed and explained with specific use-cases for availability and performance with goal-driven service resilience.
Does my dog really need a gadget?

Pet wearables increasing popularity on the market places them also in the spotlight of ACI research. Recent studies have shown that they have the potential to impact the human-pet bond, improve caregiving and increase the motivation of owners' for physical exercising with their pets. Thus pet wearables provide pets with a 'digital voice' to speak up about their needs more clearly. Despite this benefit, the adoption rate of pet wearables nowhere near that of human wearables. This paper aims to better understand the barriers towards their adoption. To this end, we present the results of a study (N=200) with pet owners investigating their amotivations, e.g., reasons for not purchasing or using a pet wearable. Our findings indicate that users are first and foremost focused on cost and durability of the devices. We present a detailed qualitative analysis of the different reasons that pet owners hold, visualizing them as a map of trade-offs that pet owners consider. Finally, we reflect on our findings in the context of the ACI agenda.
Deep Multiple Instance Learning for Human Trajectory Identification

Extracting identifiable information from human trajectories is a fundamental task in many location-based services (LBS). However, various mobility patterns underlain in human trajectories are difficult to model by existing models. Moreover, we could hardly define a clear user set for user identification because the set of users are dynamic and changing everyday. Bearing these in mind, we apply a deep multiple instance learning method to handle the multimodal mobility patterns in a weak-supervised learning way, and address the dynamic user set problems via a pairwise loss with negative sampling. We utilize a multi-head attention mechanism to automatically extract multiple aspects and match the corresponding information between query trajectories and historical trajectories. Our method shows a good identification accuracy on three human GPS trajectory data sets comparing with baseline methods.
A Comprehensive Survey of Spam Profile Detection Methods in Online Social Networks

Abstract Social networks have grown into a popular way for internet surfers to interact with friends in addition to family members, reading news, and also discuss events. Users spend more time on popular social platforms (e.g., Facebook, Twitter, etc.) storing and sharing their personal information. This fact together with the prospect of communicating thousands of users fascinates the concentration of malicious users. They exploit the implicit trust interactions concerning users with the purpose of accomplishing their malicious objectives, for instance, create malicious links inside the posts/tweets, spread fake news, send out unsolicited messages to genuine users and so on. In this paper, we reviewed various existing techniques on spam profile detection in online social networks.
Evaluation of Modifications to CPPPSNR in 360° Video Quality Assessment

360° videos are represented in spherical projection formats and the video quality of such videos is assessed using spherical objective quality metrics. Furthermore, the objective video quality between two different spherical projection formats can be evaluated using Cross projection metrics. Craster parabola, is a 2D cross projection format which is used by Craster Parabolic Peak Signal-to-Noise Ratio (CPPPSNR) metric. The existing CPPPSNR measurements do not consider the subsampling locations during the quality assessment to match the pixel density of a sphere. Nevertheless, it is vitally important to account for the oversampled projection formats and the sphere in order to be compatible with the existing video encoding architectures. To this end, the proposed improvements to the CPPPSNR locates the subsample points during craster parabolic projection and use nearest neighbor interpolation to assign pixels from the craster parabolic projection. Furthermore, in order to compensate the occurrence of oversampling, appropriate weights are applied to the corresponding pixels. The proposed method was tested with Shanghai Jiao Ton University (SJTU) Virtual Reality (VR) sequences for projection conversion. The comparison between Spherical PSNR (SPSNR) and existing CPPPSNR, validate the proposed CPPPSNR as an objective quality metric for cross projections.
Dynamic Pruning of CNN networks

A new, radical CNN dynamic pruning approach is presented in this paper, achieved by a new holistic intervention on both the CNN architecture and the training procedure, which targets to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. Our approach formulates a systematic and data-driven method for developing CNNs that are trained to eventually change size and form in real-time during inference, targeting to the smaller possible computational footprint. Results are provided for the optimal implementation on a few modern, high-end mobile computing platforms indicating a significant speed-up.
Detection of SQL Injection Attacks Based on Improved TFIDF Algorithm

Abstract Detecting SQL injection attacks (SQLIAs) effectively is one of the critical issues to be solved in Web secure fields. Aiming at the problem that the distribution of feature words in the same kind of statements is not considered when using TFIDF algorithm to vectorize the text of SQL statements, a method of detecting SQLIAs based on improved Term Frequency-Inverse Document Frequency (TFIDF) algorithm is proposed. Firstly, TFIDF algorithm is improved based on the distribution of feature words in the same kind of statements. Then, the improved TFIDF (ITFIDF) algorithm is used to vectorize the dataset of SQL statements to increase the feature weight of the SQL statement. Finally, the detection of SQLIAs is carried out based on Support Vector Machine (SVM). The experimental results show that the combination of SVM and ITFIDF has higher accuracy, recall rate and F-score compared with other similar methods. At the same time, the experimental results also show that SVM has better classification performance when dealing with this problem than other machine learning models such as DNN and Decision Tree.
Text Feature Space Optimization Using Artificial Bee Colony

A text classification system’s learning is substantially dependent on the input features and their process of extraction and selection. The solitary drive encouraging feature selection practice is to lessen the dimensionality of the problem at hand; thus, facilitating the process of classification. Among several problem areas, text categorization is one area where feature selection plays a vital role. It is well-known that text categorization suffers from the curse of dimensionality. This results in the creation of feature space which may have redundant or irrelevant features leading to the creation of a poor classifier. Therefore, to build an intelligent classifier feature, selection is an important process. This paper has a fourfold objective: Firstly, it aims to create a word to vector space using a widely used score. Secondly, it intends to optimize text feature space using a nature-inspired algorithm. Thirdly, it aims at comparing classification performance of three prominently used classifiers, SVM, Naïve Bayes, and k-Nearest Neighbors in the area of text classification. Lastly, it targets to compare metrics. Besides accuracy, to understand the consequence of optimizing feature space using nature-inspired algorithm. Standard text classification dataset, Reuters-21578, was used, and the classification accuracies reached 95.07%, 92.23%, 87.37% for SVM, Naïve Bayes, and k-Nearest Neighbors, respectively. Besides accuracy, precision, recall, and F-measure were the performance metrics. Considering the encouraging results achieved using the ABC algorithm, this method seems promising for other applications of text classification.
Hardware Implementation Aspects of a Syndrome-based Neural Network Decoder for BCH Codes

Deep-Learning-based Decoders have been recently introduced for use with short-length codes. They have been found to act as a Soft-Decision-Decoding method achieving near Maximum-Likelihood error correcting capability. However, Deep-Learning decoding methods are hard to implement as they normally require millions of operations for inference. In order for Deep-Learning decoding to be a competitive candidate for practical applications, research effort is required to reduce the computational complexity and storage requirements of the Neural Networks involved. In this work, a structured flow is presented that significantly compresses a trained Syndrome-Based Neural Network Decoder by pruning up to 80% of the network's weights and quantizing them to 8-bit fixed-point representation, with no loss in its BER performance. The attained compressed Neural Network can then be used for inference, by designing specific hardware or by using a generic Deep-Learning hardware accelerator that exploits the compressed structure of the network. The deployment of the DL Decoder in an embedded application is showcased, using the AI Edge platform by Xilinx. To accomplish this, a simple method to obtain a computationally equivalent convolutional layer from a fully-connected one is introduced. Implementation results are provided for the compressed DL Decoder, regarding throughput rate and BER performance. To our knowledge, this is the first DL decoder in hardware reported.
RBM Based Joke Recommendation System and Joke Reader Segmentation

In the recent scenario, consumers are bared to a variety of information as well as commodities which leads to a variance in their choices. Recommender systems are a way to endure this challenge. An appropriate approach in recommending jokes on the basis of their preferences will be of substantial help in the future reference of the probable jokes. It is followed by segmentation of readers that has the potential to allow analysts to address each Joke-Reader in the most efficient way. This study aims at the development of a Joke Recommendation System based on Collaborative Filtering and Joke-Reader Segmentation based on the similarities in their preference patterns. A Bernoulli Restricted Boltzmann Machine (RBM) Model is implemented for constructing the Joke Recommender and k-means Clustering Model is deployed for achieving the Joke-Reader Segments. Considering the recommendation operation altogether, it is observed that the Joke-Reader Segmentation is firmly associated with the recommended ratings.
Improving Scheduling for Irregular Applications with Logarithmic Radix Binning

Effective scheduling and load balancing of applications on massively multi-threading systems remains challenging despite decades of research, especially for irregular and data dependent problems where the execution control path is unknown until run-time. One of the most widely used load-balancing schemes used for data dependent problems is a parallel prefix sum (PPS) array over the expected amount of work per task, followed by a partitioning of tasks to threads. While sufficient for many systems, it is not ideal for massively multithreaded systems with SIMD/SIMT execution, such as GPUs. More fine-grained load-balancing is needed to effectively utilize SIMD/SIMT units. In this paper we introduce Logarithmic Radix Binning (LRB) as a more suitable alternative to parallel prefix summation for load-balancing on such systems. We show that LRB has better scalability than PPS for high thread counts on Intel's Knight's Landing processor and comparable scalability on NVIDIA Volta GPUs. On the application side, we show how LRB improves the performance of PageRank up to 1.75X using the branch-avoiding model. We also show how to better load-balance segmented sort and improve performance on the GPU.
DTW-KNN Implementation for Touch-based Authentication System

Touch dynamic is a new biological recognition method based on one's touch pattern. However, less works have been done to explore the use of motion sensors embedded in smart phone for touch dynamic to enhance the password authentication. In this paper, we propose a touch-based authentication system to enhance the protection level of traditional authentication mechanism by adding touch pattern into the password mechanism. The proposed system leverages only three motion sensors (accelerometer, magnetometer and gyroscope) to monitor users' touch behavior. After performing pre-processing and a series of in-depth analysis on these sensor data, some touch features are extracted for users to characterize their unique touch patterns. Then the K-Nearest Neighbor (KNN) with Dynamic Time Warping (DTW) distance is employed to train the model for authentication. The experimental results show that the proposed system can achieve a lowest average false acceptance rate (FAR) of 4.85% and false rejection rate (FRR) of 4.98% with 1200+ data.
A Consolidated Approach for Design Pattern Recommendation

Software design patterns are standard solutions to common problems found in software development and design. Among many other benefits, they enable the creation of reusable, extensible and easy-to-maintain software systems. However, the complexity of comprehending design patterns hinders software designer's ability to adapt software design patterns in software design and development. For novice developers, choosing the most appropriate pattern for a given design context and situation becomes a challenging task. For this reason, a design pattern recommendation system can be of great help, especially for novice designers and developers, to instantiate the right pattern in their design problems. This paper proposes a consolidated approach and presents a tool which combines text-based, case-based and question-based recommendations to suggest an adequate pattern to a design problem. The effectiveness of the tool has been tested on several scenarios used in a design pattern course. Our preliminary evaluation shows that in majority of the cases, the correct design pattern is placed in the top three.
Evaluating Session-Based Recommendation Approaches on Datasets from Different Domains

Recommending relevant items of interest for user is the main purpose of recommendation system based on long-term user profiles. However, personal data privacy is becoming a big challenge recently. Thus, recommendation system needs to reduce the dependence on user profiles while still keeping high accuracy on recommendation. Session-based recommendation is a recent proposed approach for recommendation system to overcome the issue of user profiles dependency. The relevance of problem is quite high and has triggered interest among researchers in observing activities of users. It increased a number of proposals for session-based recommendation algorithms that aiming to make prediction of next actions. In this paper, we would like to compare the performance of such algorithms by using various datasets and evaluation metrics. The most recent deep learning approach named GRU4REC [1] and simpler methods based are included in our comparison. Six real-world datasets from three different domains are included in our experiment. Our experiments reveal that in case of numerous unpopular items dataset, GRU4REC’s performance is low. However, its performance is significantly increased after applying our proposed sampling method. Therefore, our obtained results suggested that there is still room for improving deep learning session-based recommendation algorithms.