Reviewerly/exHarmony huggingface dataset:
Recommender Performance on Collaborative Filtering using Latent Topics under Several Sparsity Levels

Collaborative filtering (CF) is one of the popular methods for selecting contents suited to user’s preference. CF represents user’s preference as ratings of matrix form. In real systems and services, the percentage of missing ratings (called sparsity) is usually high. Matrix factorization (MF) is an efficient method to estimate missing ratings from a small number of ratings. There is, however, a limit to how well MF can deal with missing ratings. Collaborative topic regression (CTR) is a pioneer model, which uses not only ratings but also document information regarding items and users. CTR is quite effective in extremely high sparsity situations, but its performance may be inferior to that of MF in relatively low sparsity situations. We assume that it is important to select words in the documents based on document frequency according to sparsity. And CTR has several hyperparameters that adjust the influence strength of the ratings and the latent topics extracted from the documents. In this paper, we investigate the impact of the combination of words used in latent topic extraction and hyperparameters under several sparsity levels. These elements are likely to be closely related to each other; hence, it is important to clarify their relationships to leverage CTR. As a result of our experiments, we summarize guidelines for word selection and hyperparameter settings for leveraging CTR according to sparsity.
HGC: Hybrid Gradient Compression in Distributed Deep Learning

Distributed stochastic gradient descent (SGD) algorithms are widely deployed in training large-scale deep learning models, while the communication overhead among workers becomes the new system bottleneck. Recently, two major categories of gradient compression techniques were proposed, including gradient quantization and sparsification. At best, the gradient quantization technique can obtain a compression ratio of 32, with little impact on model convergence accuracy. The gradient sparse technique can achieve a much higher compression ratio with some loss of model accuracy. To obtain a higher communication compression ratio with the minimum model accuracy loss, we proposed a mixed compression strategy named Hybrid Gradient Compression (HGC), which combines the merits of both quantization and sparsification. We validated the efficiency of our Hybrid Gradient Compression method by testing some complex models with millions of parameters (e.g., ResNet, VGG, LSTM, etc.) on the datasets including CIFAR-10, CIFAR-100 and Penn TreeBank on a GPU cluster. According to our tests, HGC can achieve a much higher gradient compression ratio at the cost of a small accuracy loss.
HNST: Hybrid Neural State Tracker for High Speed Tracking

Object tracking is a fundamental problem in perception, which is of great importance for intelligent robots. Recent years have witnessed breakthroughs of deep neural networks (DNN) in intelligent perception with a remarkable improvement on tracking accuracy. However, the huge computing cost and high latency restrict their applications in embedded robotic platform. In contrast, traditional correlation filters-based trackers use handcraft low-level features with higher tracking speed and lighter computational overhead. Both methods have their own advantages. Integrating them may provide a complementary strategy to achieve flexibility between speed and accuracy, which promises to handle more complex scenarios. This work proposes a hybrid neural state tracker (HNST) that combines DNN-based detection and Kernelized Correlation Filter (KCF) tracking using a policy to determine when to activate detection and whether to update the template of tracking target. Furthermore, we develop a similarity discrimination and establish a hybrid neural state machine for decision-making. Through comprehensive evaluation of the speed and accuracy of multiple standard video datasets, our approach improves more than 70% on precision and 95% on AUC with real-time tracking speed (~100 FPS) on CPU over the KCF tracker baseline, indicating the great potential of HNST in high-speed tracking.
Virtual Assistant for Forensics Recovery of IoT Devices

The rapid expansion and diversity of technology throughout society have impacted the growing knowledge gap in conducting analysis on IoT devices. The IoT digital forensic field lacks the necessary tools and guidance to perform digital forensics on these devices. This is mainly attributed to their level of complexity and heterogeneity that is abundant within IoT devices-making the use of a JTAG technique one of the only ways to acquire information stored on an IoT device effectively. Nonetheless, utilizing a JTAG technique can be challenging, especially when having multiple devices with each possibly having its own configuration. To alleviate these issues within the field, we propose the development of an Internet of Things - Forensics Recovery Assistant (IoT-FRA). The IoT-FRA will offer the capabilities of an expert system to assist inexperienced users in performing forensics recovery of IoT devices through a JTAG technique and analysis on the device's capabilities to develop an organized method that will prioritize IoT devices to be analyzed.
User-Centric Item Characteristics for Modeling Users and Improving Recommendations

The PhD research presented in this paper discusses the idea of using user-centric item characteristics (UCIC) such as the eudaimonic/hedonic quality of multimedia items for achieving a better performance of the recommender systems. UCIC is the characteristics of the item that has its root in the way users perceive the item. Different users have different perceptions and therefore UCIC is a distribution of these perceptions of the item characteristic. For example, movies have a different value of UCIC concerning the induced emotion in users. Therefore, the quality of induced emotion is the characteristic of the item that its value is changed based on how the item is triggering different emotional reactions in users. One of our objectives is to predict the value of UCIC for the items. One possibility of describing UCIC can be by predicting mean and standard variation of perception of users of the specific item characteristic. Another objective is to use the variance in perception of the item characteristic in different users to personalize the recommendations based on the predicted score of the perceived item characteristic for different users. The thesis is composed of three original scientific contributions: (i) devise a method for labeling items with UCIC value and in particular the UCIC of hedonic/eudaimonic quality, (ii) devise models of user behavior based on the perception of item characteristic and in particular eudaimonic/hedonic perception, (iii) devise recommender systems with the incorporation of UCIC concepts for generating more accurate recommendations.
Dynamic maintenance model for high average-utility pattern mining with deletion operation

Abstract The high average-utility itemset mining (HAUIM) was established to provide a fair measure instead of genetic high-utility itemset mining (HUIM) for revealing the satisfied and interesting patterns. In practical applications, the database is dynamically changed when insertion/deletion operations are performed on databases. Several works were designed to handle the insertion process but fewer studies focused on processing the deletion process for knowledge maintenance. In this paper, we then develop a PRE-HAUI-DEL algorithm that utilizes the pre-large concept on HAUIM for handling transaction deletion in the dynamic databases. The pre-large concept is served as the buffer on HAUIM that reduces the number of database scans while the database is updated particularly in transaction deletion. Two upper-bound values are also established here to reduce the unpromising candidates early which can speed up the computational cost. From the experimental results, the designed PRE-HAUI-DEL algorithm is well performed compared to the Apriori-like model in terms of runtime, memory, and scalability in dynamic databases.
Data Confidentiality and Integrity in Cloud Storage Environment

Cloud services have seen a considerable increase in recent times, as the cloud allows users to outsource their data and IT resources. Storing and backing up data in the cloud have become increasingly popular. However, data security is an increasing concern. With the number of attacks on the cloud and data leakage, users are worried about their data security in the cloud. Several works dealt with data security in the cloud, but most of these solutions largely depend on providers. They do not provide users the control of the security of their data. To deal with data security concerns, we proposed a solution called Encrypt cloud that allows users to encrypt and check their files’ integrity before uploading and storing it the cloud storage. We used the encryption technique (AES 256) to ensure the confidentiality of the data. To verify the data integrity, we used the SHA 256 hash function with a two-level integrity check. Performance analysis of the AES encryption algorithm was performed to compare execution time and memory usage during the encryption and decryption process. It should be noted that decryption consumes more resources than encryption.Djeki, Essohanam Bondiombouy, Carlyna Degila, Jules
Neuronal Unit of Thoughts (NUTs); A Probabilistic Formalism for Higher-Order Cognition

A probabilistic graphical model, Neuronal Unit of Thoughts (NUTs), is proposed in this paper that offers a formalism for the integration of lower-level cognitions.Zakaria, Nordin Nodes or neurons in NUTs represent sensory data or mental concepts or actions, and edges the causal relation between them. A node affects a change in the Action Potential (AP) of its child node, triggering a value change once the AP reaches a fuzzy threshold. Multiple NUTs may be crossed together producing a novel NUTs. The transition time in a NUTs, in response to a ‘surprise,’ is characterized, and the formalism is evaluated in the context of a non-trivial application: Autonomous Driving with imperfect sensors.
Decision support system analysis for selecting a baby cream product with Preference Selection Index (PSI) Baby Sensitive Skin Under 3 Year

Abstract The aim of this study was to examine the technology for decision support in the selection of baby cream products for sensitive skin in children under three years of age. Babies’ skin sensitivity should be addressed in order to maintain the baby’s skin health, but parents should be more careful with product selection in the number of products to treat sensitive skin in babies on the market. Data sources obtained through random observations, interviews and questionnaires (parents who have babies). The solution provided is the Preference Selection Index (PSI) method, which is part of the support system for decision making. Five criteria are used as parameters when selecting sensitive skin product for children, namely: price (K1), type of skin (K2), feeling of brand (K3), interests (K4) and packaging (K5). While the alternatives used based on the interview results are cussons (alt1), johnson’s (alt2), zwitsal (alt3), pigeon (alt4), and sleek (alt5). The results of the Preference Selection Index could be used by analysis of the first recommendation of product alt1 (cussons), a value of 0.978584. The second recommendation of product alt3 (zwitsal) is a value of 0.976655. Research results are hoped to provide a wider awareness for babies when selecting sensitive skin products for three years old.
Research on the Application of Intelligent Detection Technology in Business Hall System

The existing offline business halls are unable to accurately identify customers for accurate marketing, and lack effective detection mechanisms for some abnormal behaviors, and cannot assist security personnel such as security personnel in the security work. The successful application of face recognition technology in the payment field makes it feasible to apply it in offline systems of business halls, and the development of behavior detection technology based on computer vision also makes us actively explore its application in offline scenarios of business halls. Therefore, we researched the application of intelligent detection technology including dynamic face recognition technology and behavior detection technology in offline systems of business halls, designed and implemented a prototype system based on this technology, and tested it in real scenarios. The experimental results show that it is feasible to apply the intelligent detection technology to the offline service system of the business hall.
Convolutional neural network for sapphire ingots defect detection and classification

Optical homogeneity detection can be brought into play for quality control of sapphire substrate, sapphire mobile phone screen, and sapphire window material. By detecting the optical homogeneity of the sapphire crystal, the crystal lattice distortion inside the crystal can be evaluated non-destructively and intuitively. However, due to the small birefringence coefficient of sapphire crystals, it is difficult to detect the optical homogeneity of sapphire ingots through the conventional setup. In this work, we detected the optical homogeneity of the sapphire ingot using the Focused Polarization Interference (FPI) method also known as Conoscopic Birefringence Interferometry. The main principle of this technique is that the stress induced by the lattice distortion may cause the variation of the crystal birefringence which leads to the formation of the polarization interference patterns. To quantitatively evaluate the pattern deformation, we used pattern recognition technique. However, the main issue in pattern recognition and notably images classification problems is image feature extraction and image encoding. To solve the above problem, here we showed and compared different approaches: Bag of features, ResNet-50, AlexNet, VGG16, and GoogleNet pre-trained convolutional neural network (CNN) models. The reliability, practicality, and verification of the proposed models, in this work, is according to the detection measures use accuracy, confusion matrix, and Area Under the Curve (AUC). The Bag of features model achieved the classification accuracy of 94.3%, while the ResNet-50 pre-trained convolutional neural network achieved 97.6%. The findings show that ResNet-50 is capable of correctly classifying the samples from the datasets with high true fraction value and low false positive followed by AlexNet as compare to other models like VGG16 and GoogleNet. Finally, the proposed models have been in contrast with the well-known chemical etching method. The results showed that the time required to etch one sample using chemical etching is about 10 h, while by using the proposed models, we can detect and classify the whole dataset within 80 min and 27 s. The proposed pre-trained convolutional neural network models based on sapphire crystal defects classification would potentially serve as a new tool for predicting the quality inspection-based on crystal growth technology. • Optical homogeneity detection can be used for quality control of sapphire substrate. • The small birefringence coefficient of sapphire crystals. • It is difficult to detect the optical homogeneity of sapphire ingots through the conventional setup. • Focused Polarization Interference (FPI) method, so-called Conoscopic Birefringence Interferometry is used. • Pre-trained convolutional neural network (CNN) models evaluate and classify the patterns deformations.
Event driven sensor fusion

Multi sensor fusion has long been of interest in target detection and tracking. Different sensors are capable of observing different characteristics about a target, hence, providing additional information toward determining a target’s identity. If used constructively, any additional information should have a positive impact on the performance of the system. In this paper, we consider such a scenario and present a principled approach toward ensuring constructive combination of the various sensors. We look at Decision Level Sensor Fusion under a different light wherein each sensor is said to make a decision on occurrence of certain events that it is capable of observing rather than making a decision on whether a certain target is present. These events are formalized to each sensor according to its potentially extracted attributes to define targets. The proposed technique also explores the extent of dependence between features/events being observed by the sensors, and hence generates more informed probability distributions over the events. In our case, we will study two different datasets. The first one, combines a Radar sensor with an optical sensor for detection of space debris, while the second one combines a seismic sensor with an acoustic sensor in order to detect human and vehicular targets in a field of interest. Provided some additional information about the features of the object, this fusion technique can outperform other existing decision level fusion approaches that may not take into account the relationship between different features. Furthermore, this paper also addresses the issue of coping with damaged sensors when using the model, by learning a hidden space between sensor modalities which can be exploited to safeguard detection performance.
Two-Level Scheduling Technology For Heterogeneous Clusters Using Analytical Hierarchy Processes

The cluster system presents multi-level and complex heterogeneous features with GPUs computing. Job scheduling technology determines computing resource utilization and throughput of the cluster system. On the basis of heterogeneous cluster system, the two-level scheduling technology is proposed in this paper. In the first level, a job priority scheduling algorithm based on user value is designed at the cluster level using analytical hierarchy processes. The second level of scheduling is to act on the computing nodes that contain GPUs, and design a scheduling algorithm based on GPU sharing policy that manages GPU jobs in a more fine-grained manner. Through experiments, it shows that the job priority scheduling algorithm based on user value can ensure that jobs with high user value are executed first, and fairness is guaranteed. The scheduling algorithm based on GPU sharing technology can significantly improve the utilization of GPU and video memory, reducing the average waiting time of jobs by 55.54% and the average turnaround time of jobs by 28.83%.
Steady Hands - An Evaluation on the Use of Hand Tracking in Virtual Reality Training in Nursing

Our paper describes the setup and the results of an empirical study on comparison of usability and performance while using two different input methods for training of nursing procedures in virtual reality: hand-held controller input and markerless hand tracking. As part of a research project funded by the German Ministry of Health, our study features the aseptic wound cleansing with tweezers and swabs. Our results indicate that the input method could have no significant impact on performance, but ratings on usability show a trend in favor of controller input.
Driver Attention Assistance by Pedestrian/Cyclist Distance Estimation from a Single RGB Image: A CNN-based Semantic Segmentation Approach

Automotive companies are investing a relevant amount of resources for designing autonomous driving systems, driver assistance technologies, and systems for assessing the driver's attention. In this context, two important applications consist in processing images of the surrounding environment to respectively separate the different objects in the scene (semantic segmentation) and to estimate their distances. In both applications, methods based on Deep Learning (DL) and Convolutional Neural Networks (CNN) are being increasingly used, considering LiDAR scans or RGB images. However, LiDAR scanners require dedicated sensors, high costs, and post-processing algorithms to estimate a dense depth map or a three-dimensional representation of the surrounding environment. Moreover, current methods in the literature based on RGB images do not consider the combination of semantic segmentation and depth estimation for assessing the distances of specific objects in the scene. In this paper, we propose the first method in the literature able to estimate the distances of pedestrians/cyclists from the vehicle by using only an RGB image and CNNs, without the need for any LiDAR scanner or any device designed for the three-dimensional reconstruction of the scene. We evaluated our approach on a public dataset of RGB images captured in an automotive scenario, with results confirming the feasibility of the proposed method.
Enterprise Electricity Consumption Forecasting Method Based on Federated Learning

With the development of intelligence and data construction of electric power system in China, a large amount of data accumulated by electric power enterprises provide a data base for the fine prediction of electric power consumption. High-precision power forecasting model has far-reaching influence on urban planning and construction, smart grid development and so on. The problem of power big data privacy leads to the phenomenon of Power Data Island, which leads to the shortage of the accuracy of the current power forecasting methods. Federal learning breaks the long-standing Data Island phenomenon in power industry, and satisfies the privacy and security requirements of power data in application. This paper presents a high-precision federal learning method for enterprise electricity consumption forecasting, which takes into account weather conditions and enterprise tax information. Based on the FATE platform, this method combines the enterprise electricity consumption data with the tax data, and uses the third party coordinator to conduct encryption training, then constructs the enterprise electricity consumption forecast model based on the SecureBoost algorithm. The simulation results show that the federated learning method can effectively improve the accuracy of enterprise electricity consumption forecasting model.
Information and communications technologies and multimedia content as motivational factors for new generations of students

This paper deals with the theoretical analysis of the principles of efficient use of information and communications technologies (ICT) in teaching and learning, which can contribute to the advancement of the teaching process and motivation of students/pupils in the achieving the planned activities. Apart from the theoretical analysis, the results of empirical research are also presented in the paper. They tested and practically confirmed that there is a significant influence of the application of ICT and enriching the teaching material with multimedia content and also the effects in contemporary teaching of information technology. The primary intention of this paper is to point out the importance of the introduction of ICT to the teaching process with the aim of a significant improvement of students'/pupils' learning through the increase of their motivation and efficiency in learning, and at the same time the improvement in the performance of the educational system.
Making the Future Visible: Reflections Twenty Years on

A commentary after twenty years by the author of “Making the Future Visible,” written in 1999 and previously only circulated informally. Expanding on the idea of a “psychological landscape of the future” proposed in the 1999 paper, the author recalls the background and motivation for the original paper, and proposes that there are three psychologically distinct types of future—Cognitive, Affective, and Conative—each of which should be developed in any futures work intended to lead to strategic action. The identification and development of these three types of future also provides a means of resolving the so-called “scenarios to strategy problem.”