Miracle-dz/UniFAR-FTUs huggingface dataset:
How Sensitive is Recommendation Systems' Offline Evaluation to Popularity?

Datasets used for the offline evaluation of recommender systems are collected through user interactions with an already deployed recommender system. However, such datasets can be subject to different types of biases including a system’s popularity bias. In this paper, we focus on assessing the influence of popularity on the offline evaluation of recommendation systems. Our insights from a deeper analysis based on popularity-stratified sampling reveal that the current offline evaluation of recommendation systems are sensitive to popular items, raising questions about conclusions driven from the offline comparison of recommendation models.
T-LINC Architecture with Digital Combination and Mismatch Correction in the Receiver

In this paper a novel outphasing amplification architecture, Transmitted Linear Amplification with Nonlinear Components (T-LINC), is proposed and evaluated. Different from conventional LINC techniques that combine the outphased signals before transmission, the proposed architecture transmits both outphased signals and implements the signal combination in the base-band domain at the receiver side. By digitally combining the signals in the receiver, it is easier to correct gain or phase mismatch existing between the two outphased signals, without adding any extra complexity in the transmitter. Additionally, a power combiner is no longer required in the transmitter. Simulations show that the proposed technique is able to correct transmitter gain and phase imbalance and achieve an ACPR level of -42 dBc and EVM below 2.5 % for 16-QAM signals.
HomeChain: A Blockchain-Based Secure Mutual Authentication System for Smart Homes

Increasingly, governments around the world, particularly in technologically advanced countries, are exploring or implementing smart homes, or the related smart facilities for the benefits of the society. The capability to remotely access and control Internet of Things (IoT) devices (e.g., capturing of images, audios, and other information) is convenient but risky, as vulnerable devices can be exploited to conduct surveillance or perform other nefarious activities on the users and organizations. This highlights the necessity of designing a secure and efficient remote user authentication solution. Most of the existing solutions for this problem are generally based on a single-server architecture, which has limitations in terms of privacy and anonymity (leading to users’ daily activities being predicted), and integrity and confidentiality (resulting in an unreliable behavior auditing). While blockchain-based solutions may mitigate these issues, they still face some critical challenges (e.g., providing regulation of behaviors and privacy protection of access policy). Motivated by these facts, in this article, we construct a novel secure mutual authentication system, which can be applied in smart homes and other applications. Specifically, the proposed approach integrates blockchain, group signature, and message authentication code to provide reliable auditing of users’ access history, anonymously authenticate group members, and efficiently authenticate home gateway, respectively. We also prove the security and privacy requirements, including anonymity, traceability, and confidentiality, that the proposed system satisfies, with an implementation and evaluation to demonstrate its practicality.
A new congestion-aware routing algorithm in network-on-chip: 2D and 3D comparison

Network on-Chip (NoC) is scalable, flexible, modular communication structure for Multi/Many-core architectures. It allows simpler interconnect models with higher bandwidth compared to bus-based architectures. When a message is exchanged inside an NoC, it passes through several routers. When the communication load is high, contention and congestion may appear in central routers, and lead to an increased latency and power consumption. 3D NoC has been proposed as a solution to improve the performances, especially: throughput, latency, and energy efficiency. In this paper, we first report the results of theoretical and practical comparison between 2D and 3D mesh Network-on-Chip topology. We focus on communication supports, especially routing, in the goal of reducing latency and avoiding congestion. Further, we propose a novel exhaustive routing algorithm to avoid and reduce congestion in 2D and 3D NoC. Also, we discuss the implementation of the proposed algorithm in both topologies and the trade-off between their overheads and their performances. We demonstrate the effectiveness of our algorithm using a wide set of synthetic experiments using NOXIM simulator.
Alternative prediction methods in the stock exchanges of Thailand

This paper is conducted to substantially do the two alternatives between the traditional statistical methods such as ARMA and HW models, and artificial intelligence (AI) contains KNN and ELM, respectively. To scope the main object of the paper, SET indexes are collected as the main financial variable, which are 5,472 daily observed days during 9 September 1997 to 11 June 2018. Technically, the cross-entropy (CE) analysis, MSE and RMSE calculations are computationally employed to clarify the resolution of the two computations. The empirical results state that the AI prediction can be a substitution replacing the traditional estimations, and this can strongly confirm that machine learning (ML) algorithms are continuously interested, and they are recently becoming a powerful tool for modern econometric forecasting.
Privacy Preservation in Data Centric Environment using K-Anonymity

: Due to the expansion in healthcare information systems, the availability of therapeutic reports has benefitted human administration organizations to inquire about work. In numerous cases, these are creating concerns while sharing helpful records. Protection methods for an unstructured helpful substance highlight on acknowledgment and ejection of individual identifiers from the substance, which may be missing for shielding security and data utility. Considering sensitive social protections information, protection security could be a noteworthy concern, when patients' restorative administration's data is utilized for investigation purposes. In this article, we have compared two methods K-anonymity and the inbuilt simulator ARX tool to ensure who can provide higher privacy on medical databases in data-intensive environments. The outcomes declare that the proposed approach has superior execution than those of the related works concerning variables such as information protection with k-anonymity.
ATM: An Attentive Translation Model for Next-Item Recommendation

Predicting what items a user will consume in the next time (i.e., next-item recommendation) is a crucial task for recommender systems. While the factorization method is a popular choice in recommendation, several recent efforts have shown that the inner product does not satisfy the triangle inequality, which may hurt the model's generalization ability. TransRec is a promising method to overcome this issue, which learns a distance metric to predict the strength of user–item interactions. Nevertheless, such method only uses the latest consumed item to model a user's short-term preference, which is insufficient for modeling fidelity. In this article, we propose a simple yet effective method named attentive translation model, to explicitly exploit high-order sequential information for next-item recommendation. Specifically, we construct a user-specific translation vector by accounting for multiple recent items, which encode more information about a user's short-term preference than the latest item. To aggregate multiple items into one representation, we devise a position-aware attention mechanism, learning different weights on items at different orders in a personalized way. Extensive experiments on four real-world datasets show that our method significantly outperforms several state-of-the-art methods.
Peer-assisted Data Offloading and Distributed Channel Selection for Mobile Cloud Computing

The rapid growth of intelligent applications makes it increasingly desirable to offload mobile terminals (MTs) traffic to data centers for smart computing. In this paper, we propose a distributed channel selection scheme with cooperative offloading, in which MTs help each other in each channel to offload the data for the purpose of saving energy. Particularly, MTs that are in the same channel and close to each other in a certain geographical area can form into cooperation cluster to perform local computing, then, offload the data to a cloud server in a certain wireless channel. In this way, the energy consumption for data offloading can be reduced. We adopt game theory to formulate the channel selection problem as a non-cooperation game. We then prove that the formulated game is a potential game, and, thus, guarantee the existence of Nash equilibrium. A Markov approximation approach is then applied to design a distributed channel selection algorithm so that each MT can self-organize into stability without information exchange across the whole network. Analytical and numerical results show that the distributed algorithm is efficient in comparison to the centralized optimization solutions.
Silhouette Extraction for Shadow Volumes Using Potentially Visible Sets

In this paper, we present a novel approach for accelerated silhouette computation based on potentially visible sets stored in the octree acceleration structure. The scene space, where the light source can appear, is subdivided into voxels. The octree voxels contain two precomputed sets of edges that potentially or always belong to the silhouette. We also propose a novel method of octree compression for reduction of the memory footprint of the resulting acceleration structure. Using our novel technique we were able to considerably decrease the computational complexity of ﬁnding the silhouette and reduce its sensitivity to the number of edges.
Towards smarter SDN switches: revisiting the balance of intelligence in SDN networks

Software Defined Networks (SDNs) represent a new model for building networks, in which the control plane is separated from the forwarding plane, allowing for centralised, fine grained control of traffic in the network. The benefits of SDN range widely from reducing operational costs of networks to providing better Quality of Service guarantees to its users. Its application has been shown to increase the efficiency of large networks such as data centers and improve security through Denial of Service mitigation systems and other traffic monitoring efforts. While SDN has been shown to be highly beneficial, some of its core features (e.g separation of control and data planes and limited memory) allow malicious users to carry out Denial of Service (DoS) attacks against the network, reducing its availability and performance. Denial of Service attacks are explicit attempts to prevent legitimate users from accessing a service or resource. Such attacks can take many forms but are almost always costly to its victims, both financially and reputationally. SDN applications have been developed to mitigate some forms of DoS attacks aimed at traditional networks however, its intrinsic properties facilitate new attacks. We investigate in this thesis, the opportunity for such Denial of Service attacks in more recent versions of SDN and extensively evaluate its effect on a legitimate user’s throughput. In light of the potential for such DoS attacks which specifically target the SDN infrastructure (controller, switch flow table etc), we propose that increasing the intelligence of SDN switches can increase the resilience of the SDN network by preventing attack traffic from entering the network at its source. To demonstrate this, we put forward in this thesis, designs for an intelligent SDN Switch and implement two additional functionalities towards realising this design into a software version of the SDN switch. These modules allow the switch to efficiently handle high control plane loads, both malicious and legitimate, to ensure the network continues to provide good service even under such circumstances. Evaluation of these modules indicate they effectively preserve the performance of the network under under high control plane loads far better than unmodified switches, with no notable drawbacks.
Deep Regressor Stacking for Air Ticket Prices Prediction

Purchasing air tickets by the lowest price is a challenging task for consumers since the prices might fluctuate over time influenced by several factors. In order to support users’ decision, some price prediction techniques have been developed. Considering that this problem could be solved by multi-target approaches from Machine Learning, this work proposes a novel method looking forward to obtaining an improvement in air ticket prices prediction. The method, called Deep Regressor Stacking (DRS), applies a naive deep learning methodology to reach more accurate predictions. To evaluate the contribution of the DRS, it was compared with the competence of the single-target regression and two state-of-the-art multi-target regressions (Stacked Single Target and Ensemble of Regressor Chains). All four approaches were performed based on Random Forest and Support Vector Machine algorithms over two real-life airfares datasets. After results, it was concluded DRS outperformed the other three methods, being the most indicated (most predictive) to assist air passengers in the prediction of flight ticket price.
Survey of Hand Gesture Recognition Systems

Recognition of human gestures is an important subject in computer science, especially in computer vision and sign language. It aims at interpreting human gestures by mathematical models. Gestures originate from different parts of the human body, but the most common ones emerge from the hand or face. Gestures recognition is a method to enable computers to understand and interpret the language of the human body in the best way possible and to build a bridge between humans and machines from uncomplicated user interfaces that have been command-line to graphical user interfaces GUI, so far they limit the common input on the keyboard and mouse. In this paper, we have reviewed and analyzed several methods of recognition for hand gestures including, Artificial Neural Networks (ANN), Histogram based feature, a Fuzzy Clustering algorithm, Hidden Markov Model (HMM), Condensation algorithm and Finite-State Machine (FSM).
Impact of Hovering Inaccuracy on UAV-Aided RFET

In this letter, the impact of hovering inaccuracy on the performance of unmanned aerial vehicle (UAV)-aided RF energy transfer (RFET) is investigated. Hovering inaccuracy is measured by localization and orientation mismatch of UAV while it hovers above a sensor of interest. An analytical framework is presented that captures these mismatches, and its impact on the performance of UAV-aided RFET is studied. To evaluate the performance, a metric called mismatch index is defined as the ratio of loss in harvested power due to mismatch and harvested power without mismatch. A closed-form expression of the distribution of mismatch index is obtained. A UAV-based experimental setup is developed to collect the data of hovering inaccuracy parameters, and the performance is investigated for three antenna types with different radiation patterns. It is observed that, the optimal deployment height of UAV increases as the antenna becomes more directional.
Object detection in satellite images based on human cooperative semi-self-training

Convolutional neural networks (CNNs) can effectively detect objects in satellite images. They use a large number of labeled samples for training appropriate feature extractor. However, creating labels requires significant concentration and increases the workload of users, because satellite images cover quite large areas relative to the scale of the objects. We propose a human cooperative semi-self-training (SST) framework to reduce users' burden for training a CNN. The SST cooperatively collects training samples from unlabeled samples by repeating the following two phases: self-training and user intervention. The self-training automatically constructs the training dataset and trains the CNN, while the user intervention expands the number of accurately labeled samples. Notably, the SST requests users to label samples only if the automatic training stagnates. We improve the self-training phase to reduce the frequency of user intervention through the following two modules: intelligent dataset construction and pre-training. The intelligent dataset construction module automatically collects only effective training samples based on automatic labeling and evaluation of the collected samples utilizing tentatively trained CNN, while the pre-training module facilitates training of feature extractor in the CNN by allowing the CNN to learn handcrafted image features. Introducing the pre-training module into the dataset construction can effectively improve the self-training because the quality of the automatic labeling based on the pre-trained CNN can be enhanced. The experimental results demonstrated that the CNN trained by the proposed method yielded 92% of performance with only 2.6% of the labeled samples relative to the model trained with the complete dataset.
A Review of Deep Learning based Object Detection Techniques

: The aim of object detection is to recognize instances of semantic objects belonging to a certain class within an image, accurately predict the location of the object in the image, and then to classify it according to a corresponding class label. In the past few years, there have been a lot of new and constantly improving models proposed for this task. Deep Learning based approaches, especially those involving Deep Convolutional Neural Networks, have been the most popular for good reason. In this paper, we aim to review the latest approaches in tackling the problem of object detection, while understanding the drawbacks of each approach as well as the improvements observed with the subsequent models. We then compare the results obtained by each model on popular datasets. In the last part, we aim to offer ideas for future work, scope for improvement and potential application areas.
Spectro-Temporal Representation of Speech for Intelligibility Assessment of Dysarthria

Recently, spectro-temporal representation of speech has been used in many fields of speech processing. Owing to this, we explore the use of spectro-temporal representation for speech intelligibility assessment especially for dysarthric speech. In this work, we investigate the use of spectro-temporal representations to evaluate intelligibility levels using artificial neural network (ANN) and convolutional neural network (CNN). Standard American English dysarthric databases namely Universal Access and TORGO are used for evaluation. Performance of CNN classifier is superior to ANN as it is an advanced classifier. Further, use of Time-Frequency CNN configuration proved to capture spectro-temporal variations together resulting in an improved performance compared to either Time-CNN or Frequency-CNN configurations which capture either temporal or spectral variations respectively.
Adult Learners’ Participation In A Blended Learning Environment: A Case Study On Imposed Pace Learning

Distance education provides individuals with extensive learning opportunities. Learners of all ages enjoy the opportunity to become well-equipped individuals as many educational institutions offer flexible and blended programs that can aid in personal and professional development. The current study focuses on adult learners and aims to investigate their opinions of an imposed-pace program design and this design’s reflection on their participation. This study employed a qualitative research design and interviewed 30 individuals and found that mandatory attendance increased participants’ interaction with the content by eliminating other responsibilities and obligations as excuses for non-participation. Additional factors affecting participation included instructors’ voices in a narrated online course and integration activities that allowed for self-evaluation during the course. Virtual and face-to-face interactions were also found to be important factors. Session timing, communication abilities, technical issues, and effective communication were the other emergent findings in this study.
Community Detection Based on Symbiotic Organisms Search and Neighborhood Information

Modularity optimization methods based on nature-inspired metaheuristics are popular and competent for community detection in complex networks. However, on some real-world networks with complex and vague structures, most contemporary algorithms are difficult to obtain the global optimal partition. There are two key factors that are seriously affecting the global optimization capability: one is the convergence performance of the incorporated optimization strategy and the other is the sufficient and rational utilization of network topological information. In this article, a novel community detection method is proposed, named symbiotic organisms search community detection (SOSCD). The bio-inspired metaheuristic [symbiotic organisms search (SOS)] is discretized and utilized as the optimization strategy to improve global convergence performance of modularity optimization. Meanwhile, by utilizing neighborhood information of each node to guide community optimization, two different local search (LS) schemes are designed to intensify exploitation and, thus, assisting the global search, including the neighbor-based community modification (NCM) and the neighbor-based LS (NLS). Experimental results on both synthetic and real-world networks have validated the effectiveness and superiority of the proposed operations in SOSCD. Moreover, SOSCD can significantly improve the precision and stability of the identified optimal partition, comparing with many state-of-the-art modularity optimization algorithms.
Historical and impact analysis of API breaking changes

Change is a routine in software development. In the case of APIs provided by libraries and frameworks changes can be backward-incompatible, breaking contracts with client applications. In this dissertation, we perform two studies on API breaking changes. We assess (i) the frequency of breaking changes, (ii) their behavior over time, (iii) the impact on clients, (iv) the characteristics of libraries with high frequency of breaking changes, (v) the reasons why developers introduce them, and (vi) developers awareness on the risks associated to breaking changes. Our large-scale analysis on 317 real-world Java libraries, 9K releases, and 260K client applications shows that (i) 14.78% of the API changes break compatibility, (ii) their frequency increases over time, (iii) 2.54% of API clients are impacted, and (iv) systems with higher frequency of breaking changes are larger, more popular, and more active. We also survey API developers to reveal a list of five reasons why they break APIs.