Reviewerly/exHarmony huggingface dataset:
Two-Stage Checkpoint Based Security Monitoring and Fault Recovery Architecture for Embedded Processor

Nowadays, the secure program execution of embedded processor has attracted considerable research attention, since more and more code tampering attacks and transient faults are seriously affecting the security of embedded processors. The program monitoring and fault recovery strategies are not only closely related to the security of embedded devices, but also directly affect the performance of the processor. This paper presents a security monitoring and fault recovery architecture for run-time program execution, which takes regular backup copies of the two-stage checkpoint. In this framework, the integrity check technology based on the basic block (BB) is utilized to monitor the program execution in real-time, while the rollback operation is taken once the integrity check is failed. In addition, a Monitoring Cache (M-Cache) is built to buffer the reference data for integrity checking. Moreover, a recovery strategy mainly for three tampered positions (registers in processor, instructions in Cache, and codes in memory) is provided to ensure the smooth running of the embedded system. Finally, the open RISC processor is adopted to implement and verify the presented security architecture, which has been proved to be effective for program detection in the execution of tamper attack and quick recovery of the running environment as well as code.
Facial image super-resolution guided by adaptive geometric features

Abstract This paper addresses the traditional issue of restoring a high-resolution (HR) facial image from a low-resolution (LR) counterpart. Current state-of-the-art super-resolution (SR) methods commonly adopt the convolutional neural networks to learn a non-linear complex mapping between paired LR and HR images. They discriminate local patterns expressed by the neighboring pixels along the planar directions but ignore the intrinsic 3D proximity including the depth map. As a special case of general images, the face has limited geometric variations, which we believe that the relevant depth map can be learned and used to guide the face SR task. Motivated by it, we design a network including two branches: one for auxiliary depth map estimation and the other for the main SR task. Adaptive geometric features are further learned from the depth map and used to modulate the mid-level features of the SR branch. The whole network is implemented in an end-to-end trainable manner under the extra supervision of depth map. The supervisory depth map is either a paired one from RGB-D scans or a reconstructed one by a 3D prior model of faces. The experiments demonstrate the effectiveness of the proposed method and achieve improved performance over the state of the arts.
Use of Social Media Platforms in Promoting the Academic Library Services of City College of Angeles among Students

Social media has great impact of its use by college libraries; thus, the academic library of a city college in the Philippines has been considered in this study.SmartPLs was employed to predict the use of social media platforms in promoting the academic library services of the college.Descriptive method was used, and validated structured questionnaires were distributed to 174 student-respondents.Majority use their own social media accounts.They have an access to the academic library which has three main social media platforms: Facebook, Twitter, and Instagram.The factors they consider are purpose, library account contents, and relative advantages of social media.Findings revealed that these factors influence the promotion of academic library services through social media platforms.Active usage of library social media accounts should be encouraged to provide better promotion of the library, particularly its resources and services.
Imagined paralysis alters somatosensory evoked-potentials

Recent studies employing body illusions have shown that multisensory conflict can alter body representations and modulate low-level sensory processing. One defining feature of these body illusions is that they are sensory driven and thus passive on behalf of the participant. Thus, it remained to establish whether explicit alteration of own-body representations modulates low-level sensory processing. We investigated whether tibial nerve somatosensory-evoked potentials were modulated when participants imagined paralysis of their legs and arms. Imagined paralysis of the legs decreased P40 amplitude, but not imagined paralysis of the arms. These results show modulation of early somatosensory processing via explicit, top-down alteration to the internal representation of the body. Interestingly, P40 suppression positively correlated with bodily awareness scores whereas it negatively correlated with body dissociation scores. This suggests that the ability to actively alter own-body representation and its corresponding sensory processing depends upon dispositions to attend to and focus on bodily sensations.
Not All Explorations Are Equal: Harnessing Heterogeneous Profiling Cost for Efficient MLaaS Training

Machine-Learning-as-a-Service (MLaaS) enables practitioners and AI service providers to train and deploy ML models in the cloud using diverse and scalable compute resources. A common problem for MLaaS users is to choose from a variety of training deployment options, notably scale-up (using more capable instances) and scale-out (using more instances), subject to the budget limits and/or time constraints. State-of-the-art (SOTA) approaches employ analytical modeling for finding the optimal deployment strategy. However, they have limited applicability as they must be tailored to specific ML model architectures, training framework, and hardware. To quickly adapt to the fast evolving design of ML models and hardware infrastructure, we propose a new Bayesian Optimization (BO) based method HeterBO for exploring the optimal deployment of training jobs. Unlike the existing BO approaches for general applications, we consider the heterogeneous exploration cost and machine learning specific prior to significantly improve the search efficiency. This paper culminates in a fully automated MLaaS training Cloud Deployment system (MLCD) driven by the highly efficient HeterBO search method. We have extensively evaluated MLCD in AWS EC2, and the experimental results show that MLCD outperforms two SOTA baselines, conventional BO and CherryPick, by 3.1× and 2.34×, respectively.
Electronic Money and Payments as Means of Committing Crimes

The article deals with the main problems of criminal characteristics of electronic money and payments as instruments of crime. The article reveals the economic and legal nature of electronic money (e-money). The features of e-money and its relation to electronic payments are identified. It is proved that intensive development of the IT sector results in an increasing replacement of cash by cashless methods of payment, and e-money is required for making payments online. E-money is previously created in all cases where they are stored on an electronic device and are used as means of payment, therefore they significantly reduce the level of abuse against property. Criminals in the real estate leasing sector use it as a tool when they commit cybercrime - a new place that has been transformed into a powerful source using for crime. Particular attention in this article is paid to the criminal characteristics of electronic money as an instrument of crime. Electronic money has been updated to account for a significant number of offending characters, while these crimes operate in free currency from a non-supported market and move to electronic services, they are used, and may be joined by warehouse resources, which require a criminal law level. It also indicates that e-money is being received by its billing service, and then can be exploited by attackers to trick owners who have already participated in their work. Through this process, using e-money and electronic payment is almost legal and covers the concept of legality. It is disclosed that the main complexity in detecting malicious tools creates electronic money, which makes it difficult to track the traces of such crime, which uses its delays in cyber space. The author also reveals an accurate view of electronic money resources, such as what is more likely to be abused, which is an additional element of criminal execution qualification.
Customer brand engagement impact on brand satisfaction, loyalty, and trust in the online context. Egyptian Banking Sector

This study measures the impact of customer brand engagement (CBE) on brand satisfaction, loyalty, and trust through the online brand experience in the Egyptian banking sector based on the customers' perspective.Electronic questionnaires were developed to collect data.Data collected from 392 questionnaires was analyzed by means of Structural Equation Modelling (SEM).Findings indicated that CBE has a significant positive impact on brand satisfaction, trust, and loyalty respectively via the online brand experience.This study contributes to knowledge development by focusing on the importance of CBE and its consequences in the online service sector, which is still uncovered.Besides, this study offers guidelines to the service providers with valuable meanings for CBE to enhance brand's satisfaction, trust and loyalty via developing a unique online brand experience particularly in the Egyptian banking sector, which is witnessing now a new era of digital transformation.
Real-Time Twitter Corpus Labelling Using Automatic Clustering Approach

In this paper, we present a novel automatic labelling approach for the classification of large amount of unlabelled real-time twitter datasets for textual-based Twitter Sentiment Analysis.The tweets are labelled or classified as Positive, Negative or Neutral using the novel automatic approach.The proposed approach applies an unsupervised clustering technique that would generate clusters based on the underlying patterns (finding similarities between tweets) in the collected twitter corpus.Twitter search API is used to collect real-time English tweets on several topics such as "#Demonetization", "#lockdown", and "#9pm9minutes" by the use of search operator.To analyse the sentiment from real-time tweets, labelling of the corpus is required.Manual annotation of large twitter corpus is time and labor-intensive.Moreover, domain experts are needed for labelling of tweets belonging to a particular domain.Thus, in this work, we propose the use of the improved K-mean clustering approach, which is an unsupervised way of labelling corpus, which could then be used for learning supervised models such as SVM for sentiment analysis.To make the corpus ready for clustering and to get quality clusters, we have applied some basic to advanced cleaning operations known as tweet normalization.Furthermore, extensive feature engineering is conducted to generate different types of features including POS-based (Part-of-Speech), ngrams, Twitter-specific, negation, and lexicon-based features from our collected unlabelled twitter corpus.Those features act as input to the K-mean clustering algorithm and help it in identifying patterns from the data for cluster generation.Moreover, we handle an important linguistic phenomenon namely negation before the cluster generation.Our main focus is on handling those negation tweets in which negation presence has literally no sense of negation (negation exception cases).At the end, cluster analysis is done manually to find out the sentiments expressing by tweets in a particular cluster.Accordingly, cluster classification is done and each cluster is assigned one class that is Positive, Negative, or Neutral.The main contribution of this work is the idea of amalgamation of extensive feature engineering and negation modelling with the unsupervised K-mean clustering approach for classification of large unlabelled twitter corpus.A comparative analysis of our proposed approach is done with or without negation exception cases and with random K-mean using only conventional TF-IDF as features.The proposed automatic labelling approach produces substantial results in terms of cluster quality assessed through two evaluation metrics known as inertia and silhouette score. .
Managing Bias When Library Collections Become Data

Developments in AI research have dramatically changed what we can do with data and how we can learn from data. At the same time, implementations of AI amplify the prejudices in data often framed as ‘data bias’ and ‘algorithmic bias.’ Libraries, tasked with deciding what is worth keeping, are inherently discriminatory and yet remain trusted sources of information. As libraries begin to systematically approach their collections as data, will they be able to adopt and adapt the AI-driven tools to traditional practices?&#x0D; &#x0D; Drawing on the work of the AI initiative within Stanford Libraries, the Fantastic Futures conference on AI for libraries, archives, and museums, and recent scholarship on data bias and algorithmic bias, this article encourages libraries to engage critically with AI and help shape applications of the technology to reflect the ethos of libraries for the benefit of libraries themselves and the patrons they serve. A brief examination of two core concepts in machine learning, generalization and unstructured data, provides points of comparison to library practices in order to uncover the theoretical assumptions driving the different domains. The comparison also offers a point of entry for libraries to adopt machine learning methods on their own terms.
Recommender in AI-enhanced Learning: An Assessment from the Perspective of Instructional Design

Abstract As tools for AI-enhanced human learning, recommender systems support learners in finding materials and sequencing learning paths. The paper explores how these recommenders improve the learning experience from a perspective of instructional design. It analyzes mechanisms underlying current recommender systems, and it derives concrete examples of how they operate: Recommenders are either expert-, criteria-, behavior-, or profile-based or rely on social comparisons. To verify this classification of five different mechanisms, we analyze a set of current publications on recommenders and find all the identified mechanisms with profile-based approaches as the most common. Social recommenders, though highly attractive in other sectors, reveal some drawbacks in the context of learning. In comparison, expert-based recommendations are easy to implement and often stand out as simple but effective ways for suggesting learning materials and learning paths to learners. They can be combined with other approaches based on social comparisons and individual profiles. The paper points out challenges in studying recommenders for learning and provides suggestions for future research.
Mobile information behaviour of sandwich students towards mobile learning integration at the University of Ghana

In Ghana, sandwich students are in accelerated programmes and education are offered to them during vacation periods. Like other forms of education, it has been found that the integration of mobile learning with sandwich programmes would be beneficial. However, the need to explore students' readiness and attitudes towards M-learning before its integration is crucial. This study therefore looked into sandwich students' mobile information behaviour to ascertain their readiness for M-learning adoption. Through a survey research method, the study used a quantitative approach to look into this phenomenon. Forty (40) sandwich students from the University of Ghana participated in the study and data were collected through the use of the questionnaire. The sandwich students perceived integration of M-learning to be beneficial. The results of the study also show that the participants own mobile devices, but a significant number of them were of the view that their mobile devices would not enable M-learning adoption to be effective. The study recommends training on mobile information retrieval skills to students for effective M-learning adoption.
A Modification on Key Stream Generator for RC4 Algorithm

Rivest Cipher 4 (RC4) is one of most common stream cipher, but it experience many problem, such as, there is little combination between the plaintext and cipher-text. For this reason RC4 Cipher is vulnerable to a number of attacks. Thereupon, this paper proposes a new modification of the RC4 to strong it. This achieved by modifying key-stream generator based on linear equation with four state tables for generating random numbers. Key control is used for selecting one state table to apply # operation, then performing forward and backward effects to generate the key-stream that used in encryption and decryption. Being evaluated, the results obtained from the statistical probabilities prove that our proposed algorithm is more complex than the standard algorithm by using different key lengths. Also, our proposed RC4 pass the randomness in most metrics in NIST.
Toward Visual Distortion in Black-Box Attacks

Constructing adversarial examples in a black-box threat model injures the original images by introducing visual distortion. In this paper, we propose a novel black-box attack approach that can directly minimize the induced distortion by learning the noise distribution of the adversarial example, assuming only loss-oracle access to the black-box network. The quantified visual distortion, which measures the perceptual distance between the adversarial example and the original image, is introduced in our loss whilst the gradient of the corresponding non-differentiable loss function is approximated by sampling noise from the learned noise distribution. We validate the effectiveness of our attack on ImageNet. Our attack results in much lower distortion when compared to the state-of-the-art black-box attacks and achieves $100\%$ success rate on InceptionV3, ResNet50 and VGG16bn. The code is available at https://github.com/Alina-1997/visual-distortion-in-attack.
Parameters optimization of light field micro-particle image velocimetry

The light field micro-particle image velocimetry (LF-μPIV) can record the instantaneous three-dimensional (3D) spatial position information of tracer particles in a single photographic exposure by a single camera, and thus achieve the instantaneous 3D velocity measurement of the micro-scale flow. The accuracy of the spatial positions of the reconstructed tracer particles is closely related to the spatial reconstruction resolution of the LF-μPIV system, and determines the measurement accuracy of the 3D velocity distribution. In this paper, the effects of the optical parameters of the LF-μPIV system and the spatial position of the tracer particle on the spatial resolution and reconstruction quality are numerically studied. The location of the measurement volume and the optical parameters of the LF-μPIV system are further optimized. Based on the optimized parameters, a LF-μPIV system is assembled. Finally, experiments are carried out to verify the feasibility of the optical parameters optimization from simulations and to evaluate the performance of the LF-μPIV system for three-dimensional velocity field in a micro-scale convergent channel. Results indicate that the lateral and axial reconstruction resolutions are higher in the outside and inside regions away from the focal plane, compared with those in the region near the focal plane of the objective lens, and is less affected by the microlens aperture, CCD sensor pixel size and objective lens magnification. The spatial reconstruction resolution of the LF-μPIV system at different depths is in good agreement with the simulation results, proving the effectiveness of the optical parameters optimization of the LF-μPIV system. The measurement results of 3D velocity field in a micro-scale convergent channel are consistent with the simulation results by ANSYS Fluent, further verifying the feasibility of the optimized LF-μPIV system for 3D velocity field.
The Effect of The E-Commerce Companies Benevolence, Integrity and Competence Characteristics on Consumers Perceived Trust, Purchase Intention and Attitudinal Loyalty

The purpose of this study is to reveal the effect of consumer perceptions on the benevolence, integrity and competence of e-commerce companies on consumers' perceived trust, purchase intention and attitudinal loyalty.The research data were collected by online survey method.The convenience sampling method was used in the study.The research was conducted between November 29 and December 29, 2019.As a result of the data collection process, 297 questionnaires were collected.The research model was tested with structural equation modeling by using Smart PLS.As a result of the research, significant effects of the integrity and competence characteristics of ecommerce companies on the perceived trust by the consumers was found.However, effect of the benevolence of e-commerce companies on the consumers' perceived trust wasn't found.As a result of the research, it was found that the perceived trust has significant effect on the purchase intention and the purchase intention has a significant effect on attitudinal loyalty.As a conclusion, e-commerce companies should improve benevolence, integrity and competence which are the antecedent variables affecting trust in order to increase the consumers' perceived trust.As the perceived trust increases, the level of purchase and attitudinal loyalty of consumers will increase considerably.
A Network Paradigm for Very High Capacity Mobile and Fixed Telecommunications Ecosystem Sustainable Evolution

The main objective for Very High Capacity (VHC) fixed and mobile networks is improving end-user Quality of Experience (QoE), i.e., meeting the Key Performance Indicators (KPIs) - throughput, download time, round trip time, and video delay - required by the applications. KPIs depend on the end-to-end connection between the server and the end-user device. Not only Telco operators must provide the quality needed for the different applications, but also they must address economic sustainability objectives for VHC networks. Today, both goals are often not met, mainly due to the push to increase the access networks bitrate without considering the end-to-end applications KPIs. This paper's main contribution deals with the definition of a VHC network deployment framework able to address performance and cost issues. We show that three are the interventions on which it is necessary to focus: i) the reduction of bit-rate through video compression, ii) the reduction of packet loss rate through artificial intelligence algorithms for access lines stabilization, and iii) the reduction of latency (i.e., the round-trip time) with edge-cloud computing and content delivery platforms, including transparent caching. The concerted and properly phased action of these three measures can allow a Telco to get out of the Ultra Broad Band access network “trap”as defined in the paper. We propose to work on the end-to-end optimization of the bandwidth utilization ratio (i.e., the ratio between the throughput and the bit-rate that any application can use). It leads to better performance experienced by the end-user, enables new business models and revenue streams, and provides a sustainable cost for the Telco operators. To make such a perspective more precise, the case of MoVAR (Mobile Virtual and Augmented Reality), one of the most challenging future services, is finally described.