Miracle-dz/UniFAR-FTUs huggingface dataset:
HEPATOPROTECTIVE EFFECT OF VITAMIN C ON CAPECITABINE- INDUCEDLIVER INJURY IN RATS -

Chemotherapy for malignant tumours has diversified, and recognizing its side effects has become more important than ever. Capecitabine (Xeloda) was established as a treatment option in colorectal cancer in 2001, and more recently for adjuvant treatment of colon cancer and treatment of metastatic breast cancer, but this was associated with severe toxicities. The present work investigated the protective role of vitamin C against Capecitabine–induced liver toxicity in rats. Forty male albino rats were randomly divided into four groups of 10 animals each. Group I represents the control group; Group II were administrated Capecitabine (3 mg/m2 twice daily) via gavage for two weeks. The animals of Group III received vitamin C (100 mg/kg BW) before Capecitabine administration; the animals of Group IV received vitamin C alone (100 mg/kg BW). At the end of the 2nd week, the general status of the rats, histopathology and Immunohistochemistry of the liver, and the levels of serum alanine aminotransaminase (ALT), aspartate aminotransaminase (AST), and alkaline phosphatase (ALP) levels were detected. The present results showed that Capecitabine caused significant increases in serum ALT, AST, and ALP. Also, the results of the present study showed that Capecitabine produced a severe inflammatory lesion to the liver compared to the control group. PCNA expression was increased in the liver of Capecitabine-treated rats. Administration of vitamin C exhibited significant reversal of Capecitabine-induced toxicity in the hepatic tissue. In conclusion, these results suggest that vitamin C has a protective role towards Capecitabine toxicity in the rat.
A novel integer linear programming model for routing and spectrum assignment in optical networks

The routing and spectrum assignment problem is an NP-hard problem that receives increasing attention during the last years. Existing integer linear programming models for the problem are either very complex and suffer from tractability issues or are simplified and incomplete so that they can optimize only some objective functions. The majority of models uses edge-path formulations where variables are associated with all possible routing paths so that the number of variables grows exponentially with the size of the instance. An alternative is to use edge-node formulations that allow to devise compact models where the number of variables grows only polynomially with the size of the instance. However, all known edge-node formulations are incomplete as their feasible region is a superset of all feasible solutions of the problem and can, thus, handle only some objective functions.Our contribution is to provide the first complete edge-node formulation for the routing and spectrum assignment problem which leads to a tractable integer linear programming model. Indeed, computational results show that our complete model is competitive with incomplete models as we can solve instances of the RSA problem larger than instances known in the literature to optimality within reasonable time and w.r. t. several objective functions. We further devise some directions of future research.
Factors Influencing the Intended Adoption of Digital Transformation: A South African Case Study

Organisations worldwide are facing a market pressures which are forcing them to undertake digital transformation projects or initiatives. This research study set out to explore this in more depth, asking questions about the intention of South African (SA) retail organisations to adopt digital transformation and probing into the understanding and perception of digital transformation itself, as well as future intended use cases for available digital technologies. A case study approach was deemed to be an appropriate strategy given the paucity of existing academic research on the topic. Participants in the study had a good understanding of digital transformation and digital technologies, but perceptions were mixed. The most prominent digital transformation initiatives in the SA retail industry were the adoption of cloud technologies and data analytics. The factors identified in this study, using the Technology, Organisational, and Environmental (TOE) framework, can assist retailers in their decision-making process concerning digital transformation adoption.
Towards a Catalog of Java Dependency Injection Anti-Patterns

[Context] Dependency Injection (DI) is a commonly applied mechanism to decouple classes from their dependencies in order to provide better modularization of software. In the context of Java, the availability of a DI specification and popular frameworks, such as Spring, facilitate DI usage in software projects. However, bad DI implementation practices can have negative consequences, such as increasing coupling. Even though the literature suggests the existence of DI anti-patterns, there is no detailed catalog of such bad practices. Moreover, there is no evidence on their occurrence and perceived usefulness from the developer's point of view. [Goal] Our goal is to review the reported DI anti-patterns in order to analyze their completeness and to propose and evaluate a novel catalog of DI anti-patterns in the context of Java. [Method] We propose an initial catalog containing twelve Java DI anti-patterns. We selected four open source software projects that adopt a DI framework and developed a tool to statically analyze the occurrence of the DI anti-patterns within their source code. Also, we conducted a survey through face to face interviews with three experienced developers that regularly apply DI. [Results] At least nine different DI anti-patterns appeared in each analyzed project. In addition, the feedback received from the developers confirmed their relevance and the importance of investing further effort towards a catalog. [Conclusion] The results indicate that the initial catalog contains Java DI anti-patterns that occur in practice and are useful. Sharing it with practitioners may help them to avoid such anti-patterns. Sharing it with the research community will enable further improving the catalog.
Software industry awareness on green and sustainable software engineering: a state-of-the-practice survey

Sustainable computing is a rapidly growing research area spanning several areas of computer science. In the software engineering field, the topic has received increasing attention in recent years, with several studies addressing a range of concerns. However, few studies have demonstrated the awareness of software practitioners about the underlying concepts of sustainability in the software development practice. In this effect, in this study, we aim to provide some evidence about the practitioners' perception about the adoption of sustainability in software development, under four main perspectives: economic, social, environmental and technical. To accomplish such a goal, we carried out a survey study with twenty-five software engineers involved in projects in different domains. The yielded results indicate an overall lack of knowledge about the topic, in particular regarding the concepts about sustainable software, although it is a common understanding that sustainability should be treated as a quality attribute and should support the interaction between sustainability and the software development life cycle phases. Among the observed perspectives, the respondents indicate that the technical dimension is the most relevant and explored so far. This study contributes to the field with initial evidence and can be seen as a first step towards establishing a common understanding about how the software industry is receptive to the use of sustainability concepts in software development practices.
Predicting Blood Glucose using an LSTM Neural Network

Diabetes self-management relies on the blood glucose prediction as it allows taking suitable actions to prevent low or high blood glucose level. In this paper, we propose a deep learning neural network (NN) model for blood glucose prediction. It is a sequential one using a Long-Short-Term Memory (LSTM) layer with two fully connected layers. Several experiments were carried out over data of 10 diabetic patients to decide on the model’s parameters in order to identify the best variant of it. The performance of the proposed LSTM NN measured in terms of root mean square error (RMSE) was compared with the ones of an existing LSTM and an autoregressive (AR) models. The results show that our LSTM NN is significantly more accurate; in fact, it outperforms the existing LSTM model for all patients and outperforms the AR model in 9 over 10 patients, besides, the performance differences were assessed by the Wilcoxon statistical test. Furthermore, the mean of the RMSE of our model was 12.38 mg/dl while it was 28.84 mg/dl and 50.69 mg/dl for AR and the existing LSTM respectively.
Provably Secure Dynamic ID-Based Anonymous Two-Factor Authenticated Key Exchange Protocol With Extended Security Model

Authenticated key exchange (AKE) protocol allows a user and a server to authenticate each other and generate a session key for the subsequent communications. With the rapid development of low-power and highly-efficient networks, such as pervasive and mobile computing network in recent years, many efficient AKE protocols have been proposed to achieve user privacy and authentication in the communications. Besides secure session key establishment, those AKE protocols offer some other useful functionalities, such as two-factor user authentication and mutual authentication. However, most of them have one or more weaknesses, such as vulnerability against lost-smart-card attack, offline dictionary attack, de-synchronization attack, or the lack of forward secrecy, and user anonymity or untraceability. Furthermore, an AKE scheme under the public key infrastructure may not be suitable for light-weight computational devices, and the security model of AKE does not capture user anonymity and resist lost-smart-card attack. In this paper, we propose a novel dynamic ID-based anonymous two-factor AKE protocol, which addresses all the above issues. Our protocol also supports smart card revocation and password update without centralized storage. Further, we extend the security model of AKE to support user anonymity and resist lost-smart-card attack, and the proposed scheme is provably secure in extended security model. The low-computational and bandwidth cost indicates that our protocol can be deployed for pervasive computing applications and mobile communications in practice.
Poster Abstract: A Practical Sybil-Proof Incentive Mechanism for Multichannel Allocation

Sybil attack is a classic attacking means in spectrum allocation. However, in previous auction-based mechanism, bids of fictitious identities in Sybil attack model are required to follow their own valuation functions. In this paper, we are the first to propose a novel attack model EqualSumBid Sybil to describe a more feasible Sybil attack and propose PRAM, which is a Practical sybil-pRoof Auction Mechanism for multichannel allocation. Simulations demonstrate that PRAM not only achieves truthfulness but also resists EqualSumBid Sybil attack.
CSIID: WiFi-based Human Identification via Deep Learning

With the widespread popularization of commercial off-the-shelf (COTS) WiFi devices, the device-free WiFi sensing has attracted attention extensively. However, there are only a few studies on human identification by using noncontact techniques since traditional methods are facing the problem of heavy workload and low recognition accuracy. Aiming at these issues, we propose a deep learning method, named CSIID, to analyze the gait features using Channel State Information (CSI) of COTS WiFi devices In CSIID, the convolution layers are combined with long short-term memory (LSTM) layers to extract gait features automatically from CSI data and to identify persons, which effectively reduces the need for a large amount of data preprocessing by manual feature extraction. Experimental results conducted on CSI data collected from different situations indicate that the CSIID has desirable identification accuracy. The average identification accuracy of CSIID is ranging from 97.4% to 94.8% when the number of persons is from 2 to 6.
The Hope: An Interactive Mobile Solution to Overcome the Writing, Reading and Speaking Weaknesses of Dyslexia

Dyslexia is the most common disorder in the world that have weaknesses in writing, reading and speaking. As a well spread disorder there are several stages of Dyslexia that has to recognize before giving the treatments. The current treatment is known as “Speech Therapy” which is given by a doctor or a therapist in a hospital or in a special education unit. These weaknesses are more wide spread and effects one in five people in the world. Dyslexia also runs in families. It can be recognized in kindergarten but if not it will be a huge disadvantage and will cause difficulties for further studies when the victim grows up. The people who are suffering from Dyslexia are step aside of the society and most of them are not very social and not open to the society. Therefor the treatment has to be creative and unique in order to get it in to the user. This tool named “The Hope” assembles Dyslexia therapies in a graphical and creative way to present the user to make it more accurate. Key content of this research covers selecting the Dyslexia stage, load the therapies and detect gestures and voice. The end result will be mainly based on Artificial Intelligence, Virtual reality, Image processing and Voice recognition technologies. Whoever suffers from Dyslexia or anyone who is willing to improve their writing, reading and speaking skills can use “The Hope” with a parent or a guardian and can get a clear improvement by following the therapies that the application provides daily. Application will provide an appreciation and a reward system to motivate and encourage the user to use the application daily. Following the therapies daily will improve the user skills. This tool lets users easily recognize their writing, reading and speaking weaknesses and will help to overcome them in a creative and accurate way.
The Footprints of a “Mastodon”: How a Decentralized Architecture Influences Online Social Relationships

Decentralized online social networks (DOSNs) have recently emerged as a viable solution to preserve the users' privacy and ensure higher users' control over the contents they publish. However, little is known about the backlashes that the decentralized organization and management of these platforms may have on the overlaid social network. This paper fills the gap. Specifically, we investigate how a decentralized architecture based on distributed servers impacts the structure of the users' neighborhood and their ego-networks. Our analysis relies on social data gathered from the decentralized micro-blogging platform Mastodon, the newest and fastest-growing decentralized alternative to Twitter. Our findings highlight that the social network supported by each server, namely instance, has a specific footprint in terms of degree distribution and clustered structure of the ego-networks of its members. Further, how users connect to people hosted in other instances is heavily bound by the server they are in. Moreover, users who tend to establish relationships in outer instances prefer to use a bunch of servers. Finally, we show that the ego-networks of the users are more clustered within the instance boundary, i.e. triangles are more likely to form among members of the same instance. All these findings suggest that the decentralization drives the social network to a structure that can be potentially very different from the usual one typical of centralized online social networks. Thus, the architecture of a DOSN is a factor developers and researchers should take into account when designing this kind of social platforms.
Analytical Framework for Full-Dimensional Massive MIMO With Ray-Based Channels

The performance of baseband beamforming in multi-user multiple-input multiple-output (MU-MIMO) systems has been extensively studied for simplified statistical channel models where no angular parameters are taken into account. In contrast, there is little performance analysis with ray-based models, which are more physically motivated, feature prominently in standardization and have been experimentally validated. Thus, unlike previous studies, we present a mathematical framework to analyze the performance of zero forcing (ZF) and minimum mean-squared error (MMSE) combining. Using a central result for averaging in the angular domain, we derive tight approximations for the uplink signal-to-noise ratio and signal-to-interference-and-noise ratio (SINR) for ZF and MMSE processing, respectively, and the resulting spectral efficiencies. The remarkably simple expressions offer the following insights into the effects of the propagation environment. We demonstrate an improvement in performance when moving from vertical uniform rectangular array (URA) to horizontal URA to uniform linear array (ULA) antenna configurations. There is also a corresponding increase in the robustness of the performance to propagation scenarios. We demonstrate that under specific conditions increasing the angular spread can decrease the SINR for a ULA - an unexpected behavior which we link to the effects of end-fire radiation. Furthermore, our results allow us to investigate the impact of different array configurations and system parameters on the rate of convergence to favorable propagation conditions. Finally, we evaluate the spatial correlation properties intrinsically present in ray-based models, and compare them to the commonly used simple exponential model which yields equal, fixed correlation characteristics for each user.
Slotted Hemispherical Resonators for 3-D Printed Waveguide Filters With Extended Spurious-Free Stopbands

This paper presents a comprehensive study on a new family of slotted hemispherical resonators and their applications in waveguide bandpass filters (BPFs) to extend the spurious-free stopbands. The proposed scheme focuses on suppressing unwanted spurious modes in the air-filled hemispherical resonators by adding functional slots to the metallic cavity shells. The spurious TM2m1 and TE101 modes in the hemispherical resonators are effectively suppressed by interrupting the surface current with slots, without significantly degrading the unloaded quality factor of the fundamental TM101 mode. Resonant modes in the slotted hemispherical resonators are analyzed. Design, fabrication, and measurement of $X$ -band proof-of-concept waveguide BPFs based on slotted hemispherical resonators are expounded. Each filter is monolithically prototyped by employing metallic additive manufacturing technology. The filters demonstrate good passband performance—average insertion loss: 0.6–1.1 dB, frequency shift: 0.9%–1%, and return loss: >20 dB, and more significantly extended stopbands with spurious-free frequency ratios of >1.8:1 and stopband rejections over 36 dB.
An Empirical Approach to Phishing Countermeasures Through Smart Glasses and Validation Agents

Phishing attacks have been persistent for more than two decades despite mitigation efforts from academia and industry. We believe that users fall victim to attacks not only because of lack of knowledge and awareness, but also because they are not attentive enough to security indicators and visual abnormalities on the webpages they visit. This is also probably why smart device users, who have more limited screen size and device capabilities compared to desktop users, are three times more likely to fall victim to phishing attacks. To assert our claim, we first investigated general phishing awareness among different groups of smartphone users. We then used smart eyeglasses (electro-oculographic) to experimentally measure the mental effort and vigilance exhibited by users while surfing a website and while playing an Android phishing game that we developed. The results showed that knowledge and awareness about phishing do not seem to have a significant impact on security behaviours, as knowledgeable participants exhibited insecure behaviours such as opening email attachments from unfamiliar senders. However, attentiveness was important as even participants with low cybersecurity knowledge could effectively identify attacks if they were reasonably attentive. Based on these results, we asserted that users are more likely to continue falling victim to phishing attacks due to insecure behaviours, unless tools to lessen the identification burden are provided. We thus recommended implementing a lightweight algorithm into a custom Android browser for detecting phishing sites deceptively without a user interaction. We used fake login credentials as validation agents and monitor the destination server HTTP responses to determine the authenticity of a webpage. We also presented initial evaluation results of this algorithm.
3D Human Body Inpainting using Intrinsic Statistical Shape Models

This paper presents an algorithm for inpainting 3D human body geometry. We built a Statistical Shape Model (SSM) based on the intrinsic Laplacian Coordinates (LC) to tackle the pose variation of human bodies, which is a common challenging issue in 3D human body modeling and shape analysis. We evaluate our inpainting algorithm using synthesized damages on complete human body scans, and with the groundtruth we measure the normalized reconstruction error quantitatively. Our experiments demonstrate the effectiveness of this LC-based SSM in human body inpainting.
AoI-Penalty Minimization for Networked Control Systems with Packet Loss

In this work the impact of age of information (AoI) is studied from the perspective of networked control systems (NCS), i.e., control loops that are closed over networks. We formulate the estimation problem of a linear time invariant (LTI) system and show that related performance metrics can be optimized by minimizing age-penalty functions. From the variety of possible penalties that make sense from an NCS point of view, we derive a general age-penalty minimization problem. We characterize properties of penalty functions that are trivial or non-trivial to solve and show that for non-trivial age-penalties, the optimal transmission policy over a single link with packet loss is AoI-threshold based. Then, we propose an algorithm to find the optimal threshold. Simulation results verify that threshold policies with optimal threshold can serve to optimally solve a variety of NCS related estimation problems.
Automatic Academic Confusion Recognition In Online Learning Based On Facial Expressions

Academic confusion is one of the most common academic emotions, timely identification and resolution of confusion are helpful to improve learning effect. In the research, a model is developed to identify the academic confusion in online learning based on facial expressions. The model mainly includes three parts: confusion-inducing experiments, image preprocessing, and recognition methods comparing. Firstly, a set of confusion-inducing experiments in online learning are designed. Then the images are preprocessed to improve the recognition effect. In the third part, Histogram of Oriented Gradient (HOG), Local Binary Patterns (LBP), Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are combined to form four methods of HOG-SVM, LBP-SVM, CNN and CNN-SVM, which are employed. The experimental results show that most of the methods can effectively detect students’ academic confusion, and the CNN-SVM has the best predictive performance with an average accuracy of 0.938. The Model proposed in this study can provide a technical support for learner emotional modeling of teaching assistant systems.