Miracle-dz/UniFAR-FTUs huggingface dataset:
Ethical Network Surveillance using Packet Sniffing Tools: A Comparative Study

Nowadays, with growing of computer's networks and Internet, the security of data, systems and applications is becoming a real challenge for network's developers and administrators. An intrusion detection system is the first and reliable technique in the network's security that is based gathering data from computer network. Further, the need for monitoring, auditing and analysis tools of data traffic is becoming an important factor to increase an overall system and network security by avoiding external attackers and monitoring abuse of the IT assets by employees in the workplace. The techniques that used for collecting and converting data to a readable format are called packet sniffing. Packet Sniffer is a tool that used to capture packets in binary format, converts that binary data into a readable data format and log of that captured data for analyzing and monitoring, displaying different used applications, cleartext user names, passwords, and other vulnerabilities. It is used by network administrator to keep the network is more secured, safe and to support better decision. There are many different sniffing tools for monitoring, analyzing, and reporting the network's traffic. In this paper we will compare between three different sniffing tools; TCPDump, Wireshark, and Colasoft according to various parameters such as their detection ability, filtering, availability, supported operating system, open source, GUI, their characteristics and features, qualitative and quantitative parameters. In addition, this paper may be considered as an insight for the new researchers to guide them to an overview, essentials, and understanding of the packet sniffing techniques and their working.
Self-Organizing Ultra-Dense Small Cells in Dynamic Environments: A Data-Driven Approach

This paper presents a data-driven biadaptive self-organizing network (Bi-SON) for ultra-dense small cells (UDSC), which can improve energy efficiency and reduce interference in dynamic environments, taking account of cell switching <sc>on</sc>/<sc>off</sc>, transmission power adjustment, and traffic loads simultaneously. In the first adaptation of Bi-SON, a joint traffic load and interference aware cell ranking mechanism first determines the necessary active small cells based on traffic loads, and then ranks all the active small cells based on their carried traffic load and resulting interference. Top ranked cells will transmit at the maximum power. The last ranked <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> cells will adjust the transmission power for interference reduction in the second adaptation function of Bi-SON, while maintaining the required quality of service. According to a polynomial regression learning approach, the total system throughput of UDSC is characterized as a function of <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>. Compared to the baseline case when all the cells transmit with the maximum power, our proposed Bi-SON framework can improve the throughput and energy efficiency of UDSC by 73% and 169%, respectively. However, the pure switching <sc>on/off</sc> approach can only improve the throughput and the energy efficiency of UDSC by 52% and 115%, respectively. As demonstrated, even with a simple power adaptation algorithm, a learning-based Bi-SON framework can improve the performance of UDSC by taking advantage of the pervasive availability of voluminous data.
A Latency-Optimized and Energy-Efficient Write Scheme in NVM-Based Main Memory

Nonvolatile memory (NVM) technologies are promising candidates as the next-generation main memory due to high scalability and low energy consumption. However, the performance bottlenecks, such as high write latency and low cell endurance, still exist in NVMs. To address these problems, frequent pattern compression schemes have been widely used, which however suffer from the lack of flexibility and adaptability. In order to overcome these shortcomings, we propose a well-adaptive NVM write scheme, called dynamic frequent pattern compression (DFPC), to significantly reduce the amount of write units and extend the lifetime. Instead of only using static frequent patterns in existing FPC schemes, which are predefined and not always efficient for all applications, the idea behind DFPC is to exploit the characteristics of data distribution in execution to obtain dynamic patterns, which often appear in the real-world applications. To further improve the compression ratio, we exploit the value locality in a cache line to extend the granularity of dynamic patterns. Hence, DFPC can encode the contents of cache lines with more kinds of frequent data patterns. Moreover, to further support efficient write and read operations in the context of multilevel/triple-level cell NVMs, we need to extend the DFPC to improve performance in terms of the access latency and energy consumption. We hence propose a latency-optimized and energy-efficient compression write scheme to encode the compressed data with low energy and latency states, i.e., enhanced DFPC, thus reducing the latency and energy consumption. We implement DFPC in GEM5 with NVMain and execute the applications from SPEC CPU2006 to evaluate our scheme. Experimental results demonstrate the efficacy and efficiency of DFPC. We have released the source codes for public use at Github https://github.com/dfpcscheme/DFPCScheme.
Introduction to Knowledge Translation in Health Care Research

Knowledge translation may be understood as the process of synthesizing, disseminating, and exchanging information to appropriate audiences. This process is an essential component of research, ensuring that research findings reach the knowledge users in a cost-efficient and meaningful manner. However, novice investigators may sometimes believe knowledge translation equates to presentations at research conferences and publishing in a journal. The objective of this paper is to address this misconception by providing an overview of the knowledge translation process for novice investigators. The authors will first define knowledge translation and describe its importance in health care research. Following this brief introduction, a framework for knowledge translation, the Knowledge-to-Action Cycle, will be examined to equip novice investigators with the essential knowledge and strategies they need to develop and implement successful research proposals. The information presented in this paper may enable novice investigators to understand and apply knowledge translation in their research, enhancing its applicability and usefulness. The authors conclude this paper by clarifying the various approaches, levels, and means of knowledge translation, and by constructing a brief overview of planning considerations for novice investigators engaging in knowledge translation.
Unsupervised Classification of Hydrophone Signals With an Improved Mel-Frequency Cepstral Coefficient Based on Measured Data Analysis

Recently, passive detection technology has developed the ability to detect surface ships based on the noise emissions recorded by hydrophones, making it possible in some cases to classify surface ships. One of the most concerning issues with ships and underwater targets is the current lack of reliable features for unsupervised classification. To solve this problem, this paper proposes an improved Mel-frequency cepstral coefficients (IMFCC) feature for unsupervised clustering of marine targets. As the feature extraction of hydrophone signals rely on preprocessing, the IMFCC adds cyclic modulation spectrum (CMS) and cross-correlation bispectrum (CCB) in the preprocessing module before traditional the Mel-frequency cepstral coefficient process, and the principal component analysis (PCA) is added as the backend processing module. There are four contributions as follows: First, for IMFCC extraction, it combines the advantages of the CMS, CCB, MFCC, and PCA. Second, the two common unsupervised clusters, Gaussian mixture model (GMM) and fuzzy C-means are used to evaluate the CMS, CCB, and several MFCCs—MFCC-vector quantization (MFCC-VQ), MFCC-Gaussian mixture model (MFCC-GMM), TEO-MFCC (Teager Energy Operator based MFCC), and IMFCC. Third, the performances of traditional MFCC, Teager-energy operator (TEO)-MFCC, IMFCC, MFCC-VQ, and MFCC-GMM are discussed under different dimensions, signal-to-noise ratios, distances, and depths. Finally, the experimental results prove that the IMFCC method has a strong anti-interference ability and is robust and has a high-success rate for clustering multitarget or different depth targets, which enables the IMFCC to be a reliable feature for unsupervised classification.
Second language learning from a multilingual perspective

How do people learn a second language? In this thesis, we study this question through an examination of cross-linguistic transfer: the role of a speaker’s native language in the acquisition, representation, usage and processing of a second language. We present a computational framework that enables studying transfer in a unified fashion across language production and language comprehension. Our framework supports bidirectional inference between linguistic characteristics of speakers’ native languages, and the way they use and process a new language. We leverage this inference ability to demonstrate the systematic nature of cross-linguistic transfer, and to uncover some of its key linguistic and cognitive manifestations. We instantiate our framework in language production by relating syntactic usage patterns and grammatical errors in English as a Second Language (ESL) to typological properties of the native language, showing its utility for automated typology learning and prediction of second language grammatical errors. We then introduce eye tracking during reading as a methodology for studying cross-linguistic transfer in second language comprehension. Using this methodology, we demonstrate that learners’ native language can be predicted from their eye movement while reading free-form second language text. Further, we show that language processing during second language comprehension is intimately related to linguistic characteristics of the reader’s first language. Finally, we introduce the Treebank of Learner English (TLE), the first syntactically annotated corpus of learner English. The TLE is annotated with Universal Dependencies (UD), a framework geared towards multilingual language analysis, and will support linguistic and computational research on learner language. Taken together, our results highlight the importance of multilingual approaches to the scientific study of second language acquisition, and to Natural Language Processing (NLP) applications for non-native language. Thesis Supervisor: Boris Katz Title: Principal Research Scientist Computer Science & Artificial Intelligence Lab
Violence detection based on three-stream convolutional networks

Violence detection in videos is a challenging task which has gotten much attention in the research community. In this paper, we propose a three-stream network framework for violence detection in binocular stereo vision. To capture the complementary information from the video we adopt the appearance, motion and depth information. The spatial part, we use the RGB as the individual frame appearance. Then, we use the sparse stereo matching method to extract the feature points and obtain the vision disparity of the point. The 3D coordinates of the points are calculated through the standard 3D measurement theory. The 3D motion vector conveys the movement of the camera and the objects as the motion information. Besides, the depth information flow is the third input of the network which can improved recognition rate.
A Systematic Literature Review on Teaching and Learning Introductory Programming in Higher Education

Contribution: This paper adds to the results of previous systematic literature reviews by addressing a more contemporary context of introductory programming. It proposes a categorization of introductory programming challenges, and highlights key issues for a research roadmap on introductory programming learning and teaching in higher education. Background: Despite the advances in methods and tools for teaching and learning introductory programming, dropout and failure rates are still high. Published surveys and reviews either cover papers only up to 2007, or focus on methods and tools for teaching introductory programming. Research Questions: 1) What previous skills and background knowledge are key for a novice student to learn programming? 2) What difficulties do novice students encounter in learning how to program? 3) What challenges do teachers encounter in teaching introductory programming? Methodology: Following a formal protocol, automatic and manual searches were performed for work from 2010 to 2016. Of 100 papers selected for data extraction, 89 were retained after quality assessment. Findings: The most frequently cited skills necessary for learning programming were related to problem solving and mathematical ability. Problem solving was also cited as a learning challenge, followed by motivation and engagement, and difficulties in learning the syntax of programming languages. The main teaching challenges concern the lack of appropriate methods and tools, as well as scaling and personalized teaching.
Configurable Operational Amplifier Architectures Based on Oxide Resistive RAMs

This paper introduces memristor-based operational amplifiers (OpAmps) in which semiconductor resistors are suppressed and replaced by memristors. The ability of the memristive elements to hold several resistance states is exploited to design programmable closed-loop OpAmps. An inverting OpAmp, an integrator and a differentiator are studied. Such designs are developed based on a calibrated memristor model, and offer dynamic configurability to realize different gains and corner frequencies at reduced chip area.
Image Analysis via Applied Harmonic Analysis : Perceptual Image Quality Assessment, Visual Servoing, and Feature Detection (Bildanalyse mit Methoden der angewandten harmonischen Analysis : Perzeptuelle Bildqualität, Robotersteuerung und Mustererkennung)

Certain systems of analyzing functions developed in the field of applied harmonic analysis are specifically designed to yield efficient representations of structures which are characteristic of common classes of two-dimensional signals, like images. In particular, functions in these systems are typically sensitive to features that define the geometry of a signal, like edges and curves in the case of images. These properties make them ideal candidates for a wide variety of tasks in image processing and image analysis. This thesis discusses three recently developed approaches to utilizing systems of wavelets, shearlets, and α-molecules in specific image analysis tasks. First, a perceptual image similarity measure is introduced that is solely based on the coefficients obtained from six discrete Haar wavelet filters but yields state of the art correlations with human opinion scores on large benchmark databases. The second application concerns visual servoing, which is a technique for controlling the motion of a robot by using feedback from a visual sensor. In particular, it will be investigated how the coefficients yielded by discrete wavelet and shearlet transforms can be used as the visual features that control the motion of a robot with six degrees of freedom. Finally, a novel framework for the detection and characterization of features such as edges, ridges, and blobs in two-dimensional images is presented and evaluated in extensive numerical experiments. Here, versatile and robust feature detectors are obtained by exploiting the special symmetry properties of directionally sensitive analyzing functions in systems created within the recently introduced α-molecule framework.
Potential Probiotic Properties of Lactic Acid Bacteria Isolated from Malted and Spontaneously Fermented Acha (Digitaria exilis) Flour

Aim: To isolate, characterise and identify Lactic Acid Bacteria (LAB) with potential probiotic properties from malted and fermented Acha (Digitaria exilis). Place and Duration of Study: The study was carried out at the Department of Microbiology, University of Ibadan, between May 2017 and October, 2017. Methodology: Collected acha grains was sorted, malted for 48 hours, dried milled and sieved. The flour was reconstituted, fermented spontaneously for 72 hours and sampled every 24 hours for isolation of LAB. Results: The probiotic properties of 40 LAB strains isolated were evaluated in vitro. Based on their antimicrobial activity against some common foodborne pathogen and antibiotic susceptibility pattern to standard antibiotics, 14 LAB strains were selected for further screening. They all exhibited strong antimicrobial activity against Staphylococcus aureus, Salmonella sp. , Escherichia coli, Bacillus sp. and Pseudomonas aeruginosa. All selected strains were investigated for acidic pH and bile salt tolerance, tolerance to NaCl and simulated gastric juice, cell surface characteristics which includes hydrophobicity and auto-aggregation assay. Production of DNAase, gelatinase, Exopolysaccharide and haemolytic ability were investigated for safety assessment. Five strains Original Research Article Wakil and Olorode; AIR, 17(6): 1-12, 2018; Article no.AIR. 45414 2 (L12, L15, L118, L213, L214) exhibited tolerance to 10% NaCl concentration and pH 2. All the selected 14 LAB strains resisted the bile salt up to 1.0% except L15 and tolerated simulated gastric juice with a sharp decrease by 90min followed by an increase in the count after 180 min except for isolates L12 and L17. Auto-aggregation value ranged from 0.88% to 93.71% with the highest value recorded for L17 (93.71%). LAB117 had the highest (43.39%) microbial adherence to hydrocarbon (MATH) value and least in L19 (2.08%). All the 14 LAB strains were negative to safety tests but all produced exopolysaccharide except LAB L13 and L19. Based on the morphological, biochemical and physiological characteristics, the 14 LAB isolates were identified as Lactobacillus plantarum (L15, L17, L117, L214), Lactobacillus casei (L113, L116, L213), Lactobacillus sp. (L13, L19, L22, L211), Enterococcus sp. (L12, L115) and Pediococcus sp. (L118). Conclusion: The selected 14 isolates have the probiotic properties required for use as a potential probiotic in weaning food supplements with the best probiotic properties recorded with Lactobacillus plantarum L117 strain.
Face Recognition using One-shot Learning

Face Recognition represents one of the attractive research areas. It has drawn the attention of many researchers due to its varying applications such as security, healthcare, marketing, identity authentication, surveillance etc. In this order, different face recognition algorithms have been proposed however, one algorithm that stands out in the event of limited dataset is one shot learning. “One shot” means learning from a single training item. This paper discusses a way for solving this problem. Neural networks are notorious for requiring extremely large datasets to reach a considerable accuracy. This paper proposes a method to solve this problem for the face recognition domain by bringing down the number of training samples required to just one and still achieving a decent accuracy close to 90%.
Inventor Name Disambiguation with Gradient Boosting Decision Tree and Inventor Mobility in China (1985-2016)

This paper presents the first systematic disambiguation result of all Chinese patent inventors in the State Intellectual Property Office of China (SIPO) patent database from 1985 to 2016. We provide a method of constructing high-qualitative training data from lists of rare names and evidence for the reliability of these generated labels when large-scale and representative hand-labeled data are crucial but expensive, prone to error, and even impossible to obtain. We then compare the performances of seven supervised models, i.e., naive Bayes, logistic, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), as well as tree-based methods (random forest, AdaBoost, and gradient boosting decision trees), and found that gradient boosting classifier outperforms all other classifiers with the highest F1-score and stable performance in solving the homonym problem prevailing in Chinese names. In the last step, instead of adopting the more popular hierarchical clustering method, we clustered records with the density-based spatial clustering of applications with noise (DBSCAN) based on the distance matrix predicated by the GBDT classifier. Varying across different testing data and parameters of DBSCAN, our algorithm yielded a F1-score ranging from 93.5%-99.3% with splitting error within the range 0.5%-3% and lumping error between 0.056%-0.37%. Based on our disambiguated result, we provide an overview of Chinese inventors' regional mobility.
Joint Beamforming and Jamming Optimization for Secure Transmission in MISO-NOMA Networks

Non-orthogonal multiple access (NOMA) has been developed as a key multi-access technique for 5G. However, secure transmission remains a challenge in NOMA. Especially, the user with weakest channel is most threatened by eavesdropping, due to its highest transmit power. Two schemes are proposed to generate artificial jamming at the NOMA base station (BS), aiming at disrupting the potential eavesdropping without affecting the legitimate transmission. In the first scheme, the transmit power of artificial jamming is maximized, with its received power at each receiver higher than that of other users. Thus, the jamming signal can be eliminated via successive interference cancellation before others. When the transmit power of the BS is inadequate, the transmit jamming power is maximized with the jamming signal zero-forced at each receiver. Thus, the legitimate transmission is not affected by the jamming, and the eavesdropping can be disrupted effectively. Due to the non-convexity of these two optimization problems, we first convert them to convex ones and, then, provide an iterative algorithm to solve them. Simulation results are presented to show the effectiveness of the proposed schemes in guaranteeing the security of NOMA networks.
Deep alignment network: from MIMD to SIMD platform

The paper considers the following software engineering problem for digital media: given a software tool for processing tensor signals, like Deep Neural network (DNN) defined for MIMD architecture (Multi Instruction, Multi Data), redefine this algorithm to SIMD architecture (Single Instruction, Multiple Data). While for mapping multiple instructions, the standard signal processing approach is applied, for mapping tensors of any dimensionality, 2D RGBA textures (Red, Green, Blue, and Alpha channels) are used as the target data structure. To illustrate the tensor mapping concept, Deep Alignment Network (DAN), contemporary important application for Human Computer Interfacing, is selected and its efficiency analyzed. The testbed for comparisons of DAN’s MIMD and SIMD architectures, was based on Javascript (MIMD) and WebGL (SIMD) software platforms. It appears that expected speed-up (checked for commodity personal computers) of SIMD versus MIMD architecture is on the reasonable level: 350 image frames per minute versus seven image frames per minute.