Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Advances

Internet of things (IoT) and device to device (D2D) communications are among the novel promising technologies in the current releases of 4G and they will play a fundamental role in the next generation 5G as well. In this paper, it is investigated the impact of allocation strategies that take into account the mutual interference in D2D Narrow-Band IoT terminals and cellular terminals transmitting in the same resource block. In a multi-cellular downlink context, the proposed approach and the analysis can serve also as an e ﬃ cient criterion for selecting the target S INR , useful for managing the power control in the uplink. The rate improvement, measured with the proposed approach, is between 10% and 15% w.r. t. conventional techniques.
Magnetotelluric Investigation of the Geo-Tectonic Stability of Campo-Ma’an Area in South Cameroon

The magnetotelluric prospection method has been widely used in Cameroon not only as a reconnaissance method for geophysical investigations but also to determine the nature of the subsurface and the structural settings of the various geological formations in the country that resulted from the global geo-tectonic movements. The present study is aimed at employing the magnetotelluric method to provide evidence on major tectonic dislocations which might have led to the presence of plutonic intrusions in the cratonic structure of the Ntem Complex as well as determining the nature of the contact between the sedimentary Campo Basin and the metamorphic cratonic Ntem Complex. The analyses and interpretations of iso-resistivity maps and geoelectric sections from magnetotelluric data has enabled us to show that tectonic forces at contact between the sedimentary Campo Basin and the metamorphic Ntem Complex were of three different categories: gravity gliding, magmatic upwelling and lateral compressions. The results of the action of these forces were the development of vertical and sub-vertical faulting systems at Craton-Sedimentary Basin contact and subsidence of overburden that appears to have also paved the way for the setting-up of the Ntem River bed and its flood plains. This river bed and its flood plains have been characterized as having a sedimentary deposit depth of 500 m and width of 20 km. Meanwhile, the deep-seated contact faulting systems within the two identified formations have varied fault magnitudes with a maximum of 60 km. The relatively high apparently resistivity values (up to1.0 kΩ•m) for the sedimentary and the very high values for the metamorphic formations (up to and above 10.0 kΩ•m) suggest that the whole area is at present relatively geo-tectonically stable.
This paper is included

Cryptographic operations can be prohibitively expensive for IoT and other resource-constrained devices. We introduce a new cryptographic primitive which we call Ciphertext and Signature Propagation (CSProp) in order to deliver security to the weak end-devices. CSProp is a cryptographic propagation algorithm whereby an untrusted machine sitting upstream of a lightweight device can modify an authenticated message so it can be efﬁciently veriﬁed. Unlike proxy-based solutions, this upstream machine is stateless and untrusted (making it possible for any device to serve that role), and the propagated signature is mathematically guaranteed to be valid only if the original signature is also valid. CSProp relies on RSA security and can be used to optimize any operations using the public key such as signature validation and encryption, which our experiments show are the most common public key operations in IoT settings. We test CSProp by using it to extend DNSSEC to edge devices (validation), and to optimize the performance of TLS (validation and encryption) on a range of resource constrained devices. CSProp reduces DNSSEC validation latency by 78x and energy consumption by 47 x on the Raspberry Pi Zero. It reduces TLS handshake latency and energy by an average of 8 x each. On an Arduino-based IoT board, CSProp signiﬁcantly outperforms traditional RSA public key operations ( e.g., 57 x and 36 x reductions in latency and energy consumption, respectively, for encryption).
Privacy Issues in Online Social Networks: User Behaviors and Third-Party Applications

In contemporary society, social networking websites has developed dramatically and became an indispensable component in our daily life. Since it can help create a more feature-rich online social community, third-party service has been widely adopted in online social networks (OSNs). Integrating these third-party sites and applications has not only extended business of both social network server and third party and but also promises to break down the garden walls of social-networking sites. While at the same time it dramatically raises concerns on privacy leakage. This article mainly focuses on the privacy disclosure issues caused by user’s behavior and third-party applications and websites. On the one hand, because of the diversity of usage behaviors, the revelation of personal information varies significantly. A survey is conducted to present empirical and quantitative result. On the other hand, the access mechanism between OSN and third party is not perfect enough. Besides, it could be a potential source of privacy leak that third-party services sometimes act as advertisers and information aggregators of a user's traversals. The relevant reasons and internal and external threats are presented. Finally, possible solutions to reduce the increasing information disclosure are provided. Actions should be taken along three fronts: the government, the users themselves as well as the third parties.
Centre for Quantifiable Quality of Service in Communication Systems

Visual quality is one of the most important issues in Digital Cinema applications, and the most practical method to measure visual quality is using objective metrics. Several objective metrics that take into account perceived visual quality have been developed in the past few years; one of them is based on the structural similarity paradigm. To analyze the performance of these objective metrics for digital cinema applications, a subjective visual quality assessment in digital cinema environments using contents from the Digital Cinema Initiative (DCI) Standard Evaluation Material has been conducted in a previous study. Since in practice digital cinema utilizes only high quality imagery, in this paper we analyze further a subset of highquality stimuli from the previous study.
Calling Out Bluff: Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems

Automatic scoring engines have been used for scoring approximately fifteen million test-takers in just the last three years. This number is increasing further due to COVID-19 and the associated automation of education and testing. Despite such wide usage, the AI-based testing literature of these "intelligent" models is highly lacking. Most of the papers proposing new models rely only on quadratic weighted kappa (QWK) based agreement with human raters for showing model efficacy. However, this effectively ignores the highly multi-feature nature of essay scoring. Essay scoring depends on features like coherence, grammar, relevance, sufficiency and, vocabulary. To date, there has been no study testing Automated Essay Scoring: AES systems holistically on all these features. With this motivation, we propose a model agnostic adversarial evaluation scheme and associated metrics for AES systems to test their natural language understanding capabilities and overall robustness. We evaluate the current state-of-the-art AES models using the proposed scheme and report the results on five recent models. These models range from feature-engineering-based approaches to the latest deep learning algorithms. We find that AES models are highly overstable. Even heavy modifications(as much as 25%) with content unrelated to the topic of the questions do not decrease the score produced by the models. On the other hand, irrelevant content, on average, increases the scores, thus showing that the model evaluation strategy and rubrics should be reconsidered. We also ask 200 human raters to score both an original and adversarial response to seeing if humans can detect differences between the two and whether they agree with the scores assigned by auto scores.
Journal of Computer and System Sciences

The theory of combinatorial designs has recently been used in order to build switch-centric data centre networks incorporating a large number of servers, in comparison with the popular Fat-Tree data centre network. We clarify and extend these results and prove that in these data centre networks: there are pairwise link-disjoint paths joining all the servers adjacent to some switch with all the servers adjacent to any other switch; and there are pairwise link-disjoint paths from all the servers adjacent to some switch to any identically-sized collection of target servers where these target servers need not be adjacent to the same switch. In both cases, we always control the path lengths. Our constructions and analysis are undertaken on bipartite graphs with the applications to data centre networks being easily derived. Our results show the potential of the application of results and methodologies from combinatorics to data centre network design.
Improved Minimum Error Rate Training in Moses

Improved Minimum Error Rate Training in Moses We describe an open-source software for minimum error rate training (MERT) for statistical machine translation (SMT). This was implemented within the Moses toolkit, although it is essentially standsalone, with the aim of replacing the existing implementation with a cleaner, more flexible design, in order to facilitate further research in weight optimisation. A description of the design is given, as well as experiments to compare performance with the previous implementation and to demonstrate extensibility.
Pattern recognition

Unsupervised feature selection methods try to select features which can well preserve the intrinsic structure of data. To represent such structure, conventional methods construct various graphs from data. In most cases, those different graphs often contain some consensus and complementary information. To make full use of such information, we construct multiple base graphs and learn an adaptive consensus graph from these base graphs for feature selection. In our method, we integrate the multiple graph learning and the feature selection into a uniﬁed framework, which can jointly characterize the structure of the data and select the features to preserve such structure. The underlying optimization problem is hard to solve, and we solve it via a block coordinate descent schema, whose convergence is guaranteed. The extensive experiments well demonstrate the effectiveness of our proposed framework.
Independent Component Analysis Based on Information Bottleneck

The paper is mainly used to provide the equivalence of two algorithms of independent component analysis (ICA) based on the information bottleneck (IB). In the viewpoint of information theory, we attempt to explain the two classical algorithms of ICA by information bottleneck. Furthermore, via the numerical experiments with the synthetic data, sonic data, and image, ICA is proved to be an edificatory way to solve BSS successfully relying on the information theory. Finally, two realistic numerical experiments are conducted via FastICA in order to illustrate the efficiency and practicality of the algorithm as well as the drawbacks in the process of the recovery images the mixing images.
INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

: With the increasing popularity of technologies such as Internet streaming video and video conferencing, video compression has became an essential component of broadcast and entertainment media. Motion Estimation (ME) and compensation techniques, which can eliminate temporal redundancy between adjacent frames eﬀectively, have been widely applied to popular video compression coding standards such as MPEG-2, MPEG-4. A motion estimation algorithm is to improve the performance of the existing searching algorithms at a relative low computational cost . In this paper will be provide survey of fastest block matching algorithm in motion estimation. Concentrate on study of “Hash based block matching in motion estimation. We propose a hash-based block matching scheme for the motion estimation process, which enables frame level block searching. In the proposed scheme, the blocks sharing the same hash values with the current block are selected as prediction candidates. Then the hash-based block selection is employed to select the best candidates. Achieve best coding efficiency. The proposed scheme, bit rate saving and time reduction
DEEP PPDES FOR ROUGH (LOCAL) STOCHASTIC VOLATILITY

We introduce a new deep-learning based algorithm to evaluate options in a large class of rough stochastic volatility models, possibly including a local volatility component. In this setting, we show that the pricing function is the solution to a path-dependent PDE, for which we develop a numerical scheme based on Deep Learning techniques. Numerical simulations suggest that the latter is extremely efficient, and provides a good alternative to classical Monte Carlo simulations.
Automated Keyword Filtering in Latent Dirichlet Allocation for Identifying Product Attributes From Online Reviews

Identifying product attributes from the perspective of a customer is essential to measure the satisfaction, importance, and Kano category of each product attribute for product design. This article proposes automated keyword filtering to identify product attributes from online customer reviews based on latent Dirichlet allocation. The preprocessing for latent Dirichlet allocation is important because it affects the results of topic modeling; however, previous research performed latent Dirichlet allocation either without removing noise keywords or by manually eliminating them. The proposed method improves the preprocessing for latent Dirichlet allocation by conducting automated filtering to remove the noise keywords that are not related to the product. A case study of Android smartphones is performed to validate the proposed method. The performance of the latent Dirichlet allocation by the proposed method is compared to that of a previous method, and according to the latent Dirichlet allocation results, the former exhibits a higher performance than the latter.
The Relation of CMM and Software Lifecycle Model

CMM and Software Life Cycle Model are both widely used in the IT industry. Based on the analysis of CMM and major software life cycle models, the following points were investigated: (1) CMM and Software Life Cycle Model are both results of the process of software activities and they both focus on the process of software activities. To be specific, CMM is the result of the maturity stage, and Software Life Cycle Model is the result of the early stage. (2) Owing to different development backgrounds and different problems to resolve, Software Life Cycle Model focuses on engineering activities, while CMM focuses on non- engineering activities. (3) Though the CMM authors had tried to make CMM be independent of any certain software life cycle model, the originating era of CMM did leave CMM with the brand of the waterfall model.
A Survey on Enhancing Resource Allocation in Virtual Environment

The cloud computing has become the main part networking for its vast applications all over the world. The cloud computing empowers the primary services for business point of view with the customer and also computes the variation in the resource consumption depending upon the load requirement. However, the enabling the simultaneous use of single machine over many numbers of the computer system is the most challenging thing in the cloud computing. In reality, when the workload ramp-up, techniques adopted for resource allocation cannot fulfill the speedy execution of more job, by which efficiency in the service may reduce. The efficiency can be improved by considering information on workload and analytical performance. The workload intensity in many visualized IT resource can be found to have QoS (Quality of service). Thus, the virtual machines are used to solve these issues. This paper presents a survey on resource allocation using virtual service of cloud computing in a different work situation.
Adaptive display power management for OLED displays

Mobile gaming has become increasingly popular in the past few years with the proliferation of smartphones that have the increased CPU, memory, and network (3.5G etc.) capabilities to support a vast range of interesting games. In addition, these phones also have high quality displays, such as Organic Light Emitting Diodes (OLED) displays, that allow the intricate details in games to be shown in vivid detail to end users. Unfortunately, these displays tend to consume a lot of energy - which in turn limits the amount of time that a user can spend actually playing games on these devices. In this paper, we describe a technique that makes use of saliency, with respect to the end user, to reduce the power consumption of OLED displays when they are used for games, by reducing the brightness of game areas that are not of interest currently to the game player. We evaluated our technique with a user study and showed that even for fast-paced shooting games, our solution is able to save a modest amount of power with no impact in the end user gaming experience.
Face Recognition Using Genetic Algorithm and Back Propagation Neural Network

 Abstract— In this paper, a face recognition system for personal identification and verification using Genetic algorithm and Back-propagation Neural Network is proposed. The system consists of three steps. At the very outset some pre-processing are applied on the input image. Secondly face features are extracted, which will be taken as the input of the Back-propagation Neural Network (BPN) and Genetic Algorithm (GA) in the third step and classification is carried out by using BPN and GA. The proposed approaches are tested on a number of face images. Experimental results demonstrate the higher degree performance of these algorithms.
Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python – Supplemental Material

where P (x|θ) is the likelihood of observing the data (in this case choices and RTs) given each parameter value and P (θ) is the prior probability of the parameters. In most cases the computation of the denominator is quite complicated and requires to compute an analytically intractable integral. Sampling methods like Markov-Chain Monte Carlo (MCMC) (Gamerman and Lopes, 2006) circumvent this problem by providing a way to produce samples from the posterior distribution. These methods have been used with great success in many different scenarios (Gelman et al., 2003) and will be discussed in more detail below.
Statistical Assessment of Rainfall Characteristics in Upper Blue Nile Basin over the Period from 1953 to 2014

Investigating the trends of hydro-meteorological variables and checking its variability are of great importance for water resources management and development in the River Nile basin. The present study aimed to analyze the rainfall variability and trends in the Upper Blue Nile Basin (UBNB) over a period from 1953 to 2014. Variability analysis showed that the basin has been suffering from variable rainfall events causing severe droughts and floods over different years. According to precipitation concentration index calculations, the basin had irregular and strong irregular rainfall distribution over the annual and dry seasons, while the basin had a uniform and moderate rainfall distribution during the rainy season and small rainy season. For the total annual rainfall, Mann–Kendall test indicated that, for the eastern central part of the basin, a significant increasing trend of 12.85 mm/year occurred over the studied period, while, for the southwestern part of the basin, a significant decrease of 17.78 mm/year occurred. For the rainy season, a significant increasing trend over the northeastern and eastern central parts of the basin with the magnitude of 3.330–12.625 mm/year occurred. Trend analysis was applied on the monthly averaged rainfall over the whole basin and revealed that July and August are the most contributors of rainfall to the basin with 23.32% and 22.65%. Changing point assessment revealed that at Lake Tana outlet there is a decreasing of the rainfall of 17.7% after 1977 that matched with the trend analysis results. The data and results contained herein provide updated information about the current situation in the UBNB. The results can be used to predict future precipitation and estimate the uncertainty in future precipitation prediction models.
Application of artiﬁcial intelligence technique for modelling elastic properties of 2D nanoscale material

A novel computational approach is proposed to investigate the shear modulus of graphene nanostructures. In this approach, the factors that affect the shear modulus of graphene structures are analysed using an integrated artiﬁcial intelligence (AI) cluster comprising molecular dynamics (MD) and gene expression programming. The MD-based-AI approach has the ability to formulate the explicit relationship of shear modulus graphene nanostructure with respect to aspect ratio, temperature, number of atomic planes and vacancy defects. In addition, the shear modulus of graphene predicted using an integrated MD-based-AI model is in good agreement with that of experimental results obtained from the literature. The sensitivity and parametric analysis were further conducted to ﬁnd out speciﬁc inﬂuence and variation of each of the input system parameters on the shear modulus of two graphene structures. It was found that the number of defects has the most dominating inﬂuence on the shear modulus of graphene nanostructure.
Toxicological assessment of pesticide contaminated soils with use of biotests

The paper presents the results of experiments on ecological toxicity assessment performed for 12 soil samples collected at 3 obsolete pesticide “tombs” in Poland, before their final disposal. Bioavailability of the main pollutants: p,p’-DDT, lindane and methoxychlor was assessed for selected samples by consecutive solid phase extraction using a Tenax TA sorbent. Several toxicity bioassays were also carried out, including: reducers (Microtox Soild Phase), producers (Phytotox) and consumers (tests of avoidance, acute toxicity and reproduction with use of the earthworm Eisenia foetida). Data from toxicity tests were discussed against the results of the chemical analysis of a wide range of pesticides determined by GC-ECD and GC-NPD. This part of the study enabled the evaluation of the applicability of the aforementioned bioassays in the assessment of pesticide soil pollution. Results of toxicity tests showed a slight to severe impairment of habitat function for all of the contaminated samples, which was only partially reflected by the analytical data. The most sensitive biotest was earthworm reproduction, followed by Phytotox and earthworm acute toxicity. Earthworm avoidance and Microtox tests were found to be of rather limited usability.
Experimental and numerical investigation on the convective thermal plume around the head of the standing and lying hu-man body

This paper presents a study for two thermal plumes generated by two humanoid thermal manikins, one standing and one lying down. The research was approached from a numerical and experimental perspective. The numerical model represents an operating room (OR) with two surgeons, a patient and a unidirectional air flow (UAF) diffuser. The experimental study was made in a climatic chamber, having a similar air distribution system, using particle image velocimetry (PIV) and infrared thermography (IR) measurements. The purpose of the study was to characterize the thermal plumes of the two manikins by numerical and experimental studies. The results obtained from these different approaches were compared with each other and with the ones from the literature in order to validate our numerical models.