Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Malicious Domains with Behavioral Modeling and Graph Embedding

The last decade has witnessed the explosive growth of malicious Internet domains which serve as the fundamental infrastructure for establishing advanced persistent threat command and control communication channels or hosting phishing Web sites. Given the big data nature of Internet traffic data and the ability of algorithmically generating domains and acquiring and registering the domains in a near-automated fashion, detecting malicious domains in real-time is a daunting task for security analysts and network operators. In this paper, we introduce bipartite graphs to capture the interactions between end hosts and domains, identify associated IP addresses of domains, and characterize time-series patterns of DNS queries for domains, and explore one-mode projections of these bipartite graphs for modeling the behavioral, IP-structural, and temporal similarities between domains. We employ graph embedding technique to automatically learn dynamic and discriminative feature representations for over 10,000 labeled domains, and develop an SVM-based classification algorithm for predicting malicious or benign domains. Our model makes the progress towards adapting to the changing and evolving strategies of malicious domains. The experimental results have shown that our proposed algorithm achieves an area under the curve (AUC) of 0.94 based on k-fold cross-validation. To the best of our knowledge, this is the first effort to apply the combination of behavioral modeling and graph embedding for effectively and accurately detecting malicious domains.
WiMi: Target Material Identification with Commodity Wi-Fi Devices

Target material identification is playing an important role in our everyday life. Traditional camera and video-based methods bring in severe privacy concerns. In the last few years, while RF signals have been exploited for indoor localization, gesture recognition and motion tracking, very little attention has been paid in material identification. This paper introduces WiMi, a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. We also design a new material feature which is only related to the material type and is independent of the target size. Comprehensive real-life experiments demonstrate that WiMi can achieve fine-grained material identification with cheap commodity Wi-Fi devices. WiMi can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors. Even for very similar items such as Pepsi and Coke, WiMi can still differentiate them at a high accuracy.
Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods

One of the reasons for the success of convolutional networks is their equivariance/invariance under translations. However, rotatable data such as molecules, living cells, everyday objects, or galaxies require processing with equivariance/invariance under rotations in cases where the rotation of the coordinate system does not affect the meaning of the data (e.g. object classification). On the other hand, estimation/processing of rotations is necessary in cases where rotations are important (e.g. motion estimation). There has been recent progress in methods and theory in all these regards. Here we provide an overview of existing methods, both for 2D and 3D rotations (and translations), and identify commonalities and links between them, in the hope that our insights will be useful for choosing and perfecting the methods.
ResumeGAN: An Optimized Deep Representation Learning Framework for Talent-Job Fit via Adversarial Learning

Nowadays, it is popular to utilize online recruitment services for talent recruitment and job recommendation. Given the vast amounts of online talent profiles and job-posts, it is labor-intensive and exhausted for recruiters to manually select only a few potential candidates for further consideration, and also nontrivial for talents to find the most matched job positions. Recently, some deep learning-based approaches are developed to automatically matching the talent resumes and job requirements, and have achieved encouraging performance. In this paper, we propose a novel framework that targets the same task, but integrate different types of information in a more sophisticated way and introduce adversarial learning to learn more expressive representation. In addition, we build a dataset for model evaluation and the effectiveness of our framework is demonstrated by extensive experiments.
Deep learning based task scheduling in a cloud RAN enabled edge environment

As mobile traffic continues to grow, network operators are constantly on a journey, looking for ways to optimize their network. The aim is to improve network operation performance as well as cut down cost without compromising on the overall quality of service. Many network operators as part of their optimization exercise are adopting Cloud Radio Access Network (C-RAN) and it is necessary to address the challenges that this technology poses in an edge environment. One of the major challenges or an area of improvement is how tasks are scheduled as a user moves within the edge environment. This ongoing research adopts a machine learning technique in scheduling tasks efficiently in order for applications and services to adhere to stringent performance requirements even at geographically dispersed "edge" locations.
Evaluation of soil contamination in the surroundings of Kerala Minerals and Metals Limited (KMML) industrial area in Kollam District, Kerala, South India

Toxic trace metals concentration in soil exerts a decisive impact on soil quality in an industrial area. In recent days, industrialization is growing at very faster rate than any other activities. Due to industrialization, the pollution load for water, air and soil is increasing day by day. Metal pollution in soil possess a serious threat to the human health and safety of agricultural production. An attempt is made here to study soil contamination due to industrial effluents and air pollutants in the surroundings of KMML (Kerala Minerals and Metals Ltd.) industrial area in Chavara, Kerala. To find out the soil pollution from the solid and liquid wastes produced due to the manufacturing process of titanium dioxide in the KMML industry, a detailed analysis was conducted on the physico-chemical characteristics of soil samples collected from the surrounding areas. The study on the physical and chemical characteristics of soil in the selected stations of KMML industrial area shows that soils are acidic in nature and the concentration of nutrients like organic carbon, organic matter, nitrogen, total phosphorus, sodium in the study stations near the industry were less than that of the control soils. The present study revealed that the heavy metal concentrations in all the study stations were high compared to the control station. The study shows that the soils in the surroundings of industrial area are contaminated with toxic elements than its normal distribution. Key words: Soil pollution, heavy metals, industrial area, Kerala minerals and metals limited (KMML)
Understanding Default Behavior in Online Lending

Microcredit, very small loans given out without any collaterals, is a new form of financial instrument that serves the segment of population that are typically underserved by traditional financial services. When microcredit takes the form of lending over the internet, it has the advantage of easy online application process and fast funding for borrowers, as well as attractive rate of return for individual lenders. For platforms that facilitate such activities, the key challenge lies in risk management, i.e. adequately pricing each loan's risk so as to balance borrowers' lending cost and lenders' risk-adjusted return. In fact, identifying default borrowers is of critical importance for the ecosystem. Traditionally, credit risk depends heavily on borrowers' historical loan records. However, most borrowers do not have any bureau history, and therefore cannot provide sufficient loan records. In this paper, we study default prediction in online lending by using social behavior. Specifically, we based our work on a dataset provided by PPDai, one of the leading platforms in China. Our dataset consists of over 11 million users and more than 1.5 billion call logs between them. We establish a mobile network and explore social factors that predict borrowers' default. Based on this, we focused on cheating agents, who recruit and teach borrowers to cheat by providing false information and faking application materials. Cheating agents represent a type of default, especially detrimental to the system. We propose a novel probabilistic framework to identify default borrowers and cheating agents simultaneously. Experimental results on production dataset demonstrate significant improvement over several baseline methods. Moreover, our model can effectively identify cheating agents without any labels.
Mode choice endogeneity in value of travel time estimation

Abstract It is often found that the value of travel time (VTT) is higher for car drivers than for public transport passengers. This paper examines the possible explanation that the difference could be due to a selection effect. The result is an inability to measure the effect of a mode difference, e.g., comfort, among transport modes. We specify a model that captures the mode difference through a mode dummy and use econometric techniques that allow treatment of the mode dummy as the result of an individual choice and hence endogenous. Using first a standard logit model we find a large and significant difference between the VTT for bus and car. When we control for endogeneity using instruments, the mode dummy becomes smaller and just significant. Our investigation is novel in that it allows for endogeneity in the estimation of VTT but like other applications using instruments the results indicate that we have difficulty in finding good instrumental variables.
On-line Client Association Scheme Based on Reinforcement Learning for WLAN Networks

Emergent WLAN applications demand more and more network resources. At the same time, the increasing number of users leads to dense WLAN networks. In this context, the standard client-Access Point association has been shown to reach far from optimal performance. Thanks to the SDN paradigm, centralised programmable strategies could be applied by the WLAN controllers to choose the best Access Point for each user when many Access Points are in the vicinity of a user. In this paper, we present an on-line centralized client-Access Point association scheme based on reinforcement learning which aims at minimizing the clients dissatisfaction in terms of obtained rate. The scheme is implemented in a SDN controller which pilots the Access Points. Simulation results show the superiority of our scheme over the IEEE standard signal strength based association in terms of aggregated throughput and users satisfaction.
AQUALOC: An underwater dataset for visual–inertial–pressure localization

We present a new dataset, dedicated to the development of simultaneous localization and mapping methods for underwater vehicles navigating close to the seabed. The data sequences composing this dataset are recorded in three different environments: a harbor at a depth of a few meters, a first archeological site at a depth of 270 meters, and a second site at a depth of 380 meters. The data acquisition is performed using remotely operated vehicles equipped with a monocular monochromatic camera, a low-cost inertial measurement unit, a pressure sensor, and a computing unit, all embedded in a single enclosure. The sensors’ measurements are recorded synchronously on the computing unit and 17 sequences have been created from all the acquired data. These sequences are made available in the form of ROS bags and as raw data. For each sequence, a trajectory has also been computed offline using a structure-from-motion library in order to allow the comparison with real-time localization methods. With the release of this dataset, we wish to provide data difficult to acquire and to encourage the development of vision-based localization methods dedicated to the underwater environment. The dataset can be downloaded from: http://www.lirmm. fr/aqualoc/
Empirical Relationships between Modified Mercalli Intensity and Engineering Ground-Motion Parameters in Greece

Abstract New relationships between modified Mercalli intensity (MMI) and engineering ground-motion parameters are developed for Greece. The ground-motion parameters investigated were peak ground acceleration (PGA), velocity, displacement, Arias intensity, and cumulative absolute velocity. The observed earthquake intensity is quantified in terms of the observed MMI at the recording station and the data set consists of 310 time histories recorded from 89 Greek earthquakes. The selected records were found to be characterized by high-frequency, low-energy content and short duration. Two sets of empirical relationships between MMI and the selected ground-motion parameters were derived. The first set of MMI predictive equations are independent of magnitude and epicentral distance, and they were derived by fitting the mean values of the ground-motion parameters using a weighted least-squares regression technique. The influence of magnitude, epicentral distance, and the local site conditions were incorporated into the second MMI predictive model, resulting in a decrease of the model variance. The lowest standard deviation observed for the first MMI predictive model was for PGA, while for the second MMI predictive model, Arias intensity exhibited the smallest variability. Another finding of the present study was that the local site effect has a little influence on the MMI predictive model for peak ground velocity (PGV). The proposed predictive equations are valid for MMI values IV–VIII, and some of them might be used for rapid assessment of the ground shaking and mapping damage potential.
Consumer rights in Iran’s telecom: Investigation effective drivers on permission base mobile marketing

Nowadays the SMS advertisements in Iran are sent without observing the regulations which protect the consumer rights; this practice has proved to be a major annoyance to Iranian mobile users. In this paper, the factors which need to be examined before gaining mobile user’s permission were evaluated. The hypothesized model is empirically tested using data collected from a survey in Tehran theater-e-shahr audiences. Structural Equation Modeling (SEM) was used to evaluate the causal model and Confirmatory Factor Analysis (CFA) was performed to examine the reliability and validity of the measurement model. It is concluded that mobile marketing experience extremely affects the process of getting mobile users’ permission.
Soft Frequency Capping for Improved Ad Click Prediction in Yahoo Gemini Native

Yahoo's native advertising (also known as Gemini native) serves billions of ad impressions daily, reaching a yearly run-rate of many hundred of millions USD. Driving the Gemini native models that are used to predict both click probability (pCTR) and conversion probability (pCONV) is øffset\ -- a feature enhanced collaborative-filtering (CF) based event prediction algorithm. øffset is a one-pass algorithm that updates its model for every new batch of logged data using a stochastic gradient descent (SGD) based approach. Since øffset represents its users by their features (i.e., user-less model) due to sparsity issues, rule based hard frequency capping (HFC) is used to control the number of times a certain user views a certain ad. Moreover, related statistics reveal that user ad fatigue results in a dramatic drop in click through rate (CTR). Therefore, to improve click prediction accuracy, we propose a soft frequency capping (SFC) approach, where the frequency feature is incorporated into the øffset model as a user-ad feature and its weight vector is learned via logistic regression as part of øffset training. Online evaluation of the soft frequency capping algorithm via bucket testing showed a significant $7.3$% revenue lift. Since then, the frequency feature enhanced model has been pushed to production serving all traffic, and is generating a hefty revenue lift for Yahoo Gemini native. We also report related statistics that reveal, among other things, that while users' gender does not affect ad fatigue, the latter seems to increase with users' age.
Reductions for Safety Proofs (Extended Version)

Program reductions are used widely to simplify reasoning about the correctness of concurrent and distributed programs. In this paper, we propose a general approach to proof simplification of concurrent programs based on exploring generic classes of reductions. We introduce two classes of sound program reductions, study their theoretical properties, show how they can be effectively used in algorithmic verification, and demonstrate that they are very effective in producing proofs of a diverse class of programs without targeting specific syntactic properties of these programs. The most novel contribution of this paper is the introduction of the concept of context in the definition of program reductions. We demonstrate how commutativity of program steps in some program contexts can be used to define a generic class of sound reductions which can be used to automatically produce proofs for programs whose complete Floyd-Hoare style proofs are theoretically beyond the reach of automated verification technology of today.
Delay Optimal Coding for Secure Transmission over a Burst Erasure Wiretap Channel

We consider transmissions of secure messages over a burst erasure wiretap channel under decoding delay constraint. For block codes we introduce and study delay optimal secure burst erasure correcting (DO-SBE) codes that provide perfect security and recover a burst of erasures of a limited length with minimum possible delay. Our explicit constructions of DO-SBE block codes achieve maximum secrecy rate. We also consider a model of a burst erasure wiretap channel for the streaming setup, where in any sliding window of a given size, in a stream of encoded source packets, the eavesdropper is able to observe packets in an interval of a given size. For that model we obtain an information theoretic upper bound on the secrecy rate for delay optimal streaming codes. We show that our block codes can be used for construction of delay optimal burst erasure correcting streaming codes which provide perfect security and meet the upper bound for a certain class of code parameters.
Problems and Prospects of Banana Breeding in India

Banana breeding programme in India involves maintenance of various genetic resources of banana, of which triploids constitute the maximum share over diploids or tetraploids. RAPD studies conducted in these clones exhibit many distinct genotypes. During a hybridization programme, although many crosses were made, seed set and seed germination were relatively poor in many crosses. Male fertility in banana hybrids could be assessed by pollen output per anther; pollen viability and pollen size, which vary from cross to cross, and also from ploidy to ploidy. Ploidy levels in hybrids are estimated by phenotypic appearance (scoring technique) and confirmed either by stomatal density, size and number of chloroplast per guard cell pair or root tip mitosis. However, flow cytometry appears to be the most reliable method in many disputed cases. Generation of parthenocarpic hybrids depends largely upon selection and utilization of parents with parthenocarpic pedigree in a breeding programme. Evaluation of hybrids and parents indicated the nature of inheritance with respect to plant height and suckering habit but no definite trend could be ascribed to the traits of bunch orientation. Diploid x Diploid breeding approach has led to identification of a superior triploid hybrid, NPH 02-01, while Triploid (with AB)xDiploid approach has led to the development of a promising diploid hybrid H.212 and a triploid hybrid H.96/7 (ABB). Similarly, the Triploid x Diploid breeding programme resulted in development of many potential tetraploids that need further improvement. Innovative breeding approaches through in vitro mutation breeding and in vitro polyploidazation resulted in the development of many potentially useful variants. Breeding for resistance against biotic stresses such as Fusarium wilt and nematodes holds promise in banana, and, biochemical mechanisms for resistance in resistant genotypes/hybrids have been elucidated.
Novel Smart Home Authentication Protocol LRP-SHAP

Remote control the smart home eases the burden of the user's daily tasks. Indeed, he can program smart products from anywhere in the world according to his needs. However, the sensor devices as well as the user mobile device communicate via an insecure communication channel. Thus, many attacks are possible, such as privileged-insider attack, impersonation attack, denial of service attack, and the mobile device stolen attack. In the literature, most of the current schemes for smart home remote control are not secure quite regard the aforementioned attacks. As well, they are not lightweight enough at the sensor device side. In this paper, we propose a remote user authentication scheme for a smart home environment. For the computation of a robust session key, we propose to use Elliptic Curve Cryptography. The solution is lightweight for resource-constrained devices with limited resources as the gateway node will assist in deriving the session key to the sensor device. The security of the proposed solution is proved using a formal security evaluation via the Scyther tool. Also, a performance evaluation is performed to show the effectiveness of our solution.
UNDERSTANDING LAND IDLING AS A STRATEGIC BEHAVIOUR: A BRIEF NOTE USING THE REAL OPTIONS APPROACH

One possible explanation for land idling in a rapidly transforming economy can be found by looking at the value of the option to wait and hysteresis behaviour in the land market. If investors assume that the drop is temporary and that its long term prospects outweigh current holding losses, the market will observe some form of a zone of inactivity i.e., no selling and no additional buying. The fact that landowners elect to keep their valuable land underdeveloped for prolonged periods of time suggests that idled land is more flexible and valuable than what the current market price suggests. If the revenue from farming is insufficient to sustain the activity, land is left idle; although this operational decision is open to continuous revision. The numerical example shown in the paper shows that price of land can be substantially bolstered by the option to wait embedded in land. We argue that land market prices are based on more than the landâ€™s total present value of its future income stream. In a transforming economy, the option to wait and change land-use are especially important to consider. Policy-wise, the government can discourage land idling by ensuring appropriate and suitable zoning of agricultural areas, strong adherence to the zoning conditions/plans as well as more far-sighted land conversion decisions. These efforts can go a long way to reduce the source and degree of uncertainty which is the most important cause of real option premium in land prices. Based on the nature and type of data available, future directions of research should emphasize on empirical testing of real options premium in sale prices of agricultural land.