scholarly journals Detecting Nuisance Calls over Internet Telephony Using Caller Reputation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 353
Author(s):  
Ibrahim Tariq Javed ◽  
Khalifa Toumi ◽  
Fares Alharbi ◽  
Tiziana Margaria ◽  
Noel Crespi
Keyword(s):  
Security Risk ◽  
False Detection ◽  
Short Term ◽  
New Approach ◽  
Call Duration ◽  
Internet Telephony ◽  
Proposed Model ◽  
Different Types ◽  
Reputation Model

Internet telephony permit callers to manage self-asserted profiles without any subscription contract nor identification proof. These cost-free services have attracted many telemarketers and spammers who generate unsolicited nuisance calls. Upon detection, they simply rejoin the network with a new identity to continue their malicious activities. Nuisance calls are highly disruptive when compared to email and social spam. They not only include annoying telemarketing calls but also contain scam and voice phishing which involves security risk for subscribers. Therefore, it remains a major challenge for Internet telephony providers to detect and avoid nuisance calls efficiently. In this paper, we present a new approach that uses caller reputation to detect different kinds of nuisance calls generated in the network. The reputation is computed in a hybrid manner by extracting information from call data records and using recommendations from reliable communicating participants. The behavior of the caller is assessed by extracting call features such as call-rate, call duration, and call density. Long term and short term reputations are computed to quickly detect the changing behavior of callers. Furthermore, our approach involves an efficient mechanism to combat whitewashing attacks performed by malicious callers to continue generating nuisance calls in the network. We conduct simulations to compute the performance of our proposed model. The experiments conclude that the proposed reputation model is an effective method to detect different types of nuisance calls while avoiding false detection of legitimate calls.

“The Death of Liberal Internationalism?: Donald Trump, Walmart, and the Two Koreas”

2018 ◽  
Vol 25 (2) ◽  
pp. 169-197
Author(s):  
Mitchell B. Lerner
Keyword(s):  
United States ◽  
The United States ◽  
Business Practices ◽  
Short Term ◽  
New Approach ◽  
Liberal Internationalism ◽  
The People ◽  
Modern Business ◽  
Short Time

The election of Donald J. Trump unsettled many areas of U.S. foreign policy, but few more than the nation’s relationship with Korea. This article argues that the Trump administration’s vision for the world represents a stark break from the tradition of liberal internationalism and instead seeks to take the United States down a path that reflects the modern business practices of giant American corporations. A suitable label for this vision, as the following pages will show, is “Walmart unilateralism.” This framework abandons the traditional American policies of nation building and alliances based on shared ideological values. Instead, it embraces a more short-term approach rooted in financial bottom lines, flexible alliances and rivalries, and the ruthless exploitation of power hierarchies. This new approach, this article concludes, may dramatically transform the American relationship with Korea. Walmart unilateralism in Korea almost certainly will have some short-time positive ramifications for the United States, but its larger failure to consider the history and values of the people living on the Korean Peninsula may generate serious long-term problems for the future experience of the United States in the region.

15. Democracies, Democratization, and Authoritarian Regimes

2020 ◽  
pp. 336-362
Author(s):  
Peter Ferdinand
Keyword(s):  
Economic Development ◽  
Recent Literature ◽  
Authoritarian Regimes ◽  
Short Term ◽  
National Crisis ◽  
Different Types ◽  
Types Of Democracy ◽  
The Face ◽  
Long Term Trends

This chapter focuses on democracies, democratization, and authoritarian regimes. It first considers the two main approaches to analysing the global rise of democracy over the last thirty years: first, long-term trends of modernization, and more specifically economic development, that create preconditions for democracy and opportunities for democratic entrepreneurs; and second, the sequences of more short-term events and actions of key actors at moments of national crisis that have precipitated a democratic transition — also known as ‘transitology’. The chapter proceeds by discussing the different types of democracy and the strategies used to measure democracy. It also reviews the more recent literature on authoritarian systems and why they persist. Finally, it examines the challenges that confront democracy in the face of authoritarian revival.

Integration of geological and seismological data in earthquakes occurrence models for Italy: towards a unified model for different forecast perspectives

2019 ◽  
Vol 219 (3) ◽  
pp. 2148-2164
Author(s):  
A M Lombardi
Keyword(s):  
Aftershock Sequence ◽  
Earthquake Forecasting ◽  
Short Term ◽  
Background Rate ◽  
Etas Model ◽  
Term Forecast ◽  
Earthquake Predictability ◽  
Background Seismicity ◽  
Different Types

SUMMARY The operational earthquake forecasting (OEF) is a procedure aimed at informing communities on how seismic hazard changes with time. This can help them live with seismicity and mitigate risk of destructive earthquakes. A successful short-term prediction scheme is not yet produced, but the search for it should not be abandoned. This requires more research on seismogenetic processes and, specifically, inclusion of any information about earthquakes in models, to improve forecast of future events, at any spatio-temporal-magnitude scale. The short- and long-term forecast perspectives of earthquake occurrence followed, up to now, separate paths, involving different data and peculiar models. But actually they are not so different and have common features, being parts of the same physical process. Research on earthquake predictability can help to search for a common path in different forecast perspectives. This study aims to improve the modelling of long-term features of seismicity inside the epidemic type aftershock sequence (ETAS) model, largely used for short-term forecast and OEF procedures. Specifically, a more comprehensive estimation of background seismicity rate inside the ETAS model is attempted, by merging different types of data (seismological instrumental, historical, geological), such that information on faults and on long-term seismicity integrates instrumental data, on which the ETAS models are generally set up. The main finding is that long-term historical seismicity and geological fault data improve the pseudo-prospective forecasts of independent seismicity. The study is divided in three parts. The first consists in models formulation and parameter estimation on recent seismicity of Italy. Specifically, two versions of ETAS model are compared: a ‘standard’, previously published, formulation, only based on instrumental seismicity, and a new version, integrating different types of data for background seismicity estimation. Secondly, a pseudo-prospective test is performed on independent seismicity, both to test the reliability of formulated models and to compare them, in order to identify the best version. Finally, a prospective forecast is made, to point out differences and similarities in predicting future seismicity between two models. This study must be considered in the context of its limitations; anyway, it proves, beyond argument, the usefulness of a more sophisticated estimation of background rate, inside short-term modelling of earthquakes.

scholarly journals Neural Architecture Search for a Highly Efficient Network with Random Skip Connections

2020 ◽  
Vol 10 (11) ◽  
pp. 3712
Author(s):  
Dongjing Shan ◽  
Xiongwei Zhang ◽  
Wenhua Shi ◽  
Li Li
Keyword(s):  
State Of The Art ◽  
Cell Structure ◽  
Frequency Discrimination ◽  
Search Space ◽  
Short Term ◽  
Cell Parameters ◽  
Neural Architecture ◽  
Proposed Model ◽  
Initialization Scheme

Regarding the sequence learning of neural networks, there exists a problem of how to capture long-term dependencies and alleviate the gradient vanishing phenomenon. To manage this problem, we proposed a neural network with random connections via a scheme of a neural architecture search. First, a dense network was designed and trained to construct a search space, and then another network was generated by random sampling in the space, whose skip connections could transmit information directly over multiple periods and capture long-term dependencies more efficiently. Moreover, we devised a novel cell structure that required less memory and computational power than the structures of long short-term memories (LSTMs), and finally, we performed a special initialization scheme on the cell parameters, which could permit unhindered gradient propagation on the time axis at the beginning of training. In the experiments, we evaluated four sequential tasks: adding, copying, frequency discrimination, and image classification; we also adopted several state-of-the-art methods for comparison. The experimental results demonstrated that our proposed model achieved the best performance.

scholarly journals An LSTM-Based Method with Attention Mechanism for Travel Time Prediction

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 861 ◽  
Author(s):  
Xiangdong Ran ◽  
Zhiguang Shan ◽  
Yufei Fang ◽  
Chuang Lin
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Traffic Prediction ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Departure Time ◽  
Long Short Term Memory

Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the proposed model. We use AdaGrad method for training the proposed model. Based on the datasets provided by Highways England, the experimental results show that the proposed model can achieve better accuracy than the Long Short-Term Memory and other baseline methods. The case study suggests that the departure time is effectively employed by using attention mechanism.

scholarly journals Long Short-Term Memory with Dynamic Skip Connections

2019 ◽  
Vol 33 ◽  
pp. 6481-6488 ◽  
Author(s):  
Tao Gui ◽  
Qi Zhang ◽  
Lujun Zhao ◽  
Yaosong Lin ◽  
Minlong Peng ◽  
...  
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Training Data ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Transition Functions ◽  
Proposed Model ◽  
Long Short Term Memory

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%.

Simple Techniques for Monitoring Footpath Erosion in Mountain Areas of North-West England

1977 ◽  
Vol 4 (2) ◽  
pp. 145-148 ◽  
Author(s):  
Rosalind A. Coleman
Keyword(s):  
Aerial Photography ◽  
Technical Expertise ◽  
Short Term ◽  
Mountain Areas ◽  
North West ◽  
Different Types ◽  
Different Seasons ◽  
One Year ◽  
Site Types

Very precise measurements of the movement of coarse-textured, unconsolidated materials may be meaningless. Therefore the study of individual processes operating on footpaths may require a different approach. However, for identification of changes of reasonable dimensions, methods such as those described above are inexpensive, quick, and require no technical expertise. It may be argued that, for path management, erosion that is too limited to be measured by these methods is too limited to be a problem. It can certainly be argued that the problem of spatial correlation implies a large number of measurements. What is lost in lack of precision may be more than compensated for by the gain in data from the larger area and wider variation in site-types that it is possible to monitor with such simple techniques.To monitor the effects of recreation in mountain areas, it is desirable to be able to measure any change in path-state and relate this to recreation factors at different seasons and under different sit; -conditions. Three methods of measurement have been considered in this paper, corresponding to three time-scales. Aerial photography has been used to demonstrate trends over the long term, and has proved adequate to differentiate between path sections with differing resistance to erosion.Short-term measurement has been carried out relative to known fixed positions. Two methods are suggested. One is less precise, but simple and widely applicable, and can be used for measurement intervals of six months to one year. The other is more detailed and can be used for measurement intervals of a few days, but is limited in its application by practical considerations.It is suggested that simple techniques used at a large number of different types of site may be more effective than detailed measurements at a few sites.

scholarly journals Modeling sensitivity indices of industrial enterprise organizational change

2021 ◽  
Vol 10 (45) ◽  
pp. 230-241
Author(s):  
Victoriia Bilyk ◽  
Olena Kolomytseva ◽  
Olha Myshkovych ◽  
Nataliia Tymoshyk ◽  
Denis Shcherbatykh
Keyword(s):  
Organizational Change ◽  
Theory And Practice ◽  
Organizational Changes ◽  
Short Term ◽  
Industrial Enterprises ◽  
Sensitivity Indices ◽  
Proposed Model ◽  
Development Effectiveness ◽  
Made In

Evaluation of sensitivity of commercial enterprises to organizational changes should be made in terms of short-term planning for which it is important to ensure the financial results, as well as in terms of long-term planning, which is important for non-monetary indicators of development effectiveness. To solve this problem, the paper is designed model sensitivity Descriptive indicators of industrial enterprises to organizational changes, reflecting monetary and non-monetary effects of organizational change. The authors determined that the proposed model allows for the analysis of organizational change with regard to their impact on monetary and non-monetary efficiency. This paper contributes to the theory and practice at the border to ensure a balance between short-term and long-term development of industrial enterprises. Convincingly demonstrated the possibility of using research results in practice.

scholarly journals Terrorism as a type of political communication: The use of propaganda of the deed in hegemonic struggles

2020 ◽  
Vol 72 (4) ◽  
pp. 709-732
Author(s):  
Filip Otovic-Visnjic
Keyword(s):  
Political Communication ◽  
Communication Strategies ◽  
Important Goal ◽  
Short Term ◽  
Cultural Approach ◽  
Different Types ◽  
Theoretical Conception ◽  
Terrorist Acts ◽  
Efficient Realization

The paper focuses on the communicological dimension of the terrorist act, starting from the position that the violence is used to convey various messages in a non-verbal way. Throughout the research into the propaganda of the deed, the technique on which communicational tactics of terrorists are mostly based, the author seeks to examine extensive ranges of communication strategies used by insurgent groups against dominant hegemony. By combining the elements of Jacques Ellul's theoretical conception of propaganda along with the cultural approach in the interpretation of mechanisms in which hegemony operates, the author refutes perspectives that deny rebellions? possibilities for efficient realization of their propaganda goals by using acts of violence. The author?s conclusion is based on three arguments. Firstly, for modern propaganda, provoking the behavior of the audience (ortopraxie) is a more important goal than influencing its attitudes (orthodoxy). Secondly, it is possible to notice elements in the pre-propaganda field, which evade hegemonic control, due to the contradiction between ideological narratives and the real structure, and which insurgent propaganda may utilize. Lastly - by means of terrorist acts, their performers address different types of audiences with different goals simultaneously. The author concludes that the efficiency of propaganda can be manifested in two manners: in the short term - when an act of violence represents a direct ?trigger? for the desired behavior of the audience; in the long term - by including the act and provoked behavior in the network of memories, which becomes an element of pre-propaganda that can be referred to in the future.

scholarly journals Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 214-221 ◽  
Author(s):  
Ke Sun ◽  
Tieyun Qian ◽  
Tong Chen ◽  
Yile Liang ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
State Of The Art ◽  
User Preferences ◽  
Short Term ◽  
Preference Modeling ◽  
Point Of Interest ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Novel Method ◽  
Real World Datasets

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

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