In this report we explain an alternative computational analysis to the detection diabetes Type 2 from voice, which is an end-to-end pipeline, the input to which is a speech file and the output is a prediction about its category(diseased or control), and it consists of 1) a feature extraction script to obtain richer representation of the speech signal (6000 parameters in placeof less than 20), and 2) learning and testing of a classification functionthat assigns a category to a new sample. The feature extraction can be usedtogether with the classical statistical analysis currently considered to be thegold standard in the literature on diabetes detection from voice.

Recent research describes the effect of Type 2 diabetes (T2D) on voice, suggesting that it can be diagnosed based on vocal clues. Although these studies have similar experimental designs with respect to the voice data and the analysis methods, the conclusions regarding the voice changes differ substantially and are at times contradictory. This is unexpected, since the mechanism of pathological deterioration behind the observed changes is the same. This year in an article published in J. of Voice it was suggested that vocal changes may be different among ethnicities. Before this hypothesis can be accepted, the study protocols should be improved and unified, to ensure that the empirical evidence is reliable. Additionally, given the recently published data about the temporal voice changes as a result of glucose swings, we propose that the persons in hypo- and hyperglycemic conditions should be excluded from the experiment. Since no study succeeded in diabetes detection, it is timely to mention that there is an alternative methodology for disease detection from voice, which is far more sensitive than the state of the art procedure. We propose a script that is available from the first author on request. © 2020 The Voice Foundation

Emotive speech is a non-invasive and cost-effective biomarker in a wide spectrum of neurological disorders with computational systems built to automate the diagnosis. In order to explore the possibilities for the automation of a routine speech analysis in the presence of hard to learn pathology patterns, we propose a framework to assess the level of competence in paralinguistic communication. Initially, the assessment relies on a perceptual experiment completed by human listeners, and a model called the Aggregated Ear is proposed that draws a conclusion about the level of competence demonstrated by the patient. Then, the automation of the Aggregated Ear has been undertaken and resulted in a computational model that summarizes the portfolio of speech evidence on the patient. The summarizing system has a classical emotion recognition system as its central component. The code and the medical data are available from the corresponding author on request. IEEE

In telecommunication business, a major investment goes into the infrastructure and its maintenance, while business revenues are proportional to how big, good, and well-balanced the customer base is. In our previous work we presented a data-driven analytic strategy based on combinatorial optimization and analysis of the historical mobility designed to quantify the desirability of different geo-demographic segments, and several segments were recommended for a partial reduction. Within a segment, clients are different. In order to enable intelligent reduction, we introduce the term infrastructure-stressing client and, using the proposed method, we reveal the list of the IDs of such clients. We also have developed a visualization tool to allow for manual checks: it shows how the client moved through a sequence of hot spots and was repeatedly served by critically loaded antennas. The code and the footprint matrix are available on the SourceForge. © 2019, Springer International Publishing AG, part of Springer Nature.

Emerging scenarios for the “Internet of Things” (IoT) require a dedicated software defined network over the conventional communication network provided by the different service providers and free to use communication methodologies. These IoT networks have their own dedicated requirements, based on the different stakeholders involved in it, which can be realized via dynamic context-based authentication, authorization and accounting (AAA). This AAA needs to be envisaged in a much larger perspective than the current perspective in telecom networks. As part of this study, we have identified a few external stakeholders, who are domain and IoT experts and discussed the various requirements, scenarios and change scenarios for the dedicated IoT networks. Relying on Zachman’s framework, a reference architecture that we call as “Smart AAA agent for dedicated IoT network” is presented to the domain experts and evaluated against their scenarios utilizing a scenario-based software architecture analysis method. The scenarios discussed and utilized for the analysis encompass two ends of the IoT spectrum of requirements. The medical domain scenarios have critical IoT perspective as lives and health of patients is involved, while the enterprise IoT scenarios involve huge scalability and monetizability aspect, which is very important for the industry. With this reference architecture, we demonstrate a system capable of providing a software defined network fulfilling the requirements of a dedicated IoT network as enlisted in scenarios by the external stakeholders. Furthermore, this proposed reference architecture is evaluated with a software architect and matured to its current state and made available for any future research, development or standardization for 5G and next generation networks for the Internet of Things.

The main resource for any telecom operator is the physical radio cell network. We present two related methods for optimizing utilization in radio networks: Tetris optimization and selective cell expansion. Tetris optimization tries to find the mix of users from different market segments that provides the most even load in the network. Selective cell expansion identifies hotspot cells, expands the capacity of these radio cells, and calculates how many subscribers the radio network can handle after the expansions. Both methods are based on linear programming and use mobility data, i.e., data defining where different categories of subscribers tend to be during different times of the week. Based on real-world mobility data from a region in Sweden, we show that Tetris optimization based on six user segments made it possible to increase the number of subscribers by 58% without upgrading the physical infrastructure. The same data show that by selectively expanding less than 6% of the cells we are able to increase the number of subscribers by more than a factor of three without overloading the network. We also investigate the best way to combine Tetris optimization and selective cell expansion. © 2018 The Author(s)

We define the term an Infrastructure-Stressing client. Roughly speaking, she uses the infrastructure in a taxing manner, such as always staying in the zones of high demand. We developed a method based on combinatorial optimization to reveal the Infrastructure-Stressing clients in the customer base based on trajectory information from Call Data Records. We have found that 7 % in the customer base are Infrastructure-Stressing. As was expected, a correlation exists between this quality and client's geo-demographic segment. Currently we are working on a predictive model to be able to tell an Infrastructure-Stressing client in a newcomer whose mobility is yet unknown to the operator. © 2019 Universitatsverlag Potsdam. All rights reserved.

As pointed out by Zadeh, the mission of fuzzy logic in the era of big data is to create a relevant summary of huge amounts of data and facilitate decision-making. In this study, elements of fuzzy set theory are used to create a visual summary of telecom data, which gives a comprehensive idea concerning the desirability of boosting an operator’s presence in different neighborhoods and regions. The data used for validation cover historical mobility in a region of Sweden during a week. Fuzzy logic allows us to model inherently relative characteristics, such as “a tall man” or “a beautiful woman”, and importantly it also defines “anchors”, the situations (characterized with the value of the membership function for the characteristic) under which the relative notion receives a unique crisp interpretation. We propose color coding of the membership value for the relative notions such as “the desirability of boosting operator’s presence in the neighborhood” and “how well the operator is doing in the region”. The corresponding regions on the map (e.g., postcode zones or larger groupings) are colored in different shades passing from green (1) though yellow (0.5) to red (0). The color hues pass a clear intuitive message making the summary easy to grasp. © 2019, Springer Nature Singapore Pte Ltd.

The population in Sweden is growing rapidly due to immigration. In this light, the issue of infrastructure upgrades to provide telecommunication services is of importance. New antennas can be installed at hot spots of user demand, which will require an investment, and/or the clientele expansion can be carried out in a planned manner to promote the exploitation of the infrastructure in the less loaded geographical zones. In this paper, we explore the second alternative. Informally speaking, the term Infrastructure-Stressing describes a user who stays in the zones of high demand, which are prone to produce service failures, if further loaded. We have studied the Infrastructure-Stressing population in the light of their correlation with geo-demographic segments. This is motivated by the fact that specific geo-demographic segments can be targeted via marketing campaigns. Fuzzy logic is applied to create an interface between big data, numeric methods for its processing, and a manager who wants a comprehensible summary. © 2017, Springer International Publishing AG.

A major investment made by a telecom operator goes into the infrastructure and its maintenance, while business revenues depend on how efficiently it is exploited. We present a data-driven analytic strategy based on combinatorial optimization and analysis of historical data. The data cover historical mobility in one region of Sweden during a week. Applying the proposed method in a case study, we have identified the optimal combination of geodemographic segments in the customer base, developed a functionality to assess the potential of a planned marketing campaign, and investigated how many and which segments to target for customer base growth. A comprehensible summary of the conclusions is created via execution of the queries with a fuzzy logic component. © 2017 IEEE.