Discriminating between encrypted and non-encrypted information is desired for many purposes. Much of the efforts in this direction in the literature is focused on deploying machine learning methods for the discrimination in streamed data which is transmitted in packets in communication networks. Here, however, the focus and the methods are different. The retrieval of data from computer hard drives that have been seized from police busts against suspected criminals is sometimes not straightforward. Typically the incriminating code, which may be important evidence in subsequent trials, is encrypted and quick deleted. The cryptanalysis of what can be recovered from such hard drives is then subject to time-consuming brute forcing and password guessing. To this end methods for accurate classification of what is encrypted code and what is not is of the essence. Here a procedure for discriminating encrypted code from non-encrypted is derived. Two methods to detect where encrypted data is located in a hard disk drive are detailed using passive change-point detection. Measures of performance of such methods are discussed and a new property for evaluation is suggested. The methods are then evaluated and discussed according to the performance measures. 

The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person's intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy (R2(pred)>90%). © 2022 by the authors.

Robots are being designed to communicate with people in various public and domestic venues in a perceptive, helpful, and discreet way. Here, we use a speculative prototyping approach to shine light on a new concept of robot steganography (RS): that a robot could seek to help vulnerable populations by discreetly warning of potential threats: We first identify some potentially useful scenarios for RS related to safety and security– concerns that are estimated to cost the world trillions of dollars each year–with a focus on two kinds of robots, a socially assistive robot (SAR) and an autonomous vehicle (AV). Next, we propose that existing, powerful, computer-based steganography (CS) approaches can be adopted with little effort in new contexts (SARs), while also pointing out potential benefits of human-like steganography (HS): Although less efficient and robust than CS, HS represents a currently-unused form of RS that could also be used to avoid requiring a computer to receive messages, detection by more technically advanced adversaries, or a lack of alternative connectivity (e.g., if a wireless channel is being jammed). Some unique challenges of RS are also introduced, that arise from message generation, indirect perception, and effects of perspective. Finally, we confirm the feasibility of the basic concept for RS, that messages can be hidden in a robot’s behaviors, via a simplified, initial user study, also making available some code and a video. The immediate implication is that RS could potentially help to improve people’s lives and mitigate some costly problems, as robots become increasingly prevalent in our society–suggesting the usefulness of further discussion, ideation, and consideration by designers.

What if an autonomous vehicle (AV) could secretly warn of potential threats? “Steganography”, the hiding of messages, is a vital way for vulnerable populations to communicate securely and get help. Here, we shine light on the concept of vehicular steganography (VS) using a speculative approach: We identify some key scenarios, highlighting unique challenges that arise from indirect perception, message generation, and effects of perspective-as well as potential carrier signals and message generation considerations. One observation is that, despite challenges to transmission rates and robustness, physical signals such as locomotion or sound could offer a complementary, currently-unused alternative to traditional methods. The immediate implication is that VS could help to mitigate some costly safety problems-suggesting the benefit of further discussion and ideation. © 2021. All Rights Reserved.

A new method for musical steganography for the MIDI format is presented. The MIDI standard is a user-friendly music technology protocol that is frequently deployed by composers of different levels of ambition. There is to the author’s knowledge no fully implemented and rigorously specified, publicly available method for MIDI steganography. The goal of this study, however, is to investigate how a novel MIDI steganography algorithm can be implemented by manipulation of the velocity attribute subject to restrictions of capacity and security. Many of today’s MIDI steganography methods—less rigorously described in the literature—fail to be resilient to steganalysis. Traces (such as artefacts in the MIDI code which would not occur by the mere generation of MIDI music: MIDI file size inflation, radical changes in mean absolute error or peak signal-to-noise ratio of certain kinds of MIDI events or even audible effects in the stego MIDI file) that could catch the eye of a scrutinizing steganalyst are side-effects of many current methods described in the literature. This steganalysis resilience is an imperative property of the steganography method. However, by restricting the carrier MIDI files to classical organ and harpsichord pieces, the problem of velocities following the mood of the music can be avoided. The proposed method, called Velody 2, is found to be on par with or better than the cutting edge alternative methods regarding capacity and inflation while still possessing a better resilience against steganalysis. An audibility test was conducted to check that there are no signs of audible traces in the stego MIDI files. © 2020 by the author. Licensee MDPI, Basel, Switzerland.

The Diffie–Hellman protocol, ingenious in its simplicity, is still the major solution in protocols for generating a shared secret in cryptography for e-trading and many other applications after an impressive number of decades. However, lately, the threat from a future quantum computer has prompted successors resilient to quantum computer-based attacks. Here, an algorithm similar to Diffie–Hellman is presented. In contrast to the classic Diffie–Hellman, it involves floating point numbers of arbitrary size in the generation of a shared secret. This can, in turn, be used for encrypted communication based on symmetric cyphers. The validity of the algorithm is verified by proving that a vital part of the algorithm satisfies a one-way property. The decimal part is deployed for the one-way function in a way that makes the protocol a post-quantum key generation procedure. This is concluded from the fact that there is, as of yet, no quantum computer algorithm reverse engineering the one-way function. An example illustrating the use of the protocol in combination with XOR encryption is given. © 2020 MDPI (Basel, Switzerland)

Activity recognition in smart environments is essential for ensuring the wellbeing of older residents. By tracking activities of daily living (ADLs), a person’s health status can be monitored over time. Nonetheless, accurate activity classification must overcome the fact that each person performs ADLs in different ways and in homes with different layouts. One possible solution is to obtain large amounts of data to train a supervised classifier. Data collection in real environments, however, is very expensive and cannot contain every possible variation of how different ADLs are performed. A more cost-effective solution is to generate a variety of simulated scenarios and synthesize large amounts of data. Nonetheless, simulated data can be considerably different from real data. Therefore, this paper proposes the use of regression models to better approximate real observations based on simulated data. To achieve this, ADL data from a smart home were first compared with equivalent ADLs performed in a simulator. Such comparison was undertaken considering the number of events per activity, number of events per type of sensor per activity, and activity duration. Then, different regression models were assessed for calculating real data based on simulated data. The results evidenced that simulated data can be transformed with a prediction accuracy of R² = 97.03%.

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Automatic detection and recognition of Activities of Daily Living (ADL) are crucial for providing effective care to frail older adults living alone. A step forward in addressing this challenge is the deployment of smart home sensors capturing the intrinsic nature of ADLs performed by these people. As the real-life scenario is characterized by a comprehensive range of ADLs and smart home layouts, deviations are expected in the number of sensor events per activity (SEPA), a variable often used for training activity recognition models. Such models, however, rely on the availability of suitable and representative data collection and is habitually expensive and resource-intensive. Simulation tools are an alternative for tackling these barriers; nonetheless, an ongoing challenge is their ability to generate synthetic data representing the real SEPA. Hence, this paper proposes the use of Poisson regression modelling for transforming simulated data in a better approximation of real SEPA. First, synthetic and real data were compared to verify the equivalence hypothesis. Then, several Poisson regression models were formulated for estimating real SEPA using simulated data. The outcomes revealed that real SEPA can be better approximated ( R²_pred = 92.72 % ) if synthetic data is post-processed through Poisson regression incorporating dummy variables. © 2020 MDPI (Basel, Switzerland)

Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

The phrase “most cruel and revolting crimes” has been used to describe some poor historical treatment of vulnerable impaired persons by precisely those who should have had the responsibility of protecting and helping them. We believe we might be poised to see history repeat itself, as increasingly humanlike aware robots become capable of engaging in behavior which we would consider immoral in a human–either unknowingly or deliberately. In the current paper we focus in particular on exploring some potential dangers affecting persons with dementia (PWD), which could arise from insufficient software or external factors, and describe a proposed solution involving rich causal models and accountability measures: Specifically, the Consequences of Needs-driven Dementia-compromised Behaviour model (C-NDB) could be adapted to be used with conversation topic detection, causal networks and multi-criteria decision making, alongside reports, audits, and deterrents. Our aim is that the considerations raised could help inform the design of care robots intended to support well-being in PWD.