Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating Value of Information in Future Vehicular Communications

The next generations of vehicles are expected to be equipped with sophisticated sensors to support advanced automotive services. The large volume of data generated by such applications will likely put a strain on the vehicular communication technologies, which may be unable to guarantee the required quality of service. In this scenario, it is fundamental to assess the value of information (VoI) provided by each data source, to prioritize the transmissions that have greatest importance for the target applications. In this paper, we characterize VoI in future vehicular networks, and investigate efficient data dissemination methods to tackle capacity issues. Through a simulation study, we show how analytic hierarchy multicriteria decision processes can be exploited to determine the value of sensory observations as a function of space, time, and quality criteria.
StableNet: Semi-Online, Multi-Scale Deep Video Stabilization

Video stabilization algorithms are of greater importance nowadays with the prevalence of hand-held devices which unavoidably produce videos with undesirable shaky motions. In this paper we propose a data-driven online video stabilization method along with a paired dataset for deep learning. The network processes each unsteady frame progressively in a multi-scale manner, from low resolution to high resolution, and then outputs an affine transformation to stabilize the frame. Different from conventional methods which require explicit feature tracking or optical flow estimation, the underlying stabilization process is learned implicitly from the training data, and the stabilization process can be done online. Since there are limited public video stabilization datasets available, we synthesized unstable videos with different extent of shake that simulate real-life camera movement. Experiments show that our method is able to outperform other stabilization methods in several unstable samples while remaining comparable in general. Also, our method is tested on complex contents and found robust enough to dampen these samples to some extent even it was not explicitly trained in the contents.
Towards automatic estimation of conversation floors within F-formations

The detection of free-standing conversing groups has received significant attention in recent years. In the absence of a formal definition, most studies operationalize the notion of a conversation group either through a spatial or a temporal lens. Spatially, the most commonly used representation is the F-formation, defined by social scientists as the configuration in which people arrange themselves to sustain an interaction. However, the use of this representation is often accompanied with the simplifying assumption that a single conversation occurs within an F-formation. Temporally, various categories have been used to organize conversational units; these include, among others, turn, topic, and floor. Some of these concepts are hard to define objectively by themselves. The present work constitutes an initial exploration into unifying these perspectives by primarily posing the question: can we use the observation of simultaneous speaker turns to infer whether multiple conversation floors exist within an F-formation? We motivate a metric for the existence of distinct conversation floors based on simultaneous speaker turns, and provide an analysis using this metric to characterize conversations across F-formations of varying cardinality. We contribute two key findings: firstly, at the average speaking turn duration of about two seconds for humans, there is evidence for the existence of multiple floors within an F-formation; and secondly, an increase in the cardinality of an F-formation correlates with a decrease in duration of simultaneous speaking turns.
Anomaly-based Intrusion Detection in Industrial Data with SVM and Random Forests

Attacks on industrial enterprises are increasing in number as well as in effect. Since the introduction of industrial control systems in the 1970’$s$, industrial networks have been the target of malicious actors. More recently, the political and warfare-aspects of attacks on industrial and critical infrastructure are becoming more relevant. In contrast to classic home and office IT systems, industrial IT, so-called OT systems, have an effect on the physical world. Furthermore, industrial devices have long operation times, sometimes several decades. Updates and fixes are tedious and often not possible. The threats on industry with the legacy requirements of industrial environments creates the need for efficient intrusion detection that can be integrated into existing systems. In this work, the network data containing industrial operation is analysed with machine learning- and time series-based anomaly detection algorithms in order to discover the attacks introduced to the data. Two different data sets are used, one Modbus-based gas pipeline control traffic and one OPC UA-based batch processing traffic. In order to detect attacks, two machine learning-based algorithms are used, namely SVM and Random Forest. Both perform well, with Random Forest slightly outperforming SVM. Furthermore, extracting and selecting features as well as handling missing data is addressed in this work.
LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization

More and more neural network approaches have achieved considerable improvement upon submodules of speaker diarization system, including speaker change detection and segment-wise speaker embedding extraction. Still, in the clustering stage, traditional algorithms like probabilistic linear discriminant analysis (PLDA) are widely used for scoring the similarity between two speech segments. In this paper, we propose a supervised method to measure the similarity matrix between all segments of an audio recording with sequential bidirectional long short-term memory networks (Bi-LSTM). Spectral clustering is applied on top of the similarity matrix to further improve the performance. Experimental results show that our system significantly outperforms the state-of-the-art methods and achieves a diarization error rate of 6.63\% on the NIST SRE 2000 CALLHOME database.
Keystone: A Framework for Architecting TEEs

Trusted execution environments (TEEs) are becoming a requirement across a wide range of platforms, from embedded sensors to cloud servers, which encompass a wide range of cost and power constraints as well as security threat models. Unfortunately, each of the current vendor-specic TEEs makes a xed choice in each of the design dimensions of deployability, trusted computing base (TCB), and threat model, with lile room for customization and experimentation. To provide more exibility, we present Keystone—the rst open-source framework for building customized TEEs. Keystone uses a simple abstraction of memory isolation together with a programmable layer that sits underneath untrusted components, such as the OS. We demonstrate that this is sucient to build reusable TEE core primitives, separate from platform-specic modications and required application features. us, Keystone reduces the eort for platform providers and application developers to build only those security features they need. We implement Keystone on RISC-V, an open architecture that provides a straightforward way to realize the Keystone abstractions. We showcase the benets of our design in executing standard benchmarks, applications, and kernels on various deployment platforms.
Origin of up-up-down-down magnetic order in 
Cu2GeO4

We use density-functional band-structure calculations to explore the origin of the up-up-down-down (UUDD) magnetic order in Cu$_2$GeO$_4$ with the frustrated $J_1-J_2$ spin chains coupled into layers within the spinel-like crystal structure. In contrast to earlier studies, we find that the nearest-neighbor coupling $J_1$ may be negligibly small, owing to a nearly perfect compensation of the ferromagnetic direct exchange and antiferromagnetic superexchange. Under this condition, weak symmetric anisotropy of the exchange couplings gives rise to the UUDD order observed experimentally and also elucidates the non-trivial ordering pattern between the layers, whereas a small Dzyaloshinsky-Moriya interaction causes a spin canting that may generate local electric polarization. We argue that the buckling of the copper chains plays a crucial role in the suppression of $J_1$ in Cu$_2$GeO$_4$ and sets this compound apart from other $J_1-J_2$ chain magnets.
A reverse Aldous–Broder algorithm

The Aldous/Broder algorithm provides a way of sampling a uniform spanning tree for finite connected graphs using simple random walk. Namely, start a simple random walk on a connected graph and stop at the cover time. The tree formed by all the first-entrance edges has the law of a uniform spanning tree. Here we show that the tree formed by all the last-exit edges also has the law of a uniform spanning tree. This answers a question of Tom Hayes and Cris Moore from 2010. The proof relies on a bijection that is related to the BEST theorem in graph theory.
Exemplar Getting Out the Vote With Evaluative Thinking

Democratic institutions often do not evaluate their instruments. By working closely with authorities, we developed a field experiment to examine an initiative to increase voter turnout among 18-year-olds that had not previously been evaluated. Particular attention was paid to developing an appropriate program theory and to designing the evaluation in a manner that was consistent with legal and ethical requirements. The program distributed different versions of mobilization letters to the newly enfranchised voters. The treatment effect was positive on turnout and diminished the gap in turnout across population groups, and the effects of the treatments were strongest for individuals with the lowest initial propensity to vote. Costeffectiveness analysis indicated that the price of an additional vote was approximately USD$136. Our findings influenced policy design and helped establish the principle of evaluative thinking as an integrated part of the future program.
IoT Hacking – A Primer

The Internet of Things (IoT) enables many new and exciting applications, but it also creates a number of new risks related to information security. Several recent attacks on IoT devices and systems illustrate that they are notoriously insecure. It has also been shown that a major part of the attacks resulted in full adversarial control over IoT devices, and the reason for this is that IoT devices themselves are weakly protected and they often cannot resist even the most basic attacks. Penetration testing or ethical hacking of IoT devices can help discovering and fixing their vulnerabilities that, if exploited, can result in highly undesirable conditions, including damage of expensive physical equipment or even loss of human life. In this paper, we give a basic introduction into hacking IoT devices. We give an overview on the methods and tools for hardware hacking, firmware extraction and unpacking, and performing basic firmware analysis. We also provide a survey on recent research on more advanced firmware analysis methods, including static and dynamic analysis of binaries, taint analysis, fuzzing, and symbolic execution techniques. By giving an overview on both practical methods and readily available tools as well as current scientific research efforts, our work can be useful for both practitioners and academic researchers.
Pressure image assimilation for atmospheric motion estimation

The complexity of the laws of dynamics governing 3-D atmospheric flows associated with incomplete and noisy observations make the recovery of atmospheric dynamics from satellite image sequences very difficult. In this paper, we address the challenging problem of estimating physical sound and time-consistent horizontal motion fields at various atmospheric depths for a whole image sequence. Based on a vertical decomposition of the atmosphere, we propose a dynamically consistent atmospheric motion estimator relying on a multilayer dynamic model. This estimator is based on a weak constraint variational data assimilation scheme and is applied on noisy and incomplete pressure difference observations derived from satellite images. The dynamic model is a simplified vorticity-divergence form of a multilayer shallow-water model. Average horizontal motion fields are estimated for each layer. The performance of the proposed technique is assessed using synthetic examples and using real world meteorological satellite image sequences. In particular, it is shown that the estimator enables exploiting fine spatio-temporal image structures and succeeds in characterizing motion at small spatial scales.
Relevance of Pecking Order and Trade-Off Theories in Financial Decision Making: Empirical Evidence in the Sri Lankan Companies

This study tests the relevance and the applicability of pecking order theory and trade-off theory in financial decision making using 232 companies and 1624 balanced panel data finance and Nonfinancial firms for the period of 2011-2017 listed in the Colombo Stock Exchange (CSE). Pecking order theory explains that there is not a well-defined debt equity target, and there two kinds of equity, internal and external, one at the top of the pecking order and one at the bottom. Trade-off theory identifies the optimal debt-to-equity ratio as the level at which the cost of two offset each other. This study uses the pool regression, fixed effect and random effect models. Profitability, Tangibility, Firm size, Growth opportunity and Non-debt tax shield were used as independent variables, while leverage was the dependent variable. The study finds both theories are relevant and pecking order theory is more applicable in Sri Lankan companies.
New Address Shift Linear Feedback Shift Register Generator

In this paper we introduced a design of new pseudo random generator, which generate binary sequences that would be used as an encryption key in Stream Cipher Cryptosystem (SCC). The proposed generator consists of number of Linear Feedback Shift Registers (LFSR's), which are considered the basic unit of SCC and some nonlinear functions. The proposed cryptosystem called Address Shift LFSR (ASLFSR) cryptosystem. Lastly, ASLFSR generator subjects to set of Basic Efficient Criteria (BEC) to examine its output to prove its efficiency. [DOI: 10.22401/JNUS.20. 1.20]
Response of Soft Film / Hard Substrate and Hard Film / Soft Substrate Layered Systems Under Small Mass Impact

The impact response of layered systems subjected to small mass impact is studied by the coded finite element program. To simulate impact response, an effective finite element approach in conjunction with the Sun's higher-order beam theory and Kurapati’s generalized power law is proposed. Two typical film and substrate combinations, namely, Al/Si as soft film/hard substrate and Si/Al as hard film/soft substrate layered systems are considered. From the present numerical results, it can be seen that a generalized power law applied would be very helpful for estimating the impact responses of layered systems, and impact responses of Si/Al (hard film/soft substrate) layered systems are more sensitive than those of Al/Si (soft film/hard substrate) layered systems in the same film/substrate thickness. That is, this means that soft film/hard substrate layered systems may eventually be protected from impact damage and is more impact resistant than hard film/soft substrate layered systems. And, also, we can observe that in case of Al/Si (soft film/hard substrate) and Si/Al (hard film/soft substrate) layered systems, the interface and impacted surface unlike occurring in static analysis are prone to more failure risk than the other layer, respectively. Index Term-Al/Si and Si/Al layered systems, Impact response, Small mass impact, Finite element analysis
Performance investigation of selected SQL and NoSQL databases

In the today’s high-tech world the amount of data and especially spatial data is growing from day to day. Databases are one of the best ways to store data. Several database concepts exist for storing data, while the mostly used is the relational database concept. Relational databases are very well in use for the storage of spatial and non-spatial data. But for example in social media, like Facebook or Twitter, relational databases often reach their limit of performance. For huge amounts of data and frequent data changes NoSQL-databases can be used. The paper gives an overview of selected SQL and NoSQL-databases according to their geo-functionalities. In addition it compares two document based NoSQL-databases with a relational database by several performance tests.
Reviving Legacy Enterprise Systems with Micro service-Based Architecture with in Cloud Environments

Evolution has always been a challenge for enterprise computing systems. The microservice based architecture is a new design model which is rapidly becoming one of the most effective means to re-architect legacy enterprise systems and to reengineer them into new modern systems at a relatively low cost. This architectural style has evolved based on a number of different approaches and standards. However, there are quite a few technical challenges which emerge when adopting microservices to revive a legacy enterprise system. In this paper, an evolution framework and a set of feature-driven microservices-oriented evolution rules have been proposed and applied to modernise legacy enterprise systems, with a special emphasis on analysing the implications as regards runtime performance, scalability, maintainability and testability. Testing and evaluation have been carried out in depth, aiming to provide a guidance for the evolution of legacy enterprise systems.
- 1-What is the Essence of " Music ? " A Case Study on a Japanese

In this paper, we discuss what the essence of music is, based on an audience survey to evaluate musical performances with new interfaces. We composed ten pieces by introducing various types of uncertainty (chance operations), musical scores, and instruments, and performed them at a concert for a Japanese audience of 180. From the results of our survey, we concluded that the essential characteristics of music include the human element, or human-ness, and structure in addition to melody, rhythm and harmony. Moreover, we found that subjects with experience in music tend to be more open to new forms than subjects with little or no musical experience. They also are inclined to put much faith in human-ness when they estimate the worth of beauty, pleasure and liking as well as evaluating whether a piece is "music" or not.
New media in the newsroom = more democratic news?

News journalism has undergone significant changes in recent time, the speed and intensity of which has engendered a feeling of crisis in the field. The frequency of debates about the future of news media reflect and even magnify this sense. Yet with far-reaching managerial decisions being made, journalists being made redundant and newsrooms being closed, we do indeed need to step back and reflect on the underlying trends in the change in journalism and the consequences for democracy and society.
eu CORDIAL WORDS RECOGNIZED BY THE AUTOMATA

The concept of cordial words in automata was introduced in [6]. A word w over the alphabet Σ is said to be cordial word, if |η0 − η1| ≤ 1 where η0 is the number of 0’s in w and η1 is the number of 1’s in w and the total number of 0’s and 1’s labeled on the arcs differ by atmost one . Cordial language is the language which consists only cordial words over {0, 1}. In this paper we analyze the patterns of cordial words recognized by the finite state automata. Some interesting cordial words are also obtained by duplication of the transition arcs by states. AMS Subject Classification: 05C78, 68Q45
Major involvement of CD 40 in the regulation of chemokine secretion from human peritoneal mesothelial cells

Major involvement of CD40 in the regulation of chemokine secretion from human peritoneal mesothelial cells. Background. CD40 is a member of the tumor necrosis factor (TNF) family of receptors, whose ligand, CD154, is expressed by activated mononuclear cells. CD40 activation is a major immune regulatory pathway and is important for the regulation of chemokine and cytokine secretion. This study investigates the effect of CD40 ligation on the secretion of chemokines from human peritoneal mesothelial cells (HPMC). Methods. We activated CD40 in HPMC along with combinations of TNF-a, interleukin-1 (IL-1), and interferon gamma (IFN-c ), and evaluated the mRNA levels and protein secretion of regulated upon activation, normal T-cell expressed and secreted (RANTES), monocyte chemoattractant protein-1 (MCP-1), and IL-8. Results. CD40 ligation had a small stimulatory effect on the secretion of all three chemokines, while TNF-a, IL-1 and IFN-c induced their secretion in a dose-dependent manner. The combination of CD40 ligation with either IL-1 or TNF-a increased chemokine secretion additively. IFN-c and CD40 ligation acted in synergy to induce the secretion of the mononuclear recruiting chemokines RANTES and MCP-1 (up to ∼36-fold and ∼ threefold, respectively), for which the combination of all three cytokines with CD40 ligation was extremely potent. In contrast, the secretion of the neutrophil chemoattractant IL-8, induced by CD40 ligation or by the combination of IL-1 and TNF-a, was reduced in the presence of IFN-c . Conclusion. In light of our data, it is reasonable to suggest that in the mononuclear phase of peritonitis, IFN-c and CD154, expressed by activated mononuclear cells, diminish IL-8 secretion from HPMC and thus inhibit neutrophil recruitment. At the same time, the two act in synergy to induce the secretion of RANTES and MCP-1 from HPMC. Hence, by regulating chemokine secretion, CD40 may be involved in peritonitis and in the development of late phase mononuclear predominance.
Modular Human – Robot Applications in the Digital Shopfloor Based on IEC-61499

This chapter presents the results of the conception effort done under Daedalus to transfer the technological results of IEC-61499 into the industrial domain of Human–Robot collaboration, with the aim of deploying the concept of mutualism in next-generation continuously adaptive Human–Machine interactions, where operators and robots mutually complement their physical, intellectual and sensorial capacities to achieve optimized quality of the working environment, while increasing manufacturing performance and flexibility. The architecture proposed envisions a future scenario where Human–Machine distributed automation is orchestrated through the IEC-61499 formalism, to empower worker-centred cooperation and to capitalize on both worker’s and robot’s strengths to improve synergistically their integrated effort.
Wavelet and neuro-fuzzy combination model for predicting suspended sediment transport in rivers

In this paper prediction of the suspended sediment load (SSL) of the rivers by using the available actual data from Pecos River gauging station in the USA was done. For this purpose, result of three models were compared with the actual original data. The models were conventional sediment rating curve (SRC) model, neuro-fuzzy (NF) and conjunction of wavelet analysis and neurofuzzy (WNF). In the proposed WNF model, observed time series of river discharge and SSL were decomposed at different scales by wavelet analysis. Then summed effective time series of discharge and SSL were imposed as inputs to the NF model for prediction of SSL in one day ahead. Obtained results showed that the WNF model performance was better in prediction rather than the NF and SRC models. The WNF model produced reasonable predictions for the extreme values. Furthermore, the cumulative SSL estimated by this technique was closer to the actual data than the other ones. In addition to that, the model could be employed to simulate hysteresis phenomenon, while SRC method did not able to simulate this event. Key-Words: Time series, Wavelet decomposition, Artificial intelligence, Sediment rating curve, Hysteresis.