Miracle-dz/UniFAR-FTUs huggingface dataset:
AI on the Edge: Architectural Alternatives

Since the advent of mobile computing and IoT, a large amount of data is distributed around the world. Companies are increasingly experimenting with innovative ways of implementing edge/cloud (re)training of AI systems to exploit large quantities of data to optimize their business value. Despite the obvious benefits, companies face challenges as the decision on how to implement edge/cloud (re)training depends on factors such as the task intent, the amount of data needed for (re)training, edge-to-cloud data transfer, the available computing and memory resources. Based on action research in a software-intensive embedded systems company where we study multiple use cases as well as insights from our previous collaborations with industry, we develop a generic framework consisting of five architectural alternatives to deploy AI on the edge utilizing transfer learning. We validate the framework in four additional case companies and present the challenges they face in selecting the optimal architecture. The contribution of the paper is threefold. First, we develop a generic framework consisting of five architectural alternatives ranging from a centralized architecture where cloud (re)training is given priority to a decentralized architecture where edge (re)training is instead given priority. Second, we validate the framework in a qualitative interview study with four additional case companies. As an outcome of validation study, we present two variants to the architectural alternatives identified as part of the framework. Finally, we identify the key challenges that experts face in selecting an ideal architectural alternative.
Traveling waves based fault line selection using sparse representation

Traveling wave method has been proved to be effective in fault line selection of power distribution system with the neutral non-effectively grounded. However, it can be easily disturbed by non-faulty traveling wave when selecting the line. For this reason, this paper proposes a new traveling wave line selection method based on sparse representation, in which current traveling wave signals are measured for the selection task. The algorithm of selection is based on the idea that all non-zero terms in the sparse coefficients of the measured signal are found to be associated with training samples from a single class, and the test samples can be easily assigned to the class. This makes it possible to distinguish between the non-faulty traveling wave and the real fault traveling wave.
Low-Complexity Chase Decoding of Reed-Solomon Codes Using Module

The interpolation based algebraic soft decoding yields a high decoding performance for Reed-Solomon (RS) codes with a polynomial-time complexity. Its computationally expensive interpolation can be facilitated using the module structure. The desired Gröbner basis can be achieved by reducing the basis of a module. This paper proposes the low-complexity Chase (LCC) decoding algorithm using this module basis reduction (BR) interpolation technique, namely the LCC-BR algorithm. By identifying $\eta $ unreliable symbols, $2^\eta $ decoding test-vectors will be formulated. The LCC-BR algorithm first constructs a common basis which will be shared by the decoding of all test-vectors. This eliminates the redundant computation in decoding each test-vector, resulting in a lower decoding complexity and latency. This paper further proposes the progressive LCC-BR algorithm that decodes the test-vectors sequentially and terminates once the maximum-likelihood decision decoding outcome is reached. Exploiting the difference between the adjacent test-vectors, this progressive decoding is realized without any additional memory cost. Complexity analysis shows that the LCC-BR algorithm yields a lower complexity and latency, especially for high rate codes, which will be validated by the numerical results.
CD1d Expression NKT Cells Is Independent of Peripheral Prevention of Type 1 Diabetes by Invariant

Invariant NKT (iNKT) cells can prevent diabetes by inhibiting the differentiation of anti-islet T cells. We recently showed that neither iNKT cell protection against diabetes nor iNKT cell inhibition of T cell differentiation in vitro requires cytokines such as IL-4, IL-10, IL-13, and TGF- (cid:1) . In contrast, cell-cell contacts were required for iNKT cell inhibition of T cell differentiation in vitro. The present study was designed to determine whether the CD1d molecule is involved in the inhibitory function of iNKT cells. Experiments were performed in vitro and in vivo, using cells lacking CD1d expression. The in vivo experiments used CD1d-deﬁcient mice that were either reconstituted with iNKT cells or expressed a CD1d transgene exclusively in the thymus. Both mouse models had functional iNKT cells in the periphery, even though CD1d was not expressed in peripheral tissues. Surprisingly, both in vitro inhibition of T cell differentiation by iNKT cells and mouse protection against diabetes by iNKT cells were CD1d-independent. These results reveal that iNKT cells can exert critical immunoregulatory effects in the absence of CD1d recognition and that different molecular interactions are involved in iNKT cell functions. The Journal of Immunology, 2007, 178: 1332–1340.
Simulation and Energy Management Interface of an Autonomous DC Microgrid

Considering technical constraints and incidents of network connection with photovoltaic system, the autonomous system is now considered to be a better replacement for it. Due to the intermittence of photovoltaic power production, the major problem of this system is the continuity of the service, the importance of the energy storage and the addition of conventional sources. In this paper, a simple autonomous dc microgrid model composed of PV (photovoltaic) array, dc-dc power converters, electrolytic storage, and programmable dc electronic load is built for better managing the energy. Firstly, two models reproducing the characteristic curves (V-I and V–P) of a photovoltaic panel have been compared. Then based on the historical data files of photovoltaic power generation and user consumption, a graphical interface has been created to display energy curves, which provides a direct vision for the follow-up study.
Dynamic Access Control In Iot: Monitoring User Behavior Using Smart Contracts

Dynamic Access Control in IoT (Internet of Things) through monitoring user behavior is a procedure that defines access control theory. Usually, an institution provides services to a user who owns a specific role that was issued by another institution. Such access control methods are possible in man-to-man communications only, but not in computer networks, because it is hard to establish security and limit spiteful actions through the users with high-risk values. High-risk values are caused by the users who do the tasks which they are not intended to do. In this paper, we bring forward a technique to limit malicious activities by monitoring user behavior using smart contracts. This proposed technique uses blockchain based technology and smart contracts for trust and the confirmation of user’s possession. We described the substructure, containing two main parts: smart contract and an authentication arrangement protocol.
Nodal Frequency Performance of Power Networks

This paper investigates how a disturbance in the power network affects the nodal frequencies of certain network buses. To begin with, we show that the inertia of a single generator is in inverse proportion to the initial rate of change of frequency (RoCoF) under disturbances. Then, we present how the initial RoCoF of the nodal frequencies are related to the inertia constants of multiple generators in a power network, which leads to a performance metric to analyze nodal frequency performance. To be specific, the proposed metric evaluates the impact of disturbances on the nodal frequency performance. The validity and effectiveness of the proposed metric are illustrated via simulations on a multi-machine power system.
Power System Resiliency

A power system has become fragile due to climate change. The weather events, for example, Hurricanes Katrina, Sandy in the US and Tohoku Earthquake in Japan, caused enormous economic losses and have imprinted people in making the power system more resilient under natural disasters. In order to evaluate the impacts of natural disasters on the power system, resiliency is necessarily needed. But in contrast to power system reliability, power system resiliency is less known relatively and there is no widely accepted concept for itself. Therefore, this paper shows the fundamental concept for power system resiliency, some reasons those make power system vulnerable including natural disasters, and the method to evaluate the resiliency.
Breaking the Vicious Circle: Why AI for software analytics and business intelligence does not take off in practice

In recent years, the application of artificial intelligence (AI) has become an integral part of a wide range of areas, including software engineering. By analyzing various data sources generated in software engineering, it can provide valuable insights into customer behavior, product performance, bugs and errors, and many more. In practice, however, AI for software analytics and business intelligence often gets stuck in a prototypical stage and the results are rarely used to make decisions based on data. To understand the underlying root causes of this phenomenon, we conduct both an explanatory case study and a survey on the challenges of realizing and utilizing artificial intelligence in the context of software-intensive businesses. As a result, we identify a vicious circle that prevents practitioners from moving from prototypical analytics to continuous and productively usable software analytics and business intelligence based on AI.
A Review of Cyber-Attack Methods in Cyber-Physical Power System

With the application of advanced computer and communication technologies, modern power system transformed into cyber-physical power system (CPPS). CPPS is critical infrastructure for modern society, which has complex dual-direction information flow. The information interactions make CPPS vulnerable to cyber-attacks, resulting in serious consequences in extreme cases. Some power system blackouts accidents had occurred around the world in recent years, such as Ukraine blackouts in 2015 and Venezuela blackouts in 2019. Consequently, it is necessary to summarize and analyze cyber-attack methods on CPPS for the defense against cyber-attacks. In this paper, a review of cyber-attack methods on CPPS is summarized and analyzed. Firstly, this paper summarizes the principles of different cyber-attack methods, such as false data injection attack (FDIA), denial-of-service (DoS) attack and man-in-the-middle (MITM) attack, etc. Secondly, the paper analyzes the applicable scenarios of different cyber-attack methods and studies possible attack methods on equipment and systems such as state estimation, wide area measurement systems (WAMS), smart meters and so on. Finally, according to the characteristics of different attack methods, some defense strategies are presented to against cyber-attacks.
Design of A Multi-Functional Soft Ankle Exoskeleton for Foot-Drop Prevention, Propulsion Assistance, and Inversion/Eversion Stabilization

Stroke often results in hemiplegia, which greatly affects the walking ability of the patients. We propose a multi-functional portable ankle exoskeleton for use in preventing foot-drops, assisting propulsion, and stabilizing inversion/eversion during walking to help gait rehabilitation of stroke patients. The portable ankle exoskeleton was fabricated by 3D printing a soft/rigid hybrid structure. The device was able to prevent foot-drop and assist propulsion with a bi-directional cable-driven actuation system. It also showed a capability of stabilizing inversion/eversion motions using a counter-electromotive force of two small, lightweight gear motors. The device was controlled by a microcontroller based on real-time feedback from one inertial measurement unit and a customized force sensitive resistor. The device is fully untethered with all the components integrated on-board, with a total weight of less than 1 kg. Five healthy subjects performed over-ground walking tests with the proposed ankle exoskeleton for three different walking situations (normal walking, walking with simulated foot-drop, and walking on an uneven terrain) and three walking conditions (without the exoskeleton, with the exoskeleton powered off, and with the exoskeleton powered on). From the test results, we confirmed the feasibility of the proposed ankle exoskeleton for foot-drop prevention, propulsion assistance, and inversion/eversion stabilization. The ankle exoskeleton showed a potential for wearable gait rehabilitation for stroke patients with high mobility and portability.
Risk scheduling model of wind turbines failure of Integrated Electrical and Natural-gas System with Power-to-Gas

The Integrated Electrical and Natural-gas System (IENS) based on Power-to-Gas (P2G) devices and gas-burning units with wind power is rapidly developing. The failure of wind turbines brings a greater risk to the safe operation of integrated system. The gas-burning units and P2G devices have fast and flexible power and gas control capabilities, providing an effective way to reduce the operational risk of integrated system. At present, the operation and scheduling of the IENS fails to take into account the risks of the system, and the influences of the control strategies of gas-burning units and P2G devices on the system risk cannot be ignored. The scheduling methods in independent power system are difficult to directly apply to the integrated system. Therefore, based on the operating characteristics of the IENS and the risk of wind turbines failure, a risk indicator of wind turbines failure considering the P2G devices was established. Based on this risk indicator, a multi-objective optimal scheduling model with minimum wind turbines failure risk and minimum coal consumption of coal coal-burning units was established, which is to solve the contradiction between wind turbines risk and cost during the system operation. The example shows that the multi-objective risk scheduling model of this paper can effectively reduce the operational risk of the IENS and improve the capacity of wind power consumption.
ISOLASI BAKTERI SIMBION DENGAN SPONS DARI PERAIRAN TONGKEINA, SULAWESI UTARA

Penelitian ini dilakukan dengan tujuan untuk mengisolasi bakteri yang bersimbiosis denganspons dan menentukan karakteristik morfologi serta sifat Gram dari isolat bakteri tersebut. Sampel spons diambil dari perairan Tongkeina, Sulawesi Utara. Spons digerus dan dilarutkan dalam air laut steril dan dilakukan pengenceran. Bakteri simbions spons dalam setiap pengenceran ditumbuhkan pada media Nutrient Broth. Bakteri yang tumbuh ditumbuhkan kembali pada media Nutrient Agar dengan metode cawan gores. Isolat bakteri yang tumbuh kemudian diuji sifat Gramnya. Penelitian ini memperoleh sepuluh isolat bakteri yang bersimbiosis dengan dua jenis spons dengan karaktersitik morfologi yang bervariasi. Dua dari kesepuluh isolat bakteri tergolong dalam Gram positif dengan bentuk coccus dan delapan tergolong dalam Gram negatif dengan bentuk basil (batang pendek).
Automatic detection of mental arithmetic task and its difficulty level using EEG signals

This paper presents a methodology for automatic detection of mental arithmetic tasks and detection of the difficulty level of the given task, using single channel EEG signals. It is vital to understand the response of cerebral cortices to different levels of task difficulty and give a quantitative measure to the role of different brain waves in cognitive tasks. Here, the EEG signals are decomposed using a filter bank to obtain gamma, beta, alpha, theta and delta rhythms and features such as the energy, entropy, mean and $L_{2}$ norms are evaluated for each of these rhythms. Performance of three machine learning classifiers namely; support vector machines, decision tree and quadratic discriminant analysis are compared. The algorithm detects the rest state of the brain and the active state of the brain for the given cognitive task. The EEG signals are also classified into two groups based on difficulty level of the given task.
Privacy in Software Ecosystems - An Initial Analysis of Data Protection Roles and Challenges

In the last decade, the software industry embraced a new development paradigm called software ecosystems. In this setting, multiple businesses act together as a unit and interact with a shared market for software and services. Ecosystems often result from opening a technology in the form of a platform so that it can receive external contributions from independent developers, small companies, resellers, and other actors. This open innovation phenomenon involves not just big players such as Google and Apple, but also SMEs seeking to co-create new solutions that facilitate their entrance into new markets. The challenging coordination of such distributed development must enable secure sourcing, integration, deployment, and evolution of the software solutions created by multiple complementors. An issue that has grown in importance with the introduction of the General Data Protection Regulation in 2018 is compliance with relevant data protection legislation. Our goal therefore is to analyse how privacy requirements affect software development in such ecosystems. As main contribution, we present an analysis of (i) data protection roles in a software ecosystem together with (ii) a set of challenges for data protection to platform companies, based on GDPR specification.
The Method of Increasing of CMRR for CJFET Dual Differential Input Stages for the Tasks of Processing Sensor Signals Under Conditions of Cryogenic Temperatures and Penetrating Radiation

The architecture of the CJFET dual differential input stage (DDS) architecture is available for converting differential output sensor signals. DDS is proposed, in which the circuitry solutions are available. They provide the increase of the CMRR, including in severe application (low temperatures, radiation). It is shown that due to the special construction of the output current adder in the considered DDS, the transconductance of the input common-mode signal is significantly reduced to the output DSS. The versions of practical circuits of CJFET dual differential input stages based on current mirrors and "folded" cascodes with the increased CMRR and the results of their computer simulation at cryogenic temperatures in LTspice simulation software on CJFET models are presented. The proposed DDSs are recommended to be used in the structure of low-noise analog interfaces of sensors of various physical quantities applied in medicine, high-energy physics, and space instrument engineering.
Optimum Waveguide and Pulse Shaping filter for GFDM System

Generalized Frequency Division Multiplexing (GFDM) is one of the contender waveforms to the 5G level (fifth generation) depend on multi-branch multi-carrier filter bank that can provide low OOB (out-of-band) radiation whereas OFDM (Orthogonal Frequency-Division Multiplexing) bear from various bandwidth inefficiency, since a considerable segment of every single stream packet is assigned to the cyclic prefix segments. Signal transmission at high rate of modulation over a band-limited network can produce inter symbol interferences and PAPR. But The pulse shaping filter create the transmitted signal enhanced appropriate to its drive or the network medium, generally by controlling the operational bandwidth of the communication. In this paper, the optimization of waveguide through Hilbert envelope and pulse shaping filter on inter-symbol interference and PAPR performance of the GFDM phenomenon is examined and the comparative analysis of PAPR in OFDM is too figure out.
Which Information Help agile Teams the Most? An Experience Report on the Problems and Needs

Fast feedback promotes agile teams to improve their work during the software process, making it crucial for team success. Information systems accelerate the availability of information that result in compact knowledge sources.In practice, feedback in Sprints is often limited to sole progress and performance measures, e.g., burndown charts or velocity diagrams. Sprint insights related to team dynamics are rarely considered, even though they frequently cause project failures, e.g., lack of social interaction. In this paper, we describe a survey study conducted with international members of the software engineering community to reveal which information helps agile teams the most and provides practical support in Sprints.We describe results in an experience report highlighting the frequent information problems and needs of agile teams, considering the perspective of 90 researchers and practitioners. The responses were quantitatively interpreted. The report promotes understanding about how or what kind of information would be useful for agile development teams. Moreover, it reveals what information problems were perceived as crucial for project success and avoidable, considering proper team feedback.The study endorses practical needs for system-aided feedback that supplies knowledge on the human factors in Sprints. The findings are relevant for practitioners and researchers that struggle on improving team feedback based on information needs.
SEkey: A Distributed Hardware-based Key Management System

Cryptography plays a key role in all the aspects of today cybersecurity and any cryptographic approach relies on cryptographic keys, i.e., series of bits that determine how a plain text is encrypted and decrypted, according to an agreed algorithm. The secrecy and security of an encryption key are thus crucial and fundamental: if the cryptographic key is compromised and known, everyone can decrypt a text encrypted according to the strongest encryption algorithm. As a consequence, several Key Management Systems (KMS) have been developed to easily support the management of cryptographic keys, whose number is constantly increasing, due to the amount of devices and communications that take place today, even in very restricted contexts. SEkey is a key management system developed targeting a distributed environment, where it is possible to identify a single central manager that acts as a Key Distribution Center (KDC) and many users that locally store and manage their own keys. Users, to a certain extent, can also work ‘offline’ without being always in direct communication with the central manager. SEkey is built leveraging the functionalities and physical properties of the SEcube™ Hardware Security Module (HSM). All the key values and critical information are stored inside the SEcube™ and never leave the device in clear, and all the cryptographic operations are performed by the SEcube™ itself. The guidelines provided by NIST where followed during the whole development process, guaranteeing all the most important security features and principles.
Study of Input Signal Waveforms for Precise Analog Counter

This paper introduces an improvement of precise analog counter for high-speed reinforcement learning by devising input waveform. The analog counter is a circuit that changes an output voltage according to the number of input pulses. In this circuit, as the output voltage goes up and down, the output per input waveform changes. In this paper, the output change per input waveform was compared between the case where a square wave with a period of 400 ps was input directly to an analog counter and the case where a spike signal was input, and it was found that the characteristics were improved when the spike signal was input.