Reviewerly/exHarmony huggingface dataset:
Griefing-Penalty: Countermeasure for Griefing Attack in Bitcoin-compatible PCNs.

Payment Channel Networks or PCNs have gained prominence ensuring faster relaying of transactions. However, this Layer-two solution has its own fair share of problems. Topological analysis on Lightning Network reveals that Griefing Attack is a major problem whereby an adversary intentionally exhausts the channel capacity of the network. It can be used for mounting series of targeted attacks like Denial-of-Service Attack, Node Isolation Attack and Channel Exhaustion Attack on honest participants as well. Though the attack does not always result in a direct monetary gain of the attacker, blocking of channel capacity for several days prevented several nodes from processing any future transaction request, leading to substantial collateral damage. Certain portions of the payment channel network get stalled which hampers the throughput and utility of the network. Mitigating Griefing Attack still remains an open problem. In this paper, we propose an efficient countermeasure for the attack, known as Griefing-Penalty. Mounting such an attack requires the attacker to pay a penalty proportional to the collateral cost of executing a payment. The penalty is used for compensating parties who incurred loss by locking funds. Our proposed strategy works for any timelock based payment protocol and ensures faster resolution of payments. To illustrate it, we propose a new payment protocol HTLC-GP or Hashed Timelock Contract with Griefing-Penalty. It not only preserves privacy but also ensures that an attacker cannot ascribe blame on any honest intermediary present in the path relaying a payment.
A hybrid active contour model driven by global and local intensity information

Aiming at the characteristics of intensity inhomogeneity distribution in images, a variational level set image segmentation model combining global and local intensity information is proposed. Local region information is the key to accurately segmenting images. However, the conventional CV model does not utilize the local region information, and the LBF model is susceptible to the initial outline and noise. In this paper, we present a hybrid model driven by new global and local intensity information. A new evolutionary stop function is constructed by using the principle of LBF model, and it is combined with the CV model to obtain an active contour model containing local and global information. By testing various types of real images and synthetic images, the model not only can deal with image with intensity inhomogeneity, but also reduces sensitivity of the model to the initial contour and the iteration number is also decreased.
Smart global futures: Designing affordable materialities for a better life

This chapter proposes an understanding of the design of the digital–material environment which builds on two bodies of literature: first, on discussions of ‘visions of future’ in design and technology research; and second, on the call from Design Anthropology to take seriously ethnographic research on imagination. It suggests the imaginaries are connected with a broader global future and have less significance in the design, whereas the visions of future are informed by the local experience of the designers and have a crucial role in the materialization of design projects. Visions imply a ‘willed social change’ while imaginaries are experienced as an inspiration for creativity. Both are related allies, for example in science fiction narratives, and have a central role in design. In the case of the low-cost sensor design process addressed here, citizenship, in the form of shared imaginaries, has a distinctive role among other imaginaries.
Digitization of Batak Manuscripts Using Methods Learning Vector Quantization (LVQ)

Abstract Digitalization of the Batak manuscript itself is still done manually. [2][4] Dr. Uli Kozok has made a Batak script font that can run on Windows, Linux, and Apple OSX but there is no system that can digitize Batak manuscripts properly. There is no system that can digitize the Batak manuscript itself automatically. Therefore, we need a system that can digitize Batak manuscripts. This system was built by applying the LVQ Method. Batak manuscript digitization in .jpg format is used as an image input file for the recognition process. Then image pre-processing including grayscale, contrast, thresholding, and segmentation is done in order to facilitate the recognition of Batak documents and recognition as a result of document recognition. The application of LVQ method can digitize the Batak manuscript well and in accordance with what is desired. This is supported by an accuracy rate of 97.9%. Based on system testing, the absence of training data in the input document, the letters are unclear, the position of the letters is not appropriate and the background in the document is still noise which greatly affects the success rate of the system in recognizing Batak script documents.
An Interactive Image Editing System Using an Uncertainty-Based Confirmation Strategy

We propose an interactive image editing system that has a confirmation dialogue strategy using an entropy-based uncertainty calculation on its generated images with Deep Convolutional Generative Adversarial Networks (DCGAN). DCGAN is an image generative model that learns an image manifold of a given dataset and enables continuous change of an image. Our proposed image editing system combines DCGAN with a natural language interface that accepts image editing requests in natural language. Although such a system is helpful for human users, it often faces uncertain requests to generate acceptable images. A promising approach to solve this problem is introducing a dialogue process that shows multiple candidates and confirms the user's intention. However, confirming every editing request creates redundant dialogues. To achieve more efficient dialogues, we propose an entropy-based dialogue strategy that decides when the system should confirm, and enables effective image editing through a dialogue that reduces redundant confirmations. We conducted image editing dialogue experiments using an avatar face illustration dataset for editing by natural language requests. Through quantitative and qualitative analysis, our results show that our entropy-based confirmation strategy achieved an effective dialogue by generating images desired by users.
Fuzzy Logic Application to Searchable Cryptography

Public Key Encryption with Keyword Search (PEKS) allows users to search encrypted files by a specific keyword without compromising the original data security. Almost all current PEKS schemes enable users to search exact keyword only instead of imprecise keyword (such as “latest”, “biggest”, etc.). Therefore, if the keyword is fuzzy, these PEKS schemes will be terminated and then report errors. Besides, some PEKS schemes are not secure mainly because they are vulnerable to Off-line Keyword Guessing Attack (OKGA). This research paper incorporates with Mamdani Fuzzy Inference System to PEKS for supporting Fuzzy Keyword Search. Secondly, the proposed scheme is proved to be semantic secure under the random oracle models so that it is able to resist OKGA. In addition, the new scheme allows users to search multiple keywords and therefore, it could be applied to the general public networks.
Foundations of Explainable Knowledge-Enabled Systems.

Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these approaches were tightly coupled with the capabilities of the artificial intelligence systems at the time. With the proliferation of AI-enabled systems in sometimes critical settings, there is a need for them to be explainable to end-users and decision-makers. We present a historical overview of explainable artificial intelligence systems, with a focus on knowledge-enabled systems, spanning the expert systems, cognitive assistants, semantic applications, and machine learning domains. Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.
The Spatial and Temporal Properties of eLearning: an Exploratory Study Regarding the Students’ Perspective

Abstract The paper aims to analyze and comment the current eLearning environments considering the space and time characteristics in order to better understanding of certain antecedents’ factors for eLearning system acceptance and effective use by students. The current paper illustrated the eLearning platforms – from the differences between online and traditional face to face learning to the spatial thinking and learning process – using as a case study the minds of students from a technical University of Romania.
Design and deployment of multilingual industrial voice control applications

This paper presents a concept and a demonstrator for rapid design and de-ployment of multilingual industrial voice control speech applications. The dialogues are modelled in a designer toolkit and the resources are deployed on a target embedded system. The workflow consists of distinctive design and deployment phases: definition of speech interaction by giving representative utterance examples (languagemodelling); assignment of the control functions (intents or tasks); incorporating do-main knowledge (entity definition and interfaces), definition and assignment of the acoustic feedback (text-to-speech); validation, optimization of the speech dialogue; generation of language specific resources; deployment to the target platform; itera-tive testing and optimizations. The demonstrator presents hands-free and natural lan-guage interactions with arbitrary external systems via the standard Internet of Things protocols. It does not require internet access, the speech and audio feedback processing is performed offline, ensuring privacy by design. The presented approach allows rapid prototyping of multilingual dialogues, as a part of an iterative process of assessing and improving the quality of interaction and the user experience.
Bitcoin Price Variation: An Analysis of the Correlations

The Bitcoin system is attracting a huge community both from specialists and common people, who see in it a great opportunity of investment. Thanks to the fact that the Bitcoin blockchain in publicly available, and considering that it shows properties of a real economy, Bitcoin is becoming more and more often subject of a number of studies. One of the hardest task in this field, yet interesting also from a non specialist point of view, is the bitcoin price correlation and prediction. In this paper we present a methodological framework for the bitcoin exchange graph analysis which helps in focusing only on restricted time spans that show interesting dynamics of the bitcoin price. We also present our study on three separate time spans and show that empirical correlations can be found between the bitcoin price and some bitcoin exchange graph measures. Lastly, with our framework we are also able to detect some unexpected behaviour from particular users which tend to pile up big amounts of bitcoin over the selected time spans.
Predictive maintenance oriented pattern recognition system based on ultrasound data analysis

Ultrasonic data analysis as a method of early-stage machine fault detection has been successfully utilized in the Predictive Maintenance (PdM) industry for over 25 years. The challenge with this type of machine health monitoring is the sensitivity of the data collected to the changes in machine operational condition such as load, speed and/or pressure. Typically any alarm or fault criteria threshold based on ultrasonic data would need to be sufficiently elevated in order to accommodate the machine’s variable operational conditions or the monitoring system would generate excessive false alarms due to, for example, a high load condition. A sufficiently high alarm level results in reduced accuracy in the response of the ultrasonic monitoring system to specific machine fault conditions. An integrated solution with a predictive pattern recognition system that would predict the ultrasonic data level based on machine operational conditions is proposed as a solution to the high sensitivity of the ultrasonic monitoring system. The pattern recognition algorithm is able to create dynamic or variable alarm conditions which is offered to improve the accuracy of determining the health of the machine and/or the presence of mechanical faults based on the ultrasonic data analysis.
Tight polynomial worst-case bounds for loop programs

In 2008, Ben-Amram, Jones and Kristiansen showed that for a simple programming language - representing non-deterministic imperative programs with bounded loops, and arithmetics limited to addition and multiplication - it is possible to decide precisely whether a program has certain growth-rate properties, in particular whether a computed value, or the program's running time, has a polynomial growth rate. A natural and intriguing problem was to move from answering the decision problem to giving a quantitative result, namely, a tight polynomial upper bound. This paper shows how to obtain asymptotically-tight, multivariate, disjunctive polynomial bounds for this class of programs. This is a complete solution: whenever a polynomial bound exists it will be found. A pleasant surprise is that the algorithm is quite simple; but it relies on some subtle reasoning. An important ingredient in the proof is the forest factorization theorem, a strong structural result on homomorphisms into a finite monoid.
The Impact of Using Augmented Reality on the Developing the Tenth Graders Motivation Towards Learning: An Applied Study on the Chemistry Courses.

This study aims to reveal the impact of using augmented reality on the motivation towards learning the chemistry curriculum for tenth-grade students.The researchers applied the qusi-experimental approach on a random sample of (49) students divided into two groups; control by ( 24) students, and experimental by ( 25) students.The experiment involves the application of augmented reality techniques to explain some of the basic chemistry concepts for the experimental group, and to explain the same concepts by using traditional teaching for the control group.To measure the impact size, amotivation scale has been developed and applied before and after the experiment.The results indicate that there are statistically significant differences in the level of motivation towards learning and acquiring scientific concepts in chemistry according to the method of teaching (augmented, traditional) in favour of the experimental group.In addition, the study recommends the need to adopt the use of augmented reality techniques in teaching chemistry concepts in the basic stages as part of the chemistry teaching standards.
Determinants of Deposit Mobilization in the Case of Commercial Bank of Ethiopia Selected Branches

Deposit mobilization is among one of the primary activities of banking system.Banks mobilize financial resource required for investment and economic growth from households, business, corporate bodies and government.To be effective, competitive in the market and maintain liquidity identifying factors affecting deposit mobilization is crucial.With this in mind the objective of this study is to identify determinants of bank deposit mobilization in the case of Commercial Bank of Ethiopia.To achieve this objective primary data collected through distributing 238 valid questionnaires to selected customers of the bank is primarily used.To complement primary data, secondary data is also used to some extent.Data analysis is handled quantitatively through the help of SPSS.The study findings shows that branch expansion, variety of services offered by the bank, confidentiality of customer information, trust on banking system and saving habits are found to be significant variables affecting deposit mobilization.More over interest rate and inflation are also among significant macroeconomic factors affecting deposit mobilization.Based on these findings recommendations are also forwarded.
Real-time object detection for autonomous vehicles using deep learning

Self-driving systems are commonly categorized into three subsystems: perception, planning, and control. In this thesis, the perception problem is studied in the context of real-time object detectio ...
Convolutional neural networks can learn duration for detecting pitch accents and lexical stress

The duration of syllables or words is an important correlate of prosodyand often used as a feature for automatic pitch accent detection. We have previ-ously introduced a method for pitch accent detection using a convolutional neuralnetwork (CNN) that yields good performance using low-level acoustic descriptorsalone, without any explicit duration information. In this paper, we use this modelfor various pitch accent and lexical stress detection tasks at the word and syllablelevel on the DIRNDL German radio news corpus. We show that information onword or syllable duration is encoded in the high-level CNN feature representationby training a linear regression model on these features to predict duration. The factthat this can be approximated suggests that the CNN makes use of implicit dura-tion information that is derived from the frame-based input. We also observe thatduration is only learnt in tasks where it is directly correlated with the target label.We compare two different methods of pooling that capture the input informationdifferently and show how this affects what is encoded in the output representation.
Squeezing-Enhanced Phase-Shift-Keyed Binary Communication in Noisy Channels

We address the use of squeezing in binary phase-shift-keyed (PSK) channels at fixed energy. In particular, we assess homodyne receivers against the Helstrom bound in the presence of phase noise. We also take into account possible imperfections in the generation of squeezing and the effect of losses during propagation. We find that squeezing is a useful resource if its amplitude is below a given threshold depending on the energy of the signals and on the properties of the channel. Squeezing enhancement is present also when phase-noise becomes large.
Quantum Implications of Huang's Sensitivity Theorem

Based on the recent breakthrough of Huang (2019), we show that for any total Boolean function f, the deterministic query complexity, D(f), is at most quartic in the quantum query complexity, Q(f): D(f)=O(Q(f)4). This matches the known separation (up to log factors) due to Ambainis, Balodis, Belovs, Lee, Santha, and Smotrovs (2017). We also use the result to resolve the quantum analogue of the Aanderaa-Karp-Rosenberg conjecture. We show that if f is a nontrivial monotone graph property of an n-vertex graph specified by its adjacency matrix, then Q(f)=Ω(n), which is also optimal. Learn more about Microsoft Quantum computing > Learn more about Microsoft Azure Quantum >
Stochastic gradient algorithms from ODE splitting perspective

We present a different view on stochastic optimization, which goes back to the splitting schemes for approximate solutions of ODE. In this work, we provide a connection between stochastic gradient descent approach and first-order splitting scheme for ODE. We consider the special case of splitting, which is inspired by machine learning applications and derive a new upper bound on the global splitting error for it. We present, that the Kaczmarz method is the limit case of the splitting scheme for the unit batch SGD for linear least squares problem. We support our findings with systematic empirical studies, which demonstrates, that a more accurate solution of local problems leads to the stepsize robustness and provides better convergence in time and iterations on the softmax regression problem.
Animal experiment for 5G remote robotic surgery

ObjectiveTo evaluate the feasibility of remote robotic surgery under 5G network through animal experiment.MethodsThe surgeon console and the bedside cart of the domestic Kangduo robotic system were separated and 50 kilometers apart from each other. With the 5G network interconnection jointly developed by Huawei Technologies Company and China Unicom, the surgeons remotely control the bedside cart for animal experiments to test the stability and safety of remote surgery.ResultsOn Dec.18, 2018, the experiment was successfully completed in Fuzhou, Fujian Province. The surgeon remotely controlled two robotic arms (bipolar coagulation and electrocoagulation hooks) and the lens arm, and successfully completed the wedge resection of the pig liver. The resected specimen was about 2 cm×2 cm×3 cm. The whole animal experiment took about 60 minutes and the bleeding was about 5 ml. The intraoperative high-definition 3D image and sound transmission were instantaneous and stable. The bedside robot arms were smooth and flexible. The master-slave motion tracking and mapping were correct and good. The average delay time from the actuator end motion command to the motion of robotic arms end was less than 150 ms. The robot system did not show any malfunction in software or hardware throughout the experimental process.ConclusionsThis animal experiment was the first attempt of 5G remote surgery in the world. The results preliminarily showed that the 5G remote domestic robot surgery technology was feasible. The application of remote surgery will effectively solve the contradiction between the current imbalance of supply and demand of medical resources, achieve the general application of high-quality medical resources, narrow the gap between different grades of medical resources, effectively reduce medical expenses, reduce the economic burden of patients, and have great significance for wartime medical rescue.Key words: Minimally invasive surgery; Robot; Telesurgery; Network delay; 5G network
Fast-Accurate Dual-Axis Solar Tracker Controlled by P&O Technique with Neural Network Optimization

This paper proposes a fast-accurate method to locate the sun through dual-axis sun tracker rapidly and accurately. The tracking time is reduced to decrease the power consumption and improve the dynamic performance. The controlling procedure is closed-loop combination of Multi-Layer Perceptron (MLP) neural network and Perturbation & Observation (P&O). If the sun deviates from solar panels significantly, the sun tracker will operate quickly, but inaccurately by using the neural network. But if the sun gets away slightly, the sun tracker will operate accurately, but slowly by using P&O. So, by combining these two given cases will cause to track the sun fast and accurately. The error back propagation training algorithm is firstly used to train the neural network and in the next step the Modified Particular Swarm Optimization (MPSO) method used to optimize the neural network. The results indicate that the tracking time of proposed method is 42%–49% less than other which is used for comparison. So, the sun tracker has better dynamic performance, lower power consumption and increased efficiency.