Reviewerly/exHarmony huggingface dataset:
Generating Natural Language Attacks in a Hard Label Black Box Setting.

We study an important and challenging task of attacking natural language processing models in a hard label black box setting. We propose a decision-based attack strategy that crafts high quality adversarial examples on text classification and entailment tasks. Our proposed attack strategy leverages population-based optimization algorithm to craft plausible and semantically similar adversarial examples by observing only the top label predicted by the target model. At each iteration, the optimization procedure allow word replacements that maximizes the overall semantic similarity between the original and the adversarial text. Further, our approach does not rely on using substitute models or any kind of training data. We demonstrate the efficacy of our proposed approach through extensive experimentation and ablation studies on five state-of-the-art target models across seven benchmark datasets. In comparison to attacks proposed in prior literature, we are able to achieve a higher success rate with lower word perturbation percentage that too in a highly restricted setting.
Security analysis of continuous variable quantum key distribution based on entangled states with biased correlations

Einstein-Podolsky-Rosen (EPR) entangled states can significantly enhance the secret key rate and secure distance of continuous-variable quantum key distribution (CV-QKD). In practical imperfections always exist in the preparation of two-mode squeezing (entangled states), which present an asymmetrical variance for the two quadratures. The imperfections induced by the bias effect of the entangled states are commonly treated as part of the untrusted channel to decrease the performance of the system. Here, we theoretically quantify the influence of bias effect on the secret key rate and secure distance, and propose a solution of generating unbiased entangled states protocol. The results demonstrated that the unbiased entangled states protocol guarantees the longest secure distance and highest key rate compared to that of coherent and biased entangled states.
Data Balancing Improves Self-Admitted Technical Debt Detection

A high imbalance exists between technical debt and non-technical debt source code comments. Such imbalance affects Self-Admitted Technical Debt (SATD) detection performance, and existing literature lacks empirical evidence on the choice of balancing technique. In this work, we evaluate the impact of multiple balancing techniques, including Data level, Classifier level, and Hybrid, for SATD detection in Within-Project and Cross-Project setup. Our results show that the Data level balancing technique SMOTE or Classifier level Ensemble approaches Random Forest or XGBoost are reasonable choices depending on whether the goal is to maximize Precision, Recall, F1, or AUC-ROC. We compared our best-performing model with the previous SATD detection benchmark (cost-sensitive Convolution Neural Network). Interestingly the top-performing XGBoost with SMOTE sampling improved the Within-project F1 score by 10% but fell short in Cross-Project set up by 9%. This supports the higher generalization capability of deep learning in Cross-Project SATD detection, yet while working within individual projects, classical machine learning algorithms can deliver better performance. We also evaluate and quantify the impact of duplicate source code comments in SATD detection performance. Finally, we employ SHAP and discuss the interpreted SATD features. We have included the replication package <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and shared a web-based SATD prediction tool <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with the balancing techniques in this study.
Mobilenet V2-FCD: Fake Currency Note Detection

The technology advancement has made lot of changes to printing and scanning industry. The printing and scanning technologies are affecting economy. There is a big challenge to the entire world in terms of fake currency. These real time problems can be solved easily using Deep Learning techniques. Convolution Neural Networks can be used for detecting Fake Currency note. Our proposed Mobilenetv2-FCD is trained to detect the Indian Fake currency Notes. The proposed network detects fake notes with 85% accuracy. The network can be trained for any nation’s currency and can be used accordingly.
Analysis of information security in the PoW (Proof of Work) and PoS (Proof of Stake)blockchain protocols as an alternative for handling confidential nformation in the public finance ecuadorian sector

Blockchain technology relies on a growing number of globally distributed ledgers known as blockchain. This technology was used for the creation of the cryptocurrency known as bitcoin that allows transactions to be carried out quickly and easily, without the need to use an intermediary "financial institution". The information is sent trough the protocols known as: PoW (Proof of Work) and PoS (Proof of Stake), which must guarantee confidentiality, integrity and availability of the information. The present work shows the result of a bibliographic review on the evolution of the blockchain, the PoW and PoS protocols; as well as the application of these within the framework of Ecuadorian legislation with emphasis on the evolution of risks of the PoW protocol.
Simple Storage-Saving Structure for Volume-Hiding Encrypted Multi-maps

Severe consequences in volume leakage (subject to the conditions required by specific attacks) stimulate a new research direction (Eurocrypt 2019) of volume-hiding structured encryption ( $$\mathsf {STE}$$ ), particularly encrypted multi-maps ( $$\mathsf {EMM}$$ ), in which all queries should share the same (as the largest) response size unless the scheme is lossy. Meanwhile, note that the responses are originated from the actual ciphertexts outsourced to the server. Conventional wisdom suggests that the ciphertexts (to be accessed by the server while answering a query) should also contain many dummy results to make a query look uniform with others. Supporting updates is also natural; however, attaching dummy results to a query also complicates the operation and leakage of updates, which excludes many advanced data structures, e.g., cuckoo hashing (CCS 2019). This paper proposes a space-efficient $$\mathsf {EMM}$$ without storing any dummy ciphertext, which is volume hiding against passive adversaries (SP 2021) and compatible with dynamic extensions. Its crux structure is a hash ring, which is famous for load balancing but rarely appears in any $$\mathsf {STE}$$ . Efficiency-wise, our scheme beats the state-of-the-art (Eurocrypt 2019, CCS 2019), maintaining the necessary communication overhead and downsizing the server storage to be linear in the number of values in the $$\mathsf {EMM}$$ , while ruling out any data loss due to truncations or differential privacy.
Dynamic selection and combination of one-class classifiers for multi-class classification

A natural solution to tackle multi-class problems is employing multi-class classifiers. However, in specific situations, such as imbalanced data or high number of classes, it is more effective to decompose the multi-class problem into several and easier to solve problems. One-class decomposition is an alternative, where one-class classifiers (OCCs) are trained for each class separately. However, fitting the data optimally is a challenge for OCCs, especially when it presents a complex intra-class distribution. The literature shows that multiple classifier systems are inherently robust in such cases. Thus, the adoption of multiple OCCs for each class can lead to an improvement for one-class decomposition. With that in mind, in this work we introduce the method called One-class Classifier Dynamic Ensemble Selection for Multi-class problems (MODES, for short), which provides competent classifiers for each region of the feature space by decomposing the original multi-class problem into multiple one-class problems. So, each class is segmented using a set of cluster validity indices, and an OCC is trained for each cluster. The rationale is to reduce the complexity of the classification task by defining a region of the feature space where the classifier is supposed to be an expert. The classification of a test example is performed by dynamically selecting an ensemble of competent OCCs and the final decision is given by the reconstruction of the original multi-class problem. Experiments carried out with 25 databases, 4 OCC models, and 3 aggregation methods showed that the proposed architecture outperforms the literature. When compared with the state-of-the-art, MODES obtained better results, especially for databases with complex decision regions.
Performance Analysis of Open Puff Steganography Tool Using Various Image Formats

Communication is further developed even in the digital field which nowadays a very important means of communication. For a better way of communication and end to proper message delivery it is very important to have a proper security channel. Steganography is the secure mode of digital communication by hiding any data not to be visually exposed. It is possible in various file types that are images, audio, video, text etc. Image steganography is the most widely used type of steganography. It is basically of two major types that are spatial and transform domain, spatial domain the most common technique which includes LSB, MSB and both LSB-MSB steganography. The Lsb is the most common and easily performed technique. The research is carried out on LSB technique steganography tool the OpenPuff steganography tool. Main objective of the research is to analyze the performance of the OpenPuff tool using relevance to the various image formats that are JPEG, BMP and PNG. The steganographed image form OpenPuff tool are compared on various features for the best output collection in lieu of the best format. Pixels and the bit value changes were analyzed using the counter steganalysis tool. PSNR calculation tool is used to calculate the PSNR.The image steganographed were compared on the basis of robustness, visual distortion etc. for keeping it safe from the vulnerability of attacks and mishandling. The file size that is input and output size, hiding and extraction time etc. are compared for the quality for and changes of the image in all the three format's the formats BMP, PNG and JPEG.
Improved JPEG Coding by Filtering 8 × 8 DCT Blocks

The JPEG format, consisting of a set of image compression techniques, is one of the most commonly used image coding standards for both lossy and lossless image encoding. In this format, various techniques are used to improve image transmission and storage. In the final step of lossy image coding, JPEG uses either arithmetic or Huffman entropy coding modes to further compress data processed by lossy compression. Both modes encode all the 8 × 8 DCT blocks without filtering empty ones. An end-of-block marker is coded for empty blocks, and these empty blocks cause an unnecessary increase in file size when they are stored with the rest of the data. In this paper, we propose a modified version of the JPEG entropy coding. In the proposed version, instead of storing an end-of-block code for empty blocks with the rest of the data, we store their location in a separate buffer and then compress the buffer with an efficient lossless method to achieve a higher compression ratio. The size of the additional buffer, which keeps the information of location for the empty and non-empty blocks, was considered during the calculation of bits per pixel for the test images. In image compression, peak signal-to-noise ratio versus bits per pixel has been a major measure for evaluating the coding performance. Experimental results indicate that the proposed modified algorithm achieves lower bits per pixel while retaining quality.
Quantum Computing Entrepreneurship and IEEE TEMS

Quantum computing is experiencing an inflection point. This technology is likely to facilitate great advances in many fields including logistics, chemistry simulation, or even risk analysis. It is attractive for large hardware and software companies in addition to start-up entrepreneurial enterprises. In this article, we summarize interesting insights into this field related to possibilities related to quantum computing and entrepreneurship.
PROJECT FOR TRAINING PROFESSIONAL SKILLS FOR FUTURE TEACHERS OF TECHNOLOGICAL EDUCATION

The development of social thinking, practical experience, worldview and thinking of future teachers of technological education, the improvement of their professional competence, the logic of their attitude to their profession depends in many respects on the adequate formation of their professional skills and abilities. The article analyzes the current professional activities of future teachers of technological education and their levels of practical training. The methodological and technological bases of design in the formation of professional skills and competencies of future teachers of technological education, as well as scientific considerations on the application of technological education in the teaching system have been described.
Designing a Smart Speaking System for Voiceless Community

Voiceless people are facing a lot of issues in establishing a communication with normal people. Usually, the normal people are not professionally trained in sign language, and hence, it becomes very hard to establish a proper communication. While traveling to other places with normal people, the voiceless people are facing many challenges to convey their message. In order to incorporate a simple communication, new products have been designed especially for the voiceless community to recognize voice through hand motions and gestures. This mechanism utilizes the concept of a hand motion reading system, which is integrated with flex sensors and hand gestures along with a speaker unit. The whole framework circuit is then motorized by a gatherer controller. The entire data is processed and operated with the help of Raspberry Pi. The system reads the person's hand motions by recognizing different forms of hand movements. The fully automated smart speaking system is intended to help the voiceless people to communicate with normal people by using a simple wearable device.
Research on Design and Application of Power Dispatch Based on Blockchain

With the large-scale access of renewable energy and the rapid development of electricity market operation, power dispatch urgently needs to improve the level of instant information sharing and service management capabilities. Through the blockchain technology, it can realize the credible sharing and traceability of key data, and further promote the efficiency of power dispatch collaboration management as well as the openness and transparency of the process of power dispatch. In this paper, the adaptability of blockchain and power dispatch business is analyzed, a power dispatch model with hierarchical management and hierarchical storage based on blockchain is proposed, and a fully functional dispatch blockchain platform is developed. Customized smart contracts are used to realize the automatic execution of the dispatch assessment and evaluation, and a consistency check mechanism is designed for the key data of the security and stability control device. Finally, two typical scenarios of dispatch management assessment and dispatch data management are selected for application research. The results show that the power dispatch model based on blockchain can significantly improve the transparency and operation efficiency of power dispatch, reduce the impact of the misoperation of the security and stability control device on the power grid, and promote the safe and stable operation of power grid.
On the monitorability of session types, in theory and practice

Software components are expected to communicate according to predetermined protocols and APIs. Numerous methods have been proposed to check the correctness of communicating systems against such protocols/APIs. Session types are one such method, used both for static type-checking as well as for run-time monitoring. This work takes a fresh look at the run-time verification of communicating systems using session types, in theory and in practice. On the theoretical side, we develop a formal model of session-monitored processes. We then use this model to formulate and prove new results on the monitorability of session types, defined in terms of soundness (i.e., whether monitors only flag ill-typed processes) and completeness (i.e., whether all ill-typed processes can be flagged by a monitor). On the practical side, we show that our monitoring theory is indeed realisable: we instantiate our formal model as a Scala toolkit (called STMonitor) for the automatic generation of session monitors. These executable monitors can be used as proxies to instrument communication across black-box processes written in any programming language. Finally, we evaluate the viability of our approach through a series of benchmarks.
Easy Language and beyond: How to maximize the accessibility of communication

Accessible communication is easy to find, easy to perceive, easy to understand and is related to the previous knowledge of the recipients. Target groups may have special communication needs necessitated by sensory, cognitive or psychological impairment, low education or critical life events such as illness, loss of reference persons or forced migration. When texts fail to address the needs of these target groups, they become barriers to successful interaction: sensory and cognitive barriers, media and culture barriers, language or specialized language barriers or even motoric barriers. Accessible communication comprises all measures to prevent texts (oral or written) from becoming a barrier for the intended target groups. This is a broad field of action that comprises, but goes way beyond Easy and Plain Language: A text could be written in Easy Language but the intended users have no access to it; a text may not be well perceived and therefore not understood; a text may be accessible online, but directed to users with different media preferences; a text may be easy to perceive and understand but not acceptable with respect to the politeness requirements of the users. What good are such texts to the target audience? This paper, that was held as Plenary Speech on the 2019 Klaara Network conference, focuses on Easy Language and Accessible Communication in Germany and discusses communication barriers and the conditions of communicative accessibility for people with diverse needs in the light of the following questions: What road have we travelled thus far? Where are we right now? Have we really covered all necessary aspects of accessibility? Where do we go from here?
A Hybrid Generalized Reduced Gradient-Based Particle Swarm Optimizer for Constrained Engineering Optimization Problems

A hybrid algorithm is presented that combines strong points of Particle Swarm Optimization (PSO) and Generalized Reduced Gradient (GRG) algorithm to keep a good compromise between exploration and exploitation. The hybrid PSO-GRG quickly approximates the optimum solution using PSO as a global search engine in the first phase of the search process. The solution accuracy is then improved during the second phase of the search process using the GRG algorithm to probe locally for a proper solution(s) in the vicinity of the current best position obtained by PSO. The k-nearest neighbors (k-NN)-based Purely Uniform Distributed (PUD) initial swarm is also applied to increase the convergence speed and reduce the number of function evaluations (NFEs). Hybridization between both algorithms allows the proposed algorithm to accelerate throughout the early stages of optimization using the high exploration power of PSO whereas, promising solutions will possess a high probability to be exploited in the second phase of optimization using the high exploitation ability of GRG. This prevents PUD-based hybrid PSO-GRG from becoming trapped in local optima while maintaining a balance between exploration and exploitation. The competence of the algorithm is compared with other state-of-the-art algorithms on benchmark optimization problems having a wide range of dimensions and varied complexities. Appraising offered algorithm performance revealed great competitive results on the Multiple Comparison Test (MCT) and Analysis of Variance (ANOVA) test. Results demonstrate the superiority of hybrid PSO-GRG compared to standard PSO in terms of fewer NFEs, fast convergence speed, and high escaping ability from local optima.
FADS: A framework for autonomous drone safety using temporal logic-based trajectory planning

In this work, we present an integrated Framework for Autonomous Drone Safety (FADS). The demand for safe and efficient mobility of people and goods is growing rapidly, in line with the growth in population in US urban centers. In response, new technologies to meet these urban mobility demands are also rapidly maturing in preparation for future full-scale deployment. As surface congestion increases and the technology surrounding unmanned aerial systems (UAS) matures, more people are looking to the urban airspace and Urban Air Mobility (UAM) as a piece of the puzzle to promote mobility in cities. However, the lack of coordination between UAS stakeholders, federal UAS safety regulations, and researchers developing UAS algorithms continues to be a critical barrier to widespread UAS adoption. FADS takes into account federal UAS safety requirements, UAM challenge scenarios, contingency events, as well as stakeholder-specific operational requirements. FADS formalizes these requirements, through Signal Temporal Logic (STL) representations, and a trajectory planning optimization for multi-rotor UAS fleets guarantees robust and continuous-time satisfaction of the requirements and mission objectives. The intuitive FADS user interface makes it easy to plan missions in a variety of environments; we demonstrate this through several rural and urban environment-based case studies. FADS holistically integrates high-level stakeholder objectives with low-level trajectory planning; combined with a user-friendly interface, FADS reduces the complexity of stakeholder coordination within the UAM context.
Project MultiLeap: Fusing Data from Multiple Leap Motion Sensors

Finding a simple and precise way to control the virtual environment is one of the goals of a lot of humancomputer interaction research. One of the approaches is using a Leap Motion optical sensor, which provides hand and finger tracking without the need of any hand-held device. However, the Leap Motion system currently supports only one sensor at the time. To overcome this limitation, we proposed a set of algorithms to combine the data from multiple Leap Motion sensors to increase the precision and the usability of the hand tracking. First, we suggested a way how to improve the calibration of the current hand pose alignment proposed by Leap Motion. Then, we proposed an approach to fuse the tracking data from multiple Leap Motion sensors to provide more precise interaction with the virtual world. For this, we implemented our very own algorithm for computing the confidence level of the tracking data that can be used to distinguish which Leap Motion sensor detects the tracked hands best. We implemented those algorithms into our MultiLeap library.We also created two demo scenes that we used to validate the correctness of our work - one for evaluation of the fusing algorithms and one for mimicking the interaction with control panels in a helicopter cockpit.