Reviewerly/exHarmony huggingface dataset:
Deep Learning Modulation Recognition for RF Spectrum Monitoring

This paper presents a classification Convolutional Neural Network model for modulation recognition. The model is capable of classifying 11 different modulation techniques based on their In-phase and Quadrature components at baseband. The classification accuracy is higher than 80% for signals with a Signal-to-Noise Ratio higher than 2 dB. The model performance is evaluated using the same In-phase and Quadrature component data-sets used in the state of the art. Compared to previous work, the number of parameters and multiplications/additions is reduced by several orders of magnitude. The proposed Convolutional Neural Network is implemented on FPGA and achieves the same performance as the GPU model. Compared to other FPGA implementations of RF signal classifiers, the proposed implementation classifies twice as much modulation schemes while consuming only half the dynamic power.
Extractive Multi Product-Line Engineering

Cloning is a general approach to create new functionality within variants as well as new system variants. It is a fast, flexible, intuitive, and economical approach to evolve systems in the short run. However, in the long run, the maintenance effort increases. A common solution to this problem is the extraction of a product line from a set of cloned variants. This process requires a detailed analysis of variants to extract variability information. However, clones within a variant are usually not considered in the process, but are also a cause for unsustainable software. This thesis proposes an extractive multi product-line engineering approach to re-establish the sustainable development of software variants. We propose an approach to re-engineer intra-system and inter-system clones into reusable, configurable components stored in an integrated platform and synthesize a matching multi-layer feature model.
An unsupervised learning method for named entity relation extraction of space knowledge graph

Abstract Knowledge graph is a kind of typical artificial intelligence technology, which can improve cognitive ability of machines. Space is a booming domain for human beings. In this paper, space knowledge graph is proposed in order to improve intelligence of space information system. A major obstacle of space knowledge graph construction is to identify and extract core named entities and relations in space domain. An unsupervised learning method to extract core named entities and relations is proposed in this paper. Firstly, confidence level index used to measure importance of entities and conditional confidence level index used to measure credibility of relations between entities are defined and investigated. Then, an entity and relation extraction procedure based on maximum likelihood estimation and apriori algorithm is designed. Finally, a case study is carried out and influence of super parameter setting on the extraction result is discussed. The method proposed in this paper can be used to construct space knowledge graph at a lower cost compared to supervised method, and can be extended to other domain knowledge graph construction.
Degree-anonymization using edge rotations

The Min Anonymous-Edge-Rotation problem asks for an input graph G and a positive integer k to find a minimum number of edge rotations that transform G into a graph such that for each vertex there are at least k−1 other vertices of the same degree (a k-degree-anonymous graph). In this paper, we prove that the Min Anonymous-Edge-Rotation problem is NP-hard even for k=n/q, where n is the order of a graph and q any positive integer, q≥3. We argue that under some constrains on the number of edges in a graph and k, Min Anonymous-Edge-Rotation is polynomial-time 2-approximable. Furthermore, we show that the problem is solvable in polynomial time for any graph when k=n and for trees when k=θ(n). Additionally, we establish sufficient conditions for an input graph and k such that a solution for Min Anonymous-Edge-Rotation exists.
CKG: Dynamic Representation Based on Context and Knowledge Graph

Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance. As a result, they have achieved state-of-the-art results in a large range of language tasks. However, there exists other valuable semantic information such as similar, opposite, or other possible meanings in external knowledge graphs (KGs). We argue that entities in KGs could be used to enhance the correct semantic meaning of language sentences. In this paper, we propose a new method CKG: Dynamic Representation Based on Context and Knowledge Graph. On the one side, CKG can extract rich semantic information of large corpus. On the other side, it can make full use of inside information such as co-occurrence in large corpus and outside information such as similar entities in KGs. We conduct extensive experiments on a wide range of tasks, including QQP, MRPC, SST-5, SQuAD, CoNLL 2003, and SNLI. The experiment results show that CKG achieves SOTA 89.2 on SQuAD compared with SAN (84.4), ELMo (85.8), and BERTBase (88.5).
Employing machine learning and microscopy images of <scp>AIB1</scp>‐stained biopsy material to assess the 5‐year survival of patients with colorectal cancer

Abstract Our purpose was to employ microscopy images of amplified in breast cancer 1 (AIB1)‐stained biopsy material of patients with colorectal cancer (CRC) to: (a) find statistically significant differences (SSDs) in the texture and color of the epithelial gland tissue, between 5‐year survivors and non‐survivors after the first diagnosis and (b) employ machine learning (ML) methods for predicting the CRC‐patient 5‐year survival. We collected biopsy material from 54 patients with diagnosed CRC from the archives of the University Hospital of Patras, Greece. Twenty‐six of the patients had survived 5 years after the first diagnosis. We selected regions of interest containing the epithelial gland at different microscope lens magnifications. We computed 69 textural and color features. Furthermore, we identified features with SSDs between the two groups of patients and we designed a supervised ML system for predicting the CRC‐patient 5‐year survival. Additionally, we employed the VGG16 pretrained convolution neural network to extract deep learning (DL) features, the support vector machines classifier, and the bootstrap cross‐validation method for boosting the accuracy of predicting 5‐year survival. Fourteen features sustained SSDs between the two groups of patients. The supervised ML system achieved 87% accuracy in predicting 5‐year survival. In comparison, the DL system, using images from all magnifications, gave 97% classification accuracy. Glandular texture in 5‐year non‐survivors appeared to be of lower contrast, coarseness, roughness, local pixel correlation, and lower AIB1 variation, all indicating loss of textural definition. The supervised ML system revealed useful information regarding features that discriminate between 5‐year survivors and non‐survivors while the DL system displayed superior accuracy by employing DL features.
Time-CNN and Stacked LSTM for Posture Classification

In current times, when work-from-home and remote learning have become part of our life, maintaining healthy lifestyle is very essential. The posture in which we are sitting or standing may affect our health, if not corrected consciously. In this paper, we propose a novel deep-learning model to classify nine different sitting and standing postures using data from accelerometers. Three tri-axis accelerometers are placed on the back of a person to record the data. A combination of time convolutional neural network (tCNN) and stacked long short-term memory (LSTM) is proposed for classification of the considered postures from the raw accelerometer data acquired from seven subjects. The performance of the proposed model is compared with that of individual tCNN and two different configurations of stacked LSTM models in terms of loss and classification accuracy. The combined network outperforms the other models, giving an average accuracy of 99.77%.
The fully informed mayfly optimization algorithm

Considering the similarity in structures of updating equations between the mayfly optimization (MO) and particle swarm optimization (PSO) algorithms, the fully informed MO algorithm was proposed in this paper. The updating equations were re-formulated based on the explosion and stability analysis on the PSO algorithm. Results of several kinds of simulation experiments verified that such improvement could increase the capability of individuals in the MO swarm in optimization. The fully informed MO algorithm was confirmed to be another efficient way for optimization problems.
Research on the Application of Internet Technology in Adaptive Education of Accounting Major

In recent years, with the rapid development of Internet technology, human beings have rapidly entered the "Internet +" intelligent society. The social development mode with intelligence, automation and digitization as the core has greatly changed the thinking mode of modern people and our behavior mode. Under the background of "Internet + education", adaptive education has become one of the hot topics in the application of Internet technology in education, and has aroused great concern from researchers in the field of computer and education. In the era of Internet technology and big data, accounting business and accounting information increasingly present new features such as massive data processing and real-time cloud computing. Accounting education should change with the needs of social and economic development. The application of Internet technology to the field of accounting education makes the rapid development of adaptive education, which is of double significance to the cultivation of practical accounting talents and the development of accounting discipline.
"It's our mutual responsibility to share"

While most online accounts are designed assuming a single user, past work has found that romantic couples often share many accounts. Our study examines couples' account sharing behaviors as their relationships develop. We conducted 19 semi-structured interviews with people who are currently in romantic relationships to understand couples' account sharing behaviors over the lifecycle of their relationship. We find that account sharing behaviors progress through a relationship where major changes happen at the start of cohabitation, marriage, and occasional breakup. We also find that sharing behaviors and motivations are influenced by couples' relationship ecology, which consists of the dynamics between the couples and the social environment they live in. Based on these findings, we discuss implications for further study to support couples' sharing needs at different relationship stages and identify design opportunities for technology solutions to facilitate couples' sharing.
Advances in quantum secure direct communication

The practical implementation of quantum secure direct communication (QSDC) will undoubtedly be a milestone in the development of quantum cryptography. Research is in its final phase and focuses on developing secure protocols that consider realistic constraints and are feasible within the current state of technology. Three well-known protocols ae compared, Ping-Pong, Two-Step and Deng-Long, in the context of the level of security offered and the challenges related to their implementation. This work explains how the evolution of QSDC protocols from a purely quantum formulation to hybrid classical-quantum solutions based on the Wyner wiretap channel model can solve most problems encountered. The work aims to inform scientists in other fields of quantum information processing about the latest advances in QSDC. The attached appendix introduces basic concepts for readers unfamiliar with this research area.
On the use of ant colony algorithm with weighted penalty strategy to optimize path searching

The heuristic algorithm has been commonly used in solving the shortest path optimization problem. Recently, the ant colony algorithm is a bionic algorithm with decent performance. This paper proposed an improved ant colony algorithm with weighted penalty strategy to shorten path searching distance. Firstly, the grid method is used to transform the target area into a two-dimensional environment model, and the traditional ant colony algorithm is used to plan the path on the grid environment model. Secondly, shortest path obtained by Dijkstra algorithm is compared with that of the ant colony algorithm to validate its effectiveness. Finally, a weighted penalty strategy is proposed and implemented within ant colony algorithm. The advantage of this proposed algorithm enables the ant colony search to set higher priority on those close to the optimal path, and set lower priority on the undesired path. The weighted strategy has been simulated in MATLAB and it is demonstrated the distance of the optimal path is shorten effectively compared to traditional ant colony algorithm.
Newly discovered gossanite-like and sulfide ore bodies associated with microbial activity in the Zagros ophiolites from the Rayat area of NE Iraq

In this article, we report the discovery of a gossanite-type hematite and magnetite ore associated with sulfide mineralization in the Rayat area of NE Iraq. Detailed petrographic, mineralogical, and geochemical data define the features of the gossanite and the associated sulfide-rich rocks, providing an assessment of the mineralization and its relation to hydrothermal, microbial and alteration processes. The identified gossanite-like rocks are interpreted as part of a specific submarine gossan formed on the sulfide-rich serpentinite. In the uppermost part of the studied sequence, jasperite lithofacies occur, within which millimeter-thick layered iron oxide-silica alternations are also visible and comparable to the banded iron formation (BIF). Below the jasperite, an oxidized mineralized zone (OMZ) developed, representing gossanite that is exceptionally rich in iron oxides (up to 70%), chlorite, and quartz with some minor spinel content. The lowermost part of the studied sequence is the sulfide-rich reduced mineralized zone (RMZ) composed of silica, pyrite, chalcopyrite, pentlandite, Ni-chlorite, and siderite. The transition zone (TZ), approximately 1 m thick, between the OMZ and RMZ, contains chlorite, Fe-oxides, quartz, and partially altered sulfide minerals. The potential economic significance of the ore body in addition to Fe is the silver-nickel (Ag-Ni) and cobalt (Co) mineralization, with an average Ag concentration of 5.09 wt% per sulfide grain, which occurs as an exsolution phase within the sulfidic-cherts lithofacies. The Ni concentration ranged from 363 to 4,000 mg/kg, while that of Co ranged from 90 to 441 mg/kg, depending on the lithofacies. A thin lensoid coaly bed associated with the ore body, attributable to the Ophiolitic Mélange, provides some initial clues for the epigenetic thermotectonic evolution of the studied strata. The overall data suggest that the Rayat gossanite, jasperite, and the underlying sulfide-rich zone were formed near the seafloor through a combination of initial hydrothermal alteration of serpentinites, which provided the sulfides, accompanied by the sedimentary deposition of Fe-Si-exhalatives along with the olistostromatic brecciated facies in the upper section, and the subsequent seafloor oxidation/weathering effects along microbial iron uptake. The discovery of gossanite and sulfides in the Rayat area, along with the already known chromitite deposits in the contiguous areas, highlights NE Iraq as a potential mineral-rich domain.
Automated Synthesis of Quantum Circuits Using Symbolic Abstractions and Decision Procedures

Quantum algorithms are notoriously hard to design and require significant human ingenuity and insight. We present a new methodology called Quantum Automated Synthesizer (QUASH) that can automatically synthesize quantum circuits using decision procedures that perform symbolic reasoning for combinatorial search. Our automated synthesis approach constructs finite symbolic abstract models of the quantum gates automatically and discovers a quantum circuit as a composition of quantum gates using these symbolic models. Our key insight is that most current quantum algorithms work on a finite number of classical inputs, and hence, their correctness proof relies only on reasoning about a finite set of quantum states that can be represented using finite symbolic systems. We demonstrate the potential of our approach by automatically synthesizing four quantum circuits and re-discovering the Bernstein-Vazirani quantum algorithm using state-of-the-art decision procedures. Our synthesis approach only requires distinguishing between a finite set of symbolic quantum states; for example, the synthesis of the Bernstein-Vazirani quantum algorithm only requires reasoning about the following qubit states: |0, |1, -i|0, i|1, |+, |-, e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> |1i, e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iπ/4</sup> |1 and a remaining symbolic state representing all other possible quantum states. Our approach leverages decision procedures and theorem provers to assist in the discovery of new quantum algorithms and is a step towards the automation of quantum algorithm design.
New Standards for E-Voting Systems: Reflections on Source Code Examinations

There is a difference between a system having no known attacks and the system being secure—as cryptographers know all too well. Once we begin talking about the implementations of systems this issue becomes even more prominent since the amount of material which needs to be scrutinised skyrockets. Historically, lack of transparency and low standards for e-voting system implementations have resulted in a culture where open source code is seen as a gold standard; however, this ignores the issue of the comprehensibility of that code.In this work we make concrete empirical recommendations based on our, and others, experiences and findings from examining the source code of e-voting systems. We highlight that any solution used for significant elections should be well designed, carefully analysed, deftly built, accurately documented and expertly maintained. Until e-voting system implementations are clear, comprehensible, and open to public scrutiny security standards are unlikely to improve.
Power Side-Channel Attack Detection through Battery Impedance Monitoring

In modern integrated circuits design, security is a significant concern due to power analysis based side-channel attacks. Various countermeasures such as hiding and masking have limitations, for example, performance degradation, power consumption, low scalability, and area overhead. Besides, these countermeasures cannot detect attacks and alert users to take protective steps against attack. A power side-channel attack (P- SCA) detection system is proposed in this paper through battery impedance monitoring. Due to malicious probing, a non-typical impedance change occurs in the battery. Therefore, our system monitors the battery impedance change to detect such attacks. The detection circuit consists of a 10 bit ADC, an S8050 NPN transistor, a 1N4007 diode, and sense resistors (10 Ω, 50 Ω, 1k Ω). The detection system is designed in an Arduino. Measurements show that the system can detect a P-SCA in 22.020 ms, which is well within the desired detection window. The desired detection windows for different sense resistor values are measured as well. The accuracy and power consumption of implementation are also analyzed.
High-Throughput Polynomial Multiplier Architecture for Lattice-Based Cryptography

We propose a polynomial multiplier for lattice-based cryptography that achieves a throughput of 24.2 times higher than the state-of-the-art design. We have optimized the proposed architecture for ASIC implementation, instead of FPGA or CPU implementation. We employed shift register to reorder values to avoid complex memory accesses, and we realize complete pipeline operation for higher throughput. Also, we show that raising the degree of parallelism in this design increases throughput per area. This work will lead to the acceleration of Ring-LWE and Module-LWE-based cryptography, which attracts much attention for its resistance to quantum computers and applications in fully homomorphic encryption (FHE).
‘Lex Cryptographia,’ ‘Cloud Crypto Land’ or What? – Blockchain Technology on the Legal Hype Cycle

Based on an analysis of the literature on the interaction of law and DLT/blockchain, the paper argues that hype cycle dynamics apply in legal discourse. Thinking in hype cycle categories provides a structured way for analysing the potential legal implications of a particular innovation. This critical engagement with enthusiasts, sceptics and pragmatists throughout the different stages may help to present a more realistic picture of DLT/Blockchain’s potential from a legal perspective in the short and medium term. Consequently, the paper discusses the potential for disruption to the legal system envisaged by enthusiasts at the height of inflated expectations, attempts to deconstruct the arguments levelled at the technology by its detractors during the trough of disillusionment, charts the emerging legal landscape that seeks to accommodate and harness the potential of DLT/blockchain on the slope of enlightenment, and concludes by risking a glimpse towards the plateau of productivity.
Stacked generative adversarial networks for image compositing

Abstract Perfect image compositing can harmonize the appearance between the foreground and background effectively so that the composite result looks seamless and natural. However, the traditional convolutional neural network (CNN)-based methods often fail to yield highly realistic composite results due to overdependence on scene parsing while ignoring the coherence of semantic and structural between foreground and background. In this paper, we propose a framework to solve this problem by training a stacked generative adversarial network with attention guidance, which can efficiently create a high-resolution, realistic-looking composite. To this end, we develop a diverse adversarial loss in addition to perceptual and guidance loss to train the proposed generative network. Moreover, we construct a multi-scenario dataset for high-resolution image compositing, which contains high-quality images with different styles and object masks. Experiments on the synthesized and real images demonstrate the efficiency and effectiveness of our network in producing seamless, natural, and realistic results. Ablation studies show that our proposed network can improve the visual performance of composite results compared with the application of existing methods.