Reviewerly/exHarmony huggingface dataset:
A Novel Outlier Detecting Algorithm Based on the Outlier Turning Points

Outlier detection is one of hot research direction in data mining, and has been applicable to various fields such as network anomaly detection, image abnormal analysis, etc. In recent years, many outlier detecting algorithms have been proposed. However, these outlier detecting algorithms are hard to effectively detect global outliers, local outliers and outlier clusters at the same time. In this paper, we propose a novel outlier detecting algorithm based on the following ideas: (1) the density distribution should not be changed dramatically on local area; (2) the ratio of the number of k nearest neighbors and the number of reverse k nearest neighbors should not be very big. Based on above ideas, the proposed algorithm aims to find outlier turning points, then regards all outlier turning points and its sparse neighbors as outliers. Futhermore, the proposed algorithm use natural neighbors to obtain the neighborhood parameter k adaptively. The formal analysis and extensive experiments demonstrate that this technique can detect global outliers, local outliers and outlier clusters without neighborhood parameter k.
Modeling Enablers for Blockchain Adoption in the Circular Economy

As a catalyst for the implementation of the Circular Economy (CE), blockchain is expected to introduce new business models, instill transparency and confidence in circular operations, and reshape existing institutional and economic systems. This article evaluates the critical enablers that influence technology adoption in the CE. A comprehensive literature review and feedback from experts revealed nineteen (19) critical enablers. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology was used to determine the cause-and-effect relationships between these enablers. The findings identified five critical causal enablers: transparency, security, smart contracts, traceability, and enhanced collaboration that influence the blockchain adoption in the CE.
A Super Feature Transform for Small-Size Image Forgery Detection

In this digital era, we have a wide variety of image editing software that is prone to create malicious alterations on images. Hence, the evaluation for authenticity of image contents and identification of malicious modifications is an open problem. In this work, an efficient small-size image forgery detection algorithm is presented based on Super Feature Transform - combining Super Resolution and Feature Transform. The approach enhances detection of small-size forgery by pre-processing the input image using super resolution algorithm. A robust feature transform is suggested to extract potential feature points from small-size patches with entanglement properties. Subsequently, feature matching and filtering is achieved by fuzzy threshold so that the false matches are filtered out. Also, the feature matching module employs a soft clustering to determine the matching points between identical and semi-identical feature points in different clusters. The experimental evaluations demonstrated that the proposed method outperforms existing techniques particularly when the forgery size is small and detects manifold duplicate forged regions in terms of TPR and FPR recognition rate.
Crypto-Currencies: Can Investors Rely on them as Investment Avenue?

The purpose of this study is to examine investors’ perceptions about investing in crypto-currencies. We think that investors trust in crypto-currencies is largely driven by crypto-currency comprehension, trust in government, and transaction speed. This is the first study to examine crypto-currencies from the investor’s perspective. Following that, we discover important antecedents of crypto-currency confidence. Second, we look at the government's role in crypto-currencies. The importance of this study is: first, crypto-currencies have the potential to disrupt the current economic system as the debate is all about impact of decentralization of transactions; thus, further research into how it affects investors trust is essential; and second, access to crypto-currencies. Finally, if Fin-Tech companies or banks want to enter the bitcoin industry may not attract huge advertising costs as well as marketing to soothe clients' concerns about investing in various digital currencies The research sheds light on indecisiveness in the context of marketing aspects adopted by demonstrating investors are aware about the crypto.
Research on the problem of solar energy storage system based on AHP

Solar energy storage, while currently widely used as a clean and sustainable resource, is a critical issue that impacts the widespread use of solar energy technology. Using solar power to provide electricity can overcome the constraint of avoiding geography, which is crucial for the electricity problem associated with homes built in remote areas. However, it is limited by the sun’s irradiation. We should solve the electricity storage problem to support energy requirements at night and on a cloudy day. In our paper, we are tasked to evaluate the utility of battery storage system based on the energy needs and individual’s preferences. Considering individual’s needs and preferences, we proposed a generalized evaluation model based on AHP method. The method is based on the choice of variables that influence battery storage system section in question 1 and goes through a hierarchical analysis model to calculate the weights of variables. AHP method can effectively represent the preferences of individuals. Besides, we proposed a set of functions to evaluate the satisfaction of individual under each factor by considering individual’s expected cost and energy requirements. Finally, we talk about the future of cement batteries. The benefits and drawbacks of cement batteries as energy storage equipment are examined, focusing on cost, safety, and environmental contamination concerns. Furthermore, we assessed the future utility of cement battery in terms of cost, usable capacity, continuous power rating, instantaneous rower rating, lifetime of battery, and other factors.
Proving Unfairness of Decision Making Systems Without Model Access

The problem of guaranteeing the fairness of automatic decision making systems has recently become a topic of considerable interest. Many definitions of fairness have been proposed, as well as methods aiming to achieve them while maintaining the ability to train useful models. The complimentary question of testing the fairness of an existing predictor is important both to the creators of machine learning systems, and to users. More specifically, it is important for users to be able to prove that a system that affects them is unfair, even when full and direct access to the system internals is denied. In this paper we propose a framework that allows to prove the unfairness of predictors which have known accuracy properties, without direct access to the model, the features it is based on, or even individual predictions. To do so we analyze the fairness-accuracy trade-off under the definition of demographic parity. We develop an information-theoretic method that uses only an external dataset containing the protected attributes and the targets, and provides a bound on the accuracy of any fair model that predicts the same targets, regardless of the features it is based on. The result is an algorithm that enables proof of unfairness, with absolutely no cooperation from the system owners.
Enhanced Self-Supervised Learning with Autoencoder

Contrastive learning always relies on positive or negative features. This is both its strength and weakness. As a kind of Self-Supervised Learning(SSL), contrastive learning has a strong attachment to apply random data augmentations, as such applications can generate some features required by the algorithm. This may be a bottleneck in the performance of contrastive learning. In this paper, we propose a new contrastive learning framework named ESLA which is an abbreviation of E nhanced S elf-supervised L earning with A utoEncoder. The ESLA directly augments features in latent space and improve model performance with asymmetric masked autoencoder auxiliary structures. We also use the Gaussian offset to avoid the encoder learning a degenerate mapping. From the experimental results, our method achieves good results on several benchmark datasets.
Design of Mobile App Interactive Interface Based on 3D Image Vision

With the development of science and technology and the advent of the 5G era, smartphone apps are being integrated into our daily lives, and even most of the daily activities need to be implemented by mobile apps. The importance of human-computer interaction in the era of big data is increasing day by day. Faced with this situation, the design of interactive interfaces needs to get rid of solidified thinking and transform to emotional and personalized. The user’s innovative demand for APP interface is also the main driving force for the development and innovation of interactive interface design. This paper analyzes the design method of mobile APP interactive interface based on the visual perspective of 3D images. First, through the combing of related concepts, the scope of the 3D image and the level of coverage of the mobile APP interactive interface are clarified; then, the factors affecting the user’s satisfaction with the APP interactive interface are analyzed to analyze the priority of the design of the APP interactive interface to the involved elements; finally The literature analysis method and the case analysis method focus on the analysis of examples of 3D image vision in mobile APP interactive interface design. It not only mentions the widely used 3D vision methods, but also elaborates the cutting-edge 3D technology methods that are still in the research stage. This article aims to explore the new style and future development trend of interactive interface design, in order to pave the way for future research.
Estimation of Consistent Cross-Covariance Matrices in a Multisensor Data Fusion

The article investigates the problems relevant to the fusion of data provided by sensors used for detection and tracking. One of the most burdensome problems on any data fusion in these fields of application is the search for the appropriate correlation function between the estimation errors of the tracks from the various sensors. The absence of such a function can lead to excessively optimistic cross-covariance matrices between tracks with consequent inconsistencies and missed associations. The article has the aim to provide an effective solution to this problem. The proposed technique resides to build consistent cross-covariance matrices by means of a tracking system which uses as input the simultaneous associations of the tracks of the involved sensors. Furthermore, a fusion equation, that provides an unbiased and optimal global estimate in the sense of the minimum mean square error, is proposed. The method results simple and fast as well as precise. Finally, the robustness of the algorithms is proved through dedicated simulations.
Deep Learning-Based Differential Distinguisher for Lightweight Ciphers GIFT-64 and PRIDE

Rivest in 1991 in his landmark paper Cryptography and Machine Learning noted various similarities between machine learning and cryptanalysis. This was followed by good number of research papers to apply machine learning (ML) techniques for cryptanalysis. First major breakthrough in this direction was projected by Aron Gohr at Crypto’2019. He built an efficient cryptographic distinguisher using neural networks. In line with Gohr’s work, we present two neural network-based cryptographic distinguishers against lightweight block ciphers GIFT-64 and PRIDE. Our experiments assert the capability of ML-based techniques in development of cryptographic distinguishers for block ciphers. Through our experiments, we have been able to reduce the attack complexity in comparison to earlier published results.
Automated Wheelchair Module Using Bluetooth Application

The intelligent Bluetooth controlled wheelchair described in this article may be operated via voice instructions, hand gestures, and touch buttons. Disabled persons who are unable to travel from one location to another depend on others to assist them in transporting their wheelchair. Power wheelchairs with joystick controls have been popular in recent years, although they have a few disadvantages. If the joystick fails, the wheelchair must be controlled manually until the joystick is fixed or replaced, however the project we designed can be linked to and operated using any Smartphone. They become more autonomous as a result of this Bluetooth-controlled technology, which allows them to operate the wheelchair without assistance.
Computational Sensor Fingerprints

Analysis of imaging sensors is one of the most reliable photo forensic techniques, but it is increasingly challenged by complex image processing in modern cameras. The underlying photo response non-uniformity (PRNU) is distilled into a static sensor fingerprint unique for each device. This makes it easy to estimate and spoof and limits its reliability in face of sophisticated attackers. We propose to exploit computational capabilities of emerging intelligent vision sensors to design next-generation computational sensor fingerprints. Such sensors allow for running neural network inference directly on raw pixels, which enables end-to-end optimization of the entire photo acquisition and distribution pipeline. Control over fingerprint generation allows for adaptation to various requirements and threat models. In this study we provide a detailed assessment of security properties and evaluate two approaches to prevent spoofing: fingerprint generation based on local image content and adversarial training. We found that adversarial training is currently impractical, but content fingerprints deliver good performance in the considered cross-domain (RAW-RGB) setting and could provide robust best-effort protection against photo manipulation. Moreover, computational fingerprints can alleviate other limitations of PRNU, e.g., its limited reliability for dark/texture content and expensive fingerprint storage that hinders scalability. To enable this line of work, we developed a novel open-source and high-fidelity simulation environment for modeling photo acquisition and distribution pipelines (https://github.com/pkorus/neural-imaging).
Exposing “Pluralistic Ignorance” to Reduce Youth Violence in Political Spaces

The study was inspired by the involvement of youths in political violence in Zimbabwe. It attempted to expose pluralistic ignorance concept as a way of reducing violence in political spheres. The concept of Pluralistic Ignorance argues that there are individuals who wrongly see the attitudes and deeds of peers as unlike their own when in fact they are not. It creates misunderstandings, which ensue in relation to problem behaviours which are commonly overrated. The study followed an intensive desk review conducted along a qualitative research paradigm. The concept of anti-violence as defined by the Commonwealth Commission on Respect and Understanding was applied to underpin the analysis. The study noted that the exposure of pluralistic ignorance allowed the affected youths to be able to discern the good from the bad and make sound decisions for their participation in politics. This, if appropriately applied helps reduce youth conflicts in politics in Africa.
Blockchain for Smart Cities: Findings from a Systematic Literature Review

Following a systematic literature review methodology, we focus on answering the following questions pertaining to smart cities and blockchain: (i) what was the reason that blockchain was proposed as the chosen solution? (ii) What blockchain-based applications are being proposed for smart cities? Based on 45 peer-reviewed academic studies all published in journals that met predefined search criteria, we find that the desire for high security, privacy, and trust are the reasons most cited for the use of blockchain for smart cities. Further, we discuss smart cities’ blockchain applications from the literature using a taxonomy framework. We conclude, highlighting the current blockchain challenges and future research opportunities, the skills that are needed to implement blockchain for smart cities and the need to change the current mindset of centralized control and trusted third parties to a more participative engagement model across smart cities.
Automated Return Type Annotation for Python

Despite efforts being put in the Python community to support type annotations, most Python libraries have not yet been provided with type hints, missing the opportunity to open up their code for more accessible static analysis, refactoring, and potential runtime type checkings. The Python community has started to mitigate this issue by introducing tools such as mypy to support static type checking. These tools, however, rely on library stubs that are either manually built by developers or automatically generated by industry-leading tools such as stubgen to achieve their objectives. Unfortunately, as experimentally revealed in this work, the official typeshed project supports only a limited number of functions in a small number of Python libraries, while the stubgen tool is also not reliable to generate stub files for new Python libraries. To fill this gap, we introduce a static analysis tool called TypeGen4Py, aiming to support automated return type annotation for Python functions. Experimental results on two benchmarks and real-world popular Python libraries demonstrate the effectiveness of TypeGen4Py in generating return types for Python functions. TypeGen4Py outperforms both typeshed and stubgen , as well as Pytype (by Google) by providing annotations for more functions in a set of libraries.
Trustworthiness Evaluation for Permissioned Blockchain-Enabled Applications

As permissioned blockchain becomes a common foundation of blockchain-based circumstances for current organizations, related stakeholders need a means to assess the trustworthiness of the applications involved within. It is extremely important to consider the potential impact brought by the Blockchain technology in terms of security and privacy. Therefore, this study proposes a rigorous security risk management framework for permissioned blockchain-enabled applications. The framework divides itself into different implementation domains, i.e., organization security, application security, consensus mechanism security, node management and network security, host security and perimeter security, and simultaneously provides guidelines to control the security risks of permissioned blockchain applications with respect to these security domains. In addition, a case study, including a security testing and risk evaluation on each stack of a specific organization, is demonstrated as an implementation instruction of our proposed risk management framework. According to the best of our knowledge, this study is one of the pioneer researches that provide a means to evaluate the security risks of permissioned blockchain applications from a holistic point of view. If users can trust the applications that adopted this framework, this study can contribute to the adoption of permissioned blockchain-enabled technologies. Furthermore, application providers can use the framework to perform gap analysis on their existing systems and controls and understand the risks of their applications.
A Micro-population Evolution Strategy for Loser-Out Tournament-Based Firework Algorithm

The loser-out tournament-based firework algorithm (LoTFWA) is a new baseline among firework algorithm (FWA) variants due to its outstanding performance in multimodal optimization problems. LoTFWA successfully achieves information-interaction among populations by introducing a competition mechanism, while information-interaction within each sub-population remains insufficient. To solve this issue, this paper proposes a micro-population evolution strategy and a hybrid algorithm LoTFWA-microDE. Under the proposed strategy, sparks generated by one firework make up a micro-population which is taken into the differential evolution procedure. The proposed algorithm is tested on the CEC'13 benchmark functions. Experimental results show that the proposed algorithm attains significantly better performance than LoTFWA and DE in multimodal functions, which indicates the superiority of the proposed micro-population evolution strategy.
New Infinite Family of Sequences Almost Over <i>Q</i> <sub>8</sub> With Ideal Autocorrelation

In this paper, we provide the left and right Parseval relations over the quaternions. Moreover, we construct a new infinite family of sequences almost over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q_{8} $ </tex-math></inline-formula> with period <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> by a generalization of Legendre mapping, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p\equiv 1 \pmod {8}$ </tex-math></inline-formula> is prime and the sequences have ideal autocorrelation. The sequences are symmetric when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p\equiv 1 \pmod {16}$ </tex-math></inline-formula> , and antisymmetric when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p\equiv 9 \pmod {16} $ </tex-math></inline-formula> .
Target Identification and Detection on SAR Imagery Using Deep Convolution Network

Paper captures implementation of a real-time ship target detection algorithm on SAR imagery. SAR images are independent of range and can operate at all-day and all-weather conditions. The features in SAR images, noise and clutter make it real challenging for detection of objects. An open-source SAR-ship dataset is used for detection task. The traditional methods of detection (two-stage object detector) have region proposals followed by extraction of features. The speed of the process is slow, and the accuracy is unsatisfactory. Hence, one-stage detector model (YOLOv3-16 model with 16 convolution layers) is used, which outperforms YOLOv2 by providing 89.35% mean average precision (mAP) at 0.5. The customized model YOLOv3-16 outperforms YOLOv3 in terms of inference time with nearly comparable accuracy. The prediction time is ~100 ms on YOLOv3-16 model as compared to YOLOv3 which provides ~172 ms on GPU. NVIDIA GPU (K40) hardware is used for faster training of the model with speedup of ~ 5 times over CPU (Xeon).
Cognitive Technologies in the Regional Economic Security Management

The authors proved that the potential of cognitive technologies in solving problems of regional economic security management lies in their ability to model poorly formalized processes, predict their development, and improve the quality of management decisions. Scientific novelty of the research: The author’s hypothesis that the process of managing the regional economic security to a new qualitative level is possible through the use of cognitive technologies is substantiated. Potential threats to the well-being of the region’s life are revealed. Research methods: theoretical analysis method, systematization method, comparative analysis method, system approach, and cognitive modeling method.