Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Voting Considered Harmful?

Blockchains (BC) and Distributed Ledgers (DL) offer favorable properties, especially immutability and decentralization, which are suitable for voting systems’ Bulletin Boards (BB). In recent years, an influx of BC-based voting systems have been observed. Distributing trust among multiple trustees is a crucial reason to adopt BCs and DLs in voting systems. The practical deployment must be decentralized, too, and not just done through virtualizing interconnected systems. As discussed widely, adopting a BC or DL can incur threats to a system that assumed a trusted and centralized Public Bulletin Board (PBB). Therefore, the exploitation of BCs or DLs requires careful consideration of cryptographic mechanisms and the overall system design, as well as the adversary model. Besides these operational necessities, the long-term privacy of ballots is essential. Thus, the key question investigated in this article is: Can BC, and DL-based voting systems be considered harmful? Hence, first (i) requirements of BC-based voting systems are provided, followed by (ii) terminology definitions, and (iii) complemented by the design and implementation of a fully decentralized voting system: Æternum, which achieves Unconditional Privacy (UP) and neither relies on computational hardness assumptions nor on a trusted Trusted Third Party (TTP). Achieving UP is crucial because future adversaries may be able to break hardness assumptions. Æternum does not present a Single-Point-of-Failure (SPoF) either, since (i) the PBB in use is based on a permissioned DL, and (ii) the final tally and proofs can be verified by anyone, without requiring trust in any authority.
Squiride Rank

The proposed SquiRide Rank algorithm is designed by integrating the Squirrel Search Algorithm (SSA) with the Rider Optimization Algorithm (ROA), respectively. The concept of fictional computing and the foraging behavior realize the re-ranking process more effectively in the web environment. However, the features extracted from the web pages makes the process more effective and achieve global optimal solution through the fitness measure. The proposed SquiRide Rank algorithm effectively captures and analyzes the ranking scores of different search engines in order to generate the re-ranked score result. However, the proposed SquiRide Rank algorithm provides satisfactory results using the metrics, like precision, recall, and F-measure, which acquired with the values of 0.964, 0.996, and 0.980, respectively.
Blockchain meets network analytics: a tale of heuristics, location and fraud detection

The information provided by blockchains of cryptocurrencies is immense and diverse. Blockchain information are represented by networks, the transaction network and the user network, approximated by the entity network. We review heuristics for constructing the entity network out of the transaction network, and discuss how they have been used, improved, and developed over time. We introduce network analytics applied to blockchain data, which supports finance and economics research to look into location identification, fraud detection, and price investigations of cryptocurrencies, among other topics. We inspect how they make use of network analytics, often tailored to the specific properties of blockchains. By this comprehensive overview, we intend to aid research on the use of blockchain information to understand user behaviours and the corresponding price behaviours of cryptocurrencies.
Design of Military Physical Fitness Evaluation System Based on Big Data Clustering Algorithm

The existing military fitness evaluation system has the defect of low accuracy, which cannot meet the needs of today's military field. Therefore, this paper puts forward the design and research of military physical fitness evaluation systems based on big data clustering algorithm. The big data clustering algorithm is introduced to design the military physical fitness evaluation system. The hardware unit is mainly the input unit, controller, arithmetic unit, memory, and output unit; the software module is mainly the construction module of the military physical fitness evaluation index system, the application module of big data clustering algorithm, and the database establishment module. Through the design of hardware unit and software module, the operation of the military physical fitness evaluation system is realized. The experimental results show that through data comparison, compared with the existing system, the design system has higher accuracy of military physical fitness evaluation results, which fully confirms the effectiveness and feasibility of the design system.
Computer Management of University Library Based on Deep Learning

With the development of science and technology, the information technology is advancing. The preservation of library documents is not the only standard to evaluate the quality of library work. The continuous progress of science and technology and the development of education promote the library to effectively change the traditional working mode. Computer management has become an important guarantee for the automatic management of the library, It has a great impact on the traditional library management. This paper analyzes the problems existing in the implementation of computer management in the library, and puts forward some countermeasures for the optimization of computer management, so as to lay a good foundation for improving the effectiveness of library automation management.
Non-spectral Transposition-Invariant Information in Pitch-Class Sets and Distributions

The spectral information of a pitch-class set or distribution relates to its interval content and what Ian Quinn calls its harmonic qualities, the magnitudes of a discrete Fourier transform of a pitch-class vector. The spectrum is invariant with respect to transposition and inversion, but the existence of Z-related sets, which have equivalent spectra but are not related by transposition or inversion, means that the spectrum is not a complete description of a set class. We show how to isolate transposition-invariant phase information using products of Fourier coefficients. We describe some of the mathematical features of these coefficient products and show how they encode aspects of tonality, and can be useful for analyzing non-tonal music with an example from Takemitsu's "Air" for solo flute.
Key Takeaways and Prospective Research

Abstract This chapter outlines the book’s key takeaways and future research trajectories, emphasising that Touch is a key cultural communicative aspect of social media relationships, and more broadly of digitally networked relationships. I summarise the key argument of each chapter, then drawing these arguments together, I locate and define the key aspects of “semeful sociabilities” as a means to map Touch of digitally networked social relationships and to describe the technological self as meaningful (semeful) and part of meaning-making processes. Mapping Touch is a means through which selfies and memes (as fundamental elements of everyday cultural communication and embodied networked practices) can be explored as meaningful cultural relationships, exposing the social, cultural and political realities of everyday socialities. Future trajectories for semeful sociabilities include application to a range of other memes and selfies, and to networked social relationships beyond visual. Furthermore, I propose that mapping Touch (to identify semeful social relationships) can provide insight into investigations and mapping of automated systems. Semeful sociabilities has the potential to identify and expose the semefulness (meanings) and impact of seamless systems and gain a better understanding of ourselves as humans as we design our technological futures.
Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments

Building on current work on multilingual hate speech (e.g., Ousidhoum et al. (2019)) and hate speech reduction (e.g., Sap et al. ( 2020)), we present XtremeSpeech, 1 a new hate speech dataset containing 20,297 social media passages from Brazil, Germany, India and Kenya.The key novelty is that we directly involve the affected communities in collecting and annotating the data -as opposed to giving companies and governments control over defining and combatting hate speech.This inclusive approach results in datasets more representative of actually occurring online speech and is likely to facilitate the removal of the social media content that marginalized communities view as causing the most harm.Based on XtremeSpeech, we establish novel tasks with accompanying baselines, provide evidence that cross-country training is generally not feasible due to cultural differences between countries and perform an interpretability analysis of BERT's predictions.
Data Visualization Technology in Power Grid Planning and Government Affairs Based on Association Rules Algorithm

Due to the rapid development of artificial intelligence and big data technology, State Grid has entered the era of full intelligence. The amount of data has increased exponentially. How to manage these data is a major challenge encountered in the development of smart national grid. The rapid development of big data analysis and visualization technology has opened up new development opportunities for power grid data analysis and research. This paper aims to study the data visualization technology in power grid planning and government affairs under the association rule algorithm. Based on the analysis of the application of data mining technology in the power system, the association rule algorithm and data visualization technology, it adopts the electricity consumption of an enterprise in S city. The data construction model is used for data mining and analysis to verify the effectiveness of the association rule algorithm in data analysis in power grid planning and government affairs. The experimental results show that through the analysis of association rules, the business efficiency of the power grid is continuously improved, and the best allocation of resources is finally realized.
Bento Packaging Activity Recognition with Convolutional LSTM Using Autocorrelation Function and Majority Vote

This paper reports Bento Packaging Activity Recognition Challenge by team “RitsBen” held in the International Conference on Activity and Behavior Computing (ABC 2021). Our approach used an autocorrelation function in the preprocessing to isolate the data since the dataset was given with repetitive activity. We then use a model that implements convolutional layers and LSTM. The final decision is made by majority vote using sigmoid predictions output from all body parts. The loss is calculated using BCEWithLogitsLoss for each body part. The evaluation results showed that average accuracy of 0.123 was achieved among subjects 1, 2, and 3 in leave-one-subject-out manner. However, we did not achieve high accuracy as the possibility that the extraction of repetitive actions was not correct.
Problems of Formation of Multicultural Competence in Modern Higher Education (on the Example of Creative Specialties)

Purpose The purpose of the article is to identify the main problems and principles of multicultural education in relation to higher education in Russia, as well as to confirm the implementation of these principles in teaching Chinese students creative specialties at the Khabarovsk State Institute of Culture. Methodology To achieve this goal, a theoretical justification of the concepts of “multicultural education” and “cultural competence” was carried out, three principles of multicultural education that are relevant for higher education were identified and argued. To confirm the implementation of the principles of multicultural education in the Khabarovsk State Institute of Culture, the main educational programs for creative specialties were analyzed. The method of comparison with similar programs of higher education institutions of the People’s Republic of China was applied. Results A survey of foreign students and teachers on the formation of multicultural competence and tolerant environment of the Institute was conducted. Positive results have been obtained that assess a fairly high level of development of a tolerant educational environment and multicultural education. Conclusions To improve the work, it is necessary to continue analyzing the curricula and educational programs of creative specialties of higher education institutions of the People’s Republic of China, to provide the educational process with teaching materials and manuals, including bilingual ones. Teachers of the Institute should improve their skills in the field of multicultural education.
A Histogram-Based Heuristic for an Adaptive Active Contours Color Image Segmentation

The fidelity to data (external energy) term in energy-based segmentation of scalar (single channel) images requires setting scalar values de ning the weights assigned to the inside and outside energy functional. These values are often determined empirically, which is a tedious and time consuming task. Whenit comes to color images (multi-channel), the weights become vectors, which further complicates the process of identifying the appropriate weights. In this work, a new interpretation of the weight vector is introduced. It is seen as representing the contribution of each channel in the energy functional, that is equivalent to search an optimum color space. We propose a heuristic formula for estimating the values of the weight vector. It is based on the ratio of the height to the width of the color components histograms. ....
Adapting Interaction Protocols in Multiagent Systems at Runtime: A Promising Approach for Self-Adaptive Ambient Assisted Applications

Multiagent Systems have proven to be helpful in ambient assisted applications due to their adaptive and self-adaptive potentials. This field has gained maturity and the time is therefore, ripe for congruence between specification, design, development, deployment and, evaluation of the specificities of such systems. This paper proposes a new methodology that automatically generates agents with adaptive, and self-adaptive abilities for Ambient assisted applications. Thanks to generated Adaptation Rules, the agents can change their interaction protocol or tune some parameters accordingly depending on the context application at runtime. The proposed methodology is illustrated with a person fall detection scenario. The results are very promising since they showed that it is possible to automatically generate Multiagent systems that are self-adaptive by design. Our method is generic enough so that it can be used with different algorithms for generating the Adaptation Rules. Furthermore, it can be applied to new interaction protocols and domain applications.
A Zero-Shot Image Enhancement Method Based on Human Skin Tone Reference

In this work, we introduce a zero-shot method for color enhancement in images containing people. This method is based on photo edition techniques used by professional editors. One of the techniques applied by these professionals is the adjustment of RGB curves, which affects the image colors globally. The hypothesis adopted in this investigation is that, when there are people present in an image, they become the most important element of the scene, so one of the main aspects of color adjustment is to make their skin tones look more realistic and natural, which should please most of the viewers. The proposed method employs a pipeline of classical algorithms of computer vision to mimic the process applied by professional photo editors. Experiments performed on the Adobe-MIT 5k dataset, a widely used dataset on image enhancement, show that our approach has similar performance compared to supervised data-driven state-of-art methods.
A multi strategy improved pigeon-inspired optimization algorithm

Pigeon-inspired optimization algorithm is easy to fall into local optimization and low convergence accuracy in solving nonlinear optimization problems. In this paper, an improved pigeon-inspired optimization algorithm called Gaussian mixture pigeon-inspired optimization algorithm (GPIO) is proposed. In GPIO, the cubic mapping of chaotic mapping method is used to initialize the pigeon population, which increases the diversity of the population. Gaussian mutation operator is introduced to change the shortage that pigeon swarm algorithm is easy to fall into local optimization, and improve the convergence efficiency of the algorithm. The experimental results of 19 benchmark functions show that the algorithm has better optimization ability than other swarm intelligence algorithms.
Hierarchical Reinforcement Learning

The goal of artificial intelligence is to understand and create intelligent behavior; the goal of deep reinforcement learning is to find a behavior policy for ever larger sequential decision problems.
On The Skill of Influential Predictions

The skill of a model is defined as its ability to predict better than a simpler or default model. These default models almost always exist. Models without skill should not be used. All predictions are based on models, tacit or formal. Predictions to be useful must therefore be skillful. Some predictions are influential in the sense that the observable predicted is caused, in whole or in part, by the prediction itself. Skill measures common in assessing non-influential predictions are developed here for use with influential models.
Classification of Breast Cancer Histopathology Images Using EfficientNet Architectures

In women, breast cancer is one of the common diseases found worldwide. A pathologist confirms it by observing cancerous tissues in histopathology images. Due to a lack of pathologists, the manual process of diagnosis is a time taking process and may lead to delay in prognosis and treatment of the disease. Delay in treatment may cause to affect mortality due to the disease. Computer-aided diagnosis methods can assist pathologists and reduce the time of diagnosis. The goal of this work is to apply different EfficientNet architectures for the classification of breast cancer using histopathology images. EfficientNet architectures (B0–B7) models are used to classify histopathology images of breast cancer. The accuracy of all models is evaluated on breast histopathology image dataset a publically available. EfficientNet-B2 achieved validation accuracy and validation loss and AUC score of 0.875, 0.2945, and 0.95, respectively. Among all EfficientNet architectures, it has been shown that the EfficientNet-B2 model classification performance is better. EfficientNet-B6, -B7 faced overfitting which can be reduced by applying some techniques.
Modeling Generalization in Domain Taxonomies Using a Maximum Likelihood Criterion

We define a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a domain taxonomy. This generalization lifts the set to its “head subject” node in the higher ranks of the taxonomy tree. The head subject is supposed to “tightly” cover the query set, possibly involving some errors referred to as “gaps” and “offshoots”. We develop a method to globally maximize the likelihood of a scenario involving gains and losses of the general concept manifested in a fuzzy cluster of leaf nodes of the taxonomy. Probabilities of the gain and loss events are derived from multiple runs of our earlier method of maximum parsimony starting with randomly generated values for the two parameters involved. Supplemented with fuzzy c-means clustering, this allows us to obtain meaningful generalizations for six fuzzy thematic clusters of Data Science topics using over 17000 abstracts from 17 research journals published by Springer.
A 3D Chaotic Dynamics-Assisted Color Image Authentication Technique in Multicore Milieu

The paper presents a novel, 3D Chaotic Dynamics assisted secured and imperceptible, LSB steganography technique for hiding secret information, into the RGB color components of a cover image. Random numbers generated with three dimensional chaotic function assist in embedding and extracting the secret data in the payload, enhancing the transmission security. The quality of the pseudorandom number sequences has been tested with the standard NIST test suite as a pre-embedding operation. The proposed steganography technique is highly secure, robust and fast in execution as it utilizes multicore processors available today with most of the desktop and laptop computers. Statistical analysis of the technique validates the security and robustness while scalability and speed-up analysis validate the efficiency of the proposed parallel steganography technique. Significant performance has been observed even when a large amount of data is processed.
Social Media Governance and Strategies to Combat Online Hate Speech in Germany

Concerns over online hate speech have prompted governments to strengthen social media governance. However, claims by policy makers and political activists regarding the effectiveness and likely consequences of legal regulations remain largely untested. We rely on qualitative interviews and two expert surveys to examine the behavior of public relations professionals in response to online hate speech when having the option of using the new user-complaint mechanism under the German Network Enforcement Act (NetzDG). Our findings reveal that strategies depend on whether professionals work at public sector institutions, business, or civil society organizations and political parties. Public sector institutions are likely to report to the platform, but not under NetzDG. Civil society organizations are likely to choose content moderation, counterspeech, and other forms of intervention. Businesses lack a coherent strategy to counter online hate speech. In practice, Germany’s procedural approach relying on user-complaint mechanisms to deal with online hate speech is not used by experts as a means to combat online harassment.
Frame-Based Locality Preservation Matching for Images Involving Large-Scale Transformations

Feature matching refers to the establishment of reliable correspondence between two sets of local features, which is an essential approach in remote sensing applications such as image registration and mosaicking. In this paper, a simple yet effective method, called frame-based locality preservation matching, is proposed for robust remote sensing image matching. We primarily focus on those images pairs that involve large-scale geometric transformations (e.g., extreme zoom). The key idea of our approach is to dig up the frame knowledge, such as the feature orientation and scale implied by common features like SIFT. The frame knowledge is free to obtain, and we find it to be of great significance in feature matching, especially for our focus -- large-scale geometric transformations. The proposed method can easily handle the geometric challenges and high outlier proportions, and significantly improves the performance compared to other state-of-the-art methods.
Handwritten Word Recognition on the Fundación-Osborne Dataset

Even to this day, offline handwritten text recognition still constitutes a challenging research problem, specially when it comes to perform recognition tasks on historical databases. In this context, the main aim of the present paper is to expound the results obtained after training a deep convolutional Seq2Seq network with attention mechanism using a combination of word training images from both contemporary and historical databases. In the light of the subsequent results, we discuss the effectiveness that different proportions of modern and historical text during the training process have on the final performance of the architecture concerning historical handwritten text recognition.