Reviewerly/exHarmony huggingface dataset:
Applying Geographic Information System (GIS) for Solar Power Plants Site Selection Support in Makkah

Solar energy is one of the most important components of renewable energy, which constitutes an important source of clean energy in many fields, especially water desalination and electricity generation.With the increase in electricity consumption in the Kingdom of Saudi Arabia at an annual rate of 5%, the National Initiative for the production of water and electricity was launched.The current study aims to apply a multi-standard GIS method to determine the most appropriate spatial sites for solar energy collection in the Makkah Administrative District.A set of conditions and criteria have been relied upon, whether planning criteria, environmental criteria, or an environmental criterion, to obtain a digital appropriate model that shows the best sites for constructing solar power plants.The study determined the required criteria by relying on literary studies and creating a digital geographic database for these requirements, and then integrating these requirements into an integrated geographic information system in order to obtain a spatial fit model.The results of the suitability indicate that all areas of Makkah Al-Mukarramah are suitable for the solar energy project with an appropriate percentage ranging between 30% and 80%.These results are promising for the renewable energy sector in Makkah Al-Mukarramah and should be taken into consideration.By analyzing these spatial sites and their degrees of suitability to standards, it was found that the lands that are characterized by an adequate share of more than 80% have an area of about 4000 square kilometers with a percentage of 3% of the total suitable lands.These highly suitable areas are concentrated on the governorates of the Makkah Al-Mukarramah Administrative Region, where the Taif governorate comes in the first place with 35% of the total area, followed by the two governorates of Turbah with 24%, and the Rania governorate with 14%.A digital map was made showing the spatial distribution of How to cite this paper:
Dual Distance Optimized Deep Quantization With Semantics-Preserving

Recently, quantization has been an effective technique for large-scale image retrieval, which can encode feature vectors into compact codes. However, it is still a great challenge to improve the discriminative capability of codewords while minimizing the quantization error. This letter proposes Dual Distance Optimized Deep Quantization (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ODQ) to deal with this issue, by minimizing the Euclidean distance between samples and codewords, and maximizing the minimum cosine distance between codewords. To generate the evenly distributed codebook, we find the general solution for the upper bound of the minimum cosine distance between codewords. Moreover, scaler constrained semantics-preserving loss is considered to avoid trivial quantization boundary, and ensure that a codeword can only quantize the features of one category. In contrast to state-of-the-art methods, our method has a better performance on three benchmark datasets.
Impact of Agile Scrum Methodology on Time to Market and Code Quality – A Case Study

The software industry's Development Lifecycle is changing dramatically, with traditional, heavy-weight processes giving way to simple, light-weight Agile software development (ASD) methods. ASD develops outstanding, high-quality software in short, rapid iterations that is responsive to changing business conditions. Adaptive planning, gradual design and deployment, and a time-bound strategy are all encouraged. Agile promotes a quick and flexible response to change. Agile systems have the ability to improve customer satisfaction, lower bug rates, shorten cycle times, and enable faster adaption to quickly changing company needs. Agile helps to deliver early but might compromise on the quality of software delivered. This research paper presents the performance analysis of Agile Scrum methodology towards Time to Market and Code Quality based on various project parameters like time to deliver the product, number of bugs and code smells and amount of duplicate code.
Toward Data Authenticity and Integrity for Blockchain-Based Mobile Edge Computing

Blockchain-based mobile edge computing (BMEC) solves the problem of limited computing resources of devices in the Internet of Things (IoT). However, there are some security threats in the blockchain environment, among which the attacks against consensus algorithms are particularly serious, such as double-spending attacks and long-range attacks. These attacks target the block proposal or validation process, which compromise the integrity of the BMEC by overwriting the correct block record with the wrong block record. In this article,we propose a scheme to solve the above problem by using the cooperative threshold signature and group management scheme to protect the block validity. The blockchain process of a single node is controlled in a group cooperative way, and each new block is regarded as a valid block only if it has a valid threshold signature for the group to which it belongs. Concretely, the system is initialized by a cooperative distributed key generation protocol, the device management protocol uses a resident node to ensure the security and efficiency of device join and leave, and the signature issuing protocol can tolerate a certain degree of exceptions of nodes within the group. We analyze the resistance ability of the scheme against consensus attacks, prove the security of the scheme, and analyze the computing and communication efficiency. Experimental evaluations on the protocols of the proposed scheme have been conducted on cloud virtual machines and embedded boards, including Raspberry Pi and UP2 Grove, which indicated that the scheme is efficient in practical scenarios.
CNN Based Speech and Text Translation Using Sign Language

There are around 5,00,000 deaf and hard of hearing persons in the globe. Around 60% of peoples are born as a deaf and deafeningly. These kinds of peoples are not able to understand anything like as normal peoples. The sign language (i.e., hand movements) are used for the dumb and deaf to pass their messages. These people’s faces lots of challenges on daily basis due to the relative absence of widespread sign language use in society. The helplessness to talk seems like true impairment. People with this defacement communicate in entirely several means. There are a variety of choices present for communication where sign language is one of the ways. Applications existence for sign language is necessary for dumb people so that they become able to talk easily with them who does not know sign language motions. The objective here is to take the essential acts in sustaining the language gap between conventional people and verbally challenged people. Communication becomes extremely difficult for them, the chance that they can talk is through language of sign. Though a dumb or deaf person might know language of sign, the people with whom she wishes to speak might not. Our aim to overcome this difference by using machine learning and neural networks to analyze sign language and translate it to text and voice.
Tweet2Story: A Web App to Extract Narratives from Twitter

Social media platforms are used to discuss current events with very complex narratives that become difficult to understand. In this work, we introduce Tweet2Story, a web app to automatically extract narratives from small texts such as tweets and describe them through annotations. By doing this, we aim to mitigate the difficulties existing on creating narratives and give a step towards deeply understanding the actors and their corresponding relations found in a text. We build the web app to be modular and easy-to-use, which allows it to easily incorporate new techniques as they keep getting developed.
Computational Authorship Analysis of Homeric Language

Iliad and Odyssey are products of a collective effort involving numerous authors, each contributing unknown portions of text, and it still cannot be determined whether a single individual (or distinct group of poets) contributed larger chunks of such additional verses, or even whole Books. In this work, we employed character-level statistical language modeling to analyse the computational authorship of Homeric text and study the linguistic proximity and divergence between the books of Iliad and Odyssey. We show that some pairs of books are much closer than others and that some books are linguistically far from the rest. Furthermore, we investigated the linguistic association between the Homeric poems and four Homeric hymns, showing that “To Aphrodite” is linguistically close and that “To Hermes” is linguistically far from both, Iliad and Odyssey. In a final experiment, we show that statistical language models can be used to classify excerpts between Iliad and Odyssey similarly to the average human expert.
Evolution of two-mode quantum states under a dissipative environment: Comparison of the robustness of squeezing and entanglement resources

We explore the relative robustness of single-mode squeezing and entanglement (which are quantum resources interconvertible via passive optics) for two-mode Gaussian states under different dissipative environments. When the individual modes interact with identical local baths, entanglement and squeezing decay at the same rate. However, when only one of the modes interacts with a local bath, the comparative robustness of entanglement and squeezing depends on the initial squeezing of the state. Similarly, when the system interacts with a global bath, the robustness of entanglement and squeezing depends on the initial squeezing. Thus depending on the nature of dissipative environments and the initial squeezing of the state, one can select the more robust form of resource out of squeezing and entanglement to store quantumness. This can be used to effectively enhance the performance of various quantum information processing protocols based on continuous variable Gaussian states.
Cybercrime and the Regulation of Cryptocurrencies

Cryptocurrencies are decentralized digital assets that operate as a medium of exchange with ownership records maintained on a secure blockchain. Being virtual and decentralized, cryptocurrencies are convenient, fast, and transparent. They are generally considered secure due to their foundation in cutting-edge blockchain technology. However, despite their inherent security safeguards, it is now been realized that cryptocurrencies are not completely secure against criminals. Individual users, wallet providers, and cryptocurrency exchanges have all been targeted by cybercriminals. Furthermore, regardless of the current or prospective financial standing of cryptocurrencies, the question of how existing national legislation will apply to cryptocurrencies remains unresolved. Cybercriminals exploit cryptocurrencies in several ways, utilizing them as both a tool and a target. On such a basis this article seeks to address and analyze factors surrounding the cyber-attack risks associated with cryptocurrencies. It is discussed how cryptocurrencies are being used for money laundering and payment for illegal services and goods on the Dark Web, and are becoming a target for hacking, phishing, malware, extortion, and ransomware. Finally, a comparative examination of legislative and regulatory methods in Australia, Belgium, the Netherlands, Germany, and the United States and general characteristics of the European Union's legislative framework on cryptocurrencies are presented.
Seeds: Sampling-Enhanced Embeddings

Finding a desirable sampling estimator has a profound impact on the development of static word embedding models, such as continue-bag-of-words (CBOW) and skip gram (SG), which have been generally accepted as popular low-resource algorithms to generate task-agnostic word representations. Due to the prevalence of large-scale pretrained models, less attention has been paid to these static models in the recent years. However, compared with the dynamic embedding models (e.g., BERT), these static models are straightforward to interpret, cost effective to train, and out-of-box to deploy, thus are still widely used in various downstream models until now. Therefore, it is still of considerable significance to study and improve them, especially the crucial components shared by these static models. In this article, we focus on negative sampling (NS), a key component shared by the sampling-based static models, by investigating and mitigating some critical problems of the sampling core. Concretely, we propose Seeds, a sampling enhanced embedding framework, to learn static word embeddings by a new algorithmic innovation for replacing the NS estimator, in which multifactor global priors are considered dynamically for different training pairs. Then, we implement this framework by four concrete models. For the first two implementations, namely CBOW-GP and SG-GP, both negative words and positive auxiliaries are sampled. And for the other two implementations, CBOW-GN and SG-GN, estimations are simplified by sampling only the negative instances. Extensive experimental results across a variety of standard intrinsic and extrinsic tasks demonstrate that embeddings learned by the proposed models outperform their NS-based counterparts, such as CBOW-NS and SG-NS, as well as other strong baselines.
Low personality-sensitive feature learning for radar-based gesture recognition

Radar-based sensing of gestures has gained tremendous attention with the recent advancements in radar technologies. However, evident discrepancies exist in the gesture samples due to hand flexibility and individual habits. It is challenging for traditional methods to identify the gestures from unknown data sources. Cross-person (Cross-scenario) recognition refers to a recognition where the training and test samples are from different people (scenarios), respectively. To explore how the recognition performance is affected by the individual habits, the reasons are analyzed and visualized through the experiments. On this basis, HandNet is targeted proposed for the low personality-sensitive feature learning and it has two main contributions. First, a Stepped Data Augmentation (SDA) is proposed to reduce the sample interferences by non-coherent accumulating, and capture the inter-frame dependencies. Second, a Focus on Generalization loss (FoG loss) is proposed to highlight the generalized feature learning by res tricting the distances of inter-source features. Extensive experiments demonstrate that HandNet effectively reduces the classifier’s sensitivity to the personalized habits, and outperforms the existing state-of-the-art methods on the cross-person and cross-scenario gesture recognition. To the best of our knowledge, it is the first time to dedicate to addressing the radar-based gesture recognition with low personal sensitivity, which is more suitable for practical scenarios.
On Implementation-Level Security of Edge-Based Machine Learning Models

In this chapter, we are considering the physical security of Machine Learning (ML) implementations on Edge Devices. We list the state-of-the-art known physical attacks, with the main attack objectives to reverse engineer and misclassify ML models. These attacks have been reported for different target platforms with the usage of both passive and active attacks. The presented works highlight the potential threat of stealing an intellectual property or confidential model trained with private data, and also the possibility to tamper with the device during the execution to cause misclassification. We also discus possible countermeasures to mitigate such attacks.
Visual segmentation of complex naturalistic structures in an infant eye-tracking search task

An infant's everyday visual environment is composed of a complex array of entities, some of which are well integrated into their surroundings. Although infants are already sensitive to some categories in their first year of life, it is not clear which visual information supports their detection of meaningful elements within naturalistic scenes. Here we investigated the impact of image characteristics on 8-month-olds' search performance using a gaze contingent eye-tracking search task. Infants had to detect a target patch on a background image. The stimuli consisted of images taken from three categories: vegetation, non-living natural elements (e.g., stones), and manmade artifacts, for which we also assessed target background differences in lower- and higher-level visual properties. Our results showed that larger target-background differences in the statistical properties scaling invariance and entropy, and also stimulus backgrounds including low pictorial depth, predicted better detection performance. Furthermore, category membership only affected search performance if supported by luminance contrast. Data from an adult comparison group also indicated that infants' search performance relied more on lower-order visual properties than adults. Taken together, these results suggest that infants use a combination of property- and category-related information to parse complex visual stimuli.
Integral Cryptanalysis on Reduced-Round KASUMI

Integral cryptanalysis is one of the most powerful attacks on symmetric key block ciphers. Attackers preliminarily search integral characteristics of a target cipher and use them to perform the key recovery attack. Todo proposed a novel technique named the bit-based division property to find integral characteristics. Xiang et al. extended the Mixed Integer Linear Programming (MILP) method to search integral characteristics of lightweight block ciphers based on the bit-based division property. In this paper, we apply these techniques to the symmetric key block cipher KASUMI which was developed by modifying MISTY1. As a result, we found new 4.5-round characteristics of KASUMI for the first time. We show that 7-round KASUMI is attackable with 263 data and 2120 encryptions.
Min-entropy estimation for semiconductor superlattice true random number generators

Semiconductor superlattice true random number generator (SSL-TRNG) has an outstanding practical property on high-throughput and high-security cryptographic applications. Security in random number generators is closely related to the min-entropy of the raw output because feeding cryptographic applications with insufficient entropy leads to poor security and vulnerability to malicious attacks. However, no research has focused on the min-entropy estimation based on the stochastic model for SSL-TRNG, which is a highly recommended method for evaluating the security of a specific TRNG structure. A min-entropy estimation method is proposed in this paper for the SSL-TRNG by extending the Markov stochastic model derived from the memory effects. By calculating the boundary of the transition matrix, the min-entropy result is the average value of each sample (1 bit) is 0.2487. Moreover, the experimental results show that the estimator is accurate enough to adjust compression rate dynamically in post-processing to reach the required security level, estimating entropy on the fly rather than off-line.
Bayesboost: Identifying and Handling Bias Using Synthetic Data Generators

Advanced synthetic data generators can model sensitive personal datasets by creating simulated samples of data with realistic correlation structures and distributions, but with a greatly reduced risk of identifying individuals. This has huge potential in medicine where sensitive patient data can be simulated and shared, enabling the development and robust validation of new AI technologies for diagnosis and disease management. However, even when massive ground truth datasets are available (such as UK-NHS databases which contain patient records in the order of millions) there is a high risk that biases still exist which are carried over to the data generators. For example, certain cohorts of patients may be under-represented due to cultural sensitivities amongst some communities, or due to institutionalised procedures in data collection. The under-representation of groups is one of the forms in which bias can manifest itself in machine learning, and it is the one we investigate in this work. These factors may also lead to structurally missing data or incorrect correlations and distributions which will be mirrored in the synthetic data generated from biased ground truth datasets. In this paper, we explore methods to improve synthetic data generators by using probabilistic methods to firstly identify the under-represented samples in ground truth data, and then to boost these types of data when generating synthetic samples. The paper explores attempts to create synthetic data that contain more realistic distributions and that lead to predictive models with better performance.
Spline-based Path Planning and Reconfiguration for Rigid Multi-Robot Formations

This paper presents an approach for continuous path planning in rigid formations containing nonholonomic mobile robots using Beziér-splines. Unlike most existing approaches, the focus is on maintaining a rigid formation, as required in many scenarios such as object transport, handling or assembly. In these scenarios, a constant distance between the individual robots must be kept to avoid damaging the transported object. Also, limitations regarding the velocity and acceleration of each robot have to be taken into account, as every robot traverses in a slightly different path. Especially the distance from the center of the formation to an individual robot has a big impact on the path a mobile robot has to move on to comply with the formation constraints during an orientation change of the formation. Therefore, we use splines to control and optimize the curvature of the formation’s path and limit the velocity and acceleration. The usage of splines requires the calculation of specific support points that can be interpolated. To achieve this, we calculate the formation’s diameter, including the transported object and adjust the inflation radius of the global costmap. To guarantee a collision-free movement from the start to the target position, we use an existing path planner to generate an initial path. The employed Relaxed-A*-Algorithm provides fast computation and an almost optimal initial path, although this path is not continuous. From this path, we extract a number of support points based on the size of the formation. We then fit a fifth-degree Beziér-spline through these points to smooth the discontinuous path. The final result is a computationally efficient, smooth and jerk-limited path that induces minimal disturbances to the formation control. Also, this approach allows us to continuously transition from one formation to another, which can be used to pass narrow passages. To valid the path planning approach presented in this study we showcase simulative and experimental results.