Reviewerly/exHarmony huggingface dataset:
On financial innovation in developing countries: The determinants of mobile banking and financial development in Africa

This study addresses the question of financial innovation in developing countries by focusing on the determinants of mobile banking development in Africa. Data on mobile banking across countries is partially constructed by the author, to which is applied panel data and cross-sectional econometric technics, to investigate the determinants of mobile banking development. Statistical analysis reveals that there is a positive link between the different proxies of mobile banking and an indicator of mobile banking development using the principal component analysis technique is constructed. Overall, the estimation of the four models highlights the following determinants: human capital development, credible monetary policy, infrastructure development, remittances facilitation, urbanization, trade openness and the facilitation of access to domestic credit from the banking sector. Also, the results of our robustness check are uncontroversial. A general recommendation to African governments is that they should pursue good performance in the light of the determinants highlighted above by implementing specific and well conditioned economic policies. JEL Codes: G21, R1, O4
Compact implementations of Curve Ed448 on low‐end IoT platforms

Elliptic curve cryptography is a relatively lightweight public-key cryptography method for key generation and digital signature verification. Some lightweight curves (eg, Curve25519 and Curve Ed448) have been adopted by upcoming Transport Layer Security 1.3 (TLS 1.3) to replace the standardized NIST curves. However, the efficient implementation of Curve Ed448 on Internet of Things (IoT) devices remains underexplored. This study is focused on the optimization of the Curve Ed448 implementation on low-end IoT processors (ie, 8-bit AVR and 16-bit MSP processors). In particular, the three-level and two-level subtractive Karatsuba algorithms are adopted for multi-precision multiplication on AVR and MSP processors, respectively, and two-level Karatsuba routines are employed for multi-precision squaring. For modular reduction and finite field inversion, fast reduction and Fermat-based inversion operations are used to mitigate side-channel vulnerabilities. The scalar multiplication operation using the Montgomery ladder algorithm requires only 103 and 73 M clock cycles on AVR and MSP processors.
Energy-Efficient Convolutional Neural Networks via Recurrent Data Reuse

Deep learning (DL) algorithms have substantially improved in terms of accuracy and efficiency. Convolutional Neural Networks (CNNs) are now able to outperform traditional algorithms in computer vision tasks such as object classification, detection, recognition, and image segmentation. They represent an attractive solution for many embedded applications which may take advantage from machine-learning at the edge. Needless to say, the key to success lies under the availability of efficient hardware implementations which meet the stringent design constraints.Inspired by the way human brains process information, this paper presents a method that improves the processing efficiency of CNNs leveraging their repetitiveness. More specifically, we introduce (i) a clustering methodology that maximizes weights/activation reuse, and (ii) the design of a heterogeneous processing element which integrates a Floating-Point Unit (FPU) with an associative memory that manages recurrent patterns. The experimental analysis reveals that the proposed method achieves substantial energy savings with low accuracy loss, thus providing a practical design option that might find application in the growing segment of edge-computing.
Texture retrieval using periodically extended and adaptive curvelets

Image retrieval is an important problem in the area of multimedia processing. This paper presents two new curvelet-based algorithms for texture retrieval suitable, which are suitable for use in constrained-memory devices. The developed algorithms are tested on three publicly available texture datasets: CUReT, Mondial-Marmi, and STex-fabric. Our experiments confirm the effectiveness of the proposed system. Furthermore, a weighted version of the proposed retrieval algorithm is proposed, which is shown to achieve promising results in the classification of seismic activities.
LBMM: A Load Balancing Based Task Scheduling Algorithm for Cloud

As one of the fields in Computer Science research, Cloud Computing has attracted attentions from industries as well as academia in recent years. Numerous topics have been studied related to Cloud Computing, and one of them is task scheduling. Task scheduling is the strategy to assigning various tasks to certain resources. Existing task scheduling algorithms include Min-Min, Suffrage, Max-Min and many more, in which Max-Min is efficient in minimizing the completion time of tasks and producing a good task schedule, however, it has a drawback of load unbalancing. To address this issue, we design an algorithm called LBMM for task scheduling considering load balancing as the key concept. We conduct our experiments using CloudSim package which is a framework for simulating activities in the Cloud systems. The experimental results demonstrate that our algorithm decreases the completion time and improves load balancing of resources, and it outperforms the traditional Max-Min and Min-Min.
Knowledge-Based Short Text Categorization Using Entity and Category Embedding

Short text categorization is an important task due to the rapid growth of online available short texts in various domains such as web search snippets, etc. Most of the traditional methods suffer from sparsity and shortness of the text. Moreover, supervised learning methods require a significant amount of training data and manually labeling such data can be very time-consuming and costly. In this study, we propose a novel probabilistic model for Knowledge-Based Short Text Categorization (KBSTC), which does not require any labeled training data to classify a short text. This is achieved by leveraging entities and categories from large knowledge bases, which are further embedded into a common vector space, for which we propose a new entity and category embedding model. Given a short text, its category (e.g. Business, Sports, etc.) can then be derived based on the entities mentioned in the text by exploiting semantic similarity between entities and categories. To validate the effectiveness of the proposed method, we conducted experiments on two real-world datasets, i.e., AG News and Google Snippets. The experimental results show that our approach significantly outperforms the classification approaches which do not require any labeled data, while it comes close to the results of the supervised approaches.
Nonlinear Autoregressive Moving-average (NARMA) Time Series Forecasting Using Neural Networks

In this paper, a one-step forecasting comparison using a simulated nonlinear autoregressive moving-average time series (NARMA) was conducted between two groups of neural networks. Group I is neural networks that use only autoregressive inputs, while Group II is neural networks that use autoregressive and moving-average (i.e., error feedback) inputs. Simulation results showed that the models in Group II produce more accurate forecasts as compared to the models in Group I. That means, introducing error feedback to neural networks helps in forecasting NARMA time series. Another comparison was conducted between autoregressive moving-average (ARMA) model and neural network models using the simulated NARMA time series. As expected, since it is a nonlinear time series, neural networks show better results as compared to ARMA model.
Dynamically feasible trajectory planning for Anguilliform-inspired robots in the presence of steady ambient flow

A crucial requirement for imparting autonomy to the fish-inspired robots is the trajectory planning capability that can generate obstacle-free and optimal trajectories to a commanded goal location. Research in the area of dynamics-aware trajectory planning, especially in the presence of environmental disturbances, for fish-inspired robots is scarce. Trajectory-following for fish-inspired robots, which is different from trajectory planning, has been well explored wherein feedback controllers that reject disturbances are employed to follow the pre-specified waypoints. This paper reports an optimal trajectory planning approach for Anguilliform-inspired robots based on model predictive planning framework. The robot's dynamics constraints, as well as its interaction with the surrounding flow conditions, are captured via dynamics simulations and are expressed using discretized motion primitives. The motion primitives are then used for generating a search-tree and an A*-based algorithm is used for searching the optimal trajectory. We developed a simulation-based admissible heuristic function that is used for improving the computational performance of the developed trajectory planner. The developed simulation-based heuristic function provided a computational speed-up by a factor of up to 10.3 with respect to that of the Euclidean heuristic for test cases comprising of a variety of obstacle densities, ambient flow conditions, and goal locations. The trajectories generated by the developed approach have been found to be dynamically feasible, collision-free, and optimal for the experiments reported in this paper. We believe that the developed approach can impart autonomy to the Anguilliform-inspired robots that, due to their slender and hyper-redundant structures, can perform autonomous inspection and maintenance of sub-sea structures having narrow regions, obstacles, and ambient flow and can be useful in various civil and defense applications.
Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands

The Canary Islands are a well known tourist destination with generally stable and clement weather conditions. However, occasionally extreme weather conditions occur, which although very unusual, may cause severe damage to the local economy. The ViMetRi-MAC EU funded project has among its goals, managing climate-change-associated risks. The Spanish National Meteorology Agency (AEMET) has a network of weather stations across the eight Canary Islands. Using data from those stations, we propose a novel methodology for the prediction of maximum wind speed in order to trigger an early alert for extreme weather conditions. The methodology proposed has the added value of using an innovative kind of machine learning that is based on the data stream mining paradigm. This type of machine learning system relies on two important features: models are learned incrementally and adaptively. That means the learner tunes the models gradually and endlessly as new observations are received and also modifies it when there is concept drift (statistical instability), in the modeled phenomenon. The results presented seem to prove that this data stream mining approach is a good fit for this kind of problem, clearly improving the results obtained with the accumulative non-adaptive version of the methodology.
Cryptanalysis of Reduced-Round SPECK

SPECK, a family of lightweight block ciphers proposed by the National Security Agency (NSA), is widely used under resource constrained environment.There are many cryptanalytic results on SPECK concentrated on differential and linear attacks.However, the security evaluation against other popular cryptanalysis methods seems to lag behind.In this paper, we investigate both the security of SPECK against impossible differential, zero-correlation linear, and integral attacks as well as the design choice of NSA.First, we construct the satisfiability (SAT)-based model to automatically search impossible differentials and zero-correlation linear hulls and then obtain several integral distinguishers based on the links between the zero-correlation linear hull and integral distinguisher.Second, based on the new distinguishers, we propose the first zero-correlation attack on 11-round SPECK64 and integral attack on 11-round SPECK32, SPECK48, and SPECK64 by exploring the iterated expression of the modulo subtraction operation and utilizing the partial-sum technique.Finally, we study the design principle of the rotation parameters selection of SPECK32.We show that SPECK32 with parameters (8, 3) is better than SPECK32 with the original parameters (7, 2) with respect to security against impossible differential, zero-correlation linear and integral cryptanalysis.
Steps toward a principled approach to automating cyber responses

Cyber-attackers are likely to exploit advances in artificial intelligence to achieve faster, stealthier, and more effective operational effects. Defenders need to keep pace by developing their own advances, which may preclude human-in-theloop decision making. Consequently, future systems will have to rely on automated reasoning and automated responses to ensure mission success and continuously adapt to an evolving adversary. Automated reasoning about defensive cyber responses is essentially sequential decision making based on the projection of possible futures from a current situation. This problem is especially complicated in cyberspace, however, because the current situation and future projections are highly uncertain. Our research tackles these challenges using the formal framework of partially observable Markov decision problems (POMDPs). We show how to break the “curse of dimensionality” that makes these problems intractable by computing approximate solutions using a Monte Carlo online planner that incorporates a computationally feasible simulation of the cyber security problem. Our simulation is an extension of MITRE’s Cyber Security Game simulator, which explores the mission-impact-focused strategies of an adaptive, intelligent attacker. Preliminary results on small problems, where the optimal solution can be calculated precisely, show that our approach consistently finds the optimal answer, not just a good approximation. We are in the process of increasing the fidelity of the simulator and POMDP representation to model more realistic cyber environments by increasing attacker and defender actions, increasing the variety of sensor types (including sensing of both actions and states), accounting for multiple incident effects, and improving the scaling properties.
Post-Disaster Structural Health Monitoring System Using Personal Mobile-Phones

In the aftermath of a natural disaster reconnaissance is typically conducted by teams of engineers tasked with tagging buildings according to their damage state. Tagging (red, yellow, or green) conveys information about the condition of the building (unsafe, needs further evaluation, or safe, respectively). While thorough, the process can take several days to weeks to be completed. Automated assessment is an attractive alternative to manual inspection but requires deploying a dense network of sensors at the granularity of each structure. Such a network was deemed to be impractical with respect to cost or deployment time. However, with the advent of the Internet of things (IoT) era, a massive network of citizen-owned smart devices such as tablets and smart-phones that contain vibration sensors (e.g. accelerometers) is already deployed. The objective of this work is to develop a framework that can crowd-source relatively low-quality readings from distributed smart citizen owned devices and distill that information into actionable information. This information can be provided to public safety personnel within minutes of an event, in the form of a disaster map, with buildings tagged by their most likely damage state. This paper reports on the development of an application running on a mobile phone to collect readings, and coupled to a cloud based server, used to generate the necessary tags.
Cellular Estimation Gaussian Algorithm for Continuous Domain

Optimization algorithms are important in problems of pattern recognition and artificial intelligence, i.e., the image recognition, face recognition, data analysis, optical recognition, etc. Estimation distribution algorithms (EDAs) is kind of optimization algorithms based on substituting the cross over and mutation operators of the Genetic Algorithms by the estimation and later sampling the probability distribution learned from the selected individuals. However, a weakness of these algorithms is the efficiency in terms of the number of evaluations of the fitness function. In this paper, a Cellular Gaussian Estimation Algorithm (CEGA) for solving continuous optimization problems is proposed. CEGA is derived from evidence-based learning of independence and decentralized schemes of local populations. The experimental results showed that the present proposal reduces the number of evaluations of the fitness function in the search for optimums, maintaining its effectiveness in comparison to other algorithms of state-of-art using the same benchmark of continuous functions.
Similarity-based knowledge graph queries for recommendation retrieval

Current retrieval and recommendation approaches rely on hard-wired data models. This hinders personalized customizations to meet information needs of users in a more flexible manner. Therefore, the paper investigates how similarity-based retrieval strategies can be combined with graph queries to en able users or system providers to explore repositories in the Linked Open Data (LOD) cloud more thoroughly. For this purpose, we developed novel content-based recommendation approaches. They rely on concept annotations of Simple Knowledge Organization System (SKOS) vocabularies and a SPARQL-based query language that facilitates advanced and personalized requests for openly available knowledge graphs. We have comprehensively evaluated the novel search strategies in several test cases and example application domains (i.e., travel search and multimedia retrieval). The results of the web-based online experiments showed that our approaches increase the recall and diversity of recommendations or at least provide a competitive alternative strategy of resource access when conventional methods do not provide helpful suggestions. The findings may be of use for Linked Data-enabled recommender systems (LDRS) as well as for semantic search engines that can consume LOD resources.
Conformance Test Cases for the RDF Mapping Language (RML)

Knowledge graphs are often generated using rules that apply semantic annotations to data sources. Software tools then execute these rules and generate or virtualize the corresponding RDF-based knowledge graph. RML is an extension of the W3C-recommended R2RML language, extending support from relational databases to other data sources, such as data in CSV, XML, and JSON format. As part of the R2RML standardization process, a set of test cases was created to assess tool conformance the specification. In this work, we generated an initial set of reusable test cases to assess RML conformance. These test cases are based on R2RML test cases and can be used by any tool, regardless of the programming language. We tested the conformance of two RML processors: the RMLMapper and CARML. The results show that the RMLMapper passes all CSV, XML, and JSON test cases, and most test cases for relational databases. CARML passes most CSV, XML, and JSON test cases regarding. Developers can determine the degree of conformance of their tools, and users determine based on conformance results to determine the most suitable tool for their use cases.
Using Synonym and Definition WordNet Semantic relations for implicit aspect identification in Sentiment Analysis

Sentiment analysis (SA) is the process by which information can be extracted from customer reviews to be analyzed and categorized as positive, negative or neutral opinions according to three levels of SA: document, sentence and aspect levels. In aspect level, the aim is to determinate the target of sentiment or opinion represented in customer reviews, known as aspect term identification. In this paper, we address the aspect identification task involving implicit aspect implied by adjectives and verbs. Our approach considers the training data enhancement for Naïve Bayes (NB) classifier through the use of synonym and definition relations from WordNet (WN). In our method, we use the two semantic relations differently. NB classifier was used with five unique subsets of synonym and definition words and applied on two benchmark datasets, products reviews and restaurant. The obtained results show that: (1) The use of synonym and definition relations as represented in our work supports NB classifier and (2) Synonym and Definition relation must be appropriately combined the used to better support each implicit aspect identification task involving adjectives and verbs.
Raw Data Processing Framework for IoT

The Internet of Things IoT sensors continuously generate large amount of data. Storing and managing the streaming IoT sensor data is a great challenge. IoT applications require real-time analytical query processing on stored data. To make any IoT data queriable, it needs to be loaded into any database management system traditionally. Where the high data insertion time DIT becomes the first bottle-neck. Storing data in its raw format is the most convenient solution for scientific experiments and variety of other applications where high data velocity and unknown or varying schemas exist in the approaching data. Therefore raw data storage and management techniques need to be explored to utilize very low DIT for handling streaming IoT data. Most of the work with raw data processing is dealing with the static data and how to improve query performance on the stored data. This work tries to explore possibilities in applying raw data processing technique to streaming IoT sensor data. A Raw data processing framework is proposed to handle the streaming IoT sensor data. The work tries to exploit the open source system setup to handle the streaming data. The experimental setup consists of PostgresRAW PgRAW a first mature NoDB system. Results of PgRAW shown 99.5% improvement in DIT as compared to PostgreSQL PgSQL. While the query execution time QET performance improved by 57% over the data scaling experiment from 1M to 4M for PgRAW over PgSQL.
A hybrid adaptive granular approach to Takagi–Sugeno–Kang fuzzy rule discovery

In this paper, a hybrid adaptive granular fuzzy approach to rule discovery (HGFRD) is proposed that effectively utilizes the advantages of Mamdani and Takagi–Sugeno–Kang (TSK) fuzzy structures in a unique learning process, resulting in more compactness and better accuracy of the extracted TSK models. HGFRD's primary adaptive granulation process, based on a Mamdani fuzzy structure, provides better generalization, more compactness, and a reasonable initial solution for the secondary optimization stage. On the other hand, the algorithm's fine-tuning procedure is constructed of a TSK structure, offering lower process burden, universal approximation property, and the chance of using more effective optimization methods, leading to better model accuracy. The proposed process is independent of any a priori expert knowledge since it is purely data-driven and avoids initial parameter tuning. It also functions more desirably on a broader range of problem types and structures due to its adaptive behavior over normalized data spaces. Moreover, it is theoretically proven that HGFRD's training error converges to zero in certain situations. Finally, the effect of several data preprocessing techniques such as normalization, feature selection, and outlier detection is investigated to improve HGFRD's performance. To illustrate the utility of the proposed algorithm, it is applied to ten standard benchmarks, and the results are compared against twenty-two recent competing strategies. Numerical results confirm that HGFRD reaches higher model accuracy and more compactness simultaneously.