Reviewerly/exHarmony huggingface dataset:
Exploring the Relationship Between Developer Activities and Profile Images on GitHub

In the GitHub platform, social media profile images are one of many visual components of developers. Besides, developer activities such as reporting issues or following other developers are regarded as important development and self-expression behaviors. However, to the best of our knowledge, no study has yet been conducted to study the relationship between GitHub developer activities and profile images. In this paper, we aim to investigate the relationship between developer activities and profile images to gain some insights into the developers' internal properties. During our experiments, we manually classify profile images into seven categories. Next, we investigate the relationship between developer's demographic information and developer activity. Further, using logistic regression analysis, when controlled for various variables, we statistically identify and quantify the relationships between developer activities and profile image categories. We find that several profile image categories significantly correlate with developer's demographic information and activities. We also provide a rich resource of research ideas for further study. Our examination and analysis provide insights into the developers' internal properties when using different profile images. Moreover, this study is the first step in understanding the relationship between developer activities and profile images on GitHub.
Hardware Acceleration of k-Mer Clustering using Locality-Sensitive Hashing

Clustering is an essential operation in many data analysis applications. In particular, bioinformatics and genome analysis use clustering to group similar components in sequence data, in order to find important patterns such as DNA motifs. In this paper, we present an algorithm that clusters DNA data using locality-sensitive hashing with MinHash to group similar subsequences in large Chip-seq datasets. Tested on a standard mESC dataset, the algorithm builds clusters that contain subsequences with high-score matches to known DNA motifs. We also describe the architecture and implementation of a hardware accelerator on a Xilinx Kintex-7 XC7K325T FPGA, that exploits the parallelism of the algorithm to cluster data with a throughput of one k-mer per clock cycle at 350MHz. The accelerator achieves a speedup of 91 compared to a parallel software implementation of the algorithm on a 24-core server.
Dataset

We envision a world where people-centric sensing and personalized services can be achieved without centralized data collection and processing or the reliance on video-based surveillance. To help assess the feasibility of building context-aware applications while allowing users to fully control their potentially sensitive data, we studied the feasibility of people-centric sensing using only user-side data acquisition. Using only off-the-shelf, energy restricted sensor kits in a smart environment together with an energy-efficient message exchange scheme implemented on top of Bluetooth Low Energy, the collected dataset provides insight on a continuous cyber-physical view from users' individual perspectives. The availability of the dataset encourages further studies of users' activities, for instance to perform distributed inference on users' social interactions and activity trajectories.
CIDF: A Clustering-Based Interaction-Driven Friending Algorithm for the Next-Generation Social Networks

Online social networks, such as Facebook, have been massively growing over the past decade. Recommender algorithms are a key factor that contributes to the success of social networks. These algorithms, such as friendship recommendation algorithms, are used to suggest connections within social networks. Current friending algorithms are built to generate new friendship recommendations that are most likely to be accepted. Yet, most of them are weak connections as they do not lead to any interactions. Facebook is well known for its Friends-of-Friends approach which recommends familiar people. This approach has a higher acceptance rate but the strength of the connections, measured by interactions, is reportedly low. The accuracy of friending recommendations is, most of the time, measured by the acceptance rate. This metric, however, does not necessarily correlate with the level of interaction, i.e., how much friends do actually interact with each other. As a consequence, new metrics and friending algorithms are needed to grow the next generation of social networks in a meaningful way, i.e., in a way that actually leads to higher levels of social interactions instead of merely growing the number of edges. In this paper, we develop a novel approach to build friendship recommender algorithms for the next-generation social networks. We first investigate existing recommender systems and their limitations. We also highlight the side effects of generating easily accepted but weak connections between people. To overcome the limitations of current friending algorithms, we develop a clustering-based interaction-driven friendship recommender algorithm and show through extensive experiments that it does generate friendship recommendations that have a higher probability of leading to interactions between users than existing friending algorithms.
Multi-sensor Energy Efficient Obstacle Detection

With the improvement in technology, both the cost and the power requirement of cameras, as well as other sensors have come down significantly. It has allowed these sensors to be integrated into portable as well as wearable systems. Such systems are usually operated in a hands-free and always-on manner where they need to function continuously in a variety of scenarios. In such situations, relying on a single sensor or a fixed sensor combination can be detrimental to both performance as well as energy requirements. Consider the case of an obstacle detection task. Here using an RGB camera helps in recognizing the obstacle type but takes much more energy than an ultrasonic sensor. Infrared cameras can perform better than RGB camera at night but consume twice the energy. Therefore, an efficient system must use a combination of sensors, with an adaptive control that ensures the use of the sensors appropriate to the context. In this adaptation, one needs to consider both performance and energy and their trade-off. In this paper, we explore the strengths of different sensors as well their trade-off for developing a deep neural network based wearable device. We choose a specific case study in the context of a mobility assistance device for the visually impaired. The device detects obstacles in the path of a visually impaired person and is required to operate both at day and night with minimal energy to increase the usage time on a single charge. The device employs multiple sensors: ultrasonic sensor, RGB Camera, and NIR Camera along with a deep neural network accelerator for speeding up computation. We show that by adaptively choosing the appropriate sensor for the context, we can achieve up to 90% reduction in energy while maintaining comparable performance to a single sensor system.
LoRa Posture Recognition System Based on Multi-Source Information Fusion

Human body posture recognition is a popular research topic in the field of pattern recognition. At present, with advances in big data technology and data mining algorithms, long-term posture monitoring data of specific individuals or groups can be fully utilized. This data can be applied in the context of national sports statistics, health rehabilitation, and posture monitoring for the elderly. These applications involve large group and high-frequency usage; as a result, the system must have low energy consumption, a convenient carrying system, and low cost. Based on these requirements, this paper proposes a LoRa posture recognition system based on multi-source information fusion. LoRa WAN technology has the advantages of low cost and long transmission distance. A multi-sensor and LoRa terminal node are combined to collect human posture and are convenient to carry. The system includes a posture sensor module, wireless transmission module, and posture recognition module. The posture sensor module includes three types of sensors and a microprocessor for collecting posture information. The LoRa terminal node, LoRa gateway, and network server form a wireless transmission module for remotely transmitting data from the sensors. The function of the gesture recognition module includes pre-processing the sensor signals and outputting the pose categories based on the classification model. Aiming to address the disadvantage of the low LoRa transmission rate, this paper proposes a minimum redundancy maximum relevance sequential forward selection random forest (MRMR-SFS-RF) feature selection algorithm. This algorithm selects a small number of features that are most representative and outputs them for gesture recognition to increase the frequency of the feature output and reduce the energy consumption of the thermal node. To verify the performance of the proposed algorithm, feature data is extracted based on the proposed posture recognition system, and the accuracy of posture recognition is calculated. Finally, four key features are selected from 162 dimensional features. The experimental results demonstrate that the recognition accuracy for the four features is 98.9%, which achieves the goal of maintaining high classification accuracy with a small number of features.
A New Stability Theory for Grünwald–Letnikov Inverse Model Control in the Multivariable LTI Fractional-Order Framework

The new general theory dedicated to the stability for LTI MIMO, in particular nonsquare, fractional-order systems described by the Grünwald–Letnikov discrete-time state–space domain is presented in this paper. Such systems under inverse model control, principally MV/perfect control, represent a real research challenge due to an infinite number of solutions to the underlying inverse problem for nonsquare matrices. Therefore, the paper presents a new algorithm for fractional-order perfect control with corresponding stability formula involving recently given H- and σ -inverse of nonsquare matrices, up to now applied solely to the integer-order plants. On such foundation a new set of stability-related tools is introduced, among them the key role played by so-called control zeros. Control zeros constitute an extension of transmission zeros for nonsquare fractional-order LTI MIMO systems under inverse model control. Based on the sets of stable control zeros a minimum-phase behavior is specified because of the stability of newly defined perfect control law described in the non-integer-order framework. The whole theory is complemented by pole-free fractional-order perfect control paradigm, a special case of fractional-order perfect control strategy. A significant number of simulation examples confirm the correctness and research potential proposed in the paper methodology.
An Improved Otsu Method Based on Uniformity Measurement for Segmentation of Water Surface Images

One-dimensional Otsu method is an adaptive threshold method which obtains the optimal threshold for image segmentation by the maximum between-class variance. Direct application of the Otsu segmentation method is very challenging due to the uniform background of the water surface image. In this paper, we propose an improved Otsu method based on uniformity measurement for segmentation of water surface. It achieves adaptive selection of thresholds by the uniformity measure function, so as to segment the surface floating object area better. Experimental results demonstrate that the effectiveness of the improved Otsu method is generally better than traditional Otsu method for the water surface images, which greatly improves the segmentation accuracy of images and reduces the mis-segmentation rate.
Boolean Functions for Stream Ciphers

The possibility of generating a special type of sequence, called de Bruijn sequence, at minimal hardware and software costs to implement nonlinear Boolean functions, and apply them in stream encryption systems, is the main subject of this work. The paper presents the possible structure boundaries (algebraic degree of a Boolean function, the number of monomials in a function) of iterative de Bruijn sequence bitrate generators for various generated sequence characteristics, such as linear complexity and autocorrelation function. The profile of the linear complexity of the studied sequences is closed to the expected value of the linear complexity, as well as for a truly random sequence.
An Unmanned Aerial Vehicle Path Planning Method Based on the Elastic Rope Algorithm

In this paper, we design a path planning algorithm for UAV with maneuverability constraints. In the task of UAV path planning, it is necessary to ensure that UAV does not collide with obstacles and maintain a certain safe distance. At the same time, the obtained path should meet the maneuverability requirements of UAV. According to the definition of elastic rope in [1], we analyze the limitations of the elastic rope algorithm and give the design scheme of the algorithm for UAV path planning. Finally, we design several simulation scenarios to verify the performance of the algorithm. From the simulation results, we can see that the designed algorithm has a good performance in UAV path planning.
Student Mobility Management Model in the Context of Industry 4.0

The purpose of the research is to establish behavior patterns to facilitate the mobility of the students. In this article, we present a component of a model to manage student mobility in the context of Industry 4.0. The component, called SADEAF, is a Decision Support System, based on Felder's Index of Learning Styles. With this system, the learning style of the students can be identified in a digital way. Knowing the learning style of students can facilitate the management of student mobility and, in turn, also strengthen the self-knowledge of the students. The lecturers can use this information for partitioning the students into appropriate groups in the lectures and exercise sessions in university education. In this study, the SADEAF system was applied in two universities, one in Peru and the other in Germany, between 2017 and 2018. Descriptive statistics is used to present the preliminary results concerning the learning styles among the engineering students at these two universities.
Parity-Based Concurrent Error Detection Schemes for the ChaCha Stream Cipher

We propose two parity-based concurrent error detection schemes for the Quarterround of the ChaCha stream cipher to protect from transient and permanent faults. They offer a trade-off between implementation overhead and error coverage. The second approach can detect any odd-weight error on the in-/output and intermediate signals of a Quarterround, while the first one requires less logic.
Performance improvement of unidimensional continuous-variable quantum key distribution using heralded hybrid linear amplifier

We propose an improved scheme for unidimensional continuous-variable quantum key distribution (UCVQKD) using heralded hybrid linear amplifier, aiming to simplify the implementation and improve secret key rate. Different from the symmetrical continuous-variable quantum key distribution protocol (CVQKD), this scheme modulates one quadrature of the coherent state with security insurance. The heralded hybrid linear amplifier concatenates a deterministic linear amplifier (DLA) and a noiseless linear amplifier (NLA), which can tune between the high-gain or high noise-reduction for performance enhancement. Security analysis shows that the proposed scheme can be secured under the collective attacks. Compared with traditional UCVQKD involving noiseless amplifier, the security transmission distance of proposed protocol is increased by 24 kilometers. It not only simplifies the modulation process but also has approximate performance with symmetrical CVQKD in terms of maximal security transmission distance.
Damanghor intermediate sulfidation epithermal Au mineralization, Northern Bardaskan: geology, alteration, mineralization, and geochemistry

Damanghor intermediate sulfidation epithermal Au mineralization, Northern Bardaskan: geology, alteration, mineralization, and geochemistry
A Secure Fine-Grained Identity-Based Proxy Broadcast Re-encryption Scheme for Micro-video Subscribing System in Clouds

Micro-video sharing is prevalent nowadays due to the rapid development of personal smart devices, fast growing of network bandwidth and the easy-to-use cloud computing. However, the micro-video sharing system is more vulnerable compared to the conventional video service model because of the vulnerability of the cloud and the hardware computing constraints of personal devices. Ensuring the security and privacy of the micro-videos has become critical for micro-video sharing systems. Recently, a number of encryption schemes have been proposed to protect the security of the micro-videos stored in the cloud. However, these approaches cannot support micro-video sharing functionality. Moreover, the encryption-based method cannot provide the fine-grained access control on encrypted micro-videos. In this paper, we present a notion of fine-grained identity-based broadcast proxy re-encryption to address the issue of fine-grained encrypted micro-video sharing. Further, we present a concrete scheme in the proposed notion and analyze its security. Performance and evaluation show that our proposed scheme is practical and efficient.
BP Neural Network Based Fault Prediction of Distribution Network during Typhoon Disaster

Typhoon disasters will cause faults such as reversing, disconnection, wind flashover, etc., which threaten the safe and reliable operation of the distribution network. It is necessary to predict the failure of the distribution network caused by typhoon disasters. Considering that a large number of historical data from typhoon and distribution network fault information are retained in the meteorological information acquisition system, it is very suitable to use artificial neural network to explore the correlation between meteorological information and distribution network fault information. Therefore, this paper proposes a fault prediction model of distribution tower during typhoon disaster based on BP neural network. The fault prediction model of distribution network based on BP neural network is established firstly. Then the parameters in the model are optimized by training samples. Lastly, the validity of model prediction is tested by test samples. The test results show that the proposed method can accurately predict the fault trip of the distribution network tower during typhoon disasters.
Work-in-Progress: Application of academic strategies to strengthen intrinsic motivation and reduce the number of students who abandon engineering programs at universities in Lima - North

A descriptive study of population and academic performance carried out by the statistics department of the University of Sciences and Humanities (UCH) concluded that the students of the Electronic Engineering program of the UCH, abandoned the program due to low academic performance in curriculum courses of studies. The study details, that of the total of entrants since 2008, 37% of students finished the program, of which, only 15% of students approved all the courses of the curriculum of studies, without disapproving any course; This indicator generates great concern regarding the achievement of skills and continuity of students in the program. In order to solve this problem, academic strategies were applied to strengthen intrinsic motivation, using the implementation of virtual classrooms, technical visits and seminars by experts. Achieving to date a favorable result in the academic performance and the reduction of students who leave the program.
Obstacle avoidance for robotic manipulator using Mixed reality glasses

The problem of finding the shortest path for manipulators with obstacle avoidance has many solutions. The paper presents the usage of well-known methods for solving that problem using mixed reality glasses. Glasses contain the Spatial mapping module, presented in the form of an continuously updating mesh. Analyzing space through mixed reality glasses, we have developed algorithms for obtaining the shortest path in the Cartesian and Joint spaces. In the beginning, we implement A* simple algorithm and then realized the improved RRT algorithm. The approaches were verified using Unity, Microsoft HoloLens and 6 DOF robot UR10e.
Comparison-Based Study of PageRank Algorithm Using Web Structure Mining and Web Content Mining

On the internet, a large number of information is available, which consolidates data and hyperlinks. With the large heterogeneous information resource, it is very difficult to locate the coveted data that matched with client needs and intrigue. To recover the relevant report from the user's query, we use different types of algorithms. Page Ranking is additionally one of the groundbreaking algorithms to recover the best significant records that likewise lessen the client's searching time. Web mining instruments are utilized by page ranking algorithm. Web mining device is utilized to arrange, group, and rank the report so the client can without much of a stretch finish the guide the query item and search the required data content. Mining can be done using two types, namely "Web Structure Mining" and "Web Content Mining". In Web Structure Mining, rank the pages on the preface of their hyperlinks and in Web Content Mining, rank the pages on the premise of content of the pages. This paper portrays the examination and consolidates investigation of Web Structure Mining and Web Content Mining to enhance the positioning of pages.
Labelling Hierarchical Clusters of Scientific Articles

Exploration of document collections is a complex task. One way to do this is to cluster the initial collection hierarchically and then label each cluster with a set of extracted terms. Good labelling should help exploration. We focus on the scientific domain and particularly on collections of abstracts of articles. Abstract is commonly a brief of a paper that outlines the research area, the challenge, the proposed solution and the results; so it could be used instead of a full article despite the difficulties related to its shortness. In this paper, we propose a new method HCBasic for labelling hierarchical clusters. It is particularly tuned for articles' abstracts and compared to three other methods: MTWL, hierMTWL and ComboBasic. To evaluate the quality of the labelling algorithms we did A/B testing in which eight volunteers searched for the articles that they were familiar with in the labelled cluster tree. We show that there is no single winner in terms of quality, and different methods are preferable in different cases.
E-crm Information System for Tapis Lampung SMEs

Abstract Companies must have a strong competitive strategy in the market in meeting customer needs, it is necessary an information system that applies the method of customer relationship management. Target for this research were SMEs Tapis in Lampung Indonesia. Tapis Lampung is one of the clothes used in Lampung Indonesia culture. Tapis Lampung must be introduced and disseminated, not only in Indonesia, but in overseas. Problem in this study is product orders are still coming directly to the location and via telephone, as well as product marketing through Instagram media. Orders are recorded in the book, so that data processing is less effective and efficient. There is no consumer database to see the number of existing customers, do not yet have communication and service good to consumers. The purpose of this study is to produce an information system application program using web-based E-crm method which is a strategy to increase sales, expand marketing and improve services to consumers. The application of web-based CRM methods in Tapis Lampung SMEs is important so that they can open sales opportunities globally, and have a good relationship with consumers because they can provide services to consumers that are more effective and efficient.