Reviewerly/exHarmony huggingface dataset:
Rancang Bangun Purwarupa Alat Pengupas Buah Salak Berbasis Mikrokontroler

Snakefruit is a fruit that is widely known by the people of Indonesia, many varieties of salak plants have superior properties both in terms of taste and appearance of the fruit. The process of stripping the salak fruit itself still uses the manual method (by hand) or by using a kitchen knife. This peeling process is one of the obstacles in the production of salak fruit as a basic product because it takes a lot of time to peel a single salak fruit and requires extra precision so that the hands are not injured due to the rough texture of the salak skin. So the purpose of this research is to make a prototype of a snake fruit peeler using an Arduino microcontroller as a data processor using several other supporting sensors such as the TCS3200 color sensor as a detector for the color of the skin of the snake fruit and a stepper motor to drive the wheels on the machine. to facilitate the purpose of trading business as well as how the work of the tool in an efficient method and productivity that is more competent and useful.
Construction and application of knowledge graph of Treatise on Febrile Diseases

To establish the knowledge graph of “disease-syndrome-symptom-method-formula” in Treatise on Febrile Diseases (Shang Han Lun,《伤寒论》) for reducing the fuzziness and uncertainty of data, and for laying a foundation for later knowledge reasoning and its application. Under the guidance of experts in the classical formula of traditional Chinese medicine (TCM), the method of “top-down as the main, bottom-up as the auxiliary” was adopted to carry out knowledge extraction, knowledge fusion, and knowledge storage from the five aspects of the disease, syndrome, symptom, method, and formula for the original text of Treatise on Febrile Diseases, and so the knowledge graph of Treatise on Febrile Diseases was constructed. On this basis, the knowledge structure query and the knowledge relevance query were realized in a visual manner. The knowledge graph of “disease-syndrome-symptom-method-formula” in the Treatise on Febrile Diseases was constructed, containing 6 469 entities and 10 911 relational triples, on which the query of entities and their relationships can be carried out and the query result can be visualized. The knowledge graph of Treatise on Febrile Diseases systematically realizes its digitization of the knowledge system, and improves the completeness and accuracy of the knowledge representation, and the connection between “disease-syndrome-symptom-treatment-formula”, which is conducive to the sharing and reuse of knowledge can be obtained in a clear and efficient way.
The Role of Administrative Leadership in Crisis Management in the Jordanian Ministry of Planning and International Cooperation

The main objective of the current study is to find out the relationship between leadership style and crisis management in the Jordanian Ministry of Planning and International Cooperation for the department head, managers, and senior management in the Jordanian Ministry of Planning. A quantitative methodology was used to track the goals, in which people who participated in the survey, received the highest score ever Leadership type, the study concluded that transformational leadership has become more stringent in recent decades than transactional leadership, but democratic leadership has no significant relationship with crisis management, and often the growth strategies of those organizations need to change in times of crisis, and the results from the current research will serve as a guide for the authorities government.
Remote Working in the COVID-19 Era

The COVID-19 pandemic is the largest virtual experience for online work and employee engagement in the world. Millions of employees have been required to spend extended periods of time working on digital platforms such as Zoom, Microsoft Team, and Skype. However, the nature of this work has resulted in a number of negative implications, such as lengthy work hours, virtual meetings, and the constant pressure to check and respond to business-related emails. In addition, several employees felt obligated to stay online to demonstrate that they were not avoiding their work responsibilities, which contributed to their psychological stress. In this research, a questionnaire was utilized to assess remote work throughout the COVID-19 pandemic's spread. The questionnaire consisted of four axes: work intensification, employee engagement and online presence, adapting to new working techniques, and demographic aspects of the respondents. According to the results, the abrupt change from physical to online work patterns increased work intensity, online presence, and job instability. We also observe that the transition to new methods of working from home is proceeding well.
Accelerating matrix-centric graph processing on GPUs through bit-level optimizations

Even though it is well known that binary values are common in graph applications (e.g., adjacency matrix), how to leverage the phenomenon for efficiency has not yet been adequately explored. This paper presents a systematic study on how to unlock the potential of the bit-level optimizations of graph computations that involve binary values. It proposes a two-level representation named Bit-Block Compressed Sparse Row (B2SR) and presents a series of optimizations to the graph operations on B2SR by the intrinsics of modern GPUs. It additionally introduces Deep Reinforcement Learning (DRL) as an efficient way to best configure the bit-level optimizations on the fly. The DQN-based adaptive tile size selector with dedicated model training can reach 68% prediction accuracy. Evaluations on the NVIDIA Pascal and Volta GPUs show that the optimizations bring up to 40 and 6555 for essential GraphBLAS kernels SpMV and SpGEMM, respectively, accelerating GraphBLAS-based BFS by up to 433, SSSP, PR, and CC 35, and TC 52.
Secure and Efficient Masking of Lightweight Ciphers in Software and Hardware

Abstract Masking is a well used and widely deployed countermeasure against side channel attacks, both in software and hardware. With masking comes at a great cost, search has focused on how to lower a performance penalty or find efficient masking implementation. In particular, our contribution is 2-fold: for software masking, we first find bitsliced implementations of Sbox with Multiplicative Complexity 4 and Multiplicative Depth 2, then adapt the common shares approach introduced by Coron et al. at CHES 2016 to make many cross-products $a_{i}\cdot b_{j}$ can be reuse for parallel ISW-based 32-bit nonlinear operations. Therefore, we improve the efficiency of 2$\times b/4/32$ parallel high-order masking of ISW scheme for RECTANGLE, TANGRAM and KNOT on 32-bit ARM embedded microprocessor, with roughly a 13%-34% speed-up, at cost of $(1+d) \times 32$-bit randomness. For hardware masking, 4 bit cubic Sboxes with quadratic decomposition length 2, including RECTANGLE, TANGRAM, KNOT and LWC third-round candidates, can be implemented with a 3-share and 4-share threshold implementation (TI) by decomposing cubic permutations $S$ as a composition of sub-permutations having lower algebraic degrees. We use two decomposition form: one composition of two quadratic permutations $G$ and $F$, $S = F\circ G$, is for efficiency; the other composition of some linear permutations $A_i$ and one quadratic permutation $G$, $S=A_3 \circ G \circ A_2 \circ G \circ A_1 $, is for reducing the area requirements. For $S = F\circ G$, we introduce a new approach of searching through all possible quadratic permutations $G$ with 2$^{25.71}$, which is effcient than 2$^{26.23}$ in Poschmann et al. at J. Cryptol 2011. For $S=A_3 \circ G \circ A_2 \circ G \circ A_1 $, our approach of finding $A_i$ with complexity 2$^{27.71} $, which is effcient than the method introduced by Moradi et al. at ASIACRYPT 2016. In addition, we proposes a new decomposition that $S=G \circ A_2 \circ G \circ A_1 $. We can find the fastest and the smallest hard-ware decomposition implementation of 4-bit permutations for TI with 3 and 4 shares.
Comprehensive Analysis of Learning Cases in an Autonomous Navigation Task for the Evolution of General Controllers

Robotics technology has made significant advancements in various fields in industry and society. It is clear how robotics has transformed manufacturing processes and increased productivity. Additionally, navigation robotics has also been impacted by these advancements, with investors now investing in autonomous transportation for both public and private use. This research aims to explore how training scenarios affect the learning process for autonomous navigation tasks. The primary objective is to address whether the initial conditions (learning cases) have a positive or negative impact on the ability to develop general controllers. By examining this research question, the study seeks to provide insights into how to optimize the training process for autonomous navigation tasks, ultimately improving the quality of the controllers that are developed. Through this investigation, the study aims to contribute to the broader goal of advancing the field of autonomous navigation and developing more sophisticated and effective autonomous systems. Specifically, we conducted a comprehensive analysis of a particular navigation environment using evolutionary computing to develop controllers for a robot starting from different locations and aiming to reach a specific target. The final controller was then tested on a large number of unseen test cases. Experimental results provide strong evidence that the initial selection of the learning cases plays a role in evolving general controllers. This work includes a preliminary analysis of a specific set of small learning cases chosen manually, provides an in-depth analysis of learning cases in a particular navigation task, and develops a tool that shows the impact of the selected learning cases on the overall behavior of a robot’s controller.
Persepsi Masyarakat kota Medan Mengenai PMI yang Telah Berusia 77 Tahun 

&#x0D; &#x0D; Education is an important thing, including abysmal education. The Red Cross is in the city of Medan to help the people of the city of Medan. The forms of assistance are not only material but also non-material, non-material forms include blood transfusion activities, helping flood victims and evacuating them, providing public kitchens for people in need.&#x0D; Keywords: PMI; Birthday; community
Detection of pedestrians and vehicles in autonomous driving with selective kernel networks

Abstract Accurate detection of pedestrians and vehicles on the road is an important content in autonomous driving technology. In this article, a method to optimise the object detection network using the channel attention mechanism is proposed. In general, small object detection problems and difficult sample detection problems in object detection tasks can be solved by using feature pyramids. Different from building a feature pyramid, the authors did not make extensive changes to the network, but used the channel attention mechanism to dynamically adjust the output of a layer during the feature extraction process, allowing each neuron to adjust its receptive field size adaptively according to multiple scales of the input information, so that the network pays attention to the extraction of important features, especially the features of small objects and difficult samples. In order to evaluate the performance of the proposed method, experiments were conducted on standard benchmark data sets. It has been observed that the proposed method is superior to the original object detection network in terms of the detection accuracy of pedestrians and vehicles, especially the detection of small objects.
Penerapan Teori Perilaku Informasi Menurut Kulthau di Perpustakaan

Information seeking behavior refers to patterns of human behavior related to the nature of information, both in terms of using and seeking information. The term "behavior" refers to how a person obtains information through his behavior. The Kulthau literature review method is used in this paper. According to Kulthau, information seeking behavior has several characteristics, including initiation, which is the initial stage where users are aware of their information needs, selection, where users begin to collect the information topics needed and then look for them through OPAC, browse directly to shelves or by asking users. At this point, the user is confused because he has to decide which information best suits his needs. formulation At this point, the user has determined which information best meets his needs. Users collect information that has been obtained and recorded in books, cellphones, or by taking pictures at this stage, and the last presentation is the user's feelings of satisfaction and dissatisfaction after getting information in the library.&#x0D; Keywords: information seeking behavior, kulthau, library
Enhancing satellite‐to‐ground communication using quantum key distribution

Abstract Classical Cryptography has been in use for a long time. It has been the only way of securing people's communication. However, there are some flaws observed during the execution of classical cryptography. One of them being the staunch belief that the number composed of multiplication of two large prime numbers cannot be factorised easily, which is under a threat thanks to the computational power of the quantum computers. The next flaw is the non‐detection of the hacker, both of which can be eliminated by using quantum mechanical principles for encryption purposes, which is known as quantum cryptography. Quantum Key Distribution, which provides an information‐theoretically safe solution to the key exchange problem, is the most well‐known example of quantum cryptography. The benefit of quantum cryptography is that it makes it possible to perform a variety of cryptographic operations that have been demonstrated or are hypothesised to be impractical when using solely traditional (i.e., non‐quantum) communication. Free‐space quantum communication has been successfully demonstrated across 300 m by the Indian Space Research Organization (ISRO) in March 2021. With this, ISRO is trying to achieve the same using a satellite‐based communication mechanism, which would revolutionise the mode of modern communication. It is justified that the key generation rate depends on factors like the aperture diameter of the sender and receiver, distance between them, the quantum bit error rate, and many more. The results vary with the parameters in the discussion as explained in the upcoming sections. The avenue of different types of losses that occur while transmitting at large distances, such as Atmospheric Loss, Pointing Loss and Geometric Loss, is explored.
Self‐supervised short text classification with heterogeneous graph neural networks

Abstract Short text classification has been a fundamental task in natural language processing, which benefits various applications, such as sentiment analysis, news tagging, and intent recommendation. However, classifying short texts is challenging due to the information sparsity in the text corpus. Besides, the performance of existing machine learning classification models largely relies on sufficient training data, yet labels can be scarce and expensive to obtain in real‐world text classification scenarios. In this article, we propose a novel self‐supervised short text classification method. Specifically, we first model the short text corpus as a heterogeneous graph to address the information sparsity problem. Then, we introduce a self‐attention‐based heterogeneous graph neural network model to learn short text embeddings. In addition, we adopt a self‐supervised learning framework to exploit internal and external similarities among short texts. Experiments on five real‐world short text benchmarks validate the effectiveness of our proposed method compared with the state‐of‐the‐art methods.
Selective feature fusion network for salient object detection

Abstract Fully convolutional neural networks have achieved great success in salient object detection, in which the effective use of multi‐layer features plays a critical role. Based on this advantage, many saliency detectors have emerged in recent years, and most of them designed a series of network structures to integrate the multi‐level features generated by the backbone network. However, information in different layer play different roles in saliency object detection, how to integrate them effectively is still a great challenge. In this article, a selective feature fusion network which consists of a selective feature fusion module (SFM) and an attention‐guide hierarchical feature emphasis module (AEM) is proposed. Most of the previous works mainly integrate multi‐level feature by addition and concatenation, as a difference, SFM adaptively selects the important information from the input features in the fusion, which effectively avoids introducing too much redundant information. Besides, AEM combines spatial attention and channel attention to enhance features simply and effectively by hierarchical iteration, and further improve the accuracy of salient object detection. Experiments on five datasets show that the proposed selective feature fusion method achieve satisfactory results when comparing to other state‐of‐the‐art salient object detection approaches.
Systematization of knowledge: privacy methods and zero knowledge proofs in corporate blockchains

Nowadays enterprises implement blockchain technology in various industries, including finance, logistics, and other sectors. While the core idea behind blockchain is to decentralize storage, enterprises need to address privacy issues of replicated data. One of the methods to solve privacy in the blockchain is to adjust zero-knowledge-proof protocols. We comprehensively review blockchain privacy techniques employing zero-knowledge proofs and other cryptographic techniques and discuss the World and Russian standardization processes in blockchain privacy techniques.
SIFNet: A self-attention interaction fusion network for multisource satellite imagery template matching

Multisource satellite images provide abundant and complementary earth observations, while nonlinear radiometric and geometric distortions (such as scale and rotation variations) between these multimodal images pose remarkable challenges for further remote sensing applications, such as change detection. We therefore proposed a template matching algorithm based on self-attention interactive fusion network, named SIFNet, to align multisource satellite images. First, a feature pyramid network was first conducted to extract multiscale features for each pixel, with the template and reference images as the inputs. Then, the extracted features were fused by self-attention layers in Transformer for information interaction. Third, the similarity and semantic matching loss functions were developed to convert satellite imagery registration into regression task, allowing SIFNet aligning multimodal patch images more efficiently based on point-to-point correspondence, instead of globally searching extremums as previous matching strategies did. We performed experiments based on four multimodal datasets (i.e. Google, GF-2, Landsat-8 and optical-SAR images) with various scenes to evaluate the performance and robustness of SIFNet. The results demonstrate the proposed SIFNet performed a comparable accuracy for template matching with other algorithms and was robust to geometric distortions and radiometric variations of multisource remote sensing data.
Psychometric Evaluation of the Cybersecurity Curriculum Assessment

We present a psychometric evaluation of the Cybersecurity Curriculum Assessment (CCA), completed by 193 students from seven colleges and universities. The CCA builds on our prior work developing and validating a Cybersecurity Concept Inventory (CCI), which measures students' conceptual understanding of cybersecurity after a first course in the area. The CCA deepens the conceptual complexity and technical depth expectations, assessing conceptual knowledge of students who had completed multiple courses in cybersecurity. We review our development of the CCA and present our evaluation of the instrument using Classical Test Theory and Item-Response Theory. The CCA is a difficult assessment, providing reliable measurements of student knowledge and deeper information about high-performing students.
An adaptive state estimation for tracking hypersonic glide targets with model uncertainties

To track a maneuvering hypersonic glide target, an adaptive method for state estimation is proposed with model uncertainties in this paper. Various maneuver modes lead to considerable motion model uncertainty. In the method, unknown aerodynamic accelerations are modeled as the Singer model with a small maneuver frequency. When the real motion mode deviates from the default model, the maneuver frequency is adaptively enlarged by a novel technique derived from the orthogonal principle to reduce the model error. Simultaneously, the Huber technique is adopted to address measurement model uncertainty. Two strategies are specially formulated to alleviate possible misjudgments that a type of model error activates the non-corresponding technique. The adaptive state estimation is realized under the framework of unscented Kalman filter. Through tracking different flight trajectories, simulation results demonstrate that the proposed method has stronger robustness and higher estimation accuracy than conventional methods in the presence of model uncertainties and the computation burden is significantly less than the multiple-model method.
Employees’ BYOD Security Policy Compliance in the Public Sector

The proliferation of the Bring Your Own Device (BYOD) policy has instigated a widespread change across organizations. However, employees’ compliance toward BYOD security policy remains a challenge. Building on the organizational control, security culture, and social cognitive theory, a research framework for analyzing BYOD security policy compliance factors was developed in this study. To validate the framework, 346 responses were obtained from three Critical National Information Infrastructure (CNII) agencies. Using Partial Least Square-Sequential Equation Modelling (PLS-SEM), the study confirmed that perceived mandatoriness, self-efficacy, and psychological ownership are influential in predicting BYOD security policy compliance. Specification of policy is associated with perceived mandatoriness. Self-efficacy is associated with both BYOD IT support and SETA. Further, security culture was found to have no significant relationship with BYOD security policy compliance.
Object-Assisted Question Featurization and Multi-CNN Image Feature Fusion for Visual Question Answering

Visual question answering (VQA) demands a meticulous and concurrent proficiency in image interpretation and natural language understanding to correctly answer the question about an image. The existing VQA solutions either focus only on improving the joint multi-modal embedding or on the fine-tuning of visual understanding through attention. This research, in contrast to the current trend, investigates the feasibility of an object-assisted language understanding strategy titled semantic object ranking (SOR) framework for VQA. The proposed system refines the natural language question representation with the help of detected visual objects. For multi-CNN image representation, the system employs canonical correlation analysis (CCA). The suggested model is assessed using accuracy and WUPS measures on the DAQUAR dataset. On the DAQUAR dataset, the analytical outcomes reveal that the presented system outperforms the prior state-of-the-art by a significant factor. In addition to the quantitative analysis, proper illustrations are supplied to observe the reasons for performance improvement.