Reviewerly/exHarmony huggingface dataset:
Objective quantification of nerves in immunohistochemistry specimens of thyroid cancer utilising deep learning

Accurate quantification of nerves in cancer specimens is important to understand cancer behaviour. Typically, nerves are manually detected and counted in digitised images of thin tissue sections from excised tumours using immunohistochemistry. However the images are of a large size with nerves having substantial variation in morphology that renders accurate and objective quantification difficult using existing manual and automated counting techniques. Manual counting is precise, but time-consuming, susceptible to inconsistency and has a high rate of false negatives. Existing automated techniques using digitised tissue sections and colour filters are sensitive, however, have a high rate of false positives. In this paper we develop a new automated nerve detection approach, based on a deep learning model with an augmented classification structure. This approach involves pre-processing to extract the image patches for the deep learning model, followed by pixel-level nerve detection utilising the proposed deep learning model. Outcomes assessed were a) sensitivity of the model in detecting manually identified nerves (expert annotations), and b) the precision of additional model-detected nerves. The proposed deep learning model based approach results in a sensitivity of 89% and a precision of 75%. The code and pre-trained model are publicly available at https://github.com/IA92/Automated_Nerves_Quantification .
The Comparison Performance of Digital Forensic Tools Using Additional Root Access Options

This research used MiChat and SayHi as materials for forensic investigations using three different tools, namely MOBILedit, Magnet Axiom, and Belkasoft. These three tools will show each performance in the forensic process. We also added a rooting process as an option if data cannot be extracted optimally even when using these three applications. The result of this study shows that the cases studied with processes without root access and with root access have the aim of complementing each other in obtaining evidence. So that these two processes complement each other's shortcomings. The main contribution of this research is a recommendation of a tool based on the best performance shown during the forensic process with rooting access and without rooting access. Based on the comparison, Magnet Axiom is superior with a total of 34 items of data found without root access, while MOBILedit is 30 items and 30 items for Belkasoft. While comparison using root access, Magnet Axiom and MOBILedit are superiors with a total of 36 items found in Magnet Axiom without root access, while MOBILedit is 36 items and 33 items for Belkasoft. Based on the test results, it can be concluded that the recommended tool according to the used scenario is Magnet Axiom.
Hough Transform Proposal and Simulations for Particle Track Recognition for LHC Phase-II Upgrade

In the near future, LHC experiments will continue future upgrades by overcoming the technological obsolescence of the detectors and the readout capabilities. Therefore, after the conclusion of a data collection period, CERN will have to face a long shutdown to improve overall performance, by updating the experiments, and implementing more advanced technologies and infrastructures. In particular, the largest LHC experiment, i.e., ATLAS, will upgrade parts of the detector, the trigger, and the data acquisition system. In addition, the ATLAS experiment will complete the implementation of new strategies, algorithms for data handling, and transmission to the final storage apparatus. This paper presents an overview of an upgrade planned for the second half of this decade for the ATLAS experiment. In particular, we show a study of a novel pattern recognition algorithm used in the trigger system, which is a device designed to provide the information needed to select physical events from unnecessary background data. The idea is to use a well known mathematical transform, the Hough transform, as the algorithm for the detection of particle trajectories. The effectiveness of the algorithm has already been validated in the past, regardless of particle physics applications, to recognize generic shapes within images. On the contrary, here, we first propose a software emulation tool, and a subsequent hardware implementation of the Hough transform, for particle physics applications. Until now, the Hough transform has never been implemented on electronics in particle physics experiments, and since a hardware implementation would provide benefits in terms of overall Latency, we complete the studies by comparing the simulated data with a physical system implemented on a Xilinx hardware accelerator (FELIX-II card). In more detail, we have implemented a low-abstraction RTL design of the Hough transform on Xilinx UltraScale+ FPGAs as target devices for filtering applications.
Trace Metals Evaluation of Groundwater in the Granito-Basaltic Fractured Rock Aquiferous Formations in Bafoussam, West Region—Cameroon

Bafoussam is situated between latitude 5.42N -5.60N and longitude 10.38E -10.48E, is the capital and largest city in the Western Region of Cameroon.Increased urbanization and agricultural activities have potentials for trace metal pollution of groundwater.As such, trace metals evaluation of groundwater in Bafoussam is among the most important environmental issues related to contamination from natural and anthropogenic sources.This study investigated the trace metal content of groundwater sources; determined estimates of the health hazard and pollution risk indices; assessed the health hazard and level of risk to trace metal pollution in Bafoussam.Field measurement of physicochemical parameters from 206 Dug-wells and chemical analysis of 10 representative groundwater samples determined their trace metal content using Inductively Coupled Plasma Mass Spectroscopy ICP-MS and R-mode statistical analysis; Hierarchical Cluster Analysis (HCA) and Pearson's correlation analysis (PCA) of the trace metal content with the physico-chemical parameters were carried out.Four pollution hazards were determined: the average daily dose ADD, carcinogenic risks CR, non-carcinogenic risk hazard quotient HQ and hazard index HI.Six pollution risks were determined: the Degree of contamination DC, Enrichment factor EF, Ecological risk factor Er, Ecological risk index RI, Pollution load index PLI, and geo-accumulation index Igeo.The relative abundance of trace metals levels in the sampled groundwater follows the decreasing order: Zn > Ba > Sr > Cu > Mn > Fe > Pb > Cr > Ni > Co > V > Cd > As > Li.HCA distinguishes trace metals into two clusters: cluster one is made up of 2 elements divided into two classes and cluster two is made up of 12 non soluble elements divided into three classes.
Factors Influencing Digital Transformation Adoption among Higher Education Institutions during Digital Disruption

This research aims to apply confirmatory factor analysis to identify the digital transformation components for higher education institutions. The research sample consisted of 300 personnel from agencies within higher education institutions, which are higher education institutions under the Ministry of Higher Education, Science, Research and Innovation, Thailand that use the database system on educational quality assurance called Commission on Higher Education Quality Assessment online system (CHE QA Online). The selection was the result of multi-stage random sampling from 100 higher education instructions. The research tool was an online questionnaire form on factors influencing the success of information systems in the digital transformation for higher education institutions by 5-level rating scale based on the Likert&amp;#39;s scale. The result revealed that digital transformation factor consistent with empirical data (p-value = 0.860), which consist of 6 components: 1) Strategy 2) Process 3) Product/Service 4) People 5) Data) and 6) Technology. The research findings help higher education institutions prepare for the elements necessary for the institutional transformation to a digital organization.
Research on the Influence of Inclusive Leadership Style on Employment Relationship Climate and Employee Proactive Behavior

Employee job satisfaction is important to get the attention of every leader of the organization. This is because satisfied employees can be expected to produce fast, careful and accurate work. On the other hand, dissatisfied employees often take the form of indiscipline, unwillingness to take initiative and innovate. In the end, the resulting product/service is often not as expected. In this study, leadership style is an independent variable which includes; directive leadership style; participative; supportive. Job satisfaction as the dependent variable and work climate as an intervening variable. This study aims to analyze leadership styles that can increase job satisfaction and verify that work climate variables can mediate these effects. To achieve this goal, this study uses a quantitative approach with Tadulako University employees as respondents who filled out a questionnaire, which was circulated via google form, obtained as many as 71 respondents. The data analysis technique used is Partial Least Square (PLS). The results of this study indicate that the work climate can mediate the influence of leadership style on job satisfaction, and then find a leadership style that has a positive influence on employee job satisfaction through the work climate, namely, participative and supportive leadership styles.
Design Plan for an Evolution Study of Related Characteristics of a Population

The objective is to study the evolution of different characteristics of a population through time. These response variables may be related for each experimental unit, and in addition, the observations for each response may as well be correlated with time, producing a complex correlation structure. The number of responses that can be observed is usually limited for budget, resources, or time reasons, and thus the selection of the most informative time points when data must be taken is quite convenient. This will be performed by using the optimal design of experiments techniques. Some analytical results will be shown, and the results will be applied to obtain the most convenient points when tests about two variables related with the capability of the resolution of mathematical problems in primary school students should be performed.
Improving Hardware Security on Talos II Architecture Through Boot Image Encryption

The OpenPOWER Foundation is an organization that promotes open-source high-performance hardware like the POWER9. OpenBMC is an OpenPower project that strives to produce an open-source firmware stack for Baseboard Management Controllers (BMCs). If hardware falls into the hands of competitors or bad actors, reverse engineering methods can be used to leak or manipulate sensitive information from the boot sequence. This represents a security concern because the root of trust can be invalidated. For example, since the Initial Program Load (IPL) data is frequently not encrypted and is sent over the Low Pin Count (LPC) bus, it is possible to intercept and conduct man-in-the-middle attacks to modify the boot process. The boot image flash chip could also be removed from the Talos II motherboard and examined by competing server architecture manufacturers to reveal detailed boot information. Firmware that developers deem to contain sensitive code or perform innovative operations needs to be protected before being flashed onto the boot image chip. This paper demonstrates a method to encrypt sections of the boot image by encrypting a section of the image before flashing it onto the Talos II. The encrypted image will be decrypted during the boot sequence in the Level 3 cache of the POWER9, proving that it is possible to prevent adversaries from interfering with the IPL flow or obtaining details on firmware from the flash chip. This paper presents a novel method to improve the security of the boot image on Talos II architecture by encrypting the boot firmware image and decrypting it during the boot process. The proof of concept was executed on a Raptor Engineering Talos II system running a POWER9 processor with OpenBMC firmware on the ASPEED AST2500 BMC. This research claims that this unique method increases boot time security through firmware without altering hardware.
A Study on Three Step Multifactor Authentication System for Modern Security

Abstract: This survey paper reviews all the major factors in validating three-level passwords such as one-factor authentication using passwords and two-factor authentication is not enough to provide better security to the modern digital age with remarkable advances in the field of information technology. Even when single-factor or two-factor authentication was used to secure remote access and the system, hacking tools, were simple computer programs for collecting private keys, as well as private generators have made it difficult to provide security. Security threats based on malware, such as important installed trackers, are always available to improve security risks. This is necessary the use of a safe and easy-to-use object. As a result, Three Level Security is an easy-to-use software. It also proves the use of different techniques used by different authors. Keywords: Three-level authentication, network, security, remote access, two-factor authentication
Comparison of the effect of two teaching methods on surgical technologist students' learning and satisfaction (flashcards vs. mobile-based learning)

M-learning is "learning across multiple contexts, through social and content interactions, using simple hand-held technologies, wireless and mobile network. This personal electronic device is a way to facilitate the transfer of learning and improve teaching. Hence, the researchers decided to design a mobile-based learning application to teach surgical instruments comprehensively and compare students' learning and satisfaction in both mobile-based learning and flashcards methods.An interventional study was conducted on surgical technologist students in Alborz University of medical science (2019). Students were divided into two groups: flashcards (n = 21) and mobile application learning group (n = 21). The difference of pre- and post-test scores was considered for the evaluation of learning level in two groups. Data were entered into SPSS 20.0 software and analyzed by statistics tests. Data were analyzed by a paired t-test, independent t-test, and Pearson's correlation coefficient.There was no significant difference in students' knowledge level between two groups before intervention (P = 0.87) but there was significant difference between groups after the intervention (P = 0.003). In the evaluation of student's satisfaction, the mobile application was preferred by 84.28% of students.Given the effectiveness of using both methods (flashcards and mobile application), it is recommended that these methods especially mobile application be used for surgical instruments education.
Visit the U.S. Geological Survey's National Water Dashboard

First posted March 2, 2022 For additional information, contact: Associate Director, Water Resources Mission AreaU.S. Geological Survey12201 Sunrise Valley DriveReston, VA 20192Contact Pubs Warehouse The U.S. Geological Survey National Water Dashboard supplies critical information to decision makers, emergency managers, and the public during extreme hydrologic events (such as droughts and floods) and during normal hydrologic conditions. It informs decision making that can help protect lives and property before and during extreme hydrologic events. The National Water Dashboard draws upon the extensive site-specific hydrologic data housed in the U.S. Geological Survey National Water Information System database (https://doi.org/10.5066/F7P55KJN) and also links to the U.S. Geological Survey WaterAlert system, which provides users with instant and customized updates about water conditions. Overall, the National Water Dashboard is part of the U.S. Geological Survey's effort to respond to 21st century science needs.
Exploring Indonesian EFL Writing Students’ Differences in Motivation: A Language Learning Journey to Success

Every language learner has various dreams and purposes in learning a language. Different motivations are also commonly found among EFL learners. This study reveals the motivations of 33 English language learners from Indonesia. They were all writing students of an English Language Education Program (ELEP) in a private university in Central Java, Indonesia. The classes they attended were Procedural Writing (14 students from the 2020 class year), Research Report Writing (10 students from the 2017 class year), and Research Proposal (9 students from the 2018 class year). Data were derived through questionnaires and interviews. The first finding was that 87.88% of the respondents were intrinsically motivated. However, both intrinsic and extrinsic motivation might be a big drive leading to students’ success. The second discovery was that 81.82% of the students were instrumentally rather than integratively motivated. Instrumental motivation could also be a big initiative for students to learn a language. Next, students’ ideals or dreams became the motivating factor that most students had. The last outcome was that students’ habit of delaying doing assignments, having too many assignments, and being lazy were the three major demotivating factors. This study will hopefully be useful for writing lecturers to give them more insights into and a better understanding of their students’ motivational backgrounds.
Design of Maintaining Data Security on IoT Data Transferred Through IoT Gateway System to Cloud Storage

IoT is a smart-device environment; it acts smarter and gives higher quality to enhance the user experience. IoT is an open network environment. However, IoT's security challenges are more vulnerable because the devices are openly accessible to all in the network. IoT communication can be formed in different ways, where the device generates the data is communicated to other devices or gateway or cloud. Therefore, security challenges are in everywhere IoT environment. IoT Communication considered in this paper is gateway communication. Data generated from the IoT environment are directed to the cloud over a gateway system. Hence, the data essentials to be protected from the IoT devices to the gateway system and the gateway system to the cloud storage. The cloud is a public environment that makes security is a challenge to keep data secure. Therefore, securing IoT data to the cloud environment is the most significant focus. This paper focuses on securing the data from the gateway system to the cloud storage. The paper proposes an enhanced modern symmetrical encryption to secure data to ensure data security in the travel and storage of IoT data to the cloud. The proposed Enhanced Modern Symmetric Data Encryption (EMSDE) is a block cipher encryption technique. It encrypts data by 64-bit block. Proposed encryption is tested according to time and level of security. Thingspeak stores the IoT data sent from the IoT gateway system for implementation and testing. Based on the testing, the proposed EMSDE produces better results, and it is tailored to secure data stored in the cloud from IoT devices.
Lightweight and Low-Latency AES Accelerator Using Shared SRAM

In this study, we propose a lightweight and low-latency advanced encryption standard (AES) accelerator. Instead of being connected to the bus through its own slave wrapper, the proposed AES accelerator is located within the slave wrapper of the static random-access memory (SRAM) and is directly attached to the SRAM. Hence, the AES accelerator can directly access data in the SRAM and share SRAM space for storing expanded keys, resulting in no time for transferring input and output data, no resource usage for storing keys, and no power wastage for repeated key expansion. The proposed AES accelerator has a latency of 53 clock cycles per encryption/decryption process and has a gate count of 2912 when synthesized using 28 nm process technology. The latency is similar to that of another AES accelerator with the same 32-bit data path; however, the size of the proposed accelerator is 46.0% smaller. Furthermore, compared with other AES accelerators with 8-bit data path, the proposed AES accelerator has a 3.0–22.0 times smaller latency with a slightly larger area.
Condition of Program Learning Outcomes of Physical Education Study Program from Alumni Perspective

The completeness of the Program Learning Outcomes (PLO) listed in the curriculum in a study program has been monitored through assessments carried out in each subject for students.Assessment is objective from one side of the lecturer based on students' performance attending theoretical lectures and learning experiences on campus.PLO needs to be updated according to the progress of the times continuously.The purpose of writing this article is to describe the completeness of the alumni of the study program in achieving PLO contextually when they became PE teachers at school.This descriptive research data collection uses a survey method to 73 PE teachers with essential characteristics consisting of graduation period, gender, school status, school accreditation, teacher experience, employment status, teacher level of education, amount of teaching hours, school level.Closed questionnaires were used as research instruments prepared based on the PLO applicable in the curriculum of the study program.Data analysis used percentage, chi-square, and spearman correlation.The results show that four PLOs (PLO-6, 7, 9, 10) are influenced by the variable of teacher teaching experience.The correlation that occurs is negative.That is, the longer the teaching experience, the more incomplete the PLO required.So it can be concluded that the PLO curriculum of the study program must be upgraded according to the needs of PE teachers while carrying out their duties at school.
Research on real-time facial landmark detection model that can deal with occlusion

With the wide use of face tasks on mobile terminals, facial landmark detection faces new challenges of real-time and occlusion. Therefore, this paper designs a real-time facial landmark detection model that can deal with occlusion. The model of this paper includes the backbone network and the auxiliary network. The function of the backbone network is to locate facial landmarks. The backbone network uses the lightweight network module ShufflenetV2 to ensure the realtime reasoning of the model, and uses dynamic convolution and Wing loss function to improve the positioning accuracy of the model. The function of the auxiliary network is to predict landmarks’ visibility. The auxiliary network helps the backbone network focusing attention on visible landmarks, so as to deal with occlusion. The experimental results show that the size of the model is 1.3MB, the reasoning speed of the model on Intel i5-8250u CPU is 64.63 pictures per second, and the prediction accuracy on the WFLW dataset of the model is 85.72%. The model performs better than PFLD in size, speed, and accuracy.