Miracle-dz/UniFAR-FTUs huggingface dataset:
The Evaluation of Operational Efficiency in International Disaster Public Welfare Organizations

The mission of the public welfare organizations is generally oriented to the public and based on morality. Serving as a key means to solve the problem in the non-profit organization agency, efficiency evaluation is also the premise and the foundation of accountability. Based on the data from 2010 to 2017, this paper analyzes the operational efficiency of four international disaster public welfare organizations (including GFDRR, UNISDR, OCHA, and ICRC). Given the characteristics of organizations, we extracted nine indexes of operational efficiency evaluation both in financial and non-financial aspects, and analyzed them by using the factor analysis method. The results show that the resource acquisition ability of the public welfare organization has a great influence on the operation of the organization. Additionally, the effective disclosure of the use of financial funds and the effectiveness of public welfare activities can promote public satisfaction. The beneficiary group size of the public welfare organization has a positive correlation with the management costs and staff costs.
An Image Encryption Scheme Based on DNA Computing and Multiple Chaotic Systems

A number of techniques for securing plaintext, images and video frames have been developed in cryptography using jointly DNA computing and Chaos Theory. With the advancement of DNA/quantum computing, the threats of security breaches to information have an increasing possibility. In this paper, we propose a symmetric encryption algorithm for color images by extending the current encryption/decryption techniques. Our encryption algorithm is based on three chaotic systems (PWLCM, Lorenz and 4D Lorenz-type), a Secure Hash Algorithm, a scrambler, a chaotic generator and DNA sequence based Linear Feedback Shift Register. We introduce multilevel security to increase the degree of diffusion and confusion. Through experiments, we present security analysis for key irreproducibility and sensitivity, Gray Level Co-occurrence Matrix based analysis, maximum deviation, irregular deviation, entropy, histogram, variance and correlation, number of pixel change rate, unified average cipher intensity, known/chosen-plaintext attacks, mean absolute error, robustness against noises of various types using PSNR and occlusion attacks. It is demonstrated that mostly our proposed encryption algorithm has enhanced performance as compared to contemporary works in information security, while comparable in other cases.
Suppressing Gender and Age in Face Templates Using Incremental Variable Elimination

Recent research on soft-biometrics showed that more information than just the person’s identity can be deduced from biometric data. Using face templates only, information about gender, age, ethnicity, health state of the person, and even the sexual orientation can be automatically obtained. Since for most applications these templates are expected to be used for recognition purposes only, this raises major privacy issues. Previous work addressed this problem purely on image level regarding function creep attackers without knowledge about the systems privacy mechanism. In this work, we propose a soft-biometric privacy enhancing approach that reduces a given biometric template by eliminating its most important variables for predicting soft-biometric attributes. Training a decision tree ensemble allows deriving a variable importance measure that is used to incrementally eliminate variables that allow predicting sensitive attributes. Unlike previous work, we consider a scenario of function creep attackers with explicit knowledge about the privacy mechanism and evaluated our approach on a publicly available database. The experiments were conducted to eight baseline solutions. The results showed that in many cases IVE is able to suppress gender and age to a high degree with a negligible loss of the templates recognition ability. Contrary to previous work, which is limited to the suppression of binary (gender) attributes, IVE is able, by design, to suppress binary, categorical, and continuous attributes.
Cache Replacement Strategy With Limited Service Capacity in Heterogeneous Networks

Recently, cellular networks face a huge challenge to satisfy large increment of user demands for higher-speed and lower-latency communication service. One promising solution is to apply the cache technology in edge cache networks to reduce redundant data transmission. The cache technology is effective, but the cache capacity is limited. Researchers have proposed various cache strategies to efficiently utilize the limited cache capacity. However, most existing solutions have not taken into account the service capacity limitation of mobile devices. In this paper, we propose a cache replacement strategy for heterogeneous networks considering the limitation of service capacity and user mobility. In the cache replacement strategy, we utilize the user characteristics, such as user mobility and file popularity, to estimate the user demands, and then define the system cost. We formulate the cache strategy design as a mixed integer linear programming problem to minimize the system cost, and use Lagrangian relaxation and hierarchical primal-dual decomposition method to solve this problem. Numerical results show that the proposed cache strategy can significantly reduce the system cost and increase the cache hit ratio compared to the cache strategy that does not consider the limitation of user service capacity.
Indonesian Traditional Food Image Identification using Random Forest Classifier based on Color and Texture Features

Indonesia has various cultures and consequently, it also has huge selection of traditional foods. Since there is new style of people of sharing food images in several social media before eating nowadays, people also have benefit to explore more information, such as recommendation place to eat particular food. When people travel to one place to another place they also have an intention to enjoy the local food, but some of them may not acknowledge the name of traditional food, especially for those who is not a residence in that place. By spreading images to others, it can help other people finding recommended food when visiting the same place. One way to tackle this problem is by identifying food images automatically. In this paper, we propose a new method for recognizing traditional foods in Indonesia. There are 382 images taken using smartphone camera, consisting of 33 classes as training set and 33 images as testing set. All features are extracted using color moments in RGB, LAB, and HSV color spaces and texture feature of Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM). Random Forest Classifier with different number of estimators are used to evaluate the proposed method. The result of combined features consisting of color and texture features reaches 93.5% of accuracy using 500 estimators. It proves that this method is sufficient to recognize traditional food image.
NIR-to-VIS Face Recognition via Embedding Relations and Coordinates of the Pairwise Features

NIR-to-VIS face recognition is identifying faces of two different domains by extracting domain-invariant features. However, this is a challenging problem due to the two different domain characteristics, and the lack of NIR face dataset. In order to reduce domain discrepancy while using the existing face recognition models, we propose a ’Relation Module’ which can simply add-on to any face recognition models. The local features extracted from face image contain information of each component of the face. Based on two different domain characteristics, to use the relationships between local features is more domain-invariant than to use it as it is. In addition to these relationships, positional information such as distance from lips to chin or eye to eye, also provides domain-invariant information. In our Relation Module, Relation Layer implicitly captures relationships, and Coordinates Layer models the positional information. Also, our proposed Triplet loss with conditional margin reduces intra-class variation in training, and resulting in additional performance improvements.Different from the general face recognition models, our add-on module does not need to pre-train with the large scale dataset. The proposed module fine-tuned only with CASIA NIR-VIS 2.0 database. With the proposed module, we achieve 14.81% rank-1 accuracy and 15.47% verification rate of 0.1% FAR improvements compare to two baseline models.
Survey of Hybrid VANET Design for Provisioning Infotainment Application

This paper presents a survey of various existing VANET model for provisioning real-time (i.e. safety critical application) and non-real smart infotainment application for user on the go. Further, it carries out survey of various existing hybrid VANET for provisioning QoS (Quality of services), increase network density using SDN (software defined network), fog computing and cloud computing framework, and future generation high speed wireless network such as LTE (Long term evolution), 5G (Fifth generation), CRAN (Cloud based Radio area network). However, from extensive analysis carried out it can be seen existing hybrid model do not consider feature of each framework/architecture such as VANET communication (i.e. among V2I and V2V), QoS controlling mechanism in SDN for provisioning data from cloud to VANET user and also do not provide efficient resource allocation technique in cloud computing environment to minimize computation cost. This paper shows meeting aforementioned feature in designing hybrid VANET model will aid in offering scalable and flexible smart infotainment application to VANET user. Further, the survey show how propagation model is affected due to environmental factor and presence of obstacle in LOS. Further, discusses about the need for new MAC (Medium access model). Then, discusses about selecting simulation environment/tool to evaluate performance of Hybrid VANET model. Lastly, a novel possible solution is described to overcome research problem which will be the future research direction of this survey. The proposed envisioned model will aid in offering superior performance than existing models.
Fuzzing Error Handling Code in Device Drivers Based on Software Fault Injection

Device drivers remain a main source of runtime failures in operating systems. To detect bugs in device drivers, fuzzing has been commonly used in practice. However, a main limitation of existing fuzzing approaches is that they cannot effectively test error handling code. Indeed, these fuzzing approaches require effective inputs to cover target code, but much error handling code in drivers is triggered by occasional errors (such as insufficient memory and hardware malfunctions) that are not related to inputs. In this paper, based on software fault injection, we propose a new fuzzing approach named FIZZER, to test error handling code in device drivers. At compile time, FIZZER uses static analysis to recommend possible error sites that can trigger error handling code. During driver execution, by analyzing runtime information, it automatically fuzzes error-site sequences for fault injection to improve code coverage. We evaluate FIZZER on 18 device drivers in Linux 4.19, and in total find 22 real bugs. The code coverage is increased by over 15% compared to normal execution without fuzzing.
Sclera Segmentation Benchmarking Competition in Cross-resolution Environment

This paper summarizes the results of the Sclera Segmentation Benchmarking Competition (SSBC 2019). It was organized in the context of the 12th IAPR International Conference on Biometrics (ICB 2019). The aim of this competition was to record the developments on sclera segmentation in the cross-resolution environment (sclera trait captured using multiple acquiring sensors with different image resolutions). Additionally, the competition also aimed to gain the attention of researchers on this subject of research.For the purpose of benchmarking, we have employed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1). The second dataset is the Mobile Sclera Dataset (MSD), and in this dataset, images were captured using . a mobile phone rear camera of 8-megapixels. Baseline manual segmentation masks of the sclera images from both the datasets were developed.Precision and recall-based measures were employed to evaluate the effectiveness and ranking of the submitted segmentation techniques. Four algorithms were submitted to address the segmentation task. In this paper we analyzed the results produced by these algorithms/systems, and we have defined a way forward for this problem. Both the datasets along with some of the accompanying ground truth/baseline masks will be freely available for research purposes.
Adaptive eXogenous Kalman Filter for Actuator Fault Diagnosis in Robotics and Autonomous Systems

This paper presents an algorithm for actuator fault diagnosis in robotics and autonomous systems under random uncertainties based on a cascade of nonlinear observer and linearized Kalman filter. The two-stage estimation method assumes uniform complete observability and controllability conditions and persistent excitation condition. To this end, we consider dynamical systems of robotics and autonomous systems with one-sided Lipschitz nonlinearity. To demonstrate the effectiveness of the proposed algorithm, numerical simulations in a single-link flexible joint robot are performed.
Simulation and design of 500 kV DC cable terminal accessory based on ZnO varistor microsphere composites

DC transmission has advantages over AC transmission in terms of distance and capacity and has thus received considerable research attention and development. However, the development of high-voltage DC cables is limited by the development of the associated DC cable accessories. In view of the existing problems with DC cable terminal accessories, materials with nonlinear conductivity are being applied to cable terminal accessories. Using finite element simulations, the internal electric field distribution of a DC cable terminal under a thermoelectric coupled field is calculated for different temperature gradients. It is found that cable terminal accessories made of materials with nonlinear conductivity have a stronger electric field grading effect than that of traditional cable terminal accessories. Based on the characteristics of the material with nonlinear conductivity, a simpler cable terminal accessory structure that can achieve the same electric field grading effect is proposed. The results show that the use of a material with nonlinear conductivity facilitates the development of high-voltage DC cable accessories.
Dynamic VTH Tracking for Cross-Temperature Suppression in 3D-TLC NAND Flash

The cross-temperature effect has been identified as one of the sources that limits the 3D-TLC NAND Flash reliability and performance. The physical nature of the phenomenon lies on the temperature dependency of the threshold voltage distributions that is evidenced when the memory is read at a temperature different from the one experienced during its programming. State-of-the-art solutions for mitigating the phenomenon, both at circuit and system-level, are failing to entirely cover the issues of such reliability threat. In this work we present and validate a dedicated algorithm (cross-temperature dynamic VTH tracking or CTDVT) that goes in synergy with a system like a Solid State Drive to completely suppress the cross-temperature issue.
Deep Learning for Predicting Students' Academic Performance

The use of Deep Learning to predict what happens in the future becomes more popular because of great availability of data. This study proposes the power of Deep Learning to predict Students' Academic Performance (SAP). It is happened, students pass or fail at the end of the school period. By the time, it is already too late to help the students. The prediction of SAP in advance is needed to prevent students from failing. Feedforward Neural Network is used in this study to predict whether students can achieve certain scores within a specific range score. Data from students' activities are used to predict SAP. Raw data were collected from a course attended by 90 students. The input for this study was student's activities that were recorded in the log data from the Learning Management System. The way the data were collected, pre-processed and developed are explained. The result shows that students' activity from log data varied greatly and the formed pattern is weak. The training validation model's accuracy was still around 85% with the accuracy to predict is under 50%. Thus, our initial research results indicate that Deep Learning does not perform high accuracy for predicting Students' Academic Performance because of several reasons, among others, small sample of dataset, relevance of students' activities, and so forth. Regardless of that, the study reveals that with our dataset structure of students' activities, SAP can be predicted. Further study is to investigate more relevance students' activities, more dataset of students, and a longer period of study time.
Drain Current Simulation of Molybdenum Disulfide Based Devices

This paper presents the numerical model of MoS<inf>2</inf> based transistor. The proposed structure having Molybdenum disulphide (MoS<inf>2</inf>) as channel material and SiO<inf>2</inf> as gate oxide material. Output characteristics of MoS<inf>2</inf> have been demonstrated using our proposed model. Compared with silicon, the MoS<inf>2</inf> channel material provides the better drain current due to its larger band gap, high mobility and low power consumption. The IV Characteristics for the MoS<inf>2</inf> material was measured by using the Technology Computer Aided Design (TCAD) simulation software.
Towards the Automation of a Chemical Sulphonation Process with Machine Learning

Nowadays, the continuous improvement and automation of industrial processes has become a key factor in many fields, and in the chemical industry, it is no exception. This translates into a more efficient use of resources, reduced production time, output of higher quality and reduced waste. Given the complexity of today’s industrial processes, it becomes infeasible to monitor and optimize them without the use of information technologies and analytics. In recent years, machine learning methods have been used to automate processes and provide decision support. All of this, based on analyzing large amounts of data generated in a continuous manner. In this paper, we present the results of applying machine learning methods during a chemical sulphonation process with the objective of automating the product quality analysis which currently is performed manually. We used data from process parameters to train different models including Random Forest, Neural Network and linear regression in order to predict product quality values. Our experiments showed that it is possible to predict those product quality values with good accuracy, thus, having the potential to reduce time. Specifically, the best results were obtained with Random Forest with a mean absolute error of 0.089 and a correlation of 0.978.
Non-filamentary non-volatile memory elements as synapses in neuromorphic systems

Crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing highly energy-efficient neuromorphic computing systems. For Deep Neural Networks (DNN), where information can be encoded as analog voltage and current levels, such arrays can represent matrices of synaptic weights, implementing the matrix-vector multiplication needed for algorithms such as backpropagation in a massively-parallel fashion. Previous research demonstrated a large-scale hardware-software implementation based on phase-change memories and analyzed the potential speed and power advantages over GPU-based training. In this proceeding we will discuss extensions of this work leveraging a different class of memory elements. Using the concept of jump-tables we simulate the impact of real conductance response of non-filamentary resistive devices based on ${P} r_{0}. {}_{3}Ca_{0.7}$ Mn $O_{3}$ (PCMO). With the same approach as of [1], we simulate a three-layer neural network with training accuracy >90% on the MNIST dataset. The higher ON/OFF conductance ratio of improved Al[Mo/PCMO devices together with new programming strategies can lead to further accuracy improvement. Finally, we show that the bidirectional programming of Al[Mo/PCMO can be used to implement high-density neuromorphic systems with a single conductance per synapse, at only a slight degradation to accuracy.
FluxRank: A Widely-Deployable Framework to Automatically Localizing Root Cause Machines for Software Service Failure Mitigation

The failures of software service directly affect user experiences and service revenue. Thus operators monitor both service-level KPIs (e.g., response time) and machine-level KPIs (e.g., CPU usage) on each machine underlying the service. When a service fails, the operators must localize the root cause machines, and mitigate the failure as quickly as possible. Existing approaches have limited application due to the difficulty to obtain the required additional measurement data. As a result, failure localization is largely manual and very time-consuming. This paper presents FluxRank, a widely-deployable framework that can automatically and accurately localize the root cause machines, so that some actions can be triggered to mitigate the service failure. Our evaluation using historical cases from five real services (with tens of thousands of machines) of a top search company shows that the root cause machines are ranked top 1 (top 3) for 55 (66) cases out of 70 cases. Comparing to existing approaches, FluxRank cuts the localization time by more than 80% on average. FluxRank has been deployed online at one Internet service and six banking services for three months, and correctly localized the root cause machines as the top 1 for 55 cases out of 59 cases.
Usage Intention Model for Mobile Health Application: Uses and Gratification Perspective

There is a need to improve the health status of the Indonesian people. Health information technology (HIT) like mobile health (mHealth) has the potential to improve health conditions and the quality of health services for all Indonesian people. However, the adoption rate of mHealth applications in Indonesia is still lower than other application categories. This study aims to investigate several factors or characteristics that motivate people consumption of mHealth from Uses and Gratification perspectives. The methodology used in this research is a mixed method that includes in-depth interviews and online questionnaires. The gratification factors investigated in this study were divided into three categories: Hedonic Gratification, Utilitarian Gratification, and Social Gratification. The Gratification factors was validated with questioners and identified the most significant factor is Utilitarian Gratification which was approved by 51% of respondents, and this is affected from the ease of use and information aspects which can fulfill user needs and effect on continuous intention using mHealth.