Miracle-dz/UniFAR-FTUs huggingface dataset:
The Investigative DNA

Building off previous literature on journalistic roles, this study showed that local television investigative reporters were more adversarial and placed greater value on audience appeal compared to other journalists. A factor analysis of survey responses (N = 165) revealed five functions describing investigative journalists’ perceived roles: adversarial, interpretive, entertainer, traditionalist, and mobilizer. Using Shoemaker and Reese’s hierarchical model, individual- and organization-level variables were used as predictors in multivariate regression analyses. These analyses indicated that investigative journalists at stations owned by publicly traded companies were significantly more likely to subscribe to traditional news values. Other significant predictors of investigative role included ethnicity, amount of investigative work, market size, and an organization’s emphasis on quality journalism.
Challenges Facing Integration and Use of ICT in the Management of County Governments in Kenya

Kenyan’s Vision 2030 identifies ICT as one of the core drivers of Kenya’s growth and development strategy to becoming a middle income country by the year 2030. Vision 2030stresses access to ICTs based on its potential to increase productivity and raise the competitiveness of local businesses in a knowledge-based economy. ICT integration has not reached its full potential and utilization in the County governments. This has been brought about by a number of factors. Poor and inadequate ICT facilities, poor levels of computer literacy and ICT skills, poor level of awareness of internet facilities among policy makers, government officials and the ruling class in general and minimum involvement of academic institutions in network building are challenges mitigating against the use of ICTs. The Purpose of this study is to investigate on Challenges Facing Integration and Use of ICT in the management of County Governments in Kenya. The paper used a desk study review methodology where relevant empirical literature was reviewed to identify main themes according to the global and local contexts. From the literature reviewed, the study revealed that it is an area worthy of doing research. The study found out that ICT integration has and continues to be a challenge in many county governments. Its integration is yet to reach its full potential and its utilization is limited. The literature review highlighted several factors that affect ICT integration for service delivery. These challenges include lack of enough ICT infrastructure, lack of staff capacity in terms of trainings, staff attitude towards technology adoption, poor government regulation policies and lack of management commitment. Based on the findings and conclusions, the study recommends that County Government staff should be trained and retrained in application of ICT for improved integration and use of ICT in the management of the County activities. To sustain this, there is a need for rethinking education as a whole to produce the future ICT integration implementers. Key words: ICT integration, staff capacity, ICT infrastructure, management commitment, County Government
Enhancing 5G/IoT Transport Security Through Content Permutation

Internet of Things (IoT) or massive machine-type communications (mMTCs) is one of the essential aspects addressed by 5G mobile telecommunications. Some 5G core networks will be deployed with software-defined networking (SDN) where security is an important issue. The overhead of offering security in 5G is high, which is typically incurred in encoding and decoding. This paper proposes the enhanced content permutation algorithm (eCPA) that can effectively implement secret permutation in a 5G/IoT transport network with the P4 SDN switches to protect the 5G packets (especially for IoT). The encoding/decoding speed, including packet routing, can be up to 6.4 Tb/s, which is the fastest in the world.
Foreword to the special issue of the World Conference on Information Systems and Technologies (WorldCIST 2017)

It is our great pleasure to introduce the latest special issue of the Concurrency and Computation: Practice and Experience. The issue contains the results of the latest research in the wide-ranging topics regarding computers and computational science. The contemporary problems tackled in the issue concern information technology and are connected not only with improving software and hardware but also with human-machine interfaces. Especially, this last issue is current and up-to-date as computer technologies interlocated between human-supporting machinery are a natural step in developing our civilization. The topics presented in the issue include the problems relating to modeling new computational system, monitoring health, system diagnostics, and algorithms for big data management. We are sure that every reader interested in computational engineering might find some very interesting information.
Design Guidelines of All Storage Class Memory (SCM) SSD and Hybrid SCM/NAND Flash SSD to Balance Performance, Power, Endurance and Cost

Abstract The performance, energy consumption, endurance and cost are compared between the All storage-type storage class memory (S-SCM) solid-state drive (SSD) and Hybrid memory-type SCM (M-SCM)/NAND flash SSD. A wear leveling algorithm has been proposed for the SCM due to its limited endurance. With a 25% M-SCM/NAND capacity ratio, the All S-SCM SSD is faster if the S-SCM latency is <5s. Moreover, assuming the cost ratio of M-SCM/NAND is 12, All S-SCM SSD is more cost-efficient when S-SCM/NAND cost ratio is < 4.
Fine-Tuning Gold Questions in Crowdsourcing Tasks using Probabilistic and Siamese Neural Network Models

The economic benefits of crowdsourcing have furthered its wide-spread use over the past decade. However, increasing numbers of fraudulent workers threaten to undermine the emerging crowdsourcing economy: requestors face the choice of either risk-ing low-quality results or having to pay extra money for quality safeguards such as gold questions or majority voting. The more safeguards injected into the workload, the lower are the risks im-posed by fraudulent workers, yet the higher are the costs. So, how many of them are actually needed? Is there a generally applicable number or percentage? This paper uses deep learning techniques to identify custom-tailored numbers of gold questions per worker for individually managing the cost/quality balance. Our new method follows real-life experiences: the more we know about workers before assigning a task, the clearer our belief or disbelief in this worker’s reliability gets. Employing probabilistic models, namely Bayesian belief networks and certainty factor models, our method creates worker profiles reflecting different a-priori belief values, and we prove that the actual number of gold questions per worker can indeed be assessed. Our evaluation on real-world crowdsourc-ing datasets demonstrates our method's efficiency in saving money while maintaining high-quality results.
Molecular Functions of the Chromatin Remodeler Fun30

Many studies have identified conserved ATP-dependent chromatin remodeling complexes whose functions are to modulate DNA access by relieving chromatinmediated repression. We have previously characterized Fun30 in Saccharomyces cerevisiae as a homodimer with ATP-dependent chromatin remodeling activity. Other studies have shown that Fun30 plays a role in maintaining the silenced state of subtelomeric and centromeric chromosomal regions. Fun30 has also been shown to play an important role in DNA damage repair by facilitating long range resection of DNA in Double Strand Breaks. This thesis was focuses on understanding the mechanisms by which Fun30 is involved in DNA damage repair. Results presented here show that Fun30 can anneal complementary strands of DNA that is facilitated by ATP hydrolysis and a helicase activity in the presence of trap DNA. In addition, Fun30 was found to be able to relax both negatively and positively supercoiled DNA in an ATP-independent manner and cleave a 3’ overhang in a forked DNA duplex or a duplex that has a protruding 3’. Annealing and 3’ flap endonuclease activities of Fun30 suggest a mechanism by which Fun30 can facilitates double strand break repair by the Single Strand Annealing pathway, while a potential helicase activity can facilitate Synthesis Depended Strand Annealing and as a result reduce the generation of recombination intermediates. Moreover, employing in vivo approaches, we show that Fun30 genetically interacts with the Mus81 nuclease upon chronic treatment with chemicals that stall the replication fork, suggesting that Fun30 deletion might lead to the accumulation of toxic recombination intermediates that are difficult to resolve in the absence of Mus81. We also found that Fun30 deletion affects the cell cycle progression of cells lacking TopI, without affecting the viability of the cells. This might explain a function for Fun30 in facilitating the progression of the cell cycle in the presence of torsional stress which can be induced by TopI deletion. Moreover, we found that Fun30 is not involved in removing camptothecin induced TopI/DNA complexes since no genetic interaction between Tdp1 and Fun30 was observed. Furthermore, we show that Fun30 genetically interacts with Asf1 under DNA damaging conditions, suggesting that Fun30 is required in the absence of Asf1. Finally, couple of models are proposed that explain how Fun30 annealing and nuclease activities may be important in the Single Strand Annealing pathway and how Fun30 viii helicase activity might be used to reduce the level of toxic recombination intermediates and thus maintain genomic stability, which if compromised could lead to cancer or other diseases.
Adaptive Capacity Partitioning in Cooperative Computing to Maximize Received Resources

Users in a cooperative computing environment always have a tendency to free-ride. One can employ incentive mechanisms to prevent such behavior. Some of the cooperative computing scenarios have the same access link shared between upload and download. In such a situation, increasing upload capacity decreases the download capacity and vice versa. Optimal partitioning of link capacity between upload and download needs to be done by each user to maximize its gain (i.e., download) from the network. We model this link capacity partitioning problem as a feedback control system, where feedback (resources received) decides the number of resources to be uploaded by a user. The resulting algorithm called adaptive step size (ASZ) dynamically adjusts the partitioning of link capacity to an optimal value. To compare this approach with others, a metric “level of optimality $(U)$ ” is introduced. $U$ achieved by the ASZ is closer to the optimal level than the reputation-based resource allocation policy (existing scheme), thus resulting in its better performance. The ASZ is also integrated with BitTorrent, and the simulation results show that it increases the resources received by the users. The ASZ can provide an efficient solution to the problem of optimal partitioning in real-life distributed networks due to its distributed implementation, robustness to changes in network dynamics, and compatibility with the existing partitioning schemes.
Knowledge Discovery Pada Email Box Sebagai Penunjang Email Marketing

Memanfaatkan email untuk pemasaran adalah salah satu strategi marketing yang sangat efektif dan murah. Walaupun demikian, email marketing tidak boleh dikirim secara sembarangan dan terus – menerus. Pengiriman email yang berlebihan serta tidak sesuai dengan minat atau bisnis dari orang yang menerima akan diabaikan atau bahkan dianggap spam oleh penerima. Akibat dari hal tersebut, hubungan bisnis antara perusahaan dengan client atau calon client menjadi tidak terjalin dengan baik. Maka, sebelum mengirim email promosi produk akan lebih baik jika bagian marketing mencari tahu minat dan beberapa informasi penting berkaitan dengan penerima. Informasi tentang minat atau kesukaan dari client dapat ditemukan dari kumpulan email yang pernah mereka kirim ke inbox kita. Namun menemukan minat client dari dokumen email perlu teknik khusus dalam mengolah data teks. Maka dari itu, Pada penelitian ini fokus melakukan studi dan implementasi text mining pada dokumen email. Dokumen email dianalisis dengan memperhitungkan jumlah kata yang muncul pada email tersebut. Representasi teks yang digunakan yaitu single word. Single word tersebut kemudian menjadi masukan dari proses dokumen clustering dengan algoritma K-mean pada fase data mining. Hasil dari clustering inilah yang dapat digunakan oleh pihak marketing sebagai penunjung kegiatan promosi produk. Dari hasil perancangan, pengembangan serta pengujian aplikasi terhadap dokumen email didapatkan hasil yaitu sistem sudah dapat melakukan preprocessing teks menggunakan teknik parsing, stopword removal, dan stemming sehingga menghasilkan kumpulan kata dasar (bag of word). Pada proses pencarian minat dengan term yang dimasukkan, sistem sudah dapat menemukan email client dengan arah minat tertentu menggunakan teknik TF-IDF. Pada proses pengelompokkan dokumen email menggunakan algoritma K-mean clustering pada dokumen email yang sudah dilabelkan sebelumnya memberikan akurasi 63,63% dari 11 dokumen email yang sudah digabung dari setiap pengirim. Dari hasil pengujian sistem sudah dapat digunakan sebagai dasar pemilihan calon penerima email marketing sesuai dengan term / key word yang dimasukkan serta berdasarkan kemiripan dari isi email marketing.
Maximum Likelihood Image Registration With Subpixel Accuracy

The problem addressed in this paper is to estimate, with an error which is substantially less than the dimensions of a pixel, the unknown displacement d between two images of a common scene, given only the image data. Assuming a Gauss-Markov model for the scene, the joint probability density function of the two images is obtained and an implicit expression for the maximum likelihood estimate of the displacement is found as the maximum of a functional J(c1). The sensitivity of the algorithm to the model parameters has been determined by experiments on 32 real images. The experiments show that a mean absolute error of 1/20 of a pixel dimension is achievable for rms signal to rms noise ratios down to a value of 5.
Conceptual Models of Learning Material Development Based on the Spatial Perspective

The purpose of this research is to develop a conceptual model of learning material that based on the spatial perspective. The research use development design of the Borg and Goll with eight steps are: need analysis, planning, develop preliminary form of product, preliminary field testing, main product revision, playing field testing, revision of product, and the final product revision. The subjects of the research were student of the SMA 5 Malang. The result of the research shows that to develop learning material that based on the spatial perspective can be done with the following steps: (1) determining the topic of the problem, (2) determining the location and distribution of phenomena or object that is completed by map, (3) explaining the argumentation why phenomena or object occurs at that location, (4) explaining the relation of phenomena or objects that occur with other related natural factors, (5) using spatial analysis in accordance with
Distributed Hash Table: Theory, Platforms and Applications

This SpringerBrief summarizes the development of Distributed Hash Table in both academic and industrial fields. It covers the main theory, platforms and applications of this key part in distributed systems and applications, especially in large-scale distributed environments. The authors teach the principles of several popular DHT platforms that can solve practical problems such as load balance, multiple replicas, consistency and latency. They also propose DHT-based applications including multicast, anycast, distributed file systems, search, storage, content delivery network, file sharing and communication. These platforms and applications are used in both academic and commercials fields, making Distributed Hash Table a valuable resource for researchers and industry professionals.
The Role of Quantitative Analysis in the Information Security Systems Development Lifecycle

Today’s numerous Quantitative Analysis (QA) tools have been successfully utilized to solve business problems in diverse applications. However, the application of QA tools in solving information security problems has been sparse. Devising the means and ways to use QA tools in resolving industry-wide security problems has the potential to yield enormous global economic benefit. The purpose of this paper is to explore the use of QA tools as a means of improving the processes involved in the Information Security Systems Development Lifecycle (SecSDL). Information security professionals use the SecSDL as a guide for formulating a comprehensive information security program. The paper examines the fit between QA tools and the processes of the SecSDL. A case application illustrates an example of QA tools applied specifically to risk assessment in the SecSDL.
Non Linear Features Analysis between Imaginary and Non-imaginary Tasks for Human EEG-based Biometric Identification

Electroencephalogram (EEG) is a signal contains information of brain activities. Nowadays, many types of research regarding EEG have been done such as neuromarketing. The human brain is very complicated but it contains a lot of information. EEG signal is a non-stationary signal, it changes over time and it also depends on human’s emotion, thinking and activities. Due to the uniqueness of the EEG signal, the EEG signal has the potential to be used in the human authentication system. In this paper, an imaginary task and a non-imaginary task were studied to find out which type of task is possible to be used in authentication system. In preliminary study, five subjects were volunteered and performed the motor imagery and motor execution tasks. EEGOTM sports device (ANT Neuro, Enschede, Netherlands) with 32 channels was used to record the EEG signal and the sampling frequency is set to 512 Hz. The EEG signal was analysed by using EEG signal processing namely pre-processing, feature extraction and classification. Power line interference was removed by using a notch filter. Daubechies 8 wavelet family with 5th level decomposition has been applied to remove baseline wander noise. The performance of non-linear features such as Empirical Mode Decomposition (EMD), Hurst Exponent and Entropy were examined. Random forest gives good classification accuracy for imaginary task and non-imaginary task which are 83.53% and 87.06% respectively, thus, it shows non-linear features is possible to be employed in biometric identification.
Research on Gesture Recognition Based on Improved GBMR Segmentation and Multiple Feature Fusion

Aiming at addressing the problem of interactive gesture recognition between lunar robot and astronaut, a novel gesture detection and recognition algorithm is proposed. In gesture detection stage, based on saliency detection via Graph-Based Manifold Ranking (GBMR) algorithm, the depth information of foreground is added to the calculation of superpixel. By increasing the weight of connectivity domains in graph theory model, the foreground boundary is highlighted and the impact of background is weakened. In gesture recognition stage, Pyramid Histogram of Oriented Gradient (PHOG) feature and Gabor amplitude also phase feature of image samples are extracted. To highlight the Gabor amplitude feature, we propose a novel feature calculation by fusing feature in different directions at the same scale. Because of the strong classification capability and not-easy-to-fit advantage of Adaboosting, this paper applies it as the classifier to realize gesture recognition. Experimental results show that the improved gesture detection algorithm can maintain the robustness to influences of complex environment. Based on multi-feature fusion, the error rate of gesture recognition remains at about 4.2%, and the recognition rate is around 95.8%.
CONFLLVM: Compiler-Based Information Flow Control in Low-Level Code

We present a compiler-based scheme for information flow control in low-level applications (e.g. those written in C) in the presence of an active adversary. In our scheme, the programmer marks private data by writing lightweight annotations on the top-level definitions in the source code. The compiler then uses a combination of static dataflow analysis and runtime instrumentation to prevent data leaks even in the presence of low-level attacks. To keep the overheads of the instrumentation low, the compiler uses a novel memory layout and a taint-aware form of control flow integrity. We formalize our scheme and prove its security. We have also implemented our scheme within the LLVM compiler and evaluated it on the CPU-intensive SPEC micro-benchmarks, and on larger, real-world applications, including the NGINX webserver and the OpenLDAP directory server. We find that performance overheads introduced by our instrumentation are moderate (average 12% on SPEC), and the programmer effort to port the applications is minimal.
Combinatorial Drug Design Approach for the Discovery of Potential Inhibitors against Influenza Neuraminidase

The frequent emergence, in recent years, of strains of influenza virus which are resistant to current treatment regimens, poses a devastating problem to world health, particularly in developing nations such as South Africa. This necessitates the discovery and development of novel therapeutic agents. This study employs computational methods to identify compounds which could exhibit potent inhibitory activity against neuraminidase, a crucial enzyme in the Influenza virus, with a preferably greater inhibitory activity than a known neuraminidase inhibitor, Zanamivir. Molecular docking and virtual screening were employed to identify hits that were structurally similar to Zanamivir. Potential hits were retrieved from the DrugBank database for pharmacokinetic analysis using the online software, admetSAR. Three compounds were identified after the virtual screening. Pharmacokinetic properties revealed that all three compounds adhered to the Lipinski rule of five and were structurally similar to Zanamivir. Molecular docking of all three compounds into the binding site of neuraminidase showed that compound DB03321 had better binding interactions when compared to the rest of the compounds. DB03321 formed a greater number of hydrogen bonds with the residues of the active site with the highest binding affinity of -7.9kcal/mol, when compared to other compounds obtained through screening. DB03321 showcased preferable interactions with binding site residues of neuraminidase relative to Zanamivir. These findings could serve as a step forward in the development of novel inhibitors of influenza neuraminidase, which could potentially act with greater potency than currently available therapeutic agents.
A Human Body Motion Capture System Using a Wireless Inertial Sensor

— In this study, a wireless inertial sensor system was designed for application in human motion capture. A human body node displacement and attitude data decoding strategy is proposed based on the gait analysis method, synthesizing the advantages of the complementary filter and Kalman filter methods. A human body motion capture verification platform was set up, and the performance of the wireless inertia sensor network capture system was evaluated. Results show that the designed wireless inertial sensor system can accurately measure joints in the process of human movement in real-time. The proposed attitude algorithm strategy has better accuracy in precise real-time tracking of human motion.