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Nutmeg encounters challenges in productivity, quality, climate change, Covid-19 disruptions, and stagflation, requiring efforts to enhance competitiveness and meet global market demands. This study aims to analyze Indonesian nutmeg trade performance, assess competitive advantage, and formulate essential strategies to increase its competitiveness. Using comprehensive mixed quantitative and qualitative analysis, methods include export-import market share analysis, CAGR, import dependency ratio, import-to-export ratio, RCA, RSCA, RTA, and SWOT/QSPM analysis. The data obtained from in-depth interviews with key stakeholders was used for formulating strategies enhancing nutmeg's competitiveness. The findings underscore Indonesia's significant dominance in the global nutmeg market, with a considerable 61.6% market share but its export growth rate for nutmeg was less than the import annual growth. The competitiveness indicators demonstrated a robust comparative advantage of Indonesian nutmeg trade in the global market, especially for products categorized as "nutmeg, neither crushed nor ground" and "nutmeg, crushed or ground". Indonesian also has comparative advantage among other exporter countries. Crucial strategies of internal and external perspectives, such as expanding markets, improving nutmeg quality and productivity through improving farmer capacity, technological adoption, and good agricultural practices are necessary to take into account Indonesia's nutmeg competitiveness in global markets. [ABSTRACT FROM AUTHOR]

The collaborative intrusion detection network (CIDN) framework provides collaboration capability among intrusion detection systems (IDS). Collaboration selection is done by an acquaintance management algorithm. A recent study developed an effective acquaintance management algorithm by the use of binary risk analysis and greedy-selection-sort based methods. However, most algorithms do not pay attention to the possibility of wrong responses in multibotnet attacks. The greedy-based acquaintance management algorithm also leads to a poor acquaintance selection processing time when there is a high number of IDS candidates. The growing number of advanced distributed denial of service (DDoS) attacks make acquaintance management potentially end up with an unreliable CIDN acquaintance list, resulting in low decision accuracy. This paper proposes an acquaintance management algorithm based on multi-class risk-cost analysis and merge-sort selection methods. The algorithm implements merge riskordered selection to reduce computation complexity. The simulation result showed the reliability of CIDN in reducing the acquaintance selection processing time decreased and increasing the decision accuracy. [ABSTRACT FROM AUTHOR]

The voting process is an essential part of the democratic system. As the size and breadth of voter distribution grow, the social aspects become more complex and the need to manage the voting process more efficiently and more quickly increases. This makes electronic voting (e-voting) a more interesting alternative voting technology. The voting confidence level highly depends on the ability of the system to protect the votes until the end of the process. Parameters in e-voting consist of accuracy, invulnerability, privacy, and verifiability, where verifiability is currently the most important factor in attempts to further improve the quality of e-voting technology. The availability of verifiability properties gives the voters confidence that the voting system used provides protection, both to the votes cast and to the identity of the voters themselves. Individual verifiability refers to the verifiability properties of the voting system that should be able to accommodate the needs of the voters. In this paper, an e-voting system that accommodates the individual verifiability requirements and a method for measuring the degree of individual verifiability are proposed. [ABSTRACT FROM AUTHOR]

An Intrusion Detection System (IDS) which implement a group-based classification algorithm, theoretically has the benefit of higher accuracy. Unfortunately, higher accuracy only achieved if the observed group is homogeneous from a certain distribution. Recently, a distributed denial of service (DDoS) attack consists of multiple botnets which produce multi types of traffic in one attack session. It makes the IDS suffers from decreasing accuracy as the increasing heterogeneity within the observed group. To address the problem, we propose homogeneous grouping algorithm based on triangle area Mahalanobis distance to support IDS which implement group-based data analysis. First, the Mahalanobis distance measurement was used to construct homogeneous groups. Then, the covariance matrix of each homogeneous group was classified using a decision tree classifier. Classification performance was evaluated using known KDDCup 99 dataset. The results pointed out that the used of homogeneous grouping algorithm improve the classification performance for natural and mixed random DDoS traffic. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDS) play an important role in information security. Two major problems in the development of IDSs are the computational aspect and the architectural aspect. The computational or algorithmic problems include lacking ability of novel-attack detection and computation overload caused by large data traffic. The architectural problems are related to the communication between components of detection, including difficulties to overcome distributed and coordinated attacks because of the need of large amounts of distributed information and synchronization between detection components. This paper proposes a multi-agent architecture for a distributed intrusion detection system (DIDS) based on ant-colony clustering (ACC), for recognizing new and coordinated attacks, handling large data traffic, synchronization, co-operation between components without the presence of centralized computation, and good detection performance in real-time with immediate alarm notification. Feature selection based on principal component analysis (PCA) is used for dimensional reduction of NSL-KDD. Initial features are transformed to new features in smaller dimensions, where probing attacks (Ra-Probe) have a characteristic sign in their average value that is different from that of normal activity. Selection is based on the characteristics of these factors, resulting in a two-dimensional subset of the 75% data reduction. [ABSTRACT FROM AUTHOR]

Cryptography is an important tool in e-commerce because it allows the cryptographic protection of electronic information in an e-commerce. The effectiveness of this protection depends on many variables such as length of the key, cryptographic protocols and the selection of passwords. This paper specifically discusses the selection of the keylength for elliptic curve cryptosystems. Selection of keylength is an important factor in determining the security level of the cryptography used. In this paper we discuss the determination of the key length for public key cryptography based on a set of parameters which are explicitly formulated. Another factor that determines the key length is also discussed in this paper such as the effort and costs incurred for the attack against elliptic curve cryptosystem. [ABSTRACT FROM AUTHOR]

The microcomputer system for verbal behavior analysis is built on the basis of the IBM-PC. It consists of two parts: hardware and software. The hardware is the condenser microphone and a two-channel ADC board, including: amplifiers with adjustable gain; two Bessel analog filters with cutoff frequencies of 800 Hz and 6 kHz; and two ADCs allowing sampling rate up to 40 kHz. The software is a program package which allows high precision pitch detection by means of the autocorrelaton function; finding the separate pitch periods by means of amplitude selection and parabolic interpolation; pitch period perturbation; number of subharmonic-to-fundamental frequency power; separation of segments where the signal is strictly periodic; realization of spectral analysis over these segments by means of fast Fourier and fast Walsh transforms; and evaluation of the degree of hoarseness from the spectra. Preliminary experimental research shows that the system may be very useful for analysis of the speech of patients with neurological and laryngeal diseases as well as the analysis for musical sounds. Results from analysis of pathologic voices agree with those of Kasuya et al. ['Preliminary Experiments on Voice Screening,' in Proceedings of the Symposium on Voice Acoustics and Dysphonia, Gotland, Sweden (August 1985)] and Laver et al. ['An Acoustic Screening System for the Detection of Laryngeal Pathology,' J. Phon. 14, 517-524 (1986)]. [ABSTRACT FROM AUTHOR]