Hukum Responsif: Hukum sebagai Institusi Sosial Melayani Kebutuhan Sosial dalam Masa Transisi

The purpose of this paper is to analyze more deeply responsive legal concepts developed by Nonet and Selznick, the differences between the types of responsive law to the type of autonomous laws and law as a social institutions that serve social needs in transition. The results obtained, responsive law types have prominent features, namely: a. The shift in emphasis from rules to principles and objectives; b. The importance of the character of populist either as a law purpose and how to achieve it. The main characteristics of an autonomous law types are: a. The emphasis on the rule of law as a major effort to oversee the formal and informal power. b. Free trial. c. Separation of law from politics. d. The Court can not guarantee but may seek the law is just. The law is a social institution, viewed more than a mere regulatory system and in transition meet social needs

Accountability of Village Fund Management: Case Study in Bulusuka Village, Jeneponto Regency

This study aimed to describe and analyze the accountability of Village fund management in terms of the dimensions of transparency, liability, control, responsibility, and responsiveness in Jeneponto Regency, Bontoramba District, Bulusuka Village. This research is a descriptive study using a qualitative approach that is phenomenological. Bontoramba Jeneponto Regency, South Sulawesi Province. Research informants included the internal village government, Bontoramba District Head, Regional Examination Team (TPD), Academics and NGOs. Data is collected through observation, interviews and documentaries, while data analysis is done through data collection, reduction, presentation and verification / conclusion. The results showed that the accountability of Village Fund management in terms of the dimensions of transparency, liability, control, responsibility, and responsiveness were not fully maximally managed accountably. The process of managing village funds in an accountable and transparent manner through the Musdus (hamlet deliberation) and Musdes (village deliberation) of budget designs and development programs and deliberations on budget realization and development program realization by installing information boards and billboards in public places, so that the public knows the process of managing village funds universally

Accelerator-Based Human Activity Recognition Using Voting Technique with NBTree and MLP Classifiers

In evolution and ubiquitous computing systems, accelerometer-based human activity recognition has huge potential in a large number of application domains. Accelerometer-based human activity recognition aims to identify physical activities performed by human using accelerometer; a sensor device attached to the body and returns an actual valued estimate of acceleration along the x-, y- and z-axes from which the sensor location can be estimated. In this study, an accelerator-based activity recognition model using voting technique was proposed. Two machine learning classifiers, Naïve Bayes Tree (NBTree) and Multilayer Perceptron (MLP), were used as ensemble classifiers in the voting technique. To evaluate the proposed voting technique, the performance of selected individual classifiers and existing voting technique was first examined, followed by the experiment to determine the performance of the proposed model. All of the experiments were performed using a standard dataset called Wireless Sensor Data Mining involving six physical human activities; jogging, walking, walking towards upstairs, walking towards downstairs, sitting and stand still. Results showed that the proposed voting technique with NBTree and MLP ensemble classifiers outperformed other individual classifiers and another previously suggested voting technique for accelerometer-based human activity recognition

Predictors of lymph node involvement in bladder cancer treated with radical cystectomy

Objective: To identify the clinical variables associated with prevalence of lymph node metastasis in patients with bladder cancer treated by radical cystectomy and lymphadenectomy for primary bladder cancer. Methods: Review of records of Ninety-five patients who underwent radical cystectomy and pelvic lymph node (LN) dissection during the period of 1995-2008 from a prospectively maintained database. Eighteen patients were excluded due to lack of data on the nodal status, leaving 77 evaluable patients. Associations between LN metastasis and age, gender, duration of disease, number of transurethral resection (TUR) prior to cystectomy, pathological grade and tumour stage was analyzed. Data was analyzed using the SPSS software, version 15. Statistical tests applied were independent sample t test or the Mann Whitney U test, the chi-square test and the Fischer exact test. Results: The median age of the patients was 58 years in lymph node negative group and 63 years in lymph node positive group. There were 87% males and 13% females. LN metastasis was detected in 19 (25%) patients. Mean duration of disease in LN negative patients was 537 +/- 997 days compared to 509 +/- 708 days in LN positive patients. Mean number of TUR were same in both the groups, pathological grade was not found significantly different in both groups, where as primary tumour stage was found to be significantly (p \u3c 0.05) higher in LN positive patients. Conclusions: Higher primary tumour stage at radical cystectomy is associated with higher prevalence of lymph node metastasis

Improving anomalous rare attack detection rate for intrusion detection system using support vector machine and genetic programming

Commonly addressed problem in intrusion detection system (IDS) research works that employed NSL-KDD dataset is to improve the rare attacks detection rate. However, some of the rare attacks are hard to be recognized by the IDS model due to their patterns are totally missing from the training set, hence, reducing the rare attacks detection rate. This problem of missing rare attacks can be defined as anomalous rare attacks and hardly been solved in IDS literature. Hence, in this letter, we proposed a new classifier to improve the anomalous attacks detection rate based on support vector machine (SVM) and genetic programming (GP). Based on the experimental results, our classifier, GPSVM, managed to get higher detection rate on the anomalous rare attacks, without significant reduction on the overall accuracy. This is because, GPSVM optimization task is to ensure the accuracy is balanced between classes without reducing the generalization property of SVM

A fuzzy logic approach to manage uncertainty and improve the prediction accuracy in student model design

The intelligent tutoring systems (ITSs) are special classes of e-learning systems developed using artificial intelligent (AI) techniques to provide adaptive and personalized tutoring based on the individuality of each student. For an intelligent tutoring system to provide an interactive and adaptive assistance to students, it is important that the system knows something about the current knowledge state of each student and what learning goal he/she is trying to achieve. In other words, the ITS needs to perform two important tasks, to investigate and find out what knowledge the student has and at the same time make a plan to identify what learning objective the student intends to achieve at the end of a learning session. Both of these processes are modeling tasks that involve high level of uncertainty especially in situations where students are made to follow different reasoning paths and are not allowed to express the outcome of those reasoning in an explicit manner. The main goal of this paper is to employ the use Fuzzy logic technique as an effective and sound computational intelligence formalism to handle reasoning under uncertainty which is one major issue of great concern in student model design

Solving classification problem using ensemble binarization classifier

Binarization strategy is broadly applied in solving various multi-class classification problems. However, the classifier model learning complexity tends to increase when expanding the number of problems into several replicas. One-Versus-All (OVA) is one of the strategies which transforming the ordinal multi-class classification problems into a series of two-class classification problems. The final output from each classifier model is combined in order to produce the final prediction. This binarization strategy has been proven as superior performance in accuracy than ordinal multi-class classifier model. However, learning model complexity (eg. Random Forest-RF ensemble decision trees) tends to increase when employing a large number of trees. Even though a large number of trees might produce a decent accuracy, generating time of the learning model is significantly longer. Hence, self-tuning tree parameter is introduced to tackle this matter. In such circumstances, a number of trees in the RF classifier are defined according to the number of class problem. In this paper, the OVA with self-tuning is evaluated based on parameter initialization in the context of RF ensemble decision tree. At the same time, the performance has also been compared with two classifier models such J48 and boosting for several well-known datasets

Patient\u27s outcome of bladder cancer managed by radical cystectomy with lymphadenectomy at a university hospital

Objective: To study the impact of tumour staging and nodal metastases in predicting 5- year\u27s survival after radical cystectomy and bilateral pelvic lymphadenectomy for primary bladder cancer. Methods: During the period 1995 to 2005, 58 patients underwent radical cystectomy and bilateral pelvic lymphadenectomy and urinary diversion at a University hospital. Patients were identified using medical indexing coding system (ICD 9CM) using standard key words. The patient records were analyzed and follow up data updated. Disease specific survival, death or recurrence was taken as end point.Results: Out of 58 patients, 50 (86%) were males and 8 (14%) females with a mean age of 61 +/- 13.1 years (range from 27 to 87 years). Of 58 patients, 11 (23%) were excluded from the study because of in adequate follow up. The mean follow up was 5.7 years (range, 7 months to 11 years). The overall 5 years survival was 55% with disease specific survival being 66%. Patients with pathological stage TO at cystectomy have 87% 5 years disease specific survival compared to 60%, in patients with pT4 (p = 0.705). The 5-year survival for node positive patients was 16%, compared to 60% for node negative patients (p \u3c 0.01). Conclusions: Radical cystectomy and bilateral pelvic lymphadenectomy is the standard treatment for muscle invasive and high grade T1 cancers, and as salvage for recurrent cancers. Lymphadenectomy has a potential therapeutic benefit. The pathological stage at cystectomy and nodal status are predictors of 5 years survival

Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity

Widespread research on activity recognition is becoming an imperative topic for improving the quality of human health. The fast development of sensing technology has become a fundamental platform for researchers to implement a system that could fulfill human needs. Due to privacy interests and low cost, wearable sensing technology is used in numerous physical activity monitoring and recognition systems. While these systems have proved to be successful, it is crucial to pay attention to the less relevant features to be classified. In such circumstances, it might happen that some features are less meaningful for describing the activity. Less complex and easy to understand, feature ranking is gaining a lot of attention in most feature dimension problems such as in bioinformatics and hyperspectral images. However, the improvement of ranking features in activity recognition has not yet been achieved. On the other hand, an evolutionary algorithm has proven its effectiveness in searching the best feature subsets. An exhaustive searching process of finding an optimal parameter value is another challenge. Consequently, this paper proposes a ranking self-adaptive differential evolution (rsaDE) feature selection algorithm. The proposed algorithm is capable of selecting the optimal feature subsets while improving the recognition of acceleration activity using a minimum number of features. The experiments employed real-world physical acceleration data sets: WISDM and PAMAP2. As a result, rsaDE performed better than the current methods in terms of model performance and its efficiency in the context of random forest ensemble classifiers