ANALISIS PERTANDINGAN BULUTANGKIS ANTAR MAHASISWA (STUDI PADA PERTANDINGAN BULUTANGKIS FAKULTAS ILMU OLAHRAGA DALAM TURNAMEN DIES NATALIS UNESA KE-55 TAHUN 2019)

ABSTRAK Dalam penelitian ini bertujuan untuk mengetahui prosentase bagaimana cara pemain melakukan teknik pukulan yang digunakan dalam pertandingan, melakukan teknik pukulan menghasilkan point, melakukan unforced error dan error dan pada pertandingan setengah kompetisi bulutangkis beregu Fakultas Ilmu Olahraga dalam turnamen Dies Natalis Unesa ke-55 tahun 2019. Pada kategori beregu ganda putra dan ganda campuran. Jenis penelitian yang digunakan yaitu metode kuantitatif deskriptif analisis. Hasil yang didapatkan dari analisis video yaitu Prosentase teknik pukulan yang digunakan pada pertandingan yaitu dari short service 24%, dan drive 17%. Teknik pukulan yang memiliki tingkat keberhasilan tertinggi menghasilkan point dari smash sebanyak 54% dan drive 14%. Unforced Error tertinggi pada teknik drive 12% dan Error tertinggi pada teknik smash 21%. Kata Kunci : Analisis, teknik pukulan yang digunakan, teknik pukulan menghasilkan point, unforced error dan error pukulan, Bulutangki

BUKTI TIDAK LANGSUNG SEBAGAI DASAR HAKIM MENJATUHKAN PIDANA (Putusan Nomor: 777/Pid.B/2016/PN.JKT.PST)

Salah satu dasar hakim menjatuhkan pidana dalam putusan nomor: 777/Pid.B/2016/PN.JKT.PST., menyebutkan bahwa hakim dapat menggunakan bukti tidak langsung atau circumstantial evidence dalam hal tidak ditemukannya saksi mata yang melihat pembunuhan tersebut. Hal ini bertentangan dengan KUHAP tepatnya Pasal 183 jo 184 ayat (1). Circumstantial evidence tidak dikenal dalam KUHAP. Putusan ini menimbulkan konflik norma, kekaburan hukum dan ketidakpastian hukum serta melanggar HAM Terdakwa. Rumusan masalah 1. Bagaimana kedudukan bukti tidak langsung sebagai dasar hakim menjatuhkan pidana?, 2. Bagaimana penerapan bukti tidak langsung sebagai dasar hakim menjatuhkan pidana?. Menggunakan penelitian hukum normatif dengan metode pendekatan perundang-undangan, pendekatan konsep, dan pendekatan kasus  yang didukung dengan teknik analisis preskriptif. Hasil penelitian ini, kedudukan bukti tidak langsung yang digunakan hakim menjatuhkan pidana hanya sebagai doktrin dari ahli hukum. Penerapan bukti tidak langsung dalam perkara nomor: 777/Pid.B/2016/ PN.JKT.PST, tidak dapat dibenarkan karena tidak dikenal dalam KUHAP

RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture

Deep Learning (DL) based methods have shown great promise in network intrusion detection by identifying malicious network traffic behavior patterns with high accuracy, but their applications to real-time, packet-level detections in high-speed communication networks are challenging due to the high computation time and resource requirements of Deep Neural Networks (DNNs), as well as lack of explainability. To this end, we propose a packet-level network intrusion detection solution that makes novel use of Recurrent Autoencoders to integrate an arbitrary-length sequence of packets into a more compact joint feature embedding, which is fed into a DNN-based classifier. To enable explainability and support real-time detections at micro-second speed, we further develop a Software-Hardware Co-Design approach to efficiently realize the proposed solution by converting the learned detection policies into decision trees and implementing them using an emerging architecture based on memristor devices. By jointly optimizing associated software and hardware constraints, we show that our approach leads to an extremely efficient, real-time solution with high detection accuracy at the packet level. Evaluation results on real-world datasets (e.g., UNSW and CIC-IDS datasets) demonstrate nearly three-nines detection accuracy with a substantial speedup of nearly four orders of magnitude

Interactive Time-Series of Measures for Exploring Dynamic Networks

International audienceWe present MeasureFlow, an interface to visually and interactively explore dynamic networks through time-series of network measures such as link number, graph density, or node activation. When networks contain many time steps, become large and more dense, or contain high frequencies of change, traditional visualizations that focus on network topology, such as animations or small multiples , fail to provide adequate overviews and thus fail to guide the analyst towards interesting time points and periods. Measure-Flow presents a complementary approach that relies on visualizing time-series of common network measures to provide a detailed yet comprehensive overview of when changes are happening and which network measures they involve. As dynamic networks undergo changes of varying rates and characteristics, network measures provide important hints on the pace and nature of their evolution and can guide an analysts in their exploration; based on a set of interactive and signal-processing methods, MeasureFlow allows an analyst to select and navigate periods of interest in the network. We demonstrate MeasureFlow through case studies with real-world data

Real-time Network Intrusion Detection via Decision Transformers

Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e.g., network intrusion detection from a sequence of arriving packets. Existing approaches like reinforcement learning may not be suitable for such cybersecurity decision problems, since the Markovian property may not necessarily hold and the underlying network states are often not observable. In this paper, we cast the problem of real-time network intrusion detection as casual sequence modeling and draw upon the power of the transformer architecture for real-time decision-making. By conditioning a causal decision transformer on past trajectories, consisting of the rewards, network packets, and detection decisions, our proposed framework will generate future detection decisions to achieve the desired return. It enables decision transformers to be applied to real-time network intrusion detection, as well as a novel tradeoff between the accuracy and timeliness of detection. The proposed solution is evaluated on public network intrusion detection datasets and outperforms several baseline algorithms using reinforcement learning and sequence modeling, in terms of detection accuracy and timeliness

Solid State Joining of a Cold Rolled Zr-Based Bulk Metallic Glass to a Wrought Aluminum Alloy by Power Ultrasonics

Ultrasonic metal welding (UMW) enables joining in the solid state at relative low temperatures with short cycle times. This technique is of particular interest for joining metallic glasses to each other or to other materials, because crystallization of the amorphous structure can be prevented due to the low thermal loading and the rapidity of the process. In this work, UMW is applied to join one 1 mm thick sheet of a commercial wrought aluminum alloy (AA5754) and one 0.4 mm thick strip of a commercial Zr-based bulk metallic glass (AMZ4). The introduced heat of the welding process is detected with thermocouples and thermal imaging. To investigate the strength of the joint and the influence on the microstructure, mechanical tensile tests are carried out in combination with scanning electron microscopy and differential scanning calorimetry. The results show that ultrasonic metal welding is a suitable technique to join amorphous bulk metallic glasses to crystalline aluminum alloys. The metallic glass component retains its amorphous structure in the joint, and the joint strength is higher than the strength of the Al sheet. These findings will help to develop future applications of BMG-based multi-material components, including medical tools

Development and optimization of novel sulfur-containing Ti-based bulk metallic glasses and the correlation between primarily crystallizing phases, thermal stability and mechanical properties

The effect of sulfur on the glass forming ability, thermal stability and mechanical properties of the eutectic alloy Ti33.4Zr33.3Cu33.3 was investigated by conventional X-ray diffraction, differential scanning calorimetry and 3-point flexural experiments. A novel region of bulk glass formation with a critical casting diameter of up to 4 mm was found in the quaternary Ti-Zr-Cu-S system, however, brittle fracture behavior was predominant. Various alloying strategies were employed to improve mechanical properties and a compositional transition from brittle to ductile fracture has been identified (e.g. for Ti36Zr33.5Cu24.5S6). A change of the primary precipitating phases from a C14 Laves to an intermetallic (Ti,Zr)2Cu phase can be observed, as well as a stabilization of the supercooled liquid. The origin of the thermally unstable behavior in Ti-based bulk metallic glasses is traced back to the easy formation of the icosahedral phase upon heating, which is structurally close to the supposedly predominant icosahedral short-range order in the amorphous state. The systematic study carried out in this work indicates a strong correlation between primary crystallizing phase and thermal stability, both pointing to the frozen short-range order in the amorphous state which is predetermining the mechanical properties. The transition from the Laves to the intermetallic (Ti,Zr)2Cu phase as well as the enlarged supercooled liquid region appear to be directly related to a destabilization of the icosahedral short-range order and ultimately to the improved mechanical properties