Device Fingerprinting with Magnetic Induction Signals Radiated by CPU Modules

With the widespread use of smart devices, device authentication has received much attention. One popular method for device authentication is to utilize internally measured device fingerprints, such as device ID, software or hardware-based characteristics. In this article, we propose DeMiCPU , a stimulation-response-based device fingerprinting technique that relies on externally measured information, i.e., magnetic induction (MI) signals emitted from the CPU module that consists of the CPU chip and its affiliated power-supply circuits. The key insight of DeMiCPU is that hardware discrepancies essentially exist among CPU modules and thus the corresponding MI signals make promising device fingerprints, which are difficult to be modified or mimicked. We design a stimulation and a discrepancy extraction scheme and evaluate them with 90 mobile devices, including 70 laptops (among which 30 are of totally identical CPU and operating system) and 20 smartphones. The results show that DeMiCPU can achieve 99.7% precision and recall on average, and 99.8% precision and recall for the 30 identical devices, with a fingerprinting time of 0.6~s. The performance can be further improved to 99.9% with multi-round fingerprinting. In addition, we implement a prototype of DeMiCPU docker, which can effectively reduce the requirement of test points and enlarge the fingerprinting area.

Sustainable Amelioration of Heavy Metals in Soil Ecosystem: Existing Developments to Emerging Trends

The consequences of heavy metal contamination are progressively degrading soil quality in this modern period of industry. Due to this reason, improvement of the soil quality is necessary. Remediation is a method of removing pollutants from the root zone of plants in order to minimize stress and increase yield of plants grown in it. The use of plants to remove toxins from the soil, such as heavy metals, trace elements, organic chemicals, and radioactive substances, is referred to as bioremediation. Biochar and fly ash techniques are also studied for effectiveness in improving the quality of contaminated soil. This review compiles amelioration technologies and how they are used in the field. It also explains how nanoparticles are becoming a popular method of desalination, as well as how they can be employed in heavy metal phytoremediation.

ANALISIS BIBLIOMETRIK: PERKEMBANGAN PENELITIAN DAN PUBLIKASI MENGENAI KOORDINASI PROGRAM MENGGUNAKAN VOSVIEWER

Bibliometric analysis is a popular method for exploring and analyzing large amounts of scientific data. This article is a bibliometric analysis using Scopus indexed journals on program coordination using VOSviewer application. The purpose of bibliometric analysis in this research is to identify the development of studies related to program coordination. Specifically, this article discusses citation developments, publications trends, authors’ collaboration, trending term titles, trend term author keywords, trend term abstracts, and country statistics with the topic of program coordination 1943-2021. The data was collected from Scopus database using keyword “coordination program.” Furthermore, the author uses VOSviewer software to analyze and visualize the database obtained. The results showed that the results of the citation analysis showed the number of citations per year from 1943-2021 was 4.304 citations. Irvine, M.K is a researcher who has the largest productivity, which is ten publications. In the analysis of the trend term author keywords, it can be seen that there are 676 author keys used in selected articles using a minimum of five events, resulting nine author keywords that have a strong connection. The term “care coordination” is the term most frequently used by the author with 23 entries. Moreover, the country that published the most program coordination articles was the United States with 246 articles.

Chronology of the Basalt Units Surrounding Chang’e-4 Landing Area

The Chang’e-4 (CE-4) lunar probe, the first soft landing spacecraft on the far side of the Moon, successfully landed in the Von Kármán crater on 3 January 2019. Geological studies of the landing area have been conducted and more intensive studies will be carried out with the in situ measured data. The chronological study of the maria basalt surrounding the CE-4 landing area is significant to the related studies. Currently, the crater size-frequency distribution (CSFD) technique is the most popular method to derive absolute model ages (AMAs) of geological units where no returned sample is available, and it has been widely used in dating maria basalt on the lunar surface. In this research, we first make a mosaic with multi-orbital Chang’e-2 (CE-2) images as a base map. Coupled with the elevation data and FeO content, nine representative areas of basalt units surrounding the CE-4 landing area are outlined and their AMAs are derived. The dating results of the nine basalt units indicate that the basalts erupted from 3.42 to 2.28 Ga ago in this area, a period much longer than derived by previous studies. The derived chronology of the above basalt units establishes a foundation for geological analysis of the returned CE-4 data.

Strategic Unification of Artificial Intelligence in Foreign Direct Investment Application Forms

A foreign direct investment (FDI) is a very popular method of investing overseas but different from a stock investment in a foreign company. It could be purchasing of an interest in a company by an investor located outside its borders and in most cases, governments pay special interest on them. This is a business decision to acquire a substantial stake in a foreign business or to buy it outright as to expand its operations to a new region. Embedding artificial intelligence (AI) across the business requires significant investment and a change in overall approach. It is highly constructive and productive transformation that should be planned professionally, applied systematically, and managed strategically. AI drives meaningful value to business through better decision-making and consumer-facing applications. The general perception about filling a FDI application is a cumbersome job. Some countries manage this stage very methodically and investors always give priority for them as they can commence the production/business activities within a short period. Those countries who fail to gain this competitive advantage tend to lose the FDI opportunities even if they own various other advantages of resources to attract investors. This paper attempts to evaluate the potential of embedding a strategic unification of artificial intelligence in the application forms used to fill by investors at the time of starting foreign direct investment projects.

Customer Segmentation Using Machine Learning

Nowadays Customer segmentation became very popular method for dividing company’s customers for retaining customers and making profit out of them, in the following study customers of different of organizations are classified on the basis of their behavioral characteristics such as spending and income, by taking behavioral aspects into consideration makes these methods an efficient one as compares to others. For this classification a machine algorithm named as k-means clustering algorithm is used and based on the behavioral characteristic’s customers are classified. Formed clusters help the company to target individual customer and advertise the content to them through marketing campaign and social media sites which they are really interested in.

ANALISIS RISIKO PORTOFOLIO MENGGUNAKAN METODE SIMULASI MONTE CARLO CONTROL VARIATES

Value at Risk (VaR) is a method to measure the maximum loss with a certain level of confidence in a certain period. Monte Carlo simulation is the most popular method of calculating VaR. The purpose of this study is to demonstrate control variates method as a variance reduction method that can be applied to estimate VaR. Moreover, it is to compare the results with the normal VaR method or analytical VaR calculation. Control variates method was used to find new returns from all stocks which are used as estimators of the control variates. The new returns were then used to define parameters needed to generate N random numbers. Furthermore, the generated numbers were used to find the VaR value. The method was then applied to estimate a portfolio of the game and esports company stocks that are EA, TTWO, AESE, TCEHY, and ATVI . The results show Monte Carlo simulation gives VaR of US$41.6428 within 1000 simulation, while the analytical VaR calculation or normal VaR method gives US$30.0949.

Effect of Rotten Butter Shock Load on Anaerobic Digestion of Chicken Manure

Anaerobic digestion is a popular method for improving fertilizing properties, but there is no report on the effect of shock load with butter on anaerobic digestion of chicken manure. Therefore, this study aimed to investigate the anaerobic digestion of chicken manure with butter addition. The volatile suspended solid (VSS) was set at 20g VSS/L with different butter additions from 0 to 60 g VSS/L and different oxygen flow rate (OFR) from 0 to 2.5 mL/h. The results showed that ammonia ranged from 0.072 g/L to 0.082 g/L, while the volatile acids ranged from 425 mg/L to 325 mg/L. The volatile organic acid was significantly influenced by a change in OFR compared to ammonia, while a correlation between hydrogen and hydrogen sulfide was observed. The results showed that the highest hydrogen and methane production was obtained at butter addition of 30 g VSS/L with OFR 1.4 mL/h with volumes of 78 mL and 25 L respectively. In addition, hydrogen sulfide emissions induced rapid growth with increase in butter concentration.

Study on Human Temperature Measurement by Infrared Thermography

Increased temperature in humans is one of symptoms of infectious diseases. Infrared thermography is a popular method for measuring temperature as it offers fast and non-contact temperature measurement. However, and despite many advantages, its real accuracy for human temperature measurement is not sufficient in many cases. This study was focused on a statistical evaluation of human temperature measurement reliability. The goal of the experiment was to find limitations of thermography at near-laboratory conditions. More than 300 measurements were made simultaneously by a thermography and an arm-pit thermometer on a closed group of persons during several months. The results showed that standard deviations of the performed armpit and thermographic temperature measurement were about 0.15 and 0.36 °C, respectively, but that a temperature shift and a dependence on ambient conditions can occur due to the used experimental configuration.

Sequence-to-Sequence Acoustic Modeling with Semi-Stepwise Monotonic Attention for Speech Synthesis

An encoder–decoder with attention has become a popular method to achieve sequence-to-sequence (Seq2Seq) acoustic modeling for speech synthesis. To improve the robustness of the attention mechanism, methods utilizing the monotonic alignment between phone sequences and acoustic feature sequences have been proposed, such as stepwise monotonic attention (SMA). However, the phone sequences derived by grapheme-to-phoneme (G2P) conversion may not contain the pauses at the phrase boundaries in utterances, which challenges the assumption of strictly stepwise alignment in SMA. Therefore, this paper proposes to insert hidden states into phone sequences to deal with the situation that pauses are not provided explicitly, and designs a semi-stepwise monotonic attention (SSMA) to model these inserted hidden states. In this method, hidden states are introduced that absorb the pause segments in utterances in an unsupervised way. Thus, the attention at each decoding frame has three options, moving forward to the next phone, staying at the same phone, or jumping to a hidden state. Experimental results show that SSMA can achieve better naturalness of synthetic speech than SMA when phrase boundaries are not available. Moreover, the pause positions derived from the alignment paths of SSMA matched the manually labeled phrase boundaries quite well.