Explainable AI with counterfactual paths

Explainable AI (XAI) is an increasingly important area of research in machine learning, which in principle aims to make black-box models transparent and interpretable. In this paper, we propose a novel approach to XAI that uses counterfactual paths generated by conditional permutations. Our method provides counterfactual explanations by identifying alternative paths that could have led to different outcomes. The proposed method is particularly suitable for generating explanations based on counterfactual paths in knowledge graphs. By examining hypothetical changes to the input data in the knowledge graph, we can systematically validate the behaviour of the model and examine the features or combination of features that are most important to the model's predictions. Our approach provides a more intuitive and interpretable explanation for the model's behaviour than traditional feature weighting methods and can help identify and mitigate biases in the model

Kekerabatan Genetik Anakan Alam Ulin (Eusideroxylon Zwageri Teijsm. & Binn.) Menggunakan Penanda Random Amplified Polymorphism Dna

The study aimed to assess genetic diversity and genetic relationship of ulin wildlings randomly collected from a nursery and originated from Bukit Soeharto natural forest, East Kalimantan. DNA templates were extracted from leaf samples of 1.5 years old wildings. Five RAPD primers consisted 55 polymorphic loci were used for genetic studies. Genetic diversity and relationship were analyzed using GenAlex software. The results showed moderate mean value of genetic diversity (HE=0,345, SE 0,015) of the wildings. Forty eight wildings were clustered in only 3 groups; almost all wildings (65%) were clustered in one main cluster. Moreover, 4 wildlings were clones (8%). In conclusion, the 48 wildings of ulin consisted high genetic relationship and individual clones that reflects the low genetic diversity of this species

Genetic Relationship of Ulin (Eusideroxylon Zwageri Teijsm. &Binn.) Wildlings Using Random Amplified Polymorphism Dna Markers

The study aimed to assess genetic diversity and genetic relationship of ulin wildlings randomly collected from a nursery and originated from Bukit Soeharto natural forest, East Kalimantan. DNA templates were extracted from leaf samples of 1.5 years old wildings. Five RAPD primers consisted 55 polymorphic loci were used for genetic studies. Genetic diversity and relationship were analyzed using GenAlex software. The results showed moderate mean value of genetic diversity (HE=0,345, SE 0,015) of the wildings. Forty eight wildings were clustered in only 3 groups; almost all wildings (65%) were clustered in one main cluster. Moreover, 4 wildlings were clones (8%). In conclusion, the 48 wildings of ulin consisted high genetic relationship and individual clones that reflects the low genetic diversity of this species

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions

The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art

KARAKTERISASI GEN CYTOCHROME OXIDASE SUBUNIT 1 (CO1) ELANG BRONTOK (Nisaetus cirrhatus Gmelin, JF, 1788) DAN ELANG JAWA (Nisaetus bartelsi Stresemann, 1924)

Burung merupakan satu kelas klasifikasi yang telah banyak diteliti dan menghasilkan wawasan mengenai evolusi, spesisasi dan biologi populasi. Dari kemudahan tersebut, burung menjadi kelas yang cocok utnuk eksplorasi ketepatan dan kemampuan barcode. Banyaknya informasi yang dapat diperoleh dari kajian molekuler barcode menjadi awal tujuan penelitian ini mengkaji Elang Brontok dan Elang Jawa. Kajian tersebut mencakup pada pengaruh morph Elang Brontok serta karakteristik,variasi dan kekerabatan dari Elang Brontok dan Elang Jawa. Penelitian ini memanfaatkan gen CO1 (cytochrome oxidase-1) dari DNA mitokondria sebagai gen penanda. Gen yang dapat ditemui dihampir semua spesies hewan ini diambil dari sampel darah dikertas saring dan diamplifikasi menggunakan metode PCR Direct dari protokol Phire Animal Tissue Direct PCR kit dengan beberapa primer yaitu L14731-H15454; BirdF1-BirdR1; dgLCO1490-dgHCO2198; serta UniminibarR1-UniminibarF1 sebagai uji kecocokan primer. Keempatnya mampu mengamplifikasi DNA Elang dengan salah satu primer yakni UniminibarR1- UniminibarF1 tidak mengamplifikasi pada panjang fragmen target yang diinginkan. Hasil menunjukkan Elang Jawa tidak terdapat varasi gen. Disamping Elang Jawa, penelitian ini juga menunjukkan hasil bahwa terdapat Elang Brontok memiliki variasi gen dengan jarak 0,015 dari perbandingan morph yang berbeda, sedangkan morph yang sama memiliki jarak genetik 0,001. Pohon filogenetik direkonstruksi dengan metode Maximum Likelihood pada program Mega6 dengan hasil N. cirrhatus berkelompok dengan morph yang sama dan S. phillipensis sebagai sister spesies-nya. Sedangkan N. bartelsi berkerabat dengan N. nipalensi

Quantum harmonic oscillator sonification

Presented at the 15th International Conference on Auditory Display (ICAD2009), Copenhagen, Denmark, May 18-22, 2009This work deals with the sonification of a quantum mechanical system and the processes that occur as a result of its quantum me- chanical nature and interactions with other systems. The quantum harmonic oscillator is not only regarded as a system with sonifi- able characteristics but also as a storage medium for quantum in- formation. By representing sound information quantum mechan- ically and storing it in the system, every process that unfolds on this level is inherited and reflected by the sound. The main profit of this approach is that the sonification can be used as a first in- sight for two models: a quantum mechanical system model and a quantum computation model

Sonic Explorations With Earthquake Data

Presented at the 14th International Conference on Auditory Display (ICAD2008) on June 24-27, 2008 in Paris, France.The composition ``underground sounds'' - an interdisciplinary project including a concert piece as its artistic element - deals with the phenomenon of the constantly moving, therefore resonating earth and is based on data taken from an earthquake which reached 7.8 on the Richter scale and triggered a tsunami on April 1st, 2007 close to the Solomon Islands in the Southwestern Pacific. The data from several related seismic events was provided via a real-time data server belonging to the GEOFON network of seismic stations and converted to audio data using programs specifically developed for that purpose ``underground sounds'' is not an audification; the seismometers records were used as raw material for several applications of signal processing effects. The four parts of the composition concentrate on different characteristics of seismic events including sounds of the same seismic event recorded by different stations, the filtered harmonic sounds of the measuring instruments and the output of the separation of the earthquake's impulse-like components from the earth's constant movements, each used as separate instruments in the composition

Graph-guided random forest for gene set selection

Machine learning methods can detect complex relationships between variables, but usually do not exploit domain knowledge. This is a limitation because in many scientific disciplines, such as systems biology, domain knowledge is available in the form of graphs or networks, and its use can improve model performance. We need network-based algorithms that are versatile and applicable in many research areas. In this work, we demonstrate subnetwork detection based on multi-modal node features using a novel Greedy Decision Forest with inherent interpretability. The latter will be a crucial factor to retain experts and gain their trust in such algorithms. To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well. Systems biology is a good example of a field in which statistical data-driven machine learning enables the analysis of large amounts of multi-modal biomedical data. This is important to reach the future goal of precision medicine, where the complexity of patients is modeled on a system level to best tailor medical decisions, health practices and therapies to the individual patient. Our proposed approach can help to uncover disease-causing network modules from multi-omics data to better understand complex diseases such as cancer