Hilsa is the national fish of Bangladesh. Bangladesh is earning a lot of foreign currency by exporting this fish. Unfortunately, in recent days, some unscrupulous businessmen are selling fake Hilsa fishes to gain profit. The Sardines and Sardinella are the most sold in the market as Hilsa. The government agency of Bangladesh, namely Bangladesh Food Safety Authority said that these fake Hilsa fish contain high levels of cadmium and lead which are detrimental for humans. In this research, we have proposed a method that can readily identify original Hilsa fish and fake Hilsa fish. Based on the research available on online literature, we are the first to do research on identifying original Hilsa fish. We have collected more than 16,000 images of original and counterfeit Hilsa fish. To classify these images, we have used several deep learning-based models. Then, the performance has been compared between them. Among those models, DenseNet201 achieved the highest accuracy of 97.02%.

The administration of President Joe Biden entered office on January 20, 2021, pledging a broad-strokes overhaul of how Washington interacts with the world, promising to be a distinct counterpoint to the disruptive, go-it-alone posture of former President Donald Trump, and tying stability and prosperity at home to US interests abroad in his so-called "foreign policy for the middle class". As 2021 ends, the administration has indeed sought to re-up relations with key allies and position itself as a central player in combating global crises, but has faced criticism for failing to live up to vows of a human rights-leading foreign policy and for what some have described as an over-emphasis on sweeping ideological differences at a time when global cooperation -- particularly between superpowers -- is sorely needed. "2021 was a year of transition. President Biden replaced Trump's impetuousness with pragmatism and realism. There is a greater understanding of what US policy actually is," PJ Crowley, the former US assistant secretary of state for public affairs under President Barack Obama, told Al Jazeera.

Global food insecurity is expected to worsen in the coming decades with the accelerated rate of climate change and the rapidly increasing population. In this vein, it is important to remove inefficiencies at every level of food production. The recent advances in deep learning can help reduce such inefficiencies, yet their application has not yet become mainstream throughout the industry, inducing economic costs at a massive scale. To this point, modern techniques such as CNNs (Convolutional Neural Networks) have been applied to RPQD (Raw Produce Quality Detection) tasks. On the other hand, Transformer's successful debut in the vision among other modalities led us to expect a better performance with these Transformer-based models in RPQD. In this work, we exclusively investigate the recent state-of-the-art Swin (Shifted Windows) Transformer which computes self-attention in both intra- and inter-window fashion. We compare Swin Transformer against CNN models on four RPQD image datasets, each containing different kinds of raw produce: fruits and vegetables, fish, pork, and beef. We observe that Swin Transformer not only achieves better or competitive performance but also is data- and compute-efficient, making it ideal for actual deployment in real-world setting. To the best of our knowledge, this is the first large-scale empirical study on RPQD task, which we hope will gain more attention in future works.

Shortly before 3 a.m. on July 19, 2016, U.S. Special Operations forces bombed what they believed were three Islamic State (IS) group "staging areas" on the outskirts of Tokhar, a riverside hamlet in northern Syria. They reported 85 fighters killed. In fact, they hit houses far from the front line, where farmers, their families and other local people sought nighttime sanctuary from bombing and gunfire. More than 120 villagers were killed. In early 2017 in Iraq, an American war plane struck a dark-colored vehicle, believed to be a car bomb, stopped at an intersection in the Wadi Hajar neighborhood of West Mosul. Actually, the car had been bearing not a bomb but a man named Majid Mahmoud Ahmed, his wife and their two children, who were fleeing the fighting nearby. They and three other civilians were killed. In November 2015, after observing a man dragging an "unknown heavy object" into an IS "defensive fighting position," U.S. forces struck a building in Ramadi, Iraq. A military review found that the object was actually "a person of small stature" -- a child -- who died in the strike. None of these deadly failures resulted in a finding of wrongdoing. These cases are drawn from a hidden Pentagon archive of the American air war in the Middle East since 2014. The trove of documents -- the military's own confidential assessments of more than 1,300 reports of civilian casualties, obtained by The New York Times -- lays bare how the air war has been marked by deeply flawed intelligence, rushed and often imprecise targeting and the deaths of thousands of civilians, many of them children, a sharp contrast to the U.S. government's image of war waged by all-seeing drones and precision bombs. The documents show, too, that despite the Pentagon's highly codified system for examining civilian casualties, pledges of transparency and accountability have given way to opacity and impunity. In only a handful of cases were the assessments made public. Not a single record provided includes a finding of wrongdoing or disciplinary action. Fewer than a dozen condolence payments were made, even though many survivors were left with disabilities requiring expensive medical care. Documented efforts to identify root causes or lessons learned are rare. The air campaign represents a fundamental transformation of warfare that took shape in the final years of the Obama administration, amid the deepening unpopularity of the forever wars that had claimed more than 6,000 American service members. The United States traded many of its boots on the ground for an arsenal of aircraft directed by controllers sitting at computers, often thousands of kilometers away. President Barack Obama called it "the most precise air campaign in history." This was the promise: America's "extraordinary technology" would allow the military to kill the right people while taking the greatest possible care not to harm the wrong ones. The IS caliphate ultimately crumbled under the weight of American bombing.

This work has been submitted to the IEEE for possible publication. Abstract--We comment on a recent TKDE paper [1] "Linear Approximation of F-measure for the Performance Evaluation of Classification Algorithms on Imbalanced Data Sets", and make two improvements related to comparison of F-measures for two prediction rules. We extend the "JVESR formula" We found in a recent issue of TKDE Wong's paper [1] performance of the proposed method and Wong's method on statistical comparison of F-measures for two algorithms, with the designed comparative experiments. Finally, we which is obviously an important problem. However, we conclude and discuss possible future works in Section 4. found that there are two things in [1] that need improvement.

Humans and artificial intelligences (AI) will increasingly participate digitally and physically in conflicts, yet there is a lack of trusted communications across agents and platforms. For example, humans in disasters and conflict already use messaging and social media to share information, however, international humanitarian relief organisations treat this information as unverifiable and untrustworthy. AI may reduce the 'fog-of-war' and improve outcomes, however AI implementations are often brittle, have a narrow scope of application and wide ethical risks. Meanwhile, human error causes significant civilian harms even by combatants committed to complying with international humanitarian law. AI offers an opportunity to help reduce the tragedy of war and deliver humanitarian aid to those who need it. In this paper we consider the integration of a communications protocol (the 'Whiteflag protocol'), distributed ledger technology, and information fusion with artificial intelligence (AI), to improve conflict communications called 'Protected Assurance Understanding Situation and Entities' (PAUSE). Such a trusted human-AI communication network could provide accountable information exchange regarding protected entities, critical infrastructure; humanitarian signals and status updates for humans and machines in conflicts.

With the emergence of deep learning in the last years, new opportunities arose in Earth observation research. Nevertheless, they also brought with them new challenges. The data-hungry training processes of deep learning models demand large, resource expensive, annotated datasets and partly replaced knowledge-driven approaches, so that model behaviour and the final prediction process became a black box. The proposed SyntEO approach enables Earth observation researchers to automatically generate large deep learning ready datasets and thus free up otherwise occupied resources. SyntEO does this by including expert knowledge in the data generation process in a highly structured manner. In this way, fully controllable experiment environments are set up, which support insights in the model training. Thus, SyntEO makes the learning process approachable and model behaviour interpretable, an important cornerstone for explainable machine learning. We demonstrate the SyntEO approach by predicting offshore wind farms in Sentinel-1 images on two of the worlds largest offshore wind energy production sites. The largest generated dataset has 90,000 training examples. A basic convolutional neural network for object detection, that is only trained on this synthetic data, confidently detects offshore wind farms by minimising false detections in challenging environments. In addition, four sequential datasets are generated, demonstrating how the SyntEO approach can precisely define the dataset structure and influence the training process. SyntEO is thus a hybrid approach that creates an interface between expert knowledge and data-driven image analysis.

A reliable early warning system to detect tsunamis could be a step closer thanks to research from Cardiff University. Researchers say their analysis of ocean soundwaves triggered by underwater earthquakes has enabled them to develop artificial intelligence (AI) that allow prediction of when a tsunami might occur. The results are published today in the journal Scientific Reports. It is hoped this technology could assist experts in gaining accurate real-time assessments of these geological events. Dr. Usama Kadri, from Cardiff University's School of Mathematics, said: "Tsunamis have a devastating impact on communities. Developing accurate methods to detect them quickly is key to saving lives. "Our findings show we are able to classify the type of earthquake and retrieve its main properties from acoustic signals, in near real time.

Explainable artificial intelligence (XAI) and interpretable machine learning (IML) have become active research fields in recent years (Adadi and Berrada 2018; Molnar 2019). This is a natural consequence as complex machine learning (ML) models are now applied to solve supervised learning problems in many high-risk areas: cancer prognosis (Kourou et al. 2015), credit scoring (Kvamme et al. 2018), and money laundering detection (Jullum, Løland, et al. 2020). The high prediction accuracy of complex ML models often comes at the expense of model interpretability. As the goal of science is to gain knowledge from the collected data, the use of black-box models hinders the understanding of the underlying relationship between the features and the response, and thereby curtail scientific discovery. Model explanation frameworks from the XAI field extract the hidden knowledge about the underlying data structure captured by a black-box model, and thereby make the model's decision-making process transparent. This is crucial for, e.g., medical researchers that apply an ML model to obtain well-performing predictions, but who simultaneously also strive to discover important risk factors. Another driving factor is the Right to Explanation legislation in EU's General Data Protection Regulation (GDPR) (European Commission 2016).

Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through  flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introduce a  flexible approach for the construction and selection of highly  flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically, similarly to deep learning, but have additional  flexibility on the possible types of features to be considered. This  flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions, a Bayesian approach, introducing priors for functions based on their complexity, is considered. A genetically modified mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications, we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally, we compare its predictive performance with several machine learning algorithms.