Central Intelligence Agency

<title>Abstract</title> Marketing plays a crucial role in enhancing consumer awareness and affinity towards a product. Consumer research examines the motivations, trends, and patterns underlying purchasing behavior, thereby enabling institutions to refine their offerings and marketing strategies to improve their alignment with consumer decision-making and increase purchase probability. Consumer neuroscience aims to study the neurological factors influencing consumer preference, which cannot be determined using traditional methods like questionnaires and self-assessment. Machine learning techniques have proven effective in this domain, yet deep learning architectures that effectively fuse electroencephalography (EEG) signals with eye-tracking (ET) data remain largely unexplored. In this study, we propose a novel hybrid "Wide &amp; Deep" framework for multimodal classification of consumerpreference using EEG and eye-tracking data from the Neuma dataset. The proposed architecture employs an InceptionTime network with early fusion to process concatenated multimodal input (25 channels), extracting multi-scale temporal featuresthrough parallel convolutions with varying kernel sizes (10, 20, 40). The learned 128-dimensional neural embeddings are combined with 44 time-domain statistical features (temporal mean and standard deviation across all channels) and passed to a CatBoost gradient boosting classifier, forming a neural-symbolic ensemble. A modality dropout strategy (30% probability) is introduced during training to prevent over-reliance on eye-tracking signals and force robust EEG representation learning. The framework employs a grokking-oriented training regime with extended epochs (100), high weight decay (0.02), and strict checkpointing based on validation loss. The model is evaluated using 5-fold stratified cross-validation to assess generalizability. The proposed framework achieves a mean accuracy of 77.51% (±0.49%) and a weighted F1-score of 74.60% (±0.21%) in classifying consumer preferences, representing a significant improvement over prior methods on this dataset. This study demonstrates the effectiveness of multi-scale convolutional architectures combined with gradient boosting ensemble methodsfor multimodal consumer neuroscience applications.

Abstract This chapter examines the intersection of strategic intelligence and criminal law enforcement in the United States. It discusses the differences and convergences of both worlds and how each affects the other. It deals with some of the more significant impacts of the U.S. criminal law system on intelligence operations, including surveillance, constitutional issues, and trial procedures. It surveys topics that relate to how and why intelligence operations are shaped by historical attempts by the U.S. government to accommodate imperatives required by both national security and justice. It also covers the protection of sources and methods and other intelligence community equities in the context of prosecutions and defendants’ rights.

For Infrared Image Super-Resolution (IRSR) technology, maintaining performance while reducing model complexity is critical for a wide range of applications. However, existing research on lightweight IRSR has been predominantly limited to modifying model architectures. This study proposes a new methodology that applies Knowledge Distillation, a representative model compression technique from supervised learning, to IRSR models. To this end, we extend the DCKD framework, previously used for RGB image super-resolution, to the IRSR domain and introduce new loss functions designed to maximize the preservation of key structural characteristics in infrared images, namely edge and spectral(Contourlet-domain) information. Through the proposed methodology, a lightweight student model trained with distilled knowledge from a high-performance, complex teacher model consistently achieved superior performance compared to the same architecture trained via standard supervised learning. This study demonstrates that Knowledge Distillation based methodology is effective for developing lightweight IRSR models and is expected to contribute to fields where high-efficiency IRSR is essential, such as real-time military surveillance, disaster response, and nocturnal reconnaissance.

Abstract The rise in multidrug-resistant pathogenic bacteria presents a major current challenge, highlighting the urgent need for alternatives and sustainable biocontrol strategies. Here, we report the genome analysis of a bacteriophage calledSalmonella phage TennesseeSalten and attributed to a newTequintavirusspecies. Salten was isolated following infection ofSalmonella entericasubsp.entericaserotype Tennessee (sequence type ST5018). Its genome is 109,999 bp in length and contains 220 predicted proteins on which 197 are CDS and 23 tRNAs. Compared to its closest known relative phageEscherichia phageHildyBeyeler - sharing 84.6% identity - Salten harbours 16 unique or highly divergent genes. Of these, 13 encode proteins with unknown function, one encodes for a putative adenine methyltransferase and two encode HNH homing endonucleases. Moreover, the Long Tail Fibre protein, whose structure was predicted based on that of phage T5, was highly divergent among theTequintavirusgenus.

Smart home care is the future of healthcare. Technology will continue to transform healthcare through smart home technologies, integrating telemedicine and digital tools to deliver personalized, efficient, and accessible care at home. This chapter discusses the shift from traditional clinic-based care to a system of care in the comfort of their homes, monitored and assisted by an array of interconnected devices. This future system will leverage sensors, smart appliances, digital tattoos, and telehealth platforms to monitor health in real time, aiding in early diagnosis , treatment, and improved quality of life . Ethical considerations, cost implications, integration challenges, and the potential for improved outcomes with reduced healthcare costs are explored. While promising, this paradigm shift raises questions about data privacy, inclusivity, and the need for informed consent .

Despite the great performance of language identification (LID), there is a lack of large-scale singing LID databases to support the research of singing language identification (SLID). This paper proposed a over 3200 hours dataset used for singing language identification, called Slingua. As the baseline, we explore two self-supervised learning (SSL) models, WavLM and Wav2vec2, as the feature extractors for both SLID and universal singing speech language identification (ULID), compared with the traditional handcraft feature. Moreover, by training with speech language corpus, we compare the performance difference of the universal singing speech language identification. The final results show that the SSL-based features exhibit more robust generalization, especially for low-resource and open-set scenarios. The database can be downloaded following this repository: https://github.com/Doctor-Do/Slingua.

This research aims to investigate forms of propaganda spread by the Islamic State of Iraq and Syria (ISIS) as represented in an Arabic History Coursebook they prepare for ISIS’ school environments. It utilizes Critical Discourse Analysis (CDA) as its research method to examine and reveal values and ideologies behind choices of words, grammar, and text structures. Using an ISIS’ Arabic History Coursebook entitled Al-Tarikh: li al-Shaff al-Khamis al-Ibtida'i (published in 2016/1437H) as the data source, this research is primarily focused on the analysis of clauses and phrases which contain words of daulah and qital. The data were analysed utilizing functional linguistics and propaganda techniques as its analytical framework. Results of analyses show that as an organization ISIS fights for ideologies which underlie the idea of the Islamic State and spreads values of radicalism to its targeted communities. Propaganda techniques such as name calling, glittering generality, transfer, plain folk and bandwagon are found to be dominant in ISIS’ propaganda projects.

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