Cyber Threat Intelligence (CTI) is crucial for modern cybersecurity because it provides the knowledge and insights needed to defend against a wide range of cyber threats. However, there are issues associated with incomplete and inconsistent CTI data that can lead to inaccurate threat assessments, increasing the risk of both false alarms and undetected threats. This paper introduces CLEVER, an extended version of the Malware Information Sharing Platform (MISP) platform that includes machine learning (ML) models to support the management and processing of CTI data. The models are designed to address specific challenges such as (i) prioritizing and ranking Indicators of Compromise (IoCs) based on severity and potential impact, (ii) classifying IoCs by attack type or threat, and (iii) aggregating similar IoCs into clusters. The effectiveness of the ML models employed in CLEVER has been thoroughly tested on three public CTI datasets, and the results provide encouraging outcomes in enhancing CTI management and analysis.

In the realm of Unsupervised Domain Adaptation (UDA), Vision Transformers (ViTs) have recently demonstrated remarkable adaptability surpassing that of traditional Convolutional Neural Networks (CNNs). Nevertheless, the patch-based structure of ViTs heavily relies on local features within image patches, potentially leading to reduced robustness when confronted with out-of-distribution (OOD) samples. To address this concern, we introduce a novel regularizer tailored specifically for UDA. By leveraging negative views, i.e. target-domain samples applied by negative augmentations, we make the learning process more intricate, thereby preventing models from taking shortcuts in spatial context recognition. We present a novel loss function, rooted in contrastive principles, to effectively distinguish between the negative views and original target samples. By integrating this novel regularizer with existing UDA methodologies, we guide ViTs to prioritize context relationships among local patches, thereby enhancing the robustness of ViTs. Our proposed Negative View-based Contrastive (NVC) regularizer substantially boosts the performance of baseline UDA methods across diverse benchmark datasets. Furthermore, we release new dataset, Retail-71, comprising 71 classes of images commonly encountered in retail stores. Through comprehensive experimentation, we showcase the effectiveness of our approach on traditional benchmarks as well as the novel retail domain. These results substantiate the robust adaptation capabilities of our proposed method. Our method is implemented at our repository.

Abstract Web applications created using web languages such as HTML, CSS, and JavaScript are widely used regardless of execution environments, owing to their portability and popularity. Since JavaScript language is conventionally used for complex computations in large‐scale web apps, many optimization techniques have been proposed to accelerate the JavaScript performance. However, most of these optimizations speed up JavaScript engines at the expense of consuming more memory. Hence, memory consumption becomes an additional concern in the JavaScript field. Based on the research on memory status, we unearthed that a substantial portion of the heap memory is allocated for JavaScript source code, particularly in lightweight JavaScript engines, which may range from 13.2% to a maximal 52.5% of the entire heap. To resolve this memory issue, this article suggests a new memory optimization method, called dynamic code compression that dynamically compresses the source code and keeps its compressed form instead of the source string. A novel heuristic is proposed to timely compress the source code. We also re‐design the internal structure of the JavaScript engine to efficiently compress the source code without incurring any conflict. Using our code compression method, we could reduce the entire heap memory by up to 43.3% and consistently downsize the overall heap size. From an evaluation of standard benchmarks, our approach showed just 2.7% degradation in performance with negligible compression overhead, proving the feasibility of the code compression technique.

In this paper, we derive the closed forms of residual interference that occurs over a multipath channel of a filter bank multicarrier-quadrature amplitude modulation (FBMC-QAM) system, in which the system adopts two different prototype filters to guarantee that the orthogonality condition is met for both even and odd subcarriers without using a guard interval. The filter coefficients of the two filters are designed differently to meet the orthogonality condition, and thus, their degrees of robustness are different over the multipath channel. In order to clarify the influence of the multipath channel on the performance of the FBMC-QAM, we both derive an effective channel frequency response that includes the filter response and analyze the residual interference over the multipath channel. Based on this analysis, we propose a unique scheme for both even-biased reference symbol mapping and symbol/signal-level channel estimation and equalization that is suitable for the FBMC-QAM system transceiver structure. The simulation results show that the performance of the proposed scheme is comparable to that of a conventional orthogonal frequency division multiplexing-quadrature amplitude modulation system in terms of the mean squared error, bit error rate, and effective data rate.

With the increase in flexibility and capabilities of wireless networks, the use of distributed computing over wireless network environments is being researched in order to maximize network sustainability and interoperability among distributed nodes. To this end, a new paradigm is required for optimization of a more generalized environment. This environment would include various nodes of different processing and communication abilities constrained by circuit powers and residual energy over individual dynamic wireless channels. In this paper, we present a novel strategy named link capacity-energy aware wireless distributed computing (LEA-WDC) for maximizing the lifetime of a wireless network. The major advantage of LEA-WDC is its achievement of lifetime maximization by systematically reconciling highly coupled system parameters (tasks, processing power, communication power, and residual energy) in terms of the role of nodes and the layer of each node. To attain an optimal solution, we perform unique interworking optimization via decomposition in accordance with the roles of header and slave nodes. The evaluation results of our simulation verify that the lifetime is further maximized by finding the optimal transmission power of each node according to the Shannon capacity.

In this paper, we propose a joint transmission and reception with phase control for beamforming in a multi-cell environment. For generated transmit weight vectors of multiple base stations (BSs), a mobile station (MS) calculates phases to maximize an achievable rate with low rate feedback. By using the phases, the multiple transmit weight vectors are coordinated to improve the signal to noise ratio (SNR). In order to find optimal phases, we present a phase control method for the effective channel via geometrical approach.

In this paper, we investigate efficient multi-cell cooperation based on CoMP-joint processing and transmission (CoMP-JPT) with orthogonal beamforming. Through the use of a combinatorial optimization algorithm, the optimal user scheduling for joint transmission using multiple transmitters is accomplished. The throughput of the CoMP-JPT can be significantly improved while maintaining fairness among users over a multi-cell environment.

Coordinated multiple point transmission (CoMP) technologies have recently been proposed to improve the performance of cell-edge users who suffer strong inter-cell interference. Nevertheless, as more transmitters get involved in cooperation, the complexity associated with the selection of multi-dimensional parameters increases exponentially. In this work, we investigate efficient multi-cell cooperation based on CoMP-joint processing and transmission (CoMP-JPT) with orthogonal beamforming using limited feedback. Through the utilization of combinatorial optimization, optimal user scheduling for joint transmission via multiple transmitters is accomplished, while the computational complexity is significantly reduced. In particular, a generalized beam assignment problem (GBAP) is formulated and solved using a combinatorial algorithm that is generalized in terms of the number of transmitters o\mathcal{B}o. The performance of the combinatorial orthogonal beamforming (COBF) scheme is mathematically analyzed so as to demonstrate its superiority and capability to maintain fairness among users in a multi-cell environment. In the simulation results, a performance gain of more than 50% for cell-edge users is obtained without a loss in the average throughput for the total number of users. In addition, the COBF method can reduce the complexity by more than 60% when compared to the conventional exhaustive search technique.

In a quantization based multiuser multiple-input/multiple-output (MU-MIMO) broadcast channel, the effective channel gain (i.e., the norm of a channel vector obtained from the receive combiner) needs to be considered in addition to a reduction of quantization errors for maximizing the signal-to-interference plus noise ratio (SINR). In this work, we prove that the effective channel gains in the space limited by an N_R x M_T MIMO channel form a min(M_T,N_R)-dimensional ellipsoid in the channel space. Utilizing the geometric proof, the achievable effective channel gain is derived as a function of its direction for a given MIMO channel. Based on the derivation, we finally propose a quantization vector selection criterion and an optimal receive combiner to maximize the SINR. The proposed maximum SINR based combining (MSC) is proven to be a better solution for maximizing the SINR compared to quantization based combining (QBC) and maximum gain based combining (MGC), each of which attempts to minimize quantization errors and maximize the effective channel gain.

In multi-cell downlink system, quality of service (QoS) of multiuser MIMO is an important issue, in particular, for cell-edge users. For QoS of cell-edge users in the system, the utilization of multiple-input multiple-output (MIMO) relay stations (RSs) is a promising solution that improves the signal-to-interference plus noise ratio (SINR) from neighboring base stations (BSs) to each RS. Since the capacity between the BS and the RS relies heavily on the receive performance, it is necessary to reflect interference channels in the design of the receive weight vector. In this paper, we use a geometric approach to derive the effective channel gain of the RS according to the receive weight vector. The geometric relationship between the desired and interference channels is also described. Using the description, we propose a receiver, called the maximum lower bound of expected SINR receiver (MLESR). Through a performance analysis of the MLESR over the interference-limited regime, we derive closed terms for the performance bounds in terms of the number of RS antennas, the interference channel rank, and the BS transmit power according to the rate gap of the MLESR with respect to an ideal case, i.e., a no interference case.

In this paper, a plastic organic thin-film transistor (OTFT) with high mobility formed on the polymeric gate dielectrics is presented. Flexible organic LEDs (OLEDs) operated by an OTFT are fabricated with a novel lamination method and the results are also presented. Fabrication method and the performances of white (consisting of R, G, and B) OLEDs with high efficiency, stability, and good color purity are discussed.