Reviewerly/exHarmony huggingface dataset:
Universal construction of decoders from encoding black boxes

Isometry operations encode the quantum information of the input system to a larger output system, while the corresponding decoding operation would be an inverse operation of the encoding isometry operation. Given an encoding operation as a black box from a $d$-dimensional system to a $D$-dimensional system, we propose a universal protocol for isometry inversion that constructs a decoder from multiple calls of the encoding operation. This is a probabilistic but exact protocol whose success probability is independent of $D$. For a qubit ($d=2$) encoded in $n$ qubits, our protocol achieves an exponential improvement over any tomography-based or unitary-embedding method, which cannot avoid $D$-dependence. We present a quantum operation that converts multiple parallel calls of any given isometry operation to random parallelized unitary operations, each of dimension $d$. Applied to our setup, it universally compresses the encoded quantum information to a $D$-independent space, while keeping the initial quantum information intact. This compressing operation is combined with a unitary inversion protocol to complete the isometry inversion. We also discover a fundamental difference between our isometry inversion protocol and the known unitary inversion protocols by analyzing isometry complex conjugation and isometry transposition. General protocols including indefinite causal order are searched using semidefinite programming for any improvement in the success probability over the parallel protocols. We find a sequential "success-or-draw" protocol of universal isometry inversion for $d = 2$ and $D = 3$, thus whose success probability exponentially improves over parallel protocols in the number of calls of the input isometry operation for the said case.
A likelihood ratio approach for the evaluation of single point device locations

There is growing awareness that localisation traces recovered from a mobile device should not be presented as fact. Services providing locations to smartphones are influenced by a multitude of potential error sources which may cause differing observations from where the device was at. This work provides an approach to evaluate a single localisation recovered from mobile devices and present a differentiated result to decision makers. An open source implementation is provided, allowing practitioners to evaluate based on reference data they created for their own case. The effectiveness and forensic value of this method are demonstrated using a simulated scenario illustrating the use of the approach. This study demonstrates the method using locations associated to images, and is generalised to any technology providing location to any type of mobile device.
Discovering spoliation of evidence through identifying traces on deleted files in macOS

Spoliation of evidence is a critical concern in various crimes such as information leakage, digital sexual crimes, accounting fraud, and copyright infringement. Several traditional digital forensic investigation methods such as recovery, carving, and anti-forensic behavior tracking are used to investigate these crimes. However, as technology has advanced, recovery and carving have become increasingly challenging. Shortly, data recovery will reach its technological limit, and it will be essential to obtain as much circumstantial evidence as possible based on traces of data left in suspect systems. However, no existing method can systematically track the spoliation of evidence; contemporary investigations typically depend solely on investigators’ skills and knowledge. This paper proposes a method to track deleted files by identifying and analyzing various sources that manage file-related metadata in macOS systems.
Forensic method for decrypting TPM-protected BitLocker volumes using Intel DCI

Starting from Windows 11, the Trusted Platform Module (TPM) 2.0 has become a computer requirement, providing hardware-based security capabilities. This poses a challenge to digital forensics experts, as the number of BitLocker-encrypted evidence protected by TPM tends to increase. This paper presents a forensic method for obtaining the BitLocker Volume Master Key (VMK) from TPM-protected evidence using Intel DCI technology and reverse engineering techniques. It shows how to enable Intel DCI in the firmware, reverse the Windows Boot Manager UEFI application, and debug the target computer using a USB 3 A–A cable to retrieve the VMK from memory. We have effectively applied the presented method on a computer with a 7th-generation Intel processor containing a BitLocker-encrypted volume with TPM protection and Windows 11 Pro. As a result, we were able to fully decrypt the BitLocker volume with the VMK and gain data access. We consider, however, that the success of the presented method depends on the ability to enable Intel DCI in the target computer, which may not be feasible in every system.
Harder, better, faster, stronger: Optimising the performance of context-based password cracking dictionaries

Passwords have been the prevailing method of authentication since their inception more than 50 years ago, a trend which has no signs of slowing down in the foreseeable future. They are an integral part of the security of digital persons, systems and critical data, and yet, they often remain the weakest entry point to a digital system. A password itself is indeed an extension of its creator and therefore can be exploited by malicious actors leveraging available contextual information about a target password creator. Recent research has shown that bespoke password candidate lists, generated based on available contextual information, can positively impact the password cracking processes. This paper introduces an innovative methodology for composing a contextual wordlist and ranking the password candidates in order to maximise the chance of early success. The aim of the proposed approach is to support digital forensic investigators in their criminal investigation – especially when time is of the essence. This paper describes the implementation of this methodology and provides an overview of several experimental results demonstrating the advantages of this approach. These results demonstrate that by going through a harder, more rigorous password candidate selection process, better dictionaries can be generated that, in a faster timeframe, can crack stronger passwords.
Interpreting the location data extracted from the Apple Health database

Smart fitness tracking devices are becoming more prevalent in criminal investigations. The Apple Health database for Apple Watch and iPhone fitness activity contains a diverse range of digital traces such as exercise or location information. In this paper, we provide an explanation of the database structure and an SQLite query-based approach for as complete database structure oversight as possible. We extract and link time zones to timestamps and limited geo-coordinate data to workout activity, in the context of case studies using a rich 5+ year long real database of one of the authors. Additionally we investigate the impacts of a major iOS change with iOS 16 on these insights to deepen the understanding of the data at hand. We found that the accuracy of geolocation coordinates, time zone entries, and workout information may not necessarily be accurate or consistent and requires contextual interpretation. We demonstrate the forensic value of the database in the context of informing an investigation by establishing context to a victim's or suspect's activities.
Artificial Intelligence in Education

The systematic integration of artificial intelligence (AI) into the education system enables the education system to overcome any challenges. AI can simplify the basic teaching tasks, and facing the major educational institution challenges, such as raising the efficiency of teachers, anticipating the learners' different requirements, and providing tools that enable teachers to perform their mission effectively, can provide the teacher with all needed information for evaluation, improvement of performance, and for q students quickly and effectively.
AUTOMATED PARAMETER SEARCH FOR FRACTAL FUNCTIONS IN DATA FITTING PROBLEMS

Parameter optimization is an important problem in the theory of fractal interpolation functions and data fitting. In the literature, the methods for determining values of parameters in data fitting problems are developed only for affine and recurrent affine fractal interpolation functions. There is still no direct method to determine the optimum values of parameters for various types of fractal functions. The aim of this paper is to apply an automated parameter search algorithm from machine learning to the problem of parameters optimization when we use a class of fractal functions to fit a given data set. We first establish a finite set of fractal interpolation functions, and then consider three linear combinations of these functions to fit a given data set. We apply Optuna to find the optimum values of hyperparameters in our approach that minimize the given empirical error. Two examples are given to show the results. Our approach can be applied to many types of fractal functions, and even their linear combinations. The advantage of tuning hyperparameters of fractal functions by AI-based algorithms is that it is not limited to the construction methods and forms of fractal functions.
Effects of the pandemic on education, training, teaching work and student learning

Approximately two years ago, the Coronavirus was declared a worldwide pandemic by the WHO and despite the fact that today the situation is different, a vaccine could be found that would reduce the impact on deaths and contagion, it will be an anecdote that will remain in the memory of all those who lived and were contemporary to the situation. This event not only directly affected health, but also had very large consequences in terms of economics, the completely collapsed health and education systems and without many tools to deal with this situation. In this article, the effects of the pandemic on education, training, teaching work and student learning will be exposed, mainly because it is very possible that, in several years, the wounds that the pandemic left in education being one of the deepest and most affected. The virtual learning modalities that were used in this context were ICT and for this, not only the teaching commitment to use this didactic tool, but also a State that guaranteed access to receive connectivity and monitoring of the right to quality education taking decisions that arise directly in the educational system, teacher training and student learning.
Strategic planning of human resources as an entrance to enhance marketing capabilities: A case study of King Abdullah II Center for Design and Development (KADDB)

The study aims to identify the impact of strategic planning of human resources in enhancing marketing capabilities at the King Abdullah II Center for Design and Development (KADDB). The descriptive and analytical methods were used to understand the effect of human resource planning in enhancing marketing capabilities. The research population includes all departments in the respective center. As for the sample, it consisted of 65 marketing and sales managers and workers in the marketing departments. A self-administered questionnaire was used to collect the research data. The results showed a significant effect of strategic planning for human resources in enhancing marketing capabilities at KADDB (R2 = 0.619, sig &amp;amp;lt; 0.05). It was also found that strategic planning for human resources contributes to increasing the leader ability to do their work diligently and proficiently and improving the ability to organize work, distribute duties to subordinates, and coordinate efforts. Therefore, this study recommends giving strategic planning to human resources the importance it deserves for its active role in enhancing marketing capabilities at KADDB.
Local Viterbi property in decoding

Abstract The article studies the decoding problem (also known as the classification or the segmentation problem) with pairwise Markov models (PMMs). A PMM is a process where the observation process and the underlying state sequence form a two-dimensional Markov chain, a natural generalization of hidden Markov model. The standard solutions to the decoding problem are the so-called Viterbi path—a sequence with maximum state path probability given the observations—or the pointwise maximum a posteriori (PMAP) path that maximizes the expected number of correctly classified entries. When the goal is to simultaneously maximize both criterions—conditional probability (corresponding to Viterbi path) and pointwise conditional probability (corresponding to PMAP path)—then they are combined into one single criterion via the regularization parameter $C$. The main objective of the article is to study the behaviour of the solution—called the hybrid path—as $C$ grows. Increasing $C$ increases the conditional probability of the hybrid path and when $C$ is big enough then every hybrid path is a Viterbi path. We show that hybrid paths also approach the Viterbi path locally: we define $m$-locally Viterbi paths and show that the hybrid path is $m$-locally Viterbi whenever $C$ is big enough. This all might lead to an impression that when $C$ is relatively big then any hybrid path that is not yet Viterbi differs from the Viterbi path by a few single entries only. We argue that this intuition is wrong, because when unique and $m$-locally Viterbi, then different hybrid paths differ by at least $m$ entries. Thus, when $C$ increases then the different hybrid paths tend to differ from each other by larger and larger intervals. Hence the hybrid paths might offer a variety of rather different solutions to the decoding problem.
Insider employee-led cyber fraud (IECF) in Indian banks: from identification to sustainable mitigation planning

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival.
Training Feedforward Neural Networks Using an Enhanced Marine Predators Algorithm

The input layer, hidden layer, and output layer are three models of the neural processors that make up feedforward neural networks (FNNs). Evolutionary algorithms have been extensively employed in training FNNs, which can correctly actualize any finite training sample set. In this paper, an enhanced marine predators algorithm (MPA) based on the ranking-based mutation operator (EMPA) was presented to train FNNs, and the objective was to attain the minimum classification, prediction, and approximation errors by modifying the connection weight and deviation value. The ranking-based mutation operator not only determines the best search agent and elevates the exploitation ability, but it also delays premature convergence and accelerates the optimization process. The EMPA integrates exploration and exploitation to mitigate search stagnation, and it has sufficient stability and flexibility to acquire the finest solution. To assess the significance and stability of the EMPA, a series of experiments on seventeen distinct datasets from the machine learning repository of the University of California Irvine (UCI) were utilized. The experimental results demonstrated that the EMPA has a quicker convergence speed, greater calculation accuracy, higher classification rate, strong stability and robustness, which is productive and reliable for training FNNs.
Improving Many-to-Many Neural Machine Translation via Selective and Aligned Online Data Augmentation

Multilingual neural machine translation (MNMT) models are theoretically attractive for low- and zero-resource language pairs with the impact of cross-lingual knowledge transfer. Existing approaches mainly focus on English-centric directions and always underperform compared to their pivot-based counterparts for non-English directions. In this work, we aim to build a many-to-many MNMT system with an emphasis on the quality of non-English directions by exploring selective and aligned online data augmentation algorithms. Based on our findings showing that the augmented synthetic samples are not “the more, the better” we propose selective online back-translation (SOBT) and thoroughly study different selection criteria to pick suitable samples for training. Furthermore, we boost SOBT with cross-lingual online substitution (CLOS) to align token representations and encourage transfer learning. Our intuition is based on the hypothesis that a universal cross-lingual representation leads to a better multilingual translation performance, especially for non-English directions. Comparing to previous state-of-the-art many-to-many MNMT models and conventional pivot-based methods, experiments on IWSLT2014 and OPUS-100 translation benchmarks show that our approach achieves a competitive or even better performance on English-centric directions and achieves up to ∼12 BLEU for non-English directions. All of our models and codes are publicly available.
TMVDPatch: A Trusted Multi-View Decision System for Security Patch Identification

Nowadays, the time lag between vulnerability discovery and the timely remediation of the vulnerability is extremely important to the current state of cybersecurity. Unfortunately, the silent security patch presents a significant challenge. Despite related work having been conducted in this area, the patch identification lacks interpretability. To solve this problem, this paper first proposes a trusted multi-view security patch identification system called TMVDPatch. The system obtains evidence from message commit and code diff views respectively, and models the uncertainty of each view based on the D-S evidence theory, thereby providing credible and interpretable security patch identification results. On this basis, this paper performs weighted training on the original evidence based on the grey relational analysis method to improve the ability to make credible decisions based on multi-views. Experimental results show that the multi-view learning method exhibits excellent capabilities in terms of the complementary information provided by control dependency and data dependency, and the model shows strong robustness across different hyperparameter settings. TMVDPatch outperforms other models in all evaluation metrics, achieving an accuracy of 85.29% and a F1 score of 0.9001, clearly verifying the superiority of TMVDPatch in terms of accuracy, scientificity, and reliability.
Incremental Connected Component Detection for Graph Streams on GPU

Studies on the real-time detection of connected components in graph streams have been carried out. The existing connected component detection method cannot process connected components incrementally, and the performance deteriorates due to frequent data transmission when GPU is used. In this paper, we propose a new incremental processing method to solve the problems found in the existing methods for detecting connected components on GPUs. The proposed method minimizes the amount of data to be sent to the GPU by determining the subgraph affected by the graph stream update and by detecting the part to be recalculated. We consider the number of vertices to quickly determine the connected components of a graph stream on the GPU. An asynchronous execution method is used to shorten the transfer time between the CPU and the GPU according to real-time graph stream changes. In order to show that the proposed method provides fast incremental connected component detection on the GPU, we evaluated its performance using various datasets.
Combination of Loss-based Active Learning and Semi-supervised Learning for Recognizing Entities in Chinese Electronic Medical Records

The recognition of entities in an electronic medical record (EMR) is especially important to downstream tasks, such as clinical entity normalization and medical dialogue understanding. However, in the medical professional field, training a high-quality named entity recognition system always requires large-scale annotated datasets, which are highly expensive to obtain. In this article, to lower the cost of data annotation and maximizing the use of unlabeled data, we propose a hybrid approach to recognizing the entities in Chinese electronic medical record, which is in combination of loss-based active learning and semi-supervised learning. Specifically, we adopted a dynamic balance strategy to dynamically balance the minimum loss predicted by a named entity recognition decoder and a loss prediction module at different stages in the process. Experimental results demonstrated our proposed framework’s effectiveness and efficiency, achieving higher performances than existing approaches on Chinese EMR entity recognition datasets under limited labeling resources.
A Context-focused Attention Evolution Model for Aspect-based Sentiment Classification

Due to their inherent capability in the semantic alignment of aspects and their context words, Attention and Long-Short-Term-Memory (LSTM) mechanisms are widely adopted for Aspect-Based Sentiment Classification (ABSC) tasks. Instead, it is challenging to handle long-range word dependencies on multiple entities due to the deficiency in attention mechanisms. To solve this problem, we propose a Context-Focused Aspect-Based Network to align attention before LSTM, making the model focus more on aspect-related words and ignore irrelevant words, improving the accuracy of final classification. This can either alleviate attention distraction or reinforce the text representation ability. Experiments on two benchmark datasets show that the results achieve respectable performance compared to the state-of-the-art methods available in ABSC. Our approach has the potential to improve classification accuracy by adaptively adjusting the focus on context.