Reviewerly/exHarmony huggingface dataset:
Interpretable Federated Transformer Log Learning for Cloud Threat Forensics

Threat detection and forensics have become an imperative objective for any digital forensic triage.Supervised approaches have been proposed for inferring system and network anomalies; including anomaly detection contributions using syslogs.Nevertheless, most works downplay the importance of the interpretability of a model's decision-making process.In this research, we are among the first to propose an interpretable federated transformer log learning model for threat detection supporting explainable cyber forensics.The proposed model is generated by training a local transformer-based threat detection model at each client in an organizational unit.Local models learn the system's normal behavior from the syslogs which keep records of execution flows.Subsequently, a federated learning server aggregates the learned model parameters from local models to generate a global federated learning model.Log time-series capturing normal behavior are expected to differ from those possessing cyber threat activity.We demonstrate this difference through a goodness of fit test based on Karl-Pearson's Chi-square statistic.To provide insights on actions triggering this difference, we integrate an attention-based interpretability module.We implement and evaluate our proposed model using HDFS, a publicly available log dataset, and an in-house collected and publicly-released dataset named CTDD, which consists of more than 8 million syslogs representing cloud collaboration services and systems compromised by different classes of cyber threats.Moreover, through different experiments, we demonstrate the log agnostic capability and applicability of our approach on realworld operational settings such as edge computing systems.Our interpretability module manifests significant attention difference between normal and abnormal logs which provide insightful interpretability of the model's decision-making process.Finally, we deem the obtained results as a validation for the appropriate adoption of our approach in achieving threat forensics in the real world.
Non-Autoregressive Mandarin-English Code-Switching Speech Recognition

Mandarin-English code-switching (CS) is frequently used among East and Southeast Asian people. However, the intra-sentence language switching of the two very different languages makes recognizing CS speech challenging. Meanwhile, the recent successful non-autoregressive (NAR) ASR models remove the need for left-to-right beam decoding in autoregressive (AR) models and achieved outstanding performance and fast inference speed, but it has not been applied to Mandarin-English CS speech recognition. This paper takes advantage of the Mask-CTC NAR ASR framework to tackle the CS speech recognition issue. We further propose to change the Mandarin output target of the encoder to Pinyin for faster encoder training and introduce the Pinyin-to-Mandarin decoder to learn contextualized information. Moreover, we use word embedding label smoothing to regularize the decoder with contextualized information and projection matrix regularization to bridge that gap between the encoder and decoder. We evaluate these methods on the SEAME corpus and achieved exciting results.
Expressive Voice Conversion: A Joint Framework for Speaker Identity and Emotional Style Transfer

Traditional voice conversion (VC) has been focused on speaker identity conversion for speech with a neutral expression. We note that emotional expression plays an essential role in daily communication, and the emotional style of speech can be speaker-dependent. In this paper, we study a technique to jointly convert the speaker identity and speaker-dependent emotional style, that is called expressive voice conversion. We propose a StarGAN-based framework to learn a many-to-many mapping across different speakers, that takes into account speaker-dependent emotional style without the need for parallel data. To this end, we condition the generator on emotional style encoding derived from a pre-trained speech emotion recognition (SER) model. The experiments validate the effectiveness of our proposed framework in both objective and subjective evaluations. To our best knowledge, this is the first study on expressive voice conversion.
Development of Local Content Teaching Material for the History of Wajo

This study aims to develop local content reading books for the history of Wajo in an effort to add insight for educators and students from aspects of local history in Wajo Regency. The development of reading books follows 3 (three) stages from 4 (four) stages of development in the Four D development model. The research subjects consisted of validators as media experts and material experts, two history teachers as practitioners, and 60 students as a test subject. Data collection techniques used questionnaires and interviews, which were analyzed qualitatively and quantitatively. The results of the study showed that: students needed interesting history learning and motivated students to learn more about history so there was a need for reading books that support learning the history of local content in the Wajo Region. Local history was seen as more interesting because it was more contextual than national history. The development of teaching material for the history of Wajo was realized into a digital book (e-book) through the stages of validation and testing to determine the level of practicality. The validity of reading books was carried out by validation by teaching material experts, media experts and Wajo historians. The practicality of this reading book reaches 75.22% in the good category so that it is suitable for use and does not need revision.
Optimizing Stabilizer Parities for Improved Logical Qubit Memories

We study variants of Shor's code that are adept at handling single-axis correlated idling errors, which are commonly observed in many quantum systems. By using the repetition code structure of the Shor's code basis states, we calculate the logical channel applied to the encoded information when subjected to coherent and correlated single qubit idling errors, followed by stabilizer measurement. Changing the signs of the stabilizer generators allows us to change how the coherent errors interfere, leading to a quantum error-correcting code which performs as well as a classical repetition code of equivalent distance against these errors. We demonstrate a factor of $3.78\ifmmode\pm\else\textpm\fi{}1.20$ improvement of the logical $T{2}^{*}$ in a distance-3 logical qubit implemented on a trapped-ion quantum computer. Even-distance versions of our Shor-code variants are decoherence-free subspaces and fully robust to identical and independent coherent idling noise.
Machine Learning-Assisted Differential Distinguishers for Lightweight Ciphers

At CRYPTO’19, Gohr first introduces the deep learning-based cryptanalysis on round-reduced SPECK. Using a deep residual network, Gohr trains several neural network-based distinguishers on 8-round SPECK -32/64. The analysis follows an “all-in-one” differential cryptanalysis approach, which considers all the output differences effect under the same input difference. Usually, the all-in-one differential cryptanalysis is more effective compared to the one using only one single differential trail. However, when the cipher is non-Markov or its block size is large, it is usually very hard to fully compute. Inspired by Gohr’s work, we try to simulate the all-in-one differentials for non-Markov ciphers through machine learning. Our idea here is to reduce a distinguishing problem to a classification problem so that it can be efficiently managed by machine learning. As a proof of concept, we show several distinguishers for four high-profile ciphers, each of which works with trivial complexity. In particular, we show differential distinguishers for 8-round GIMLI-HASH, GIMLI-CIPHER and GIMLI-PERMUTATION; 3-round ASCON-PERMUTATION; 10-round KNOT-256 permutation and 12-round KNOT-512 permutation; and 4-round CHASKEY- PERMUTATION. Finally, we explore more on choosing an efficient machine learning model and observe that only a three-layer neural network can be used. Our analysis shows the attacker is able to reduce the complexity of finding distinguishers by using machine learning techniques.
Distance Education on the Basis of Innovative Technologies. Problems of the Primary School Teacher Training in Ukraine

In the article, the authors analyze international experience of scientists, the Ukrainian realities and prospects for selection of expedient innovation tools. The purpose of such selection is to optimize distance education in the primary school teacher training in Ukraine on the basis of innovative technologies. The relevance of the topic is determined by a number of contradictions between the need for innovations in distance education and objective obstacles on the way to this. The purpose of the article is formulation of selection criteria, selection of the most urgent and most relevant accessible technologies and the theoretical substantiation of their application (on the example of teaching and learning the discipline “Mathematics”). The methodology involved includes the following methods: searching, sampling (according to relevance, effectiveness, regional features) and theoretical. Methods of modeling, generalization and forecasting were applied partly. The result of the work is substantiation of various types of innovative lectures as the best method of distance teaching and selection of specific technologies which (according to the didactic evidence) are the most effective. Also, the efficiency of the Moodle platform is described as the most universal access tool. Along with the literature review, fragments of discussion and comparison took place, which testified that the Ukrainian problem of innovative distance education belongs to the resource and psychological (stereotypes) area. International significance. The article is interesting for the reformers of education in developing countries and the scientists who follow the world tendencies in the education digitalization.
Voice Conversion for Dubbing Using Linear Predictive Coding and Hidden Markov Model

Dubbing is a term used to describe filling in the sound on film or video. Voice conversion can be done to support dubbing, for purposes such as obtaining a child’s voice for dubbing on children’s films. However, problems frequently occur with this process, including difficulty finding children’s voice resources and difficulty getting children to express the desired tone and mood while recording. Therefore, in this study, we propose a method for creating a cross-gender and age voice conversion from adult voices to children’s voices. The feature extraction method that is used is Linear Predictive Coding, and the modeling method is the Hidden Markov Model. The parts synthesized are fundamental frequency (F0) and spectral content. From the simulation test, the best results for the voice conversion are achieved by Linear Predictive Coding order 19. The best state of Hidden Markov Model modeling is the 5th state. F0 Root Mean Square Error of adult men to children after the conversion increased by 57.7%, while the F0 Root Mean Square Error of adult women to children after the conversion increased by 15.29%. Root Mean Square Error Cepstral after conversion increased by 43.69%. A subjective test was also performed in terms of the mean opinion score. In terms of similarities, mean opinion score testing for Hidden Markov Model has an average value of 2.64, and in terms of quality, testing mean opinion score for Hidden Markov Model has an average value of 3.23. It is hoped that this proposed method can be used in real terms for dubbing in the film industry, especially for Indonesian dialogue.
Analysis of the NHSX Contact Tracing App ‘Isle of Wight’ Data Protection Impact Assessment

This note examines the published data protection impact assessment (DPIA) released by NHSX in relation to their contact tracing/proximity tracing app. It highlights a range of significant issues which leave the app falling short of data protection legislation. It does this in order so that these issues can be remedied before the next DPIA is published. The main issues this note focuses on are the following:Personal data- The DPIA must not claim this data is anonymous, or that the app preserves anonymity, as under UK law, it does not.- The document (and associated public messaging) must be changed throughout to reflect the fact that it is not the case that personal data about a user is only uploaded with a user’s permission, as other people upload data revealing a user's social interactions.User rights- The lawful basis for a blanket refusal of the right to erasure is unspecified by NHSX in this DPIA.- The NHSX App unlawfully designs out the right to access when there is a legal obligation to design it in.- If the controller plans to, as with the right to erasure and the right to access, refuse all attempts at the right to object, this needs a justification in the DPIA.Monitoring and automated decision making- The DPIA must acknowledge the NHSX App systematically monitors publicly accessible spaces.- The DPIA does not set out a valid lawful basis for the solely automated, significant decision-making it correctly identifies as occurring.- The information contained in the document embedded in the DPIA describing the logic of automated decisions must be provided under GDPR, article 13.Prior consultation and e-Privacy- The Information Commissioner must be consulted prior to processing within the meaning of GDPR, art 36, not just briefed.- The DPIA should explain how the The Privacy and Electronic Communications Regulations are complied with, both in relation to Bluetooth usage and in relation to embedded trackers.The note does not consider alternative architectures or less intrusive means to achieve the purposes of the NHSX app, although these are critical issues that this DPIA could be argued as failing to assess. This note is unable to assess the risks of the app as provided by the DPIA as all the risks have been redacted.
On the Sanctuary of a Combined Confusion and Diffusion based scheme for Image Encryption

Confusion and diffusion are the frequently used embryonics in multimedia (image) encryption systems. Multimedia data protection against cryptanalysis can be effectually fortified by these techniques. Due to inherent properties of images such as high inter-pixel redundancy and bulk data capacity, encryption is performed in two stages: Confusion and Diffusion. In this article, a combined Pseudo Hadamard transformation in the confusion stage and Gingerbreadman chaotic substitution in the diffusion stage are used in the encryption phase of the algorithm. The strong correlation between contiguous elements in the host image is effectually reduced using Pseudo Hadamard transformation and entropy in the cipher image is enhanced using Gingerbreadman chaotic substitution. Secrete key length used in the algorithm is 128 bits, these are the initial conditions for Gingerbreadman chaotic generator. The elements of S-box in the substitution stage are considered from this random sequence generator. Experimental exploration including information entropy, correlation analysis, sensitivity analysis, key space analysis and computational complexity have been performed on set of standard images. Results obtained are better compared to many existing systems.
Students' performance analysis system using cumulative predictor algorithm

Towards automation to do mundane tasks and the expectations for students already equipped with good programming skills is on the rise. In parallel, there has been a rising number of students who find it difficult to attain the skills necessary in order to get the dream IT job they desire. The aim of this project is to bridge the gap between the employer and the future employee of the company by the use of SPAS at college level. Student performance analysis system (SPAS) is an online web application system which enables students to know prior hand if their level of skills for the placement is enough to get placed or not, given the necessary inputs. SPAS has an intelligent learning algorithm which utilises a rich database, analyses the records of previous students' traits and develops a model for further prediction. The performance evaluation of students by SPAS is by the cumulative predictor algorithm involving generation of several random forest trees on the available data. SPAS learns and creates its model reaching higher accuracy with increasing data availability.
Recognizing Falls, Daily Activities, and Health Monitoring by Smart Devices

One of the biggest challenges in ageing societies is to improve life, health, safety, and support of the elderly population in their daily life.Currently, the number of elderly people living alone is increasing every year.Living alone allows more freedom but raises the risk of serious injuries or fatal accidents.Falls are the key cause of significant health problems, especially for an elderly person who lives alone.Moreover, vital signs such as heart rate, balancing activities, and environmental context are crucial in relation to the user's condition.To assist people living alone and improve their health quality, we firmly believe that the advances in Smart Devices, Smart Environment, and Internet of Things paradigms are very helpful for developing a fall and activity recognition system.We propose a system using an unobtrusive device consisting of a smartwatch and a smartphone to identify falls and thirteen daily activities (e.g., walking, running, typing, and waving the hand).The events leading to a fall, the speed of falling down, the heart rate while doing an activity, and the time passed since the fall are important data that we store to help a doctor diagnose and rehabilitate a patient.Environment sensors are used to indicate the indices of ambient conditions such as temperature, humidity, brightness, and motion detected.Suitable machine learning techniques are used for daily activity recognition, and the processing time for classification was compared on the basis of a smartwatch and an Amazon Web Services (AWS) cloud server.Threshold-based health risk analysis models are utilized for abnormal activity recognition and heart rate and heat index (temperature and humidity) determination.The system issues different types of notifications such as warning messages, sounding alarms, and phone calls to related persons such as family members, caregivers, or doctors.Various easy-to-understand visualizations are presented to track and monitor the subjects in real time, including heart rate, daily activity summary, health risk status, and environmental information.
Fusion of Wearable and Contactless Sensors for Intelligent Gesture Recognition

Multi-Sensor Data Fusion In article number 1900088, Hadi Heidari and co-workers describe a smart sensor organizer hierarchical support vector machine, made up of multilayer support vector machines, to cognitively fuse different sensors for better performance of a complex sensing system. This multisensory fusion mechanism is validated by a combination of wearable wrist-worn and contactless radar sensors for novel and accurate gesture recognition.
Word Representation

Abstract Word representation, aiming to represent a word with a vector, plays an essential role in NLP. In this chapter, we first introduce several typical word representation learning methods, including one-hot representation and distributed representation. After that, we present two widely used evaluation tasks for measuring the quality of word embeddings. Finally, we introduce the recent extensions for word representation learning models.
A Study of Software Development Cost Estimation Techniques and Models

SDCE (Software Development Cost Estimation) has always been an interesting and budding field in Software Engineering. This study supports the SDCE by exploring its techniques and models and collecting them in one place. This contribution in the literature will assist future researchers to get maximum knowledge about SDCE techniques and models from one paper and to save their time. In this paper, we review numerous software development effort and cost estimation models and techniques, which are divided into different categories. These categories are parametric models, expertise-based techniques, learning-oriented techniques, dynamicsbased models, regression-based techniques, fuzzy logic-based methods, size-based estimation models, and composite techniques. Some other techniques which directly do not lie in any specific category are also briefly explained. We have concluded that no single technique is best for all situations; rather they are applicable in different nature of projects. All techniques have their own pros and cons and they are challenged by the rapidly changing software industry. Since no single technique gives a hundred percent accuracy, that is why one technique and model should not be preferred over all others. We recommend a hybrid approach for SDCE because in this way the limitations of one model and technique are complemented by the merits of the other model/technique. We also recommend a model calibration to obtain accurate results because if a model was developed in a different environment, we cannot expect reliable estimates from it in a completely new environment.
Blockchain, Securities Markets, and Central Banking

Distributed ledger technology, a variant of which is blockchain technology, represents one of the most important innovations of the FinTech revolution. Academics, policy-makers, and market participants are experimenting with the technology with the aim of enhancing the functioning of financial markets. Industry consortia are being formed by the biggest financial institutions in the world seeking to leverage the use of the technology, in order to improve the clearing and settlement process. Furthermore, central banks in advanced and developing economies are examining the potential of using the technology in market infrastructures operated by central banks and are even exploring the possibility of issuing digital base money. Nevertheless, the widespread adoption of distributed ledger technology as envisioned by its ardent supporters encounters considerable legal obstacles, including the numerous new regulations imposed on financial markets and market participants in the aftermath of the Global Financial Crisis. This chapter seeks to disentangle the myths from the realities of the so-called distributed ledger technology or blockchain revolution and discusses how the legal regime can act both as an impediment and a catalyst to the widespread adoption of the technology.
Computational linguistic retrieval framework using negative bootstrapping for retrieving transliteration variants

In NLP, one of the imperative and relatively less mature area is transliteration. During transliteration, issues like language identification, script specification arise in mixed script queries. To overwhelm these issues, we propose a new technique called negative bootstrapping with frequent matrix apriori for transliteration. Roman script is widely used in web search query for searching contents. The major challenge that the system face to process transliterated word is because of its existence in more than one form. The experimental evaluation has been done to check transliteration accuracy along with language identification against established methods. The paper offers a high-principled answer to handle multiple scripts used in a document leading to the problems of term matching and committing variations in spelling while searching the contents. The problem is modelled collectively with the deep-learning design and achieves significantly better results when applied to n-gram approach on the benchmark dataset.