Reviewerly/exHarmony huggingface dataset:
Breaking KASLR on Mobile Devices without Any Use of Cache Memory

Microarchitectural attacks utilize the performance optimization constructs that have been studied over decades in computer architecture research and show the vulnerability of such optimizations in a realistic framework. One such highly performance driven vulnerable construct is speculative execution. In this paper, we focus on the problem of breaking the kernel address-space layout randomization (KASLR) on modern mobile devices without using cache memory as a medium of observation. However, there are some challenges to breaking KASLR on ARM CPUs. The first challenge is that eviction strategies on ARM CPUs are slow, and the microarchitectural attacks exploiting the cache as a covert channel cannot be implemented on modern ARM CPUs. The second challenge is that non-canonical addresses are stored in the store buffer, although they are invalid. As a result, previous microarchitectural attacks distinguish such addresses as valid kernel addresses erroneously.
Text detection method based on HDBNet in natural scenes

To solve the problem of large expansion offset of text detection in natural scenes, a text detection method based on HDBNet is proposed. First, the probability map of the text region is obtained by segmenting the image. The binary map is obtained by using the differentiable binarization of the probability map. The bounding box of the text region can be obtained by looking for the connected region on the binary map. Then, aiming at the problem of large expansion offset of text detection in natural scenes, a scheme of height prediction is adopted to compensate for the expansion loss caused by the width-to-height ratio. Finally, experiments show that when the depth network structure is ResNet-50, the Precision, Recall and F-measure of the proposed HDBNet method in TD500 data set are 0.9196, 0.8058 and 0.8590, respectively. Moreover, in MLT data set, the Precision, Recall and F-measure of the proposed HDBNet method are 0.9393, 0.7884 and 0.8573 respectively. The results of HDBNet are higher than those of the comparison methods. Therefore, compared with the comparison methods, the proposed HDBNet method can significantly improve the performance of the text detection system.
An Alternative Fashion to Automate the Appropriateness of ALT-Text using Microsoft Computer Vision API

Designing and releasing of software’s in production that contains images takes a lot of time due to the need of finding ALT-text attributes for the images embedded in the applications. This paper automates the task of writing ALT-text attributes in HTML, especially if image integration is large with the use of python PIP package and Microsoft Computer Vision API. This will save huge time and efforts of the developers by automating the task of captioning images manually up to a great extent. The challenge that confronts us is the quality of annotations generated by the machine with respect to the human generated annotations. To study the appropriateness of the captions delivered by APIs, a blend of human and machine assessment was used. We have noticed a high similarity in human and machine generated annotations as we obtained individual and cumulative BLEU score metric . Another metric is confidence score with a percentage mean of 0.5 .Also, we have calculated the time taken per caption which is 1.6 seconds per image which took 6.01 minutes to caption 200 images.
A Brief Overview of Digital Forensics

This article discusses the branch of forensic sciences referred to as Digital Forensics. It explores each phase of the digital forensic lifecycle and takes an in-depth look at how casework is typically conducted. A discussion of both the historical context of digital forensics as well as emerging standards are also examined. The article concludes with a discussion of what the future holds for digital forensics.
Payment Systems

Fintech refers to current advancements in the financial sector. The burgeoning sub-discipline of digital forensics that focuses on financial technologies addresses distinct and consequential digital evidence concerns. Fintech for payments, funds transfer, and other financial operations are being introduced by the digital revolution of society. Criminals use and abuse financial technologies for fraud, extortion, money laundering, and underground criminal activity financing. Fintech and digital payment investigation must be regarded as a new technological subdiscipline within the digital forensics landscape. The digital forensics community is in a prime position to supply practitioners with research that will strengthen investigations involving Fintech and technical financial activities. Fintech provides more options for online con artists, including persuading victims to acquire a transferrable object of value. An example of a fraudulent scheme is a con artist posing as an authoritative figure in order to convince victims to purchase digital gift cards and provide the card usage code to the culprit. A review of an incident offers lessons that can be used to prevent future fraud-related losses and illustrates the multidisciplinary approach required to manage an incident and prevent future occurrences. An event response can be divided into four distinct phases: (1) fraud identification, (2) crisis management, (3) post-incident analysis, and (4) reporting to various communities. Lessons can be drawn from previous incidents in order to highlight the importance of forensic knowledge and crime analysis in interpreting the information conveyed by digital traces. From that information, innovative cross-disciplinary models can be developed for preventing, detecting, analysing, investigating, and responding to online fraud.
American Society of Crime Laboratory Directors (ASCLD)

The American Society of Crime Laboratory Directors (ASCLD) is a nonprofit professional society of forensic laboratory directors and forensic science managers working together to provide excellence in forensic science through leadership and innovation.
Packaging and Labelling of Evidence

Protecting and recording the scene, adopting correct collection processes, and finally, securing transportation of evidence will largely participate to both the analytical and judicial chains of custody, inescapable conditions to ascertain scene reconstruction and the juridically agreed solving of the incident.
Forensic Inference From Telecommunications Provider Call Data Records: Historic Cell Site Analysis

The data stored on mobile phones is a common source of investigative and evidential material, but there may be further useful information available such as Call Data Records (CDRs), held by network operators for billing purposes. This information is potentially available to be used within criminal investigations. Historic Cell Site Analysis involves the analysis of these records, in conjunction with other information such as survey and geographic data, to determine areas where a mobile device may, or may not, have been at the time of call activity recorded by the network. The evidence provided by cell site practitioners therefore often includes expert opinion concerning whether data might be expected given the positions taken by prosecution and/ or defence and to provide speculative scenarios which might generate the data considered. While primarily focussed on the situation for the UK; the technologies described below are those used in the UK, are defined in international standards, and are implemented in many other countries. Other mobile phone network operators and jurisdictions will have largely similar technical and legislative frameworks, but potentially with local differences.
Physical Match

A physical match, also known as “fracture match” or “physical fit”, is defined as “the examination of two or more objects, through physical, optical, or photographic means, which permit one to conclude that the objects were either one entity or held bonded together in a unique arrangement”. Physical match is viewed as a strong way for establishing common origin. Physical match has a long history of acceptance in courts of law, and in the forensic literature, it is often regarded as strong conclusive evidence. Physical match can be found in various materials such as glass, plastic, and metallic objects. When the matching surface is large or the matching fracture line is long, the amount of information is sufficient, and there is no doubt that the match is correct. The pieces will fit together in a manner that will convince every layman of the true match. Based on the Daubert ruling in the United States, every scientific examination should be able to demonstrate its known or potential “error rate” in order to demonstrate reliability for admissibility. The scientific process that strengthens the expert׳s conclusion exists, and the “potential error rate” can be shown. Therefore, the admissibility of the physical match comparison should stand in court.
Organization of Scientific Area Committees (OSAC) for Forensic Science

The National Institute of Standards and Technology (NIST) established the Organization of Scientific Area Committees (OSAC) for Forensic Science in 2014 to address a lack of discipline-specific forensic science standards. OSAC’s mission is to strengthen the nation׳s use of forensic science by facilitating the development of technically sound standards and guidelines and encourage their use throughout the forensic science community. OSAC’s 800-plus members and affiliates work in forensic laboratories and other institutions around the country and have expertise in 22 forensic disciplines, as well as scientific research, measurement science, statistics, law, and policy. OSAC drafts and evaluates forensic science standards via a transparent, consensus-based process that allows for participation by all stakeholders.
Motor Vehicle Digital Forensics

Regulations, technical advancement, and consumer preferences are driving significant change in the motor vehicle business. There are a multitude of data in modern-day motor vehicles and although the sub-discipline is in its infancy, motor vehicle digital forensics allows examiners to access a wide range of digital evidence from motor vehicles. The evidence will be increasingly used in vehicle-related investigations over the coming decade. Driving-related data, personal data (e.g., call logs, contact lists, SMS messages, photographs, and videos), and other communication data are all generated and transmitted in the telematics and infotainment systems which can send, receive, and retain data. Over the next decade, digital evidence from the Telematics/Infotainment System will most likely have the largest impact on vehicle-related investigations.
Document Analysis, History, and Foundations

The act of forgery – producing a copy of a document, signature, or handwriting – has existed since the development of writing. Although early courts may have relied on “recognition witnesses,” this shifted in the early part of the twentieth century, specifically for English-speaking countries. Today, forensic document examiners (FDEs) must be trained and highly skilled to identify forgery from the crudest attempt to the most skilled simulations. FDEs examine cases to determine the origin, history, and authenticity of documents, handwriting, and signatures. FDEs must follow a scientific method to analyze, compare, and evaluate evidence and do so without bias. Once a determination has been reached, reports and even expert testimony may be required to convey the expert’s opinions as well as the specific analysis and method. The examination of documents themselves may seem mundane, but they play a significant role in helping find answers to resolve cases involving estate settlements, elder abuse, murder, and even suicide. There have been many publicized cases where the handwriting was utilized and examined; sometimes these cases were solved like the Howard Hughes will case, sometimes creative techniques were required as used in the Mormon forgery murders, and sometimes cases go unsolved such as the Zodiac killer.
Internet of Things (IoT) Forensics

The “Internet of Things” (IoT) is a relatively new development describing the network of physical objects that have the ability to sense or react to their environment. It is expected that the IoT will grow to massive proportions generating huge amounts of data about individuals and their environments. That data can potentially provide substantial contextual information to criminal investigations. However, there is great variability in the format and structure of the generated data, compounded by the potentially multiple locations (device, smart phone, network and cloud) in which the data for a specific event might be located. The complexity of forensic examination and analysis further compounded by the vast range of IoT manufacturers and their offerings who based their designs and supporting architecture based on their own company philosophy. Given that IoT forensics is a new field, analysis and examination techniques are very experimental at this stage.
Certification and Licensing of Forensic Science Practitioners

Certification and licensing are mechanisms available for use by the forensic science and criminal justice communities to help ensure the quality of reported observations, findings, and test results. Unlike accreditation, which addresses quality with respect to the forensic science service provider, certification and licensing are used to specifically address the qualifications and competence of the practitioners who conduct and report on the actual work. The main goal of both certification and licensing is to help ensure public confidence in the quality of the work conducted and reported on by the individual practitioners.
Mobile Device Forensics

The use of mobile devices is ubiquitous throughout most of the world, no matter the status of a community’s economic development. Therefore, mobile devices are the most common source of digital evidence in criminal and civil investigations. Although mobile device forensics originated from computer forensics and is considered to be a sub-discipline of digital forensics, there are significant differences in the principles and examination method between the two. Consequently, mobile device forensics is a specialist area of practice in its own right due to the different data structures and data storage strategies; and the highly dynamic and rapidly evolving consumer markets in hardware, software (both operating system and “shipped with” apps) and third party apps. Mobile forensics can directly link information to suspects due to the highly personal nature of devices, their security features and their function as a silent witness due to their constant accompaniment with their owner.
Child Exploitation

The Internet provides new opportunities for the sexual exploitation of children. Individuals with a sexual interest in children may use the Internet in several ways, such as obtaining or sharing child abuse material, grooming children online, or communicating with other individuals with a sexual interest in children. This chapter will focus on ICT (Information and Communications Technology)-facilitated child sexual exploitation and abuse (I-CSEA) and the forensic techniques that may contribute to uncovering and solving such crimes.
Handwriting

The examination of handwriting has long been undertaken for the purpose of determining whether a particular known writer signed a questioned document or wrote a questioned entry. This article introduces the theory and practice of forensic handwriting and signature examination. Movement science is briefly explained to help understand the development of handwriting features, and in particular, within- and between-writer variation. Disguise and simulation in handwriting is discussed. The method that forensic handwriting examiners use to compare handwriting is outlined, and reporting approaches summarized. Digitally captured writing is defined and the complexities of examining this kind of handwriting are highlighted.
Bayesian Networks and Influence Diagrams

Bayesian networks are graphical models that have been developed in the field of artificial intelligence as a framework to help researchers and practitioners apply probability theory to inference problems of substantive size as encountered in real-world applications. Influence diagrams (Bayesian decision networks) extend Bayesian networks to a modeling environment for coherent decision analysis under uncertainty. This article provides an overview of these methods and explains their contribution to the body of formal methods for the study, development and implementation of probabilistic procedures for assessing the probative value of scientific evidence and the coherent analysis of related questions of decision-making.
European Network of Forensic Science Institutes (ENFSI)

The European Network of Forensic Science Institutes is a professional association established to share knowledge, exchange experiences, and seek mutual agreements in the field of forensic science.
Standards and Conformity Assessment

Standards and conformity assessment are important to strengthening the science and quality of forensic science. Standards provide a foundation for consistency between processes related to testing, accreditation, management systems, and personnel certification. This article provides an overview of U.S. and international documentary standards and conformity assessment concepts as they relate to forensic science service providers (FSSPs) and discusses the current state of standards development and associated challenges for implementation.
Modus Operandi

Modus Operandi (MO) is a Latin term that means method of operating. It refers generally to the manner in which a crime has been committed. A criminal’s modus operandi is made up of choices and behaviors that are intended to assist the criminal in the completion of the crime. In other words, an offender’s modus operandi refers to how they committed a crime. It is separate from the offender’s motives or signature behaviors, as these reflect why an offender commits a particular crime. Investigators have long understood that learning the materials, methods and techniques criminals use to commit crime is in fact the best way to investigate, identify, and ultimately apprehend them. This has traditionally required that the best investigators become living encyclopedias of criminal cases and related criminal activity. This in turn informs their investigative strategy – especially in those cases involving criminals, or specific types of offenses, that are repetitious. Modus Operandi Analysis is a key feature in Crime Reconstruction, Crime Scene Analysis, and Criminal Profiling – all of which relate to different kinds of expert testimony provided in court by forensic scientists in general, and forensic criminologists in specific. This article discusses Modus Operandi including the historical use, crime analysis, elements of MO, destabilizing factors, and X-factor.
EdgeSelect: Smartwatch Data Interaction with Minimal Screen Occlusion

We present EdgeSelect, a linear target selection interaction technique that utilizes a small portion of the smartwatch display, explicitly designed to mitigate the ‘fat finger’ and screen occlusion problems, two of the most common and well-known challenges when interacting with small displays. To design our technique, we first conducted a user study to answer which segments of the smartwatch display have the least screen occlusion while users are interacting with it. We use results from the first experiment to introduce EdgeSelect, a three-layer non-linear interaction technique, which can be used to interact with multiple co-adjacent graphs on the smartwatch by using a region that is the least prone to finger occlusion. In a second experiment, we explore the density limits of the targets possible with EdgeSelect. Finally, we demonstrate the generalizability of EdgeSelect to interact with various types of content.
Multi-level Fusion of Multi-modal Semantic Embeddings for Zero Shot Learning

Zero shot learning aims to recognize objects whose instances may not be covered by the training data. To generalize knowledge from seen classes to the novel ones, semantic space is built to embed knowledge from various views into multi-modal semantic embeddings. Existing semantic embeddings neglect the relationships between classes which are essential to transfer knowledge between classes. Moreover, existing zero shot learning models ignore the complementarity between semantic embeddings from different modalities. To tackle these problems, in this work, we resort to graph theory to explicitly model the interdependence between classes and then obtain new modal semantic embeddings. Furthermore, we pioneer to propose a multi-level fusion model to effectively combine knowledge encoded in multi-modal semantic embeddings together. By the virtue of subsequent fusion block, the results of multi-level fusion can be furtherly enriched and fused. Experiments show that our model could achieve promising results on various datasets. Ablation study suggests that our method is well suited for zero shot learning.
POLLY: A Multimodal Cross-Cultural Context-Sensitive Framework to Predict Political Lying from Videos

Politicians lie. Frequently. Depending on the country they are from, politicians may lie more frequently on some topics than others. We develop the novel concept of a tripartite “VAT” graph (Video-Article-Topic) with three types of nodes: videos (with a politician featured in each), news articles that mention the politician, and topics discussed in the videos or articles. We develop several novel types of audio and video deception scores for each audio/video, as well as a topic deception score and an edge deception score for each edge in the graph. Our POLLY (POLitical LYing) system builds upon past work by others to generate predictions for whether a politician is lying or not. We test POLLY on a novel dataset (which will be made publicly available upon publication of this paper) consisting of 146 videos and 6337 news articles involving 73 politicians from 18 countries from all major continents. We show that POLLY achieves AUC and F1 scores over 77%, beating out several baselines. We further show that POLLY is robust to translation errors made by Google Translate.
The Effects of an Embodied Pedagogical Agent’s Synthetic Speech Accent on Learning Outcomes

Modern text-to-speech engines can be an effective speech choice for embodied virtual pedagogical agents. However, it is not known how synthesized accents influence learning outcomes and perceptions of the agent. In this paper, we conducted a between-subjects experiment (n=60) to determine the effect of a pedagogical agent’s machine synthesized text-to-speech accent (United States English or Indian English) on learning outcomes and perceptions of the agent for students in the United States. Our results indicate that learner gender interacts with synthesized speech accent to significantly affect learning outcomes and perceptions of the agent. Our results reveal that a foreign synthetic speech accent may affect the learning outcomes of female university students (n=30), but not male university students (n=30). Finally, our results indicate that learner gender interacts with synthesized speech accent to affect perceptions of the pedagogical agent’s human-likeness. We provide novel insights on the differences between male and female learners for interactions with pedagogical agents with synthetic TTS accents.