Reviewerly/exHarmony huggingface dataset:
Intelligent System of Productivity Monitoring and Organic Garden Marketing Based on Digital Trust with Multi-criteria Decision-Making Method

The problem of food security in Indonesia is a serious problem that needs to be immediately sought for alternative solutions. The rapid development of information technology should be one of the great opportunities for improving the management of agricultural production and marketing in Indonesia and supporting sustainable food security programs. Organic farming that relies on natural inputs has complexities, especially since Organic is a process claim, not a product claim. Therefore, IT-based organic production and marketing management for group or independent garden scales will encourage the realization of food self-sufficiency in Indonesia. This study aims to build an intelligent system for monitoring the productivity and marketing of organic gardens based on digital trust by applying the Multi-Criteria Decision Making (MCDM) Method. The research partner is the Indonesian Agri Empowered SME, which is involved in the management and development of organic farmers, including marketing organic agricultural products. They must still integrate information technology to support effective and efficient organic farming management. The application enables Farmer Partner and BAI administrators to monitor the development of organic cultivation inventory. With this, it is possible to follow up on information as soon as possible. Organic agriculture employs manure or organic liquid fertilizer, pure water for watering, and organic pesticides. The soil conditions utilized in organic agriculture must be free of chemical residues from conventional agriculture. Customers can place orders, make payments, and request a shipping address based on their desired destination.
Sustainable Material-Based Bedside Table Equipped with a Smart Lighting System

The availability of wood in nature cannot keep up with the growing number of people's needs. Numerous wood processing methods, including plywood, blockboard, and veneer, have evolved to fulfill the demands of the solid wood furniture business. Various synthetic materials, such as high-pressure laminate (HPL), have also been circulating for the covering materials used in furniture finishing. Despite being processed, materials used in production, including furniture creation, create waste. This study aims to produce a bedside table from waste materials, including the primary and finishing components. This furniture has a smart lighting system using an Arduino Uno microcontroller and PIR and LDR sensors. Design exploration is employed in this qualitative research project to create bedside tables with smart lighting features out of woodworking waste. This study tries to define, ideate, and create a prototype. The first practical step is designing and creating a bedside table prototype with an automatic light function that can only turn on in low light settings. The two sensors were function-tested before being incorporated into the furniture. Based on the findings of multiple experiments, the sensor was declared successful and installed on the bedside table. This product uses sustainable materials in its creation, reusing plywood and HPL waste material to make a bedside table. Additionally, this table uses sustainable energy in the form of energy-saving lights and efficient use, which is only performed at night or in low-light situations.
Wireless Sensor Collar for Automatic Recognition of Canine Agility Activities

Canine agility is a rapidly growing sport where dogs and their handlers navigate obstacle courses. Recent studies show that over 40% of all agility dogs suffer an injury while training or competing. By collecting and analyzing sensor measurements from a wearable computer while dogs participate in the sport, we hope to better inform dog handlers and trainers and improve the performance and overall health of their dogs. As a first step towards this long-term project goal, we present the initial validation of a bespoke collar-worn activity tracker and machine learning classifier for recognizing agility activities. The ability to classify agility activities from collar-worn sensors will provide the groundwork for further analysis of relative activity exertion levels, activity variance with repetitions, and gait regularity. To validate our system, we conducted a pilot study of six dogs performing a short agility course including three different agility obstacles. Our wireless sensor collar was able to provide data in real-time via WiFi while dogs navigated the obstacles. Our MINIROCKET-based machine learning classifier achieved 85% accuracy across the pilot study data.
Arboretum: A Planner for Large-Scale Federated Analytics with Differential Privacy

Federated analytics is a way to answer queries over sensitive data that is spread across multiple parties, without sharing the data or collecting it in a single place. Prior work has developed solutions that can scale to large deployments with millions of devices but, due to the distributed nature of federated analytics, these solutions can support only a limited class of queries - typically various forms of numerical queries, which can be answered with lightweight cryptographic primitives. Supporting richer queries, such as categorical queries, requires heavier cryptography, whose cost can quickly exceed even the resources of a powerful data center.
Automated Verification of an In-Production DNS Authoritative Engine

This paper presents DNS-V, a verification framework for our in-production DNS authoritative engine, which is the core of our DNS service. The key idea for automated verification in general is based on the layered verification principle. However, we face the challenge that our in-production DNS authoritative engine lacks modularity, more specifically, as can be seen with unclean interfaces and poor data structure encapsulation. This makes the layered verification hard to apply. To address this challenge, we propose a summarization approach that performs full-path symbolic execution to accumulate all path conditions and computation effects, and then represents a module's behavior in an abstract form as a set of input-effect pairs. In addition, for portability to future iterated versions of our DNS authoritative engine, we identify common dependency library modules that remain stable across different versions, and carefully design their abstractions to make them amenable to automated reasoning. Our framework has been successful in identifying and preventing tens of critical bugs in different versions of our DNS authoritative engine from reaching production, with a porting effort of less than one person-week.
Determining cybersecurity culture maturity and deriving verifiable improvement measures

Purpose The human factor is the most important defense asset against cyberattacks. To ensure that the human factor stays strong, a cybersecurity culture must be established and cultivated in a company to guide the attitudes and behaviors of employees. Many cybersecurity culture frameworks exist; however, their practical application is difficult. This paper aims to demonstrate how an established framework can be applied to determine and improve the cybersecurity culture of a company. Design/methodology/approach Two surveys were conducted within eight months in the internal IT department of a global software company to analyze the cybersecurity culture and the applied improvement measures. Both surveys comprised the same 23 questions to measure cybersecurity culture according to six dimensions: cybersecurity accountability, cybersecurity commitment, cybersecurity necessity and importance, cybersecurity policy effectiveness, information usage perception and management buy-in. Findings Results demonstrate that cybersecurity culture maturity can be determined and improved if accurate measures are derived from the results of the survey. The first survey showed potential for improving the dimensions of cybersecurity accountability, cybersecurity commitment and cybersecurity policy effectiveness, while the second survey proved that these dimensions have been improved. Originality/value This paper proves that practical application of cybersecurity culture frameworks is possible if they are appropriately tailored to a given organization. In this regard, scientific research and practical application combine to offer real value to researchers and cybersecurity executives.
gSampler: General and Efficient GPU-based Graph Sampling for Graph Learning

Graph sampling prepares training samples for graph learning and can dominate the training time. Due to the increasing algorithm diversity and complexity, existing sampling frameworks are insufficient in the generality of expression and the efficiency of execution. To close this gap, we conduct a comprehensive study on 15 popular graph sampling algorithms to motivate the design of gSampler, a general and efficient GPU-based graph sampling framework. gSampler models graph sampling using a general 4-step Extract-Compute-Select-Finalize (ECSF) programming model, proposes a set of matrix-centric APIs that allow to easily express complex graph sampling algorithms, and incorporates a data-flow intermediate representation (IR) that translates high-level API codes for efficient GPU execution. We demonstrate that implementing graph sampling algorithms with gSampler is easy and intuitive. We also conduct extensive experiments with 7 algorithms, 4 graph datasets, and 2 hardware configurations. The results show that gSampler introduces sampling speedups of 1.14--32.7× and an average speedup of 6.54×, compared to state-of-the-art GPU-based graph sampling systems such as DGL, which translates into an overall time reduction of over 40% for graph learning. gSampler is open-source at https://tinyurl.com/29twthd4.
How large language models can be used in the field of orthodontics

How large language models can be used in the field of orthodontics - JCO- Print ISSN No: - Online ISSN No:- 2582-0478 Article DOI No:- 10.18231/j.jco.2023.042, Journal of Contemporary Orthodontics-J Contemp Orthod
On the Utility of Virtual On-body Acceleration Data for Fine-grained Human Activity Recognition

Previous work has demonstrated that virtual accelerometry data, extracted from videos using cross-modality transfer approaches like IMUTube, is beneficial for training complex and effective human activity recognition (HAR) models. Systems like IMUTube were originally designed to cover activities that are based on substantial body (part) movements. Yet, life is complex, and a range of activities of daily living is based on only rather subtle movements, which bears the question to what extent systems like IMUTube are of value also for fine-grained HAR? In this work we first introduce a measure to quantitatively assess the subtlety of human movements that are underlying activities of interest–the motion subtlety index (MSI)–which captures local pixel movements and pose changes in the vicinity of target virtual sensor locations, and correlate it to the eventual activity recognition accuracy. We explore for which activities with underlying subtle movements a cross-modality transfer approach works, and for which not. As such, the work presented in this paper allows us to map out the landscape for IMUTube-like system applications in practical scenarios.
C-Auth: Exploring the Feasibility of Using Egocentric View of Face Contour for User Authentication on Glasses

C-Auth is a novel authentication method for smart glasses that explores the feasibility of authenticating users using the facial contour lines from the nose and cheeks captured by a down-facing camera in the middle of the glasses. To evaluate the system, we conducted a user study with 20 participants in three sessions on different days. Our system correctly authenticates the target participant versus the other 19 participants (attackers) with a true positive rate of 98.0% (SD: 2.96%) and a false positive rate of 4.97% (2.88 %) across all three days. We conclude by discussing current limitations, challenges, and potential future applications for C-Auth.
Generating Virtual On-body Accelerometer Data from Virtual Textual Descriptions for Human Activity Recognition

The development of robust, generalized models for human activity recognition (HAR) has been hindered by the scarcity of large-scale, labeled data sets. Recent work has shown that virtual IMU data extracted from videos using computer vision techniques can lead to substantial performance improvements when training HAR models combined with small portions of real IMU data. Inspired by recent advances in motion synthesis from textual descriptions and connecting Large Language Models (LLMs) to various AI models, we introduce an automated pipeline that first uses ChatGPT to generate diverse textual descriptions of activities. These textual descriptions are then used to generate 3D human motion sequences via a motion synthesis model, T2M-GPT, and later converted to streams of virtual IMU data. We benchmarked our approach on three HAR datasets (RealWorld, PAMAP2, and USC-HAD) and demonstrate that the use of virtual IMU training data generated using our new approach leads to significantly improved HAR model performance compared to only using real IMU data. Our approach contributes to the growing field of cross-modality transfer methods and illustrate how HAR models can be improved through the generation of virtual training data that do not require any manual effort.
Input Interface with Touch and Non-touch Interactions using Atmospheric Pressure for Hearable Devices

A hearable device is a wearable computer that is worn on the ear. In addition to offering conventional music listening functions when utilized as earphones, a hearable device can be linked to smartphones and various onboard sensors to recognize user actions and voice assistants. Although some devices recognize command operations when the earpiece is touched with a hand directly, there are limitations related to the shape of the earpiece and the problem of false recognition that occurs when the earpiece is touched unintentionally. Hands-free input methods utilizing voice assistants and acceleration sensors that measure head movement are available, but these run into problems such as low command recognition accuracy due to noise in public spaces and low social acceptability. In this study, we implement a device that measures the atmospheric pressure in the ear canal and around the ear by installing an atmospheric pressure sensor inside canal-type earphones. We propose a method that recognizes 12 types of gesture based on the pattern of pressure change caused by pressing and releasing the earphone with a finger (touch interaction) and by covering the auricle with a hand and putting pressure on it (non-touch interaction). In our method, six types of gesture are performed with two different interaction methods. We evaluated the recognition accuracy of each gesture by having five participants perform each gesture 50 times. Our findings showed that the “quick press and quick release” gestures were recognized with 0.99 accuracy for touch and 0.82 for non-touch.
WeightMorphy: A Dynamic Weight-Shifting Method to Enhance the Virtual Experience with Body Deformation

We propose WeightMorphy, a hand-mounted system designed to improve teleoperation manipulation’s operability and immersive experience by changing the moment of inertia. This system reduces the discrepancy between the shape of the virtual hand and its corresponding moment of inertia, enabling instantaneous control by the user while maintaining accuracy. We have provided a detailed description of the design and concept of our system and conducted experiments to examine the effect of shifting the center of gravity on the operability of the deformable virtual hand using WeightMorphy.
"\"Hello I am here\": Proximal Nonverbal Cues Role in Initiating Social Interactions in VR"

Virtual Reality (VR) has revolutionized social interactions, but limited field of view (FoV) remains a significant obstacle. Users often fail to notice others within the virtual environment, hindering social engagement. To facilitate initiating social interactions, we developed a novel social signaling technique that utilizes proximal nonverbal cues to indicate users’ location, name, and interests within a social distance. In a 2 × 2 mixed user study, we found that this technique greatly enhanced social presence and interaction quality among users with prior social ties. Our signaling technique has tremendous potential to facilitate social interactions across various social virtual events, such as staff meetings and reunions.
Robo-Coverstitch: Coverstitched Auxetic Shape Memory Actuators For Soft Wearable Devices

As wearable technologies proliferate there is a growing need for compact, easy-to-produce textile-based mechanical actuators. This paper presents a novel coverstitched auxetic actuator using shape memory alloys (SMAs) – which we call the Robo-coverstitch – that can be easily sewn onto fabrics using an industrial coverstitch machine. Multiple Robo-coverstitch samples were manufactured and tested to characterize their force vs. strain properties. The samples were actuated in both the length- and width-wise direction between 0-15% strain at constant electrical current (0.4A). The actuators produced increased force in both length- and width-wise directions when actuated (i.e., auxetic actuation behavior), and these active forces increased with actuator strain. The Robo-coverstitch offers an unobtrusive mechanical actuation solution that can be readily deployed into commercial garments.
RSA cryptanalysis — Fermat factorization exact bound and the role of integer sequences in factorization problem

Large composite integer factorization algorithms still need to be more efficient and, thus, not operate linearly. The RSA popular cryptographic algorithm strength solely depends on factoring the modulus N. This study proposes a technique based on the Fermat factorization theorem to factor modulus N and decreases processing time, obtaining greater algorithmic and storage complexities. We analyse the Fermat factorization theorem and show why it can factorize numbers in constant time complexity (i.e. O(1)) if the difference is slight. We also derive the exact factorization bound for the Fermat factorization method. Furthermore, we link the Fermat factorization with integer sequences and raise further research questions on how our technique can be generalized to factoring N regardless of the difference between the two prime numbers p and q by linking the Fermat factorization theorem with integer sequences. In the end, We address each thought and raise further research questions showing that identifying formulas for some integer sequences can be the key to factoring any composite integer in a linear time.
Siloz: Leveraging DRAM Isolation Domains to Prevent Inter-VM Rowhammer

Today's cloud DRAM lacks strong isolation primitives, highlighted by Rowhammer bit flips. Rowhammer poses an increasing threat to cloud security/reliability, given (1) DRAM activation rates in commodity and malicious workloads already exceed Rowhammer thresholds, and (2) thresholds are decreasing in newer DRAM. Deployed hardware mitigations remain vulnerable, turning cloud providers toward software defenses. However, existing defenses incur high performance or memory overhead or contain significant protection gaps.
Acto: Automatic End-to-End Testing for Operation Correctness of Cloud System Management

Cloud systems are increasingly being managed by operation programs termed operators, which automate tedious, human-based operations. Operators of modern management platforms like Kubernetes, Twine, and ECS implement declarative interfaces based on the state-reconciliation principle. An operation declares a desired system state and the operator automatically reconciles the system to that declared state.
MEMTIS: Efficient Memory Tiering with Dynamic Page Classification and Page Size Determination

The evergrowing memory demand fueled by datacenter workloads is the driving force behind new memory technology innovations (e.g., NVM, CXL). Tiered memory is a promising solution which harnesses such multiple memory types with varying capacity, latency, and cost characteristics in an effort to reduce server hardware costs while fulfilling memory demand. Prior works on memory tiering make suboptimal (often pathological) page placement decisions because they rely on various heuristics and static thresholds without considering overall memory access distribution. Also, deciding the appropriate page size for an application is difficult as huge pages are not always beneficial as a result of skewed accesses within them. We present Memtis, a tiered memory system that adopts informed decision-making for page placement and page size determination. Memtis leverages access distribution of allocated pages to optimally approximate the hot data set to the fast tier capacity. Moreover, Memtis dynamically determines the page size that allows applications to use huge pages while avoiding their drawbacks by detecting inefficient use of fast tier memory and splintering them if necessary. Our evaluation shows that Memtis outperforms state-of-the-art tiering systems by up to 169.0% and their best by up to 33.6%.
Turbo: Effective Caching in Differentially-Private Databases

Differentially-private (DP) databases allow for privacy-preserving analytics over sensitive datasets or data streams. In these systems, user privacy is a limited resource that must be conserved with each query. We propose Turbo, a novel, state-of-the-art caching layer for linear query workloads over DP databases. Turbo builds upon private multiplicative weights (PMW), a DP mechanism that is powerful in theory but ineffective in practice, and transforms it into a highly-effective caching mechanism, PMW-Bypass, that uses prior query results obtained through an external DP mechanism to train a PMW to answer arbitrary future linear queries accurately and "for free" from a privacy perspective. Our experiments on public Covid and CitiBike datasets show that Turbo with PMW-Bypass conserves 1.7 -- 15.9× more budget compared to vanilla PMW and simpler cache designs, a significant improvement. Moreover, Turbo provides support for range query workloads, such as timeseries or streams, where opportunities exist to further conserve privacy budget through DP parallel composition and warm-starting of PMW state. Our work provides a theoretical foundation and general system design for effective caching in DP databases.
Information Flow Tracking for Heterogeneous Compartmentalized Software

We are now seeing increased hardware support for improving the security and performance of privilege separation and compartmentalization techniques. Today, developers can benefit from multiple compartmentalization mechanisms such as process-based sandboxes, trusted execution environments (TEEs)/enclaves, and even intra-address space compartments (i.e., intra-process or intra-enclave). We dub such a computing model a “hetero-compartment” environment and observe that existing system stacks still assume single-compartment models (i.e., user space processes), leading to limitations in using, integrating, and monitoring heterogeneous compartments from a security and performance perspective.