Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduced‐dimension STAP using a modified generalised sidelobe canceller for collocated MIMO radars

This study addresses the problem of beam-space post-Doppler reduced-dimension (RD) space-time adaptive processing (STAP) using a modified generalised sidelobe canceller (GSC) in an airborne collocated multiple-input multiple-output (MIMO) radar. Steering vectors corresponding to the nulls of beamforming are used to construct the blocking matrix (BM) of the GSC processor with optimal signal to clutter plus noise ratio. However, it is difficult to determine the beampattern nulls for a collocated MIMO radar which is equivalent to a virtual array with coherent amplitude weights. To build a valid BM of the GSC-based STAP processor, the authors propose a BM modification method based on the modified Gram-Schmidt orthogonalisation algorithm. The authors prove that the modified RD-GSC processor can achieve the performance equal to that of the fully optimal processor conditionally. The authors also show that the proposed method has reduced computational complexity and fast convergence rate. Numerical simulations are presented to demonstrate the effectiveness of the proposed methods.
PEAK TO AVERAGE POWER RATIO (PAPR) REDUCTION TECHNIQUE IN ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING (OFDM) USING BLOCK CODING

Orthogonal Frequency Division Multiplexing (OFDM) signal is considered a good candidate for wireless systems because it offers diversity gain in frequency selective channels. As in other multicarrier schemes, however, OFDM suffers from high peak to average power ratio (PAPR). This is a major drawback of the scheme and ways of minimizing the PAPR have been researched. Block coding scheme is the technique to reduce the peak-to-average power ratio of OFDM signals and also to detect transmission errors. The reason is that in the time domain, a multicarrier signal is the sum of many narrowband signals. At some time instances, this sum is large and at other times is small, which means that the peak value of the signal is substantially larger than the average value. This high PAR is one of the most important implementation challenges that face OFDM, because it reduces the efficiency. The main purpose in this project, is to make a comparison over the PAPR reduction technique using block coding and without block coding. The capability of Block Coding scheme to reduce the Bit Error Rate (BER) in an OFDM system was also measured. The simulation developed in Matlab simulation environment.
Anaerobic digestion of grass-cuttings under mesophilic and regulated digester pressure

The successful functioning and stability of an anaerobic digester depends on the interplay of several factors, each of which is very important to the success of the system as a whole. pH is central to the whole system as it dictates the survival for the bacteria. In this study the effect of digester pressure was investigated at a fixed temperature of 36°C. The digester pressure was manipulated using back pressure regulator. Grass cuttings were used as feed material to the digester. Three pressures of 2bar, 4bar and 6bar were investigated for a period of ten days. The characteristics and methane yield achieved when digesting grass cuttings under constant digester pressure (gauge pressure) suggested that it is possible to produce biogas that has minimal amount of CO2. The highest methane compositions at 0bar, 2bar, 4bar and 6bar were 55.77, 62.2, 65.8 and 71.2% and carbon dioxide compositions were 58.85, 35.2, 32.5 and 26.2%. The amount of CO2 decreased significantly with increased pressure and the pH values dropped to 7.01, 6.96 and 6.78 respectively with increase in pressure.
Tile-based Lightweight Integer Compression in GPU

GPUs are increasingly used for high-performance and interactive data analytics workloads due to their capability to accelerate computation using massive parallelism. A key constraint of GPU-based data analytics today is the limited memory capacity in GPU devices. Data compression is a powerful technique that can mitigate the capacity limitation in two ways: (1) fitting more data into GPU memory and (2) speeding up data transfer between CPU and GPU. However, compression schemes for GPU today are still limited in compression ratio and/or decompression speed. We identify two limiting factors of existing approaches. First, existing decompression solutions require multiple passes of scanning the global memory to decode layers of compression schemes, incurring significant memory traffic and hurting performance. We present the tile-based decompression model to decompress encoded data in a single pass over global memory and inline with query execution. Second, we develop an efficient implementation of bit-packing-based compression schemes and their optimization techniques in the context of GPU. Our evaluation shows that our schemes can achieve similar compression rates to the best state-of-the-art compression schemes in GPU (i.e., nvCOMP) while being 2.2× and 2.6× faster in decompression speed and query running time.
How Do Software Companies Deal with Artificial Intelligence Ethics? A Gap Analysis

The public and academic discussion on Artificial Intelligence (AI) ethics is accelerating and the general public is becoming more aware AI ethics issues such as data privacy in these systems. To guide ethical development of AI systems, governmental and institutional actors, as well as companies, have drafted various guidelines for ethical AI. Though these guidelines are becoming increasingly common, they have been criticized for a lack of impact on industrial practice. There seems to be a gap between research and practice in the area, though its exact nature remains unknown. In this paper, we present a gap analysis of the current state of the art by comparing practices of 39 companies that work with AI systems to the seven key requirements for trustworthy AI presented in the “The Ethics Guidelines for Trustworthy Artificial Intelligence”. The key finding of this paper is that there is indeed notable gap between AI ethics guidelines and practice. Especially practices considering the novel requirements for software development, requirements of societal and environmental well-being and diversity, nondiscrimination and fairness were not tackled by companies.
Canonical typing and pi-conversion

In usual type theory, if a function f is of type (T -+ (j' and an argument a is of type CT, then the type of fa is immediately given to be (7' and no mention is made of the fact that what has happened is a form of ,B-conversion. A similar observation holds for the generalized Cartesian product types, IIx;(T.T. In fact, many versions of type theory assume that f3 holds of both types and terms, yet only a few attempt to study the theory where terms and types are really treated equally and where .B-cOllversion is used for both. A unified treatment however, of types and terms is becoming illdispensible especially in the approaches which try to generalise lllany systems under a unique one. For example, [Barendregt 91J provides the Barendregt cube and the Pure Type Systems (PTSs) which are a generalisation of many type theories. Yet even such a generalisation does not use ,a-conversion for both types and tenllS. This is unattractive, in a calculus where types have the same syntax as terms (such as the calculi of the cube or the PTSs). For example, in those systems, even though compatibility holds for the typing of abstraction, it does not hold for the typing of application. That is, even though M : N =} Ay,p.M : ITy,p.N holds, the following does not hold: M: N =} MP: NP. Based on this observation, we present a A-calculus in which the conversion rules apply to types as well as tenus. Abstraction and application, moreover, range over both types and terms. We extend the calculus with a canonical type operator T in order to associate types to terms. The type of fa will then be Fa, where F is the type of f and the statement r ~ t : (T from usual type theory is split in two statements in our system: f ft and rtf, t) = (T. Such a splitting enables us to discuss the two questions of the typability of a term and of what is its type separately. Again we believe that this splitting is important and should be usually considered. As a demonstration of what we can do with our calculus, we interpret Church's .\_ in our calculus. This enables us to view our approach as an attempt to extend A-+ with a unified treatment for type and term substitution and conversion and at splitting r ~ t : (T in the two statements: f ft and rtf, t) =1' (T. Such an approach should eventually be used to deal with the Barendregt cube and the Pure Type Systems.
Designing technologies to support everyday mindfulness beyond meditation for mental well-being

Mindfulness is a powerful tool to help individuals increase their awareness of the present moment non-judgmentally, thereby supporting mental well-being. While numerous mindfulness technologies and resources are available for teaching novices to do meditation, there is little research on supporting people to apply the awareness they have learned in mindfulness meditation to their daily activities. Taking a research-though-design approach, my dissertation work will explore the design of mindfulness technologies that go beyond supporting meditation. I aim to combine semi-structured interviews and co-design workshops with mindfulness practitioners to understand the characteristics of daily mindfulness experiences and the design space of technologies to support this practice. I also plan to apply an autobiographical design approach to obtain a nuanced understanding of the lived experience of embedded mindfulness in daily life. This dissertation work has the potential to inform the design of mindfulness technology to be better situated in one’s daily activities and support meaningful mindfulness journeys.
Voice Snapping: Inclusive Speech Interaction Techniques for Creative Object Manipulation

Voice input holds significant potential to support people with physical impairments in producing creative visual design outputs, although it is unclear whether well-established interaction methods used for manipulating graphical assets within mainstream creative applications (typically operated via a mouse, keyboard, or touch input) also present benefits for speech interaction. We present three new voice controlled approaches utilizing interface snapping techniques for manipulating a graphical object's dimensions: NoSnap, UserSnap, and AutoSnap. A user evaluation with people who have physical impairments (N=25) found that each method enabled participants to successfully control a graphical object's size across a series of design tasks, although the automated snapping approach utilized within AutoSnap was found to be more efficient, accurate, and usable. Subjective feedback from participants also highlighted a strong preference for AutoSnap over the other techniques in terms of efficiency and ease of use.
EVSec: An Approach to Extract and Visualize Security Scenarios from System Logs

Logs, a.k. a. execution traces, provide a glimpse into the functionalities of running systems that have poor, incomplete, or outdated documentation. Logs contain a rich amount of information that can be used to facilitate troubleshooting/debugging, track events, detect security breaches, maintain regulatory requirements, and profile user behavior and workload. Driven by the growing complexity of today’s software platforms, reverse engineering of high-level models from system logs has gained momentum in recent years. In this paper, we introduce EVSec, an approach to extract and visualize security scenarios from system logs. The collected logs are first merged, filtered, labeled, and segmented into execution phases. The resulting phases are then visualized using the ITU-T standard, Use Case Maps (UCM) notation, extended with security annotations. We show the applicability of our proposed EVSec approach using two real-world security features, namely, Cisco IOS Login block and Cisco Unicast Reverse Path Forwarding (uRPF).
Hierarchical Entity Resolution using an Oracle

In many applications, entity references (i.e., records) and entities need to be organized to capture diverse relationships like type-subtype, is-A (mapping entities to types), and duplicate (mapping records to entities) relationships. However, automatic identification of such relationships is often inaccurate due to noise and heterogeneous representation of records across sources. Similarly, manual maintenance of these relationships is infeasible and does not scale to large datasets. In this work, we circumvent these challenges by considering weak supervision in the form of an oracle to formulate a novel hierarchical ER task. In this setting, records are clustered in a tree-like structure containing records at leaf-level and capturing record-entity (duplicate), entity-type (is-A) and subtype-supertype relationships. For effective use of supervision, we leverage triplet comparison oracle queries that take three records as input and output the most similar pair(s). We develop HierER, a querying strategy that uses record pair similarities to minimize the number of oracle queries while maximizing the identified hierarchical structure. We show theoretically and empirically that HierER is effective under different similarity noise models and demonstrate empirically that HierER can scale up to million-size datasets.
In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle

Stochastic gradient descent (SGD) is the cornerstone of modern ML systems. Despite its computational efficiency, SGD requires random data access that is inherently inefficient when implemented in systems that rely on block-addressable secondary storage such as HDD and SSD, e.g., in-DB ML systems and TensorFlow/PyTorch over large files. To address this impedance mismatch, various data shuffling strategies have been proposed to balance the convergence rate of SGD (which favors randomness) and its I/O performance (which favors sequential access). In this paper, we first conduct a systematic empirical study on existing data shuffling strategies, which reveals that all existing strategies have room for improvement---they suffer in terms of I/O performance or convergence rate. With this in mind, we propose a simple but novel hierarchical data shuffling strategy, CorgiPile. Compared with existing strategies, CorgiPile avoids a full data shuffle while maintaining comparable convergence rate of SGD as if a full shuffle were performed. We provide a non-trivial theoretical analysis of CorgiPile on its convergence behavior. We further integrate CorgiPile into PostgreSQL by introducing three new physical operators with optimizations. Our experimental results show that CorgiPile can achieve comparable convergence rate to the full shuffle based SGD, and 1.6X-12.8X faster than two state-of-the-art in-DB ML systems, Apache MADlib and Bismarck, on both HDD and SSD.
Fast-Response Half-Wave Inductor Current Sensor for 30-MHz Buck Converter

A sense-FET-based inductor current sensor for a buck converter operating at 30 MHz switching frequency is reported in this work. The proposed sensor with cross-couple structure and improved Miller compensation features over 200 MHz unity gain bandwidth (UGB) while guaranteeing enough phase margin. With this ultra-wide bandwidth, the response time of the sensor is greatly improved and settles within 4 ns.
Theory and Practice of Provenance

Provenance is metadata about the origin, history, or derivation of something; in computer science, provenance usually describes some informational artifact, such as a dataset, an executable program, a news article, or a chart or graph in a scientific publication. Notably, provenance is closely related to issues of explanation, accountability, transparency and ethics. Indeed, these and related issues are the subject of extensive investigation in multiple areas of research such as Scientific Workflows, Databases, Machine Learning and Artificial Intelligence. TaPP, the international workshop on Theory and Practice of Provenance, is widely considered to be the premier venue dedicated to provenance. In 2022, it is held for the first time in conjunction with ACM SIGMOD.
Toward Investigating the Violations Roles of Pattern Grime Occurrence in Software Design Patterns: Violations Roles of Pattern Grime

Design patterns (DPs) are recurring solutions for software design problems. They are recommended and employed for their benefits and impact on software quality. However, the improper extension and implementation of design patterns raise the emergence of accumulating bad smells in DPs structure and behavior. Pattern grime occurrence is one of the bad smells in the design pattern. Grime highly reduces design pattern usability, testability, and adaptability, and even prevents their proper implementation. Despite that, pattern grime occurrence may have many severe violations and impacts on design patterns. For instance, grime occurrence contributes to build-up code smells, violates object-oriented principles, and degenerates pattern quality and code structure. This research focuses on investigating the violations roles of grime occurrence in design patterns and aims to propose a taxonomy for pattern grime violation types, to take the first step to develop a prediction model to detect pattern grime occurrence in the software design pattern. Thus, this research outlines a research project targeting the main research question: “how pattern grime is correlated, and how this might lead to violate design pattern principles, structure, and quality? We plan to answer this question through various stages. First, we investigate the commonalities and variability of pattern grime occurrence on design patterns. Secondly, we identify the types of grime roles violation in DPs to propose a taxonomy of pattern grime violation types. The results of these stages will help to identify a prediction model to predict pattern grime in the software design patterns.
Editorial: Conceptualizing and Using Theory in Computing Education Research

There has been considerable recent interest in a number of questions related to theory within computing education research. In this editorial, we summarize some of this recent interest, discuss the process in which this special issue came to be, and how papers were selected for inclusion. We end with a brief summary of each of the six papers appearing in this special issue, highlighting the relevance of each to this issue's theme.
Cloud-Native Transactions and Analytics in SingleStore

The last decade has seen a remarkable rise in specialized database systems. Systems for transaction processing, data warehousing, time series analysis, full-text search, data lakes, in-memory caching, document storage, queuing, graph processing, and geo-replicated operational workloads are now available to developers. A belief has taken hold that a single general-purpose database is not capable of running varied workloads at a reasonable cost with strong performance, at the level of scale and concurrency people demand today. There is value in specialization, but the complexity and cost of using multiple specialized systems in a single application environment is becoming apparent. This realization is driving developers and IT decision makers to seek databases capable of powering a broader set of use cases when looking to adopt a new database. Hybrid transaction and analytical (HTAP) databases have been developed to try to tame some of this chaos. In this paper we introduce SinglestoreDB (S2DB), formerly called MemSQL, a distributed general-purpose SQL database designed to have the versatility to run both operational and analytical workloads with good performance. It was one of the earliest distributed HTAP databases on the market. It can scale out to efficiently utilize 100s of hosts, 1000s of cores and 10s of TBs of RAM while still providing a user experience similar to a single-host SQL database such as Oracle or SQL Server. S2DB's unified table storage runs both transactional and analytical workloads efficiently with operations like fast scans, seeks, filters, aggregations, and updates. This is accomplished through a combination of rowstore, columnstore and vectorization techniques, ability to seek efficiently into a columnstore using secondary indexes, and using in-memory rowstore buffers for recently modified data. It avoids design simplifications (i.e., only supporting batch loading, or limiting the query surface area to particular patterns of queries) that sacrifice the ability to run a broad set of workloads. Today, after 10 years of development, S2DB runs demanding production workloads for some of the world's largest financial, telecom, high-tech, and energy companies. These customers drove the product towards a database capable of running a breadth of workloads across their organizations, often replacing two or three different databases with S2DB. The design of S2DB's storage, transaction processing, and query processing were developed to maintain this versatility.
Tuning Hierarchical Learned Indexes on Disk and Beyond

Entry retrieval-a process to retrieve rows whose field(s) associates with the given key(s)-is one of the core operations in databases. Classical indexes such as B-tree [2, 4] and skip list recursively partition the key space into a hierarchical structure. Such a structure retrieves an entry by traversing the path of partitions that enclose the given key, effectively reducing uncertainty of key's position as the traversal proceeds. Recently, index designers have developed interests in learned indexes, a concept introduced by [13]. Using patterns in key-position pairs, a learned model can provide a significantly higher information gain about the key's position than a pessimistic partitioning index can [8]. Many works have successfully outperformed classical indexes by multiple factors in latency and memory usage. Overall, they incorporate various combinations of models, partitioning, error corrections (a.k.a. last mile search), mutability, and tunable parameters [6, 9, 10, 12, 16] among many other works.
FlavorDesigner App: Capturing Multisensory Experiences and Crafting Personalized Flavors for Cueing their Recall

Food experiences integrate multisensory bodily experiences within social interactions infused with rich emotional meaning. This makes food a promising material to be leveraged in the design of novel interactions. However, this richness also raises challenges for food-based design, as current technologies for capturing food experiences have limitedly accounted for their multisensory qualities. We present the design of FlavorDesigner app, a mobile application aimed to support the capture of multisensory food experiences and the crafting of personalized flavor cues to support their later recall. The app interface was evaluated through workshops with 12 participants. Findings outline richer understandings of capturing multisensory experiences, both live and remembered, vocabulary to inform conversations about them, rationale for our app design, and three implications for design and design research including evocative representations for capturing taste and smell; interactive, engaging and valid sensory evaluation scales; and new classes of technologies for food-based multisensory interactions.