This paper aims to reverse engineer the tradingstrategy of an actively managed mutual fund by identifyingtechnical patterns in their trading. Investment strategies formany institutional investors consists of both fundamental andtechnical analysis. The purpose of the paper is to explore towhich extent the latter can be used to predict the trading actionsby taking some commonly used technical indicators as input invarious machine learning algorithms to assess patterns betweenthem and the trading of the fund. Furthermore, the technicalindicators’ ability to predict future prices is analysed using thesame methods. The results are not sufficiently clear to suggestthat the fund uses technical indicators to begin with, let alonewhich ones. As for the prediction of future prices, the technicalindicators appear to have some predictive ability. / Syftet med denna rapport är att prediktera handeln i en aktivt förvaltad aktiefond med hjälp av fyra maskininlärningsmetoder. Investeringsstrategier kombinerar i regel två analysmetoder, fundamental respektive teknisk analys. Avsikten med rapporten är att utforska huruvida det sistnämnda kan användas för att förutspå fondens handel genom att använda ett antal vanligt förekommande tekniska indikatorer och medelst maskininlärningsmetoder söka efter mönster mellan dessa och handeln. Vidare innefattar även studien en analys över hur väl tekniska indikatorer predikterar upprespektive nedgångar på aktiepriser. Vad gäller investeringsstrategierna återfanns inga tydliga samband mellan de utvalda indikatorerna och transaktionerna. Resultaten för andra delen av studien tyder på viss prediktiv förmåga för tekniska indikatorer på marknadsrörelser. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

Water monitoring is an essential process that managesthe well-being of freshwater ecosystems. However, it isgenerally an inefficient process as most data collection is donemanually. By combining wireless sensor technology and machinelearning techniques, projects such as iWater aim to modernizecurrent methods. The purpose of the iWater project is to developa network of smart sensors capable of collecting and analyzingwater quality-related data in real time.To contribute to this goal, a comparative study between theperformance of a centralized machine learning algorithm thatis currently used, and a distributed model based on a federatedlearning algorithm was done. The data used for training andtesting both models was collected by a wireless sensor developedby the iWater project. The centralized algorithm was used asthe basis for the developed distributed model. Due to lack ofsensors, the distributed model was simulated by down-samplingand dividing the sensor data into six data sets representing anindividual sensor. The results are similar for both models andthe developed algorithm reaches an accuracy of 98.41 %. / Vattenövervakning är en nödvändig processför att få inblick i sötvattensekosystems välmående. Dessvärreär det en kostsam och tidskrävande process då insamling avdata vanligen görs manuellt. Genom att kombinera trådlössensorteknologi och maskininlärnings algoritmer strävar projektsom iWater mot att modernisera befintliga metoder.Syftet med iWater är att skapa ett nätverk av smarta sensorersom kan samla in och analysera vattenkvalitetsrelaterade datai realtid. För att bidra till projektmålet görs en jämförandestudie mellan den prediktiva noggrannheten hos en centraliseradmaskininlärningsalgoritm, som i nuläget används, och endistribuerad modell baserad på federerat lärande. Data somanvänds för träning och testning av båda modellerna samladesin genom en trådlös sensor utvecklad inom iWater-projektet.Den centraliserade algoritmen användes som grund för denutvecklade distribuerade modellen. På grund av brist på sensorersimulerades den distribuerade modellen genom nedprovtagningoch uppdelning av data i sex datamängder som representerarenskilda sensorer. Resultaten för båda modellerna var liknandeoch den utvecklade algoritmen har en noggrannhet på 98.41 % / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

With the rapid increase of Internet of Things-devices(IoT), demand for new machine learning algorithms and modelshas risen. The focus of this project is implementing a federatedlearning (FL) algorithm to detect anomalies in measurementsmade by a water monitoring IoT-sensor. The FL algorithm trainsacross a collection of decentralized IoT-devices, each using thelocal data acquired from the specific sensor. The local machinelearning models are then uploaded to a mutual server andaggregated into a global model. The global model is sent back tothe sensors and is used as a template when training starts againlocally. In this project, we only have had access to one physicalsensor. This has forced us to virtually simulate sensors. Thesimulation was done by splitting the data gathered by the onlyexisting sensor. To deal with the long, sequential data gatheredby the sensor, a long short-term memory (LSTM) network wasused. This is a special type of artificial neural network (ANN)capable of learning long-term dependencies. After analyzing theobtained results it became clear that FL has the potential toproduce good results, provided that more physical sensors aredeployed. / I samband med den snabba ökningen avInternet of Things-enheter (IoT) har efterfrågan på nya algoritmeroch modeller för maskininlärning ökat. Detta projektfokuserar på att implementera en federated learning (FL) algoritmför att detektera avvikelser i mätdata från en sensorsom övervakar vattenkvaliteten. FL algoritmen tränar en samlingdecentraliserade IoT-enheter, var och en med hjälp av lokaldata från sensorn i fråga. De lokala maskininlärningsmodellernaladdas upp till en gemensam server och sammanställs till englobal modell. Den globala modellen skickas sedan tillbaka tillsensorerna och används som mall när den lokala träningen börjarigen. I det här projektet hade vi endast tillgång till en fysisksensor. Vi har därför varit tvungna att simulera sensorer. Dettagjordes genom att dela upp datamängden som samlats in frånden fysiska sensorn. För att hantera den långa sekventiella dataanvänds ett long short-term memory (LSTM) nätverk. Detta ären speciell typ av artificiellt neuronnät (ANN) som är kapabeltatt minnas mönster under en längre tid. Efter att ha analyseratresultaten blev det tydligt att FL har potentialen att produceragoda resultat, givet att fler fysiska sensorer implementeras. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

Anomaly detection in Internet of Things (IoT) systems has become an increasingly popular field of research as the number of IoT devices proliferate year over year. Recent research often relies on machine learning algorithms to classify sensor readings directly. However, this approach leads to solutions being non-portable and unable to be applied to varying IoT platform infrastructure, as they are trained with sensor data specific to one configuration. Moreover, sensors generate varying amounts of non-standard data which complicates model training and limits generalization. This research focuses on addressing these problems in three ways a) the creation of an IoT Testbed which is configurable and parameterizable for dataset generation, b) the usage of system performance metrics as the dataset for training the anomaly classifier which ensures a fixed dataset size, and c) the application of Symbolic Aggregate Approximation (SAX) to encode patterns in system performance metrics which allows our trained Long Short-Term Memory (LSTM) model to classify anomalies agnostic to the underlying system configuration. Our devised IoT Testbed provides a lightweight setup for data generation which directly reflects some of the most pertinent components of Industry 4.0 pipelines including a MQTT Broker, Apache Kafka, and Apache Cassandra. Additionally, our proposed solution provides improved portability over state-of-the-art models while standardizing the required training data. Results demonstrate the effectiveness of utilizing symbolized performance metrics as we were able to achieve accuracies of 95.87%, 87.33%, and 87.47% for three different IoT system configurations. The latter two accuracies represent the model’s ability to be generalized to datasets generated from differing system configurations.

Yes / The Atkinson-Shiffrin modal model forms the foundation of our understanding of human memory. It consists of three stores (Sensory Memory (SM), also called iconic memory, Short-Term Memory (STM), and Long-Term Memory (LTM)), each tuned to a different time-scale. Since its inception, the STM and LTM components of the modal model have undergone significant modifications, while SM has remained largely unchanged, representing a large capacity system funneling information into STM. In the laboratory, visual memory is usually tested by presenting a brief static stimulus and, after a delay, asking observers to report some aspect of the stimulus. However, under ecological viewing conditions, our visual system receives a continuous stream of inputs, which is segmented into distinct spatio-temporal segments, called events. Events are further segmented into event-segments. Here we show that SM is not an unspecific general funnel to STM but is allocated exclusively to the current event-segment. We used a Multiple-Object Tracking (MOT) paradigm in which observers were presented with disks moving in different directions, along bi-linear trajectories, i.e., linear trajectories, with a single deviation in direction at the mid-point of each trajectory. The synchronized deviation of all of the trajectories produced an event stimulus consisting of two event-segments. Observers reported the pre-deviation or the post-deviation directions of the trajectories. By analyzing observers' responses in partial- and full-report conditions, we investigated the involvement of SM for the two event-segments. The hallmarks of SM hold only for the current event segment. As the large capacity SM stores only items involved in the current event-segment, the need for event-tagging in SM is eliminated, speeding up processing in active vision. By characterizing how memory systems are interfaced with ecological events, this new model extends the Atkinson-Shiffrin model by specifying how events are stored in the first stage of multi-store memory systems.

The effects of short-term meditation is a debated subject. There is studies that indicates that there is none or limited effect. Research of mindfulness meditation has also shown positive effects on working memory related tasks and sustained attention, but it can also show reduction of stress and depression. This study evaluate the effects of short-term guided meditation in a group of 10 persons in comparison with a control group. Results indicated no difference in memory tasks such as digit-span but the experimental group showed significant improvements in self-reported stress and mindful assets such as Acting with awareness and Acceptance without judgement. The results are consistent with previous research in this area. / Effekten av kortsiktig meditation är omdiskuterad. Det finns studier som indikerar att det inte finns någon eller limiterad effekt. Tidigare forskning kring meditation har också visat positiva effekter på arbetsrelaterade uppgifter och bibehållen uppmärksamhet men även på stressreducering och depression. Den här studien utvärderar effekten av guidad meditation vid tre tillfällen på en experimentgrupp av tio personer i jämförelse med en kontrollgrupp. Resultaten visar inte att mindfulness hade signifikant påverkan på arbetsminnet men signifikant positiv påverkan på stressreducering och på mindfulnessdrag som Agera med medvetenhet och Acceptera utan fördomar. Resultaten går i linje med tidigare forskning inom det här området.

The purpose of this study was to determine the effect on selected Upward Bound students' vocabulary retention rate and attitude toward computers when using color in a computer assisted instructional (CAI) program. Past research on the use of color in the educational process does not answer questions about possible effects it may have when used in CAI programs. Specific areas addressed by this study include: (1) differences in color computer assisted instructional software and achromatic versions of the lesson, (2) differences in the short-term vocabulary retention rate for color versus achromatic versions, (3) differences in the long-term vocabulary retention rate for color versus achromatic versions, (4) differences on the affective attitude scale for color versus achromatic versions, (5) differences in short-term memory based on gender and computer experience, (6) differences in long-term memory based on gender and computer experience and (7) differences on the affective attitude scale based on gender and computer experience. Subjects in the experiment were high school students participating in Upward Bound programs at Texas Christian University and the University of North Texas. A pretestposttest design was used and data were obtained from seventy-one students. A CAI program presented students with twenty words and definitions via a drill and practice mode. The words came from Schuster's list of rare and seldom used words considered easy to learn. Two computer systems were used in this study, achromatic and color. Students completed the Computer Attitude Scale at the beginning and end of the CAI lesson. A pretest, immediate posttest and two week delayed posttest were administered to both experimental groups. Analysis of the data revealed a significant difference in long-term memory based on gender and computer experience. Girls using the color version of the lesson scored significantly higher on the delayed posttest than girls using the achromatic version.

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Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.